Reviewerly/exHarmony huggingface dataset:
Image Quality Assessment Based on Sparse Neighbor Importance

In this paper, the image quality assessment problem is discussed from the perspective of sparse coding and a new automatic image quality assessment algorithm is presented. Specifically, the input image is first divided into non-overlapping blocks and sparse coding is used to reconstruct a central sub-block using neighboring sub-blocks as dictionaries. Two-dimensional sparse vectors from each neighboring sub-block are devised as importance maps, which are then used in similarity measurements between the reference and distorted images. The proposed method was compared with several recently introduced shallow and deep methods across four datasets and multiple distortion types. The experimental results obtained show that it has a strong correlation with the Human Visual System and outperforms its counterparts.
A Deep-ConvLSTM Collision Prediction Model for Manipulators in Dynamic Environment

Obstacle avoidance is one of the fundamental problems in human-robot collaboration (HRC) studies. The close proximity between robots and human usually leaves robots a short period of time to re-plan a safe motion, especially when facing non-static obstacles. Therefore, to identify collisions in advance and mitigate the computational efforts regarding robot motion re-planning, this paper proposes a network-based stop-go algorithm that uses only images capturing the states of the robot arm and a non-static obstacle without accessing any robot dynamics. In particular, a deep convolutional long-short-term memory (ConvLSTM) neural network is first developed to learn the spatial features of images, and predict both the robot arm and the non-static obstacle states five steps in advance. Next, the predictions are set back to the robot arm so that the robot arm would halt the current task when a potential future collision is identified. Eventually, the robot arm resumes the task after the non-static obstacle is clear. Extensive numerical studies have been conducted to validate the effectiveness of the proposed trajectory prediction scheme in presence of obstacles.
Linking FrameNet to Russian Resources

In this paper we study three approaches for creating a Frame-Net-like resource for Russian, which can be very useful for semantic analysis of Russian texts. These approaches include two methods of linking the FrameNet frames with Russian words: via WordNet and via parallel corpus. Also we study a method for connecting FrameNet and Russian semantic resource FrameBank. We plan to use created bilingual resource fir developing a new Russian semantic resource.
CLUSTERIZATION METHOD BASED ON BREADTH FIRST SEARCH OR BFS FOR A GRAPH

Clusterization is one of the types of algorithms of unsupervised learning. The idea behind it is that an algorithm learns patterns from untagged data. Such type of algorithm helps to find unseen dependencies in the untagged data itself. This paper presented algorithms based on Breadth-First Search or BFS for a Graph. The method was built based on the basic theory of clusterization. To the theory of clusterization, the calculated distance between the two farthest points in the cluster should be less than the distance between the closest two points from different clusters. By this rule, we defined that two parameters of the method should be the maximum distance between points by which these can be connected and assumed to be in one cluster. The second had to be the maximum distance in the cluster, aka the cluster’s diameter. A cluster’s diameter is the farthest distance between two points within a cluster. With these hyperparameters and the defined distance method, we can assume that every point is a vertex of a graph, two points within the threshold of the distance between pairs of ones are neighbours, and count the connection between counts as an edge of a graph. The group of connected vertexes or a particular vertex is a graph. The diameter hyperparameter ought to keep the data homogeneity in a cluster. We can define every graph as a cluster with defined rules based on previous assumptions. Later in this paper will be visualized the clusterization of three-dimensional data points. We took one of the most popular clusterization dataset – the iris dataset for visualizing purposes. The paper contains several examples of clusterization of the dataset with different hyperparameters. We took KMeans [3] as an example of the clusterization method. The method based on BFS is a flexible clusterization method that relies on meta-information about distancing between data points.
Virtual Interactive Imagery: Virtual Reality in the Treatment of Obesity

This paper proposes a multidisciplinary framework that applies psychological treatment. The framework uses virtual reality to apply guided imagery scenarios, where a person imagines a certain situation positively, to better deal with similar ones in reality. The idea is to use virtual reality to achieve self-efficacy; a self-belief that empowers success. The proposed concept of "Virtual Interactive Imagery" represents immersing users in virtual images to live and interact in a predeveloped scenario positively, instead of having people depend on a facilitator to guide them through imagination. The paper focuses on obesity treatment and the role of imagination in human weight loss. The methodology of creating a virtual system is presented, and a proof of concept for the system is developed and tested through a usability test.
FD-CAM: Improving Faithfulness and Discriminability of Visual Explanation for CNNs

