Reviewerly/exHarmony huggingface dataset:
Speck-R: An ultra light-weight cryptographic scheme for Internet of Things

Lightweight cryptography (LWC) is an interesting research area in the field of information security. Some limitations like: increased components usage, time consumption, power consumption and memory requirement mandate the need for lightweight cryptography. One of the proposed algorithms in this field is Speck which was designed by the National Security Agency (NSA) in June 2013. In this paper, we propose a new ultra-lightweight cryptographic algorithm based on Speck known as Speck-R. Speck-R is a hybrid cipher, combining ARX architecture with a dynamic substitution layer. The novelty in this paper resides in adding a key-dynamic substitution layer that changes according to a dynamic key. With this modification, the number of rounds can be reduced from 26 (in Speck) to 7 (in Speck-R). Thus, the main contribution of this paper consists in reducing the execution time of Speck by at least 18% on limited devices to reach a reduction of 77% while keeping a high level of security. To backbone Speck-R’s security, different security and statistical tests are exerted on Speck-R. In addition, a real hardware implementation on three different famous IoT devices is also presented where Speck-R outperformed Speck in terms of execution times. Finally, extensive tests show that Speck-R possesses the necessary criteria to be considered as a good cipher scheme that is suitable for lightweight devices.
Seeing Pedestrian in the Dark via Multi-Task Feature Fusing-Sharing Learning for Imaging Sensors

Pedestrian detection is an essential problem of computer vision, which has achieved tremendous success under controllable conditions using visible light imaging sensors in recent years. However, most of them do not consider low-light environments which are very common in real-world applications. In this paper, we propose a novel pedestrian detection algorithm using multi-task learning to address this challenge in low-light environments. Specifically, the proposed multi-task learning method is different from the most commonly used multi-task learning method—the parameter sharing mechanism—in deep learning. We design a novel multi-task learning method with feature-level fusion and a sharing mechanism. The proposed approach contains three parts: an image relighting subnetwork, a pedestrian detection subnetwork, and a feature-level multi-task fusion learning module. The image relighting subnetwork adjusts the low-light image quality for detection, the pedestrian detection subnetwork learns enhanced features for prediction, and the feature-level multi-task fusion learning module fuses and shares features among component networks for boosting image relighting and detection performance simultaneously. Experimental results show that the proposed approach consistently and significantly improves the performance of pedestrian detection on low-light images obtained by visible light imaging sensor.
Random-based networks with dropout for embedded systems

Abstract Random-based learning paradigms exhibit efficient training algorithms and remarkable generalization performances. However, the computational cost of the training procedure scales with the cube of the number of hidden neurons. The paper presents a novel training procedure for random-based neural networks, which combines ensemble techniques and dropout regularization. This limits the computational complexity of the training phase without affecting classification performance significantly; the method best fits Internet of Things (IoT) applications. In the training algorithm, one first generates a pool of random neurons; then, an ensemble of independent sub-networks (each including a fraction of the original pool) is trained; finally, the sub-networks are integrated into one classifier. The experimental validation compared the proposed approach with state-of-the-art solutions, by taking into account both generalization performance and computational complexity. To verify the effectiveness in IoT applications, the training procedures were deployed on a pair of commercially available embedded devices. The results showed that the proposed approach overall improved accuracy, with a minor degradation in performance in a few cases. When considering embedded implementations as compared with conventional architectures, the speedup of the proposed method scored up to 20× in IoT devices.
Answer Category-Aware Answer Selection for Question Answering

As a key problem in artificial intelligence, question answering (QA) has always been a topic of intensive research. Most existing methods cast question answering as an answer selection task. The size of the candidate answer pool is usually very large, so it is difficult to accurately select the correct answer. One of the solutions is to narrow the range of candidate answer pool based on the category labels of the answers. However, QA tasks in reality usually only provide the category label of the question but not the category label of the answer. Based on this observation, we propose an Answer Category-Aware Answer Selection system (ACAAS), which jointly leverage unlabelled answer data and labelled question category data to generate answer category pseudo-labels in a joint embedding space. Experimental results on two public QA datasets demonstrate the effectiveness of the proposed method.
China Earthquake Administration: Chinese Seismic Network

Guanghui Dai and Yanru An report on the Chinese Seismic Network for the Summary of the Bulletin of the ISC.
Short-Term Photovoltaic Power Generation Prediction Based on LightGBM-LSTM Model

