Reviewerly/exHarmony huggingface dataset:
A Proposed Frequent Itemset Discovery Algorithm Based on Item Weights and Uncertainty

Most frequent itemset mining algorithms (FIMA) discover hidden relationships from unrelated items. They find the most frequent itemsets depending only on the frequency of the item's existence in the dataset. These algorithms give all items the same importance, and neglect the differences in importance of the items. They assume the full certainty of data, but in most cases, real word data may be uncertain. As a result, the data could be incomplete and/or imprecise. These two problems are the most common challenges that face FIMA algorithms. Some new algorithms proposed some solutions to face these two issues separately. In other words, some algorithms handle item importance only, and others handle uncertainty only. Few algorithms dealt with the two issues together. In this article, the single scan for weighted itemsets over the uncertain database (SSU-Wfim) is proposed. It depends on the single scan frequent itemsets algorithm (SS_FIM), and enhances it to deal with weighted items in an uncertain database. SSU_WFIM deals with the uncertainty of data by giving each item in a transaction an additional value to indicate occurrence likelihood. It gives the items different values to define the weight of them. It uses a table called Ptable to save the items and their probability values. This table is used to generate all possible candidates itemsets. The results indicate the high performance in aspects of runtime, memory consumption and scalability of SSU-Wfim comparing with the UApriori algorithm. The proposed algorithm saves time and memory with a percentage exceeds 70% for all tested datasets.
The Use of Grey Literature Review as Evidence for Practitioners

Context: In the last years, diverse research areas increased their interest in Grey Literature (GL). In Software Engineering (SE), SE practitioners became heavy consumers of GL, by way of contrast to traditional research papers. Problem: This is unfortunate, in particular, although the increase of Systematic Literature Reviews (SLR) published, researchers, claim to the lack of them of connection to the practice. Goal: Propose and evaluate a set of guidelines to help SE researchers to conduct a Grey Literature Review (GLR) that are more in line with practitioners' needs. Method: First, we are conducting a tertiary study to understand how secondary studies use GL. Second, we plan to employ qualitative research with researchers of SLRs and SE practitioners. Third, we plan to review and analyze the use of GL source according to the context in SE. Fourth, we plan to conduct a GLR. Finally, we plan to perform and evaluate our guideline. Preliminary Results: The tertiary study retrieved a total of 14,043 papers. We removed the duplicate studies and also which were not peer-reviewed articles. Currently, we are solving the conflicts of disagreement of the selection process. Conclusions: We present preliminary findings, show our proposed approach and the next steps.
Barriers of Requirement Change Management Process in the Context of Global Software Development

Global Software Development (GSD) is gaining fame every day, we need to understand the GSD environment to make project management more effective and successful. There are many difficulties and challenging activities in GSD environment and one of them is Requirement Change Management (RCM) process. Previous research shows that RCM process is very vague and cannot be used properly in GSD context due to numerous reasons. To address these issue, this study investigates the barriers of RCM Process in context of GSD. Firstly, we identify all the barriers of RCM process in GSD environment. Then we validate these barriers from industry. Finally, we quantify these barriers by using statistical tools.
Recent Developments of Deep Heterogeneous Information Network Analysis

Recently, there is a surge of research on employing Heterogeneous Information Networks (HIN) to model complex interaction system, where networks compose of different types of nodes or links, since HIN contains richer structure and semantic information. Many researches develop structural analysis approaches by leveraging the rich semantic meaning of structural types of objects and links in the networks. Furthermore, recent advancement on deep learning and network embedding poses new opportunities and challenges to mine HIN, and heterogeneous network embedding, even heterogeneous graph neural network, is becoming a hot topic. In this tutorial, we will give a survey on recent developments of heterogeneous information network analysis, especially on newly emerging heterogeneous network embedding. This tutorial shall help researchers and practitioners to share new techniques for identifying and analyzing relationships in networks that integrate multiple types or sources of information.
Adaptive Nonlinear Guidance Law Using Neural Networks Applied to a Quadrotor

The NonLinear Guidance Law (NLGL) is a geometric algorithm commonly employed to solve the path following problem on different unmanned vehicles. NLGL is simple (does no depend on the model of the vehicle), effective and has only one tunning parameter. Its control parameter (L) depends on various factors, such as the velocity of the vehicle, the shape of the reference path and the dynamics of the vehicle. This paper analyses the effect of parameter L on the performance of NLGL when it is applied to a quadrotor vehicle. An Adaptive NLGL, which includes a velocity reduction term, is proposed. Stability proofs are given. Simulation results show that the proposed algorithm enhances the performance of the standard NLGL. Furthermore, it has no parameters to tune.
A Unified Generation-Retrieval Framework for Image Captioning

