Reviewerly/exHarmony huggingface dataset:
Emulation Framework for Distributed Large-Scale Systems Integration

Recent trends in systems engineering include integration of very large-scale systems, which entails significant challenges when they are geographically dispersed. In these scenarios, intelligent integration of distributed large-scale systems requires significant coordination among hardware elements as well as all software components. The approach of integrated systems (both computing platform and experimental equipment) for end-to-end orchestration is called federation. Virtual frameworks can aid in the testing, assessment, and implementation of a functional system of interconnected resources. We present an emulation framework that replicates the software environments of multi-site federations of computing systems and instruments. Our emulation framework allows systems engineers to reduce development cost and avoid disruptions to production infrastructure. Our framework was effectively used to develop and test software modules for various tasks including container orchestration and instrument access. For performance assessment, however, the emulated framework is severely limited in providing accurate network and IO measurements at 10 Gbps and higher data rates. The data transfer performance profiles estimated using these emulated measurements are usually inaccurate for high bandwidth and high latency connections, since emulation does not accurately reflect the critical network transport dynamics. We utilize measurements from a physical testbed with hardware network emulators to obtain data transfer profiles that closely match the expected profiles for the emulated federations. We show the effectiveness of our approach by an illustrative example of integrated (federated) multi-site ultra large-scale systems that are connected via high speed wide area networks.
Glass Virtual Try-On

Abstract: Virtual try-on is a technology that allows people to virtually check the appearance of accessories, makeup, hairstyle, hair color, clothes and potentially more on themselves. The virtual try-on presents many advantages over real try-on, it speeds up the process providing the possibility to test hundreds of products without the need to reach a physical store. The try-on process is performed on a 3D face reconstructed from the input image allowing the user to see the virtual face and glasses from different viewpoints. The try-on process is fully automated and does not require the user to provide anything else but the picture and name of the glasses frames to test. Keywords: Virtual try-on, Virtual glasses, Augmented reality
What Kinds of Connectives Cause the Difference Between Intuitionistic Predicate Logic and the Logic of Constant Domains?

It is known that intuitionistic Kripke semantics can be generalized so that it can treat arbitrary propositional connectives characterized by truth functions. Our previous work studied how the choice of propositional connectives changes the relation between classical and intuitionistic propositional logics, and showed that the set of valid sequents in intuitionistic propositional logic coincides with the set of valid sequents in classical propositional logic if and only if the connectives we choose are all monotonic. In this paper, we extend the generalized Kripke semantics to first-order logic and study how the choice of connectives changes the relation between intuitionistic predicate logic and the logic of constant domains. In the case of the usual connectives $$\lnot $$ , $$\wedge $$ , $$\vee $$ , and $$\rightarrow $$ , it is well-known that the presence of disjunction causes the difference between these two logics. Generalizing this result to general propositional connectives, we give a simple necessary and sufficient condition for the set of valid sequents in intuitionistic predicate logic to coincide with the set of valid sequents in the logic of constant domains.
The Attitude of Hotel Employees to Interact With Collaborative Robotic Technologies

The adoption of robotic technologies and artificial intelligence is having a significant impact on service businesses. The transformation of work activity and personnel remains the subject of numerous discussions, which determines the relevance of empirical research in this area. The article presents some results of studying the attitude of the Russian hotels’ employees to the possibility of working together with robotic technologies. A self-completion questionnaire was used as a primary data collection tool. 167 forms were completed and were suitable for analysis, the data of which were analyzed using the SPSS software. The results showed that while employees agreed to interact with robotic technologies and indicated that robots are helpful, enjoyable, and productive, they also had concerns that robots would control all work. In addition, the widespread introduction of robots will lead to social problems such as loss of contact with people, which can have a negative impact on psychosocial well-being in the workplace. The results of the study are of practical importance for the owners and managers of enterprises in the hospitality industry and necessitate further in-depth study of the human-machine collaboration in the service sector.
Surgeon experience of mixed reality headset technology during the COVID-19 pandemic: a multicenter international case series in orthopedic surgery

There is interest in using reality technologies within the medical sphere and specific focus within orthopedic surgery.[1][1] Mixed reality (MR) is a type of reality technology that allows for a digital image to be both superimposed and controlled by the user on top of their normal visual field.
Research on Robot Trajectory Planning Algorithm Based on Improved Genetic Algorithm

