Reviewerly/exHarmony huggingface dataset:
Reliability Analysis of Multi-Objective Spatio-Temporal Segmentation of Human Motion in Video Sequences

In view of the problem of uneven distribution of edge contour of multi-target human motion image in video sequence, which leads to the decline of target detection ability, an algorithm of multi-target spatial-temporal segmentation of human motion in video sequence based on edge contour feature detection and block fusion is proposed. Firstly, a multi-target spatial-temporal detection model of human motion in video sequence was constructed, extracting video image frame sequence, using discrete frame fusion method to segment and fuse moving target image, matching moving multi-target in video sequence, secondly segmenting motion features in moving target image, combining with SURF algorithm (speeded up robust features, accelerated robust features) to detect and extract human motion objects in video sequence. The experimental results show that the gray histogram of human motion multi-target space-time segmentation is close to the original image histogram, and the detection and recognition ability of human motion target is improved.
Channel Effects on Surrogate Models of Adversarial Attacks against Wireless Signal Classifiers

We consider a wireless communication system that consists of a background emitter, a transmitter, and an adversary. The transmitter is equipped with a deep neural network (DNN) classifier for detecting the ongoing transmissions from the background emitter and transmits a signal if the spectrum is idle. Concurrently, the adversary trains its own DNN classifier as the surrogate model by observing the spectrum to detect the ongoing transmissions of the background emitter and generate adversarial attacks to fool the transmitter into misclassifying the channel as idle. This surrogate model may differ from the transmitter's classifier significantly because the adversary and the transmitter experience different channels from the background emitter and therefore their classifiers are trained with different distributions of inputs. This system model may represent a setting where the background emitter is a primary user, the transmitter is a secondary user, and the adversary is trying to fool the secondary user to transmit even though the channel is occupied by the primary user. We consider different topologies to investigate how different surrogate models that are trained by the adversary (depending on the differences in channel effects experienced by the adversary) affect the performance of the adversarial attack. The simulation results show that the surrogate models that are trained with different distributions of channel-induced inputs severely limit the attack performance and indicate that the transferability of adversarial attacks is neither readily available nor straightforward to achieve since surrogate models for wireless applications may significantly differ from the target model depending on channel effects.
Using the Poly-encoder for a COVID-19 Question Answering System

To combat misinformation regarding COVID-19 during this unprecedented pandemic, we propose a conversational agent that answers questions related to COVID-19.We adapt the Poly-encoder (Humeau et al., 2020) model for informational retrieval from FAQs.We show that after fine-tuning, the Poly-encoder can achieve a higher F1 score.We make our code publicly available for other researchers to use.
Motivation for choosing educational-professional trajectory and freshmen’s academic expectations

The paper considers the relevance of the university’s being students-oriented, shows the need to study the students’ learning motivation at the stage of university entrance, and presents the results of an empirical study. The purpose of the study is to identify the freshmen’s motives for choosing an educational-professional trajectory, awareness of their professional choice and academic expectations. To examine this phenomenon 247 first-year IT-students participated in the study. The results showed that the students are confident in their choice of major and university. Prestigious, financial and self-realization motives prevail among the motives for choosing an educational-professional trajectory. Freshmen’s expectations from studying at a university are associated with personal and professional development, activity in learning, and the satisfaction of cognitive and social needs. The results also showed the existence of a holistic system of subjective motivational foundations of students’ educational and professional activities. This system includes interrelated components: the motives for choosing an educational-professional trajectory, awareness of professional choice and academic expectations.
The Effectiveness of Web Systems and Mobile Applications for their End-Users

Nowadays, people are turning to the internet to search for information and are completing their work using web systems or mobile applications as their medium.This study was conducted to determine the relationship between web systems and end-user effectiveness and performance.This study collected information using primary data, with a population of 200 students from University Sultan Zainal Abidin (UniSZA) in the Faculty of Informatics and Computing.According to Krejcie and Morgan's 1970 table, the sample size is 132.By using SPSS software, the Pearson Correlation formula tests the input.Based on the testing data, the effectiveness of the user using a web based system is 0.633, whereas the effectiveness of using a mobile application is 0.973.In conclusion, there is a moderate relationship between using web systems and the effectiveness of the end-user.Furthermore, there is a strong relationship between using mobile applications and the effectiveness of the end-user.This study recommends that future researchers and the developers of web systems or mobile applications should design better and more suitable questionnaires to achieve excellent results.Developers need to be aware of the relationship between web systems and mobile applications and their end-users so that they can improve their systems and applications.
Modeling the Spatial Distribution of Soil Heavy Metals Using Random Forest Model—A Case Study of Nairobi and Thirirka Rivers’ Confluence

