Reviewerly/exHarmony huggingface dataset:
Detecting Advanced Persistent Threat in Edge Computing via Federated Learning

Advanced Persistent Threat (APT) is one of the most menacing and stealthy multiple-steps attacks in the context of information systems and IoT-related applications. Recently, with increasing losses to organizations caused by APT, its detection has attracted more attention in both academia and industry. However, conventional attack detection methods cannot be used to defense APT ideally for the following reasons: 1) misuse-based mechanisms require too much expert knowledge of APT attacks; 2) anomaly-based strategies lead to many false positives; 3) machine learning-based solutions lack training dataset that describes APT patterns. Thus, we propose a novel detection system in edge computing systems based on federated learning, named FLAPT, to detect APT attacks. The federated model can learn various APT attack patterns by maintaining a global model across multiple clients. The experimental results demonstrate that our proposed system can detect various attacks including real-life APT campaigns with high detection accuracy and low false alarm rate.
Implementation of The Introduction of Skin Diseases Based on Augmented Reality

Indonesia is a tropical country that has high rainfall. Environmental conditions greatly affect health conditions in humans such as skin health examples. Many people are still confused to identify the skin disease they are suffering from. This has a negative impact on people's lives. In this research, an application is made that is useful for introducing and informing various types of skin diseases with the Convolutional Neural Network (CNN) algorithm and implementation for information in introducing this skin disease using augmented reality (AR). This application is created using the Java programming language and is implemented on smartphones with the Android operating system.From the test results obtained, the classification results obtained when detecting skin diseases get conditions in accordance with or the same as the trained data. At a light intensity of 355 lux, the highest accuracy was obtained for measles skin disease. For the detection of skin diseases using different distances, the optimal distance to get a high accuracy value is the distance of 8 cm.
ALEX: Active Learning based Enhancement of a Classification Model's EXplainability

An active learning (AL) algorithm seeks to construct an effective classifier with a minimal number of labeled examples in a bootstrapping manner. While standard AL heuristics, such as selecting those points for annotation for which a classification model yields least confident predictions, there has been no empirical investigation to see if these heuristics lead to models that are more interpretable to humans. In the era of data-driven learning, this is an important research direction to pursue. This paper describes our work-in-progress towards developing an AL selection function that in addition to model effectiveness also seeks to improve on the interpretability of a model during the bootstrapping steps. Concretely speaking, our proposed selection function trains an 'explainer' model in addition to the classifier model, and favours those instances where a different part of the data is used, on an average, to explain the predicted class. Initial experiments exhibited encouraging trends in showing that such a heuristic can lead to developing more effective and more explainable end-to-end data-driven classifiers.
Reverse Engineering the Stream Prefetcher for Profit

Micro-architectural attacks exploit timing channels at different micro-architecture units. Some of the micro-architecture units like cache automatically provide the timing difference (the difference between a hit and a miss). However, there are other units that are not documented, and their influence on the timing difference is not fully understood. One such micro-architecture unit is an L2 hardware prefetcher named Streamer. In this paper, we reverse-engineer the Stream prefetcher, which is commercially available in the Intel machines. We perform a set of experiments and provide our observations and insights. Further, we use these observations to construct a cross-thread covert channel using the Stream prefetcher, with an accuracy of 91.3% and a bandwidth of 54.44 KBps.
Adaptive Localisation for Unmanned Surface Vehicles Using IMU-Interacting Multiple Model

Unscented Kalman Filter (UKF) remains to be a prevalent multi-sensor fusion method in many practices, including navigational tracking for Unmanned Surface Vehicles (USVs). This paper suggests that results from UKF fusion is unsatisfactory for USVs’ relatively smooth path due to UKF’s lack of versatility. Hence, it is proposed here that by replacing the UKF with Interacting Multiple Model (IMM), estimation results will better represent USV’s movement. Furthermore, this paper proposes slight modification to the IMM in order to heighten the algorithm’s confidence in switching modes. By exploiting angular velocity information from Inertial Measurement Unit (IMU), an independent mode probability can be obtained which is then injected into the IMM. Computer simulations based on maritime operations were done to show that the proposed IMU-based IMM is able to react faster to mode changes, giving more reliable outcomes.
Transaction Deanonymization in Large-Scale Bitcoin Systems via Propagation Pattern Analysis

