Reviewerly/exHarmony huggingface dataset:
Android Web Security Solution using Cross-device Federated Learning

Over the last one decade or so, Machine Learning has changed the global technology landscape with applications in almost all disciplines and verticals. Mobile and Web Security is an important research area in which researchers have been trying to apply Machine Learning, but data privacy concerns and high data communication costs to a central Machine Learning server have limited its use. Federated Learning is emerging as a promising solution which addresses privacy concerns and drastically reduces communication costs. In Federated Learning, data from individual devices is not communicated to a central server and model learning happens in a distributed manner. In this paper, we propose a Federated Learning solution for security of Android based devices. Mobile and Web Security solutions have evolved from signature-based detections to building Machine Learning models which are trained over large centralized malware repositories. We have used Federated Learning to learn security patterns from users' browsing data, which resides on individual devices and will never leave the devices. Federated Learning preserves users' privacy as it shares with the central server only the model that it learns from users' browsing data, and not the data itself. This way each mobile platform trains its own web security model from its data, and shares it to the centralized server. The centralized server aggregates these trained models received from numerous mobile devices and compiles an aggregated global model, which in turn is sent to mobile devices for inference. Mobile security solutions based on this concept create a sustained self-evolving security ecosystem, in which millions of mobile platforms share their learned models to form a robust distributed security paradigm. The results obtained using Federated Learning are found to be comparable with the results of centralized Machine Learning.
Deep Reinforcement Learning based Recommender System with State Representation

With the scale of E-commerce increasing year by year, the importance of recommender systems is getting increasing attention. Based on deep reinforcement learning, we can model the recommendation task as an interactional and sequential decision procedure between the system and users, instead of a static process. It can improve the recommendation quality to a large extent. By integrating a state representation module, the quality of modeling interaction between users and system can be improved. In this article, we propose a recommender system based on user-commodity state representation integrated deep reinforcement learning, named UCSRDRL, and conduct experiments on the datasets offered by FUXI AI Lab and the outcome performs better than the baseline. The score of UCSRDRL ranked third in the competition.
Comment on “Particle Swarm Optimization Based Highly Nonlinear Substitution-Boxes Generation for Security Applications”

This report presents a comment on the article “Particle Swarm Optimization Based Highly Nonlinear Substitution-Boxes Generation for Security Applications”, that was recently published in IEEE Access (Volume 8, 2020, date of publication June 23, 2020). This article explores a new s-box generation method, which based on the particle swarm optimization. The authors claim, that their proposed technique they surpasses the known results. In particular, they assert that the s-boxes, generated by them, (the article lists three examples in Tables 1–3) are comparable in nonlinearity to the best-known results, or even outperform them. In addition, the authors argue that the new s-boxes are able to offer better resistance against linear cryptanalysis than the most recent optimization-based s-boxes. We show that this is not the case. Indeed, the resistance against linear cryptanalysis is expressed with the indexes of the s-box nonlinearity. The nonlinearity is estimated as the minimum nonlinearity of the component Boolean functions, that describes the certain s-box. However, the authors of the article erroneously estimate the nonlinearity of only the coordinate Boolean functions. Any component Boolean function has to be highly nonlinear, and we show that in the examples given by the authors, there are functions with very low value of the nonlinearity. Therefore, the authors' general conclusion about the better resistance against linear cryptanalysis is also not confirmed. Nevertheless, it should be noted that s-box generation method, based on the particle swarm optimization, is the promising direction that needs the further researches. However, it is necessary to revise the analysis of the performance of this technique (see Section 4) and to clarify the obtained dependences (see Fig. 2–4) and the gained insights.
The Marriage of Quantum Computing and Interactive Storytelling

Interactive storytelling is playing an ever-increasing role in game design and developing immersive gaming experiences. Essentially, it is based on a continuous dialogue between game (narrative) designer and the player, in which the designer provides the setting, circumstances and the narrative corresponding to the game’s aim, while the player experiences a unique version of that world, based on their own decisions and actions made for that setting. Apart from traditional approaches, we are proposing that quantum computing can be used improving interactive storytelling. Quantum computing is an emerging technology that commits in solving challenges that may accelerate scientific achievements in many areas. In this chapter, we aim at presenting the use of IBM’s quantum computing procedural generation in creating an immersive narrative-driven role-playing game for commercial distribution on PC and next generation of consoles. We will describe the processes used in utilizing quantum technology in creating interactive storytelling experiences and provide a case study in which game narrative design can be transformed to these new application areas powered by quantum mechanics.
Understanding Query Combination Behavior in Exploratory Searches