New intelligent industrial robots require the ability to move reliably in restricted spaces with obstacles or work pieces, whether in welding, assembly or grinding operations. In this paper, a kinematic model of the robot is developed using the standard D-H method and the principle of linkage velocity transfer control is described, followed by a smooth trajectory path algorithm for the robot based on kinematic constraints. The improved genetic algorithm reorders the path points, which can effectively re-generate a feasible path for the path points with mixed directional constraints.
A Multi-feature Fusion Method with Attention Mechanism for Long Text Classification

As for the situation that the text content is long and contains much information irrelevant to the subject, which affects the performance of text classification. This paper proposes a multi-feature fusion method with attention mechanism for long text classification. Long text can be regarded as a hierarchical structure of sentences composed of words and paragraphs composed of sentences. Firstly, sentences are encoded and attention mechanism is introduced to aggregate into sentence level representation according to the different contributions of words. Then, based on the contribution of sentence level, aggregate the representation of growing text level. In sentence coding, based on the global target vector, convolutional neural network is used to extract the local features of words and average representation features of words, so as to further enhance the semantic representation of text. Finally, the important information features of long text content are fused and classified in the linear layer. The experimental results on manually processed THUCNews data show that the model has excellent classification performance in long text data with hierarchical structure, and the classification accuracy can reach 0.952.
Discussion Paper: The Integrity of Medical AI

Deep learning has proven itself to be an incredible asset to the medical community. However, with offensive AI, the technology can be turned against medical community; adversarial samples can be used to cause misdiagnosis and medical deepfakes can be used fool both radiologists and machines alike. In this short discussion paper, we talk about the issue of offensive AI and from the perspective of healthcare. We discuss how defense researchers in this domain have responded to the threat and their the current challenges. We conclude by arguing that conventional security mechanisms are a better approach towards mitigating these threats over algorithm based solutions.
Black-box and Target-specific Attack Against Interpretable Deep Learning Systems

Deep neural network models are susceptible to malicious manipulations even in the black-box settings. Providing explanations for DNN models offers a sense of security by human involvement, which reveals whether the sample is benign or adversarial even though previous studies achieved a high attack success rate. However, interpretable deep learning systems (IDLSes) are shown to be susceptible to adversarial manipulations in white-box settings. Attacking IDLSes in black-box settings is challenging and remains an open research domain. In this work, we propose a black-box version of the white-box AdvEdge approach against IDLSes, which is query-efficient and gradient-free without obtaining any knowledge of the target DNN model and its coupled interpreter. Our approach takes advantage of transfer-based and score-based techniques using the effective microbial genetic algorithm (MGA). We achieve a high attack success rate with a small number of queries and high similarity in interpretations between adversarial and benign samples.
Post-breach Recovery

Server breaches are an unfortunate reality on today's Internet. In the context of deep neural network (DNN) models, they are particularly harmful, because a leaked model gives an attacker "white-box'' access to generate adversarial examples, a threat model that has no practical robust defenses. For practitioners who have invested years and millions into proprietary DNNs, e.g. medical imaging, this seems like an inevitable disaster looming on the horizon.
Exploring a Multi-Device Immersive Learning Environment

Though virtual reality has been used for more than one decade to support learning, technology is now mature and cheap enough, and students have the required digital fluency to reach real settings. Immersive technologies have also demonstrated that they not only are engaging, but they can also reinforce learning and improve memory. This work presents a preliminary study on the advantages of using an immersive experience to help young students understand genetic editing techniques. We have relied upon the CHIC Immersive Bubble Chart, a VR (Virtual Reality) multi-device visualization of the most relevant topics in the domain. We tested the CHIC Immersive Bubble Chart by asking a group of 29 students to explore the information space by interacting with two different devices: a desktop and a VR headset. The results show that they mainly preferred the VR headset finding it more engaging and useful. As a matter of fact, during the evaluation, the students kept exploring the space even after the assigned time slot.
A Two-stage Refinement Network for Nuclei Segmentation in Histopathology Images

Histopathology images are used to assess the status of certain biological structures and to diagnose diseases such as cancer. In computer-assisted diagnosis (CAD), nuclear segmentation for histopathology images is an essential prerequisite. In recent years, deep-learning technology has been gaining popularity in the field of nuclei segmentation. However, nuclei segmentation is still faced with challenging a lot of difficulties due to (1) staining intensity inhomogeneity, (2) background noise caused by preprocessing, (3) blurred boundaries due to a large number of overlapping cells. Furthermore, in histopathology imaging, the number of data samples in the dataset is relatively low, preventing deep convolutional neural networks (CNNs) from segmenting nuclei images with high accuracy like in other vision applications. To overcome the above difficulties, we propose a two-stage deep learning network for nuclei segmentation tasks. It is the first stage network responsible for coarse segmentation, and the second stage network for refined segmentation. In comparison with traditional network architectures, our method achieves near SOTA performance in the nuclei segmentation task.
Combinatorial Optimization Method Based on Hierarchical Structure in Solution Space with Function Improving Degeneracy

