Reviewerly/exHarmony huggingface dataset:
Implementation and Performance Evaluation of Various Reversible Vedic Multiplier Architectures for Reversible Digital Filters

Reversible logic has emerged as an alternative technology to the traditional logic due to the narrowed scope for continued transistor scaling which is considered to be the primary factor driving the semiconductor industry, by the exponential growth of leakage current and other short channel effects that degrades the performance of the device. Reversible design eliminates information loss and thus reduces the power dissipation. Vedic multiplier belongs to the class of fast multipliers that incredibly improves the overall system efficiency. Thus, a reversible Vedic multiplier effectively combines both the low-power advantage of reversible design and the high-speed advantage of a Vedic multiplier. This paper presents the implementation of various architectures of reversible Vedic multiplier units based on Urdhva Tiryagbhyam Sutra and comparison of its performance in terms of the cost metrics like gate count, auxiliary inputs, garbage output and quantum cost along with factors like the power consumption and area. We have implemented six different architectures of the reversible Vedic Multiplier and compared its performance. The design is implemented using Xilinx 14.7 ISE using Verilog HDL on Spartan 6 FPGA.
Improving the Environmental Adaptability of Conditional Imitation Learning Driving Model

Conditional imitation learning has been proved to be a great policy in autonomous driving due to both its offline characteristics and good performance which is an End-to-end visual-based driving policy. However, due to the complexity of the real world, the images captured under different environments have interference factors, such as raindrops, strong light, etc., which bring challenges to the model&#x2019;s perception and decision-making. To eliminate the influence of natural disturbances under different environments, we proposed a model named TCILRS. We evaluated our model on CARLA, a great simulator, and find that our model plays better than CILRS.
Cryptanalysis of an additively homomorphic public key encryption scheme

• We proposed an attack algorithm for an ElGamal-based homomorphic encryption scheme constructed by Bae et al. [11] . • The proposed attack is thorough and efficient. • The proposed attack algorithm can be considered when designing ElGamal-based encryption schemes. Based on the ElGamal cryptosystem, Bae et al. ( J. Netw. Comput. Appl. , vol. 59, pp. 333–344, 2016) proposed a new homomorphic encryption algorithm to reduce the network traffic and data processing time brought by their data communication and aggregation scheme. However, there is no security analysis of this encryption algorithm in their scheme. In this paper, we analyze the security of their proposed encryption algorithm and prove that it is not secure.
A Novel Unet Decoding Strategy for Cervical Cell Mass Segmentation

A novel Unet decoding strategy for segmentation of cervical cell mass is proposed, which is inspired by the sub-pixel convolution. Our proposed method avoids the artificial information added by interpolation method and the noise introduced by transpose convolution method due to padding. After the implementation of the proposed method on our in-house dataset, the quantitative evaluation shows that the sub-pixel convolution has not only higher metric score but also the stable performance, and our proposed method can be used as a promising decoding method for Unet to segment the cellular clump of the cervical cell images.
The European Commission contribution to cybersecurity through the ECHO project

This paper introduces the work carried out within project ECHO (European network of Cybersecurity centres and competence Hub for innovation and Operations), one of the four pilot projects financed under the H2020 framework aiming to connect and share knowledge across multiple domains, while building a common cybersecurity strategy for Europe, inside the framework of the European Cybersecurity Competence Center located in Bucharest. The question is: what can EU countries do to strengthen the Union’s cyber defence and ensure a secure cybersecurity ecosystem that would protect citizens, economy and infrastructures from cyber-attacks? The first step is to assure that EU citizens are aware of these risks and are ready to fight them. To this purpose, ECHO project develops: (1) a marketplace of multi-sector services coming from multiple cyber ranges, the E-FCR (ECHO Federated Cyber Range), to be applied in several areas, with particular attention to the Healthcare sector, (2) a distributed platform, the E-EWS (ECHO Early Warning System), for information sharing across organizational boundaries that provides companies with a common operational cyber threat situation picture tailored for different types of users (from technical cyber experts to management executives). This paper gives an overview of the architecture of these solutions, highlighting why they could become paramount for a standardised cybersecurity training at European and international levels.
Improving Object Detection Using Weakly-Annotated Auxiliary Multi-Label Segmentation

