Reviewerly/exHarmony huggingface dataset:
Existence of primitive 2-normal elements in finite fields

An element α∈Fqn is normal over Fq if B={α,αq,αq2,⋯,αqn−1} forms a basis of Fqn as a vector space over Fq. It is well known that α∈Fqn is normal over Fq if and only if gα(x)=αxn−1+αqxn−2+⋯+αqn−2x+αqn−1 and xn−1 are relatively prime over Fqn, that is, the degree of their greatest common divisor in Fqn[x] is 0. Using this equivalence, the notion of k-normal elements was introduced in Huczynska et al. (2013): an element α∈Fqn is k-normal over Fq if the greatest common divisor of the polynomials gα[x] and xn−1 in Fqn[x] has degree k; so an element which is normal in the usual sense is 0-normal. Huczynska et al. made the question about the pairs (n,k) for which there exist primitive k-normal elements in Fqn over Fq and they got a partial result for the case k=1, and later Reis and Thomson (2018) completed this case. The Primitive Normal Basis Theorem solves the case k=0. In this paper, we solve completely the case k=2 using estimates for Gauss sum and the use of the computer, we also obtain a new condition for the existence of k-normal elements in Fqn.
BEAT: Considering question types for bug question answering via templates

Software bugs are ubiquitous in the process of software development and evolution. To accelerate bug fixing, developers need to quickly obtain bug information and understand the bugs at hand. Many existing approaches often do not try to understand the semantic information of bug data and just directly use keyword matching techniques to obtain more bug information. However, they often fail to fully express the users’ intents, and the results obtained are often irrelevant to the input queries. To alleviate this problem, this paper presents a novel approach named considering question types for bug question answering via templates (BEAT). We utilize templates to understand the natural language questions through factual triples. These templates can fully express users’ intents and generate the structured queries based on these intents. Then, answers are automatically generated by executing the structured query over the prepared RDF file. Empirical study demonstrates that BEAT is effective to automatically generate the answers, and the F1-score values of Mozilla and Eclipse project are 0.76 and 0.73, respectively, which are better than existing Q&A approaches.
Beyond the dark web: navigating the risks of cannabis supply over the surface web

Common depictions of buying and selling illicit drugs online centre on how drug market actors engage in dark web drug cryptomarkets, but the supply of illicit drugs also takes place in ‘plain site’ on the surface web. Drawing on netnographic observations and qualitative interviews with hard-to-reach buyers and vendors (n = 20), this paper explores LeafedOut, a specific, popular surface web platform, that provides a conduit for local cannabis exchanges. We found that the platform enabled easy access and supply at the local level but increased some specific risks to those involved. Actors neutralised the perceived risks of drug supply over this surface web platform through the broader societal normalisation of cannabis use/supply, adopting encrypted messaging applications to cover ‘digital traces’, and developing various methods to establish trust with an exchange partner (e.g. review systems, sending selfies with drug paraphernalia, selectively choosing meet-up locations). This paper expands our understanding of the growing number of online illicit drug markets by shifting attention from dark web cryptomarkets to the much more widely accessed surface/clear web. Theoretical implications for the study of trust and risk in online illicit drug market exchanges are also considered.
FedPARL: Client Activity and Resource-Oriented Lightweight Federated Learning Model for Resource-Constrained Heterogeneous IoT Environment

Federated Learning (FL) is a recently invented distributed machine learning technique that allows available network clients to perform model training at the edge, rather than sharing it with a centralized server. Unlike conventional distributed machine learning approaches, the hallmark feature of FL is to allow performing local computation and model generation on the client side, ultimately protecting sensitive information. Most of the existing FL approaches assume that each FL client has sufficient computational resources and can accomplish a given task without facing any resource-related issues. However, if we consider FL for a heterogeneous Internet of Things (IoT) environment, a major portion of the FL clients may face low resource availability (e.g., lower computational power, limited bandwidth, and battery life). Consequently, the resource-constrained FL clients may give a very slow response, or may be unable to execute expected number of local iterations. Further, any FL client can inject inappropriate model during a training phase that can prolong convergence time and waste resources of all the network clients. In this paper, we propose a novel tri-layer FL scheme, Federated Proximal, Activity and Resource-Aware 31 Lightweight model (FedPARL), that reduces model size by performing sample-based pruning, avoids misbehaved clients by examining their trust score, and allows partial amount of work by considering their resource-availability. The pruning mechanism is particularly useful while dealing with resource-constrained FL-based IoT (FL-IoT) clients. In this scenario, the lightweight training model will consume less amount of resources to accomplish a target convergence. We evaluate each interested client's resource-availability before assigning a task, monitor their activities, and update their trust scores based on their previous performance. To tackle system and statistical heterogeneities, we adapt a re-parameterization and generalization of the current state-of-the-art Federated Averaging (FedAvg) algorithm. The modification of FedAvg algorithm allows clients to perform variable or partial amounts of work considering their resource-constraints. We demonstrate that simultaneously adapting the coupling of pruning, resource and activity awareness, and re-parameterization of FedAvg algorithm leads to more robust convergence of FL in IoT environment.
Deep Learning Based Sentiment Analysis On COVID-19 Public Reviews

