Reviewerly/exHarmony huggingface dataset:
Fall Detection using Body Geometry in Video Sequences

According to the World Health Organization, falling of the elderly is a major health problem that causes many injuries and thousands of deaths every year. This increases pressure on health authorities to provide daily health care, reliable medical assistance, reduce fall damages and improve the elderly quality of life. For that, it is a priority to detect or predict falls accurately. In this paper, we present a fall detection approach based on human body geometry inferred from video sequence frames. We calculate the angular information between the vector formed by the head centroid of the identified facial image and the center hip of the body and the vector aligned with the horizontal axis of the center hip. Similarly, we calculate the distance between the vector formed by the head and the body center hip and the vector formed on its horizontal axis; we then construct distinctive image features. These angles and distances are then used to train a two-class SVM classifier and a Long Short-Term Memory network (LSTM) on the calculated angle sequences to classify falls and no-falls activities. We perform experiments on the Le2i fall detection dataset. The results demonstrate the effectiveness and efficiency of the developed approach.
An Image-Based Method for 3D Human Shapes Retrieval

Automatically retrieving 3D human shapes from a single 2D image is a challenging problem. The 2D nature of one image makes it difficult to infer depth, pose and style. We propose a novel method for 3D human shape retrieval based on a single image. We present a single-network approach for keypoints detection, which entails simultaneous localization of internal region keypoints and the outer contour keypoints. The network is trained by using multi-task learning, which can handle scale differences between body/foot and face/hand keypoints through an improved architecture. Based on the keypoints, we can estimate the 3D pose, which is used for 3D pose retrieval. From the outer contour keypoints, the 2D closed boundary curve can be automatically generated. We formulate the 2D curve to 3D human shapes similarity calculation as an energy minimization problem for more sophisticated retrieval. Experimental results show that our method can achieve satisfactory retrieval performance on the two benchmark datasets.
Assessing the Development of Soft Skills for Project Management using PBL: A Case Study

This research-to-practice full paper presents an investigation on how to develop soft skills for project management using PBL in the context of undergraduate degree programs of higher education in computing. Projects are a key way to create value and benefits in organizations and the project manager plays a critical role in the leadership of a project team in order to achieve the project's objectives. In order to fulfill this role, the project manager must have some technical and soft skills. In a related perspective, there are several studies concerned with the comparison between what is covered by Computer Science Education and what are the software industry needs. Such studies point out in which areas graduates most frequently do not meet industry expectations, including technical skills and also soft skills. In this context, among the several approaches that have been proposed and applied to improve Computer Science Education, we highlight Project Based Learning (PBL). Aiming to address the lack of approaches focused on the development of soft skills in technology students, in this paper we discuss the conduction of a case study in Information Systems courses, adopting the PBL approach. In this case study, a real project was developed by the students, and soft skills were evaluated before and after performing the project, in order to verify if the PBL approach had any impact on their development. As a result, it was possible to identify improvements in the development of six out of the seven skills assessed, which indicates a satisfactory result for our study.
Highly Fluent Sign Language Synthesis Based on Variable Motion Frame Interpolation

Sign Language Synthesis (SLS) is a domain-specific problem where multiple sign language words are stitched to generate a whole sentence in video, which serves to facilitate communications between the hearing-impaired people and healthy population. This paper presents a Variable Motion Frame Interpolation (VMFI) method for highly fluent SLS in scattered videos. Existing approaches for SLS mainly focus on mechanical virtual human technology, lacking high flexibility and natural effect. Also, the representative solutions to interpolate frames usually assume that the motion object moves at a constant speed which is not suitable for predicting the complex hand motion in frames of scattered sign language videos. To address the above issues, the proposed VMFI adopts acceleration to predict more accurate interpolated frames based on an end-to-end convolutional neural network. The framework of VMFI consists of variable optical flow estimation network and high-quality frame synthesis network that can approximate and fuse the intermediate optical flow to generate interpolated frames for synthesis. Experimental results on our realistic collected Chinese sign language dataset demonstrate that the proposed VMFI model achieves efficiency by performing better in PSNR (Peak Signal to Noise Ratio), SSIM (Structural Similarity) and MA (Motion Activity) and gets higher score in MOS (Mean Opinion Score) than other two representative methods.
Institutions

