Reviewerly/exHarmony huggingface dataset:
Postural Activity During Use of a Head-Mounted Display: Sex Differences in the “Driver–Passenger” Effect

Motion sickness is common in virtual environments. The risk of motion sickness varies widely between individuals and across situations. The subjective experience of motion sickness often is preceded by distinctive patterns of movement in the control of head and body posture. Previous research has documented reliable sex differences in the kinematics of postural activity, as well as reliable differences in postural activity between participants who were in control of a virtual vehicle and participants who were not. We asked whether postural precursors of motion sickness would simultaneously be influenced by individual and situational factors. We analyzed movement of the head and torso while seated participants were exposed to a driving video game presented through a head-mounted display. Half of the participants were women, and half were men. Using a yoked-control design, half of the participants controlled the virtual vehicle ( Drivers ), whereas half watched previously recorded vehicle trajectories ( Passengers ). The maximum exposure duration was 15 min, but participants were instructed to discontinue participation immediately if they experienced any symptoms of motion sickness, however mild. We analyzed movement kinematics not only in terms of sex and vehicle control but also in terms of participants who did or did not report motion sickness. Movement differed between Drivers and Passengers, in terms of both the spatial magnitude and multifractality of movement. The spatial magnitude of movement was simultaneously influenced by sex (men vs. women) and vehicle control (Drivers vs. Passengers). In addition, in statistically significant interactions, we identified postural precursors of motion sickness that differed between Drivers and Passengers and, separately, between Drivers and Passengers as a function of sex. The results are consistent with a prediction of the postural instability theory of motion sickness etiology and shed new light on the multifactorial origins of postural precursors of motion sickness in virtual environments.
Empathy-driven Arabic Conversational Chatbot

Conversational models have witnessed a significant research interest in the last few years with the advancements in sequence generation models. A challenging aspect in developing human-like conversational models is enabling the sense of empathy in bots, making them infer emotions from the person they are interacting with. By learning to develop empathy, chatbot models are able to provide human-like, empathetic responses, thus making the human-machine interaction close to human-human interaction. Recent advances in English use complex encoder-decoder language models that require large amounts of empathetic conversational data. However, research has not produced empathetic bots for Arabic. Furthermore, there is a lack of Arabic conversational data labeled with empathy. To address these challenges, we create an Arabic conversational dataset that comprises empathetic responses. However, the dataset is not large enough to develop very complex encoder-decoder models. To address the limitation of data scale, we propose a special encoder-decoder composed of a Long Short-Term Memory (LSTM) Sequence-to-Sequence (Seq2Seq) with Attention. The experiments showed success of our proposed empathy-driven Arabic chatbot in generating empathetic responses with a perplexity of 38.6, an empathy score of 3.7, and a fluency score of 3.92.
LEARNING ABOUT THE REDUCTION OF FOOD WASTE USING BLOCKCHAIN TECHNOLOGY

Farmers need to be efficient and dedicate a lot of time in order to sustain the quality of their animals which are in their care. The most convenient and good quality - price ratio should be chosen for the feed of animals. Blockchain is used in a virtual space to store and share information over a network of users. This is done using the open source Hyperledger Fabric platform. The transactions can be viewed by all the other users in real time. These transactions are stored as JSONs inside CouchDB NoSQL database which supports queries on a large volume of data. When using this technology, the farmer can know with whom the supplier for animal feed collaborated with. The history of the transactions are not saved in just one place. In this way, it is more difficult to hack and provide implausible information. An e-learning platform was created where the farm's user can post information, respectively new blocks about the animal's birth, vaccinations, medicines, including the location of the livestock. The same e-learning platform is accessible from the mobile phone. By using the blockchain technology, anyone, including the client from the shop can know a lot about the origin of the products. Fake origins of food are much more difficult to hide. Fraud is also limited. The system monitored the traceability of dairy products inside a Romanian farm. Data about fodder provider and quality, cow productive performances and health and dairy products process were obtained and analyzed by students who will become specialists at all the levels of the food chain. Blockchain is the technology which in case of a dairy products contamination, the origin of the farm is traced in just a couple of seconds. In this way just a batch of dairy products is removed from distribution, leading to the reduction of food waste.
The Good, The Bad and The Ugly: Pitfalls and Best Practices in Automated Sound Static Analysis of Ethereum Smart Contracts