Sentiment Analysis is a classification task in order to identify public reviews about different issues like product reviews, movie reviews, restaurant reviews, political opinions, and other current issues by extracting the public reviews from Social Media, and other Micro blogging sites. As we all know Coronavirus Disease 2019 (COVID-19) is still a global issue for entire world and people are expressing their emotions, thoughts, and opinions about this issue with help of Twitter, Facebook, and other Media. In this paper we have collected public tweets from Twitter which are talked about the COVID-19 global pandemic and applied a Convolutional Neural Network with Bidirectional Long-Short Term Memory (CNN-Bi-LSTM) hybrid Deep Learning algorithm to detect the user's outlook on this pandemic whether they have positive feelings, negative feelings, or neutral feelings. The proposed method used preprocessing techniques to clean the data and used a word embedding pre-trained model to extract word embedding for rare words in our corpus with the help of FastText and Globe pre-trained models. The CNN-Bi-LSTM hybrid model evaluated using accuracy, precision, recall, and f1 evaluation techniques. The experimental result has been shown 99.33% accuracy using CNN-Bi-LSTM with FastText pre-trained model, and 97.55% accuracy using CNN-Bi-LSTM with GloVe pre-trained model.
The ObReco-360°: a new ecological tool to memory assessment using 360° immersive technology

Abstract One important feature of a neuropsychological test is its ecological validity, which defines how much patients’ test scores are linked to real-life functioning. However, many of the currently available neuropsychological tools show low to moderate levels of ecological validity. Virtual reality (VR) emerged as a possible solution that might enhance the ecological value of standard paper-and-pencil tests, thanks to the possibility of simulating realistic environments and situations where patients can behave as they do in real life. Moreover, a recent kind of virtual environments, the 360° spherical photos and videos, seems to guarantee high levels of graphical realism and lower technical complexity than standard VR, despite their limitations concerning interactive design. In this pilot study, we tested the possible application of 360° technology for the assessment of memory, developing an adaptation of a standardized test. We focused on Free Recall and Recognition accuracies as indexes of memory function, confronting and correlating the performances obtained by the participants in the standard and in the 360° test. The results, even if preliminary, support the use of 360° technology for enhancing the ecological value of standard memory assessment tests.
DTransE: Distributed Translating Embedding for Knowledge Graph

Knowledge graphs play an important role in many applications, such as link prediction and question answering. Translating embedding for knowledge graphs is done with the aim of encoding structured information on entities and their rich relations in a low-dimensional embedding space. TransE is one of the most important methods in translation-based models, and uses translation invariance to implement translating embedding for knowledge graphs. In this line of work, translating embedding models represent the relation as a translation from the head entity to the tail entity and have achieved impressive results. Currently, the TransE model is only developed on single-node machines. Unfortunately, the computing and storage capacities of a single machine can easily reach their limits as knowledge graphs become larger and more complex, which limits the application scope of TransE. In order to solve this problem, we propose a distributed TransE method, known as DTransE, which can utilize distributed computing resources to calculate knowledge graph embeddings. However, building a distributed TransE is complicated and involves challenges of knowledge graph partitioning and computation. To solve these challenges, we provide a high-quality edge partitioning algorithm for the power-law graph by considering the high-degree and low-degree vertices with adaptive weights, which can balance the workload. By using the unactivated Gather-Apply-Scatter model on TransE, the processes periodically exchange messages in a loop. The irregular data distribution among the processes is also optimized to further accelerate communication. As far as we know, this is the first work on a distributed TransE method. We use link prediction to evaluate the DTransE in a distributed environment. Experiments show that, compared to the original TransE method, our proposed DTransE is, on average, 24.5 times faster with a minimum loss of accuracy; compared to the state-of-the-art parallel TransE implementation, DTransE is two times faster on average.
Decision Tree for Sequences

