Reviewerly/exHarmony huggingface dataset:
When Is a Recommendation Model Wrong? A Model-Agnostic Tree-Based Approach to Detecting Biases in Recommendations

Most of the recommendation algorithms are tuned to optimize the global objective function. However, the distribution of error may differ dramatically among different combinations of attributes, and such design of algorithms may lead to propagating the hidden data biases. In this work, we propose a model-agnostic technique to detect the combinations of user and item attributes correlated with unequal treatment by the recommender. Our results on a popular movie dataset show that the proposed technique can detect hidden biases for different popular recommendation algorithms. The debiasing techniques may be applied for improving the performance of the discriminated cases.
Multi-level feature driven storage management of surveillance videos

Surveillance videos in smart environments have become commodities nowadays, which enable many novel applications, including various video analytics that turn videos into semantic results. In addition to live feeds, surveillance videos may be saved in a storage server for on-demand user-defined queries in the future. Different from on-demand video streaming servers, whose design objective is to maximize the user-perceived video quality, a surveillance video storage server has limited space and must retain as much information as possible while reserving sufficient space for incoming videos. In this article, we design, implement, optimize, and evaluate a multi-level feature driven storage server for diverse-scale smart environments, which can be buildings, campuses, communities, and cities. We focus on the design and implementation of the storage server and solve two key research problems in it, namely: (i) efficiently determining the information amount of incoming videos and (ii) intelligently deciding the qualities of videos to be kept. In particular, we first analyze the videos to derive approximate information amount without overloading our storage server. This is done by formally defining the information amount based on multi-level (semantic and visual) features of videos. We then leverage the information amounts to determine the optimal downsampling approach and target quality level of each video clip to save the storage space, while preserving as much information amount as possible. We rigorously formulate the above two research problems into mathematical optimization problems, and propose optimal, approximate, and efficient algorithms to solve them. Besides a suite of optimization algorithms, we also implement our proposed system on a smart campus testbed at NTHU, Taiwan, which consists of eight smart street lamps. The street lamps are equipped with a wide spectrum of sensors, network devices, analytics servers, and a storage server. We compare the performance of our proposed algorithms against the current practices using real surveillance videos from our smart campus testbed. Our efficient algorithms outperform the current practices in multiple dimensions, meaning we: (i) achieve a mere 7% approximation gap on captured information amount compared to the optimal solutions, (ii) save almost 3 times more clips after a week, (iii) achieve 58% less per-query error on average, (iv) always terminate in less than 100 ms, (v) do not consume excessive storage space, and (vi) scale well with larger storage spaces.
An Analysis of Vibration Characteristic of Thai Traditional Xylophone (Ranad-Ek)

This research studied features of a Thai traditional xylophone (Ranad-Ek). The characteristic vibrations of Ranad pieces were considered by the measurement of frequencies using MATLAB and the results compared with finite element analysis (FEA). Three pieces of Ranad were investigated in this study: lowest tone, middle tone and highest tone. It was found that characteristic vibrations of Ranad pieces were similar to vibration of free-free beam as the simulation results of frequencies corresponded to experimental results from MATLAB. The effects of the geometry and weight of lead pieces used in tuning a Ranad piece were also analyzed. Two shapes of lead weights were considered, i.e. a hemispherical shape and a rectangular shape. The weight of lead attached to Ranad pieces and sound frequencies exhibited an inverse relationship. On the other hand, the lead geometry had insignificant effect on the Ranad piece's frequency.
An Analytical and Comparative Study of Hospital Re-admissions in Digital Health Care

Medical re-admissions are expensive and indicate poor quality of hospitals. A remarkable re-admission rate has a huge financial impact on the patients and the hospital. Through the increasing use of medical health records, enormous data is available to us for review which can identify high-risk patients effectively and decrease the mortality rate. According to the 2010 Affordable Care Act, hospitals may be penalized for re-admitting patients within 30 days of discharge. However, hospitals claim that the root cause of re-admissions lies similar to the populations served. In this paper, various re-admission prediction techniques that have been used earlier to estimate the re-admission rate of patients after being discharged from the hospital have been discussed. This article will also give a summary of various socioeconomic and demographic factors that play a vital role in medical re-admissions. Moreover, it will cover some of the measures that can be taken to reduce hospital re-admissions.
Professional formation of the teachers under the conditions of digital transformation of higher education