Class activation map (CAM) has been widely studied for visual explanation of the internal working mechanism of convolutional neural networks. The key of existing CAM-based methods is to compute effective weights to combine activation maps in the target convolution layer. Existing gradient and score based weighting schemes have shown superiority in ensuring either the discriminability or faithfulness of the CAM, but they normally cannot excel in both properties. In this paper, we propose a novel CAM weighting scheme, named FD-CAM, to improve both the faithfulness and discriminability of the CAM-based CNN visual explanation. First, we improve the faithfulness and discriminability of the score-based weights by performing a grouped channel switching operation. Specifically, for each channel, we compute its similarity group and switch the group of channels on or off simultaneously to compute changes in the class prediction score as the weights. Then, we combine the improved score-based weights with the conventional gradient-based weights so that the discriminability of the final CAM can be further improved. We perform extensive comparisons with the state-of-the-art CAM algorithms. The quantitative and qualitative results show our FD-CAM can produce more faithful and more discriminative visual explanations of the CNNs. We also conduct experiments to verify the effectiveness of the proposed grouped channel switching and weight combination scheme on improving the results. Our code is available at https://github.com/crishhh1998/FD-CAM.
Elicitation of Interaction Techniques with 3D Data Visualizations in Immersive Environment using HMDs

The design practices of current techniques for interacting with 3D data visualizations offered by state-of-the-art immersive head-mounted displays (HMD) are constrained by technological limitations and designer bias. We argue that understanding user-preferred interaction techniques with 3D data visualizations can lead to improved usability by helping system designers or architects formulate a set of interactions that are most intuitive and natural to the users.
Time for Fun: BattleJong, the Server

In my professional capacity as an architect and team lead, I am sometimes asked by junior developers what they can do to improve their skills. Should they watch YouTube videos about various topics in software development? Are there good books they should read? What about side projects?
Mixed Multi-relational Representation Learning for Low-Dimensional Knowledge Graph Embedding

Hyperbolic embeddings have recently received attention in machine learning because of their better ability to handle hierarchical data than Euclidean embeddings. Moreover, Hyperbolic models are also being developed for multi-relational knowledge graphs which contain multiple hierarchical relationships and have achieved promising results, such as MuRP, HyperKG, and ROTH. However, not all data is hierarchical. We also found that most of the geometry models were trained to attain good results on high-dimensional embeddings and low-dimensional embeddings are often of little interest. Besides, neural networks and graph networks models have had an impressive performance. However, they also require a relatively high-dimensional embedding to achieve good results, making them limited to use in large-scale knowledge graphs. To address these issues, in this paper, we introduce a new model named MuREL (Multi-Relational Euclidean Lorentzian), which learns a mixed embedding between two spaces, Euclidean and Lorentzian. They are not only suitable for a variety of data types but also work well on low-dimensional embeddings. Experiments on standard benchmark datasets from the task of link prediction show that our model outperforms existing Euclidean and Hyperbolic models, especially at lower dimensionality.
Learning Search Region Update for Roubst Tracking

Visual object tracking is a challenging task in computer vision. In tracking a target, trackers will encounter a series of factors such as camera motion (CM), background clutter (BC), and illumination variation (IV). It is easy for the existing trackers to deal with the above problems, however, handling the long-time full occlusion (FOC) is difficult. In this article, we collect a vehicle tracking dataset named VIP-FOC with the attribute of full occlusion. We propose a long-term tracking method based on the time series trajectory prediction model. When the tracking target disappears, the tracker uses the trajectory to infer the position of the tracking target. If the tracking target reappears in the view again, the tracker uses the appearance and trajectory to re-lock the tracking target. Experiments on OTB2015, VOT2019, and VIP-FOC databases show that our tracker can solve the long-term occlusion problem successfully and holds good performance on public datasets.
Large-View 3D Color Face Reconstruction from Dingle Image via UV Location Map and CGAN

Image-based 3D reconstruction has been a hot topic in computer vision research. Since 3D face model can convey more information, it possesses expansive application prospects in comparison with the 2D face image, such as more precise identity information recognition signs, more accurate emotional expression media, etc. In order to reconstruct a realistic 3D color face model from a single large-view 2D face image, a simple but effective method is proposed. First, the encoder-decoder network is applied to generate the 2D UV location map from the original RGB image. Then a simple convolution neural network is applied to regress the 3D face from the UV location map. Finally, a network based on conditional generative adversarial networks (CGAN) is proposed to make up for the missing of UV texture caused by self-occlusion in the case of large pose. Compared with existing 3D face reconstruction methods in Stirling/ESRC 3D Face Database, proposed method can improve the accuracy. More important, a complete and realistic 3D face model can be reconstructed from the large-view face image even in the increasingly complex background.
Blockchain Certificates for Pedigree Management

