Reviewerly/exHarmony huggingface dataset:
FrequentNet : A Novel Interpretable Deep Learning Model for Image Classification

This paper has proposed a new baseline deep learning model of more benefits for image classification. Different from the convolutional neural network(CNN) practice where filters are trained by back propagation to represent different patterns of an image, we are inspired by a method called "PCANet" to choose filter vectors from basis vectors in frequency domain like Fourier coefficients or wavelets without back propagation. Researchers have demonstrated that those basis in frequency domain can usually provide physical insights, which adds to the interpretability of the model by analyzing the frequencies selected. Besides, the training process will also be more time efficient, mathematically clear and interpretable compared with the "black-box" training process of CNN.
Linguistic features evaluation for hadith authenticity through automatic machine learning

Abstract There has not been any research that provides an evaluation of the linguistic features extracted from the matn (text) of a Hadith. Moreover, none of the fairly large corpora are publicly available as a benchmark corpus for Hadith authenticity, and there is a need to build a ‘gold standard’ corpus for good practices in Hadith authentication. We write a scraper in Python programming language and collect a corpus of 3,651 authentic prophetic traditions and 3,593 fake ones. We process the corpora with morphological segmentation and perform extensive experimental studies using a variety of machine learning algorithms, mainly through automatic machine learning, to distinguish between these two categories. With a feature set including words, morphological segments, characters, top N words, top N segments, function words, and several vocabulary richness features, we analyze the results in terms of both prediction and interpretability to explain which features are more characteristic of each class. Many experiments have produced good results and the highest accuracy (i.e. 78.28%) is achieved using word n-grams as features using the Multinomial Naive Bayes classifier. Our extensive experimental studies conclude that, at least for Digital Humanities, feature engineering may still be desirable due to the high interpretability of the features. The corpus and software (scripts) will be made publicly available to other researchers in an effort to promote progress and replicability.
Container Introspection: Using External Management Containers to Monitor Containers in Cloud Computing

Cloud computing plays an important role in today's Internet environment, which meets the requirements of scalability, security and reliability by using virtualization technologies. Container technology is one of the two mainstream virtualization solutions. Its lightweight, high deployment efficiency make container technology widely used in large-scale cloud computing. While container technology has created huge benefits for cloud service providers and tenants, it cannot meet the requirements of security monitoring and management from a tenant perspective. Currently, tenants can only run their security monitors in the target container, but it is not secure because the attacker is able to detect and compromise the security monitor. In this paper, a secure external monitoring approach is proposed to monitor target containers in another management container. The management container is transparent for target containers, but it can obtain the executing information of target containers, providing a secure monitoring environment. Security monitors running inside management containers are secure for the cloud host, since the management containers are not privileged. We implement the transparent external management containers by performing the one-way isolation of processes and files. For process one-way isolation, we leverage Linux namespace technology to let management container become the parent of target containers. By mounting the file system of target container to that of the management container, file system one-way isolation is achieved. Compared with the existing host-based monitoring approach, our approach is more secure and suitable in the cloud environment.
A preliminary study on photogrammetry for the FAST main reflector measurement

Abstract The Five-hundred-meter Aperture Spherical radio Telescope (FAST) is the largest single-dish radio telescope in the world. In this paper, we apply photogrammetry to measure local area surface accuracy of the FAST main reflector. Contrast with the existing photogrammetric methods that need to stick the cooperation target on the panel. In this paper, we directly detect nodes according to their natural feature. Analyzing the FAST reflector composition, we propose a two-step Hough transform method for node detection. Due to the high similarity of the neighboring area around nodes, it is hard to match two images by the feature matching method. Therefore, we apply the nodes combination approach to obtain the homography matrix between two photos for nodes matching. After nodes detection and matching, triangulation and bundle adjustment algorithms are adopted for 3D reconstruction. During the experiment, the adjusted node position deform a local area of the FAST main reflector into a spherical cap with a radius of 300 m. The experimental result shows that the measurement accuracy of the sphere with a radius of 300 m is 12.299 mm, indicating that it is feasible to apply photogrammetry for the FAST main reflector measurement.
What Children Learn in Smart-Thing Design at a Distance: An Exploratory Investigation

