Showing papers in "IEEE Intelligent Systems in 2018"

Multimodal Sentiment Analysis: Addressing Key Issues and Setting Up the Baselines

Abstract: We compile baselines, along with dataset split, for multimodal sentiment analysis. In this paper, we explore three different deep-learning-based architectures for multimodal sentiment classification, each improving upon the previous. Further, we evaluate these architectures with multiple datasets with fixed train/test partition. We also discuss some major issues, frequently ignored in multimodal sentiment analysis research, e.g., the role of speaker-exclusive models, the importance of different modalities, and generalizability. This framework illustrates the different facets of analysis to be considered while performing multimodal sentiment analysis and, hence, serves as a new benchmark for future research in this emerging field.

OntoSenticNet: A Commonsense Ontology for Sentiment Analysis

Abstract: In this work, we present OntoSenticNet, a commonsense ontology for sentiment analysis based on SenticNet, a semantic network of 100,000 concepts based on conceptual primitives. The key characteristics of OntoSenticNet are: (i) the definition of precise conceptual hierarchy and properties associating concepts and sentiment values; (ii) the support for connecting external information (e.g., word embedding, domain information, and different polarity representations) to each individual defined within the ontology; and (iii) the capability of associating each concept with annotations contained in external resources (e.g., documents and multimodal resources).

From Raw Data to Smart Manufacturing: AI and Semantic Web of Things for Industry 4.0

Abstract: AI techniques combined with recent advancements in the Internet of Things, Web of Things, and Semantic Web-jointly referred to as the Semantic Web-promise to play an important role in Industry 4.0. As part of this vision, the authors present a Semantic Web of Things for Industry 4.0 (SWeTI) platform. Through realistic use case scenarios, they showcase how SweTI technologies can address Industry 4.0s challenges, facilitate cross-sector and cross-domain integration of systems, and develop intelligent and smart services for smart manufacturing.

Detecting Personal Intake of Medicine from Twitter

Abstract: Mining social media messages such as tweets, blogs, and Facebook posts for health and drug related information has received significant interest in pharmacovigilance research. Social media sites (e.g., Twitter), have been used for monitoring drug abuse, adverse reactions to drug usage, and analyzing expression of sentiments related to drugs. Most of these studies are based on aggregated results from a large population rather than specific sets of individuals. In order to conduct studies at an individual level or specific groups of people, identifying posts mentioning intake of medicine by the user is necessary. Toward this objective we develop a classifier for identifying mentions of personal intake of medicine in tweets. We train a stacked ensemble of shallow convolutional neural network (CNN) models on an annotated dataset. We use random search for tuning the hyper-parameters of the CNN models and present an ensemble of best models for the prediction task. Our system produces state-of-the-art results, with a micro-averaged F-score of 0.693. We believe that the developed classifier has direct uses in the areas of psychology, health informatics, pharmacovigilance, and affective computing for tracking moods, emotions, and sentiments of patients expressing intake of medicine in social media.

How Will the Internet of Things Enable Augmented Personalized Health

Abstract: The Internet of Things refers to network-enabled technologies, including mobile and wearable devices, which are capable of sensing and actuation as well as interaction and communication with other similar devices over the Internet. The IoT is profoundly redefining the way we create, consume, and share information. Ordinary citizens increasingly use these technologies to track their sleep, food intake, activity, vital signs, and other physiological statuses. This activity is complemented by IoT systems that continuously collect and process environment-related data that has a bearing on human health. This synergy has created an opportunity for a new generation of healthcare solutions.