Certificate management has greatly benefited from Blockchain technology in a variety of businesses for many reasons. Educational institutions use Blockchain to manage degree certificates for its reliability, immutability, provability, etc. However, there are some requirements in certificate management that are needed to address, e.g., the certificate ownership that can be transferred. This article designs a new blockchain certificate management with NFT technology for Brahman cattle pedigree certificate system in Thailand. The system can address the transferable ownership of certificates, effectively. It also addresses issues of Brahman pedigree and family tree. Moreover, the whole system is designed and implemented without use of external databases. All operations are done within Blockchain.
An Efficient Multi-keyword Searchable Attribute-Based Encryption Scheme

With the large-scale application of cloud storage services, the secure sharing and privacy protection of data has become an increasingly serious issue. Attribute-based searchable encryption has become the key to solving this problem due to its advantages of fine-grained access control and privacy-preserving search. Therefore, this paper proposes an efficient multi-keyword searchable ciphertext-policy attribute-based encryption scheme for these problems in practical applications. On the one hand, in order to solve the problem that single keyword search can no longer meet people's needs in a large amount of data, we propose multi-keyword search and preprocess keywords before searching to prevent semi-trusted cloud servers from trying to obtain private data; On the other hand, partial decryption work is undertaken by the intermediate server, which not only improves the efficiency of multi-keyword search, but also reduces the overhead caused by users’ local computing. In terms of security, proof is given from indistinguishability under chosen-plaintext attack and trapdoor indistinguishability.
PRNU-based Image Forgery Localization With Convolutional Neural Network

The device fingerprint, photo-response non-uniformity (PRNU), has attracted great interest in image tampering detection and localization. The classical PRNU-based tampering detection generally depends on the correlation analysis, with the normalized correlation and hand-crafted predictor. The predictor detects unreliable regions and determines them as forgery, regardless of other information. However, the operation is arbitrary and the auxiliary information provided by such a predictor is hard to achieve satisfactory results. Motivated by this, we propose a lightweight forgery detection strategy, where a localization result is directly predicted by a supervised convolutional neural network (CNN). For the first time, CNN is introduced to compute the correlation coefficient in PRNU-based forgery detection. We perform an extensive evaluation in both pixel-level and image-level experiments, and the results show that the proposed method achieves significant performance gains.
An Image Enhancement Method Based on Multi-scale Fusion

To solve the problem of low contrast and poor visual effect of video capture image in coal mine underground, a method based on multi-scale fusion is adopted to enhance coal mine underground image. Firstly, the improved homomorphic filtering and the contrast-limited adaptive histogram equalization are used respectively for the to obtain an underground image with uniform brightness and a contrast enhanced. Then, the enhanced image is obtained by multi-scale fusion of the two images with above processing. Experimental results show that the algorithm can solve the problem of distortion, and effectively improve the contrast and brightness in the image.
Investigation and Analysis of Online Teaching Documents Based on Data Mining Technology

The development and education of big data (BD) and new technologies have provided technical support for education and teaching reform and promoted the progress of online teaching. In order to study the innovative research and development of the online teaching management model of higher vocational education under the background of BD, this article is based on online teaching management, using case analysis, document analysis and other methods to collect data from the database and build it based on online teaching. Model, and read and analyze a large number of relevant documents through the literature survey method. The experimental results proved that more than 50% of the population were dissatisfied with the current teaching system and believed that it did not play its due role, and more than 70% agreed with the reform of the teaching model. The experimental results also show that with the development of BD, online teaching is becoming more and more perfect. The current online teaching data is about 80% better than five years ago, and students’ acceptance of online education is getting higher and higher. This shows that, as a trend, online teaching has become more and more important.
Optimization of CMOS Voltage reference with Prediction based on Multi-group Hierarchical Collaborative Evolution and GBDT

An automatic design system for the voltage reference circuit is presented in this paper. The multi-group hierarchical collaborative evolution algorithm is proposed to automatically generate the circuit structures and intelligently optimize the parameters of the circuits. In order to improve the convergence speed and reduce the optimization time, the gradient boosting decision tree (GBDT) algorithm is introduced to establish the circuit prediction model. In this paper, the automatic design of the CMOS reference source circuit is implemented in a standard 0.18μ m process. Compared with previous work, the number of iterations is reduced by 20%, the optimization time is decreased by 20.48%, the line sensitivity and temperature coefficient are improved by 65.42% and 15.47%, respectively, and the circuit power consumption is reduced by 22.50%.
A Fuzzy Logic Approach to a Hybrid Lexicon-Based Sentiment Analysis Detection Tool Using Healthcare Covid-19 News Articles

