Reviewerly/exHarmony huggingface dataset:
Local feature extracted by the improved bag of features method for person re-identification

Person re-identification is to recognize a pedestrian who has been observed at different places in monitoring system, which is done by computer automatically. Important practical significance can be found in this paper on improving the intelligence of monitoring system. There are two challenging problems needed to be solved for person re-identification, which is feature representation and matching. Designing a suitable feature representation method is both challenging and crucial. Ideally, the extracted features should be robust enough to cope with the various situations of the monitoring environment, such as posture difference, illumination changing, and shooting angle difference, etc. There are a lot of challenges for feature matching too, such as small sample size, inter-class confusion, and intra-class variation, etc. In order to meet these challenges, a new person re-identification scheme was put forward in this paper. The following three aspects would be used to describe it. First, an improved BOF feature extraction algorithm based on SURF was put forwarded, SURF algorithm extracts the preliminary feature descriptor and generates visual dictionary, so the influence factors such as illumination and scale invariants can be deal with. And then, covariance descriptor was adopted by the algorithm. It has high robustness, and matching accuracy can be improved too for the case of small sample. Finally, an effective classifier was designed by LIBSVM based on improved BOF algorithm, so the efficiency of the person re-identification algorithm can be improved. The proposed method was compared with the current mainstream algorithm through experiment, and it can be found through the experimental results that it is effective to solve the difficult problems for person re-identification.
Vertical stripe correction in Hyperion image using wavelet transformation and singular value decomposition (SVD)

Vertical striping are quite common in Hyperion images. The removal of these stripes turns out to be an important step of noise correction along with atmospheric and other artifact correction. Often these corrections are applied on radiometric values in spatial domain or fequency domain. We have proposed a novel approach to correct these vertical stripes using wavelet transformation and singular value decomposition. In the first step, we proposed stripe identification based on the Eigen ratio of first and second Eigen values of vertical components (decomposed using wavelet transformation). This ratio is supposed to be very high for striped images and minimal for stripe free images. This is because of the contribution to first Eigen value of high frequency stripes. These high frequency stripes contributes tremendously to the first Eigen because of their dominance in vertical component. We have also proposed a threshold function to identify the bands that needs to be corrected. After the stripes identification and correction algorithm is appllied on respective bands, it is observed that proposed method successfully removed striping noise with high accuracy. Upon comparing the results of proposed method with various other methods, it is found that proposed method have an edge and performing better than rest of the methods. To assess the results, SNR value was calculated for all methods and found highest for the proposed method except Local Threshold approach. Local Threshold have better SNR probably because of intact noise (even in visual inspection but also present in frequency plot of vertical wavelet component) in these methods.
Guidelines For Pursuing and Revealing Data Abstractions

Many data abstraction types, such as networks or set relationships, remain unfamiliar to data workers beyond the visualization research community. We conduct a survey and series of interviews about how people describe their data, either directly or indirectly. We refer to the latter as latent data abstractions. We conduct a Grounded Theory analysis that (1) interprets the extent to which latent data abstractions exist, (2) reveals the far-reaching effects that the interventionist pursuit of such abstractions can have on data workers, (3) describes why and when data workers may resist such explorations, and (4) suggests how to take advantage of opportunities and mitigate risks through transparency about visualization research perspectives and agendas. We then use the themes and codes discovered in the Grounded Theory analysis to develop guidelines for data abstraction in visualization projects. To continue the discussion, we make our dataset open along with a visual interface for further exploration.
Building Trust in Human-Machine Partnerships

Artificial intelligence (ai) is bringing radical change to our lives. Fostering trust in this technology requires the technology to be transparent, and one route to transparency is to make the decisions that are reached by ais explainable to the humans that interact with them. This paper lays out an exploratory approach to developing explainability and trust, describing the specific technologies that we are adopting, the social and organizational context in which we are working, and some of the challenges that we are addressing.
Concept Drift Detection and Adaptation for Robotics and Mobile Devices in Federated and Continual Settings

Service robots and other smart devices, such as smartphones, have access to large amounts of data suitable for learning models, which can greatly improve the customer experience. Federated learning is a popular framework that allows multiple distributed devices to train deep learning models remotely, collaboratively, and preserving data privacy. However, little research has been done regarding the scenario where data distribution is non-identical among the participants and it also changes over time in unforeseen ways, causing what is known as concept drift. This situation is, however, very common in real life, and poses new challenges to both federated and continual learning. In this work, we propose an extension of the most widely known federated algorithm, FedAvg, adapting it for continual learning under concept drift. We empirically demonstrate the weaknesses of regular FedAvg and prove that our extended method outperforms the original one in this type of scenario.
Rapid Generation of Challenging Simulation Scenarios for Autonomous Vehicles Based on Adversarial Test

