Reviewerly/exHarmony huggingface dataset:
Implementation of artificial neurons with tunable width via magnetic anisotropy

We report an experimental implementation of width-tunable neurons to train a binary neural network. The angle-dependent magnetic behavior in an oxide thin film highly mimics neurons with width-controllable activation window, providing an opportunity to train the activation functions and weights toward binary values. We apply this feature to train the MNIST dataset using a 684-800-10 fully connected network and achieve a high accuracy of 97.4%, thus opening an implementation strategy toward training neural networks.
A Classical Cipher-Playfair Cipher and Its Improved Versions

In today's communications world, protecting data security cannot be ignored. Cryptography, which plays a pivotal role in information security, has become an indispensable and important part of information security. Cryptography is to study secret communication for adopting a kind of secret protection for the information to be transmitted. It is a technical science that studies the preparation and deciphering of codes. Classical encryption can be divided into two categories: transposition ciphers and substitution ciphers. This paper will mainly study a kind of substitution ciphers-Playfair cipher and review three improved versions of the 3D Playfair cipher. Then, a conclusion was drawn for these three improved versions of Playfair cipher. Finally looks forward to the future development of Playfair cipher and cryptography.
Rate-1 Quantum Fully Homomorphic Encryption

Secure function evaluation (SFE) allows Alice to publish an encrypted version of her input m such that Bob (holding a circuit C) can send a single message that reveals C(m) to Alice, and nothing more. Security is required to hold against malicious parties, that may behave arbitrarily. In this work we study the notion of SFE in the quantum setting, where Alice outputs an encrypted quantum state \(\left|\psi \right>\) and learns \(C(\left|\psi \right>)\) after receiving Bob's message.
Fault Detection Model for Software Correctness and Reliability

We evaluate various technical developments in software testing and fault detection that are currently advanced as potential breakthroughs in determining software correctness and reliability. Testimatica Model was developed for testing the correctness of the entire structure of software for purpose of detecting software faults using phase-to-phase approach. Findings reveals that the test based on the design using the developed testing model when evaluated against other testing/ fault detecting approaches can be a more powerful tool for checking if the structures at the design level of many software systems are correct. The findings also reveal that the model improves customer satisfaction, speeds up the development process and improves productivity of software development teams. The model is very unique and novel as it deals with the entire structure of the design on a phase-wise basis; and not just the control structures of software as reported in literature thus radically implicating a novel pathway for software development firms to develop error free products to achieve software reliability. More quantitative facts on the impact of the developed model on software reliability can be substantially proposed and improved on..
Lessons learned from requirements gathering for virtual reality simulators

This paper shows how current collaborative virtual environments (VEs) such as Mozilla Hubs and AltspaceVR can aid in the task of requirements gathering in VR for simulation and training. We performed a qualitative study on our use of these technologies in the requirements gathering of two projects. Our results show that requirements gathering in virtual reality has an impact on the process of requirements identification. We report advantages and shortcomings that will be of interest to future practitioners. For example, we found that VR sessions for requirements gathering in current VEs could benefit from better pointers and better sound quality. Current VEs are useful for the requirements gathering task in the development of VR simulators and VR training environments.
Definition and Induction of a Specification Order Relation between Capabilities

Capability-driven engineering plays a key role in advanced information systems. Service discovery and business process reuse, for instance, are firstly based on the functionalities they achieve. Semantic models based on state shift fail to provide scalable solutions for the discovery and reuse of software artifacts based on their functionalities. In this paper, we are interested in defining and inferring a specification partial order relation that enables building capability-based indexing structures. We rely on our previously developed capability model which describes capabilities via ontological features that characterize and specify the functionality achieved by a given service. We formally define a partial order specification relation between two capabilities. Moreover, we define a set of inference rules enabling the deduction of such partial order relation. The implementation details of our approach are presented, with an in-depth evaluation on shipping services, for a fair assessment of its usefulness and convenience.
AGAT: Building and evaluating binary partition trees for image segmentation