With the rapid development of deep learning techniques, the performance of object detection has increased significantly. Recently, several approaches on joint learning of object detection and semantic segmentation have been proposed to exploit the complementary benefits of the two highly correlated tasks. In this work, we propose a weakly-annotated auxiliary multi-label segmentation network that boosts object detection performance without additional computational cost at inference. The proposed auxiliary segmentation network is trained using weakly-annotated dataset and therefore does not require expensive pixel-level annotations for training. Different from the previous approaches, we use multi-label segmentation to jointly supervise auxiliary segmentation and object detection for better occlusion handling. The proposed method can be integrated with any one-stage object detector such as RetinaNet, YOLOv3, YOLOv4, or SSD. Our experimental results on the MS COCO dataset show that the proposed method can improve the performance of popular one-stage object detectors without slowing down the inference speed regardless of the sub-optimal training sample selection schemes.
Composite Radar Modulation Identification by Transfer Learning

Radar Signal Modulation Identification plays an important role in electronic countermeasures (ECM) in order to involve radar parameter estimation and radar state prediction. Most of the traditional radar signal modulation sensing techniques are used for known signal and cannot distinguish the unknown composite modulation signal. In this chapter, a new radar signal modulation sensing algorithm using Transfer Learning with the framework of a deep convolutional neural network (CNN) is presented. Using this method, it is possible to distinguish the unknown composite modulation signal with limited training data. Furthermore, a good classification accuracy on testing dataset base on Transfer Learning CNN model is achieved with this method. Several similarity cases are also studied to verify the adaptability of the proposed method.
An Efficient Spam Review Detection Using Active Deep Learning Classifiers

Online fake reviews and ratings is making a big impact In order to purchase or subscribe to the online services. So it is important to detect fake review from e-commerce sites. So review spam detection is more important nowadays. There are many research have been done in this area but no one can detect review spam efficiently with high accuracy. This is known as review spamming. We integrate SVM which is a supervised method and unsupervised methods (rating consistency check, question in reviews, all capital letter reviews, link in a review etc.).
Positive Correlation Based Efficient High Utility Pattern Mining Approach

The problem of high utility pattern (HUP) mining is an interesting task and comes with various applications. It has been argued in various past studies that the patterns with strong mutual correlation are more useful for decision making. A more powerful tool that takes the inherent correlation into account is more desirable. This paper presents an Efficient Correlated High Utility Miner (ECHUM) that considers the positive correlation among the items to find correlated high utility itemsets. The ECHUM uses a list structure is to avoid multiple scanning of the dataset. The search space is significantly reduced by using two upper-bounds named sub-tree utility and the local utility. Also, several database projection methods and transaction merging methods are used to reduce the complexity. Several pruning strategies are adopted to make the algorithm fast and memory efficient. A variety of experiments conducted shows that ECHUM is 2–3 times faster, and the memory usage is also 3–4 times lesser than the existing algorithm.
Image Contrast Enhancement with High Dynamic Range using Singlescale Retinex

This paper presents a contrast enhancement method for images with low contrast areas due to uneven illumination. The multi-scale retinex (MSR) is an excellent contrast enhancement method for such images. However, MSR contains many parameters to be determined and it is not easy to determine those parameters properly. In the proposed method, the dynamic range of the entire image is maintained by adding the single scale retinex (SSR) result to the scaled original image, and the contrast is effectively improved. The proposed method has few parameters to be determined. Therefore, it is clarified that the parameters can be determined by specifying the difference between the average values of the whole image before and after enhancement.
Video Shot Retrieval Using Multi-feature Approach

The evolution of Internet and mobile technologies has raised the growth of video data that spur demand for efficient browsing and retrieval technologies. Content-Based Video Retrieval (CBVR) is evolved progressively to retrieve desired videos proficiently from large repositories based on the video content. In this aspect, an efficient CBVR method is presented using multiple features from video shot keyframes. The approach constructs histogram for Sobel magnitude of V-channel from HSV image and also local binary cumulative sum variance pattern for grayscale image. Histograms, thus, constructed are concatenated to build multiple feature vector database. Further, shot matching process is established by applying Euclidean distance between shot keyframe and query keyframe features. Experimentation was carried out on UCF YouTube action benchmark dataset to analyze the efficiency of the proposed algorithm. Video shot retrieved results depict significant improvement of the presented CBVR approach in comparison with baseline algorithm in terms of evaluation metric.
Automatic evaluating of Russian-Arabic machine translation quality using METEOR method

Given the huge development in the field of machine translation (MT) system and the emergence of the need for high speed and extreme accuracy in the transmission of information from one language to another, the need to design an optimum new systems and compare them with the traditional available systems to determine the efficiency of the new systems and identify the weaknesses to be developed in the proposed new systems. The aim of this work is to conduct an evaluation study of the performance and efficiency of the RAMT system. The evaluation has been conducted by comparing this system with the well-known google translation system by conducting a translation of 1000 Russian sentences in METEOR (Metric for Evaluation of Translation with Explicit ORdering) method. The obtain results show that the RAMT system efficiency is about 51.57%, while the efficiency of Google translation’s system is about 48.43%. The comparison results indicate that the RAMT system is more effective and give a better translation accuracy than the Google translations system.
Influence of Probing Action Costs on Adversarial Decision-Making in a Deception Game

