Reviewerly/exHarmony huggingface dataset:
Side-channel attack-resistant AES S-box with hidden subfield inversion and glitch-free masking

Abstract A side-channel attack (SCA)-resistant AES S-box implementation is proposed, which is an improvement from the power-aware hiding (PAH) S-box but with higher security and a smaller area. We use the composite field approach and apply the PAH method to the inversion in the nonlinear kernel and a masking method to the other parts. In addition, a delay-matched enable control technique is used to suppress glitches in the masked parts. The evaluation results show that its area is contracted to 63.3% of the full PAH S-box, and its power-delay product is much lower than that of the masking implementation. The leakage assessment using simulation power traces concludes that it has no detectable leakage under t -test and that it at least can thwart the moment-correlation analysis using 665 000 noiseless traces.
Investigating the integration of user values with design rationale and its effects on HCI design artifacts

User values are a significant part of designing software, but are not always considered the main focus. When performing decision-making activities, user values can get lost among the designers' own biases. This work studies the integration of user values with a design rationale technique, namely Questions, Options, and Criteria (QOC), and also how this integration reflects on a resulting design artifact (a MoLIC interaction diagram). We performed two separate studies with Computer Science students in Rio de Janeiro. We found that user values can be integrated into design rationale using different notations, such as highlighting the values (through circling or underlining) stated in the options or criteria. The decisions made with the QOC method using user values did result in relevant impacts on MoLIC diagrams. Participants who designed for a persona with strong values had richer results than those who used a "generic" persona. In a second study, we found that designers can recognize when user values are embedded into design artifacts. These results are encouraging to continue research regarding user values, with possibilities of developing new methods or updating existing techniques and notations such as QOC or MoLIC to support user values.
Subjective Quality Evaluation of Light Field Data Under Coding Distortions

This contribution presents the subjective evaluation of the compressed light field datasets obtained with four state-of- the-art codecs: two from the JPEG Pleno Light Field Verification Model and two recent methods for which codecs are publicly available. To the best of our knowledge, currently no subjective testing has been carried out to compare the performances of the four considered codecs. The evaluation methodology is based on Bradley-Terry scores, obtained from pairwise comparisons of the four codecs at four target bit-rates, for four light field datasets. The subset of pairs for which the comparisons are performed is selected according to the square design method, under two design variants, resulting in two datasets of subjective results. The analysis of the collected data, obtained by ranking the subjective scores of the codecs at various bitrates, shows high correlation with the available objective quality metrics.
Facilitating Autonomous Vehicle Research and Development Using Robot Simulators on the Example of a KAMAZ NEO Truck

With the widespread of research in the field of autonomous vehicles the value and impact of various simulators increase dramatically as they allow for quick and safe experimentation with the design of a vehicle, environment and driving scenarios. In this paper, the authors demonstrate how autonomous vehicle research and development can be facilitated by open-source robot simulators based on the experience gained from a robotized KAMAZ NEO truck industrial project. In particular, the Webots robot simulator was applied for 3D reconstruction of the experimental test-site for vehicle motion simulation and development of a web-based dashboard for controlling and monitoring the autonomous vehicle both in the simulation and the real-world.
Deep Learning-Enabled Real Time In-Site Quality Inspection Based On Gesture Classification

In this paper we present a novel method for performing in site real time quality inspection (QI) and consequently, digitalization of manual processes performed by human workers. It complements and improves our previous work in this area, which makes use of telemetry gathered from a smartwatch to classify manual actions as successful or unsuccessful. This new methodology provides the worker with a real time capable, robust and more accurate quality inspector. This work enhances the existing system through the elimination of input from the user by making use of a BIOX bracelet that detects gestures. The signal processing and classification methods are simplified and optimised by using assembled neural networks thus merging together the data gathered from multiple signal sources. Consequently, the overall QI system is improved with around 70%, thus furthering the necessary development needed to have a system ready to be used on a production environment.
A Real-Time Visual Tracking for Unmanned Aerial Vehicles with Dynamic Window

Visual object tracking is one of the basic topic in computer vision that has been studied for decades. As deep neural network has been applied in trackers, their tracking accuracy has been greatly improved and achieved state-of-the-art performance in recent years. Due to the special viewing-angle of UAV, the video sequences captured are high resolution and large in size. So, when applying these trackers to UAVs for tracking a target, the processing speed is hard to reach real-time requirement. In this paper, we propose a visual object tracking framework, DyWinSiam, which can speed up the tracking process significantly compared with state-of-the-art deep neural network trackers, yet it still achieve compatible accuracy.
Compression Artifacts Image Patch database for Perceptual Quality Assessment