When I was a child I devoured mystery novels about the Hardy boys, two squeaky-clean teen detectives who solved surprisingly serious crimes plaguing the otherwise sleepy small town of Bayside, a fictional locale on the Atlantic coast of the United States. But eventually I learned of a crime in these novels even more shocking than the counterfeiting, smuggling, drug running, and murder that the Hardy boys routinely uncovered: the author, Franklin W. Dixon, was an imposter, a mere pseudonym for many different writers churning out titles for the Stratemeyer Syndicate, a fiction factory that mass-produced novels for children and teenagers. I felt betrayed. And foolish. Frank and Joe Hardy could sniff out an international spy ring in a week, but this ruse operated under my nose for years, and even then someone else blew the whistle on Dixon for me.
Spatial Temporal Attention Graph Convolutional Networks with Mechanics-Stream for Skeleton-Based Action Recognition

The static relationship between joints and the dynamic importance of joints leads to high accuracy in skeletal action recognition. Nevertheless, existing methods define the graph structure beforehand by skeletal patterns, so they cannot capture features considering the relationship between joints specific to actions. Moreover, the importance of joints is expected to be different for each action. We propose spatial-temporal attention graph convolutional networks (STA-GCN). It acquires an attention edge that represents a static relationship between joints for each action and an attention node that represents the dynamic importance of joints for each time. STA-GCN is the first method to consider joint importance and relationship at the same time. The proposed method consists of multiple networks, that reflect the difference of spatial (coordinates) and temporal (velocity and acceleration) characteristics as mechanics-stream. We aggregate these network predictions as final result. We show the potential that the attention edge and node can be easily applied to existing methods and improve the performance. Experimental results with NTU-RGB+D and NTU-RGB+D120 demonstrate that it is possible to obtain a attention edge and node specific to the action that can explain behavior and achieves state-of-the-art performances.
Paid and Anonymous Usage of Cloud Software

A solution is proposed in this paper to implement paid and anonymous use of cloud software. It addresses a popular cloud computing service, cloud software, and considers the requirement of some users to protect their privacy. It enables a software user to access a paid cloud software anonymously such that his input to the software and the output of the software to him cannot be linked to his identity. Firstly, a software user buys an anonymous token from a software provider. The anonymous token proves the user’s access privilege to the software and does not reveal his identity. Then the user communicates with the software provider anonymously through a two-way onion routing network, submitting his input and anonymous token and obtaining the output from the software. The two-way onion routing network employs symmetric encryption and decryption and an efficient key exchange mechanism and so does not compromise efficiency of the new scheme.
Environmentally Friendly Technologies

Any technology that is developed and used will have certain components or constituting mechanisms such as materials used, procedure, or technological process involved. The quality of life on planet with respect to environment such as fresh air to breathe, clean water for drinking, and other purposes are essential for all forms of life on earth. Therefore, whatever developments are undertaken should not degrade our environment. It is essential to bear in mind that whatever development, be it construction, new power plants, transportation modes, etc., should be carefully weighed in terms of its contribution to the environment, especially if there are any negative aspects that emerge from use of machinery, equipment, technologies, etc. We should analyze any technology that we use in terms of its cost-benefit study. Here in this chapter, the authors have discussed environmentally friendly technologies for our sustainability to use the natural resources wisely with some case studies of natural as well as manmade disasters.
Exploration of a Prediction Model of Aggression in Children Using Bayesian Networks

Aggression formed in children lasts for their lifetime and it often produces serious antisocial problems. Therefore, a lot of studies have been conducted for this subject in the educational domain, but the studies adopting data mining are relatively few yet. In this paper, the authors adopt Bayesian Networks to find which variables are related to aggression and to evaluate how the variables affect it. Markov Blanket and IMDB method are used to learn a Bayesian Network and to find the relevant variables. In the results, “social withdrawal”, “depression”, “mobile phone dependency”, “grade of Korean”, “attention” and “school activity” are extracted as the relevant variables to aggression. Also, this research investigates which variables are most influencing to aggression by changing the probabilities of variables in the learned Bayesian Network.
Event Detection and Classification Algorithm using Wavelet and Machine Learning Technique for Vibration fence PIDS

