Reviewerly/exHarmony huggingface dataset:
Multiplication Through a Single Look-Up-Table (LUT) in CNN Inference Computation

Parameter quantization with lower bit width is the common approach to reduce the computation loads in CNN inference. With the parameters being replaced by fixed-width binaries, multiplication operations can be replaced by the look-up-table (LUT), where the multiplier-multiplicand operands serve as the table index, and the precalculated products serve as table elements. Because the histogram profiles of the parameters in different layers/channels differ significantly in CNN, previous LUT-based computation methods have to use different LUTs for each layer/channel, and consequently demand larger memory space along with extra access time and power consumption. In this work, we first normalize the parameters Gaussian profiles of different layers/channels to have similar means and variances, and further quantize the normalized parameters into fixed width through nonlinear quantization. Because of the normalized parameters profile, we can use one single compact LUT ( <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> entries) to replace all multiplication operations in the whole network. Furthermore, the normalization procedure also reduces the errors induced from quantization. Experiments demonstrate that with a compact 256-entry LUT, we can achieve the accuracy comparable to the results from 32-bit floating-point calculation; while significantly reducing the computation loads and memory spaces, along with power consumption and hardware resources.
A new quantum cryptanalysis method on block cipher Camellia

Symmetric cryptography is expected to be quantum safe when long-term security is needed. Kuwakado and Morii gave a 3-round quantum distinguisher of the Feistel cipher based on Simon's algorithm. However, the quantum distinguisher without considering the specific structure of the round function is not accurate enough. A new quantum cryptanalysis method for Feistel structure is studied here. It can make full use of the specific structure of the round function. The properties of Camellia round function and its linear transformation P are taken into account, and a 5-round quantum distinguisher is proposed. Then, the authors follow a key-recovery attack framework by Leander and May, that is, Grover-meet-Simon algorithm, and give a quantum key-recovery attack on 7-round Camellia in Q2 model with the time complexity of 224. It is the very first time that the specific structure of the round function is used to improve quantum attack on Camellia.
Self-supervision Adversarial Learning Network for Liver Lesion Classification

The lack of training samples is one of the main factors affecting the development of deep learning methods. Deep learning models often fail to learn useful features and have serious over-fitting problems when lacking of training data. In this work, we exploit two popular unsupervised learning techniques: adversarial learning and self-supervised learning, which is aimed at mine more useful representations and relieve over-fitting problems. Our training scheme is mainly divided into three steps. Firstly, we train a self-supervision network with unsupervised learning to extract obvious features from our liver lesion samples and these features will be transferred to next step. Secondly, we use the final output feature map generated by self-supervision network to train a discriminator by adversarial learning. Finally, the backbone network is trained under the constraint of discriminator and classifier. Our main idea is to train a discriminator with adversarial learning and self-supervised learning. Then, we use the discriminator to constrain the backbone network, which is aimed to reduce the backbone network solution search space. In particular, Different from generating data with GAN, we use GAN to feature adversarial learning for feature augmentation. Our experiments on liver lesion classification in CT show an average accuracy as 92.51% compared with the baseline training scheme, which demonstrates our proposed method can mime useful features and relieve over-fitting problem. It can assist physicians in the early detection and treatment of liver lesions.
Interoperability Types Classifications: A Tertiary Study

Interoperability is the ability of heterogeneous systems to interact and exchange information efficiently and effectively through a planned process. Interoperability has several faces and usually, the syntactic interoperability type is pointed out as the most basic one for keeping information systems interoperable. Several other types of interoperability, such as semantics, pragmatics, and organizational have been addressed as important non-functional requirements for information systems. Still lacking consensus, there are several terms used to characterize interoperability. Thus, can bring confusion and ambiguity in the use of these terms, making it difficult to have a comprehensive view of related or similar interoperability proposals. While some primary studies address specific solutions for interoperability aspects, secondary studies seek to summarize some research directions and practical knowledge. This paper presents a tertiary study performed to investigate the current research state on interoperability, organizing knowledge that has already been defined about interoperability types. We conducted a tertiary study to achieve the stated goal based on a set of three research questions. After searching, we selected and analyzed 15 secondary studies to answer the formulated research questions. Although twenty-seven different interoperability types were found, a specific subset is the most cited among these secondary studies. Its results can help to identify points that still require further investigation in the interoperability research field.
Efficient Graph Collaborative Filtering via Contrastive Learning