As the development of the autonomous vehicles, the test and evaluation of the autonomy of autonomous vehicles has receiving considerable attentions from both academic and industrial research fields recently. How to rapid identify the performance boundaries of autonomous vehicles and then to accelerate the testing has been considered as one of the most important problems. Simulation test and challenging scenarios are considered as two effective ways to solve above problems. In this research, a framework of rapid generation of challenging simulation scenarios based on adversarial test was proposed. An optimization model based on newly developed indicators, i.e., safety, smoothness, sharpness and smartness, was proposed to assess the autonomy of autonomous vehicles. Due to the high dimensionality of the configuration space and computation complexity of the optimization problem, a quantum genetic algorithm was employed to solve the optimization problem owing to its high search ability and lower population size required. A simulation system based on Prescan, Carsim and Simulink was built to validate the effectiveness of the proposed method. Comparative experiments also were conducted to illustrate the efficiency of the proposed optimization algorithm.
Blog text quality assessment using a 3D CNN-based statistical framework

Aiming at the problem that blog texts are the streaming data captured by different acquisition modality, each kind of which has its particular quality evaluation mode, this paper proposes a text quality evaluation (TQA) model based on 3D CNN correlated with blog text data. In order to achieve accurate TQA value, the model adopted a Bi-LSTM-based architecture to process video-related blog text as auxiliary part to provide additional information for our TQA architecture. First, the auxiliary part constructs feature vector for each video-related text by the model originating from Bi-LSTM and Seq2Seq. Then, the feature vector was feed to a well-trained decoder to reconstruct the original input data. Then, the feature vector complied with the blog textual data are inputted into end-to-end TQA modal based on the 3D CNN straightly. Comprehensive experimental results on the blog text/video dataset from the well-known truism website “http://www.mafengwo.cn/” have shown that the proposed model reflects the subjective quality of online texts more accurately, and has better overall blog TQA assessment performance than the other state-of-the-art non-reference methods.
The effects of proficiency and textual enhancement technique on noticing

Textual enhancement (TE) refers to manipulation of a text to make specific linguistic targets more perceptually salient. Typically, the purpose of TE is to implicitly draw learners’ attention to target forms, as it has been postulated that noticing the gap between one’s interlanguage and the target is a first step towards acquisition (Smith, 1991, 1993). This study investigated the effects of two forms of TE on the acquisition of the English third-person singular /~s/ and /~es/ forms among 382 Japanese university students. Two grammaticality judgement tasks in a pre/posttest design indicated small, but significant, increases among the two experimental groups compared to a comparison group (unenhanced text), and a control group (no treatment). The relative effect of language proficiency was also examined, with students of lower proficiency in each experimental group showing greater improvement over their control groups than their counterparts of higher proficiency. Post-interviews revealed that participants generally had not been successful at noticing the target form, and that they were predominately engaged in processing the readings for content, not form. Overall results suggest that TE is slightly effective, but it is argued that its effects may be enhanced by combining it with training students in effective learning strategies.
Horror Laboratory and Forest Cabin - A Horror Game Series for Desktop Computer, Virtual Reality, and Smart Substitutional Reality

The games Horror Laboratory and Horror Cabin were designed to investigate ethic issues of virtual reality (VR), however, the presented work focuses on their technical implementation. Each game runs on desktop computer (PC), VR, and extended VR, namely smart substitutional reality (SSR). SSR describes the integration of smart home devices into VR as well as matching the real and virtual environment. Thereby it enriches the games by adding sensory stimuli. A study was conducted to, inter alia, compare the effects of the games' presentation on the perceived presence and the induced emotions. Also, effects of desensitization were examined. Selected results show higher spatial presence for VR and SSR compared to the PC condition. There were no differences regarding the users' emotional experience nor regarding desensitization effects. Further analysis will be part of future work.
Synthesis of Characteristic Descriptors for the Detection of Distress

The success of automated distress detection to a large extent depends on the proper choice of machine learning methods and the appropriate representation of data. In the present study, we evaluate five methods for the synthesis of characteristic descriptors that are appropriate in automated distress detection. These allow efficient data representations and contribute towards a significant reduction of the computational demands. Based on these methods, we synthesized nine alternative characteristic descriptors, which were evaluated in a common experimental protocol. The experimental setup relied on a dataset collected from approximately 6000 oncological patients at different stages of therapy. The dataset consists of the binary responses to specific questions in a purposely-designed questionnaire for self-evaluation of the degree of distress. The experimental results show that the characteristic descriptor referred to as KR8 outperforms the others in terms of detection accuracy without a significant increase in the time required for modeling and classification.
M-Throttled: Dynamic Load Balancing Algorithm for Cloud Computing