Predicting photovoltaic power generation is one of the emphases of energy management system. In recent years, with the increasingly serious environmental pollution, the use of artificial intelligence technology to study the world's largest clean energy solar energy is of great significance. Other than the existing work, this paper proposes a fusion model based on LightGBM and LSTM to forecast short-term photovoltaic power generation. For obtaining the best prediction results, MLP is applied for dynamic weight allocation and combination to generate the prediction results. Based on the real photovoltaic power generation data collected by a photovoltaic power station in China, our model was compared with three traditional models, including XGBoost model, LightGBM model and LSTM model. Besides, the prediction results of four models in a day with enough light and four models in a day with less light are compared. The experiment result indicates that the model proposed in this paper has higher accuracy in short-term photovoltaic power generation forecast and It has good adaptability to the nonlinear feature of illumination.
RETRACTED: Research on Optimization of College English Classroom Teaching Based on Computer Network Environment

Abstract In recent years, under the influence of the continuous reform of the education system, colleges and universities have set off a large-scale teaching reform, which also includes college English education. With the wide application of computer information technology and network technology, many English teachers have joined a new technological revolution in order to carry out English teaching with the help of computer information technology and network technology, thus promoting the improvement of teaching quality and efficiency.
An Investigation of Few-Shot Learning in Spoken Term Classification

In this paper, we investigate the feasibility of applying few-shot learning algorithms to a speech task. We formulate a user-defined scenario of spoken term classification as a few-shot learning problem. In most few-shot learning studies, it is assumed that all the N classes are new in a N-way problem. We suggest that this assumption can be relaxed and define a N+M-way problem where N and M are the number of new classes and fixed classes respectively. We propose a modification to the Model-Agnostic Meta-Learning (MAML) algorithm to solve the problem. Experiments on the Google Speech Commands dataset show that our approach outperforms the conventional supervised learning approach and the original MAML.
RETRACTED: Network Information Security Risk Assessment Based on Artificial Intelligence

Abstract With the development of society and the progress of human civilization, science and technology are more and more widely used in people’s daily production and life, and artificial intelligence technology is also more and more widely used in this era. With the advent of the information age, the development of the network is faster and faster, and the popularization rate is higher and higher. The network information security problem has gradually become a hot issue of social concern question. With the increasingly open network environment, network attack tools become easier to obtain, more automated, more intelligent, which poses a huge challenge to information security protection. Therefore, in order to deal with the network information security problems in this information age, this paper studies the method of network information security risk assessment and analysis based on artificial intelligence, through in-depth study of network information security, simulate attacks on network information, so as to find the weakness of the current network information security system. In the research, a large number of network information security vulnerabilities are found by simulating the attack of network information, so as to repair the vulnerabilities one by one. Through the analysis, this paper studies the Content Filtering Gateway filtering system to filter out 94.8% of the virus and garbage, improve the network information security rate.
Locally private non-asymptotic testing of discrete distributions is faster using interactive mechanisms

We find separation rates for testing multinomial or more general discrete distributions under the constraint of local differential privacy. We construct efficient randomized algorithms and test procedures, in both the case where only non-interactive privacy mechanisms are allowed and also in the case where all sequentially interactive privacy mechanisms are allowed. The separation rates are faster in the latter case. We prove general information theoretical bounds that allow us to establish the optimality of our algorithms among all pairs of privacy mechanisms and test procedures, in most usual cases. Considered examples include testing uniform, polynomially and exponentially decreasing distributions.
Improved Local Citation Recommendation Based on Context Enhanced with Global Information

Local citation recommendation aims at finding articles relevant for given citation context. While most previous approaches represent context using solely text surrounding the citation, we propose enhancing context representation with global information. Specifically, we include citing article’s title and abstract into context representation. We evaluate our model on datasets with different citation context sizes and demonstrate improvements with globally-enhanced context representations when citation contexts are smaller.
Entanglement and entropy squeezing in the system of two qubits interacting with a two-mode field in the context of power low potentials

Abstract We study the dynamics of two non-stationary qubits, allowing for dipole-dipole and Ising-like interplays between them, coupled to quantized fields in the framework of two-mode pair coherent states of power-low potentials. We focus on three particular cases of the coherent states through the exponent parameter taken infinite square, triangular and harmonic potential wells. We examine the possible effects of such features on the evolution of some quantities of current interest, such as population inversion, entanglement among subsystems and squeezing entropy. We show how these quantities can be affected by the qubit-qubit interaction and exponent parameter during the time evolution for both cases of stationary and non-stationary qubits. The obtained results suggest insights about the capability of quantum systems composed of nonstationary qubits to maintain resources in comparison with stationary qubits.
Conceptual Characterization of Cybersecurity Ontologies