In exploratory search, users sometimes combine two or more issued queries into new queries. We present such a kind of search behavior as query combination behavior. We find that the queries after combination usually can better meet users’ information needs. We also observe that users combine queries for different motivations, which leads to different types of query combination behaviors. Previous work on understanding user exploratory search behaviors has focused on how people reformulate queries, but not on how and why they combine queries. Being able to answer these questions is important for exploring how users search and learn during information retrieval processes and further developing support to assist searchers. In this paper, we first describe a two-layer hierarchical structure for understanding the space of query combination behavior types. We manually classify query combination behavior sessions from AOL and Sogou search engines and explain the relationship from combining queries to success. We then characterize some key aspects of this behavior and propose a classifier that can automatically classify types of query combination behavior using behavioral features. Finally, we summarize our findings and show how search engines can better assist searchers.
Learn from Each Other: Comparison and Fusion for Medical Segmentation Loss

In recent years, medical image segmentation has been attracting intensive attention from both the industries and academia. Various loss functions have been employed for Medical Image Segmentation toward methods of deep learning. However, little research to date has focused on the summary and analysis of the effects of different loss functions on medical image segmentation. Some existing work focused on the performance of different loss functions did not address the fusion of different types of loss functions. Moreover, some experiments that compare the performance of different loss functions are often limited to brains, full-body regions, etc. There is a lack of study on the segmentation subjects of small volume and large numbers like nucleus for various loss functions. In this paper, we first provide a comprehensive survey on recent progress in Medical Image Segmentation for different loss functions. Then, we compare and analyze the performance of nucleus image segmentation tasks using fusion loss in which a new loss function is proposed. Furthermore, we explain the reason and mechanism underlying the better performance of fusion loss function for nucleus segmentation. Finally, we give suggestions for choosing loss function in medical image segmentation. We believe the analysis on loss function may benefit the industries and academia in Medical Image Segmentation and continuous optimization of the loss function.
Bring Globality Into Convolutional Neural Networks for Wireless Interference Classification

In wireless communication, wireless interference classification (WIC) is considered as one of the most promising technologies in military and civilian scenarios. Recently, deep learning (DL) based methods have achieved superb performance for WIC. However, conventional convolutional neural networks (CNNs) based methods are particularly difficult to model long-range dependencies due to the local computational characteristics of convolutional operations, limiting recognition ability for WIC. Motivated by the self-attention mechanism in transformer for natural language processing (NLP), we propose to bring globality into CNN (BGCNN) by incorporating the transformer framework into CNNs in this letter. The BGCNN combines the advantages of CNNs in extracting low-level features and the merits of transformers in establishing long-range dependencies. Furthermore, we propose a novel multi-scale fusion mechanism for BGCNN to further improve recognition ability for WIC. Experimental results demonstrate that the proposed networks have performance and computation speed advantages over traditional DL-based models.
Anonymization Based on Improved Bucketization (AIB): A Privacy-Preserving Data Publishing Technique for Improving Data Utility in Healthcare Data

Data sharing is essential in present biomedical research. A large quantity of medical information is gathered and for different objectives of analysis and study. Because of its large collection, anonymity is essential. Thus, it is quite important to preserve privacy and prevent leakage of sensitive information of patients. Most of the Anonymization methods such as generalisation, suppression and perturbation are proposed to overcome the information leak which degrades the utility of the collected data. During data sanitization, the utility is automatically diminished. Privacy Preserving Data Publishing faces the main drawback of maintaining tradeoff between privacy and data utility. To address this issue, an efficient algorithm called Anonymization based on Improved Bucketization (AIB) is proposed, which increases the utility of published data while maintaining privacy. The Bucketization technique is used in this paper with the intervention of the clustering method. The proposed work is divided into three stages: (i) Vertical and Horizontal partitioning (ii) Assigning Sensitive index to attributes in the cluster (iii) Verifying each cluster against privacy threshold (iv) Examining for privacy breach in Quasi Identifier (QI). To increase the utility of published data, the threshold value is determined based on the distribution of elements in each attribute, and the anonymization method is applied only to the specific QI element. As a result, the data utility has been improved. Finally, the evaluation results validated the design of paper and demonstrated that our design is effective in improving data utility.
An Overview of Different Approaches for Ternary Reversible Logic Circuits Synthesis Using Ternary Reversible Gates with Special Reference to Virtual Reality