Smart things are everywhere, from smart watches to smart toys. Children are used to them but they do not know how they are made. Engaging children in the design of smart things can help them learn of them and develop their own. Smart-thing design with children was usually held in person, in informal settings, with a learning by-doing and playful approach, and collaboratively, so as to sustain children’s learning over time. Recently the COVID pandemics has made this type of design with children impossible, e.g., due to physical distancing restrictions. Smart-thing design with children has thus adapted to the mutated settings and moved at a distance. What children can learn when designing at a distance, though, remains an open question. This paper tackles such issue. It reports on at-a-distance design workshops with 20 children, aged 8–16 years old. In this paper, data are processed in relation to children’s learning of smart-thing programming, before and after the workshops. Results indicate that design workshops, held at a distance, can help children learn how to program their own smart thing ideas. A significant positive correlation was also found between age and learning.
Performance Analysıs of Different Classıfıcatıon Algorıthms for Bank Loan Sectors

The proposed research work aims to develop a novel algorithm to make predictions for various financial institutions to safeguard themselves from fraudsters, and at the same time to ease the pre-sanction process for availing loan and its related verification process. Currently, in the post pandemic world, the proposed algorithm is very essential for the financial institutions, as the rate of loan procurement by individuals has been unprecedently increased and at the same time the chances of loan default has also been increased. For all these cases, first, the bank requires to analyze their Credit Information Bureau India Limited [CIBIL] score and check whether they had done loan repayments within an appropriate time period. Data mining plays a key role to solve such problems and also different algorithms are available in the machine learning domain. Among that, K-nearest neighbor, decision tree, support vector machine, and logistic regression models are taken into consideration for performing data classification with good accuracy. In the present work, the performance of each algorithm is analyzed. The experiments were carried out using python. The accuracy of classifiers will be analyzed by using the following metrics such as Jaccard index, F1-score, and Log loss. This helps to find the best algorithm for classification and the potential of customer, which is much higher than the data mining classification algorithm, and thus it proves to be very helpful for bank officers for sanctioning loan.
PanoSynthVR: View Synthesis From A Single Input Panorama with Multi-Cylinder Images

We introduce a method to automatically convert a single panoramic input into a multi-cylinder image representation that supports real-time, free-viewpoint view synthesis for virtual reality. We apply an existing convolutional neural network trained on pinhole images to a cylindrical panorama with wrap padding to ensure agreement between the left and right edges. The network outputs a stack of semi-transparent panoramas at varying depths which can be easily rendered and composited with over blending. Initial experiments show that the method produces convincing parallax and cleaner object boundaries than a textured mesh representation.
Control Based Motion Planning Exploiting Calculus of Variations and Rational Functions: A Formal Approach

This paper presents a control-based trajectory generation approach for unmanned aerial vehicles (UAVs) under dynamic constraints. It exploits the concept of optimal control to find closed-form differential equations that satisfy any arbitrary dynamic limitation mapped into kinematic constraints. Pontryagin’s Minimum Principle applies to derive a set of differential equations in which the dynamic environment is considered in the constrained Hamiltonian function. In particular, we aim to minimize the L2-norm of the control input avoiding dynamic obstacles, given initial and final boundary conditions. Lastly, this paper proposes a novel interpolation algorithm based on rational functions, referred to as rational recursive smooth trajectory (RRST) method. The method generates an analytic expression that approximates the control inputs, for which no closed-form solutions are in general attainable.
Comparison of Transfer Learning Behaviour in Violence Detection with Different Public Datasets

The detection and recognition of violence have been area of interest to research, mainly in surveillance, Human-Computer Interaction and information retrieval for video based on content. The primary purpose of detecting and recognizing violence is to automatically and in real-time recognize violence. Hence, it is a crucial area and object of several studies, as it will enable systems to have the necessary means to contain violence automatically. In this sense, pre-trained models are used to solve general problems of recognition of violent activity. These models were pre-trained with datasets from: hockey fight; movies; violence in real surveillance; and fighting in real situations. From this pre-training models, general patterns are extracted that are very important to detect violent behaviour in videos. Our approach uses a state-of-the-art pre-trained violence detection model in general activity recognition tasks and then tweaks it for violence detection inside a car. For this, we created our dataset with videos inside the car to apply in this study.
PyFlies: A Domain-Specific Language for Designing Experiments in Psychology