The authors have proposed a combinatorial optimization method based on hierarchical structure in solution space which method performs for several types of combinatorial optimization problems at a high level. In this letter, we indicate an issue which occurs deterioration of search efficiency and propose a method which can solve the issue. The usefulness of the developed method is confirmed by numerical experiments using benchmark problems.
A Keyword Based Scientometric Analysis of Semantic Web Research in Tourism

The economy of many nations sustain on tourism which is an information intensive industry and requires semantic handling of data. Semantic web research is an area that deals with technologies for semantic handling of data. In this study, the results of an investigation on the extent of semantic web research in the field of tourism have been presented. For this purpose, data was retrieved from the Scopus database. Scientometric methods that help in keyword-based analysis were applied for analyzing the data using the R software environment.
SQUAD

Latency quantiles measurements are essential as they often capture the user's utility. For example, if a video connection has high tail latency, the perceived quality will suffer, even if the average and median latencies are low. In this work, we consider the problem of approximating the per-item quantiles. Elements in our stream are (ID, latency) tuples, and we wish to track the latency quantiles for each ID. Existing quantile sketches are designed for a single number stream (e.g., containing just the latency). While one could allocate a separate sketch instance for each ID, this may require an infeasible amount of memory. Instead, we consider tracking the quantiles for the heavy hitters (most frequent items), which are often considered particularly important, without knowing them beforehand.
On the coset graph construction of distance-regular graphs

We show that no more new distance-regular graphs in the tables of the book of (Brouwer, Cohen, Neumaier, 1989) can be produced by using the coset graph of additive completely regular codes over finite fields.
Improving software fault prediction in imbalanced datasets using the under-sampling approach

To make most software defect-free, a considerable amount of budget needs to be allocated to the software testing phase. As each day goes by, this budget slowly rises, as most software grows in size and complexity, which causes an issue for specific companies that cannot allocate sufficient resources towards testing. To tackle this, many researchers use machine learning methods to create software fault prediction models that can help detect defect-prone modules so that resources can be allocated more efficiently during testing. Although this is a feasible plan, the effectiveness of these machine learning models also depends on a few factors, such as the issue of data imbalance. There are many known techniques in class imbalance research that can potentially improve the performance of prediction models through processing the dataset before providing it as input. However, not all methods are compatible with one another. Before building a prediction model, the dataset undergoes the preprocessing step, the under-sampling, and the feature selection process. This study uses an under-sampling process by employing the Instance Hardness Threshold (IHT), which reduces the number of data present in the majority class. The performance of the proposed approach is evaluated based on eight machine learning algorithms by applying it to eight moderate and highly imbalanced NASA datasets. The results of our proposed approach show improvement in AUC and F1-Score by 33% and 26%, respectively, compared to other research work in some datasets.
Intelligent Decision Method of Multi-Agricultural Commodity Model Based on Machine Learning

China Agricultural Outlook publishes the outlook report on major agricultural commodities and the estimates of the supply–demand balance sheet of agricultural commodities in the next decade, so as to release agricultural information and guide the development of modern agriculture. A large amount of data has been accumulated in the work associated with agriculture outlook in recent years. This paper will optimize the agriculture commodity model through deep learning and create an analysis tool based on long short-term memory (LSTM) deep learning by comprehensively considering the key factors of supply and demand of agricultural commodities including the output, consumption and price and combining the impact of complex natural, social and economic factors. In this way, the close relevance of different varieties and multi-variable strong coupling of the analysis and prediction model of major agricultural commodities can be solved. The “random sampling” and “stress on causality” of traditional complex agriculture analysis models are replaced by the “whole data” and “emphasis on relevance over causality”. The intelligent decision method of agricultural commodity model based on deep learning proposed in this study can effectively improve the analysis efficiency and accuracy of multi-variety coupling model of agricultural commodities (at least by 15%), and enhance the intelligence of the supply–demand analysis and prediction, especially with the accumulation of future data, the prediction accuracy will continue to improve. Machine learning has been regarded as an effective method to provide forecast and early-warning of future agricultural development in a timely manner based on real-time monitoring of agricultural data.
Solving a puzzle with atomic qubits

A quantum com puter makes light work of the maximum independent set problem.
Why should we care about the body?