Quantum computing has been projected as an alternative to classical computing and promises to offer feasible solutions to problems that are considered intractable today. It has also been observed that information storage, processing, and communication can be improved to a great extent by the use of quantum mechanical effects like non-classical correlation and superposition of states. In a reversible circuit, we have a cascade of reversible gates each of which carry out some bijective mapping between inputs and outputs. They are also structurally different from conventional circuits in the sense that fanout and feedback connections are not permitted. In a binary quantum system expressed as a quantum circuit, the number of circuit lines represent the basic unit of information being processed (called quantum bits or qubits). Specifically, qutrit is the unit of information for a three-valued quantum system and is represented in one of the states such as $$|0\rangle $$ , $$|1\rangle $$ , and $$|2\rangle $$ . As we know, qutrits are very much expensive resources in the synthesis process, and thus reducing the number of qutrits plays a major role during synthesis. The idea behind this work is to make the early researchers in this specific area to get a comprehensive understanding of ternary reversible logic synthesis. Hence, this work addresses the different ternary reversible logic synthesis approaches and thus allow the researchers to gain more knowledge about the process of ternary reversible logic synthesis in an efficient manner and also gives an insight of how quantum computing can be further explored in the broad area of augmented reality and virtual reality.
Composition of Large Modular Ontologies Based on Structure

Ontologies are commonly used in data integration because of their expression of knowledge as a formal description of a subject of interest. Typically, the construction of large ontologies necessitates the reuse of existing ones. Modularity is a fundamental need for collaborative ontology engineering and web reuse of distributed ontology. In this scenario, combining ontology modules to get a global ontology is crucial since it needs a solution to the mismatches between the comparative concepts. This paper proposes an approach to automatically compose ontology modules. The technique suggested is based on the proposed approach on similarity measures computed against concept names, attributes, and relationships, in order to allow the merging of ontology modules into a global one.
Improving Smart Contract Transaction Performance in Hyperledger Fabric

Block chain is a time-stamped series of records of data that cannot be altered and is managed by a cluster of systems not owned by a single person, company, or entity. A chaincode, also known as a smart contract, is a digital code that controls the transfer of cryptocurrencies or assets directly, among the parties under specific conditions. A major drawback with regards to Blockchain as a technology is the fact that its transaction speed is less compared to the centralized system.In the future, for Blockchain technology to be accepted in industries, there is a need for improvement with regards to the speed.To counter this drawback, we have come up with this paper in which an approach to increase the speed of transactions by changing the current Hashing Algorithm (SHA3) in the Hyperledger Fabric to Blake3 has been done.
Automatic detection of actionable findings and communication mentions in radiology reports using natural language processing

To develop and validate classifiers for automatic detection of actionable findings and documentation of nonroutine communication in routinely delivered radiology reports.Two radiologists annotated all actionable findings and communication mentions in a training set of 1,306 radiology reports and a test set of 1,000 reports randomly selected from the electronic health record system of a large tertiary hospital. Various feature sets were constructed based on the impression section of the reports using different preprocessing steps (stemming, removal of stop words, negations, and previously known or stable findings) and n-grams. Random forest classifiers were trained to detect actionable findings, and a decision-rule classifier was trained to find communication mentions. Classifier performance was evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity.On the training set, the actionable finding classifier with the highest cross-validated performance was obtained for a feature set of unigrams, after stemming and removal of negated, known, and stable findings. On the test set, this classifier achieved an AUC of 0.876 (95% CI 0.854-0.898). The classifier for communication detection was trained after negation removal, using unigrams as features. The resultant decision rule had a sensitivity of 0.841 (95% CI 0.706-0.921) and specificity of 0.990 (95% CI 0.981-0.994) on the test set.Automatic detection of actionable findings and subsequent communication in routinely delivered radiology reports is possible. This can serve quality control purposes and may alert radiologists to the presence of actionable findings during reporting.• Classifiers were developed for automatic detection of the broad spectrum of actionable findings and subsequent communication mentions in routinely delivered radiology reports. • Straightforward report preprocessing and simple feature sets can produce well-performing classifiers. • The resultant classifiers show good performance for detection of actionable findings and excellent performance for detection of communication mentions.
Low-Cost Domain Adaptation for Crop and Weed Segmentation