The paper presents a design of a real-time algorithm to detect and classify an event that is occurring on a perimeter fence based on the Triboelectric sensor. The intrusion events like climbing, cutting the fence etc. produces vibrations on the fence. These vibrations are detected by Triboelectric cable which runs alongside the fence. Event detection and classification algorithm has been designed based on Wavelet and Machine learning techniques. Field data were collected for basic simulated events such as hand climb, metal tap and wood tap. These signals are pre-processed first before passing to a further part of the algorithm. The Discrete Wavelet Transform (DWT) is used here to extract features from the pre-processed data. These features are used to detect and further classify events using supervised machine learning methods like Support Vector Machine (SVM), decision trees and k-Nearest Neighbours (k NN). The accuracy and complexity of these methods were compared, and based on this analysis a machine learning method has been selected for designing the classification algorithm. The model trained by the selected classification method is further used to derive the formulation required for the classification. The derived formulation is used in designing a real-time algorithm for event detection and classification. The algorithm has been implemented on Digital Signal Processor (DSP) - Field Programmable Gate Array (FPGA) based hardware to detect and classify the events occurring on the fence in real-time.
Grouping Feature Learning for Giant Panda Face Recognition

The giant panda (panda) has lived on the earth for at least eight million years, and as an endangered species, it has received extensive attention from scholars from all walks of life. As an important part of the panda population investigation, the individual identification of pandas can not only provide useful indications but also verify the effectiveness of protection measures. Some work has introduced image processing techniques and deep learning techniques to help researchers identify pandas using face images of a panda. In this paper, we proposed a grouping feature learning method for panda face recognition. In particular, we designed a feature mapping module which can be easily embedded into the existing feature extractors, and a grouping loss function is adopted to constrain the feature mapping, allows the learned similar features to be aggregated together and increase generalization. We use the open captive panda dataset to verify our method, and the results show that our method is effective.
How Fast and Effectively Can Code Change History Enrich Stack Overflow?

Stack Overflow (SO) is one of the most popular Q&A sites for not only providing valuable information to software developers but also encouraging the sharing of knowledge and problem solving. Unfortunately, the information provided by SO is not always sufficient for developers. In this paper, we empirically show how fast and effectively historical code changes can substitute for missing or unanswered SO articles. Developers in all around the world encounter many problems daily and their trial-and-error experiences to resolve the problems are accumulated in the code change history. The extracted source code differences are expected to provide valuable information to developers before the questions and answers are posted on SO. In our study, we focus on the usage of APIs as the topic of SO articles, because many developers are interested in API programming and suffer from the problems related to API usage. We extracted 4,780 code differences from 713 repositories of Android applications (F-Droid). As a result, we found that 64% of SO articles on Android APIs are related to code differences, whereas 44% of code differences are related to SO articles. Not a few code differences appear before the corresponding SO articles are actually posted. The median of time lag between the first appearance of code changes and the first actual SO postings is 22 months.
FORMATION OF ETHNO-PEDAGOGICAL COMPETENCE IN GENERAL EDUCATION TEACHERS DURING ADVANCED TRAINING: NORTHERN SIBERIA EXPERIENCE

The article addresses the problem of formation of ethno-pedagogical competence in the conditions of advanced teacher training. The study aimed to analyze the ethno-pedagogical competence of a teacher as a pedagogical concept, to highlight its components, and to theoretically substantiate pedagogical conditions that contribute to the formation of ethno-pedagogical competence of a teacher during advanced teacher training. The study targeted teachers of general education institution in Northern Siberia, Russia, and relied on such methods as theoretical analysis, empirical methods (survey, questionnaire), and mental simulation of pedagogical conditions. The survey revealed the teachers have aneed for expanding their ethno-pedagogical knowledge and the insufficient manifestation of the components of ethno-pedagogical competence. This induced the search for learning conditions that could form the ethno-pedagogical competence in secondary education teachers in the conditions of advanced teacher training. The learning conditions include, first, the selection of training modules with informative content on disciplines that provide a motivationally valuable, cognitive, activity-related orientation and make up the core of ethno-pedagogical competence; second, the involvement of teachers in solving problems and discussing cases that reflect the regional-ethnic trends; and, third, the involvement of teachers into design and research activities. When implemented, these learning conditions will contribute to the training of a teachercompetentin ethno-pedagogical issues.
Leveraging Anomaly Detection for Proactive Application Monitoring

