Reviewerly/exHarmony huggingface dataset:
Attention on Global–Local Representation Spaces in Recommender Systems

In this study, we present a novel clustering-based collaborative filtering (CF) method for recommender systems. Clustering-based CF methods can effectively deal with data sparsity and scalability problems. However, most of them are applied to a single representation space, which might not characterize complex user–item interactions well. We argue that the user–item interactions should be observed from multiple views and characterized in an adaptive way. To address this issue, we leveraged the global and local properties to construct multiple representation spaces by learning various training datasets and loss functions. An attention network was built to generate a blended representation according to the relative importance of the representation spaces for each user–item pair, providing a flexible way to characterize diverse user–item interactions. Substantial experiments were evaluated on four popular benchmark datasets. The results show that the proposed method is superior to several CF methods where only one representation space is considered.
Multisv: Dataset for Far-Field Multi-Channel Speaker Verification

Motivated by unconsolidated data situation and the lack of a standard benchmark in the field, we complement our previous efforts and present a comprehensive corpus designed for training and evaluating text-independent multi-channel speaker verification systems. It can be readily used also for experiments with dereverberation, denoising, and speech enhancement. We tackled the ever-present problem of the lack of multi-channel training data by utilizing data simulation on top of clean parts of the Voxceleb corpus. The development and evaluation trials are based on a retransmitted Voices Obscured in Complex Environmental Settings (VOiCES) corpus, which we modified to provide multi-channel trials. We publish full recipes that create the dataset from public sources as the MultiSV dataset, and we provide results with two of our multi-channel speaker verification systems with neural network-based beamforming based either on predicting ideal binary masks or the more recent Conv-TasNet.
A New Approach to Constructing Decentralized Identifier for Secure and Flexible Key Rotation

Owing to the introduction of blockchain (BC) technology, a decentralized identity (DID) model has been proposed to replace conventional identity models based on centralized authorities. The BC platform operated by various participants provides a new root-of-trust functionality for entity identification and access control. Each entity generates and registers its own identifier and credential (public key) to the BC such that any entity can obtain the other entity’s public key. When the corresponding private key is compromised, the key rotation to generate and register a new key pair should be performed. However, the current approach for cryptographically binding a decentralized identifier with a public key induces a serious security problem that results in both identity-stealing attacks and multiple identifiers for a single entity. A new DID to address the security problem above is proposed herein, which is based on a newly proposed cryptographic primitive (infinite one-way hash chain), as well as its security analysis and performance evaluation on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Hyperledger Fabric</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Contiki Cooja</i> simulator. To demonstrate the applicability of the proposed DID to various security protocols, an authenticated key exchange protocol is also designed.
Challenges of Managing Information Security during the Pandemic

The COVID-19 pandemic of 2019 surprised information security practitioners in the organizations due to the change imposed on employees’ work routines. Employees were asked to work from home, and therefore changes were necessary to reduce information security risks actively. The abrupt change of work environments brought many challenges to the practitioners, which caused them to make decisions regarding organizational information security. This article aims to uncover those challenges through an ethnography study within an organization during the fourteen months of teleworking. On an overarching level, we found four challenges to be of concern: technical security, regulations and policies, employee awareness of security issues, and, finally, preparedness for the new work environment of teleworking. We believe that the challenges brought by the analysis will inspire discussions about the future of research and practice regarding information security management in case of disasters.
A Effective Algorithm for Skew Correction in Text Images

This paper proposes an effective corner detection method for skew correction in text images. The idea of corner detection is to build an RGB background mapping function based on similar features of the same region in the background. The RGB background mapping function separates the target area from the image and determines the corner points. Finally, the target area is corrected according to the corner point using perspective transformation. From the experimental results, it is shown that the proposed method can correctly make the corner point detection in the image and produce easy-to-read text images after skew correction.
Enhancing Prototypical Few-Shot Learning By Leveraging The Local-Level Strategy

Aiming at recognizing the samples from novel categories with few reference samples, few-shot learning (FSL) is a challenging problem. We found that the existing works often build their few-shot model based on the image-level feature by mixing all local-level features, which leads to the discriminative location bias and information loss in local details. To tackle the problem, this paper returns the perspective to the local-level feature and proposes a series of local-level strategies. Specifically, we present (a) a local-agnostic training strategy to avoid the discriminative location bias between the base and novel categories, (b) a novel local-level similarity measure to capture the accurate comparison between local-level features, and (c) a local-level knowledge transfer that can synthesize different knowledge transfers from the base category according to different location features. Extensive experiments justify that our proposed local-level strategies can significantly boost the performance and achieve 2.8%–7.2% improvements over the baseline across different benchmark datasets, which also achieves the state-of-the-art accuracy.
A Local Planning Method Based on Minimum Snap Trajectory Generation and Traversable Region for Inspection of Airport Roads