The delivery of unbiased news articles in the healthcare sector is one of the prominent problems in the fight against unvaccinated individuals as Covid-19 causes great skepticism among many groups of people. Companies such as Facebook have already integrated AI models for context to content that is rated by third-party fact-checkers to detect misinformation (“Fonctionnement du programme de vérification tierce de Facebook,” Fonctionnement du programme de vérification tierce de Facebook. https://www.facebook.com/journalismproject/programs/third-party-fact-checking/how-it-works?locale = fr_FR (accessed Sep. 06, 2021)). In this paper we use Natural Language Processing (NLP) and Sentiment Analysis to derive the content of news articles, an API is integrated to gather news articles from various sources using the newsapi (“News API – Search News and Blog Articles on the Web,” News API. https://newsapi.org (accessed Oct. 28, 2021)). Applying VADER, Text Blob, and Flair rule-based lexicons, we create a hybrid approach from the lexicons and combine each method, we then present a novel Fuzzy-Logic Lexicon Mamdani Rule-Base Multi Inference System (FLLMRBMIF) that can generate a final sentiment from each output of polarity, we then classify into a positive, neutral and negative result. The results demonstrate that it is possible to integrate a tool to classify the sources in real-time allowing more insightful information on biased news stories.
AI Ethics and Rule of Law

After entering the twenty-first century, the two most significant changes in technology are artificial intelligence and genetic engineering. The two domains have a lot in common: firstly, their applications possess great diffusion effect, which will fundamentally change the operation law of some industries and economic institutions; secondly, their applications also bring enormous ethical risks. Genetic engineering will undoubtedly change the boundaries of social equity and the basic laws of nature, while AI is changing the ethics of human cognition and the corresponding legal ethics; thirdly, both technologies are shaping the future of human civilization. Almost all novels or monographs related to the prediction of human future will be discussed more or less from genetic engineering and AI. Therefore, understanding the boundaries of the two fields is comprehending the prospects of human imagination in the future.
A Probabilistic Approach to Analyzing Agent Relations in Three-Way Conflict Analysis Based on Bayesian Confirmation

Conflict analysis is commonly based on a conflict situation involving agents and their ratings or attitudes toward a set of issues. Analyzing the relationships between agents is one of the essential topics in conflict analysis. Alliance, conflict, and neutrality are three typical relations. The majority of existing research adopts an auxiliary function that uses $$+1$$ , $$-1$$ , and 0 to denote these three relations concerning a single issue. An auxiliary function is aggregated for a group of issues, which is primarily limited to taking the average in the existing works. Moreover, computing the values of an auxiliary function is also associated with potential semantics issues. This paper proposes a probabilistic approach to analyzing agent relations, which is very different from the current approaches. Bayesian confirmation is adopted to explore how a rating confirms or disconfirms the alliance/conflict relation between two agents. Accordingly, we construct three regions of confirmatory, disconfirmatory, and neutral ratings. Three types of confirmation rules are induced from these regions and used to devise appropriate strategies in maintaining and developing relations with agents.
Design and Implementation of Dual-Axis Solar Tracking System for Maximizing the Efficiency of Solar Cells

Nowadays, with various technological developments, the energy consumption to production ratio is becoming a severe challenge. Though scientists have already made great strides in this area, using both renewable and nonrenewable sources, the latter has a limitation, of not being enough to fulfill the requirements of existing world needs and also has a significant contribution to Global Warming. For that reason, during the past few years the focus has shifted toward renewable sources. Solar energy is primary source of renewable resources. There is a need for developing methods for utilizing solar energy more effectively and productively, by increasing the absorption amount of solar energy. For this purpose, in this research, a dual-axis solar tracking system accompanied by a sensor; that is capable to follow Sun’s trajectory by automatically changing its orientation has been designed. An Automatic control system that precisely positions the photovoltaic cell panel according to sun orientation for maximizing the efficiency of the system has been designed and implemented. Solar tracking cells produce energy during the daytime in excess amount than required, so in our design, we modulate a system for storing this additional energy for later use. This system is critically important during the night, and under cloudy conditions or storms, where the sun is not producing the required amount of energy. Our main approach is to develop a cost-effective design of a solar tracking system that will efficiently increase the performance of the system with additional benefit of a tracking system requiring low maintenance cost. Another focus is to develop a design, as simple as possible, because complexities in design may ultimately result in reduced life of the system. In the end, efficiency analysis has been done to check whether a solar tracking system with all its complexities is worth it or not. Detailed analysis of tracking system components i.e sensors and actuators is presented, which is to help the selection of the most effective design.