Reviewerly/exHarmony huggingface dataset:
Keyword Extraction from Scientific Research Projects Based on SRP‐TF‐IDF

Keyword extraction by Term frequency-Inverse document frequency (TF-IDF) is used for text information retrieval and mining in many domains, such as news text, social contact text, and medical text. However, keyword extraction in special domains still needs to be improved and optimized, particularly in the scientific research field. The traditional TF-IDF algorithm considers only the word frequency in documents, but not the domain characteristics. Therefore, we propose the Scientific research project TF-IDF (SRP-TF-IDF) model, which combines TF-IDF with a weight balance algorithm designed to recalculate candidate keywords. We have implemented the SRP-TF-IDF model and verified that our method has better precision, recall, and F1 score than the traditional TF-IDF and TextRank methods. In addition, we investigated the parameter of our weight balance algorithm to find an optimal value for keyword extraction from scientific research projects.
A Novel Hybrid Path Planning Method Based on Q-Learning and Neural Network for Robot Arm

Path planning for robot arms to reach a target and avoid obstacles has had a crucial role in manufacturing automation. Although many path planning algorithms, including RRT, APF, PRM, and RL-based, have been presented, they have many problems: a time-consuming process, high computational costs, slowness, non-optimal paths, irregular paths, failure to find a path, and complexity. Scholars have tried to address some of these issues. However, those methods still suffer from slowness and complexity. In order to address these two limitations, this paper presents a new hybrid path planning method that contains two separate parts: action-finding (active approach) and angle-finding (passive approach). In the active phase, the Q-learning algorithm is used to find a sequence of simple actions, including up, down, left, and right, to reach the target cell in a gridded workspace. In the passive phase, the joints angles of the robot arm, with respect to the found actions, are obtained by the trained neural network. The simulation and test results show that this hybrid approach significantly improves the slowness and complexity due to using the simplified agent-environment interaction in the active phase and simple computing the joints angles in the passive phase.
Security Vulnerability Detection Using Deep Learning Natural Language Processing

Detecting security vulnerabilities in software before they are exploited has been a challenging problem for decades. Traditional code analysis methods have been proposed, but are often ineffective and inefficient. In this work, we model software vulnerability detection as a natural language processing (NLP) problem with source code treated as texts, and address the auto-mated software venerability detection with recent advanced deep learning NLP models assisted by transfer learning on written English. For training and testing, we have preprocessed the NIST NVD/SARD databases and built a dataset of over 100,000 files in C programming language with 123 types of vulnerabilities. The extensive experiments generate the best performance of over 93% accuracy in detecting security vulnerabilities.
QUAC-TRNG: High-Throughput True Random Number Generation Using Quadruple Row Activation in Commodity DRAM Chips

True random number generators (TRNG) sample random physical processes to create large amounts of random numbers for various use cases, including security-critical cryptographic primitives, scientific simulations, machine learning applications, and even recreational entertainment. Unfortunately, not every computing system is equipped with dedicated TRNG hardware, limiting the application space and security guarantees for such systems. To open the application space and enable security guarantees for the overwhelming majority of computing systems that do not necessarily have dedicated TRNG hardware (e.g., processing-in-memory systems), we develop QUAC-TRNG, a new high-throughput TRNG that can be fully implemented in commodity DRAM chips, which are key components in most modern systems.QUAC-TRNG exploits the new observation that a carefully-engineered sequence of DRAM commands activates four consecutive DRAM rows in rapid succession. This QUadruple ACtivation (QUAC) causes the bitline sense amplifiers to non-deterministically converge to random values when we activate four rows that store conflicting data because the net deviation in bitline voltage fails to meet reliable sensing margins.We experimentally demonstrate that QUAC reliably generates random values across 136 commodity DDR4 DRAM chips from one major DRAM manufacturer. We describe how to develop an effective TRNG (QUAC-TRNG) based on QUAC. We evaluate the quality of our TRNG using the commonly-used NIST statistical test suite for randomness and find that QUAC-TRNG successfully passes each test. Our experimental evaluations show that QUAC-TRNG reliably generates true random numbers with a throughput of 3.44 Gb/s (per DRAM channel), outperforming the state-of-the-art DRAM-based TRNG by 15.08× and 1.41× for basic and throughput-optimized versions, respectively. We show that QUAC-TRNG utilizes DRAM bandwidth better than the state-of-the-art, achieving up to 2.03× the throughput of a throughput-optimized baseline when scaling bus frequencies to 12 GT/s.
Tracking by Joint Local and Global Search: A Target-Aware Attention-Based Approach