The security inspection of airport Roads is an important assignment of the aviation safety. However, the traditional manual ways can’t satisfy the requirements for security and high precision. So, it is necessary to replace the traditional methods with the airport road safety inspection robots. To guarantee the precision of inspection and the security of the inspection equipment, the generated trajectory of inspection robots is demanded to be of high stability. In order to achieve this, in this paper, the minimum snap trajectory method is introduced. Some improvements are also made to solve the problems coming from the application of the method in mobile robots. The proposed method generates a local path from the local map by ARA*, then the traversable region is got according to the local path and local map, finally a stable local trajectory is generated by the improved minimum snap trajectory method.
Mapping Nearest Neighbor Compliant Quantum Circuits Onto a 2-D Hexagonal Architecture

Quantum algorithms can be described as quantum circuits and are supposed to be carried out on an ideal quantum device that is far from current ones. The current quantum devices have a significant limitation on the connectivity between quantum bits. In other words, a quantum bit is only allowed to interact with its nearest neighbors (NNs). In reality, quantum bits have to be placed on a grid, where the connectivity between quantum bits is predefined. The predefined connectivity of a grid further determines the possible range of architectures of a quantum device after the placement of quantum bits. In this article, we propose to place quantum bits based on a 2-D hexagonal architecture rather than a 2-D Cartesian architecture. To validate the effectiveness, we leverage a workflow for mapping NN compliant quantum circuits onto targeting grids, where the workflow consists of a global reordering strategy and a local reordering strategy. With the advantages of the hexagonal grid, the overhead of making quantum circuits NN compliant is reduced significantly compared with the Cartesian grid. We also provide a comprehensive set of ablation analyses to gain a better understanding of the contributions of the components within our workflow. According to the experimental results, when changing the grid type from Cartesian to hexagonal, the global reordering strategy is crucial for small quantum circuits. In contrast, the local reordering strategy is more important than the global reordering strategy for large quantum circuits.
Design of a Chatbot for People Under Distress using Transformer Model

As indicated by the WHO, the variety of mental issues incorporates dementia, discouragement, schizophrenia and other psychoses, intellectual disabilities and developmental disorders. Sadness alone influences in excess of 300 million individuals. Indeed, even with admittance to proficient medical care and social administrations, those tormented with emotional well-being conditions wonder whether or not to profit from the therapy because of the stigma encompassing the disease. Accordingly, to this we would like to build an artificial intelligent agent that responds and helps the patient using an interactive chat bot. With the recent changes in Deep learning technology and specifically in NLP with models such as BERT and Open-AI GPT getting efficient contextual responses is not a task too hard. The chatbot will be able to identify, address and help relieve the mental stress of the user through soothing message responses similar to a therapist’s conversation. Also, other relaxation techniques are provided on the same platform for further support to the distressed user.
Analysis and Issues of Artificial Intelligence Ethics in the Process of Recruitment

Artificial intelligence has evolved into a virtual human. Similar to the standards and ethics of humans, Artificial intelligence also has standards and ethics. On the other hand, Artificial Intelligence [AI] technologies should also incorporate moral values. Today, the amount of work done at the university has increased. The amount of time spent for the recruitment of the required faculty has become important for the lifetime of universities. Artificial Intelligence [AI] can be used for the process of recruitment; it will save a lot of time for recruitment board. The primary motivation of this research work is to explore all the AI tools used for recruiting and the challenges of incorporating AI tools in the process of recruitment.
Optimized Interpolation of Four-Term Karatsuba Multiplication and a Method of Avoiding Negative Multiplicands