Modeling the spatial distribution of soil heavy metals is important in determining the safety of contaminated soils for agricultural use.This study utilized 60 topsoil samples (0 -30 cm), multispectral images (Sentinel-2), spectral indices, and ancillary data to model the spatial distribution of heavy metals in the soils along the Nairobi River.The model was generated using the Random Forest package in R. Using R 2 to assess the prediction accuracy, the Random Forest model generated satisfactory results for all the elements.It also ranked the variables in order of their importance in the overall prediction.Spectral indices were the most important variables within the rankings.From the predicted topsoil maps, there were high concentrations of Cadmium on the easterly end of the river.Cadmium is an impurity in detergents, and this section is in close proximity to the Nairobi water sewerage plant, which could be a direct source of Cadmium.Some farms had Zinc levels which were above the World Health Organization recommended limit.The Random Forest model performed satisfactorily.However, the predictions can be improved further if the spatial resolutions of the various variables are increased and through the addition of more predictor variables.
Application ofVirtual Reality Technology in College Tennis Teaching

Objective: To test the advantages and disadvantages of virtual reality technology in college tennis teaching. Methods: 32 male students of school tennis team in XX University were randomly divided into experimental group (n=16) and control group (n=16). The experimental group and the control group had 8 weeks of tennis learning (The same learning content), during which the experimental group had MTC Intelligent Tennis System in the course practice stage. Results: 1) The scores of the forehand and backhand batting techniques and the action technique evaluation in the experimental group were significantly better than those before the experiment (p<; 0.05). 2) The control group was significantly better than the control group (p<; 0.05). 3) After the experiment, the scores of the forehand and backhand strokes in the experimental group were significantly better than those in the control group (p<; 0.05). 4) After the experiment, there was no significant difference between the experimental group and the control group (p<; 0.05). Conclusion: 1) Virtual reality technology can more intuitively demonstrate the standard technical actions and timely compare and correct the simulated actions. 2) Virtual reality technology can carry out accurate technical movement track comparison exercise, and speed up the speed of technical movement consolidation. 3) Virtual reality technology can establish personal technical files, personalized tracking and comparison, and facilitate technical action evaluation. 4) Whether virtual reality technology can improve the effect of practice is still lack of sufficient evidence.
P2ExNet: Patch-Based Prototype Explanation Network

Deep learning methods have shown great success in several domains as they process a large amount of data efficiently, capable of solving complex classification, forecast, segmentation, and other tasks. However, they come with the inherent drawback of inexplicability limiting their applicability and trustworthiness. Although there exists work addressing this perspective, most of the existing approaches are limited to the image modality due to the intuitive and prominent concepts. Conversely, the concepts in the time-series domain are more complex and non-comprehensive but these and an explanation for the network decision are pivotal in critical domains like medical, financial, or industry. Addressing the need for an explainable approach, we propose a novel interpretable network scheme, designed to inherently use an explainable reasoning process inspired by the human cognition without the need of additional post-hoc explainability methods. Therefore, class-specific patches are used as they cover local concepts relevant to the classification to reveal similarities with samples of the same class. In addition, we introduce a novel loss concerning interpretability and accuracy that constraints P2ExNet to provide viable explanations of the data including relevant patches, their position, class similarities, and comparison methods without compromising accuracy. Analysis of the results on eight publicly available time-series datasets reveals that P2ExNet reaches comparable performance when compared to its counterparts while inherently providing understandable and traceable decisions.
Detecting Uninitialized Variables in C++ with the Clang Static Analyzer

Uninitialized variables have been a source of errors since the beginning of software engineering. Some programming languages (e.g. Java and Python) will automatically zero-initialize such variables, but others, like C and C++, leave their state undefined. While laying aside initialization in C and C++ might be a performance advantage if an initial value can't be supplied, working with such variables is an undefined behavior, and is a common source of instabilities and crashes. To avoid such errors, whenever meaningful initialization is possible, it should be used. Tools for detecting these errors run time have existed for decades, but those require the problematic code to be executed. Since in many cases the number of possible execution paths are combinatoric, static analysis techniques emerged as an alternative. In this paper, we overview the technique for detecting uninitialized C++ variables using the Clang Static Analyzer, and describe various heuristics to guess whether a specific variable was left in an undefined state intentionally. We implemented a prototype tool based on our idea and successfully tested it on large open source projects.
Speech behaviour of Internet users in conflict communication