AGAT is a Java library dedicated to the construction, handling and evaluation of binary partition trees, a hierarchical data structure providing multiscale partitioning of images and, more generally, of valued graphs. On the one hand, this library offers functionalities to build binary partition trees in the usual way, but also to define multifeature trees, a novel and richer paradigm of binary partition trees built from multiple images and/or several criteria. On the other hand, it also allows one to manipulate the binary partition trees, for instance by defining/computing tree cuts that can be interpreted in particular as segmentations when dealing with image modeling. In addition, some evaluation tools are also provided, which allow one to evaluate the quality of different binary partition trees for such segmentation tasks. AGAT can be easily handled by various kinds of users (students, researchers, practitioners). It can be used as is for experimental purposes, but can also form a basis for the development of new methods and paradigms for construction, use and intensive evaluation of binary partition trees. Beyond the usual imaging applications, its underlying structure also allows for more general developments in graph-based analysis, leading to a wide range of potential applications in computer vision, image/data analysis and machine learning.
Static Structure of Smart Card based Cloud Voting System

In a democratic country like India, unbiased voting process represents the people’s mandate for governance. It involves active participation of its primary stakeholders like voter, election commission, political parties and their nominated candidates etc. It is important to keep track of voting trends conducted through multiple phases so as to record people’s mandate, poll swings during this entire process. Nowadays, if voting is conducted only through conventional approach, chances of manipulation by adversaries can be noticed. In this case, advanced technologies like Cloud Computing, Internet of Things (IoT), Edge Computing etc. may be blended with conventional voting process to provide an integrated, electronic voting environment. Here the authors have proposed a Cloud Voting System (CVS) using a multivariate electronic card namely, Multipurpose Electronic Card (MEC). This multivariate electronic smart card will deliver various types of electronic services to citizen through public cloud. Citizen (i.e. Voter) will use this electronic interface to cast vote through cloud medium. As a result, massive amount of data will be mounted over the proposed Cloud Voting System, which should be managed in real time scenario without any delay. This research work intends to resolve the latency problem of cloud voting system, which is noticed during fast data transmission between all the stakeholders of cloud voting system i.e. Voter, Election Commission, Presiding Officer, etc. To achieve this objective Cloud Computing and Edge Computing techniques are used in real time scenario. To simulate the real world scenario, it is essential to study the static structure of this proposed cloud voting system. In this paper authors have shown the static structure of cloud voting system using the Class Diagram of Unified Modeling Language (UML).
Parameterized cast calculi and reusable meta-theory for gradually typed lambda calculi

Abstract The research on gradual typing has led to many variations on the Gradually Typed Lambda Calculus (GTLC) of Siek &amp; Taha (2006) and its underlying cast calculus. For example, Wadler and Findler (2009) added blame tracking, Siek et al . (2009) investigated alternate cast evaluation strategies, and Herman et al . (2010) replaced casts with coercions for space efficiency. The meta-theory for the GTLC has also expanded beyond type safety to include blame safety (Tobin-Hochstadt &amp; Felleisen, 2006), space consumption (Herman et al ., 2010), and the gradual guarantees (Siek et al ., 2015). These results have been proven for some variations of the GTLC but not others. Furthermore, researchers continue to develop variations on the GTLC, but establishing all of the meta-theory for new variations is time-consuming. This article identifies abstractions that capture similarities between many cast calculi in the form of two parameterized cast calculi, one for the purposes of language specification and the other to guide space-efficient implementations. The article then develops reusable meta-theory for these two calculi, proving type safety, blame safety, the gradual guarantees, and space consumption. Finally, the article instantiates this meta-theory for eight cast calculi including five from the literature and three new calculi. All of these definitions and theorems, including the two parameterized calculi, the reusable meta-theory, and the eight instantiations, are mechanized in Agda making extensive use of module parameters and dependent records to define the abstractions.
Twitter Sentiment Analysis Based on Daily Covid-19 Table in Turkey

The coronavirus epidemic, which began to affect the whole world in early 2020, has become the most talked about agenda item by individuals. Individuals announce their feelings and thoughts through various communication channels and receive news from what is happening around them. One of the most important channels of communication is Twitter. Individuals express their feelings and thoughts by interacting with the tweets posted. The aim of this study is to analyze the emotions of the comments made under the "daily coronavirus table" shared by the Republic of Turkey Ministry of Health and to measure their relationship with the daily number of cases and deaths. In the study, emotional classification of tweets was implemented using LSTM, GRU and BERT methods from deep learning algorithms, and the results of all three algorithms were compared with the daily number of cases and deaths.
Deep Learning-Based Raindrop Quantity Detection for Real-Time Vehicle-Safety Application