Ethereum smart contracts are distributed programs running on top of the Ethereum blockchain. Since program flaws can cause significant monetary losses and can hardly be fixed due to the immutable nature of the blockchain, there is a strong need of automated analysis tools which provide formal security guarantees. Designing such analyzers, however, proved to be challenging and error-prone. We review the existing approaches to automated, sound, static analysis of Ethereum smart contracts and highlight prevalent issues in the state of the art. Finally, we overview eThor, a recent static analysis tool that we developed following a principled design and implementation approach based on rigorous semantic foundations to overcome the problems of past works.
Investigating response time and accuracy in online classifier learning for multimedia publish-subscribe systems

The enormous growth of multimedia content in the field of the Internet of Things (IoT) leads to the challenge of processing multimedia streams in real-time. Event-based systems are constructed to process event streams. They cannot natively consume multimedia event types produced by the Internet of Multimedia Things (IoMT) generated data to answer multimedia-based user subscriptions. Machine learning-based techniques have enabled rapid progress in solving real-world problems and need to be optimised for the low response time of the multimedia event processing paradigm. In this paper, we describe a classifier construction approach for the training of online classifiers, that can handle dynamic subscriptions with low response time and provide reasonable accuracy for the multimedia event processing. We find that the current object detection methods can be configured dynamically for the construction of classifiers in real-time, by tuning hyperparameters even when training from scratch. Our experiments demonstrate that deep neural network-based object detection models, with hyperparameter tuning, can improve the performance within less training time for the answering of previously unknown user subscriptions. The results from this study show that the proposed online classifier training based model can achieve accuracy of 79.00% with 15-min of training and 84.28% with 1-hour training from scratch on a single GPU for the processing of multimedia events.
Rigid commutators and a normalizer chain

The novel notion of rigid commutators is introduced to determine the sequence of the logarithms of the indices of a certain normalizer chain in the Sylow 2-subgroup of the symmetric group on 2^n letters. The terms of this sequence are proved to be those of the partial sums of the partitions of an integer into at least two distinct parts, that relates to a famous Euler's partition theorem.
THE INTERACTION BETWEEN FOREIGN DIRECT INVESTMENT, FOREIGN PORTFOLIO INVESTMENT AND ECONOMIC GROWTH: THE CASE OF SUB- SAHARAN AFRICAN COUNTRIES

There are contradicting arguments in the literature examining the influence of foreign investment on economic growth in Sub Saharan Africa. Some researchers claim that high level of volatility, rising current account deficit, lack of developed financial markets and low quality of regulatory framework would generate economic losses for developing countries in Sub Saharan Africa when they liberalized their capital flows. However, some studies focus on growth enhancing effect of foreign investment to be a remedy for low capacity of accumulated savings in Sub Saharan Africa. The current study brings new evidence about the role of foreign portfolio investment and foreign direct investment on economic growth for countries in Sub Saharan Africa. Due to the endogenenity issue, we have used panel VAR methodology to estimate three simultaneous equations system. By analyzing 25 Sub Saharan African countries over the 1990-2016 period, we found that foreign direct investment and foreign portfolio investment are complements and they have positive significant impacts on economic growth.
Taming the Blockchain Beast? Regulatory Implications for the Cryptocurrency Market

This paper uses a unique dataset of 120 regulatory events from five classes to test the relevance of the regulatory framework for cryptocurrency value. Time-series market-wide estimates and panel estimates for 300 individual coins and tokens show statistically and economically significant impact of anti-money laundering and issuance regulation. Tighter regulation and more active role of government decrease cryptocurrency prices, evidencing that potentially lower risks and wider adoption commonly attributed to the establishment of the regulatory framework do not compensate for respective efficiency and consumer utility losses. The market is generally efficient in reflecting regulatory information in cryptocurrency prices.
The development of statistical methods for the diagnosis of neurological diseases based on multiparameter analysis of brain activity

Abstract The application of data science for the analysis of biomedical data presented as time series allows using new methods of analysis of living systems. Therefore, using these methods it could be possible to discover new diagnostic criteria for neurological diseases. In this paper, in the framework of Memory Functions Formalism, one of the approaches of statistical physics, we analyze recordings of signals of the human brain cortex in the sleep state of 19 patients with nocturnal frontal lobe epilepsy and 8 healthy subjects. We observe alterations of dynamic parameter behavior at the pathology and in healthy subjects. Furthermore, we reveal significant alterations in brain rhythms manifestations at statistical memory functions power spectra of patients with epilepsy and healthy subject. As a result, we show that the application of the statistical analysis of electroencephalogram recordings, after appropriate verification, can be helpful in the search of diagnostic criteria of nocturnal frontal lobe epilepsy.
Digital Pathology During the COVID-19 Outbreak in Italy: Survey Study