The use of new and more sustainable technologies in agriculture is important to reduce the need for agrochemicals and improve energy efficiency. Many of the recent approaches in this area are based on computer vision algorithms. However, due to the great variability of the scenes that can occur in an agriculture field, the domain shift phenomenon is a relevant problem in this area. Domain adaptation attempts to mitigate this data variability problem. In this work, we propose a low-cost domain adaptation method between agriculture domains for image segmentation. Our approach performs domain adaptation by changing the amplitude of the low-frequency spectrum of images along with contrast limited adaptive histogram equalization (CLAHE), which is an efficient replacement for a commonly used image-to-image translation methods, such as Cyclegan, drastically reducing the number of parameters and improving quality in cases where these models do not maintain semantic consistence between translations.
Intelligent Phishing Url Detection: A Solution Based On Deep Learning Framework

On the Internet, every different day, kinds of attacks are deployed on innocent users. Among all, phishing is the most severe attack in which users lose their credentials or private information and their financial status quickly. The attacker uses their credibility or sensitive information to harm the target or victim. The attacker is clever and uses different strategies to fetch user-sensitive information. The existing techniques fail to overcome these issues to some extent. This work focuses on discovering the essential features that help to differentiate the legitimate and illegitimate URLs. We applied a deep learning technique on the benchmark datasets to identify the pattern of phishing URLs. We used gradient boosted decision trees algorithm to train our model and applied the regular deeply connected neural network layers in various sequences and Adam optimizer. The most found patterns will help the system to detect phishing URLs and avoid phishing. We consider the accuracy, Ff-score, and Root Mean Square Error (RMSE) as our evaluation metrics for model evaluation. The results show that the trained model can achieve an approximately 92% accuracy and 94% f-score.
Identification of Student’s Critical Thinking Skills on Cryptogamic Based on Digital Argumentation Performance through Online Learning

This study was conducted to identify students' critical thinking and argumentation skills based on digital argumentation (DA) performed through eLMA-based online learning.The research method was descriptive qualitative.The data collection technique in this research was digital argumentation which was reflected through the arguments expressed online and digital documentation quantification by rubric critical thinking and DA skills.The data of critical thinking and DA skills analysis descriptively, whereas the correlation between critical thinking and DA skill was analyzed with Pearson correlation test.The results showed that students' critical thinking skill based on digital argumentation (DA) performance through eLMA-based online learning on Cryptogamic course is at moderate level which composed of high level of 42%, moderate level of 29%, and low level of 29%.The average of students' DA skill has the same category as critical thinking skill at moderate category with slightly different distribution level, however.Student DA skills with category high, moderate, and low levels are 41%, 24%, and 35%, respectively.The Pearson correlation analysis showed that there was relationship in strong category between students' critical thinking and DA skills based on digital argumentation (DA) performance through eLMA-based online learning.
Streaming Sparse Graphs using Efficient Dynamic Sets

We present the SSTGraph framework for the storage and analysis of dynamic graphs. Its performance matches or exceeds state-of-the-art static graph engines and supports streaming updates. SSTGraph builds on top of the tinyset parallel, dynamic set data structure. Tinyset implements set membership in a shallow hierarchy of sorted packed memory arrays to achieve logarithmic time access and updates, and it scans in optimal linear time. Tinyset uses space comparable to that of systems that use data compression while avoiding compression’s computation and serialization overhead.SSTGraph outperforms other streaming, dynamic graph engines on a suite of four graph algorithms. Our evaluation includes a comparison with the Aspen streaming graph system. SSTGraph reduces runtime by 40% on average, updates are 2x-5x faster on batch sizes up to 10 million, and graphs are smaller. The partitioned data structure scales well and runs on billion edge graphs in just 15 GB of memory.
Hybrid Classic-Quantum Neural Networks for Image Classification

In the last decade, quantum computing (QC) has shown its great potential in advancing research in many fields. Here we introduce a new quantum-classical neural network, by combining quantum computing and classical computing in a hybrid neural network (HNN) that can be trained to perform image classification. The HNN on the basis of the classic convolutional neural network (CNN) with quantum circuit is considered for image classification problem. The various configurations of HNN were investigated where QC with different number of qubits were used and compared. The HNN configurations were trained, validated, and tested on the more complex CIFAR10 and CIFAR100 datasets in addition to our previous attempts on the simpler MNIST, notMNIST, MNIST Fashion datasets. Performance of HNN was compared for multiclass classification on these datasets for different number of classes (from 2 to 10) using QCs with correspondent number of qubits (from 2 to 4). The metrics measured (accuracy and loss) during these experiments support our assumption about feasibility of HNN application for multiclass classification problems.