scholarly journals Exploiting Serialized Fine-Grained Action Recognition Using WiFi Sensing

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Weiyuan Tong ◽  
Rong Li ◽  
Xiaoqing Gong ◽  
Shuangjiao Zhai ◽  
Xia Zheng ◽  
...  
Keyword(s):  
Nonverbal Communication ◽  
Hand Movement ◽  
User Studies ◽  
Machine Learning Techniques ◽  
Use Case ◽  
Chinese Calligraphy ◽  
Gesture Interaction ◽  
Fine Grained ◽  
Learning Techniques ◽  
Natural Interactions

Gestures serve an important role in enabling natural interactions with computing devices, and they form an important part of everyday nonverbal communication. In increasingly many application scenarios of gesture interaction, such as gesture-based authentication, calligraphy, sketching, and even artistic expression, not only are the underlying gestures complex and consist of multiple strokes but also the correctness of the gestures depends on the order at which the strokes are performed. In this paper, we present WiCG, an innovative and novel WiFi sensing approach for capturing and providing feedback on stroke order. Our approach tracks the user’s hand movement during writing and exploits this information in combination with statistical methods and machine learning techniques to infer what characters have been written and at which stroke order. We consider Chinese calligraphy as our use case as the resulting gestures are highly complex, and their assessment depends on the correct stroke order. We develop a set of analyses and algorithms to overcome many issues of this challenging task. We have conducted extensive experiments and user studies to evaluate our approach. Experimental results show that our approach is highly effective in identifying the written characters and their written stroke order. We show that our approach can adapt to different deployment environments and user patterns.

scholarly journals Non Verbal Communication for Analyzing the Malpractice in Examinations– An Overview

2018 ◽  
Vol 7 (2.20) ◽  
pp. 227
Author(s):  
Kujani T ◽  
Sathya T ◽  
Bhuvanya R ◽  
Uma S
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Interpersonal Communication ◽  
Nonverbal Communication ◽  
Current Situation ◽  
Verbal Communication ◽  
Machine Learning Techniques ◽  
The People ◽  
Learning Techniques

Nonverbal communication can specify the psychosomatic behavior of people involved in interpersonal communication. Many researchers have specified the importance of gesture intimation. In the paper, we shall apply the previously used computer vision hardware and Machine Learning techniques for capturing the postures students undergoing examination in the classroom atmosphere. The main       intention is to classify the people who involved in misbehavior such as copying, prompting answers, sharing the answer scripts and any other such practices. Current situation prevailing is though a physical monitor, invigilator is available in the exam hall the students     attempt to misbehave in various ways mentioned above. We discuss about the techniques to be employed to get an analysis of the       behavior of each student involved in exam. 

Inducing and Refining Topics for Web Query Classification Using a Semantic Network

2018 ◽  
Vol 15 (2) ◽  
pp. 595-600
Author(s):  
R. Sathish Kumar ◽  
M. Chandrasekaran
Keyword(s):  
Query Expansion ◽  
Web Search ◽  
Semantic Network ◽  
Machine Learning Techniques ◽  
Search Query ◽  
Fine Grained ◽  
Computational Overhead ◽  
Learning Techniques ◽  
Query Classification ◽  
The Web

Web query classification, the task of inferring topical categories from a web search query is a non-trivial problem in Information Retrieval domain. The topic categories inferred by a Web query classification system may provide a rich set of features for improving query expansion and web advertising. Conventional methods for Web query classification derive corpus statistics from the web and employ machine-learning techniques to infer Open Directory Project categories. But they suffer from two major drawbacks, the computational overhead to derive corpus statistics and inferring topic categories that are too abstract for semantic discrimination due to polysemy. Concepts too shallow or too deep in the semantic gradient are produced due to the wrong senses of the query terms coalescing with the correct senses. This paper proposes and demonstrates a succinct solution to these problems through a method based on the Tree cut model and Wordnet Thesarus to infer fine-grained topic categories for Web query classification, and also suggests an enhancement to the Tree Cut Model to resolve sense ambiguities.

scholarly journals The Promise and Pitfalls of Conflict Prediction: Evidence from Colombia and Indonesia

