Reviewerly/exHarmony huggingface dataset:
Trading Privacy through Randomized Response

Personal information is valuable for organizations and companies providing targeted and customized services. On the other hand, data owners are not willing to share their private information (e.g., spending habits and monthly purchases) due to privacy concerns. In order to incentivize the data owners to share their data, the organizations have to pay each data owner adequate compensation. In this context, privacy can be considered a personal commodity: the data owners may share their personal information if the organizations pay them sufficiently. In this paper, we consider a mechanism design problem between a data buyer (e.g., a company) and multiple data owners. In the mechanism design problem, the buyer is willing to make a payment for a desired level of accuracy and data quality, and the data owners will release their information after receiving a sufficient amount of compensation. To measure the privacy guarantee of an algorithm, we use the concept of differential privacy and use the randomized response algorithm to generate differentially private data. In contrast to existing works that study a mechanism design problem for generating a single differentially private linear query through the Laplace mechanism, we consider a more general scenario. In particular, the number of queries or the type of query does not affect our incentive mechanism, and our framework is able to generate any type of query without any limitation on how many times the buyer asks for a query.
A Sentiment Analysis and Unsupervised Learning Approach to Digital Violence Against Women: Monterrey Case

Until a few years ago, Violence Against Women took place both in public and private spaces, but it has now broken into Digital Space, adopting more symbolic expressions. Violence Against Women in Digital Space has recently been legally typified in Mexico, giving the appropriate framework to address it. There have been some important related works from Data Science approaches but mainly on cyberbullying and in detection of language patterns through supervised algorithms. This article seeks to address Violence Against Women in Digital Space in Monterrey, Mexico through Sentiment Analysis and Unsupervised Learning Techniques. The hypothesis is that Sentiment Analysis can associate sentiments to specific subjects that will lead to identify potential Digital Violence Against Women. We will work with Spanish-language text data from microblogging social network, Twitter, datasets.
On the time-lag in the dating of historical settlements: Reply to Kolář and Szabó (2021)

Settlement history is an interdisciplinary topic which connects history, archaeology, paleoecology, historical geography and other scientific disciplines. In Central Europe, one of important questions regards the dating of origin of medieval settlements. In 2020, our team published a study comparing the dating obtained from medieval written records with archaeological data (Fanta et al., 2020). Recently, Kolář and Szabó (2021) wrote a comment in which they criticised several aspects of our original study. They also suggested a new interpretation of our data based on their own reanalysis, the results of which are strikingly different from ours. We admit that many remarks raised by our colleagues are relevant and meaningful (filtering and selection of archaeological data, pottery chronology); that said, we believe that most of them have a negligible effect on the main results of our study. The reanalysis of our data by Kolář and Szabó is, however, fundamentally flawed. Their conclusion that “time lag between historical and archaeological dating can increase with time” has no backing in data; it is a mere statistical artifact produced by using inappropriate calculations and misinterpreting the results. In conclusion: A careful and correct analysis of our data indicates that the systematic upward bias in dating based on written sources is larger for earlier settlements.
A Novel Perspective of Text Classification by Prolog-Based Deductive Databases

Natural language processing has been studied extensively worldwide and has been implemented into various applications, including text classification. Especially, the significant development of social networking platforms has led to a considerable increase in data. Thus, it becomes the fertile data domain to carry out a series of studies on text classification. Various studies on this task are conducted in many languages but still have many limitations with Vietnamese. This is why we aim to do this study to classify Vietnamese texts from two Vietnamese benchmark datasets. Despite many studies on machine learning models in this study, any research work using facts and rules in a deductive database to classify Vietnamese text classification has not been studied. In particular, we design a system architecture based on facts and rules in a deductive database for text classification in Vietnamese. Our experiments show our results are positive on two Vietnamese datasets. The best performances from the experiments achieve 93.18% of F1-score for the UIT-ViNames dataset, 76.79% and 69.96% for the sentiment detection and the topic classification on the UIT-VSFC dataset, respectively. Although the experimental results are not better than the previous studies, these results are the premise for developing solutions for natural language processing problems on the deductive database, a successful pilot in implementing text classification on the Prolog-based deductive database.
Efficient Large Scale Inlier Voting for Geometric Vision Problems