The raindrops on the glass will affect driving safety, such as rear-view camera, outside mirror and windshield, etc. This article proposed a robust raindrop detection using deep learning on embedded platform with AI accelerator for real-time implementation. A training model is established through a convolution neural network (CNN)-like architecture to classify the images by the vehicle camera into three classes: no rain, heavy rain, and light rain. The classification results are used to control the speed of the motor to implement an automatic wiper control system. The training model, ResNet, is used to classify the image with good tradeoff between the computational cost and accuracy. For real-time application, the camera module on the Google Coral Dev board on embedded system platform is used to test the video stream and to estimate the performance of this system. Results show that the recognition accuracy reaches 95%, and the processing speed can achieve 20 frames per second (fps) on the embedded system.
Synthesis of a Calibration-Free Visual Feedback Controller for an Inverted Pendulum Using a Fisheye Lens

This article considers the synthesis of feedback controllers for a class in which strong robust stability is required against sensing errors. The motivation dealing with this class arises from the stabilization problem of a cart-driven inverted pendulum by visual feedback control using a fisheye lens. While fisheye lenses guarantee a wide-angle of view, a large image distortion at off-center area is inevitable. Therefore this study first derives a geometric measurement model of a pendulum angle using a general fisheye lens model. A new design method for robust controllers for our problem is then proposed. Finally, a robust feedback controller that allows lens distortion is designed. The effectiveness of the controller based on the proposed model and the proposed design method is verified through numerical simulations and experiments.
Blind Channel Estimation for MIMO Systems via Variational Inference

In this paper, we investigate the blind channel estimation problem for MIMO systems under Rayleigh fading channel. Conventional MIMO communication techniques require transmitting a considerable amount of training symbols as pilots in each data block to obtain the channel state information (CSI) such that the transmitted signals can be successfully recovered. However, the pilot overhead and contamination become a bottleneck for the practical application of MIMO systems with the increase of the number of antennas. To overcome this obstacle, we propose a blind channel estimation framework, where we introduce an auxiliary posterior distribution of CSI and the transmitted signals given the received signals to derive a lower bound to the intractable likelihood function of the received signal. Meanwhile, we generate this auxiliary distribution by a neural network based variational inference framework, which is trained by maximizing the lower bound. The optimal auxiliary distribution which approaches real prior distribution is then leveraged to obtain the maximum a posterior (MAP) estimation of channel matrix and transmitted data. The simulation results demonstrate that the performance of the proposed blind channel estimation method closely approaches that of the conventional pilot-aided methods in terms of the channel estimation error and symbol error rate (SER) of the detected signals even without the help of pilots.
Cloud Computing An Empowering Technology: Architecture, Applications and Challenges

Cloud computing is an emerging technology that supports wide range of applications. The low cost of cloud computing and its scalability advantages make it the driving force for small businesses, especially in the developing world. Cloud computing deploys numerous applications which include business, data storage, backup, education, entertainment, and management-based applications. It has a huge potential to reach millions of users. There are several types of service delivery, but the main are Infrastructure as a Service (IaaS), Software as a Service (SaaS) and Platform as a Service (PaaS). This paper explores the different concepts involved in cloud computing, importance of clouds and highlights various opportunities. Paper also presents key points that why this technology needs to be successful. Finally, it addresses various issues, challenges in cloud computing that it should deal with.
Automatic Caption Generation via Attention Based Deep Neural Network Model

The ever increasing visual and multimedia data on the internet has led to the requirement of visual content understanding in the domain of multimedia analysis and computer vison. Natural language descriptions of the visual content can contribute a lot in this area. Image captioning intends to generate textual descriptions for an image which can be used further for visual analysis and understanding the semantics of the content. Various approaches and techniques have been proposed for this problem and in recent times deep learning based models particularly those which have incorporated attention mechanism have produced better caption generators. The attention-based models tend to visualize what is seen prominently in the image and hence, are capable of producing better captions of an image. In this work, an automatic caption generator model based on attention mechanism has been implemented and the experimental results have been discussed. The model consists of a Convolutional Neural Network (CNN) encoder along with a Gated Recurrent Unit (GRU) as a Recurrent Neural Network (RNN) decoder with a local attention module.
Evaluation of Text Representation Method to Detect Cyber Aggression in Hindi English Code Mixed Social Media Text