Anomaly detection is one of the popular research fields in Machine Learning. Also, this is one of the key techniques in system and application monitoring in Industry. Anomaly detection comprises of outlier detection and identifying novelty from the data - it is a process to understand the deviation of an observation from existing observations [12] and identifying the new observations. Carrying out anomaly detection in an enterprise application is a challenge as there are complex processes to gather and analyze functional and non-functional logs of unlabeled data. In this paper we are proposing an unsupervised learning process with log featurization incorporating time window to detect outliers and novel errors from enterprise application logs.
LibGuides: Women's Studies: Getting Started

Highlighting resources that support women's studies at the UTSA Library. Researchers in this area may also want to look at more focused guides such as Women's History.
Accommodating Shortened Term Lengths in a Capstone Course using Minimally Viable Prototypes

This Full Paper presents a novel way of implementing the first half of a two-semester software design and implementation capstone course. The paper compares prior work from traditional (15-week) terms to a compressed (7-week) format and a significantly compressed (4-week) format employing this approach. To maximize competitiveness, many programs are under pressure to do more with less: competing to provide more outcomes and more practical real-world experiences in less time and with fewer courses. These challenges become especially significant in capstone courses, where students, still learning, must design and implement solutions consolidating a wide variety of skills and knowledge covered in the curriculum.The capstone course spans two semesters; the first semester focuses on design and the second semester focuses on implementation. In the standard approach, teams meet with the client and develop design documents including use cases, user interface sketches, and data models. In this approach, rather than starting from scratch, students received artifacts from a previous capstone course employing a consultancy model. Artifacts included a request for proposal (RFP), a minimally viable prototype (MVP) partial implementation, and corresponding design documents. The work reviews students' ability to effectively incorporate the provided artifacts to produce a high quality design for complete implementation of the project.The work includes an evaluation of first-semester team deliverables generated under both the compressed and significantly compressed schedules and relates the expectations and outcomes to those in a traditional-length term. This approach offers a common means to assess schedule impacts and tailor the course in accordance with the relative concentration of contact hours associated with the schedule. This paper describes variations in expectations and options for MVP, client interaction schedules (including frequency and duration), and discusses the associated impacts as perceived by clients and mentors on engagement and student learning outcomes. Finally, the paper offers specific recommendations and lessons learned for those educators that are - or plan to - offer capstone courses under a highly compressed schedule.
Methods for Extracting Changes in Human Relationships on Web Based on Commonality Analysis of Graphs

Along with the rise of intellectual activities archived on the Internet, it has increasingly been important to understand human relationships on the Web. Understanding the connection between people is important in deeply exploring a particular person and information about the person. However, there is a limit to understanding human relationships manually due to Web bloat. Moreover, relationships greatly change with the passage of time, such as when the organization to which a person belongs changes. These factors have made it difficult to understand relationships. Against these problems, there have been several methods for visualizing human relationships as networks. However, they have not been effective enough because target people and connection factors are limited, and changes in relationships are not taken into account. Therefore, this research is aimed at developing novel support for understanding human relationships. In this paper, we propose methods for dynamically extracting changes in human relationship networks on the Web. Specifically, we initially develop methods for extracting relationships in accordance with the person of interest and relationship factors. We then develop methods for expressing the extracted human relationships as a network. Moreover, we develop methods for extracting the common parts of two human-relationship networks and significantly changed portions by comparing the networks. We conducted experiments and discuss the effectiveness of the proposed methods on the basis of the results.