2021 ◽  
pp. 1-45
Author(s):  
Samuel Bazzi ◽  
Robert A. Blair ◽  
Christopher Blattman ◽  
Oeindrila Dube ◽  
Matthew Gudgeon ◽  
...  
Keyword(s):  
Early Warning ◽  
Early Warning Systems ◽  
Machine Learning Techniques ◽  
Annual Data ◽  
Fine Grained ◽  
Learning Techniques ◽  
One Year ◽  
Rich Data ◽  
Socioeconomic Data ◽  
Conflict Prediction

How feasible is violence early-warning prediction? Columbia and Indonesia have unusually fine-grained data. We assemble two decades of local violent events alongside hundreds of annual risk factors. We attempt to predict violence one year ahead with a range of machine learning techniques. Our models reliably identify persistent, high-violence hot spots. Violence is not simply autoregressive, as detailed histories of disaggregated violence perform best, but socioeconomic data substitute well for these histories. Even with unusually rich data, however, our models poorly predict new outbreaks or escalations of violence. These “best case” scenarios with annual data fall short of workable early-warning systems.

Use Case—Fraud Detection Using Machine Learning Techniques

2021 ◽  
pp. 33-49 ◽  
Author(s):  
Philipp Enzinger ◽  
Sangmeng Li
Keyword(s):  
Machine Learning ◽  
Fraud Detection ◽  
Machine Learning Techniques ◽  
Use Case ◽  
Learning Techniques

scholarly journals Machine Learning, Ethics and Law

2019 ◽  
Vol 23 ◽  
Author(s):  
Seumas Miller
Keyword(s):  
Machine Learning ◽  
Ethical Issues ◽  
Personal Information ◽  
Political Advertising ◽  
Legal Issues ◽  
Machine Learning Techniques ◽  
Political Actors ◽  
Fine Grained ◽  
Moral Principles ◽  
Learning Techniques

Recent revelations concerning data firm Cambridge Analytica’s illegitimate use of the data of millions of Facebook users highlights the ethical and, relatedly, legal issues arising from the use of machine learning techniques. Cambridge Analytica is, or was – the revelations brought about its demise - a firm that used machine learning processes to try to influence elections in the US and elsewhere by, for instance, targeting ‘vulnerable’ voters in marginal seats with political advertising. Of course, there is nothing new about political candidates and parties employing firms to engage in political advertising on their behalf, but if a data firm has access to the personal information of millions of voters, and is skilled in the use of machine learning techniques, then it can develop detailed, fine-grained voter profiles that enable political actors to reach a whole new level of manipulative influence over voters. My focus in this paper is not with the highly publicised ethical and legal issues arising from Cambridge Analytic’s activities but rather with some important ethical issues arising from the use of machine learning techniques that have not received the attention and analysis that they deserve. I focus on three areas in which machine learning techniques are used or, it is claimed, should be used, and which give rise to problems at the interface of law and ethics (or law and morality, I use the terms “ethics” and “morality” interchangeably). The three areas are profiling and predictive policing (Saunders et al. 2016), legal adjudication (Zeleznikow, 2017), and machines’ compliance with legally enshrined moral principles (Arkin 2010). I note that here, as elsewhere, new and emerging technologies are developing rapidly making it difficult to predict what might or might not be able to be achieved in the future. For this reason, I have adopted the conservative stance of restricting my ethical analysis to existing machine learning techniques and applications rather than those that are the object of speculation or even informed extrapolation (Mittelstadt et al. 2015). This has the consequence that what I might regard as a limitation of machine learning techniques, e.g. in respect of predicting novel outcomes or of accommodating moral principles, might be thought by others to be merely a limitation of currently available techniques. After all, has not the history of AI recently shown the naysayers to have been proved wrong? Certainly, AI has seen some impressive results, including the construction of computers that can defeat human experts in complex games, such as chess and Go (Silver et al. 2017), and others that can do a better job than human medical experts at identifying the malignancy of moles and the like (Esteva et al. 2017). However, since by definition future machine learning techniques and applications are not yet with us the general claim that current limitations will be overcome cannot at this time be confirmed or disconfirmed on the basis of empirical evidence.

scholarly journals A Random Forest-Based Accuracy Prediction Model for Augmented Biofeedback in a Precision Shooting Training System

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4512
Author(s):  
Junqi Guo ◽  
Lan Yang ◽  
Anton Umek ◽  
Rongfang Bie ◽  
Sašo Tomažič ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Prediction Model ◽  
Hand Movement ◽  
Training System ◽  
The Other ◽  
Machine Learning Techniques ◽  
Movement Error ◽  
Learning Techniques ◽  
Biofeedback System