Tracking-by-detection is a very popular framework for single-object tracking that attempts to search the target object within a local search window for each frame. Although such a local search mechanism works well on simple videos, however, it makes the trackers sensitive to extremely challenging scenarios, such as heavy occlusion and fast motion. In this article, we propose a novel and general target-aware attention mechanism (termed TANet) and integrate it with a tracking-by-detection framework to conduct joint local and global search for robust tracking. Specifically, we extract the features of the target object patch and continuous video frames; then, we concatenate and feed them into a decoder network to generate target-aware global attention maps. More importantly, we resort to adversarial training for better attention prediction. The appearance and motion discriminator networks are designed to ensure its consistency in spatial and temporal views. In the tracking procedure, we integrate target-aware attention with multiple trackers by exploring candidate search regions for robust tracking. Extensive experiments on both short- and long-term tracking benchmark datasets all validated the effectiveness of our algorithm.
FEXNet: Foreground Extraction Network for Human Action Recognition

As most human actions in video sequences embody the continuous interactions between foregrounds rather than the background scene, it is significant to disentangle these foregrounds from the background for advanced action recognition systems. In this paper, therefore, we propose a Foreground EXtraction (FEX) block to explicitly model the foreground clues to achieve effective management of action subjects. In particular, the designed FEX block contains two components. The first part is a Foreground Enhancement (FE) module, which highlights the potential feature channels related to the action attributes, providing channel-level refinement for the following spatiotemporal modeling. The second phase is a Scene Segregation (SS) module, which splits feature maps into foreground and background. Specifically, a temporal model with dynamic enhancement is constructed for the foreground part, reflecting the essential nature of the action category. While the background is modeled using simple spatial convolutions, mapping the inputs to the consistent feature space. The FEX blocks can be inserted into existing 2D CNNs (denoted as FEXNet) for spatiotemporal modeling, concentrating on the foreground clues for effective action inference. Our experiments performed on Something-Something V1, V2 and Kinetics400 verify the effectiveness of the proposed method.
A multilingual offensive language detection method based on transfer learning from transformer fine-tuning model

Offensive communications have invaded social media content. One of the most effective solutions to cope with this problem is using computational techniques to discriminate offensive content. Moreover, social media users are from linguistically different communities. This study aims to tackle the Multilingual Offensive Language Detection (MOLD) task using transfer learning models and the fine-tuning phase. We propose an effective approach based on the Bidirectional Encoder Representations from Transformers (BERT) that has shown great potential in capturing the semantics and contextual information within texts. The proposed system consists of several stages: (1) Preprocessing, (2) Text representation using BERT models, and (3) Classification into two categories: Offensive and non-offensive. To handle multilingualism, we explore different techniques such as the joint-multilingual and translation-based ones. The first consists in developing one classification system for different languages, and the second involves the translation phase to transform all texts into one universal language then classify them. We conduct several experiments on a bilingual dataset extracted from the Semi-supervised Offensive Language Identification Dataset (SOLID). The experimental findings show that the translation-based method in conjunction with Arabic BERT (AraBERT) achieves over 93% and 91% in terms of F1-score and accuracy, respectively.
Asset criticality and risk prediction for an effective cybersecurity risk management of cyber-physical system

Risk management plays a vital role in tackling cyber threats within the cyber-physical system (CPS). It enables identifying critical assets, vulnerabilities and threats and determining suitable proactive control measures for the risk mitigation. However, due to the increased complexity of the CPS, cyber-attacks nowadays are more sophisticated and less predictable, which makes risk management task more challenging. This paper aims for an effective cybersecurity risk management (CSRM) practice using assets criticality, predication of risk types and evaluating the effectiveness of existing controls. We follow a number of techniques for the proposed unified approach including fuzzy set theory for the asset criticality, machine learning classifiers for the risk predication and comprehensive assessment model (CAM) for evaluating the effectiveness of the existing controls. The proposed approach considers relevant CSRM concepts such as asset, threat actor, attack pattern, tactic, technique and procedure (TTP), and controls and maps these concepts with the VERIS community dataset (VCDB) features for the risk predication. The experimental results reveal that using the fuzzy set theory in assessing assets criticality supports stakeholder for an effective risk management practice. Furthermore, the results have demonstrated the machine learning classifiers exemplary performance to predict different risk types including denial of service, cyber espionage and crimeware. An accurate prediction of risk can help organisations to determine the suitable controls in proactive manner to manage the risk.
Tokenized index funds: A blockchain-based concept and a multidisciplinary research framework

