Reviewerly/exHarmony huggingface dataset:
COVID-19 Tracking Applications: A Human-Centric Analysis

The year 2020 will always be remembered with the imprints left by COVID-19 on our lives. While the pandemic has had many undesirable effects for the whole world, one of its biggest side effects has been the fast digital transformation that has taken place, which was already in progress with the Industry 4.0 era. The readily available technology and wireless communications infrastructures paved the way for a myriad of digital technologies for the containment of the disease using mobile contact tracing applications developed by health authority organizations in many countries. The mounting privacy concerns especially with Bluetooth-enabled proximity tracing and centralized tracking technologies used by these applications have given rise to the development of new privacy-preserving contact tracing protocols. Although these new protocols have alleviated the privacy concerns of citizens to a certain extent, widespread adoption is still far from being the reality. In this paper, we analyze existing contact tracing technologies from a human-centric standpoint by focusing on their privacy implications. We present our comprehensive dataset consisting of the contact tracing application usage information in 94 countries and provide results of a multinational survey we have conducted on the sentiments of people regarding contact tracing applications. The survey results demonstrate that privacy concerns are still the leading deterrent for people when deciding whether to use these applications. Nevertheless, it is a globally accepted argument that the most effective and fastest method for contact tracking will be digital technologies free from human errors and manual procedures. Accordingly, it is concluded that a policy of developing decentralized tracking solutions based entirely on user privacy should be followed, in which independent trusted third parties assume the role of authority in the system architecture, if absolutely necessary, in order to effectively combat the pandemic worldwide. An important feature of the systems to be developed to pave the way for widespread use is to provide the users the right to be forgotten.
Employing Multi-cues to Identify Event Factuality

Event factuality represents the factual nature of events in texts, and describes whether an event is a fact, a possibility, or an impossible situation. Previous work usually used the embedding of event sentence to represent the event factuality, ignoring the other helpful evidence, such as negative words, speculative words and time words. To address the above issue, this paper introduces various kinds of effective cues, i.e., time cue, negative cue and speculative, to a BERT-based convolutional neural network to identify Chinese sentence-level event factuality. Experimental results on the Chinese DLEF corpus showed that our model outperforms the baseline BERT on macro and micro F1 by 3.64% and 3.77%, respectively. Moreover, the training time of our model is just one-fifth of the benchmark.
Using project-based approach to teach design patterns: An Experience Report

In a majority of Indian Engineering colleges, Software Engineering tends to be considered as a theoretical course. More specifically, software design is taught using traditional lecture based teaching methods with minimal hands-on experience on working with team based projects. As part of this paper, we share our experiences in teaching code smells and design patterns using project-based learning approach to students in a Software Engineering course. We tracked students’ ability to detect code smells and codify design patterns through code evaluation. A confidence survey conducted at the end of the course had a positive feedback. We believe the approach can be repeated across engineering colleges in India to teach Software Engineering design.
A Performance Evaluation of 3D Deep Learning Algorithms for Crime Classification

This paper presents a study on crime classification using two 3D deep learning algorithms, i.e., 3D Convolutional Neural Network and the 3D Residual Network. The Chicago crime dataset, which has 7.29 million records, collected from 2001 to 2020, is used for training the models. The models are evaluated by using F1 score, Area Under Receiver Operator Curve (AUROC), and Area Under Curve - Precision Recall (AUCPR). Furthermore, the effectiveness of spatial grid resolutions on the performance of the models is also evaluated. Results show that the 3D ResNet-18 achieved the best performance with an F1 score of 0.9985, whereas the 3D CNN achieved an F1 score of 0.9979, during training with a spatial resolution of 16 pixels. Furthermore, the 3D ResNet-18 achieved an accuracy of 0.92 and the 3D CNN achieved an accuracy of 0.87 during model testing. In terms of future work, we intend to test these algorithms on multi-label classification and regression crime problems, improve the performance of the 3D CNN by adding RNN layers, and evaluate the implementation of 3D ResNeXt for crime prediction and classification.
Convention of Application Service Providers (ASP) to Applied Cloud Computing Circumvents Defeat