Cybersecurity is known as the practice of protecting systems from digital attacks. Organizations are seeking efficient solutions for the management and protection of their assets. It is a complex issue, especially for great enterprises, because it requires an interdisciplinary approach. The kinds of problems enterprises must deal with and this domain complexity induces misinterpretations and misunderstandings about the concepts and relations in question. This article focus on dealing with Cybersecurity from an ontological perspective. The first contribution is a search of previously existing works that have defined Cybersecurity Ontologies. The paper describes the process to search these works. The second contribution of the paper is the definition of characteristics to classify the papers of Cybersecurity Ontologies previously found. This classification aims to compare the previous works with the same criteria. The third contribution of the paper is the analysis of the results of the comparison of previous works in the field of Cybersecurity Ontologies. Moreover, the paper discusses the gaps found and proposes good practice actions in Ontology Engineering for this domain. The article ends with some next steps proposed in the evolution towards a pragmatic and iterative solution that meets the needs of organizations.
Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors

The ability to predict and plan into the future is fundamental for agents acting in the world. To reach a faraway goal, we predict trajectories at multiple timescales, first devising a coarse plan towards the goal and then gradually filling in details. In contrast, current learning approaches for visual prediction and planning fail on long-horizon tasks as they generate predictions (1) without considering goal information, and (2) at the finest temporal resolution, one step at a time. In this work we propose a framework for visual prediction and planning that is able to overcome both of these limitations. First, we formulate the problem of predicting towards a goal and propose the corresponding class of latent space goal-conditioned predictors (GCPs). GCPs significantly improve planning efficiency by constraining the search space to only those trajectories that reach the goal. Further, we show how GCPs can be naturally formulated as hierarchical models that, given two observations, predict an observation between them, and by recursively subdividing each part of the trajectory generate complete sequences. This divide-and-conquer strategy is effective at long-term prediction, and enables us to design an effective hierarchical planning algorithm that optimizes trajectories in a coarse-to-fine manner. We show that by using both goal-conditioning and hierarchical prediction, GCPs enable us to solve visual planning tasks with much longer horizon than previously possible.
Simple and Optimal Methods for Stochastic Variational Inequalities, II: Markovian Noise and Policy Evaluation in Reinforcement Learning

The focus of this paper is on stochastic variational inequalities (VI) under Markovian noise. A prominent application of our algorithmic developments is the stochastic policy evaluation problem in reinforcement learning. Prior investigations in the literature focused on temporal difference (TD) learning by employing nonsmooth finite time analysis motivated by stochastic subgradient descent leading to certain limitations. These limitations encompass the requirement of analyzing a modified TD algorithm that involves projection to an a priori defined Euclidean ball, achieving a nonoptimal convergence rate and no clear way of deriving the beneficial effects of parallel implementation. Our approach remedies these shortcomings in the broader context of stochastic VIs and in particular when it comes to stochastic policy evaluation. We developed a variety of simple TD learning type algorithms motivated by its original version that maintain its simplicity, while offering distinct advantages from a nonasymptotic analysis point of view. We first provide an improved analysis of the standard TD algorithm that can benefit from parallel implementation. Then we present versions of a conditional TD algorithm (CTD), that involves periodic updates of the stochastic iterates, which reduce the bias and therefore exhibit improved iteration complexity. This brings us to the fast TD (FTD) algorithm which combines elements of CTD and the stochastic operator extrapolation method of the companion paper. For a novel index resetting step size policy FTD exhibits the best known convergence rate. We also devised a robust version of the algorithm that is particularly suitable for discounting factors close to 1.
AE TextSpotter: Learning Visual and Linguistic Representation for Ambiguous Text Spotting