In response to the bleak prospects of today’s financial markets, a wave of financial and technological innovations emerges, bringing about potential benefits but also new challenges. For instance, tokenized securities are a new kind of blockchain-based asset enabling price stability, programmability, pseudonymity, and transaction efficiency, while also introducing new regulatory challenges and uncertainties. Conversely, index funds are an established investment device enabling broad diversification in a cost-effective, tax-efficient, and transparent way, while potentially also contributing to concentration of market power, intermediation cost, access barriers for underbanked or impoverished investors, increased market volatility, and human behavioral challenges. This paper conceptually develops Tokenized Index Funds as a hybrid approach that combines the benefits of tokenized securities and index funds while alleviating some of their drawbacks. Based thereupon, a corresponding multidisciplinary research framework is presented, with sample research questions along the activities of design and features, business and economics, management and organization, and law and regulation. • Blockchain can help to combine benefits of tokenized securities and index funds. • Benefits include cost-efficiency, inclusion, automation, transparency, and nudging. • Tokenized index funds enable predictable volatility and long-term growth. • Social, economic, technological, and ethical challenges must be considered. • Open questions concern design, business models, management, and regulation.
Certificateless Elliptic Curve Aggregate Signcryption Scheme

Driven by new situation of “Internet +,” Internet has achieved the integrated development with all walks of life. Among them, the fifth generation is a key technology to promote the deep integration of Internet-of-Things equipment, cloud computing, blockchain and other trades. Hence, it is necessary for IoTs to consider the cost and efficiency of authentication and confidentiality of the communication. For effectively solving the above problems, we devise certificateless elliptic curve aggregate signcryption (CL-ECASC) scheme for IoTs that can improve the authentication efficiency, realize data confidentiality, and avoid the problems of complex certificate management and key escrow. Under the hardness of discrete logarithm and computational DiffieHellman problems on elliptic curve, CL-ECASC is proved to has the IND-CCA2 security (indistinguishability under the adaptive chosen-ciphertext attacks) and UF-CMA security (existentially unforgeable under the adaptive chosen-message attacks). CL-ECASC has relatively faster computation efficiency and lower communication cost, and so it is suitable for secure transmission of the information in the previously mentioned environments.
GFE: General Knowledge Enhanced Framework for Explainable Sequential Recommendation

It is vital for sequential recommendation to provide accurate and explainable results for user, which can help them make better decisions. In this paper, we develop a General Knowledge Enhanced Framework for Explainable Sequential Recommendation (GFE) to capture user’s fine-grained preferences and dynamic preferences evolution. Specifically, the fine-grained preferences are modeled as intrinsic interests and external potential interests, which can be captured by sequential-aware interest and knowledge-aware interest modules respectively. Moreover, the high-order paths between each user-item pair are generated with the help of the knowledge graph, which contain abundant high-order semantic relevance among entities. To make better use of this character, we propose a hierarchical self-attention mechanism to aggregate the high-order semantic information from these knowledge paths, thus discovering user’s dynamic preferences evolution. According to these abilities, GFE can provide more reasonable explanations from the views of microcosm and macrocosm. Unlike other traditional explainable sequential recommendation models, GFE has the strong generalization to integrate with other pure sequential recommendation models and endow explainability to them. Based on this nature, we combine the GFE with two state-of-the-art sequential recommendation models and further propose two GFE-based models, called GFE-SASRec and GFE-TiSASRec, to show the availability of GFE. Finally, experiments conducted on three real-world public datasets demonstrate the state-of-the-art performance and the strong explainability of GFE.
Line Art Colorization with Concatenated Spatial Attention

Line art plays a fundamental role in illustration and design, and allows for iteratively polishing designs. However, as they lack color, they can have issues in conveying final designs. In this work, we propose an interactive colorization approach based on a conditional generative adversarial network that takes both the line art and color hints as inputs to produce a high-quality colorized image. Our approach is based on a U-net architecture with a multi-discriminator framework. We propose a Concatenation and Spatial Attention module that is able to generate more consistent and higher quality of line art colorization from user given hints. We evaluate on a large-scale illustration dataset and comparison with existing approaches corroborate the effectiveness of our approach.
Partially Encrypted Multi-Party Computation for Federated Learning