Cloud computing environment can also be called as Internet-based computing process in which there is no limitation of work. There are multiple numbers of data center (DC) available for solving multiple user requests coming from a different user base (UB). The data center is capable of negotiating multiple instructions simultaneously. But the instructions are submitted to the DC randomly. Thus, there is chance of overload for a particular DC. Hence, the load balancing plays a vital role in cloud computing to maintain the performance of the computing environment. In this research article, we have implemented throttled, round-robin, and the shortest job first loadbalancing algorithm. Also, we have proposed one more algorithm called M-throttled which has the high performance compared to others. We have taken different parameters such as overall response time and DC processing time for comparison. These are simulated by taking closest data center policy in CloudSim environment.
Quantum Advantage in Cryptography with a Low-Connectivity Quantum Annealer

The application in cryptography of quantum algorithms for prime factorization fostered the interest in quantum computing. However, quantum computers, and particularly quantum annealers, can also be helpful to construct secure cryptographic keys. Indeed, finding robust Boolean functions for cryptography is an important problem in sequence ciphers, block ciphers, and hash functions, among others. Due to the super-exponential size $\mathcal{O}(2^{2^n})$ of the associated space, finding $n$-variable Boolean functions with global cryptographic constraints is computationally hard. This problem has already been addressed employing generic low-connected incoherent D-Wave quantum annealers. However, the limited connectivity of the Chimera graph, together with the exponential growth in the complexity of the Boolean function design problem, limit the problem scalability. Here, we propose a special-purpose coherent quantum annealing architecture with three couplers per qubit, designed to optimally encode the bent function design problem. A coherent quantum annealer with this tree-type architecture has the potential to solve the $8$-variable bent function design problem, which is classically unsolved, with only $127$ physical qubits and $126$ couplers. This paves the way to reach useful quantum supremacy within the framework of quantum annealing for cryptographic purposes.
The IEEE Style: Peculiarities of the Format and Application Prospects

This article analyses a problematic issue related to the concept of academic integrity, i.e. keeping to the rules of citing references while writing scientific works. A number of professional editions, primarily in the field of technical sciences, have been examined as to the observance of these rules. A special attention is drawn to the style of citing references worked out by Institute of Electrical and Electronics Engineers - the IEEE Style. In particular the basic formats for citing both print and online books, translated books and books with a chapter title, periodicals, conferences and conference proceedings, courses, patents, standards, theses and dissertations, technical reports, software, manuals, video materials, datasets, etc. have been analysed in the article. Admitting that bibliographic activity determines the level of a scientist's culture, and proper citing references is an important indicator of the quality of a scientific work as well as guarantee that many scientists will learn it, we have proved importance of the issue in the context of integrating of Ukrainian science into the world scientific space and becoming a part of the scientific communication system.
Text Input Methods in Virtual Reality using Radial Layouts

Currently, the most popular text input method in VR is Controller Pointing (CP). While this method is easy and intuitive to use, it requires users to have steady hands, and the overlaying of the keyboard onto the virtual scene occludes a part of the scene. In this work, we proposed two new text input methods: Sector Input and T9VR , that utilize a circular keyboard that is attached to the HTC Vive controller. A preliminary study with 24 subjects was conducted to explore the potential of the proposed methods, in comparison with CP. While CP performed significantly better than the proposed methods in terms of objective typing speed and error rate, T9VR was able to match with CP in a number of subjective measures.
Learning to Answer Complex Questions with Evidence Graph

Text-based end-to-end question answering (QA) systems have attracted more attention for their good robustness and excellent performance in dealing with complex questions. However, this kind of method lacks certain interpretability, which is essential for the QA system. For instance, the interpretability of answers is particularly significant in the medical field, in that interpretable answers are more credible and apt to acception. The methods based on knowledge graph (KG) can improve the interpretability, but suffer from the problems of incompleteness and sparseness of KG. In this paper, we propose a novel method (EGQA) to solve complex question answering via combining text and KG. We use Wikipedia as a text source to extract documents related to the question and extract triples from the documents to construct a raw graph (i.e., a small-scale KG). Then, we extract the evidence graphs from the raw graph and adopt Attention-based Graph Neural Network (AGNN) to embed them to find the answer. Our experiments conduct on a real medical dataset Head-QA, which shows that our approach can effectively improve the interpretability and performance of complex question answering.
Dual Phone AR