Current decision trees such as C4.5 and CART are widely used in different fields due to their simplicity, accuracy and intuitive interpretation. Similar to other popular classifiers, these tree-based classification algorithms are developed for fixed-length vector data and suffer from intrinsic limitations in handling complex data such as sequences. To tackle the discrete sequence classification task, the dominant strategy is to adopt a two-step procedure: first transform the sequential dataset into a vector dataset and then apply existing tree-based classifiers on the new vector data. However, such methods are highly dependent on the feature generation procedure and some features that are critical to the tree construction may be missed. To alleviate these issues, we present a new tree-based sequence classification method, which is able to construct a concise decision tree from the feature space that is composed of all subsequences present in the training sequences. Experimental results on fourteen real datasets show that our method can achieve better performance than those state-of-the-art sequence classification algorithms. The source codes of our method are available at: https://github.com/ZiyaoWu/SeqDT.
Active jamming recognition based on bilinear EfficientNet and attention mechanism

As electromagnetic environments are increasingly complex, there are more kinds of radar jamming signals. Active jamming recognition has problems of the low recognition accuracy and the high computational complexity, especially under a low jamming-to-noise ratio (JNR). Herein, a deep learning network based on bilinear EfficientNet and attention mechanism is proposed to recognise and classify eight kinds of jamming signals automatically. Firstly, the one-dimensional interference signal is transformed into a two-dimensional time–frequency image by the time–frequency analysis. Based on the transfer learning of EfficientNet-B3, the effective features of the time–frequency image are automatically extracted by the two-way network with attention mechanism, and the active interference classification is realised. The experimental results show that the method's overall recognition rate for eight kinds of interference signals is more than 97.5% when the JNR is −8 dB and is close to 100% at −2 dB.
A High-Performance CNN-Applied HEVC Steganography Based on Diamond-Coded PU Partition Modes

High efficiency video coding (HEVC) is the latest high-performance video coding standard, and HEVC video steganography has become a new way to hide data for covert communication. This paper proposes a novel multilevel steganography algorithm based on diamond-encoded prediction unit (PU) partition modes. The PU modes of smaller <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$8\times 8$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$16\times 16$</tex-math></inline-formula> CUs are selected as carriers for information hiding. The diamond-coding rules are adopted to enhance the expressive ability of limited PU types, allowing them to carry more information under limited modification. Based on the encoded PU partition modes, three different embedding levels with different capacities are proposed. This paper’s most outstanding contribution is the introduction of convolutional neural networks (CNNs) for the first time to improve visual quality and reduce steganographic video bitrate increases. Experimental results show that the embedding capacity of the proposed algorithm is significantly higher than the state-of-the-art work at the same bitrate, whether in high- or low-resolution HEVC videos. At the same time, the visual quality of the steganographic videos is excellent, and the resistance to video steganalysis is strong.
Transformer based network for Open Information Extraction

Research on Open Information Extraction (Open IE) has made great progress in recent years; it is the task that detects a group of structured, machine-readable statements usually represented in triple form or n-ary relation statements. Open IE is among the core areas of the territory of Natural Language Processing (NLP), and these extractions decompose grammatically complex sentences in a corpus into the relationships they represent, which can be leveraged for various downstream tasks. Even though a lot of work has been done in this direction, there are still many issues with the existing strategies. Most of the previous Open IE systems employ a group of artificially constructed patterns to detect and extract relational tuples from a sentence in a corpus, and these patterns are either automatically learned from annotated training examples or hand-crafted. Such an approach faces some issues, the first is that it requires a lot of manpower. Secondly, they used many NLP tools, therefore, error accumulation in the procedure can negatively impact the results. In this paper, we propose an Open IE approach based on the Transformer architecture. To verify our approach, we make a study using a large and public benchmark dataset, and the experimental results showed that our model achieves a better performance than many existing baselines.
Blockchain-based Cross-border E-business Payment Model

With the characteristics of decentralization, persistency, anonymity and auditability, blockchain technology is a new tool to solve the product traceability, information security and privacy, payment efficiency and cost reduction in cross-border e-business. This paper summarizes the problems of traditional cross-border e-business payment forms, analyzes the probability of blockchain technology application in cross-border e-business payment field. The research focuses on blockchain-based letter of credit payment under cross-border B2B e-business condition, introducing a blockchain-based payment application framework. Following the framework, this study discusses the hierarchy and functional modules of a blockchain-based cross-border e-business payment platform. This paper proposes three levels chain structure and data classification management to construct an alliance chain with traders, customers, banks and regulators in the cross-border e-business.
A 2021 update on cancer image analytics with deep learning