Deception, an act of misleading into false belief, has been proven to be an effective method to counter cyber-attacks. Although prior research in deception in cybersecurity has focused on the network size and the proportion of honeypots in games, there has been little research on the influence of probing action costs on adversarial decisions. In this research, using a deception game (DG), we investigate the impact of different cost functions in the probe stage on adversarial decisions. The DG involved a game DG (n, k, γ), where n was the number of webservers, k was the number of honeypot webservers in the network, and γ was the number of probes an adversary could make before choosing to attack the network. In an experiment, three between-subject conditions that differed in the cost of probing actions included: increasing-cost (40 participants), constant-cost (40 participants), and no-cost (40 participants). In increasing-cost, the cost for probing honeypots increased linearly over trials. However, in constant-cost, the cost for probing a honeypot webserver remained constant. In no-cost, probing a webserver did not cost the adversary. Results revealed that the probing cost did not influence the probe and attack actions and that there was a significant interaction between different cost conditions and the regular webserver probe actions over blocks. The main implication of the research is that the probing action costs may not influence adversarial decisions.
Improving the accuracy of the machine learning predictive models for analyzing CHD dataset

The problem to classify big data is an important one in machine learning. There are multiple ways to classify data, but the support vector machine (SVM) has become a great tool for the data scientist. In this paper we examine several modifications of the support vector machine algorithm that achieve better efficiency in terms of accuracy, F1 precision and CPU time when classifying test observations in comparison to the standard SVM algorithm. To make the modifications faster than standard SVM we use a special methodology which splits the input dataset into n folds and combine it with input data transformations. Each time we execute the process, one of the folds is saved as a test subset and the rest of the folds are applied for training. The process is executed n times. In the proposed methodology we are looking for the pair of subsets which produces the highest accuracy result. This pair is saved as an output SVM model.
vCDS: A Virtualized Cross Domain Solution Architecture

With the paradigm shift to cloud-based operations, reliable and secure access to and transfer of data between differing security domains has never been more essential. A Cross Domain Solution (CDS) is a guarded interface which serves to execute the secure access and/or transfer of data between isolated and/or differing security domains defined by an administrative security policy. Cross domain security requires trustworthiness at the confluence of the hardware and software components which implement a security policy. Security components must be relied upon to defend against widely encompassing threats – consider insider threats and nation state threat actors which can be both onsite and offsite threat actors – to information assurance. Current implementations of CDS systems use suboptimal Trusted Computing Bases (TCB) without any formal verification proofs, confirming the gap between blind trust and trustworthiness. Moreover, most CDSs are exclusively operated by Department of Defense agencies and are not readily available to the commercial sectors, nor are they available for independent security verification. Still, more CDSs are only usable in physically isolated environments such as Sensitive Compartmented Information Facilities and are inconsistent with the paradigm shift to cloud environments. Our purpose is to address the question of how trustworthiness can be implemented in a remotely deployable CDS that also supports availability and accessibility to all sectors. In this paper, we present a novel CDS system architecture which is the first to use a formally verified TCB. Additionally, our CDS model is the first of its kind to utilize a computation-isolation approach which allows our CDS to be remotely deployable for use in cloud-based solutions.
An Experience with the Implementation of a Rule-Based Triggering Recommendation Approach for Mobile Devices

In the current Big Data era, mobile context-aware recommender systems can play a key role to help citizens and tourists to make good decisions. Ideally, these systems should be proactive, able to detect the right moment and place to offer suggestions of a specific type of item or activity to the user. For this purpose, push-based recommender systems can be used, exploiting context rules to decide when a specific type of recommendation should be triggered.
Comparing the Popularity of Testing Careers among Canadian, Indian, Chinese, and Malaysian Students

This study attempts to understand motivators and de-motivators that influence the decisions of software students to take up and sustain software testing careers across four different countries, Canada, India, China, and Malaysia. Towards that end, we have developed a cross-sectional, but simple, survey-based instrument. In this study we investigated how software engineering and computer science students perceive and value what they do and their environmental settings. This study found that very few students are keen to take up software testing careers - why is this happening with such an important task in the software life cycle? The common advantages of a software testing career are learning opportunities and easiness of the job and the common drawbacks are tediousness, complexity, and missing the opportunity to do (software) development. Our findings highlight the importance of depicting software testing activities as a set of human-dependent tasks, and emphasizes the need for research that critically examines the way in which software testers view testing activities.
Constraining Weighted Word Co-occurrence Frequencies in Word Embeddings