Outlier rejection and, equivalently, inlier set optimization is a key ingredient in numerous applications in computer vision such as filtering point-matches in camera pose estimation or plane and normal estimation in point clouds. Several approaches exist, yet at large scale we face a combinatorial explosion of possible solutions and state-of-the-art methods like RANSAC, Hough transform, or Branch&Bound require a minimum inlier ratio or prior knowledge to remain practical. In fact, for problems such as camera posing in very large scenes these approaches become useless as they have exponential runtime growth.To approach the problem, we present an efficient and general algorithm for outlier rejection based on “intersecting” k-dimensional surfaces in R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sup> . We provide a recipe for formulating a variety of geometric problems as finding a point in R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sup> which maximizes the number of nearby surfaces (and thus inliers). The resulting algorithm has linear worst-case complexity with a better runtime dependency on the requested proximity of a query to its result than competing algorithms, while not requiring domain specific bounds. This is achieved by introducing a space decomposition scheme that bounds the number of computations by successively rounding and grouping surfaces. Our recipe and open-source code <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> enables anybody to derive such fast approaches to new problems across a wide range of domains. We demonstrate the approach on several camera posing problems with a large number of matches and low inlier ratio, achieving state-of-the-art results at significantly lower processing times.
Detecting Real-Time Correlated Simultaneous Events in Microblogs: The Case of Men’s Olympic Football

Although many predictive models have been designed to detect real-time events, there is still little progress in characterizing simultaneous events. Simultaneous events found in the sport domain can be used to understand how several correlated incidents occur at the same time to describe a specific phenomenon. We proposed a novel mechanism that uses Twitter messages in order to predict emotions associated with the final football match between Brazil and Germany in Rio Olympics 2016. Users’ opinions and their sentiments were extracted from the obtained tweets using the K-means clustering algorithm and the SentiStrength technique. We also applied the “Multi-label” classification technique in conjunction with the “Binary Relevance” (BR) method. The results showed that NaiveBayes was able to predict the match outcomes and related emotions with an accuracy value of 81% and a hamming loss value of 16%. This study provides a robust approach to successfully detect real-time events using social media platforms. It also helps football clubs to characterize matches during the time span of the game. Finally, the proposed method contributes to the decision-making process in the sport domain.
Research on Classification Method of Network Resources Based on Modified SVM Algorithm

According to the traditional classification method of network capital resources, there are some problems such as low efficiency, low recall rate, and low precision rate of information. Therefore, this paper proposes a new classification method of network capital resources based on SVM algorithm. Firstly, the original sample data are analyzed by principal component analysis to realize the design of resource classification process. Then, the dimension reduction of network resources data is realized by word segmentation and denoising. Thirdly, the reduced sample data are trained by the SVM classifier, and the best parameters of the reduced data are obtained by the grid search method. Lastly, the search range of SVM classifier parameters based on the original sample data is reset, so as to quickly obtain the best SVM classifier parameters of the original sample data and realize the classification. The experimental results show that this method can improve the recall and precision of network resource information and shorten the classification time of network resources.
AFRIFASHION1600: A Contemporary African Fashion Dataset for Computer Vision

This work presents AFRIFASHION1600, an openly accessible contemporary African fashion image dataset containing 1600 samples labelled into 8 classes representing some African fashion styles. Each sample is coloured and has an image size of 128 x 128. This is a niche dataset that aims to improve visibility, inclusion, and familiarity of African fashion in computer vision tasks.AFRIFASHION1600 dataset is available here.
Foresight

Spatiotemporal variation in cellular bandwidth availability is well-known and could affect a mobile user's quality of experience (QoE), especially while using bandwidth intensive streaming applications such as movies, podcasts, and music videos during commute. If such variations are made available to a streaming service in advance it could perhaps plan better to avoid sub-optimal performance while the user travels through regions of low bandwidth availability. The intuition is that such future knowledge could be used to buffer additional content in regions of higher bandwidth availability to tide over the deficits in regions of low bandwidth availability. Foresight is a service designed to provide this future knowledge for client apps running on a mobile device. It comprises three components: (a) a crowd-sourced bandwidth estimate reporting facility, (b) an on-cloud bandwidth service that records the spatiotemporal variations in bandwidth and serves queries for bandwidth availability from mobile users, and (c) an on-device bandwidth manager that caters to the bandwidth requirements from client apps by providing them with bandwidth allocation schedules. Foresight is implemented in the Android framework. As a proof of concept for using this service, we have modified an open-source video player---Exoplayer---to use the results of Foresight in its video buffer management. Our performance evaluation shows Foresight's scalability. We also showcase the opportunity that Foresight offers to ExoPlayer to enhance video quality of experience (QoE) despite spatiotemporal bandwidth variations for metrics such as overall higher bitrate of playback, reduction in number of bitrate switches, and reduction in the number of stalls during video playback.
A GM-CPHD Filtering Algorithm Assisted by Luminance Information