Recent image captioning approaches are typically trained on generation-based or retrieval-based approaches. Both methods have their advantages but limited by the disadvantages. In this paper, we propose a Unified Generation-Retrieval framework for Image Captioning (UGRIC) by using adversarial learning. Different from previous methods, the proposed UGRIC model leverages the informative contents of N-best response candidates provided by the retrieval-based model to enhance the generation-based method. In addition, to further improve the informativeness of the generated caption, we employ copying mechanism to choose words from the retrieved candidate captions and put them into proper positions of the output sequence. Experiments on MSCOCO dataset demonstrate the effectiveness of the UGRIC model through various evaluation metrics.\footnoteCode and data are available at: \urlhttp://tinyurl.com/y6z2x6ho.
Tree-Gated Deep Mixture-of-Experts for Pose-Robust Face Alignment

Face alignment consists of aligning a shape model on a face image. It is an active domain in computer vision as it is a preprocessing for a number of face analysis and synthesis applications. Current state-of-the-art methods already perform well on “easy” datasets, with moderate head pose variations, but may not be robust for “in-the-wild” data with poses up to 90°. In order to increase robustness to an ensemble of factors of variations (e.g., head pose or occlusions), a given layer (e.g., a regressor or an upstream CNN layer) can be replaced by a Mixture of Experts (MoE) layer that uses an ensemble of experts instead of a single one. The weights of this mixture can be learned as gating functions to jointly learn the experts and the corresponding weights. In this paper, we propose to use tree-structured gates which allows a hierarchical weighting of the experts (Tree-MoE). We investigate the use of Tree-MoE layers in different contexts in the frame of face alignment with cascaded regression, firstly for emphasizing relevant, more specialized feature extractors depending of a high-level semantic information such as head pose (Pose-Tree-MoE), and secondly as an overall more robust regression layer. We perform extensive experiments on several challenging face alignment datasets, demonstrating that our approach outperforms the state-of-the-art methods.
An exploration of effective fuzzing for side‐channel cache leakage

Summary Adversaries can compute the secret information of a program, such as the key for encryption routines, from side channels in the light of timing‐based and access‐based CPU cache behaviours. As a result, it is crucial to understand whether a program is vulnerable to side‐channel cache leakage or not. Yet how we can find out such a vulnerability in a program remains a problem. In this paper, we revisit this problem and contemplate a test‐generation methodology, which, in both timing‐based and access‐based dimensions, systematically discovers the cache side‐channel leakage of an arbitrary software program. At the core of our test‐generation framework is an algorithm that explores the program's input space and adapts at runtime according to observed cache performance in the executed tests. We have implemented our test generator for timing‐based and access‐based attack tests and evaluated it with open‐source subject programs, including ones from OPENSSL and Linux GDK libraries. Our extensive evaluation effectively discloses the vulnerabilities of these real‐world software to both timing‐based and access‐based cache attacks. We also empirically show that our test generator achieves higher and comparable effectiveness, respectively, in simulations and real hardware platforms with regard to revealing cache side‐channel leakage than do state‐of‐the‐art fuzz testing tools.
Communication-Efficient and Byzantine-Robust Distributed Learning

We develop a communication-efficient distributed learning algorithm that is robust against Byzantine worker machines. We propose and analyze a distributed gradient-descent algorithm that performs a simple thresholding based on gradient norms to mitigate Byzantine failures. We show the (statistical) error-rate of our algorithm matches that of Yin et al.~\cite{dong}, which uses more complicated schemes (coordinate-wise median, trimmed mean). Furthermore, for communication efficiency, we consider a generic class of $\delta$-approximate compressors from Karimireddi et al.~\cite{errorfeed} that encompasses sign-based compressors and top-$k$ sparsification. Our algorithm uses compressed gradients and gradient norms for aggregation and Byzantine removal respectively. We establish the statistical error rate for non-convex smooth loss functions. We show that, in certain range of the compression factor $\delta$, the (order-wise) rate of convergence is not affected by the compression operation. Moreover, we analyze the compressed gradient descent algorithm with error feedback (proposed in \cite{errorfeed}) in a distributed setting and in the presence of Byzantine worker machines. We show that exploiting error feedback improves the statistical error rate. Finally, we experimentally validate our results and show good performance in convergence for convex (least-square regression) and non-convex (neural network training) problems.
Multimodal Attention Network for Trauma Activity Recognition from Spoken Language and Environmental Sound