A Multimodal Approach for the Safeguarding and Transmission of Intangible Cultural Heritage: The Case of i-Treasures

Abstract: Intangible Cultural Heritage (ICH) creations include, amongst other, music, dance, singing, theatre, human skills, and craftsmanship. These cultural expressions are usually transmitted orally and/or using gestures and are modified over a period of time, through a process of collective recreation. As the world becomes more interconnected and many different cultures come into contact, local communities run the risk of losing important elements of their ICH, while young people find it difficult to maintain the connection with the cultural heritage treasured by their elders. In this paper, we present a novel holistic approach for the safeguarding and transmission of ICH that goes beyond the mere digitization of ICH content. Based on multisensory technology for the capturing of ICH, the proposed approach enables the generation of completely novel cultural content. High-level semantics are extracted from the acquired data, enabling researchers to identify possible implicit or hidden correlations between different ICH expressions or interpretation styles and study the evolution of a specific ICH. These data, coupled with other cultural resources, are accessible through the i-Treasures Web-platform, which provides the means for supporting knowledge exchange between researchers as well as know-how transmission from ICH bearers to apprentices.

Ensemble Algorithms for Microblog Summarization

Abstract: We investigate whether off-the-shelf summarization algorithms can be combined to produce better quality summaries. To this end, we propose ensemble schemes that can combine the outputs of multiple base summarization algorithms, to produce summaries better than what is generated by any of the base algorithms.

Explaining Explanation, Part 4: A Deep Dive on Deep Nets

Abstract: This is the fourth in a series of essays about explainable AI. Previous essays laid out the theoretical and empirical foundations. This essay focuses on Deep Nets, and con-siders methods for allowing system users to generate self-explanations. This is accomplished by exploring how the Deep Net systems perform when they are operating at their boundary conditions. Inspired by recent research into adversarial examples that demonstrate the weakness-es of Deep Nets, we invert the purpose of these adversar-ial examples and argue that spoofing can be used as a tool to answer contrastive explanation questions via user-driven exploration.

Next-Generation Smart Environments: From System of Systems to Data Ecosystems

Abstract: Digital transformation is driving a new wave of large-scale data-rich smart environments with data on every aspect of our world. The resulting data ecosystems present new challenges and opportunities in the design of intelligent systems and system of systems.

Identifying SCADA Systems and Their Vulnerabilities on the Internet of Things: A Text-Mining Approach

Abstract: Supervisory Control and Data Acquisition (SCADA) systems allow operators to control critical infrastructure. Vendors are increasingly integrating Internet technology into these devices, making them more susceptible to cyberattacks. Identifying and assessing vulnerabilities of SCADA devices using Shodan, a search engine that contains records about publicly available Internet-connected devices, can help mitigate cyberattacks. The authors present a principled approach to systematically identify all SCADA devices on Shodan and then assess the vulnerabilities of the devices with a state-of-the-art tool.

No, That Never Happened!! Investigating Rumors on Twitter

Abstract: Rumors are the statements which cannot be verified for correctness. Intuitively, a fact statement cannot be a rumor or vice-versa. The spread of misinformation is, especially important in the context of breaking news, often starting off as unverified information in the form of a rumor. These rumors then spread to a large number of users, influencing perception and understanding of events, despite being unverified. Misinformation often creates a confusing and chaotic scenario, which leads to poor decision-making or many inhuman consequences. Social media rumors that are later proven false can have harmful consequences both for individuals and for society. For instance, a rumor in 2013 about the White House having been bombed, injuring Barack Obama, spooked stock markets in the United States. In another instance, rumors played a vital role in deadly 2011 London riots. Rumor detection and its support invite great interest from various organizations and government agencies. By keeping track of such information, they can take preventative measures to maintain good decorum. Therefore, determining the authenticity of the circulating misinformation (rumors) in a timely manner is very crucial.

Autonomous Nuclear Waste Management

Abstract: Redundant and nonoperational buildings at nuclear sites are decommissioned over a period of time. The process involves demolition of physical infrastructure resulting in large quantities of residual waste material. The resulting waste materials are packed into import containers to be delivered for postprocessing, containing either sealed canisters or assortments of miscellaneous objects. At present postprocessing does not happen within the United Kingdom. Sellafield Ltd. and National Nuclear Laboratory are developing a process for future operation so that upon an initial inspection, imported waste materials undergo two stages of postprocessing before being packed into export containers, namely sort and segregate or sort and disrupt . The postprocessing facility will remotely treat and export a wide range of wastes before downstream encapsulation. Certain wastes require additional treatment, such as disruption, before export to ensure suitability for long-term disposal. This paper focuses on the design, development, and demonstration of a reconfigurable rational agent-based robotic system that aims to highly automate these processes removing the need for close human supervision. The proposed system is being demonstrated through a downsized, lab-based setup incorporating a small-scale robotic arm, a time-of-flight camera, and high-level rational agent-based decision making and control framework.