This article explores the expression of impoliteness of Internet users in confrontational threads. This approach involves the identification of speech aggression markers typical of a certain type of discourse. This makes it possible to define the roles and attitudes of partici­pants in the conflict interaction. The authors employed methods of discourse analysis and corpus tools of data collection and marking. Seven confrontational threads in the communities of the social network "Vkontakte" were analysed. The authors identified language markers of the switch from cooperative to confrontational speech interaction. The authors investigated the typical roles of communicators in a confrontational thread, and observed the dynamics of using aggressive language tools for each of the proposed strategies. The article describes typi­cal situations for the confrontational threads: reliability of the information presented, lack of the user's intention to check facts, and the violation of spelling and punctuation rules. Con­frontations around these situations tend to lead to more complex conflicts and are character­ized as motives typical of impolite communication.
Robust Bayesian cluster enumeration based on the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si18.svg"><mml:mi>t</mml:mi></mml:math> distribution

A major challenge in cluster analysis is that the number of data clusters is mostly unknown and it must be estimated prior to clustering the observed data. In real-world applications, the observed data is often subject to heavy tailed noise and outliers which obscure the true underlying structure of the data. Consequently, estimating the number of clusters becomes challenging. To this end, we derive a robust cluster enumeration criterion by formulating the problem of estimating the number of clusters as maximization of the posterior probability of multivariate tν distributed candidate models. We utilize Bayes’ theorem and asymptotic approximations to come up with a robust criterion that possesses a closed-form expression. Further, we refine the derivation and provide a robust cluster enumeration criterion for data sets with finite sample size. The robust criteria require an estimate of cluster parameters for each candidate model as an input. Hence, we propose a two-step cluster enumeration algorithm that uses the expectation maximization algorithm to partition the data and estimate cluster parameters prior to the calculation of one of the robust criteria. The performance of the proposed algorithm is tested and compared to existing cluster enumeration methods using numerical and real data experiments.
3D Object Recognition Using X3D and Deep Learning

In this paper, a method of recognizing a 3D object using a machine learning algorithm is described. 3D object data sets consisting of geometric polygons are analyzed by Keras, a deep learning API that learns composition rules of data sets. A 3D object can be recognized by applying a composition rule to the object. Data sets for various types of objects have been experimented with. 100 objects per shape were used to learn the rules and different objects were used for evaluating the rules. Seven types of 3D objects were experimented with. In addition, evaluation results for different numbers of 3D objects were compared.
Opponent's Preference Estimation Considering Their Offer Transition in Multi-Issue Closed Negotiations

In recent years, agreement technologies have garnered interest among agents in the field of multi-agent systems. Automated negotiation is one of the agreement technologies, in which agents negotiate with each other to make an agreement so that they can solve conflicts between their preferences. Although most agents keep their own preferences private, it is necessary to estimate the opponent's preferences to obtain a better agreement. Therefore, opponent modeling is one of the most important elements in automated negotiating strategy. A frequency model is widely used for opponent modeling because of its robustness against various types of strategy while being easy to implement. However, existing frequency models do not consider the opponent's proposal speed and the transition of offers. This study proposes a novel frequency model that considers the opponent's behavior using two main elements: the offer ratio and the weighting function. The offer ratio stabilizes the model against changes in the opponent's offering speed, whereas the weighting function takes the opponent's concession into account. The two experiments conducted herein show that our proposed model is more accurate than other frequency models. Additionally, we find that the agent with the proposed model performs with a significantly higher utility value in negotiations.
Painting Music: Using artificial intelligence to create music from live painted drawings

This article describes the development of artificial intelligence (AI) techniques that monitor a painting or drawing evolving in real time and produce musical notes that relate to the individual elements of art as the artwork develops on the canvas. The article describes the practical approach required to capture the artwork unfolding in real time and then describes the framework used to develop the correlations between visual art and music. The AI technique exploits these areas of similarity within the two distinct artforms in order to respond to the live-painted elements and produce musical notes that reflect the development of the evolving artwork. A prototype of this system was implemented in a live stage performance at Aberdeen May Festival 2019 whose narrative centred on the question Is AI good or bad? Other outputs of this project are a 20-minute film and a body of (tangible) visual artwork for digital platforms and gallery environments informed and inspired by AI. The integration of these disciplines through AI transforms a static artform into one that is dynamic, interactive, transformational, transient and temporal.
Institutional factors of inclusive growth: Evidence from Cte divoire