Trauma activity recognition aims to detect, recognize, and predict the activities (or tasks) during trauma resuscitation. Previous work has mainly focused on using various sensor data including image, RFID, and vital signals to generate the trauma event log. However, spoken language and environmental sound, which contain rich communication and contextual information necessary for trauma team cooperation, are still largely ignored. In this paper, we propose a multimodal attention network (MAN) that uses both verbal transcripts and environmental audio stream as input; the model extracts textual and acoustic features using a multi-level multi-head attention module, and forms a final shared representation for trauma activity classification. We evaluated the proposed architecture on 75 actual trauma resuscitation cases collected from a hospital. We achieved 71.8% accuracy with 0.702 F1 score, demonstrating that our proposed architecture is useful and efficient. These results also show that using spoken language and environmental audio indeed helps identify hard-to-recognize activities, compared to previous approaches. We also provide a detailed analysis of the performance and generalization of the proposed multimodal attention network.
Credit Card Fraud Detection with Machine Learning Methods

With the increase in credit card usage of people, the credit card transactions increase dramatically. It is difficult to identify fraudulent transactions among the vast amount of credit card transactions. Although credit card fraud is limited in number of transactions, it causes serious problems in terms of financial losses for individuals and organizations. Even though large number of studies has been conducted to solve this problem, there is no generally accepted solution. In this paper, a publicly available data set is used. The unbalance problem of the data set was solved by using hybrid sampling methods together. On this data set, comparative performance evaluations have been conducted. Different from other studies, the Area Under the Curve (AUC) metric, which expresses the success in such data sets, has also been used in addition to standard performance metrics. Since it is also important to quickly detect credit card fraud transactions; the running time of different methods is also presented as another performance metric.
Efektivitas E-Learning Tingkat Sekolah Menengah Atas Kota Padang

The research is motivated by the problem of learning media from a number of schools in Padang that are not innovating in accordance with the development of the technological revolution even though the school facilities and infrastructure are sufficient for making technology-based learning media especially the Internet. The purpose of this study was to determine the effectiveness of the use of cloud computing learning media as a learning support media, in this case in the form of E-Learning. The population of this research was 71 high school students in Padang City with a sample of SMAN 4, 5 and 14 Padang. Based on the results of the calculation of effectiveness, the effectiveness level is classified as effective with an average percentage of 78.95%. So it can be concluded that the use of cloud computing as a learning support media is categorized as an effective media.
Fringe Phase-Shifting Field Based Fuzzy Quotient Space-Oriented Partial Differential Equations Filtering Method for Gaussian Noise-Induced Phase Error

Traditional filtering methods only focused on improving the peak signal-to-noise ratio of the single fringe pattern, which ignore the filtering effect on phase extraction. Fringe phase-shifting field based fuzzy quotient space-oriented partial differential equations filtering method is proposed to reduce the phase error caused by Gaussian noise while filtering. First, the phase error distribution that is caused by Gaussian noise is analyzed. Furthermore, by introducing the fringe phase-shifting field and the theory of fuzzy quotient space, the modified filtering direction can be adaptively obtained, which transforms the traditional single image filtering into multi-image filtering. Finally, the improved fourth-order oriented partial differential equations with fidelity item filtering method is established. Experiments demonstrated that the proposed method achieves a higher signal-to-noise ratio and lower phase error caused by noise, while also retaining more edge details.
A 45nm High-Throughput and Low Latency AES Encryption for Real-Time Applications

In this paper, we propose a high-throughput and low-latency AES architecture for wideband and real-time applications such as surveillance cameras, video conference, motion detection, IoT gateways, data store encryption... Our design uses an outer round pipeline technique to achieve high throughput. The design has been modelled in RTL VHDL and then synthesized with a 45nm CMOS technology using Synopsys Design Compiler. The implementation results show that the proposed architecture achieves a throughput of 111.3Gbps and a latency of 12.6ns at the maximum operating frequency of 870MHz. With the same 45nm CMOS technology, our design has area efficiency (2.4 times) and energy efficiency (4.7 times) higher than other related works.
Multi-path multimodal authentication system for remote information system

Today, the usage of cloud information system is ubiquitous. Users have access to information systems for various purposes (banking systems, medical systems, systems for obtaining government services, cloud data storage, etc.) through mobile user terminals or specialized means. Such systems don't longer require to run applications and Internet services that need storage and computation. But they must guarantee the security of user's information. One of the modern procedures which provide information security in an information system is multimodal authentication. We propose multi-path multimodal authentication system. We suggest using multi-path scenario for authenticated data transmission thus increasing the security level. It is supposed that multimodal data is separated into parts and transmitted through various paths. Thus, make the interception of multimodal information more complicated for the adversary.
DeepMEN: Multi-model Ensemble Network for B-Lymphoblast Cell Classification