Organizations have gotten subject to regularly increasing procedures of automated statistics also profligate interchange speeds. Specialists, for example, conjecture that the finish of secluded registering is close by and that in the following decade, most organizations will have made the move to utility processing. To reduce incidentals although, by way of so far accomplishing gradually composite data innovation supervisions, plentiful establishments went towards solicitation specialist organizations. For the reasons that of unfortunate strategies of accomplishment, finished contention, besides underprivileged network availability also, statistics transfer capacity, frequent Active Server Page’s disappeared through the website smash. Poles apart Active Server Pages, in any case, who grabbed network directions proposal also unaffected network transmission, remained all around put using initial cloud adopters. Through the prolonged penetration also, allocation speed of Internet providers nowadays, healthier in demand approaches, a wide variation of contribution, disseminated subtracting is remaining absent as of the Active Server Page defeat and remains positioned towards progress as a suffering world view in the process.
Gesture Classification Algorithm Based on SVDD-EPF

In the conventional gesture recognition algorithm such as Support Vector Machine (SVM) and Dynamic Time Warping (DTW), the calculation time and accuracy remain to be improved when they face a large number of data to be processed. Therefore, a new algorithm based on Support Vector Domain Description (SVDD) acting on functions is proposed to improve gesture identification. This method mainly uses effective interception data to replenish the length of different gesture data to the same, and generates a continuous and smooth function. On this basis, the SVDD algorithm acts on the extreme points of the function, so the method is simply referred as SVDD-EPF. The results show that the gesture recognition rate of the SVDD-EPF algorithm is higher than that of the traditional SVM algorithm, that is, a better recognition state can be achieved. If it is applied to a mobile phone unlock, it is expected to become a new generation of unlock mode.
Ref-CAMLA: A Reference Architecture for Context Aware Mobile Learning Applications

Context Aware Mobile Learning (CAML) provides learning material tailored to the need and particular circumstances of the learner. This paper proposes a reference architecture named Ref-CAMLA to support the development of future CAML applications that would help in achieving; standardization, reduce development cost, and shorter time to market. The evaluation results indicate that Ref-CAMLA contains the necessary elements required of the reference architecture and solves the motivated problem in this research.
WITHDRAWN: Study for electrical breakdown within dielectric liquids due to streamer discharge in rod-to-plane electrodes configuration

In this paper, a computer simulation is done to study the impact of the radius of anode electrode on behavior of electrical discharge in liquid dielectric (nitrogen and water). Different radiuses of anode electrode (rod) were used to determine the breakdown voltage values within 2 mm gap length. This simulation is based on the bubble model for the determination of the initiation and growth of the streamer discharge. Laplace’s equation is solved numerically with the use of finite element approach which was known to be an accurate numerical technique, to determine the electric field. A numerical simulation was executed for tracking streamer discharge between both electrodes for the gap length distance. The results of the simulation have shown the streamer channels’ path and their branches from anode to cathode. DC applied voltage varies, and minimal value which has resulted in streamer for bridging the gap and touches the plane has been specified as the voltage of the breakdown. It has been discovered that the voltage of the breakdown is increased with the increase of anode electrode (rod) radiuses. The breakdown voltage is affected strongly by radiuses of electrodes (anode). Also, the effects of rod radius on the streamer propagation and branching.
An Empirical Analysis on the Role of WSDL Metrics in Web Service Anti-Pattern Prediction

Service-Oriented Architecture (SOA) is one of the most well-known models for designing web systems. SOA system evolution and maintenance is challenging because of its distributive nature and secondly due to the demand of designing high-quality, stable interfaces. This evolution leads to a problem called Anti-patterns in web services. It is observed that these anti-patterns negatively impact the evolution and maintenance of software systems, making the early detection and correction of them a primary concern for the software developers. The primary motivation of this work is to investigate the relationship between the Web Service Description Language(WSDL) metrics and anti-patterns in web services. This research aims to develop an automatic method for the detection of web service anti-patterns. The core idea of the methodology defined is to identify the most crucial WSDL metrics with the association of various feature selection techniques for the prediction of anti-patterns. Experimental results show that the model developed by using all the WSDL quantity metrics(AM) shows a bit high performance compared to the models developed with the other metric sets. Experimental results also showed that the performance of the models generated using Decision Tree(DT) and Major Voting Ensemble(MVE) is high compared to the models generated using other classifier techniques.
Applications of Fuzzy Logic in Bioinformatics: A Survey

This chapter contains a survey on the basic structures of some bio-mathematical models based on fuzzy logic and explains and elaborates different complicated features and importance of the models along with their applications. We also provide a developmental approach of study to understand causes of disease to analyse the information and treatment of data and to improve control measures for various models through mathematical structural way. Our primary subjects of discussion are FRB models and FRBD models with the help of linguistic variables and If-Then fuzzy rules. We also discuss some defuzzification methods such as centre of maximum method, mean of maximum method and centre of area method.
A Residual Network and FPGA Based Real-Time Depth Map Enhancement System

