Reviewerly/exHarmony huggingface dataset:
Implementation Vulnerability Analysis: A case study on ChaCha of SPHINCS

Deployment of Post Quantum Cryptography(PQC) primitives ensures forward secrecy for today's communication against tomorrow's quantum adversary. However, these primitives have to be realized in conventional hardware, which may be vulnerable to side-channel attacks. Therefore, vulnerability analysis of these primitives is essential before deployment. In this paper, a NIST favoured digital signature primitive- SPHINCS is taken for analysis. ChaCha and BLAKE are ciphers that form the building blocks of SPHINCS. These ciphers are based on Addition, Rotation, and XOR(ARX) operations. The literature review has shown ARX ciphers to be vulnerable against implementation attacks. In this work, an effective countermeasure for the aforementioned building blocks is explored. This is achieved through the following: Parallel Prefix Adders are taken for addition operation in these ciphers instead of the native adder in Electronic Design Automation(EDA) tools. Distinct profiles are created which include the cipher using a particular adder with its best-suited implementation style. An optimized version of Threshold Implementation(TI) is adopted on the profiles as a countermeasure for the attacks on the unprotected implementations. Finally, we evaluate the protected profiles' resistance using Test Vector Leakage Assessment(TVLA) and Deep learning techniques. Such an analysis that follows a generic framework will be straightforward to automate. We believe this will serve to be useful for standardization of Threshold Schemes.
Unsupervised Motion Representation Enhanced Network for Action Recognition

Learning reliable motion representation between consecutive frames, such as optical flow, has proven to have great promotion to video understanding. However, the TV-L1 method, an effective optical flow solver, is time-consuming and expensive in storage for caching the extracted optical flow. To fill the gap, we propose UF-TSN, a novel end-to-end action recognition approach enhanced with an embedded lightweight unsupervised optical flow estimator. UF-TSN estimates motion cues from adjacent frames in a coarse-to-fine manner and focuses on small displacement for each level by extracting pyramid of feature and warping one to the other according to the estimated flow of the last level. Due to the lack of labeled motion for action datasets, we constrain the flow prediction with multi-scale photometric consistency and edge-aware smoothness. Compared with state-of-the-art unsupervised motion representation learning methods, our model achieves better accuracy while maintaining efficiency, which is competitive with some supervised or more complicated approaches.
A Financial Transaction Methods Based on MapReduce Technology and Blockchain

With the widespread application of mobile internet 5G and blockchain technology, the research and application of blockchain big data analysis and mining has been paid more and more attention by enterprises, universities and research institutions. The use of MapReduce technology to analyze and explore financial transaction data such as banking, insurance, securities, funds, futures and foreign exchange has strong application requirements and academic research significance. This paper first analyzes the characteristics of MapReduce and technology and financial transaction, and then introduces the related work of MapReduce and financial transaction. Moreover, through analysis and comparison of MapReduce and financial transaction technologies, it discusses the financial data transaction method of MapReduce technology. Finally, from the perspective of new technologies, this paper also puts forward some valuable prospects and suggestions for the sustainable development of MapReduce technology in financial transaction methods.
Theoretical and experimental studies on the vibration of membranes of Mridangam

A notable exception to percussion instruments not having harmonic overtones are drums belonging to the Indian musical family, particularly Tabla and Mridangam. A common aspect in both these instruments is the presence of an applied mass on the membrane to change its density distribution. While considerable work has been done to improve such membrane models for Tabla, relatively less has been done for Mridangam. It has been shown in past literature that the first few overtones are nearly and almost exclusively harmonic for such drums. Such a behaviour has been attributed to the aforementioned applied mass. While this inference has remained the same after improvements in membrane models of Tabla, the same is not true for Mridangam. The importance of this work is to show that while the applied mass does in fact lead to nearly harmonic overtones it doesn't do so exclusively (i.e. there are non–harmonic overtones present as well). In this work, a new membrane model for Mridangam based on extensive measurements and testing is developed. The pseudospectral method and FEM are used to solve the vibration problem using this new membrane model. Since the results from simulation based on the current membrane model present new findings that were not captured by simpler models of the past, experimental modal analysis was done to support these findings and to ascertain the assumption of treating the multiple layers as a single layer of effective density. A reconciliation is provided to the apparent harmonic nature of these drums by studying the transient response of membrane under excitation by Chaapu stroke and comparing it to similar studies done in the literature. Such a study serves to illustrate that there are perhaps some other reasons as to why these non–harmonic overtones might be suppressed. It is important to note that the current work deals exclusively with the vibration of the Mridangam membrane and does not consider any other effects like that of the air-cavity or the shells, which may or may not affect the acoustic response of the actual instrument.
A Geometric Algorithm for Fault-Tolerant Classification of COVID-19 Infected People