Bitcoin is a digital currency payment system, which bases on the property of decentralization and anonymization of Blockchain. Researches on transaction deanonymization for the Bitcoin system may not associate anonymous transactions with the IP addresses (physical identity) of the originator accurately and may consume network resources excessively. In this paper, we propose an approach to obtain the originating transactions through analyzing the propagation information. We calculate a pattern matching score by combining the propagation pattern extraction and the node weight assignment. Through carrying out the experiments in the real Bitcoin system, we effectively match the originating transactions with the target node, which reaches a precision of 81.3% and is 30% higher than the state-of-the-art method.
Study on the Transaction Management Mode of Virtual Power Plants Based on Blockchain Technology

Blockchain technology is a decentralized data storage method. The application of blockchain technology to virtual power plants will have an important impact on its operation mode. This paper first expounds the blockchain technology and analyzes the applicability of its combination with virtual power plants. On this basis, it designs the virtual power plant transaction and management framework based on blockchain technology, and establishes the power transaction mode of virtual power plants based on blockchain technology. Finally, on the Ethereum platform, the simulation test is carried out. The results show that the virtual power plant transaction mode based on blockchain technology, combined with the continuous double auction mechanism, can realize the peer-to-peer transaction of distributed resources and loads within the virtual power plant, promote the local elimination of distributed energy source, and reduce the cost of power transaction and energy loss.
Flight Dynamics and Control of an eVTOL Concept Aircraft with a Propeller-Driven Rotor

The objective of this investigation is threefold. First, to assess the flight dynamics of an electric vertical take-off and landing (eVTOL) concept aircraft with a propeller-driven rotor. Second, to develop an automatic flight control system (AFCS) for this concept aircraft. Third, to verify the potential safety benefits of the concept aircraft by analyzing the autorotation performance following a total loss of power. The paper begins with an overview of the design of the aircraft and description of the simulation model, including a detailed discussion on the inflow model of the propellers that drive the main rotor. Next, the flight dynamics are assessed at hover and in forward flight. An AFCS based on dynamic inversion is developed to provide stability and desired response characteristics about the roll, pitch, yaw, and heave axes for speeds ranging from hover to 80 kt. Additionally, an RPM governor is implemented to hold the main rotor angular speed constant at its nominal value. Finally, simulations that make use of the AFCS are performed to analyze the autorotation performance following total loss of power.
Information extraction from digital social trace data with applications to social media and scholarly communication data

Information extraction (IE) aims at extracting structured data from unstructured or semi-structured data. The thesis starts by identifying social media data and scholarly communication data as a special case of digital social trace data (DSTD). This identification allows us to utilize the graph structure of the data (e.g., user connected to a tweet, author connected to a paper, author connected to authors, etc.) for developing new information extraction tasks. The thesis focuses on information extraction from DSTD, first, using only the text data from tweets and scholarly paper abstracts, and then using the full graph structure of Twitter and scholarly communications datasets. This thesis makes three major contributions. First, new IE tasks based on DSTD representation of the data are introduced. For scholarly communication data, methods are developed to identify article and author level novelty [Mishra and Torvik, 2016] and expertise. Furthermore, interfaces for examining the extracted information are introduced. A social communication temporal graph (SCTG) is introduced for comparing different communication data like tweets tagged with sentiment, tweets about a search query, and Facebook group posts. For social media, new text classification categories are introduced, with the aim of identifying enthusiastic and supportive users, via their tweets. Additionally, the correlation between sentiment classes and Twitter meta-data in public corpora is analyzed, leading to the development of a better model for sentiment classification [Mishra and Diesner, 2018]. Second, methods are introduced for extracting information from social media and scholarly data. For scholarly data, a semi-automatic method is introduced for the construction of a large-scale taxonomy of computer science concepts. The method relies on the Wikipedia category tree. The constructed taxonomy is used for identifying key computer science phrases in scholarly papers, and tracking their evolution over time. Similarly, for social media data, machine learning models based on human-in-the-loop learning [Mishra et al., 2015], semi-supervised learning [Mishra and Diesner, 2016], and multi-task learning [Mishra, 2019] are introduced for identifying sentiment, named entities, part of speech tags, phrase chunks, and super-sense tags. The machine learning models are developed with a focus on leveraging all available data. The multi-task models presented here result in competitive performance against other methods, for most of the tasks, while reducing inference time computational costs. Finally, this thesis has resulted in the creation of multiple open source tools and public data sets (see URL below), which can be utilized by the research community. The thesis aims to act as a bridge between research questions and techniques used in DSTD from different domains. The methods and tools presented here can help advance work in the areas of social media and scholarly data analysis.
Deep Adaptive Feature Aggregation in Multi-task Convolutional Neural Networks