Multi-party computation (MPC) allows distributed machine learning to be performed in a privacy-preserving manner so that end-hosts are unaware of the true models on the clients. However, the standard MPC algorithm also triggers additional communication and computation costs, due to those expensive cryptography operations and protocols. In this paper, instead of applying heavy MPC over the entire local models for secure model aggregation, we propose to encrypt critical part of model (gradients) parameters to reduce communication cost, while maintaining MPC's advantages on privacy-preserving without sacrificing accuracy of the learnt joint model. Theoretical analysis and experimental results are provided to verify that our proposed method could prevent deep leakage from gradients attacks from reconstructing original data of individual participants. Experiments using deep learning models over the MNIST and CIFAR-10 datasets empirically demonstrate that our proposed partially encrypted MPC method can reduce the communication and computation cost significantly when compared with conventional MPC, and it achieves as high accuracy as traditional distributed learning which aggregates local models using plain text.
Omnimatte: Associating Objects and Their Effects in Video

Computer vision is increasingly effective at segmenting objects in images and videos; however, scene effects related to the objects—shadows, reflections, generated smoke, etc.—are typically overlooked. Identifying such scene effects and associating them with the objects producing them is important for improving our fundamental understanding of visual scenes, and can also assist a variety of applications such as removing, duplicating, or enhancing objects in video. In this work, we take a step towards solving this novel problem of automatically associating objects with their effects in video. Given an ordinary video and a rough segmentation mask over time of one or more subjects of interest, we estimate an omnimatte for each subject—an alpha matte and color image that includes the subject along with all its related time-varying scene elements. Our model is trained only on the input video in a self-supervised manner, without any manual labels, and is generic—it produces omnimattes automatically for arbitrary objects and a variety of effects. We show results on real-world videos containing interactions between different types of subjects (cars, animals, people) and complex effects, ranging from semitransparent elements such as smoke and reflections, to fully opaque effects such as objects attached to the subject. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
A Spacecraft Dataset for Detection, Segmentation and Parts Recognition

Virtually all aspects of modern life depend on space technology. Thanks to the great advancement of computer vision in general and deep learning-based techniques in particular, over the decades, the world witnessed the growing use of deep learning in solving problems for space applications, such as self-driving robot, tracers, insect-like robot on cosmos and health monitoring of spacecraft. These are just some prominent examples that has advanced space industry with the help of deep learning. However, the success of deep learning models requires a lot of training data in order to have decent performance, while on the other hand, there are very limited amount of publicly available space datasets for the training of deep learning models. Currently, there is no public datasets for space-based object detection or instance segmentation, partly because manually annotating object segmentation masks is very time consuming as they require pixel-level labelling, not to mention the challenge of obtaining images from space. In this paper, we aim to fill this gap by releasing a dataset for spacecraft detection, instance segmentation and part recognition. The main contribution of this work is the development of the dataset using images of space stations and satellites, with rich annotations including bounding boxes of spacecrafts and masks to the level of object parts, which are obtained with a mixture of automatic processes and manual efforts. We also provide evaluations with state-of-the-art methods in object detection and instance segmentation as a benchmark for the dataset. The link for downloading the proposed dataset can be found on https://github.com/Yurushia1998/SatelliteDataset.
Intra-Pulse Modulation Recognition of Dual-Component Radar Signals Based on Deep Convolutional Neural Network

In an ever-increasingly complicated electromagnetic environment with explosive radar signals density, accurate and fast recognition of dual-component radar signals has become an urgent problem in the current radar reconnaissance system. This letter proposes a novel multi-class learning framework based on deep convolutional neural network (DCNN) for recognizing eight types of randomly overlapping dual-component radar signals. The framework mainly includes dual-component radar signals preprocessing, DCNN model that aimed to extract more effective dual-component radar signals features, and multi-class classification. The results shown that the average classification accuracy of dual-component radar signals can be up to 96.17% when the signal-to-noise ratio (SNR) is −8 dB, which demonstrates the superior performance over others. The proposed model possesses the larger improvement for 4FSK, BPSK, EQFM, and FRANK, especially at the lower SNR. This work provides a sound guidance for further improving multi-component radar signals recognition.
Shape and Material Capture at Home

In this paper, we present a technique for estimating the geometry and reflectance of objects using only a camera, flashlight, and optionally a tripod. We propose a simple data capture technique in which the user goes around the object, illuminating it with a flashlight and capturing only a few images. Our main technical contribution is the introduction of a recursive neural architecture, which can predict geometry and reflectance at 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> ×2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> resolution given an input image at 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> ×2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> and estimated geometry and reflectance from the previous step at 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k−1</sup> ×2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k−1</sup> . This recursive architecture, termed RecNet, is trained with 256×256 resolution but can easily operate on 1024×1024 images during inference. We show that our method produces more accurate surface normal and albedo, especially in regions of specular highlights and cast shadows, compared to previous approaches, given three or fewer input images.