As the world is struggling against COVID-19 pandemic, and unfortunately no certain treatments are discovered yet, prevention of further transmission by isolating infected people has become an effective strategy to overcome this outbreak.That is why scaling up COVID-19 testing is strongly recommended.However, depending on the time tests are performed, they may have a high rate of false-negative results.This inaccuracy of COVID-19 testing is a challenge against controlling the pandemic.Therefore, in this paper we propose a geometric classification algorithm that is fault-tolerant to handle the inaccuracy of tests.So, in a metropolis of n people, let w + r be the number of cases that are tested, where r is the number of positive, while w is the number of negative COVID-19 cases, and k is an upper bound on the number of false-negative COVID-19 cases.The proposed algorithm takes O(r • (log r + log w) + w 3 + w log(hR)) time for isolating all positive cases together with at most k (according to the rate of error of testing) possibly positive (false-negative) cases from the rest of the people.The term hR in the time complexity is the size of convex hull of the set of positive cases, and obviously k ∈ O(w).For simplicity of this isolation, we consider a simple convex shape (a triangle) for this classification algorithm.
Prototype-Based Personalized Pruning

Nowadays, as edge devices such as smartphones become prevalent, there are increasing demands for personalized services. However, traditional personalization methods are not suitable for edge devices because retraining or finetuning is needed with limited personal data. Also, a full model might be too heavy for edge devices with limited resources. Unfortunately, model compression methods which can handle the model complexity issue also require the retraining phase. These multiple training phases generally need huge computational cost during on-device learning which can be a burden to edge devices. In this work, we propose a dynamic personalization method called prototype-based personalized pruning (PPP). PPP considers both ends of personalization and model efficiency. After training a network, PPP can easily prune the network with a prototype representing the characteristics of personal data and it performs well without retraining or finetuning. We verify the usefulness of PPP on a couple of tasks in computer vision and Keyword spotting.
NFC and IoT-based electronic health card for elementary students using sensor fusion method

Purpose In the context of overcoming malnutrition in elementary school children and increasing public awareness of this issue, the Indonesian Government has created a “Card for Healthy School Children” (KMS-AS) program in the form of a paper health card. However, currently, the KMS-AS record data are still written on paper, which is less effective in terms of the health process. An integrated measuring device and an online data-recording system are needed to promote children’s health and facilitate access and transfer of data from one place to another. This study aims to develop NFC and IoT-based KMS-AS using sensor fusion method. Design/methodology/approach The results of this study show that an integrated measuring device for weight, height, body temperature and Spo2 level can be connected with mobile and Web applications using IoT technology, facilitating data recording and monitoring of children’s nutritional status. The sensor fusion method was used for the classification of nutritional status and health status, based on the results of measurement tools. Near field communication (NFC) technology was used to facilitate user identification when making measurements. Findings The results show that KMS-AS can facilitate classification of nutritional status and children's health status. Measurement and classification data can be monitored via Web and mobile applications. The accuracy of height, weight, body temperature and Spo2 measurements was 98.21%, 98.59%, 98.93% and 98.93%, respectively. Originality/value In this research, the authors successfully produced a system using sensor fusion method for measuring body weight, height, temperature and Spo2 level, which is integrated and can be connected to mobile applications and the Web using the IoT and NFC.
Fighting Alcohol Craving Using Virtual Reality: the Role of Social Interaction

Craving is a cause of relapse in patients suffering from a substance use disorder. Cue-exposure therapy builds on eliciting feelings of craving in patients in safe and controlled environments to condition them to control these feelings. Different efficient and resource-friendly methods of eliciting craving exist, (such as written material, still pictures, etc.). However, these methods create knowledge and skill transfer gaps between therapy sessions and real life scenarios. Virtual reality allows more true-to-life experiences, and research demonstrates its capabilities in eliciting craving in patients. Studies have identified different environments that elicits craving, suggesting bars to be one of the most effective ones. Research also suggests the presence of others to be an effective method of eliciting craving in users. However, the effect of social interaction has not yet been explored. Therefore, this paper presents a virtual bar with the purpose to investigate whether social interaction affects alcohol craving in users. The VR intervention is designed with close cooperation with a psychiatrist experienced in working with individuals suffering from alcohol use disorder. In this paper, we present the designed and developed VR intervention and discuss how an experiment can be conducted after the COVID-19 shutdowns.
Securing E-payment Systems by RFID and Deep Facial Biometry