The article is devoted to the problem of using digital technologies for the professional formation of future primary school teachers. The authors note the relevance of this problem in connection with the digital transformation of higher education. It is emphasized that both Russian and foreign researchers define professional competence as the leading feature of professional development in the conditions of digitalization of education, reflecting all aspects of the professional activity of a teacher. In the article, the authors describe an effective way of using digital technologies for the professional formation of primary school teachers. Based on the content analysis of the regulatory database and scientific researches, it is noted that the study of this problem in the system of Russian higher pedagogical education is a separate direction under study linked both with the prospective development of education as a whole and risk factors. The article substantiates the need for professional formation of the future primary school teachers under the conditions of digitalization of the educational process and traditional forms of education. The authors have developed a model of professional formation of future primary school teachers under the conditions of digitalization of higher pedagogical education, and presented the components of digitalization of higher pedagogical education. This research is addressed to the teaching staff of higher educational institutions, and to future teachers.
Student Perception of Online Education Supported in the COVID-19 Period

The coronavirus pandemic has brought many changes, requiring the identification of new ways of working, interaction and lifestyle. This created a context conducive to questions about the social order, the need for investment and the areas where such investment is needed in the future. Strengths and weaknesses were visible, both in a global context and in a European context; new challenges have arisen that society as a whole has to face. Digital technologies are a key component in collective efforts to address the virus and support new ways of living and doing business in this exceptional time. The European Commission has taken various measures to address the various aspects of the pandemic, and digital technologies play a vital role in this. Ensuring infrastructure, connectivity and rules of use are an important goal to enable the European (but also global) population to remain active and safe. Through this paper, we aimed to measure students' perception of online courses, the technology used to conduct online courses and the quality of teaching by teachers. The study involved, between October 1-15, 2020, a number of 120 students of the Faculty of Law and Administrative Sciences (Ovidius University of Constanta).
Web-Based Collaborative VR Training System and Its Log Functionality for Radiation Therapy Device Operations

This paper introduces several additional functionalities that improve the web-based collaborative VR training system for the radiation therapy device operations already proposed by the authors. Medical students have to train as a therapist if they want to be so. However, radiation therapy devices are very expensive and dangerous, so any training systems without using the devices are needed. The authors have already proposed the web-based collaborative VR training system for the radiation therapy device operations. However, there still remains several functionalities to be implemented in order to improve the system. This time, the authors implemented some of them. The significant one is the addition of log functionality. In this paper, the authors introduce these new functionalities.
Investigating the Effectiveness of User Onboarding Solutions With Eye Tracking: A Case Study on Paint 3D

This article examines the user’s first experience with a new software application called the user onboarding process. In addition, we conducted a series of usability experiments with 18 participants, which were supported by eye tracking technology, to explore the effectiveness of different user onboarding solutions, exemplified by the newest version of Paint. We also explored users’ feedback on onboarding solutions in a series of interviews. The results showed that user onboarding solutions are generally effective, making it worthwhile for computer software companies to provide them to support new users’ activity as well as to increase the value of their products.
Learning to Listen to Data: Voice Interaction, Sonification, and Narrative

My dissertation explores how to help people learn to listen to data through new voice-first technological capabilities, by combining narrative storytelling and data sonification. For example, what if a voice assistant could help you craft personalized sounds that meaningfully represent your emotions, log them over time, and listen back to those sounds and reflect on your behaviors? Beyond simply helping general audiences engage with data, I hope these kinds of interactions can support people as they develop new capacities to learn and think with data and sound, bringing acts of everyday data interpretation to new audiences and contexts.
Introduction to GraphBLAS 2.0