Depth maps obtained through sensors are often unsatisfactory because of their low-resolution and noise interference. In this paper, we propose a real-time depth map enhancement system based on a residual network which uses dual channels to process depth maps and intensity maps respectively and cancels the preprocessing process, and the algorithm proposed can achieve real-time processing speed at more than 30 fps. Furthermore, the FPGA design and implementation for depth sensing is also introduced. In this FPGA design, intensity image and depth image are captured by the dual-camera synchronous acquisition system as the input of neural network. Experiments on various depth map restoration shows our algorithms has better performance than existing LRMC, DE-CNN and DDTF algorithms on standard datasets and has a better depth map super-resolution, and our FPGA completed the test of the system to ensure that the data throughput of the USB 3.0 interface of the acquisition system is stable at 226 Mbps, and support dual-camera to work at full speed, that is, 54 fps@ (1280 × 960 + 328 × 248 × 3).
Iot Network Behavioral Fingerprint Inference With Limited Network Traces For Cyber Investigation

The development and adoption of Internet of Things (IoT) devices will grow significantly in the coming years to enable Industry 4.0. Many forms of IoT devices will be developed and used across industry verticals. However, the euphoria of this technology adoption is shadowed by the solemn presence of cyber threats that will follow its growth trajectory. Cyber threats would either embed their malicious code or attack vulnerabilities in IoT that could induce significant consequences in cyber and physical realms. In order to manage such destructive effects, incident responders and cyber investigators require the capabilities to find these rogue IoT, contain them quickly and protect other legitimate IoTs from attacks. Such online devices may only leave network activity traces. A collection of relevant traces could be used to infer the IoT's network behavioral fingerprints and in turn could facilitate investigative find of these IoT. However, the challenge is how to infer these fingerprints when there are limited network activity traces. This research proposes a novel model construct that learns to infer the network behavioral fingerprint of specific IoT based on limited network activity traces using a One-Class Time Series Meta-learner called DeepNetPrint. Our research demonstrated our model to perform comparative well to supervised machine learning model trained with lots of network activity traces to identify IoT devices.
The Security Threat of Adversarial Samples to Deep Learning Networks

With the prosperity of artificial intelligence, research on machine learning becomes a hot issue globally. Generative Adversarial Networks expose the huge security risks of machine learning. After the creation, GAN has achieved good performance in image generation, automatic image coloring, and data enhancement. With the improvement of the ability to generate samples against the deep learning network, generating malicious samples against the target learning model to achieve the deceptive discriminator becomes an effective and harmful attacking method. At present, some efficient attack methods have been proposed for different types of learning networks and different types of sample data. This paper mainly discusses the vulnerability of deep learning networks and several attack methods based on adversarial samples.
A two-stage balancing strategy based on data augmentation for imbalanced text sentiment classification

In practice, the class imbalance is prevalent in sentiment classification tasks, which is harmful to classifiers. Recently, over-sampling strategies based on data augmentation techniques have caught the eyes of researchers. They generate new samples by rewriting the original samples. Nevertheless, the samples to be rewritten are usually selected randomly, which means that useless samples may be selected, thus adding this type of samples. Based on this observation, we propose a novel balancing strategy for text sentiment classification. Our approach takes word replacement as foundation and can be divided into two stages, which not only can balance the class distribution of training set, but also can modify noisy data. In the first stage, we perform word replacement on specific samples instead of random samples to obtain new samples. According to the noise detection, the second stage revises the sentiment of noisy samples. Toward this aim, we propose an improved term weighting called TF-IGM-CW for imbalanced text datasets, which contributes to extracting the target rewritten samples and feature words. We conduct experiments on four public sentiment datasets. Results suggest that our method outperforms several other resampling methods and can be integrated with various classification algorithms easily.
An Efficient Image Codec Using Spline-Based Directional Lifting Wavelet Transform and an Optimized EZW Algorithm

This paper proposes a new image compression system with an optimal spline-wavelet transform based on adaptive directional lifting and an improved Embedded zero-tree wavelet (EZW) coding algorithm. The performance of the proposed scheme is evaluated using objective and subjective criteria. Experimental results have shown promising performance compared with the results obtained by the JPEG2000 standard.
Creating Content and Use Cases for Testing Semantic Correlation Rules in Content Delivery