Camera Placement Based on Vehicle Traffic for Better City Security Surveillance

Abstract: Security surveillance is important in smart cities. Deploying numerous cameras is a common approach. Given the importance of vehicles in a metropolis, using vehicle traffic patterns to strategically place cameras could potentially facilitate security surveillance. This article constitutes the first effort toward building the link between vehicle traffic and camera placement for better security surveillance.

Instance-based Domain Adaptation via Multiclustering Logistic Approximation

Abstract: With the explosive growth of the Internet online texts, we could nowadays easily collect a large amount of labeled training data from different source domains. However, a basic assumption in building statistical machine learning models for sentiment analysis is that the training and test data must be drawn from the same distribution. Directly training a statistical model usually results in poor performance, when the training and test data have different distributions. Faced with the massive labeled data from different domains, it is therefore important to identify the source-domain training instances that are closely relevant to the target domain, and make better use of them. In this work, we propose a new approach, called multiclustering logistic approximation (MLA), to address this problem. In MLA, we adapt the source-domain training data to the target domain via a framework of multiclustering logistic approximation. Experimental results demonstrate that MLA has significant advantages over the state-of-the-art instance adaptation methods, especially in the scenario of multidistributional training data.

Transfer Learning for User Action Identication in Mobile Apps via Encrypted Trafc Analysis

Abstract: Recent academic studies have demonstrated the possibility of inferring user actions performed in mobile apps by analyzing the resulting encrypted network traffic. Due to the multitude of app versions, mobile operating systems, and device models (collectively referred to in this paper as configurations) previous approaches are not applicable to real life settings. In this work, we ex-tend the ability of these approaches to generalize across different configurations. We treat the different configurations as a case for transfer learning, and adapt the co-training method to sup-port the transfer learning process. Our approach leverages a small number of labeled instances of encrypted traffic from a source configuration, in order to construct a classifier capable of identi-fying a users actions in a different (target) configuration which is completely unlabeled. Experi-ments on real datasets collected from different applications on Android devices show that the proposed method achieves F1 measures over 0.8 for most of the considered user actions.

Explaining Explanation, Part 3: The Causal Landscape

Abstract: This is the third in a series of essays about explanation. After laying out the core theoretical concepts in the first article, including aspects of causation and abduction, the second article presented some empirical research to reveal the great variety of purposes and types of causal reasoning, as well as a number of different causal explanation patterns. Taking the notion of reasoning patterns a step further, the author describes a method whereby a decision maker can go from a causal explanation to a viable course of action for making positive change in the future, not to mention aiding decision making in general.

Probing the Limits of Anomaly Detectors for Automobiles with a Cyberattack Framework

Abstract: Modern automobiles are controlled by computers that are increasingly connected to the outside world and therefore vulnerable to cyberattacks. Defending cars against such attacks requires a multifaceted approach to improving security, but the last line of defense is detecting those attacks within the data traffic exchanged by the vehicles controller computers. To identify this malicious traffic, the authors created anomaly detectors using recurrent neural networks and multivariate Markov chains. However, evaluating these detectors is difficult because there are currently few examples of attack traffic. The authors solved this by creating an attack framework that describes automotive cyberattack characteristics, thereby enabling the simulation of attacks and allowing comprehensive testing of our anomaly detectors.

Agent-Based Modeling for Complex Financial Systems

Abstract: Todays global financial marketplace is best understood as a complex network of interacting market systems, in which events of world-wide significance unfold on timescales that are barely within the ability of humans to comprehend. Many researchers have concluded that the dynamics of networked market systems are better under-stood as complex adaptive systems, in which independent software components interact without centralized control or oversight.