The majority of studies in psychology are nowadays performed using computers. In the past, access to good quality software was limited, but in the last two decades things have changed and today we have an array of good and easily accessible open-source software to choose from. However, experiment builders are either GUI-centric or based on general-purpose programming languages which require programming skills. In this paper, we investigate an approach based on domain-specific languages which enables a text-based experiment development using domain-specific concepts, enabling practitioners with limited or no programming skills to develop psychology tests. To investigate our approach, we created PyFlies, a domain-specific language for designing experiments in psychology, which we present in this paper. The language is tailored for the domain of psychological studies. The aim is to capture the essence of the experiment design in a concise and highly readable textual form. The editor for the language is built as an extension for Visual Studio Code, one of the most popular programming editors today. From the experiment description, various targets can be automatically produced. In this version, we provide a code generator for the PsychoPy library while generators for other target platforms are planned. We discuss the language, its concepts, syntax, some current limitations, and development directions. We investigate the language using a case study of the implementation of the Eriksen flanker task.
Dealing with Privacy for Protecting Information

Privacy is being a trending topic in cybersecurity context not only because it is related to current regulations such as GDPR, but also because it has an impact on every citizen within this digitalized world. In fact, there is a huge number of software systems managing or processing information related to individuals in somehow, and therefore software developments producing these systems must consider specific privacy measures. Privacy by design concept and shift left strategies are considering privacy as a special topic to be treated along the software development project. In this context, Very Small Companies are required to modify their development processes for including privacy. This paper provides a modification of the ISO/IEC 29110 basic profile, a set of activities for designing a privacy preserving approach, and the results of applying differential privacy mechanisms with an illustrative example.
Examining the Influence of Participating in a Cyber Defense Track on Students’ Cybersecurity Knowledge, Awareness, and Career Choices

Facing the ever-increasing cyberattacks from hackers, some of whom are sponsored by nations/states, the USA is in desperate need of improving cybersecurity education to develop strengthened defense against current and future cyber threats. In particular, colleges/universities throughout the nation are being called upon to modernize computing programs in order to produce well-rounded professionals that are aware of and capable of mitigating cyber threats. The purpose of this mixed methods case study was to examine the influence of participating in the Cyber Defense Track (CDT) on students’ cybersecurity knowledge, awareness, and career choices. A purposeful sample of undergraduate students enrolled in the CDT was administered pre-/post-surveys and assessments and participated in focus groups. Results indicated an increased level of (a) cybersecurity knowledge, (b) awareness of cybersecurity-related practices, and (c) interest in a cybersecurity-related career field.
One-Model Ensemble-Learning for Text Recognition of Historical Printings

In this paper, we propose a novel method for Automatic Text Recognition (ATR) on early printed books. Our approach significantly reduces the Character Error Rates (CERs) for book-specific training when only a few lines of Ground Truth (GT) are available and considerably outperforms previous methods. An ensemble of models is trained simultaneously by optimising each one independently but also with respect to a fused output obtained by averaging the individual confidence matrices. Various experiments on five early printed books show that this approach already outperforms the current state-of-the-art by up to 20% and 10% on average. Replacing the averaging of the confidence matrices during prediction with a confidence-based voting boosts our results by an additional 8% leading to a total average improvement of about 17%.
Patent Protection for Blockchain - Related Inventions in China

The patents policy about block chain technology are widely focused by many country,In this paper, two aspect about block-chain patent application has been analyzed in order to enhance the possibility of authorization, one is patent application description which should be made the detailed improvements and the other is the claims which should record the related technical features, algorithms and methods. and the conclusion is that block chain patent applications, especially involving encrypted digital currencies is examined strictly than common technology,especially involving digital currencies.
Effectiveness of Social Interactivity in Merchant Websites on Emotional and Behavioral Responses

In a context of hyper connectivity, the designers of commercial websites are constantly seeking to generate favorable psychological states among internet users and to re-enchant them. This research aims to study the effect of the interaction between the social dimensions of interactivity on psychological states and the approach behavior of the e-consumer. Experimentation is chosen as the most appropriate method for testing the proposed model. An online experiment was conducted with 662 internet users. A merchant website was designed for the purposes of the study incorporating the interaction forms investigated. The results of this research underline the power of the social dimension of interactivity in the mediated market environments and show that a socially interactive site can generate the user's flow state, as well as a feeling of being physically present in a remote environment. This relation is moderated by the perceived risk.
Detecting Sudden Variations in Web Apps Code Smells’ Density: A Longitudinal Study