What can it mean to say that musical activity is embodied? What are the implications for music making as understood through this lens? In this chapter, I discuss how enactive and, moreover, enactive-ecological approaches to music cognition have been fundamental in the development of my creative music practice over the last 14 years. The emerging field of enactive music cognition has gained traction in recent years as a radical alternative to certain theories of embodied music cognition, which remain limited in their focus on representational – or skull-bound – accounts of musical activity. Coming out of non-traditional areas of the cognitive sciences, the enactive approach suggests that cognition emerges out of the developmental bodily processes of sense-making and identity-forming. I outline how these ideas have allowed me to develop a performance practice within the fields of live electronic music and sonic art through the use of highly gestural, haptic, and tangible electronic and digital technologies. In each case, what is demonstrated is that the technological interventions have been important not in terms of their ‘innovative’ qualities per se, but rather in their ability to afford collaborative, participatory, and highly social modes of living musical lives.
Informing Future Gesture Elicitation Studies for Interactive Applications that Use Radar Sensing

We show how two recently introduced visual tools, RepliGES and GEStory, can be used conjointly to inform possible replications of Gesture Elicitation Studies (GES) with a case study centered on gestures that can be sensed with radars. Starting from a GES identified in GEStory, we employ the dimensions of the RepliGES space to enumerate eight possible ways to replicate that study towards gaining new insights into end user’s preferences for gesture-based interaction for applications that use radar sensors.
Multi-prior underwater image restoration method via adaptive transmission

Captured underwater images usually suffer from severe color cast and low contrast due to wavelength-dependent light absorption and scattering. These degradation issues affect the accuracy of target detection and visual understanding. The underwater image formation model is widely used to improve the visual quality of underwater images. Accurate transmission map and background light estimation are the keys to obtaining clear images. We develop a multi-priors underwater image restoration method with adaptive transmission (MUAT). Concretely, we first propose a calculation method of the dominant channel transmission to cope with pixel interference, which combines two priors of the difference between atmospheric light and pixel values and the difference between the red channel and the blue-green channel. Besides, the attenuation ratio between the superior and inferior channels is adaptively calculated with the background light to solve the color distortion and detail blur caused by the imaging distance. Ultimately, the global white balance method is introduced to solve the color distortion. Experiments on several underwater scene images show that our method obtains accurate transmission and yields better visual results than state-of-the-art methods.
Analysis of the effects of the free semester system on academic achievement and career maturity

In this study, we examined the longitudinal effect of the free semester system on middle school students’ academic achievement and career maturity using the Korean Education Longitudinal Study data, cohort 2013. We performed Difference-in-Difference (DiD) method to analyze whether the free semester system has effects on academic achievement and career maturity using the model moderated by parental academic support. We furthermore utilized quantile DiD method to examine whether the effects of the free semester system differ across the distribution of academic achievement and career maturity. As a result, we first found the significant positive effect of the free semester system on middle school students’ academic achievement and career maturity. Second, we found that parental academic support mediated its effects on math performance. Third, our findings indicate that the free semester system improves students’ academic achievement and career maturity, especially among students who performed to the bottom of its distribution. Based on these findings, practical implications and future research are discussed.
E-Connect for Agro Products Using Supply Chain with Micro-Finance: A Blockchain Approach

The computerized world coordinates with customary cycles and keeps a nature venture track of the client of the rural product from the maker. Agro-Connect proposes collaboration among ranchers and customers participation utilizing blockchain for cultivation. Here, the ranchers can post their current harvests and production of their homestead through conveyed public record. The purchasers can go through subtleties and verify the rancher&#x0027;s believability in view of the past development and supply. This makes a straightforward and sealed advanced market stage for ranch items. In this way, the agreement can be shaped between the rancher and the purchaser, to such an extent that the buyer shall finance singular harvests or a ground shall get the harvest from the homestead or the benefit level of its reasonable worth. Measurement of yield and assemble the believability of ranchers and a buyer in light of their past encounters in the agro market. One of the momentous advancements, Blockchain, is an incredible approach to dealing with the nature of the production network on the board since it utilizes the disseminated public general record. Blockchain innovation ends up being useful in the store network area in an accompanying way: diminish mistakes, keep away from item delays, dispose of fake exercises, further develop the executives, increment buyer/provider trust, etc. In the event of the absence of straightforwardness, Blockchain gives kept data about an assortment of exchanges in merchandise and additional benefits, which are recorded and followed progressively.