Ground truth is one of the most important component for training, testing, and benchmarking algorithms for objective quality assessment In this paper, we propose an image patch quality database with compression artifacts. We create a new database of image patches with High Efficiency Video Coding (HEVC) compression artifacts. Then, the subjective test is conducted in a controlled environment to obtain the ground truth of image patch quality, where we collect differential mean opinion scores (DMOS) from a larger amount of observers. Finally, the rank order correlation factors between DMOS and a set of popular image quality metrics are calculated and presented. The proposed database is expected for learning patch based IQA model for block size in video rate-distortion optimization.
Sentiment Polarity of Programmers in an Open Source Software Project

Context: During the implementation of issues filed in open source software projects, programmers engage and interact in discussions on how to implement them. These discussions provide evidence to investigate emotionally loaded practices embraced by programmers. They interact to explain their point of view regarding the project and the issue under analysis. Objective: Analyze programmers sentiment polarity in an open source software project having releases as a reference for the analysis. Methods: We conducted an exploratory study to characterize the sentiment polarity of comments registered in issues associated with releases of the Moby open source software project. Results: The quantitative analysis identified sentiment polarity variations throughout consecutive releases in line with specific functionalities. Based on a qualitative analysis, we identified these functionalities and specific group of programmers that contributed to those results. Conclusions: We identified initial evidence to contribute for the understanding of the causes underlying the influence of the sentiments of the developers in the context of releases of open source software projects.
Co-channel Modulation Recognition Based on Deep Learning

Modulation recognition is widely used in both military and civil fields. With the complexity of electromagnetic environment, modulation recognition technology is needed to adapt to the situation of large dynamic SNR and multi signal coexistence. In this paper, a modulation recognition algorithm based on deep learning is proposed, which directly extracts the features of IF signals and simplifies the design of artificial features. Two classifiers are used to ensure the correct rate with lower complexity in the case of single signal, while the accuracy rate and noise robustness are higher than traditional methods when multiple signals with time-frequency aliasing coexist.
A Study on the Importance of Linguistic Suffixes in Maithili POS Tagger Development

This paper presents our study on the effect of morphological inflections in the performance of a Maithili Part of Speech (POS) tagger. In the last few years, substantial effort is devoted to developing morphological analyzers and POS taggers in several Indian languages including Hindi, Bengali, Tamil, Telugu, Kannada, Punjabi and Marathi. But we did not find any open POS tagger or morphological analyzers in Maithili. However, Maithili is one of the official languages of India with around 50 million native speakers. So, we worked on developing a POS tagger in Maithili. For the development, we used a manually annotated in-house Maithili corpus containing 52,190 tokens. The tagset contains 27 tags. We first trained conditional random fields (CRF) classifier with various combination of word unigram, bigram, fixed-length suffix, and prefix features. There we observed that the fixed-length suffixes do not show the expected accuracy improvement. However, during the manual corpus annotation, we observed that suffixes played as a helpful clue. So, instead of using the fixed-length suffixes, we worked on identifying the morphological inflections in Mathili. When we used these morphological suffixes in the system, we found a noticeable performance improvement.
Automatic Question Generation In Education Domain Based On Ontology

Generating questions at various difficulty levels require significant time and are challenging. It requires specific knowledge and skills. The quality of the generated questions would depend on the question maker's ability to relate information and represent it in the form of a question. Thus, it is difficult to maintain the quality consistency of a large set of questions. This study introduces an ontology-based approach for automating the generation of questions to maintain consistency of question quality. Information related to the ontology is broken down into information categories in the format of SPARQL queries. The queries are then converted into questions. Experts were asked to validate the generated questions. Based on our experiments, the accuracy of the generated questions reaches 86%.
Occlusion-Aware Discriminative Networks for Visual Object Tracking