In this paper, we presented a method of minimizing the number of overlapped partial products in the accumulation of four-term Karatsuba multiplication. This method reduced the summation of 13 overlapped partial products to 9 and the summation of 40 overlapped partial products to 24 in the case of four-term Karatsuba multiplication and eight-term Karatsuba multiplication, respectively. Moreover, to deal with the problem of negative multiplicands introduced by the choice in the evaluation of four-term Karatsuba multiplication, we further presented a method of converting negative multiplicands to non-negative multiplicands. In this way, our method would lead to the reduction of summation complexity and not using signed multipliers. Compared to the original Karatsuba multiplication designs, our proposed Karatsuba-like multiplication with optimal interpolation would lead to up to 7.7&#x0025; reduction in ADP (area-delay-product) in ASIC implementations, and up to 20.2&#x0025; reduction in ADP compared with built-in designs of Synopsys Design Compiler.
The commonly used algorithms to optimize a neural network in supervised learning: Overview, and comparative study

The neural network training algorithms or optimizers had already been a lively research topic for several years because they are a crucial part of the neural network structure. That is why; the choice of the training algorithm that can be used to optimize a neural network is one of the most important phases in the neural network's building. Therefore, it is necessary to choose the most adequate optimization algorithm for the desired application, in order to achieve a model that can deal with the best performances. In these papers, we are interested in the training algorithms used in supervised learning. Therefore, we present an overview and a comparative study between the most common algorithms employed to justify the choice of an optimizer, which can deal with high performances.
Subword-Based Compact Reconstruction for Open-Vocabulary Neural Word Embeddings

The methodology of neural word embeddings has become an important fundamental resource for tackling many applications in the artificial intelligence (AI) research field. They have successfully been proven to capture high-quality syntactic and semantic relationships in a vector space. Despite their significant impact, neural word embeddings have several disadvantages. In this paper, we focus on two issues regarding well-trained word embeddings: (i) the massive memory requirement and (ii) the inapplicability of out-of-vocabulary (OOV) words. To overcome these two issues, we propose a method of reconstructing pre-trained word embeddings by using subword information that can effectively represent a large number of subword embeddings in a considerably small fixed space while preventing quality degradation from the original word embeddings. The key techniques of our method are twofold: memory-shared embeddings and a variant of the key-value-query self-attention mechanism. Our experiments show that our reconstructed subword-based word embeddings can successfully imitate well-trained word embeddings in a small fixed space while preventing quality degradation across several linguistic benchmark datasets and can simultaneously predict effective embeddings of OOV words. We also demonstrate the effectiveness of our reconstruction method when it is applied to downstream tasks, such as named entity recognition and natural language inference tasks.
Detection System of Truck Blind Area Based on YOLOv3

This letter presents a blind area detection system of the truck based on YOLOv3 algorithm, aiming at the problem that the driver cannot fully observe the surrounding environment because of the multi-directional blind area of the truck. Firstly, the system modified the loss function and the number of anchor frames to realize multi-scale detection. Then it also improved the feature extraction network, the detection accuracy and speed has been greatly improved. In addition, the system also uses a residual neural network as the feature extraction layer to make the prediction output module faster. Finally, Yolov3 draws on the idea of feature pyramid network to predict multi-scale feature graphs, that is, at three different scales, each cell on each scale will predict three boundary boxes. The simulation results show that the blind spot detection system based on YOLOV3 can achieve real-time detection effect, and improve the accuracy of blind spot detection on the condition that the detection speed is maintained.
Dissimilarity-Based Regularized Learning of Charts

Chart images exhibit significant variabilities that make each image different from others even though they belong to the same class or categories. Classification of charts is a major challenge because each chart class has variations in features, structure, and noises. However, due to the lack of affiliation between the dissimilar features and the structure of the chart, it is a challenging task to model these variations for automatic chart recognition. In this article, we present a novel dissimilarity-based learning model for similar structured but diverse chart classification. Our approach jointly learns the features of both dissimilar and similar regions. The model is trained by an improved loss function, which is fused by a structural variation-aware dissimilarity index and incorporated with regularization parameters, making the model more prone toward dissimilar regions. The dissimilarity index enhances the discriminative power of the learned features not only from dissimilar regions but also from similar regions. Extensive comparative evaluations demonstrate that our approach significantly outperforms other benchmark methods, including both traditional and deep learning models, over publicly available datasets.
Analysis of Maximal Shift Algorithm and Rabin-Karp Algorithm in Graphic Design Dictionary