Background Transition to digital pathology usually takes months or years to be completed. We were familiarizing ourselves with digital pathology solutions at the time when the COVID-19 outbreak forced us to embark on an abrupt transition to digital pathology. Objective The aim of this study was to quantitatively describe how the abrupt transition to digital pathology might affect the quality of diagnoses, model possible causes by probabilistic modeling, and qualitatively gauge the perception of this abrupt transition. Methods A total of 17 pathologists and residents participated in this study; these participants reviewed 25 additional test cases from the archives and completed a final psychologic survey. For each case, participants performed several different diagnostic tasks, and their results were recorded and compared with the original diagnoses performed using the gold standard method (ie, conventional microscopy). We performed Bayesian data analysis with probabilistic modeling. Results The overall analysis, comprising 1345 different items, resulted in a 9% (117/1345) error rate in using digital slides. The task of differentiating a neoplastic process from a nonneoplastic one accounted for an error rate of 10.7% (42/392), whereas the distinction of a malignant process from a benign one accounted for an error rate of 4.2% (11/258). Apart from residents, senior pathologists generated most discrepancies (7.9%, 13/164). Our model showed that these differences among career levels persisted even after adjusting for other factors. Conclusions Our findings are in line with previous findings, emphasizing that the duration of transition (ie, lengthy or abrupt) might not influence the diagnostic performance. Moreover, our findings highlight that senior pathologists may be limited by a digital gap, which may negatively affect their performance with digital pathology. These results can guide the process of digital transition in the field of pathology.
Examining Autocompletion as a Basic Concept for Interaction with Generative AI

Abstract Autocompletion is an approach that extends and continues partial user input. We propose to interpret autocompletion as a basic interaction concept in human-AI interaction. We first describe the concept of autocompletion and dissect its user interface and interaction elements, using the well-established textual autocompletion in search engines as an example. We then highlight how these elements reoccur in other application domains, such as code completion, GUI sketching, and layouting. This comparison and transfer highlights an inherent role of such intelligent systems to extend and complete user input, in particular useful for designing interactions with and for generative AI. We reflect on and discuss our conceptual analysis of autocompletion to provide inspiration and a conceptual lens on current challenges in designing for human-AI interaction.
Cascaded optical resonator-based programmable photonic integrated circuits

Programmable photonic integrated circuits have mainly been developed based on the single wavelength channel operation of fundamental building blocks consisting of Mach-Zehnder interferometers (MZIs) with tunable phase shifters. We propose and study optical circuit models consisting of cascaded optical resonators that enable the independent operation of multiple wavelength channels in a more compact footprint than the conventional MZIs. By adopting experimental values reported for silicon micro-ring resonators, the fidelities of different types of 2×2 unitary transformations and higher-dimensional unitary transformations are examined by employing the Reck algorithm and the Clements algorithm.
A Survey on Privacy Preservation in Location-Based Mobile Business

In mobile business (m-business), a client sends its exact locations to service providers. This data may involve sensitive and private personal information. As a result, misuse of location information by the third party location servers creating privacy issues for clients. This paper provides an overview of the privacy protection techniques currently applied by location-based mobile business. The authors first identify different system architectures and different protection goals. Second, this article provides an overview of the basic principles and mechanisms that exist to protect these privacy goals. In a third step, the authors provide existing privacy protection measures.
Probabilistic Models with Deep Neural Networks

Recent advances in statistical inference have significantly expanded the toolbox of probabilistic modeling. Historically, probabilistic modeling has been constrained to very restricted model classes, where exact or approximate probabilistic inference is feasible. However, developments in variational inference, a general form of approximate probabilistic inference that originated in statistical physics, have enabled probabilistic modeling to overcome these limitations: (i) Approximate probabilistic inference is now possible over a broad class of probabilistic models containing a large number of parameters, and (ii) scalable inference methods based on stochastic gradient descent and distributed computing engines allow probabilistic modeling to be applied to massive data sets. One important practical consequence of these advances is the possibility to include deep neural networks within probabilistic models, thereby capturing complex non-linear stochastic relationships between the random variables. These advances, in conjunction with the release of novel probabilistic modeling toolboxes, have greatly expanded the scope of applications of probabilistic models, and allowed the models to take advantage of the recent strides made by the deep learning community. In this paper, we provide an overview of the main concepts, methods, and tools needed to use deep neural networks within a probabilistic modeling framework.
Robust Super-Resolution of Real Faces Using Smooth Features