Economic growth is important but not sufficient to generate a sustainable increase in individual welfare.Inclusiveness attributes to growth, the dual virtue of widening the space of economic and social opportunities; while ensuring a better application of distributive equity.In this perspective, Ali and Son suggest that growth is inclusive when combined with high income and equity.This study tries to verify this assertion in Côte d'Ivoire.This study focuses on ARDL Bounds approach for testing cointegration to measure the contribution of institutional factors to inclusive growth in Côte d'Ivoire over the period of 1984 to 2018.The International Country Risk Guide (ICRG) Index is used as institutional factors.The findings of empirical analysis suggest that only government stability as institutional factors have greatly and statically significant effect on inclusive growth in the short and long run.
Understanding the meaning of emoji in mobile social payments: Exploring the use of mobile payments as hedonic versus utilitarian through skin tone modified emoji usage

Despite research establishing emojis as sites of critical racial discourse, there is a paucity of literature examining their importance in the increasingly popular context of mobile payments. This is particularly important as new forms of social payment platforms such as Venmo bridge the seamlessness of mobile payments with the vibrant communicative practices of social networks. As such, they provide a unique medium to examine how emojis are used within the context of digital consumption, and by extension, self-representation. This study analyzes approximately 325 million public transactions on the U.S. payment platform Venmo to understand whether emoji usage in mobile payments is more hedonic or utilitarian. We then explore how race is represented across emoji usage on Venmo via tone-modified emojis, a subset of emojis whereby users can choose a skin tone. We found that while emojis in general are used for more hedonic purposes than utilitarian ones, darker tone-modified emojis indicate a proportionately higher use in hedonic consumption as compared to lighter tone-modified emojis, and also show a higher representation of utilitarian categories in transactions. Thematic analysis revealed that subsets with darker tone-modified emojis have a greater lexical variety and engage in more playful uses of emoji in mobile payments
Circular Pattern Matching with k Mismatches

The k-mismatch problem consists in computing the Hamming distance between a pattern P of length m and every length-m substring of a text T of length n, if this distance is no more than k. In many real-world applications, any cyclic shift of P is a relevant pattern, and thus one is interested in computing the minimal distance of every length-m substring of T and any cyclic shift of P. This is the circular pattern matching with k mismatches (k-CPM) problem. A multitude of papers have been devoted to solving this problem but, to the best of our knowledge, only average-case upper bounds are known. In this paper, we present the first non-trivial worst-case upper bounds for the k-CPM problem. Specifically, we show an $$\mathcal {O}(nk)$$-time algorithm and an $$\mathcal {O}(n+\frac{n}{m}\,{\small k^5})$$-time algorithm. The latter algorithm applies in an extended way a technique that was very recently developed for the k-mismatch problem [Bringmann et al., SODA 2019].
DNN-Based Full-Band Speech Synthesis Using GMM Approximation of Spectral Envelope

We propose a speech analysis-synthesis and deep neural network (DNN)-based text-to-speech (TTS) synthesis framework using Gaussian mixture model (GMM)-based approximation of full-band spectral envelopes. GMMs have excellent properties as acoustic features in statistic parametric speech synthesis. Each Gaussian function of a GMM fits the local resonance of the spectrum. The GMM retains the fine spectral envelope and achieve high controllability of the structure. However, since conventional speech analysis methods (i.e., GMM parameter estimation) have been formulated for a narrow-band speech, they degrade the quality of synthetic speech. Moreover, a DNN-based TTS synthesis method using GMM-based approximation has not been formulated in spite of its excellent expressive ability. Therefore, we employ peak-picking-based initialization for full-band speech analysis to provide better initialization for iterative estimation of the GMM parameters. We introduce not only prediction error of GMM parameters but also reconstruction error of the spectral envelopes as objective criteria for training DNN. Furthermore, we propose a method for multi-task learning based on minimizing these errors simultaneously. We also propose a post-filter based on variance scaling of the GMM for our framework to enhance synthetic speech. Experimental results from evaluating our framework indicated that 1) the initialization method of our framework outperformed the conventional one in the quality of analysis-synthesized speech; 2) introducing the reconstruction error in DNN training significantly improved the synthetic speech; 3) our variance-scaling-based post-filter further improved the synthetic speech.