人教版高中英语新课程教材中,语言运用（Using Language）是每个单元必不可少的部分,提供了围绕单元中心话题的听、说、读、写的综合性练习,是单元中心话题的延续和升华.如何设计Using Language部分的教学,使自己的教学模式既不落俗套,又能真正体现新课程标准所倡导的教学理念,正是广大一线英语教师一直努力探索的问题.

打磨Using Language,倡导新理念

https://www.healio.com/psychiatry/journals/psycann/1976-12-6-12/{806b8887-c02e-40c8-a228-57f1cdb2bdba}/the-stress-of-life.pdf

The stress of life

What is Twitter

In the current age of the Fourth Industrial Revolution (4IR or Industry 4.0), the digital world has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc. To intelligently analyze these data and develop the corresponding smart and automated applications, the knowledge of artificial intelligence (AI), particularly, machine learning (ML) is the key. Various types of machine learning algorithms such as supervised, unsupervised, semi-supervised, and reinforcement learning exist in the area. Besides, the deep learning, which is part of a broader family of machine learning methods, can intelligently analyze the data on a large scale. In this paper, we present a comprehensive view on these machine learning algorithms that can be applied to enhance the intelligence and the capabilities of an application. Thus, this study’s key contribution is explaining the principles of different machine learning techniques and their applicability in various real-world application domains, such as cybersecurity systems, smart cities, healthcare, e-commerce, agriculture, and many more. We also highlight the challenges and potential research directions based on our study. Overall, this paper aims to serve as a reference point for both academia and industry professionals as well as for decision-makers in various real-world situations and application areas, particularly from the technical point of view.

/pdf/machine-learning-algorithms-real-world-applications-and-3ah2ghoulk.pdf

Machine Learning: Algorithms, Real-World Applications and Research Directions

BACKGROUND
Depression, anxiety and somatization are the most common mental disorders in primary care as well as medical specialty populations; each is present in at least 5-10% of patients and frequently comorbid with one another. An efficient means for measuring and monitoring all three conditions would be desirable.


METHODS
Evidence regarding the psychometric and pragmatic characteristics of the Patient Health Questionnaire (PHQ)-9 depression, generalized anxiety disorder (GAD)-7 anxiety and PHQ-15 somatic symptom scales are synthesized from two sources: (1) four multisite cross-sectional studies (three conducted in primary care and one in obstetric-gynecology practices) comprising 9740 patients, and (2) key studies from the literature that have studied these scales.


RESULTS
The PHQ-9 and its abbreviated eight-item (PHQ-8) and two-item (PHQ-2) versions have good sensitivity and specificity for detecting depressive disorders. Likewise, the GAD-7 and its abbreviated two-item (GAD-2) version have good operating characteristics for detecting generalized anxiety, panic, social anxiety and post-traumatic stress disorder. The optimal cutpoint is > or = 10 on the parent scales (PHQ-9 and GAD-7) and > or = 3 on the ultra-brief versions (PHQ-2 and GAD-2). The PHQ-15 is equal or superior to other brief measures for assessing somatic symptoms and screening for somatoform disorders. Cutpoints of 5, 10 and 15 represent mild, moderate and severe symptom levels on all three scales. Sensitivity to change is well-established for the PHQ-9 and emerging albeit not yet definitive for the GAD-7 and PHQ-15.


CONCLUSIONS
The PHQ-9, GAD-7 and PHQ-15 are brief well-validated measures for detecting and monitoring depression, anxiety and somatization.

Iconography : The Patient Health Questionnaire Somatic, Anxiety, and Depressive Symptom Scales: a systematic review

Notifications are extremely beneficial to users, but they often demand their attention at inappropriate moments. In this paper we present an in-situ study of mobile interruptibility focusing on the effect of cognitive and physical factors on the response time and the disruption perceived from a notification. Through a mixed method of automated smartphone logging and experience sampling we collected 10372 in-the-wild notifications and 474 questionnaire responses on notification perception from 20 users. We found that the response time and the perceived disruption from a notification can be influenced by its presentation, alert type, sender-recipient relationship as well as the type, completion level and complexity of the task in which the user is engaged. We found that even a notification that contains important or useful content can cause disruption. Finally, we observe the substantial role of the psychological traits of the individuals on the response time and the disruption perceived from a notification.