Abstract Deep learning (DL)‐based interpretation of medical images has reached a critical juncture of expanding outside research projects into translational ones, and is ready to make its way to the clinics. Advances over the last decade in data availability, DL techniques, as well as computing capabilities have accelerated this journey. Through this journey, today we have a better understanding of the challenges to and pitfalls of wider adoption of DL into clinical care, which, according to us, should and will drive the advances in this field in the next few years. The most important among these challenges are the lack of an appropriately digitized environment within healthcare institutions, the lack of adequate open and representative datasets on which DL algorithms can be trained and tested, and the lack of robustness of widely used DL training algorithms to certain pervasive pathological characteristics of medical images and repositories. In this review, we provide an overview of the role of imaging in oncology, the different techniques that are shaping the way DL algorithms are being made ready for clinical use, and also the problems that DL techniques still need to address before DL can find a home in clinics. Finally, we also provide a summary of how DL can potentially drive the adoption of digital pathology, vendor neutral archives, and picture archival and communication systems. We caution that the respective researchers may find the coverage of their own fields to be at a high‐level. This is so by design as this format is meant to only introduce those looking in from outside of deep learning and medical research, respectively, to gain an appreciation for the main concerns and limitations of these two fields instead of telling them something new about their own. This article is categorized under: Technologies &gt; Artificial Intelligence Algorithmic Development &gt; Biological Data Mining
Voxelvideos for Entertainment, Education, and Training

Volumetric videos allow for a true three-dimensional experience where users can freely choose their viewing angles and be actually immersed in a video clip. High quality video productions are gaining attention and first volumetric video recordings are commercially provided at select places. Unfortunately, the production process is very time, labour, and technology-resource intensive, which requires specialist hardware, software, and production experts. A “youtube-like” production, distribution, and experience system would be desirable. Here we present an approach which allows for the creation and interactive replay of three-dimensional videoclips using a novel voxel-based platform—voxelvideos. We can show that our voxelvideos experienced in virtual and augmented reality are effective, enjoyable, and perceived as useful. We hope that our approach and findings will encourage researchers, media experts, and hobbyists to experiment with voxelvideos as a new form of affordable media production and experience.
Effect of noise on remote preparation of an arbitrary single‐qubit state

The effect of noise on the transmission of quantum state has attached much interest recently. Here we investigate the effect of different types of quantum noise on deterministic exact remote preparation of an arbitrary single-qubit state. Four types of noise which usually encountered in application of quantum remote state preparation, including bit-flipping, phase-flipping, depolarizing and amplitude damping, are considered. The average fidelities of remote state preparation for any combinations of noise are calculated. It is shown that more fidelity can be achieved via less entanglement in single-qubit remote preparation when part or all of the entangled qubits are subjected to amplitude damping noise. It is shown that in several situations more noise may leading to more fidelity in remote state preparation. Moreover, we discuss the effect of noise on probabilistic remote state preparation.
Learning with noisy labels method for unsupervised domain adaptive person re-identification

Unsupervised domain adaptive (UDA) person re-identification (re-ID) aims to adapt the model trained on a labeled source domain to an unlabeled target domain. For pseudo-label-based UDA methods, pseudo labels noise is the main problem for model degradation and the factors that cause noise are complex. In this paper, a novel learning with noisy labels (LNL) method for UDA person re-ID is proposed to address this problem by analyzing the noise data itself. LNL learns with noise data from two aspects, including noise correction and noise resistance. According to the idea of neighbor consistency, pseudo labels correction (PLC) based on sample similarity is designed to correct the noisy pseudo labels before training. In order to solve the problem of noise labels in deep learning, noise recognition based on similarity and confidence relationship (SACR) is designed. Then, an easy-to-hard model collaborative training (MCT) strategy is developed, which can resist noise during the training process and obtain a more robust training model. To further avoid overfitting of noisy samples, the re-weighting (RW) method is employed in MCT. The proposed LNL model achieves considerable results of 75.2%/88.9% and 62.5%/77.4% mAP/Rank-1 on DukeMTMC-reID-to-Market-1501 and Market-1501-to-DukeMTMC-reID UDA tasks.
Active dropblock: Method to enhance deep model accuracy and robustness

In this study, we investigated a means to improve the robustness of deep network training on visual recognition tasks without sacrificing accuracy. The contribution of this work can reduce the dependence on model decay to gain a strong defense against malicious attacks, especially from adversarial samples. There are two major challenges in this study. First, the model defense capability should be strong and improved over the training stage. The other is that the degrading of the model performance must be minimized to ensure visual recognition performance. To tackle these challenges, we propose active dropblock (ActDB) by incorporating active learning into a dropblock. Dropblock effectively perturbs the feature maps, thus enhancing the invulnerability of gradient-based adversarial attacks. In addition, it selects an optimal perturbation solution to minimize the objective loss function, thereby reducing the model degradation. The proposed organic integration successfully solved the model robustness and accuracy simultaneously. We validated our approach using extensive experiments on various datasets. The results showed significant gains compared to state-of-the-art methods.