Mobile Augmented Reality applications have become increasingly popular, however the possible interactions with AR content are largely limited to on-screen gestures and spatial movement. There has been a renewed interest in designing interaction methods for mobile AR that go beyond the screen. Mobile phones present a rich range of input, output, and tracking capabilities, and have been used as controllers for Virtual Reality and head-mounted Augmented Reality applications. In this project, we explore the use of a second phone as a controller for Mobile AR. We developed ARTWO, an application that showcases Handheld Dual Phone AR through a series of small demos in which a second phone can be used to perform basic tasks such as pointing, selecting, and drawing, in the context of real use cases. We believe that the Dual Phone AR approach can help address many of the issues faced when using conventional mobile AR interactions, and also serves as a stepping stone to the general use of phones with head-mounted AR systems in the near future.
Entity-Based Short Text Classification Using Convolutional Neural Networks

It is beyond human capabilities to analyze a huge amount of short text produced on the World Wide Web in the form of search queries, social media platforms, etc. Due to many difficulties underlying short text for automated processing, i.e, sparsity and insufficient context, the traditional text classification approaches cannot easily be applied to short text. This study discusses a Convolutional Neural Network (CNN) based approach for short text classification. Given a short text, the model generates the text representation by leveraging words together with the entities. To validate the effectiveness of the model, several experiments have been conducted on different datasets. The results suggest that the proposed model is capable of performing short text classification with a high accuracy and outperforms the baseline.
Multi-Atlas Based Adaptive Active Contour Model with Application to Organs at Risk Segmentation in Brain MR Images

Accurate delineation of organs at risk (OARs) is critical in radiotherapy. Manual delineation is tedious and suffers from both interobserver and intraobserver variability. Automatic segmentation of brain MR images has a wide range of applications in brain tumor radiotherapy. In this paper, we propose a multi-atlas based adaptive active contour model for OAR automatic segmentation in brain MR images. The proposed method consists of two parts: multi-atlas based OAR contour initiation and an adaptive edge and local region based active contour evolution. In the adaptive active contour model, we define an energy functional with an adaptive edge intensity fitting force which is responsible for evaluating contour inwards or outwards, and a local region intensity fitting force which guides the evolution of the contour. Experimental results show that the proposed method achieved more accurate segmentation results in brainstem, eyes and lens automatic segmentation with the Dice Similar Coefficient (DSC) value of 87.19%, 91.96%, 77.11% respectively. Besides, the dosimetric parameters also demonstrate the high consistency of the manual OAR delineations and the auto segmentation results of the proposed method in brain tumor radiotherapy. The geometric and dosimetric evaluations show the desirable performance of the proposed method on the application of OARs segmentations in brain tumor radiotherapy.
RevPHiSeg: A Memory-Efficient Neural Network for Uncertainty Quantification in Medical Image Segmentation

Quantifying segmentation uncertainty has become an important issue in medical image analysis due to the inherent ambiguity of anatomical structures and its pathologies. Recently, neural network-based uncertainty quantification methods have been successfully applied to various problems. One of the main limitations of the existing techniques is the high memory requirement during training; which limits their application to processing smaller field-of-views (FOVs) and/or using shallower architectures. In this paper, we investigate the effect of using reversible blocks for building memory-efficient neural network architectures for quantification of segmentation uncertainty. The reversible architecture achieves memory saving by exactly computing the activations from the outputs of the subsequent layers during backpropagation instead of storing the activations for each layer. We incorporate the reversible blocks into a recently proposed architecture called PHiSeg that is developed for uncertainty quantification in medical image segmentation. The reversible architecture, RevPHiSeg, allows training neural networks for quantifying segmentation uncertainty on GPUs with limited memory and processing larger FOVs. We perform experiments on the LIDC-IDRI dataset and an in-house prostate dataset, and present comparisons with PHiSeg. The results demonstrate that RevPHiSeg consumes \({\sim }30\%\) less memory compared to PHiSeg while achieving very similar segmentation accuracy.
A Rigorous Link Between Self-Organizing Maps and Gaussian Mixture Models

This work presents a mathematical treatment of the relation between Self-Organizing Maps (SOMs) and Gaussian Mixture Models (GMMs). We show that energy-based SOM models can be interpreted as performing gradient descent, minimizing an approximation to the GMM log-likelihood that is particularly valid for high data dimensionalities. The SOM-like decrease of the neighborhood radius can be understood as an annealing procedure ensuring that gradient descent does not get stuck in undesirable local minima. This link allows to treat SOMs as generative probabilistic models, giving a formal justification for using SOMs, e.g., to detect outliers, or for sampling.