Weighted word co-occurrence frequencies are considered the bedrock of word embeddings. Also known as a low-dimensional numerical representation, word embeddings capture word pair frequencies extracted from a corpus in an unsupervised manner. The rendering of word embeddings can be considered a two-step process with the first s tep i nvolving t he building of the word context matrix then using a matrix factorization method to reduce the dimensionality. In this research study, word embeddings are constructed from scratch in building the word context matrix and Truncated Singular Value Decomposition is applied to the matrix. Five experimental values are defined for constraining the frequency weights in the word embeddings, which are then evaluated in word similarity and sequence labeling tasks with results reported. The word similarity task shows comparable results across all experimental constraint values. Overall comparable results are also achieved in the sequence labeling task. The experiments conducted in this study have shown promising results, which will entail future work with evaluation on other tasks.
Finite mixture modeling of change point processes to discover opioid prescribing patterns: A case study of automated reports and consolidated ordering system data

One key challenge of evaluating the effectiveness of opioid-related policies is to determine whether policy changes are associated with opioid prescription and usage. We explored this issue by employing a finite mixture of change point processes applied to US state-level opioid prescription data from 2006 to 2014. We identified clusters of states based on patterns of opioid prescription dosage per capita. The produced partitionings demonstrate some degree of geographic proximity when examined on the US map. Among the four drugs examined - methadone, buprenorphine, oxycodone, and hydrocodone – change points are detected for all drug classes except hydrocodone.
Detecting Abnormal and Dangerous Activities Using Artificial Intelligence on The Edge for Smart City Application

Artificial Intelligence (AI) and Internet of Things (IoT) technologies have developed rapidly in recent years. AI on the Edge technology combined with IoT technology are very potential for smart city applications, the security protection is one of the very important problem in smart city. This study proposes a solution for detecting abnormal and dangerous activities using AI on the edge which can be applied in smart city applications. This project aims at developing a system which can detect abnormal and dangerous activities using Deep learning model on the edge computer. The video signal from the camera will be processed by embedded computer Jetson Nano, which is implemented with deep learning models to detect some abnormal and dangerous activities such as human without facemask in the SARS-CoV-2 pandemic areas or man with gun and knife in the city public areas..., the information of detected abnormal activities will be sent to cloud server through the IoT system. YOLOv5 deep learning model is selected to implement in this system, thousands of abnormal activities have been collected to train the model. A prototype abnormal and dangerous activities detection system has been designed and implemented in practical testing areas, which has very high accuracy detection result. Based on these initial results of the proposed solution we can develop some practical applications for smart city to detect and track different kinds of abnormal human activities in smart city for security issues.
Blockchain Based Lateral Transshipment

Organizations may opt for lateral transfer when an inventory of products needs to be fulfilled relatively quickly to the normal supply chain. They can make these transfers within their own structure, and in some cases, it may be necessary to perform this transfer with different organizations. In this study, we propose a blockchain-based lateral transfer model in which different organizations will meet their needs from each other without the need for a third party. While organizations supply the products they need from each other, they can reduce transportation costs by making the transportation process collectively. As far as we know, how blockchain technology can be used in lateral transfers is a first in the literature.
Spontaneous Speech Summarization: Transformers All The Way Through

This paper proposes a speech summarization system for spontaneous speech. The proposed system consists of speech segmentation, speech recognition, and extractive text summarization modules. We utilize the Transformer architecture for all modules, enabling us to achieve outstanding performance by capturing global and local context information from the sequence thanks to the self-attention mechanism. Furthermore, we introduce a novel data augmentation method for speech summarization using the results of speech segmentation and recognition modules. The proposed data augmentation addresses each sentence boundary's ambiguity in spontaneous speech, making it possible to improve the robustness for speech segmentation and recognition errors. We conduct an experimental evaluation using the Corpus of Spontaneous Japanese, which consists of Japanese speech such as lecture and conference talks. Through the experimental evaluation, we investigate individual performance and each module's relationship in terms of text summarization performance and demonstrate the effectiveness of the proposed data augmentation method.
Sketch face Recognition using Deep Learning

With the improvemet of profound understanding in deep learning, face recognition innovation depending on CNN has in reality wound up being the large approach taken. In this paper, convolutional and down-sample layers of CNN are constructed using convolution function. Python's theano library is used for processing pictures. Therefore to beautify on the CNN adaptation the convolution and tasting layers are joined straight away proper right into a solitary layer.