In recent years, convolutional neural networks have achieved great results in many image classification tasks, including the field of medical image processing. However, for leukemic B-lymphoblast cells, the traditional convolutional neural network has some problems. On the one hand, there is a strong individual difference in the cell image, that is, there is a big difference between the cells of different subjects, and this difference may exceed the difference between abnormal cells and normal cells. On the other hand, the two cell types appear very similar. People without expertise can’t tell if a cell is abnormal. In order to solve the above problems, we proposed Deep Multi-model Ensemble Network (DeepMEN). We trained a series of deep learning models and selected the six models with the most potential as candidates. We trained the six models respectively and used the model ensemble technology to fuse the six models. In addition, we use the pseudo-label and Test Time Augmentator (TTA) to reduce covariance shifts caused by individual differences. Finally, we obtained a weighted F1-score of 0.903 in the preliminary test set and 0.8856 in the final test set.
Design of Blockchain System in BDCP Using Hyperledger Fabric

With the development of the network, the number of digital productions is increasing rapidly and produce the demand for copyright protection of them. However, for a long time, both technological and institutional efforts have made some progress, they have not solved this problem very well. The phenomenon of infringement and piracy is still common among the network. In recent years, with the emergence of blockchain, it is hopeful to propose an excellent solution for this problem by taking its trustworthiness as a bridge and combining encryption technology and protection institution. Taking the idea of the Big Data Copyright Protection system as an example, this paper puts forward a detailed design scheme for the blockchain subsystem. The scheme shows the implementation of system's trustworthiness and a prototype is established using Hyperledger Fabric. We also take an experiment to test the performance of the core operations of the prototype with the observation of the resource utilization which proves that blockchain technology can meet most of the application demands. The goal of blockchain technology and continuous development are expected to bring better experience in the future.
A Continuous User Authentication System Based on Galvanic Coupling Communication for s-Health

Smart health (s-health) is a vital topic and an essential research field today, supporting the real-time monitoring of user’s data by using sensors, either in direct or indirect contact with the human body. Real-time monitoring promotes changes in healthcare from a reactive to a proactive paradigm, contributing to early detection, prevention, and long-term management of health conditions. Under these new conditions, continuous user authentication plays a key role in protecting data and access control, once it focuses on keeping track of a user’s identity throughout the system operation. Traditional user authentication systems cannot fulfill the security requirements of s-health, because they are limited, prone to security breaches, and require the user to frequently authenticate by, e.g., a password or fingerprint. This interrupts the normal use of the system, being highly inconvenient and not user friendly. Also, data transmission in current authentication systems relies on wireless technologies, which are susceptible to eavesdropping during the pairing stage. Biological signals, e.g., electrocardiogram (ECG) and electroencephalogram (EEG), can offer continuous and seamless authentication bolstered by exclusive characteristics from each individual. However, it is necessary to redesign current authentication systems to encompass biometric traits and new communication technologies that can jointly protect data and provide continuous authentication. Hence, this article presents a novel biosignal authentication system, in which the photoplethysmogram (PPG) biosignal and a galvanic coupling (GC) channel lead to continuous, seamless, and secure user authentication. Furthermore, this article contributes to a clear organization of the state of the art on biosignal-based continuous user authentication systems, assisting research studies in this field. The evaluation of the system feasibility presents accuracy in keeping data integrity and up to 98.66% accuracy in the authentication process.
Evolving spiking neural networks methods for classification problem: a case study in flood events risk assessment

&lt;p&gt;Analysing environmental events such as predicting the risk of flood is considered as a challenging task due to the dynamic behaviour of the data. One way to correctly predict the risk of such events is by gathering as much of related historical data and analyse the correlation between the features which contribute to the event occurrences. Inspired by the brain working mechanism, the spiking neural networks have proven the capability of revealing a significant association between different variables spike behaviour during an event. Personalised modelling, on the other hand, allows a personal model to be created for a specific data model and experiment. Therefore, a personalised modelling method incorporating spiking neural network is used to create a personalised model for assessing a real-world flood case study in Kuala Krai, Kelantan based on historical data of 2012-2016 provided by Malaysian Meteorological Department. The result shows that the method produces the highest accuracy among the selected compared algorithms.&lt;/p&gt;
Artificial Intelligence for Sport Activitity Recognition

This paper presents and explains an implementation of an Artificial Neural Network approach for sport activities (gestures) detection and recognition using PIQ ROBOT device. Tennis was chosen as an example of sports activities. The development of artificial intelligence has given rise to gesture-recognition-based devices. The global gesture recognition market size was valued at USD 6.22 billion in 2017 and it is likely to reach USD 30.6 billion by 2025. This paper starts our ambitious research in the area of artificial neural networks for activity recognition in the sport.