Recently, discriminative networks for visual object tracking have been prevailing in visual tracking community due to their powerful discriminative ability. However, when the target is occluded by other semantic or non-semantic backgrounds, most approaches can be fragile since they merely rely on the target appearance to estimate the location of the object in each frame. In this paper, we firstly design a cascaded pyramid module to fuse different parts of target feature map in order to resolve semantic occlusion and improve its robustness. Meanwhile, we propose a spatial attention block which can effectively provide the prior structure information for our module to handle non-semantic occlusion. We then integrate these two proposed module into discriminative networks and training them in an end-to-end fashion. Extensive experiments on OTB-2015, VOT2018, and TrackingNet benchmarks demonstrate that our approach can not only effectively handle occlusion situations but also achieve state-of-the-art performance while running at more than 30 FPS.
Research on Application of Robot and IOT Technology in Engineering Quality Intelligent Inspection

In recent years, the project quality detection profession actively explore the practical application of robot and Internet of things (IOT) technology; parts of the processes have realized IOT and man-machine linkage. This paper applies robot and the IOT in engineering quality inspection, in the perception layer of intelligent inspection architecture, through the RFID identification equipment, QR code identification equipment, AGV transport robots and reclaiming robots, the whole process of feeding, reading sample information, testing and cutting of concrete sample is automatically completed, realize "blind sample management" and "automatic inspection". In the network layer of intelligent inspection architecture, the wireless interconnection between samples and inspection information system is established to realize the effective collection, transmission and storage of the inspection information. In the application layer of intelligent inspection architecture, through the central gateway controller, wireless communication module and personal mobile phone terminal, the laboratory air conditioner, temperature collector, humidity collector, humidifier and gateway controller is connected to each other, to realize remote monitoring and management of the intelligent inspection laboratory. Finally, the application of robot and IOT in the three-layer of intelligent inspection architecture will promote engineering quality inspection to rapid development of automation and intelligence.
Graphoid properties of concepts of independence for sets of probabilities

We examine several concepts of independence associated with (1) credal sets, understood as sets of probability measures, (2) sets of full conditional probabilities, (3) sets of lexicographic probabilities, and (4) sets of desirable gambles. Concepts of independence are evaluated with respect to the graphoid properties they satisfy, as these properties capture important abstract features of "independence". We emphasize the analysis of sets of probability measures as this is a popular formalism, looking at versions of epistemic, confirmational, and type-5 independence that are based on regular conditioning, as well as complete and strong independence. We then examine analogous concepts of independence for sets of full conditional probabilities, sets of lexicographic probabilities, and sets of desirable gambles.
Inter-image Affinity based Interactive Video Object Segmentation

A procedure of interactive video object segmentation is cutting out a target object in a video by repeatedly providing user-annotations (points, lines, etc.) to an algorithm. In this paper, the procedure is achieved by generating the inter-image affinity matrix between the target frame and the annotated frame, and the other matrix between the previous frame and the target frame. By utilizing the two inter-image affinity matrices, we transport encoded features of the annotated frame and the previous frame for the target object. Through the experimental result, we show that exploiting the inter-image affinity matrices are effective for conducting the interactive video object segmentation.
Enriching Optical Flow with Appearance Information for Action Recognition

Optical flow is a widely used data source for learning motion information, but the complete loss of appearance information limits its ability for action recognition. Therefore we think of enriching optical flow frames with supplementary appearance information to form a new motion data source denoted as Appearance-Supplemented Optical Flow (ASOF). Specifically, we propose a data embedding layer and a stagewise training method to mitigate the scale-wise and density-wise data distribution divergence between the RGB and optical flow frames respectively. We conduct experiments on three benchmark datasets: UCF101 [1], HMDB51 [2] and SomethingSomething-V1 [3]. The results show that our methods can prominently improve optical flow stream recognition accuracy, and further improve the performances of two-stream (score fusion with the RGB stream) with only a little storage increase.
An Optimized Graph-based Clustering for Multi-database Mining

Multinational corporations have multiple databases distributed throughout their branches, which store millions of transactions per day. For business applications, identifying disjoint clusters of homogeneous databases contributes to learning the common patterns among customers and also increases profits by targeting potential clients in the future. In this paper, we present an effective approach to search for the optimal clustering of multiple transactional databases in a weighted undirected similarity graph. To assess the clustering quality, we use dual gradient descent to minimize a constrained quasi-convex loss function whose parameters will determine the edges needed to form the optimal database clusters in the graph. Therefore, finding the global minimum is guaranteed in a finite and short time compared with the existing non-convex objectives where all possible candidate classifications are generated to find the ideal clustering. Moreover, our algorithm does not require specifying the number of clusters a priori and uses a disjoint-set forest data structure to maintain and keep track of the clusters as they are updated. We have performed extensive experiments on public data samples and compared our algorithm with one of the best previous algorithms for clustering multiple databases. Our experimental study shows that our algorithm performs better than the previous algorithms in terms of accuracy and running time.
Construction of knowledge graph of forest musk deer based on BiLSTM-CRF model and DPA method