The GraphBLAS is a set of basic building blocks for constructing graph algorithms in terms of linear algebra. They are first and foremost defined mathematically with the goal that language bindings will be produced for a wide range of programming languages. We started with the C programming language and over the last four years have produced multiple versions of the GraphBLAS C API specification. In this paper, we describe our next version of the C GraphBLAS specification. It introduces a number of major changes including support for multithreading, import/export functionality, and functions that use the indices of matrix/vector elements. Since some of these changes introduce small backwards compatibility issues, this is a major release we call GraphBLAS 2.0.
Incorporating Price into Recommendation with Graph Convolutional Networks

In this work, we aim at developing an effective method to predict user purchase intention with the focus on the price factor in recommender systems. The main difficulties are two-fold: 1) the preference and sensitivity of a user on item price are unknown, which are only implicitly reflected in the items that the user has purchased, and 2) how the item price affects a users intention depends largely on the product category, that is, the perception and affordability of a user on item price could vary significantly across categories. Towards the first difficulty, we propose to model the transitive relationship between user-to-item and item-to-price, taking the inspiration from the recently developed Graph Convolution Networks (GCN). The key idea is to propagate the influence of price on users with items as the bridge, so as to make the learned user representations be price-aware. For the second difficulty, we further integrate item categories into the propagation progress and model the possible pairwise interactions for predicting user-item interactions. We conduct extensive experiments on two real-world datasets, demonstrating the effectiveness of our GCN-based method in learning the price-aware preference of users.
On Saving Outliers for Better Clustering over Noisy Data

Clustering is often distracted by errors, frequently observed in almost all areas, ranging from online questionnaire to sensor reading in IoT. The dirty data values not only make themselves (the corresponding tuples) outlying, but also mislead the clustering of remaining tuples, e.g., mistakenly splitting a cluster into two or distorting the cluster center. The reason is that the traditional clustering methods either simply ignore the outliers such as DBSCAN or assign them to the closest clusters anyway, e.g., in K-Means. In this paper, we propose to save the outliers for better clustering. The idea is to adjust the erroneous values (often minimally) of the outlier in order to make it appear normally. That is, the tuples after adjusting values are no longer outlying, and thus will be clustered without distracting others. The outlier saving by value adjustment is designed to work with any clustering methods (e.g., DBSCAN or K-Means). Our technical contributions include: (1) showing NPhardness of the outlier saving problem for clustering, (2) deriving lower and upper bounds of the optimal solutions, and (3) devising approximation algorithm with performance guarantees referring to the aforesaid bounds. Experiments on datasets with real-world outliers demonstrate the higher accuracy of our proposal, compared to the state-of-the-art approaches. Remarkably, we show that the adjusted data with outlier saving indeed improve significantly clustering, as well as other applications such as classification and record matching.
Detection of Electronic Devices in real images using Deep Learning Techniques

Object Detection from real world scenario is a subset of Computer Vision, that uses state-of-the-art algorithms and techniques in deep learning to identify and locate the objects in an image or video. Latest advancements in Deep Learning, especially Convolutional Neural Networks (CNN) and in the field of image processing has further improved the process of object detection. Deep learning algorithms that are developed over the years aim to solve several challenges associated with object detection which includes localizing the object in an image, classifying the object correctly with a high confidence score and realtime detection of objects. The performance of the existing algorithms involves a tradeoff between accuracy and detection speed. Algorithms like Faster Region based Convolutional Neural Networks (R-CNN) and Single Shot Detector (SSD) that achieved high accuracy in classifying objects were slow in detecting the objects. Such algorithms were not able to keep up with the pace of detection with video input in realtime and thus were not suitable for implementation in critical applications. The drawbacks associated with these algorithms can be eliminated by following a unified one-state approach. The approach is to fully identify and classify the required objects of interest by passing the image only once through the network. This approach thus decreases detection time considerably. You Only Look Once (YOLO) family of algorithms is one such single shot detector that uses CNNs to detect objects. In our work, we have used the YOLOv3 algorithm to develop a model that detects electronic devices. The model was also tested against realtime input from webcam and mean Average Precision (mAP) of YOLOv3 has been computed and compared with another model developed using Faster R-CNN.
A Belief Theory-Based Decision System for Breast Cancer Data