Collaborative filtering (CF) aims to make recommendations for users by detecting user’s preference from the historical user–item interactions. Existing graph neural networks (GNN) based methods achieve satisfactory performance by exploiting the high-order connectivity between users and items, however they suffer from the poor training efficiency problem and easily introduce bias for information propagation. Moreover, the widely applied Bayesian personalized ranking (BPR) loss is insufficient to provide supervision signals for training due to the extremely sparse observed interactions. To deal with the above issues, we propose the Efficient Graph Collaborative Filtering (EGCF) method. Specifically, EGCF adopts merely one-layer graph convolution to model the collaborative signal for users and items from the first-order neighbors in the user–item interactions. Moreover, we introduce contrastive learning to enhance the representation learning of users and items by deriving the self-supervisions, which is jointly trained with the supervised learning. Extensive experiments are conducted on two benchmark datasets, i.e., Yelp2018 and Amazon-book, and the experimental results demonstrate that EGCF can achieve the state-of-the-art performance in terms of Recall and normalized discounted cumulative gain (NDCG), especially on ranking the target items at right positions. In addition, EGCF shows obvious advantages in the training efficiency compared with the competitive baselines, making it practicable for potential applications.
Man Overboard: Fall detection using spatiotemporal convolutional autoencoders in maritime environments

Man overboard incidents in a maritime vessel are serious accidents where, the efficient and rapid detection is crucial in the recovery of the victim. The severity of such accidents, urge the use of intelligent systems that are able to automatically detect a fall and provide relevant alerts. To this end the use of novel deep learning and computer vision algorithms have been tested and proved efficient in problems with similar structure. This paper presents the use of a deep learning framework for automatic detection of man overboard incidents. We investigate the use of simple RGB video streams for extracting specific properties of the scene, such as movement and saliency, and use convolutional spatiotemporal autoencoders to model the normal conditions and identify anomalies. Moreover, in this work we present a dataset that was created to train and test the efficacy of our approach.
Generating combinations on the GPU and its application to the k-subset sum

Efficiently representing and generating combinations can allow the seamless visualization, sampling, and evaluation of combinatorial architectures. In this paper, being relevant to tackle resource allocation problems ubiquitously, we address the subset sum problem by (1) using gradient-free optimization with a number-based representation of the combinatorial search space and by (2) generating combinations with minimal change order through parallel reductions in the GPU.
A Comprehensive Review on Ontology and Semantic Web

The historical backdrop of man-made brainpower shows that information is basic for every frame works. Much of the time, better information can be more significant for settling an assignment than better calculations. To have really shrewd frameworks, information should be caught, prepared, reused, and conveyed. Ontologies supports every one of these undertakings. Current World Wide Web (WWW) is an enormous library of interlinked archives that are moved by digital media and introduced to individuals. It has developed from hypertext frameworks, yet the thing that matters is that anybody can add to it. This likewise implies the nature of data or even the ingenuity of archives cannot be by and large ensured. Current WWW contains a ton of data and information; however, machines for the most part serve just to convey and introduce the substance of reports depicting the information. Individuals need to interface all the wellsprings of significant data and decipher them themselves. Semantic Web is a push to upgrade current Web with goal that computers can process data introduced on Web and interface it to assist people with finding required information. The main point of Semantic Web is to share information rather than archives. This paper shows the overview of ontology and Semantic Web through Web mining.
An efficient machine learning approach for activity recognition

Abstract The population of older people in western nations are growing drastically. Independent lifestyle is their preference and this leads to fall instances often. Falls of this kind results in severe health issues or sometimes causes deadly damages to the elderly people. Considering this problem, it is highly important to come up with fall discovery systems. A machine learning framework has been proposed in this regard which covers both fall discovery and day to day movement recognition. Utilizing acceleration and speed inputs from dual unrestricted repositories are highly helpful in diagnosing to the maximum of seven movements. Acceleration and angular velocity parameters can be used to extract the attributes that are relevant to time and frequency domain. These attributes are then offered to a classification algorithm. An attempt has been made to check the outcomes of four different procedures for categorizing manual movements. The above-mentioned procedures are the Artificial Neural Network (ANN), K-Nearest Neighbours (KNN), Quadratic Support Vector Machine (QSVM), and FP growth. Power spectral density of the acceleration is used to maximize the outcomes of the classifier. Acceleration data alone is considered for activity discovery in the initial step. Experimental results show that the KNN, ANN, QSVM, and FP growth procedures can attain correctness of 81.2%, 87.8%, 93.2%, and 94.1%. The accuracy of fall detection touches 97.2% and 99.1% with no false positive values for the QSVM and FP growth procedures. As a next phase, the attributes can be taken autocorrelation function and the power spectral density of the acceleration and the angular velocity data that obviously maximizes the classification correctness. Projected attributes are adopted to achieve notable improvement in accuracy of 85.8%, 91.8%, 96.1%, and 97.7% for the KNN, ANN, QSVM, and FP growth algorithm. The accurateness of fall discovery touches 100% for both the QSVM and FP growth procedures in the absence of any incorrect alarm, which is the finest attainable performance.
A Survey on Streaming Adaptation Techniques for QoS and QoE in Real-Time Video Streaming