AI and Fake News

Abstract: Fake news and propaganda are not new phenomena but when powered by modern information dissemination and AI technologies, they are manifesting themselves at scales and in ways previously not possible. This paper describes several human frailties that make today's “fake news” possible together with several AI-based technologies that can help defeat or defend those frailties. Our goal is to explore ways in which AI can play a role in the “fake news” arena.

Trends and Controversies

Abstract: Performing covert biometric recognition in surveillance environments has been regarded as a grand challenge, considering the adversity of the conditions where recognition should be carried out (e.g., poor resolution, bad lighting, off-pose and partially occluded data). This special issue compiles a group of approaches to this problem.

Multivariate Correlation Entropy and Law Discovery in Large Data Sets

Abstract: Over the past several centuries, many important natural laws have been discovered by scientists, which have not only changed our viewpoints about nature but also affected our lives significantly. Today, automatic discovery of meaningful laws from data beyond two variables becomes an important task of our time. Here, we propose two multivariate correlation measures, namely, the multivariate correlation entropy (MCE) and the multivariate incorrelation entropy (MIE), which can be used to measure the strength of the correlation among multiple variables. Using MIE makes it possible to directly detect linear relations existing in large data sets. In addition, more complicated nonlinear multivariate laws can be discovered using a function dictionary.

Generating Hard to Comprehend Fake Documents for Defensive Cyber Deception

Abstract: Existing approaches to cyber defense have been inadequate at defending the targets from advanced persistent threats (APTs). APTs are stealthy and orchestrated attacks, which target both corporations and governments to exfiltrate important data. In this paper, we present a novel comprehensibility manipulation framework (CMF) to generate a haystack of hard to comprehend fake documents, which can be used for deceiving attackers and increasing the cost of data exfiltration by wasting their time and resources. CMF requires an original document as input and generates fake documents that are both believable and readable for the attacker, possess no important information, and are hard to comprehend. To evaluate CMF, we experimented with college aptitude tests and compared the performance of many readers on separate reading comprehension exercises with fake and original content. Our results showed a statistically significant difference in the correct responses to the same questions across the fake and original exercises, thus validating the effectiveness of CMF operations to mislead.

Building a Globally Optimized Computational Intelligent Image Processing Algorithm for On-Site Inference of Nitrogen in Plants

Abstract: Estimating nutrient content in plants is a crucial task in the application of precision farming. This work will be more challenging if it is conducted nondestructively based on plant images captured in the field due to the variation of lighting conditions. This paper proposes a computational intelligence image processing to analyze nitrogen status in wheat plants. We developed an ensemble of deep learning multilayer perceptron-using committee machines for color normalization and image segmentation. This paper also focuses on building a genetic-algorithm-based global optimization to fine tune the color normalization and nitrogen estimation results. We discovered that the proposed method can successfully normalize plant images by reducing color variabilities compared to other color normalization techniques. Furthermore, this algorithm is able to enhance the nitrogen estimation results compared to other non-global optimization methods as well as the most renowned SPAD meter based nitrogen measurement.

Investigative Knowledge Discovery for Combating Illicit Activities

Abstract: Developing scalable, semi-automatic approaches to derive insights from a domain-specific Web corpus is a longstanding research problem in the knowledge discovery community. The problem is particularly challenging in illicit fields, such as human trafficking, where traditional assumptions concerning information representation are frequently violated. In this article, we describe an end-to-end investigative knowledge discovery system for illicit Web domains. We built and evaluated a prototype, involving separate components for information extraction, semantic modeling and query execution, on a real-world human trafficking Web corpus containing 1.3 million pages, with promising results.

Predicting Adversarial Cyber-Intrusion Stages Using Autoregressive Neural Networks

Abstract: Current cybersecurity approaches are response-driven and ineffective, as they do not account for dynamic adversarial movement. Using empirical evidence from observations of two Red Team-Blue Team cybersecurity training exercises held at Idaho National Laboratory and the Michigan Cyber Range, we used four different models to make temporal predictions of how adversaries progress through cyberattacks: nonlinear autoregressive (NAR) neural network, NAR neural network with exogenous input (NARX), NAR neural network for multi-steps-ahead prediction, and autoregressive integrated moving average (ARIMA). The obtained results demonstrate that the trained models can capture different variations in adversarial movement across the two datasets with reliable accuracy.