Security is a major concern in Electronic Payment (E-Payment) systems. Usually, these systems are protected against illegal users, so-called hackers, by different means, such as Personal identification numbers (PINs), passwords, cards, etc. However, these hackers may manage to bypass this protection by having recourse to different strategies. Many techniques have been proposed to counter hacking attempts; however, there are still situations where an illegal user may succeed to access the E-payment system easily by stealing from a legal user its payment card. The use of Artificial Intelligence methods for face authentication, like deep learning, has made facial biometry a highly developing and accurate technology, especially in the past decade. In this paper, we propose the joint use of deep learning-based facial biometry and RFID cards to reinforce the security of an E-Payment system. By doing so, we ensure that a user should be physically present carrying his RFID card to be able to access the E-Payment system. We have tested three deep learning-based face authentication models and validated them on MUCT and CASIA Face-V5 datasets, to choose the most suitable one for our proposed secured E-Payment system, obtaining top verification rates of 99.90% and 99.26%, respectively. Two versions of this system are proposed; in the first version, which is based on a Personnel Computer (PC) and a Raspberry card, face authentication is implemented in a PC and the control of the RFID reader is performed by a Raspberry Pi 3, whereas in the second version, which may be considered as an embedded system, all the job is accomplished by the Raspberry Pi.
RL4HCI: Reinforcement Learning for Humans, Computers, and Interaction

Reinforcement learning (RL) is emerging as an approach to understand intelligence in both humans and machines. However, if RL is to have a meaningful impact in human–computer interaction, it is critical that these two threads are integrated. This is required for genuinely interactive RL-based systems which take into account user capacities and preferences. This workshop will build a community and form a research agenda for investigating RL in HCI.
WNTC: An Efficient Weight News Text Classification Model

News text classification plays an important part in the field of news recommendation and intelligent office. Based on the above problem, we use the real open-source data “Sogou CS” as the research object of this paper to carry out related work, test the accuracy of multiple AI algorithm models, use models with good prediction results, and calculate weights based on the accuracy of each model to form a composite model, named WNTC model. Experimental results on the above real dataset show that our WNTC model significantly outperforms the traditional model, in detail, the accuracy, recall rate, F1-score of the model is 94.27%, 96.02%, 95.14%, which is improved to be compared to the prediction effect of a single AI algorithm model.
Hiding Data into Blockchain-based Digital Video for Security Protection

The legitimate owner interests protection of traditional digital videos has become an increasingly demand with the rapid development of internet video applications. The development of Blockchain, which provides decentralization, traceability, and high credibility attributes has provided a technical inspiration for digital video legitimate interests protection. In this paper, we propose a novel and effective security protection framework by hiding secret data in the digital video of the Blockchain network. On the Blockchain network side, we upload the secret data referred to video to Blockchain network; on the traditional server side, we use smart contract to manipulate the procedures of embedding and extraction of secret data which has been embedded into digital video. Then, we could verify, trace and protect the secret data which are stored into Blockchain network and digital video contents itself, respectively. It is proven that the proposed framework could achieve high efficiency and accurate performance on digital video security protection.
A Methodology for Optimizing the Objective Function in a High-dimensional Space Using the Swarm Intelligence Algorithm

A formal definition of the particle swarm method and the software algorithm based on it is given. The influence of the algorithm parameters on the global minimum search efficiency is studied using the example of a multiparameter function. The best parameters that allow achieving an average error from 0.05 % for a two-parameter function to 0.13 % for a function with 5 parameters are determined. It is found that increasing the swarm size and applying an increased number of iterations in a single numerical calculation are not the most effective means of increasing accuracy. It is shown that several sequential independent calculations, from the results of which the best one is selected, make the most sense. In this case, the required error of up to 1 % was guaranteed to be achieved by using 3 sequential calculations with a swarm size of 15 particles and 500 iterations on each calculation. High-precision calculations with an error of no more than 0.1 % required 10 sequential calculations with a swarm size of 30 particles and 1000 iterations in the calculation, the total amount of computational work was 0.3 million conditional iterations. When using a single calculation, an error of 0.13 % was achieved when performing at least 1.25 million conditional iterations.
An Ethical Code for Commercial VR/AR Applications