Real low-resolution (LR) face images contain degradations which are too varied and complex to be captured by known downsampling kernels and signal-independent noises. So, in order to successfully super-resolve real faces, a method needs to be robust to a wide range of noise, blur, compression artifacts etc. Some of the recent works attempt to model these degradations from a dataset of real images using a Generative Adversarial Network (GAN). They generate synthetically degraded LR images and use them with corresponding real high-resolution (HR) image to train a super-resolution (SR) network using a combination of a pixel-wise loss and an adversarial loss. In this paper, we propose a two module super-resolution network where the feature extractor module extracts robust features from the LR image, and the SR module generates an HR estimate using only these robust features. We train a degradation GAN to convert bicubically downsampled clean images to real degraded images, and interpolate between the obtained degraded LR image and its clean LR counterpart. This interpolated LR image is then used along with it’s corresponding HR counterpart to train the super-resolution network from end to end. Entropy Regularized Wasserstein Divergence is used to force the encoded features learnt from the clean and degraded images to closely resemble those extracted from the interpolated image to ensure robustness.
A Discriminative Locality-Sensitive Dictionary Learning With Kernel Weighted KNN Classification for Video Semantic Concepts Analysis

Video semantic concept analysis has received a lot of research attention in the area of human computer interactions in recent times. Reconstruction error classification methods based on sparse coefficients do not consider discrimination, essential for classification performance between video samples. To further improve the accuracy of video semantic classification, a video semantic concept classification approach based on sparse coefficient vector (SCV) and a kernel-based weighted KNN (KWKNN) is proposed in this paper. In the proposed approach, a loss function that integrates reconstruction error and discrimination is put forward. The authors calculate the loss function value between the test sample and training samples from each class according to the loss function criterion, and then vote on statistical results. Finally, this paper modifies the vote results combined with the kernel weight coefficient of each class and determine the video semantic concept. The experimental results show that this method effectively improves the classification accuracy for video semantic analysis and shorten the time used in the semantic classification compared with some baseline approaches.
Stateless Distributed Ledgers

In public distributed ledger technologies (DLTs), such as Blockchains, nodes can join and leave the network at any time. A major challenge occurs when a new node joining the network wants to retrieve the current state of the ledger. Indeed, that node may receive conflicting information from honest and Byzantine nodes, making it difficult to identify the current state. In this paper, we are interested in protocols that are stateless, i.e., a new joining node should be able to retrieve the current state of the ledger just using a fixed amount of data that characterizes the ledger (such as the genesis block in Bitcoin). We define three variants of stateless DLTs: weak, strong, and probabilistic. Then, we analyze this property for DLTs using different types of consensus.
Global Negative Correlation Learning: A Unified Framework for Global Optimization of Ensemble Models

Ensembles are a widely implemented approach in the machine learning community and their success is traditionally attributed to the diversity within the ensemble. Most of these approaches foster diversity in the ensemble by data sampling or by modifying the structure of the constituent models. Despite this, there is a family of ensemble models in which diversity is explicitly promoted in the error function of the individuals. The negative correlation learning (NCL) ensemble framework is probably the most well-known algorithm within this group of methods. This article analyzes NCL and reveals that the framework actually minimizes the combination of errors of the individuals of the ensemble instead of minimizing the residuals of the final ensemble. We propose a novel ensemble framework, named global negative correlation learning (GNCL), which focuses on the optimization of the global ensemble instead of the individual fitness of its components. An analytical solution for the parameters of base regressors based on the NCL framework and the global error function proposed is also provided under the assumption of fixed basis functions (although the general framework could also be instantiated for neural networks with nonfixed basis functions). The proposed ensemble framework is evaluated by extensive experiments with regression and classification data sets. Comparisons with other state-of-the-art ensemble methods confirm that GNCL yields the best overall performance.
Towards a Better Integration of Fuzzy Matches in Neural Machine Translation through Data Augmentation

We identify a number of aspects that can boost the performance of Neural Fuzzy Repair (NFR), an easy-to-implement method to integrate translation memory matches and neural machine translation (NMT). We explore various ways of maximising the added value of retrieved matches within the NFR paradigm for eight language combinations, using Transformer NMT systems. In particular, we test the impact of different fuzzy matching techniques, sub-word-level segmentation methods and alignment-based features on overall translation quality. Furthermore, we propose a fuzzy match combination technique that aims to maximise the coverage of source words. This is supplemented with an analysis of how translation quality is affected by input sentence length and fuzzy match score. The results show that applying a combination of the tested modifications leads to a significant increase in estimated translation quality over all baselines for all language combinations.