Artificial Intelligence, Rationality, and the World Wide Web

Abstract: Scholars debate whether the arrival of articial super intelligence-a form of intelligence that signicantly exceeds the cognitive performance of humans in most domains- would bring positive or negative consequences. I argue that a third possibility is plausible yet generally overlooked: for several different reasons, an articial superintelligence might choose to exert no appreciable effect on the status quo ante (the already existing collective superintelligence of commercial cyberspace). Building on scattered insights from web science, philosophy, and cognitive psychology, I elaborate and defend this argument in the context of current debates about the future of articial intelligence.

Neural Cross-Session Filtering: Next-Item Prediction Under Intra- and Inter-Session Context

Abstract: Classic recommender systems (RSs) often repeatedly recommend similar items to user historical profiles or recent purchases. For this, session-based RSs (SBRSs) are extensively studied in recent years. Current SBRSs often assume a rigid-order sequence, which does not fit in many real-world cases. In fact, the next-item recommendation depends on not only current session context but also historical sessions which are often neglected by current SBRSs. Accordingly, an SBRS over relaxed-order sequences with both intra- and inter-context is more pragmatic. Inspired by the successful experience in modern language modeling, we design an efficient neural architecture to model both intra- and inter-context for next item prediction.

Activity Recognition for a Smartphone and Web-Based Human Mobility Sensing System

Abstract: Activity-based models in transport modeling and prediction are built from a large number of observed trips and their purposes. However, data acquired through traditional interview-based travel surveys is often inaccurate and insufficient. Recently, a human mobility sensing system, called Future Mobility Survey (FMS), was developed and used to collect travel data from more than 1,000 participants. FMS combines a smartphone and interactive web interface in order to better infer users activities and patterns. This paper presents a model that infers an activity at a certain location. We propose to generate a set of predictive features based on spatial, temporal, transitional, and environmental contexts with an appropriate quantization. In order to improve the generalization performance of the proposed model, we employ a robust approach with ensemble learning. Empirical results using FMS data demonstrate that the proposed method contributes significantly to providing accurate activity estimates for the user in our travel-sensing application.

GLARE-SSCPM: An Intelligent System to Support the Treatment of Comorbid Patients

Abstract: The development of software tools supporting physicians in the treatment of comorbid patients is a challenging goal and a “hot topic” in medical informatics and artificial intelligence. Computer interpretable guidelines (CIGs) are consolidated tools to support physicians with evidence-based recommendations in the treatment of patients affected by a specific disease. However, the application of two or more CIGs on comorbid patients is critical, since dangerous interactions between actions from different CIGs may arise. GLARE-SSCPM is the first tool supporting, in an integrated way, the knowledge-based detection of interactions, the management of the interactions, and the final “merge” of (part of) the CIGs operating on the patient. GLARE-SSCPM is characterized by being very supportive to physicians, providing them support for focusing, interaction detection, and for a “hypothesize and test” approach to manage the detected interactions. To achieve such goals, it provides advanced artificial intelligence techniques. Preliminary tests in the educational context, within the RoPHS project, have provided encouraging results.

Adaptive Biometric Systems using Ensembles

Abstract: With the increased availability of online services, enhanced authentication mechanisms-including biometric systems-are necessary. However, recent studies show that biometric features can change. Consequently, recognition performance can be affected over time. Adaptive biometric systems that can automatically adapt the biometric reference have been proposed to deal with this problem. Frequently, these systems use query samples classified as genuine to adapt the biometric reference. Despite good results, there are concerns regarding their robustness. This article investigates using an ensemble of classifiers to increase these systems robustness. Ensembles can improve the recognition performance of decision models, providing a more stable classification decision. The authors explore questions regarding the application of ensembles to adaptive biometric systems using one-class classification algorithms, and offer a proposal to automatically adapt the meta classifier over time.