Nowadays Graphic Design becomes a visual communication media that combines appearances, letters, and images as a language to communicate visually. There are many beginner circles and communities that have not yet known various terms in the world of graphic design. Thus, an Android-based graphic design terms dictionary is needed, that is equipped with a speech recognition feature that can make it easier for users in recognizing graphic design terms by applying the Maximal Shift algorithm and Rabin-Karp algorithm while comparing the time complexity and running time. On implementation, a search using the Maximal Shift algorithm takes an average time of 13,8 ms and the Rabin-Karp algorithm takes an average time of 42,1 ms. The complexity for the Maximal Shift algorithm is obtained from the minshift phase, the preQsbc phase, the orderPattern phase, the preAdaptedGs phase, and matchshift phase while the complexity for Rabin-Karp algorithm is obtained from the preprocessing phase and the searching phase. The speech recognition feature that is utilized for the speech to text process works well but noise factor can affect the result of the speech to text process.
Sequent Calculi for the Propositional Logic of HYPE

Abstract In this paper we discuss sequent calculi for the propositional fragment of the logic of HYPE. The logic of HYPE was recently suggested by Leitgeb ( Journal of Philosophical Logic 48:305–405, 2019) as a logic for hyperintensional contexts. On the one hand we introduce a simple $$\mathbf{G1}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>G</mml:mi> <mml:mn>1</mml:mn> </mml:mrow> </mml:math> -system employing rules of contraposition. On the other hand we present a $$\mathbf{G3}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>G</mml:mi> <mml:mn>3</mml:mn> </mml:mrow> </mml:math> -system with an admissible rule of contraposition. Both systems are equivalent as well as sound and complete proof-system of HYPE. In order to provide a cut-elimination procedure, we expand the calculus by connections as introduced in Kashima and Shimura ( Mathematical Logic Quarterly 40:153–172, 1994).
Self-orthogonal quasi-abelian codes are asymptotically good

Let F be the finite field with q=ps elements, where p is an odd prime and s is a positive integer. Suppose that gq−1(x) is the inverse function of gq(x)=1−hq(x), where hq(x) is the q-ary entropy. In this paper we construct a class of random self-orthogonal quasi-abelian codes of index 2p over the finite field F, characterize the cumulative weight enumerator of such random codes by means of a blend of representation theory and probabilistic arguments, and then prove that for any given δ∈(0,gq−1(1p)), the probability that the cumulative weight enumerator is at most δ converges to 0. As a consequence, the class of self-orthogonal quasi-abelian codes of index 2p is asymptotically good.
Virtual Reality to Mixed Reality Graphic Conversion in Unity

To date in the defence industry, virtual reality (VR) headsets are used primarily for training and simulation, while mixed reality (MR) headsets can be used in military operations. However, technological hardware advancements that blend VR and MR headset capabilities will result in increasing convergence of VR and MR applications. Accordingly, our primary objective in the current work was to present guidelines for Unity developers to convert graphical VR content to MR content in the Unity game engine. Guidelines herein address how to change camera settings to adapt from VR to MR applications, how to convert graphical user interfaces (GUIs) from VR to MR, and how to place graphical objects in MR environments. Another important objective of this work was to describe a user-controlled GUI we developed that allows real-time, progressive conversion of graphics from full VR to various levels of MR in a MR headset. This GUI provides end-users flexibility to customize their environment for gaming, training, and operational applications along an extended reality spectrum. In our future work, we are developing a tool that automates the VR to MR graphic conversion process. This tool, the guidelines, and the GUI will help researchers compare different levels of graphic content displayed in the scene and investigate the user-centered challenges that arise. It will also help Unity developers in defence, gaming, and commercial industries generate a scene in VR and quickly convert it to MR, saving the cost associated with generating separate MR scenes from scratch for multiple applications.
Monocular Human Depth Estimation Via Pose Estimation

We propose a novel monocular depth estimator, which improves the prediction accuracy on human regions by utilizing pose information. The proposed algorithm consists of two networks — PoseNet and DepthNet — to estimate keypoint heatmaps and a depth map, respectively. We incorporate the pose information from PoseNet to improve the depth estimation performance of DepthNet. Specifically, we develop the feature blending block, which fuses the features from PoseNet and DepthNet and feeds them into the next layer of DepthNet, to make the networks learn to predict the depths of human regions more accurately. Furthermore, we develop a novel joint training scheme using partially labeled datasets, which balances multiple loss functions effectively by adjusting weights. Experimental results demonstrate that the proposed algorithm can improve depth estimation performance significantly, especially around human regions. For example, the proposed algorithm improves the depth estimation performance on the human regions of ResNet-50 by 2.8% and 7.0% in terms of $\delta _{1}$ and RMSE, respectively, on the proposed HD + P dataset.