Information retrieval (IR) systems like content delivery portals (CDP) help users to complete processes like searching tasks. However, these systems are facing difficulties like a lack of context or an abundance of content within the required information. An approach to solve this problem is the creation of microDocs with semantic correlation rules (SCR). The objective of this paper is to examine the realization of SCR in CDP according to use cases by focusing on the impact SCR have on the effectiveness of CDP for IR. SCR can be implemented in various creation systems. Furthermore, the paper focuses on the exemplary development of SCR in a component content management system (CCMS). Therefore, a system-based evaluation was conducted. In addition a test collection, following an ontology and a corresponding metadata architecture was created to evaluate the impact of SCR on CDP. The evaluation includes three systems, representing the different types of CDP. It uses methodology precision and recall, as well-fitting methods for the intended purpose. To conclude and finalize, testing results will be summed up, interpreted and the findings will be provided with an outlook.
Efficient Authentication Technique in Medical Application by Using Homomorphic Encryption on Finger Print and RFID Tags

Nowadays, Internet of things had made easier to connect any devices and bring everything online with enormous amounts of new data which will make our life easier and smarter. Researchers estimated that 50 billion new devices will be connected together online in the next 5 years. For such networks, security is main concern as devices retrieve data from sensors and provides valuable information for many applications. In Internet of things, devices have high capability of computing and storing information in those with different constraints as resources, memory, bandwidth, and battery by reliable transportation over networks. In order to secure, many different techniques are present on of which is providing high security is radio frequency identification (RFID) tags security of sender node (i.e., patient) and receiver node (i.e., doctor) is very important. Using RFID with some Encryption will provide high security. In this paper, we proposed a system which will be highly secure in medical applications for patient to securely pass there inputs and report to doctors without man-in-the-middle attack. In this paper, we have identified an approach of combining homomorphic encryption on biometric authentication and using RFID tag identification using Internet of things. To implement this concept, RFID send tag information of patient which will be encrypted data with it finger print by applying homomorphic encryption is sent and receiver will able to decrypt that by following set of rules. A server will perform homomorphic encryption and using feature extraction for authentication of a Patient.
Complex-Valued Embeddings of Generic Proximity Data

Proximities are at the heart of almost all machine learning methods. In a more generic view, objects are compared by a (symmetric) similarity or dissimilarity measure, which may not obey particular mathematical properties. This renders many machine learning methods invalid, leading to convergence problems and the loss of generalization behavior. In many cases, the preferred dissimilarity measure is not metric. If the input data are non-vectorial, like text sequences, proximity-based learning is used or embedding techniques can be applied. Standard embeddings lead to the desired fixed-length vector encoding, but are costly and are limited in preserving the full information. As an information preserving alternative, we propose a complex-valued vector embedding of proximity data, to be used in respective learning approaches. In particular, we address supervised learning and use extensions of prototype-based learning. The proposed approach is evaluated on a variety of standard benchmarks showing good performance compared to traditional techniques in processing non-metric or non-psd proximity data.
Enhanced Substitution Cipher Technique for Image Encryption

Now a days, Information Security plays an important role in this modern world. With the global usage of the Internet, virtually world is interconnected to each other, while it has created a lot of risks to the users who are involved in it. The users, businesses and organizations worldwide are suffering from a continuousproblems from attackers and hackers, who have a variety of tools and techniques in order to break the secret information and make changes to the data, which leads to security issues. One of the most important aspect is transmission of message such as text, image over the network securely. Many cryptographic algorithms have been proposed for secured transmission so that no other person can gain the access of transmitted data. The Encryption algorithm plays an important role to transmit data through the network. In this paper, an image data is considered, generally each and every pixel is encoded with a secret key and transmitted and decrypted at the receiver side. In some algorithms, the blocks of pixels are encoded with differentkeys. In this paper a new methodology introduced to perform the encryption with some variation in substitution cipher algorithm on split sub matrices with different keys instead of sequence block of pixels. By applying this methodology, it increases the security a lot for the information and can be easily transmitted in the network.
Specific Area Style Transfer on Real Time Video

Since deep learning applications in object recogni-tion, object detection, segmentation, and image generation are needed increasingly, related research has been actively conducted. In this paper, using segmentation and style transfer together, a method of producing desired images in the desired area in real-time video is proposed. Two deep neural networks were used to enable as possible as in real-time with the trade-off relationship between speed and accuracy. Modified BiSe Net for segmentation and Cycle GAN for style transfer were processed on a desktop PC equipped with two RTX-2080-Ti GPU boards. This enables real-time processing over SD video in decent level. We obtained good results in subjective quality to segment Road area in city street video and change into the Grass style at no less than 6(fps).