The commercial VR/AR marketplace is gaining ground and is becoming an ever larger and more significant component of the global economy. While much attention has been paid to the commercial promise of VR/AR, comparatively little attention has been given to the ethical issues that VR/AR technologies introduce. We here examine existing codes of ethics proposed by the ACM and IEEE and apply them to the unique ethical facets that VR/AR introduces. We propose a VR/AR code of ethics for developers and apply this code to several commercial applications.
A Rapid Dehazing Model in USV Imaging System based on End-to-End Convolutional Network

Due to the inclement weather at sea, the pictures collected by USV will always be affected by fog, degrading in quality. In order to improve the quality of images collected by USV, convolutional neural networks (CNN) are widely used in image defogging. In this article, we proposed a CNN-based end-to-end defogging neural network, using dense connection blocks and the attention mechanism to obtain image information. Since convolutional neural networks have the advantages of local perception and parameter sharing, we have changed the traditional apriori method for atmospheric scattering models. We use multi-scale learning to capture image information and use the attention mechanism to suppress some irrelevant details. Experiments have shown that our method has a good effect on USV image dehazing problem.
Multiple-step Sampling for Dense Object Detection and Counting

A multitude of similar or even identical objects are positioned closely in dense scenes, which brings about difficulties in object-detecting and object-counting. Since the poor performance of Faster R-CNN, recent works prefer to detect dense objects with the utilization of multi-layer feature maps. Nevertheless, they require complex post-processing to minimize overlap between adjacent bounding boxes, which reduce their detection speed. However, we find that such a multi-layer prediction is not necessary. It is observed that there exists a waste of ground-truth boxes during sampling, causing the lack of positive samples and the final failure of Faster R-CNN training. Motivated by this observation we propose a multiple-step sampling method for anchor sampling. Our method reduces the waste of ground-truth boxes in three steps according to different rules. Besides, we balance the positive and negative samples, and samples at different quality. Our method improves base detector (Faster R-CNN), the detection tests on SKU-110K and CARPK benchmarks indicate that our approach offers a good trade-off between accuracy and speed.
Research on 3D Contrast Image Segmentation Based on an Improved Otsu Algorithm

Abstract This article mainly introduces typical segmentation methods of medical images, especially when using the advantages of threshold segmentation when segmenting blood vessels, and it is effective when selecting the principle of maximum between-class variance and global threshold. Sex is explained. Based on this, the key role played by the local threshold in the segmentation of angiographic images is explained. In addition, the block method used in this article is a fixed size. Considering when the block size can show the best segmentation effect, the article proposes an index that can determine the block size, namely block the rate of change of the internal standard deviation of the image, and the proposed method is also demonstrated.
On some concepts of nonlinear dynamics suitable for use in linguistics

Abstract In this paper, we describe hypothetical possibilities for applying nonlinear dynamics methods to linguistic research. We suggest to refer to a large text corpus (meta-book of the writer, national language, language of the branch of knowledge) as a fractal object and to measure its fractal dimension (by Hausdorff). We consider the question of practical calculation of fractal dimension, as well as the question of motivation for applying the fractal concept to the language. In addition, we propose to apply some methods of the qualitative theory of differential equations to modelling the growth of the natural language vocabulary.
Real-time Pedestrian Lane Detection for Assistive Navigation using Neural Architecture Search

Pedestrian lane detection is a core component in many assistive and autonomous navigation systems. These systems are usually deployed in environments that require realtime processing. Many state-of-the-art deep neural networks only focus on detection accuracy but not inference speed. Without further modifications, they are not suitable for real-time applications. Furthermore, the task of designing a high-performing deep neural network is time-consuming and requires experience. To tackle these issues, we propose a neural architecture search algorithm that can find the best deep network for pedestrian lane detection automatically. The proposed method searches in a network-level space using the gradient descent algorithm. Evaluated on a dataset of 5,000 images, the deep network found by the proposed algorithm achieves comparable segmentation accuracy, while being significantly faster than other state-of-the-art methods. The proposed method has been successfully implemented as a real-time pedestrian lane detection tool.