Code smells are considered potentially harmful to software maintenance. Their introduction is dependent on the production of new code or the addition of smelly code produced by another team. Code smells survive until being refactored or the code where they stand is removed. Under normal conditions, we expect code smells density to be relatively stable throughout time. Anomalous (sudden) increases in this density are expected to hurt maintenance costs and the other way round. In the case of sudden increases, especially in pre-release tests in an automation server pipeline, detecting those outlier situations can trigger refactoring actions before releasing the new version.This paper presents a longitudinal study on the sudden variations in the introduction and removal of 18 server code smells on 8 PHP web apps, across several years. The study regards web applications but can be generalized to other domains, using other CS and tools. We propose a standardized detection criterion for this kind of code smell anomalies. Besides providing a retrospective view of the code smell evolution phenomenon, our detection approach, which is particularly amenable to graphical monitoring, can make software project managers aware of the need for enforcing refactoring actions.
The Cost of Adaptivity in Security Games on Graphs

The security of cryptographic primitives and protocols against adversaries that are allowed to make adaptive choices (e.g., which parties to corrupt or which queries to make) is notoriously difficult to establish. A broad theoretical framework was introduced by Jafargholi et al. [Crypto’17] for this purpose. In this paper we initiate the study of lower bounds on loss in adaptive security for certain cryptographic protocols considered in the framework. We prove lower bounds that almost match the upper bounds (proven using the framework) for proxy re-encryption, prefix-constrained PRFs and generalized selective decryption, a security game that captures the security of certain group messaging and broadcast encryption schemes. Those primitives have in common that their security game involves an underlying graph that can be adaptively built by the adversary.
Topological distance-constrained feature descriptor learning model for vessel matching in coronary angiographies

Feature matching technology is vital to establish the association between virtual and real objects in virtual reality and augmented reality systems. Specifically, it provides them with the ability to match a dynamic scene. Many image matching methods, of which most are deep learning-based, have been proposed over the past few decades. However, vessel fracture, stenosis, artifacts, high background noise, and uneven vessel gray-scale make vessel matching in coronary angiography extremely difficult. Traditional matching methods perform poorly in this regard. In this study, a topological distance-constrained feature descriptor learning model is proposed. This model regards the topology of the vasculature as the connection relationship of the centerline. The topological distance combines the geodesic distance between the input patches and constrains the descriptor network by maximizing the feature difference between connected and unconnected patches to obtain more useful potential feature relationships. Matching patches of different sequences of angiographic images are generated for the experiments. The matching accuracy and stability of the proposed method is superior to those of the existing models. The proposed method solves the problem of matching coronary angiographies by generating a topological distance-constrained feature descriptor.
KABI: Class-Incremental Learning via knowledge Amalgamation and Batch Identification

In class-incremental learning setting, classes are typically presented batch by batch over time. Incremental learning often suffers from catastrophic forgetting: the performance on previous classes abruptly degrades when adapting a model to new classes. We find that incremental models trained using knowledge distillation are skilled at discriminating classes within a batch, whereas they have confusion among classes in different batches. We propose a class-incremental learning approach with knowledge amalgamation and batch identification (KABI), which can effectively alleviate catastrophic forgetting. The idea is to first train an expert model for new classes at current state, and then train an amalgamation model by amalgamating knowledge from the expert model and the amalgamation model of previous state to discriminate different classes within a batch, and particularly train a batch identifier to discriminate different batches. We conduct extensive experiments on three datasets: MNIST, CIFAR-100, ILSVRC 2012, and show that KABI outperforms the second-best approach by 1.29%, 14.26% and 21.51% respectively. Surprisingly, classification accuracies of our approach are even sometimes higher than the oracle results which is obtained by training a model using all training samples from all classes.
An Efficient and Secure Blockchain-Based SVM Classification for a COVID-19 Healthcare System

The recent coronavirus (COVID-19) pandemic has brought the world to an apocalyptic standstill with huge economic burden and catastrophic healthcare consequences. COVID-19 pandemic has neither clinically proven vaccine nor drugs so far. It is now obvious that the world is in dire need for non-clinical, real-time, faster and cost-effective and secure smart solutions for monitoring, contact tracing, and diagnosing/detecting COVID-19 patients and hence mitigating the burden on healthcare systems. Therefore, Machine learning approaches can be leveraged in all aspects that could impact the patients and the future care guidelines. However, ensuring the data privacy, security and the conformity to data protection regulations will become even more of a challenge. This leads to the requirement for secured and privacy-preserving machine learning mechanisms for COVID-19-based healthcare applications. In this paper, we present a blockchain-based privacy-preserving support vector machine (SVM) classification over vertically partitioned IoMT data for a clinical decision support (CDS) system. The proposed system does not require any intervention or direct interactions between data owners. Both local training and building the global classification model run on verifiable and private smart contracts rather than relying on untrusted third parties. We find that the proposed system is more secure and efficient.