Belief theory involving the use of Shafer's model and source combination rules has found wide use for the development and improvement of classification models. Availability of theoretical constructs for making the best use of available information including the ability to deal with ignorance makes it an attractive option. Representation of ignorance means that we can deal with hard to classify samples by deferring the decision rather than taking an erroneous one. This makes it especially useful in the biomedical field where the cost of making an error is high. In this paper, we develop a belief theory-based classification model and apply it to the Wisconsin breast cancer database. The proposed method is compared with an existing belief theory-based classifier, the conventional support vector machine classifier and other recent approaches using different performance parameters such as error rate, false-positive rate, false negative rate, and confusion matrix. The proposed model is found to outperform the approaches under consideration and is also able to deal with data samples having missing feature values effectively.
Introducing Tagasaurus, an Approach to Reduce Cognitive Fatigue from Long-Term Interface Usage When Storing Descriptions and Impressions from Photographs

Digital cameras and mobile phones have given people around the world the ability to take a large number of photos and store them on their computers. As these images serve the purpose of storing memories and bringing them to mind in the potentially far future, it is important to also store the impressions a user may have from them. Annotating these images can be a laborious process and the work here presents an application design and functioning implementation, which is openly available now, to ease the effort of this task. It also draws inspiration from interface developments of previous applications such as the Nokia Lifeblog and the Facebook user interface. A different mode of sentiment entry is provided where users interact with slider widgets rather than select a emoticon from a set to offer a more fine grained value. Special attention is made to avoid cognitive strain by avoiding nested tool selections.
Development trends in practical training of college students in the context of digital transformation of education

The study proves the key role of practical training as a basic constituent of the content of secondary vocational education, represented by both apical and implicit components. The study’s goal is to identify and highlight the development trends of practical training of college students in the new digital society. The study was carried out on the basis of the Polytechnic College No. 8 and the Russian State Agrarian University – Moscow Timiryazev Agricultural Academy. Basic methods involved the analysis of scientific and pedagogical literature, regulatory documents, SWOT analysis, generalization, observation, pedagogical experiment. The authors make a strong case for improving the practice-oriented component of vocational education in the context of it digital transformation. For this purpose, network interaction is actively implemented as a prototype of the dual education system. Analyzing the case of a polytechnic college, the authors show the advantages of using computer models in the process of practical preparation, including the SinuTrain program, which can fully simulate the operation of numerically controlled metal-working machines on a personal computer. In the context of the digital transformation of education, an important trend is the use of VR/AR technologies in the training process. The paper outlines the effectiveness of cooperation between the college and partner enterprises in preparing students for the demonstration exam. The authors prove the necessity of transforming practical training in colleges into a system of dual education on the basis of network interaction. This can be implemented in the form of study and production clusters.
Optimizing polarization dehazing

In the process of haze removal by the polarization method, it is very important to obtain accurate polarization information. On the one hand, the influence of noise can be effectively suppressed by appropriately weighted amplifying the airlight degree of polarization and the airlight corresponding to an object at an infinite distance. On the other hand, the scattering energy in channels [Formula: see text] and [Formula: see text] is larger, so it can reflect the actual polarization information violently. Analysis indicates the appropriately weighted amplification for the airlight degree of polarization in these two channels that can improve their estimation accuracy to a certain extent, it makes the color of the restored images more natural, and the details of the images are also more abundant. Moreover, in some special scenes, the polarization of the direct transmission could not be ignored on account of its important contribution to the total polarization. Hence, when the polarization of the direct transmission needs to be involved, only the polarization of direct transmission that contributes significantly to the total polarization is considered, and the polarization of the direct transmission in other scenes is ignored. This can still effectively eliminate the color distortion of the special scenes in the recovery results, and can greatly reduce the time consumption.
Model of the text classification system using fuzzy sets

Classification of work’s subject area by keywords is an actual and important task. This article describes algorithms for classifying keywords by subject area. A model was developed using both algorithms and tested on test data. The results were compared with the results of other existing algorithms suitable for this tasks. The obtained results of the model were analysed. This algorithm can be used in real-life tasks.
An Upcycled IoT

An upcycled approach uses everyday objects as design material for IoT systems by enabling users to make their “dumb” objects “smart.” Adopting this approach, IoT Codex realizes a new socially informed, context-aware computing and end-user programming.