Web services are products in the era of service-oriented computing and cloud computing. Considering the information overload problem arising from the task of selecting web services, a recommendation system is by far the most effective solution for performing such selections. However, users calling a limited number of services will cause severe data sparseness and a weak correlation with services. In addition, fully mining the semantic features and knowledge features of the text description is also a major problem that needs to be solved urgently. This paper proposes a deep knowledge-aware approach which introduces knowledge graph and knowledge representation into web service recommendation for the first time. We solve the data sparse problem and optimize the user’s feature representation. In this approach, an attention module is introduced to model the impact of tags for the candidate services on different words of user queries, and a deep neural network is used to model the high-level features of user-service invocation behaviors. The results of experiments demonstrate that the proposed approach can achieve better recommendation performance than other state-of-the-art methods.

Deep knowledge-aware framework for web service recommendation

This paper presents the results that were obtained from WASSA 2022 shared task on predicting empathy, emotion, and personality in reaction to news stories. Participants were given access to a dataset comprising empathic reactions to news stories where harm is done to a person, group, or other. These reactions consist of essays and Batson’s empathic concern and personal distress scores. The dataset was further extended in WASSA 2021 shared task to include news articles, person-level demographic information (e.g. age, gender), personality information, and Ekman’s six basic emotions at essay level Participation was encouraged in four tracks: predicting empathy and distress scores, predicting emotion categories, predicting personality and predicting interpersonal reactivity. In total, 14 teams participated in the shared task. We summarize the methods and resources used by the participating teams.

WASSA 2022 Shared Task: Predicting Empathy, Emotion and Personality in Reaction to News Stories

ion: Soft Clustering Data Normalization PreProcessing Stage 1 DGDS Big Data set from the SGSC Project

A study of multivariate behavior and anomaly patterns : tensor decomposition for multiway big data

Autism spectrum disorder (ASD) is a very high-flying area of research in the current era owing to its limited and on-going exploration. This chapter aims to bridge the gap of such late realization of autistic feature through machine intervention commonly known as computer vision. In this chapter, basic summarization of important characteristic features of autism and how those features could be measured and altered before a human could recognize are proposed. The chapter proposes a model for activity identification of the autistic child through video recordings. The approach is modelled in a way that consists of two phases: 1) Optical flow method detects the unusual frames based on motion pattern. 2) Each of these detected frames are fed to convolution neural network, which is trained to extract features and exactly classify if the particular frame under consideration belongs to usual or unusual class. This examines the various activities, time delay, and factors influencing the motion of the autistic child under constrained scenarios proving maximum accuracy and performance.

Activity Recognition System Through Deep Learning Analysis as an Early Biomarker of ASD Characteristics

Unmanned aerial vehicles (UAVs), commonly known as drones, have the potential to enable a wide variety of beneficial applications in areas such as monitoring and inspection of physical infrastructure, smart emergency/disaster response, agriculture support, and observation and study of weather phenomena including severe storms, among others. However, the increasing deployment of amateur UAVs (AUAVs) places the public safety at risk. A promising solution is to deploy surveillance UAVs (SUAVs) for the detection, localization, tracking, jamming and hunting of AUAVs. Accurate localization and tracking of AUAV is the key to the success of AUAV surveillance. In this article, we propose a novel framework for accurate localization and tracking of AUAV enabled by cooperating SUAVs. At the heart of the framework is a localization algorithm called cooperation coordinate separation interactive multiple model extended Kalman filter (CoCS-IMMEKF). This algorithm simplifies the set of multiple models and eliminates the model competition of each motion direction by coordinate separation. At the same time, this algorithm leverages the advantages of fusing multi-SUAV cooperative detection to improve the algorithm accuracy. Compared with the classical interacting multiple model unscented Kalman filter (IMMUKF) algorithm, this algorithm achieves better target estimation accuracy and higher computational efficiency, and enables good adaptability in SUAV system target localization and tracking.