/pdf/my-phone-and-me-understanding-people-s-receptivity-to-mobile-2qx882t3wz.pdf

My Phone and Me: Understanding People's Receptivity to Mobile Notifications

An increasing number of notifications demanding the smartphone user's attention, often arrive at an inappropriate moment, or carry irrelevant content. In this paper we present a study of mobile user interruptibility with respect to notification content, its sender, and the context in which a notification is received. In a real-world study we collect around 70,000 instances of notifications from 35 users. We group notifications according to the applications that initiated them, and the social relationship between the sender and the receiver. Then, by considering both content and context information, such as the current activity of a user, we discuss the design of classifiers for learning the most opportune moment for the delivery of a notification carrying a specific type of information. Our results show that such classifiers lead to a more accurate prediction of users' interruptibility than an alternative approach based on user-defined rules of their own interruptibility.

/pdf/designing-content-driven-intelligent-notification-mechanisms-35mu5sj3pp.pdf

Designing content-driven intelligent notification mechanisms for mobile applications

Mobile notifications are increasingly used by a variety of applications to inform users about events, news or just to send alerts and reminders to them. However, many notifications are neither useful nor relevant to users' interests and, also for this reason, they are considered disruptive and potentially annoying. In this paper we present the design, implementation and evaluation of PrefMiner, a novel interruptibility management solution that learns users' preferences for receiving notifications based on automatic extraction of rules by mining their interaction with mobile phones. The goal is to build a system that is intelligible for users, i.e., not just a "black-box" solution. Rules are shown to users who might decide to accept or discard them at run-time. The design of PrefMiner is based on a large scale mobile notification dataset and its effectiveness is evaluated by means of an in-the-wild deployment.

/pdf/prefminer-mining-user-s-preferences-for-intelligent-mobile-4dlf7bxxvd.pdf

PrefMiner: mining user's preferences for intelligent mobile notification management

Neural Architecture Search (NAS) is quickly becoming the standard methodology to design neural network models. However, NAS is typically compute-intensive because multiple models need to be evaluated before choosing the best one. To reduce the computational power and time needed, a proxy task is often used for evaluating each model instead of full training. In this paper, we evaluate conventional reduced-training proxies and quantify how well they preserve ranking between multiple models during search when compared with the rankings produced by final trained accuracy. We propose a series of zero-cost proxies, based on recent pruning literature, that use just a single minibatch of training data to compute a model's score. Our zero-cost proxies use 3 orders of magnitude less computation but can match and even outperform conventional proxies. For example, Spearman's rank correlation coefficient between final validation accuracy and our best zero-cost proxy on NAS-Bench-201 is 0.82, compared to 0.61 for EcoNAS (a recently proposed reduced-training proxy). Finally, we use these zero-cost proxies to enhance existing NAS search algorithms such as random search, reinforcement learning, evolutionary search and predictor-based search. For all search methodologies and across three different NAS datasets, we are able to significantly improve sample efficiency, and thereby decrease computation, by using our zero-cost proxies. For example on NAS-Bench-101, we achieved the same accuracy 4× quicker than the best previous result.

/pdf/zero-cost-proxies-for-lightweight-nas-2tr6d7s07t.pdf

Zero-Cost Proxies for Lightweight NAS

Remarkable advances in smartphone technology, especially in terms of passive sensing, have enabled researchers to passively monitor user behavior in real-time and at a granularity that was not possible just a few years ago. Recently, different approaches have been proposed to investigate the use of different sensing and phone interaction features, including location, call, SMS and overall application usage logs, to infer the depressive state of users. In this paper, we propose an approach for monitoring of depressive states using multi-modal sensing via smartphones. Through a brief literature review we show the sensing modalities that have been exploited in the past studies for monitoring depression. We then present the initial results of an ongoing study to demonstrate the association of depressive states with the smartphone interaction features. Finally, we discuss the challenges in predicting depression through multimodal mobile sensing.

/pdf/towards-multi-modal-anticipatory-monitoring-of-depressive-18a46g9rne.pdf

Abhinav Mehrotra

Papers

My Phone and Me: Understanding People's Receptivity to Mobile Notifications

Designing content-driven intelligent notification mechanisms for mobile applications

PrefMiner: mining user's preferences for intelligent mobile notification management

Zero-Cost Proxies for Lightweight NAS

Towards multi-modal anticipatory monitoring of depressive states through the analysis of human-smartphone interaction