In the military, police, security companies, and shooting sports, precision shooting training is of the outmost importance. In order to achieve high shooting accuracy, a lot of training is needed. As a result, trainees use a large number of cartridges and a considerable amount of time of professional trainers, which can cost a lot. Our motivation is to reduce costs and shorten training time by introducing an augmented biofeedback system based on machine learning techniques. We are designing a system that can detect and provide feedback on three types of errors that regularly occur during a precision shooting practice: excessive hand movement error, aiming error and triggering error. The system is designed to provide concurrent feedback on the hand movement error and terminal feedback on the other two errors. Machine learning techniques are used innovatively to identify hand movement errors; the other two errors are identified by the threshold approach. To correct the excessive hand movement error, a precision shot accuracy prediction model based on Random Forest has proven to be the most suitable. The experimental results show that: (1) the proposed Random Forest (RF) model achieves the prediction accuracy of 91.27%, higher than any of the other reference models, and (2) hand movement is strongly related to the accuracy of precision shooting. Appropriate use of the proposed augmented biofeedback system will result in a lower number of rounds used and shorten the precision shooting training process.

Hand Movement-Based Diabetes Detection Using Machine Learning Techniques

2021 ◽  
Vol 9 (4) ◽  
pp. 234
Author(s):  
Mutaz Al-Tarawneh ◽  
Mustafa Muheilan ◽  
Ziyad Al Tarawneh
Keyword(s):  
Machine Learning ◽  
Hand Movement ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals Mining knowledge graphs to map heterogeneous relations between the internet of things patterns

2021 ◽  
Vol 11 (6) ◽  
pp. 5066
Author(s):  
Vusi Sithole ◽  
Linda Marshall
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Machine Learning Techniques ◽  
Small Scale ◽  
The Internet ◽  
Use Case ◽  
Object Oriented Design ◽  
Learning Techniques ◽  
Knowledge Graphs ◽  
The Internet Of Things

<span lang="EN-US">Patterns for the internet of things (IoT) which represent proven solutions used to solve design problems in the IoT are numerous. Similar to object-oriented design patterns, these IoT patterns contain multiple mutual heterogeneous relationships. However, these pattern relationships are hidden and virtually unidentified in most documents. In this paper, we use machine learning techniques to automatically mine knowledge graphs to map these relationships between several IoT patterns. The end result is a semantic knowledge graph database which outlines patterns as vertices and their relations as edges. We have identified four main relationships between the IoT patterns-a pattern is similar to another pattern if it addresses the same use case problem, a large-scale pattern uses a small- scale pattern in a lower level layer, a large pattern is composed of multiple smaller scale patterns underneath it, and patterns complement and combine with each other to resolve a given use case problem. Our results show some promising prospects towards the use of machine learning techniques to generate an automated repository to organise the IoT patterns, which are usually extracted at various levels of abstraction and granularity.</span>

scholarly journals The Promise and Pitfalls of Conflict Prediction: Evidence from Colombia and Indonesia

2019 ◽  
Author(s):  
Samuel Bazzi ◽  
Robert Blair ◽  
Chris Blattman ◽  
Oeindrila Dube ◽  
Matthew Gudgeon ◽  
...  
Keyword(s):  
Hot Spots ◽  
Early Warning Systems ◽  
Machine Learning Techniques ◽  
Warning Systems ◽  
Fine Grained ◽  
Learning Techniques ◽  
One Year ◽  
Rich Data ◽  
Conflict Prediction ◽  
Annual Risk

Policymakers can take actions to prevent local conflict before it begins, if such violence can be accurately predicted. We examine the two countries with the richest available sub-national data: Colombia and Indonesia. We assemble two decades one fine- grained violence data by type, alongside hundreds of annual risk factors. We predict violence one year ahead with a range of machine learning techniques. Models reliably identify persistent, high-violence hot spots. Violence is not simply autoregressive, as detailed histories of disaggregated violence perform best. Rich socio-economic data also substitute well for these histories. Even with such unusually rich data, however, the models poorly predict new outbreaks or escalations of violence. \Best case" scenarios with panel data fall short of workable early-warning systems.

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