Collaborative unmanned aerial systems for effective and efficient airborne surveillance

Today’s world is witnessing great increase in the use of Social Media. People use them as a platform to share their feelings, emotions and experiences along with lot of personal information. All such information could be used in advantageous ways to help increase the Business and understand the user need. Personality prediction has gained lot of focus nowadays. It studies behavior of users and reflects the thinking, feelings etc. Traditional ways to take survey that was time consuming, for large number of users there is need of automatic prediction. Users are dynamic and can have their account on multiple platforms as they can have multi-context information. This survey gives an overview of different strategies used to predict the personality and behavior using the content available on social sites. Ability to predict user’s personality traits can help to build many customized services or products. Finally, the last section gives the Future trends and directions. Keywords—Personality prediction; Social Media

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Personality Prediction from Social Media Text: An Overview

Due to heavy traffic the network monitoring is very difficult and cumbersome job, hence the probability of network attacks increases substantially. So there is the need of extraction anomalies. Anomaly extraction means to find flows associated with the anomalous events, in a large set of flows observed during an anomalous time interval. Anomaly extraction is very important for root-cause analysis, network forensics, attack mitigation and anomaly modeling. To identify the suspicious flows, we use meta-data provided by several histogram based detectors and then apply association rule with multidimensional mining concept to find and summarize anomalous flows. By taking rich traffic data from a backbone network, we show that our technique effectively finds the flows associated with the anomalous events. So by applying multidimensional mining rule to extract anomaly, we can reduce the work-hours needed for analyzing alarms and making anomaly systems more effective.

Multidimensional Data Mining for Anomaly Extraction

Now a day, the mass events like political rallies, musical concerts, sports matches, school gathering and pilgrimage become an issue of the public security. So, Management of crowd at public places becomes a big issue. Crowd congestion prevents the smooth flow of the crowd and causes the unsafe condition; thereby causing many people lost their life. So, it is important to overcome such situation to provide the safety on time. It can be achieved by using the video monitoring device footage along with optical flow and standard deviation model to detect the crowd congestion and providing warning for managing the crowd. Higher the congestion level higher is the risk of life and occurrences of uncertainty. Based on the level of congestion and movement it is possible to predict the future flow of crowd in that direction.

Motion detection using optical flow and standard deviation

Smartphone applications are gaining popularity and with time many complex and computation/resource intensive applications are coming to market. These applications require more resources and consume lot of battery power, but the improvisation in battery capacity has been lagged far behind as compared to other components of mobile devices. On other hand, clouds are rich in resources and computational power. Therefore, by utilizing cloud resources for application execution we can reduce battery consumption and in turn increases battery charging intervals. This all is known as cyber foreign or computation offloading in Moble Cloud Computing. There are various factors affecting computation offloading like bandwidth. So, we had considered bandwidth as major factor in proposed offloading decision making algorithm. Our algorithm is based on divide and conquer strategy. This system focuses on time and energy saving.

Time and Energy Saving through Computation Offloading with Bandwidth Consideration for Mobile Cloud Computing

With constant research in the last decade, Object Detection has become one of the rapidly evolving sub-fields of Deep Learning. As a result, the complexity and applications of Object Detection models have grown, necessitating larger datasets and multi-format labelled annotations for training and testing. To aid with the annotation work, a variety of tools based on various technologies, techniques, and features have been developed. However, some of the tools only cater to specific standards, requirements and problem domain. Researchers have observed that a drop in the quality of labelled annotations can have unintended consequences for model performance. This study provides an overview of various annotation tools with the goal of assisting researchers in determining the appropriate tool for their needs. The work will also serve as a reference for developers as they work on subsequent features to improve or create new tools.

Mamta Bhamare

Papers

Personality Prediction from Social Media Text: An Overview

Multidimensional Data Mining for Anomaly Extraction

Motion detection using optical flow and standard deviation

Time and Energy Saving through Computation Offloading with Bandwidth Consideration for Mobile Cloud Computing

A Review of Image Annotation Tools for Object Detection