Abstract Optical tracking of small and weak targets in space, has problems such as a large and unknown number of targets, dense clutter, close measurement of neighboring targets, and false detection and missed detection. In this paper, a Gaussian mixture cardinalized probability hypothesis density (GM-CPHD) filtering algorithm assisted by luminance information is proposed. By establishing the luminance likelihood function, the luminance information is introduced into the CPHD filtering process, and the Gaussian mixture implementation method is given. The simulation results indicate that the proposed algorithm can effectively adapt to the dense clutter environment, and compared with the traditional GM-CPHD filtering algorithm, its tracking accuracy for multiple targets is significantly improved.
Simulated or Physical? An Empirical Study on Input Validation for Context-aware Systems in Different Environments

Context-Aware Systems (a.k.a. CASs) integrate cyber and physical space to provide context-aware adaptive functionalities. Building context-aware systems is challenging due to the uncertainty of the real physical environment. Therefore, input validation for context-aware systems plays a significant role in keeping the systems executing safely. Input validation approaches have been proposed to monitor and guard the executions of context-aware systems. However, few of these works (17%, 2 out of 12) evaluated their approaches with a real context-aware system in a real physical environment. In this paper, we study and compare the effectiveness of input validation approaches for context-aware system in both a simulated and a physical environment. We built a testing platform, RM-Testing, based on DJI RoboMaster S1 robot car. We implemented three up-to-date input validation approaches, and evaluated their effectiveness in improving the success rate of the robot car’s executions. The results show that the selected input validation approaches are effective in guarantee the safe execution of context-aware systems, which improve the success rate by 82% in the simulated environment, and 50% in the physical environment. However, the effectiveness of these approaches does vary in different environment. Thus, we believe that such CASs-based input validation works should be evaluated in the physical environment to better validate their effectiveness and usefulness.
Steganalysis of Low Embedding Rate CNV-QIM in Speech

To address the difficulty of detecting low embedding rate and high-concealment CNV-QIM (complementary neighbor vertices-quantization index modulation) steganography in low bit-rate speech codec, the code-word correlation model based on a BiLSTM (bi-directional long short-term memory) neural network is built to obtain the correlation features of the LPC codewords in speech codec in this paper.Then, softmax is used to classify and effectively detect low embedding rate CNV-QIM steganography in VoIP streams.The experimental results show that for speech steganography of short samples with low embedding rate, the BiLSTM method in this paper has a superior detection accuracy than state-of-the-art methods of the RNN-SM (recurrent neural network-steganalysis model) and SS-QCCN (simplest strong quantization codeword correlation network).At an embedding rate of 20% and a duration of 3 s, the detection accuracy of BiLSTM method reaches 75.7%, which is higher than that of RNN-SM by 11.7%.Furthermore, the average testing time of samples (100% embedding) is 0.3 s, which shows that the method can realize real-time steganography detection of VoIP streams.
The Role of Embodiment and Simulation in Evaluating HCI: Theory and Framework

In this paper, we argue that embodiment can play an important role in the evaluation of systems developed for Human Computer Interaction. To this end, we describe a simulation platform for building Embodied Human Computer Interactions (EHCI). This system, VoxWorld, enables multimodal dialogue systems that communicate through language, gesture, action, facial expressions, and gaze tracking, in the context of task-oriented interactions. A multimodal simulation is an embodied 3D virtual realization of both the situational environment and the co-situated agents, as well as the most salient content denoted by communicative acts in a discourse. It is built on the modeling language VoxML, which encodes objects with rich semantic typing and action affordances, and actions themselves as multimodal programs, enabling contextually salient inferences and decisions in the environment. Through simulation experiments in VoxWorld, we can begin to identify and then evaluate the diverse parameters involved in multimodal communication between agents. VoxWorld enables an embodied HCI by situating both human and computational agents within the same virtual simulation environment, where they share perceptual and epistemic common ground. In this first part of this paper series, we discuss the consequences of embodiment and common ground, and how they help evaluate parameters of the interaction between humans and agents, and demonstrate different behaviors and types of interactions on different classes of agents.
Semantics in Sensor Networks: An Ontology for Dynamic Transfer Behavior in Calibrated Sensors

The enrichment of raw sensor data with contextual information is essential to enabling interoperability and the automation of data analysis in Industry 4.0 applications. In this paper an ontology to describe the dynamic transfer behavior of calibrated sensors in a metrologically consistent manner is presented. By merging and extending commonly used ontologies, a new ontology is constructed that describes the mathematical details of transfer models in a consistent and machine-interpretable manner. Incorporating measurement uncertainty as an integral part of the ontology, the applicability of the model is demonstrated for a calibrated sensor with a defined transfer model as well as through a series of Competency Questions.
Automatic Rocks Segmentation Based on Deep Learning for Planetary Rover Images