Video streaming is a dominant application in our day-to-day life and is accounting for the majority of the traffic over the Internet. HTTP streaming with dynamic adaptation (DASH) is accommodating prominent streaming services such as YouTube, Netflix, Amazon Video, and Hulu. It is a challenging task to stream the video up to the end-user's level of expectation for the service providers due to the sudden fluctuations in bandwidth availability and many more factors over the network. At present, video streaming is rated based on network-centric (QoS—quality of service) and user-centric (QoE—quality of experience) parameters. The perception of the viewer's video quality is measured by QoE metrics. In this contemporary survey, we manifest solely state-of-the-art bitrate dynamic adaptation algorithms for streaming over HTTP. The main contribution of this paper is focusing on video coding standards, diversified bitrate adaptation techniques, network-centric parameters like bandwidth, throughput, packet loss, delay or jitter which account for the QoS, QoE subjective and objective metrics, and influencing factors like initial delay, stalling rates, and quality oscillations which are taken into account. We also consider the factors for bitrate adaptation techniques like bandwidth estimation, playback buffer status, device configuration, content type, and viewer expectations. We make a note of machine learning (ML) algorithms which play a vital role in real-time data streaming. This comprehensive survey is useful to the researchers to predict the suitable bitrate chunk by the adaptation technique according to the network conditions as well as take the client-side factors into an account to optimize the viewer's QoE in real-time multimedia streaming.
Side Channel Attack Countermeasure for Low Power Devices with AES Encryption

The advancement of Internet of Things produces a massive increase in the use of low-power devices, which can contain sensitive information. Most of these devices do not have the necessary security to protect their information. Therefore, the work aims is analyze and compare countermeasures for SCA in low-power devices with 128-AES encryption. In this scenario, we analyze existing countermeasures and conclude that algorithm-based countermeasures are more suitable for low-power devices. Subsequently, we run a set of tests to understand the scope of CPA attacks and thus establish a countermeasure that is tailored to these devices. Finally, we propose a countermeasure based on byte logic, comparing it with traditional countermeasures of the same type.
VoiceWriting: a completely speech-based text editor

Assistive technologies, mostly based on speech recognition and synthesis, help visually-impaired people writing text on digital devices. However, they do not fully support non-sequential text editing without the use of sight. This paper discusses the design of the interaction protocol underlying the first prototype of a text editor that is especially designed for people with very poor eyesight. It does not require the visual localization of text for non-sequential editing of multiple-paragraph documents and only exploits voice and ”uninterpreted” keyboard input, namely the outmoded ”press any key” for mode-switching. Preliminary tests complete the paper.
Elo-based similar-strength opponent sampling for multiobjective competitive coevolution

Multiobjective evolution and competitive coevolution are sub-fields of evolutionary computation which each significantly complicate the concept of fitness. Together, these make multiobjective competitive coevolution very difficult to work with by combining multiple objective values with non-absolute fitness, preventing the use of many established techniques for improving performance in multiobjective or competitive coevolutionary algorithms. Nonetheless, multiobjective scenarios arise frequently in competitive coevolution, such as whenever coevolving agents must consider costs for their actions. This paper proposes a new evaluation method of pairing opponents with similar skill levels in each objective, so that evaluations more efficiently distinguish the performance of similar individuals. This is enabled through the use of per-objective Elo ratings as a surrogate fitness function that prevents bias against individuals assigned stronger opponents. Ratings can further be assigned for asymmetric, non-zero-sum objectives such as cost, allowing individuals to be paired with opponents that incidentally challenge those asymmetric objectives. Mixed results are presented, showing significant benefits from pairing similar opponents, but finding that the use of Elo rating instead of raw fitness harms evolution. A novel statistical test for comparing multiobjective coevolutionary algorithms is also introduced.
Blockchain: A New Safeguard to Cybersecurity