with the widespread growth in modern technologies and social media networks, people spend their massive amount of time communicating and gathering information online from the web. This phenomenon leads to an increase in the number of active social media users from multilingual societies over every year along with a major challenge to monitor aggressive and harmful content posted informally onto the large-scale social media. A recent study showed the victims of cyber aggression suffer from various impacts as depression, suicide attempts or must leave the social media platform which focuses on emerging need to automatically understand such type of offensive content. The majority of the text in social media belongs to non-English language but research has so far concentrated on English texts only hence text understanding is the major issue in social media as non-English speakers do not always use Unicode to write in their language, they use phonetic typing, frequently insert English elements and mix multiple languages. In our work, we studied already existing work deeply and investigate multiple text embedding techniques onto cyber aggression detection dataset having a challenging issue of Hindi English code mixed text understanding and revealed that character-based embedding is performing best in noisy data and can be enhanced with inclusion only aggressive words density as a feature without in-depth preprocessing. Also, our model overcomes the constraint of the availability of pre-trained word embedding.
Relational perspectives as situated visualizations of art collections

Abstract With relational perspectives, we examine the potential of traversing cultural collections along the richness of individual facets and relations through the process of developing an exploration-centered visualization. Cultural collections can contain thousands of artifacts, of which each typically possesses a diverse set of properties and individual facets constituting a unique relationship to the rest of the collection. Therefore, to create an appropriate representation of the complex data of each underlying artifact, oftentimes, it is not only interesting to get an overview of the entire collection from one perspective, but to explore the specific context of an artifact and the various relations it may have to other items. To investigate the potential of relational perspectives, we selected an art collection as a particularly promising case study. By following and reporting from a collaborative and iterative design process with an art museum, we developed a web interface that contrasts a collection overview with three situated visualizations each exposing a different kind of relational perspective on the art collection from an artifact.
The Influence of Feedback Types on the Use of Automation During Learning

This study investigated two feedback types to see if there was a relationship between level of elaborative feedback and participant’s ability to learn a task while also looking at their use of automation. The task was a rule-based problem solving task where participants needed to learn the rules of selecting one pair (Ranger and Hiker) on a gridded map. Ten trials were randomly presented to participants who were asked to make pair selections based on rules (two stated and one unknown to the participant when starting) of correct matches. Results indicated that feedback type significantly influenced accuracy, while trial difficulty influenced use of automation. Results from this study can be applied in education and training of declarative knowledge tasks when rules must be inferred.
Individual Experience and Engagement in Avatar-Mediated Environments: The Mediating Effect of Interpersonal Attraction

The application of different virtual reality interventions has been recently gaining interest from researchers and practitioners. Due to the current COVID-19 pandemic, higher education institutions increased the use of these technologies to help create much value for educators and students. The relationship between individual experience and engagement in avatar-mediated environments has garnered empirical support, but still little is known about the mediating mechanisms underlying this relation. This study investigated the mediating role of interpersonal attraction (e.g., social, physical, and task attraction) in the link between individual experience and engagement in an avatar-mediated learning environment. A total of 112 participants (21–23 years; 73 male and 39 female) participated in online peer learning sessions in Second Life. After controlling for demographic covariates, the results showed that (1) individual experience was positively associated with students’ engagement; (2) social, physical, and task attraction was found to positively mediate the link between experience and engagement. The outcomes from this study provide valuable implications for promoting online learners’ engagement in avatar-mediated environments.
Improved prior selection using semantics in maximum a posteriori for few-shot learning

Few-shot learning is to recognize novel concepts with few labeled samples. Recently, significant progress has been made to address the overfitting caused by data scarcity, especially those on modeling the distribution of novel categories given a single point. However, they often deeply rely on the prior knowledge from base set, which is generally hard to define, and its selection can easily bias the learning. A popular pipeline is to pretrain a feature extractor with base set and generate statistics from them as prior information. Since pretrained feature extractor cannot extract accurate representations for categories have never seen, and there is only 1 or 5 support images from novel categories, making it hard to acquire accurate priors, especially when they are far away from the class center. To address these issues, in this paper, we base our network on Maximum a posteriori (MAP), proposing a strategy for better prior selection from base set. We specially introduce semantic information, which are learned from unsupervised text corpora and easily available, to alleviate biases caused by unrepresentative support samples. Our intuition is that when the support from visual information is biased, semantics can provide strong prior knowledge to assist learning. Experimental results on four few-shot benchmarks also show that it outperforms the state-of-the-art methods by a large margin, improves around 2.08%∼12.68% than the best results in each dataset on both 1- and 5-shot tasks.