Accurate detection and segmentation of obstacles is the key to the smooth operation of the planetary rovers and the basic guarantee of scientific exploration mission. The traditional method of rock segmentation based on boundary detector is affected by the change of illumination and dust storms. To address this problem, this paper proposes an improved U-net-based architecture combined with Visual Geometry Group (VGG) and dilated convolutional neural network for the segmentation of rocks from images of planetary exploration rovers. The proposed method also has a contracting path and an expansive path to get high-resolution output similar with U-Net. In the contracting path, the convolution layers in U-Net are replaced by the convolutional layers of VGG16. Inspired by the dilated convolution, the multiscale dilated convolution in the expansive path is proposed. Furthermore, our method is further optimized in the expansive path. To evaluate the proposed method, extensive experiments on segmentation with the Mars dataset have been conducted. The experimental results demonstrate that the proposed method produces accurate semantic segmentation and identification results automatically and outperforms state-of-the-art methods.
Measuring the impact of a library-hosted showcase event

In 2015, an academic library at a R1 institution initiated an annual showcase event, to acknowledge the significant achievements of university faculty. The event honors faculty who authored a book, participated in a major performance or exhibition, commercialized new technologies and inventions, or received a major research grant during the previous calendar year. The event intends to cultivate collaboration among the honorees while also increasing the visibility of the university libraries and their services. The authors used a series of surveys to elicit feedback from honored faculty and their administrators who attended the events. Analysis of the data indicates that the event is valuable to attendees and it increases their awareness and use of specific library resources.
Overcoming the Complexities in Decision-Making for Enterprise Software Products: Influence of Technological Factors

Technological aspects of an Information Technology (IT) innovation have a vital role in organization’s decision for adopting the IT innovation. Due to innovations in distributed computing, customers has the provision to avail Software products in two delivery models namely On-Premise model and Cloud computing model. Each deployment model possesses advantages and disadvantages one over the other and at the same time organizations are posed with the challenge of adopting one among them. Relative advantage is one of the key parameters in decision making, it has an impact on the decision maker in terms of cost, performance, utilization etc. Complexity is how the user perceives the ease of use for an innovation. The other technical aspects are Compatibility with existing infrastructure, Data security, Data privacy, and Data backup. Observing the results of the software product under trial period and making comparisons between the two kind of delivery models helps in adopting the software product model in long term usage. In this research paper, we have determined and evaluated the technological aspects of enterprise software product which helps organization’s in decision-making for adopting software product’s delivery model. The Technological context comprises of technical characteristics of a software product which can ease the adoption of software model. Complexity, Compatibility, Relative advantage, Trialability, Observability, Data Privacy, Data Security, Data backup, Data Accessibility, Data Location are the major factors identified in this study. Focused group discussion and interviews were conducted with the organizations for finding as how various technological factors has an impact on the decision of organization’s in the adoption of software products in the form of on-premise model or the cloud computing model.
On self-correcting logic circuits of unreliable gates

The following statements are proved: 1) for any integer m ≥ 3 there is a basis consisting of Boolean functions of no more than m variables, in which any Boolean function can be implemented by a logic circuit of unreliable gates that self-corrects relative to certain faults in an arbitrary number of gates; 2) for any positive integer k there are bases consisting of Boolean functions of no more than two variables, in each of which any Boolean function can be implemented by a logic circuit of unreliable gates that self-correct relative to certain faults in no more than k gates; 3) there is a functionally complete basis consisting of Boolean functions of no more than two variables, in which almost no Boolean function can be implemented by a logic circuit of unreliable gates that self-correct relative to at least some faults in no more than one gate.
Challenges and Applications of Face Deepfake

With the development of Generative deep learning algorithms in the last decade, it has become increasingly difficult to differentiate between what is real and what is fake. With the easily available “Deepfake” applications, even a person with less computing knowledge can also produce realistic Deepfake data. These fake data have many benefits while on the other hand, it can also be used for unethical and malicious purposes. Deepfake can be anything fake data generated by using deep learning methods. In this study, we focus on Deepfake with respect to face manipulation. We represent the currently used algorithms and datasets are represented for creating Deepfake. We also study the challenges and the real-world applications in which the benefits, as well as the drawbacks of using Deepfake, are being pointed out.