Scene text spotting aims to detect and recognize the entire word or sentence with multiple characters in natural images. It is still challenging because ambiguity often occurs when the spacing between characters is large or the characters are evenly spread in multiple rows and columns, making many visually plausible groupings of the characters (e.g. “BERLIN” is incorrectly detected as “BERL” and “IN” in Fig. 1(c)). Unlike previous works that merely employed visual features for text detection, this work proposes a novel text spotter, named Ambiguity Eliminating Text Spotter (AE TextSpotter), which learns both visual and linguistic features to significantly reduce ambiguity in text detection. The proposed AE TextSpotter has three important benefits. 1) The linguistic representation is learned together with the visual representation in a framework. To our knowledge, it is the first time to improve text detection by using a language model. 2) A carefully designed language module is utilized to reduce the detection confidence of incorrect text lines, making them easily pruned in the detection stage. 3) Extensive experiments show that AE TextSpotter outperforms other state-of-the-art methods by a large margin. For example, we carefully select a set of extremely ambiguous samples from the IC19-ReCTS dataset, where our approach surpasses other methods by more than 4%.
A Multilingual Neural Machine Translation Model for Biomedical Data

We release a multilingual neural machine translation model, which can be used to translate text in the biomedical domain.The model can translate from 5 languages (French, German, Italian, Korean and Spanish) into English.It is trained with large amounts of generic and biomedical data, using domain tags.Our benchmarks show that it performs near stateof-the-art both on news (generic domain) and biomedical test sets, and that it outperforms the existing publicly released models.We believe that this release will help the large-scale multilingual analysis of the digital content of the COVID-19 crisis and of its effects on society, economy, and healthcare policies.We also release a test set of biomedical text for Korean-English.It consists of 758 sentences from official guidelines and recent papers, all about COVID-19.
OneNet: Towards End-to-End One-Stage Object Detection.

End-to-end one-stage object detection trailed thus far. This paper discovers that the lack of classification cost between sample and ground-truth in label assignment is the main obstacle for one-stage detectors to remove Non-maximum Suppression(NMS) and reach end-to-end. Existing one-stage object detectors assign labels by only location cost, e.g. box IoU or point distance. Without classification cost, sole location cost leads to redundant boxes of high confidence scores in inference, making NMS necessary post-processing. To design an end-to-end one-stage object detector, we propose Minimum Cost Assignment. The cost is the summation of classification cost and location cost between sample and ground-truth. For each object ground-truth, only one sample of minimum cost is assigned as the positive sample; others are all negative samples. To evaluate the effectiveness of our method, we design an extremely simple one-stage detector named OneNet. Our results show that when trained with Minimum Cost Assignment, OneNet avoids producing duplicated boxes and achieves to end-to-end detector. On COCO dataset, OneNet achieves 35.0 AP/80 FPS and 37.7 AP/50 FPS with image size of 512 pixels. We hope OneNet could serve as an effective baseline for end-to-end one-stage object detection. The code is available at: \url{this https URL}.
Verification of an Optimized NTT Algorithm

The Number Theoretic Transform (NTT) is an efficient algorithm for computing products of polynomials with coefficients in finite fields. It is a common procedure in lattice-based key-exchange and signature schemes. These new cryptographic algorithms are becoming increasingly important because they are quantum resistant. No quantum algorithm is known to break these lattice-based algorithms, unlike older schemes such as RSA or elliptic curve cryptosystems. Many implementations and optimizations of the NTT have been proposed in the literature. A particular efficient variant is due to Longa and Naehrig. We have implemented several of these variants, including an improved version of the Longa and Naehrig algorithm. An important concern is to show that numerical overflows do not happen in such algorithms. We report on several attempts at automatically verifying the absence of overflows using static analysis tools. Off-the-shelf tools do not work on the NTT code. We present a specialized abstract-interpretation method to solve the problem.
Adversarial Reconstruction CNN for Illumination-Robust Frontal Face Image Recovery and Recognition

This article proposes an adversarial reconstruction convolution neural network (ARCNN) for non-uniform illumination frontal face image recovery and recognition. The proposed ARCNN includes a reconstruction network and a discriminative network. The authors employ GAN framework to learn the reconstruction network in an adversarial manner. This article integrates gradient loss and perceptual loss terms, which are able to preserve the detailed and spatial structure image information, into the overall reconstruction loss function to constraint the reconstruction procedure. Experiments are conducted on the typical illumination-sensitive dataset, extended YaleB dataset. The reconstructed results demonstrate that the proposed ARCNN approach can remove the illumination and shadow information and recover natural uniform illuminated face image from non-uniform illuminated ones. Face recognition results on the extended YaleB dataset demonstrate that the proposed ARCNN reconstruction procedure can also preserve the discriminative information of face image for classification task.