Abstract Forest musk deer is a national key protected species, but due to overhunting and habitat destruction, wild musk deer has disappeared in large areas. Constructing a knowledge graph in the field of forest musk deer can standardize the relevant knowledge in the field of forest musk deer and lay a good foundation for wildlife protection and ecological construction. In order to construct the knowledge map of the forest musk deer, the entities were further subdivided first, and the BiLSTM-CRF model was used to identify the entities of the BIO-labeled data. After analysis, it was found that the model used in the experiment performed better. Secondly, the Dependency Parsing Analysis method was used to identify the relationship, and the relationship between the entities was smoothly extracted.Finally, the Neoj4 graph database was used to realize the storage and visualization of the knowledge graph of the forest musk deer. This research transforms the unstructured text in the field of forest musk deer into structured data. Through the systematic sorting of relevant knowledge in the field of forest musk deer, it can provide wildlife protection, artificial breeding of forest musk deer, intelligent knowledge services and other aspects some new technical means in the field of forest musk deer.
Development and implementation of special natural-historical museum educational programs and projects

The basic requirements for the development and application of interactive museum lessons in the form of games that maximally complement the school curriculum. Special approaches to teaching in the walls of the natural-historical museum of children of the Alpha generation are considered. It is emphasized that the main source of implementation of pedagogical game scenarios is a lesson, an important aspect of which is the effective selection of methods and techniques. There are obtained experience of children's shares and various events that enhance the museum audience.
A Web Application Load Prediction Model Using Recurrent Neural Network in Cloud

Nowadays, Cloud computing environment attracts many application developers to deploy their Web applications on cloud data centers. However, the Web applications workload continuously fluctuates depending on the user behavior, so it very hard to predict. Predicting future request workload is very important because it can help Cloud service providers automatically adjust resources online to maintain the Service Level Agreement (SLA). This paper proposes a Web application load prediction model by using Recurrent Neural Network (RNN) to forecast request workload in the future. The model uses a special type of RNN called Bi-directional Long Short-term Memory Network (Bi-LSTM). The experiment result is evaluated with real workload history from World Cup 1998 Website. Results show that our proposed model can improve around 50% prediction accuracy compared to regular LSTM and have an acceptable prediction speed for applying to real-time circumstances like the auto-scaling system.
Predicting the Basic Level in a Hierarchy of Concepts

The “basic level”, according to experiments in cognitive psychology, is the level of abstraction in a hierarchy of concepts at which humans perform tasks quicker and with greater accuracy than at other levels. We argue that applications that use concept hierarchies could improve their user interfaces if they ‘knew’ which concepts are the basic level concepts. This paper examines to what extent the basic level can be learned from data. We test the utility of three types of concept features, that were inspired by the basic level theory: lexical features, structural features and frequency features. We evaluate our approach on WordNet, and create a training set of manually labelled examples from different part of WordNet. Our findings include that the basic level concepts can be accurately identified within one domain. Concepts that are difficult to label for humans are also harder to classify automatically. Our experiments provide insight into how classification performance across different parts of the hierarchy could be improved, which is necessary for identification of basic level concepts on a larger scale.
Bayesian Fusion of Multiview Human Crowd Detections for Autonomous UAV Fleet Safety

In this paper, a Bayesian method for fusing multiple visual human crowd detections (in the form of heatmaps) under an autonomous UAV fleet deployment setting is proposed, aiming at enhanced vision-assisted human crowd avoidance in line with common UAV safety regulations. 2D crowd heatmaps are derived using deep neural human crowd detectors on multiple UAV camera streams covering the same large-scale area over time (e.g., when each drone tracks a different target). Then, these heatmaps are back-projected onto the 3D terrain of the navigation environment. The projected crowd heatmaps are fused by exploiting a Bayesian filtering approach that favors newer crowd observations over older ones. Thus, during flight, an area is marked as crowded (therefore, a no-fly zone) if all, or most, UAV-mounted visual detectors have recently and confidently indicated crowd existence on it. Empirical evaluation on synthetic multiview video sequences depicting human crowds in outdoor environments verifies the efficiency of the proposed method against the no-fusion case.