Blockchain and Cybersecurity are two vivid technologies that have developed tremendous consideration in recent years. This chapter presents a structure of existing and possible utilization of blockchain-based cybersecurity solutions for attackers and defenders to evaluate whether the convergence will steer the results for one of these groups. It focuses on the blockchain architecture and clarifies the ideas and different features such as decentralization, trustworthiness, smart contracts, and immutability. It attempts to feature the role of Blockchain in molding the eventual fate of Cyber Security. It also deals with trending cryptocurrencies like bitcoin and Ethereum which are gaining prominence in the recent times and will revolutionize the way people trade.
Attacking asymmetric cryptosystem based on phase truncated Fourier fransform by deep learning

Most of optical encryption systems are symmetric cryptosystems. The plaintext and the ciphertext in optical image encryption are related linearly. The security of the system needs to be strengthened. The asymmetric cryptosystem based on phase truncated Fourier transforms (PTFT) makes the security of the encryption system greatly improved by its nonlinear phase truncation. Deep learning (DL) as a method of machine learning was proposed decades ago. With the development of computer’s performance, the practicality of deep learning proves to be more and more obvious. Recently, deep learning has been effectively used in many fields such as biomedicine, object detection, etc. The good results have been achieved. In this article proposed is the attack to the PTFT encryption system by deep learning. Through the PTFT encryption system, we construct a plaintext-ciphertext paired image dataset and then train it by residual network (ResNet). There are two problems encountered by the traditional neural network model. One is vanishing or named exploding gradient, which makes training effect difficult to converge and the other is a degradation phenomenon. When continuing to increase the number of layers for a suitable depth model, the model accuracy will decline which is not caused by overfitting. This problem can be solved by the ResNet to a certain extent by directly bypassing and then taking the input information to the output to protect the integrity of the information. The biggest difference between ordinary directly connected convolutional neural networks and ResNet is that the ResNet has many bypass branches that directly connect the input to the subsequent layers, so that the subsequent layers can directly learn the residuals. The ResNet can automatically learn the decryption characteristics of the encryption system. Finally, the test set is used to test the decryption performance of the trained model. The data show that the model can restore the image with high quality and the model has a certain anti-noise ability. Compared with the two-step iterative amplitude recovery algorithm, the the method proposed in this paper can recover high quality image.
Design and Implementation of Intelligent Semiconductor Thermostat Based on Arduino MCU

In order to reduce Freon emissions from conventional refrigerators, the intelligent semiconductor thermostat based on Arduino is designed in this paper. The intelligent thermotank is a comprehensive complex system which integrates many functions such as semiconductor refrigeration/heating and temperature detection and display. The intelligent thermotank is controlled by Arduino MCU. The heating and refrigeration temperature can be set manually and the thermotank have perfect function of display and protection by collaboration of components. The detailed design procedure is presented in this paper, including hardware and software system. The functional and feasibility experiment results is given at the end of the paper which show the thermostat is stable and environmentally friendly.
Underwater image restoration by structured light and flood light imaging

An underwater optical imaging system is indispensable for many oceanic engineering tasks, yet still plagued by poor visibility conditions. The serious degradation of underwater image results from light scattering and absorption. Removal of the backscattered light is the focus issue of underwater imaging technology to improve the image visibility, particularly in turbid water. In this paper, we present an approach for underwater image recovery using structured light imaging and flood light imaging to compose a combined imaging model with which the backscatter component is completely offset. The convolutional image is obtained using the structured light scanning imaging mode where the backscatter intensity is proportional to that of the flood light image with an unknown scale parameter. An algorithm to refine the matching of the backscatter components of both the convolutional image and the flood light image is proposed. Thus, subtraction of both images gives rise the combined imaging model without the backscatter component. Consequently, image restoration is completed by employing the deconvolution process. Results of underwater experiments are given.
Leveraging Zipf’s Law to Analyze Statistical Distribution of Chinese Corpus

In term of Chinese natural language processing, it exits one particular problem that how to choose the strategy of word segmentation, which commonly includes char-based and word-based. Targeted at sentiment analysis of short text comparing with long text, the word-based segmentation faces the other problem that there are the more ambiguous or unregistered words in context of short text. The feature extraction done by the different Chinese Word Segmentation impact the statistic distribution of features, and further the accuracy of sentiment analysis. This paper evaluates five Chinese segmentation strategy effect on Sentiment Analysis of Short Text. We chose two word-based Chinese Word Segmentation (CWS), and three char-based n-gram and made usage of Zipf's law to quantify and present the result of word segmentation.