Convolutional Neural Network (CNN) based multi-task learning methods have been widely used in a variety of applications of computer vision. Towards effective multi-task CNN architectures, recent studies automatically learn the optimal combinations of task-specific features at single network layers. However, they generally construct an unchanged operation of feature aggregation after training, regardless of the characteristics of input features. In this paper, we propose a novel Adaptive Feature Aggregation (AFA) layer for multi-task CNNs, in which a dynamic aggregation mechanism is designed to allow each task to adaptively determine the degree to which the feature aggregation of different tasks is needed according to the feature dependencies. On both pixel-level and image-level tasks, we demonstrate that our approach significantly outperforms the previous state-of-the-art methods of multi-task CNNs.
Hybrid Intelligent Multi-agent System for Power Restoration

The problem of restoration of the power grid after shutdowns requires extensive heterogeneous knowledge, has high combinatorial complexity, many limitations and conditions, including limitation on the decision-making time. Under such conditions, traditional abstract-mathematical methods are irrelevant to the complexity of the control object, and solving the problem by expert team is irrelevant to its dynamism. In this regard, hybrid intelligent multi-agent system that model collective heterogeneous thinking processes during the decision-making under the guidance of a facilitator are proposed to solve problems in dynamic environments, in particular distribution grid restoration-planning problem. The paper discusses the model, the functional structure, and the collective heterogeneous thinking protocol of such systems.
Evidence for embodied cognition in immersive virtual environments using a second language learning environment

Immersive virtual environments (IVEs) are increasingly being explored as potential educational tools. However, it is unclear which aspects of IVEs contribute to learning, including hardware modalities and learner responses (e.g. motivation, usability, cognitive load and presence). One IVE hardware modality particularly backed by theory is embodied controls, with their potential for leveraging embodied cognition for enhanced learning outcomes. This paper explores if embodied controls can be leveraged to enhance learning in an IVE by comparing language learning outcomes from an IVE using embodied controls, and a non-embodied control. It explores two words classes - verbs and nouns - to examine if there is a difference in learning outcome for embodied controls with actions (verbs) and object interactions (nouns). This paper also explores co-variables often linked with IVE learning (motivation, presence, cognitive load) to understand why learning gain occurs. It finds that leveraging embodied controls provides better learning outcomes, with no impact on cognitive load. It also finds that the benefit does not correlate with motivation or presence ratings, suggesting that embodiment-induced motivation or immersion is not the cause of the learning enhancements, and therefore this could be evidence for embodied cognition-based learning in IVEs.
DistilSum:

A popular choice for extractive summarization is to conceptualize it as sentence-level classification, supervised by binary labels. While the common metric ROUGE prefers to measure the text similarity, instead of the performance of classifier. For example, BERTSUMEXT, the best extractive classifier so far, only achieves a precision of 32.9% at the top 3 extracted sentences ([email protected]) on CNN/DM dataset. It is obvious that current approaches cannot model the complex relationship of sentences exactly with 0/1 targets. In this paper, we introduce DistilSum, which contains teacher mechanism and student model. Teacher mechanism produces high entropy soft targets at a high temperature. Our student model is trained with the same temperature to match these informative soft targets and tested with temperature of 1 to distill for ground-truth labels. Compared with large version of BERTSUMEXT, our experimental result on CNN/DM achieves a substantial improvement of 0.99 ROUGE-L score (text similarity) and 3.95 [email protected] score (performance of classifier). Our source code will be available on Github.
Modern e-learning technologies: analysis of the impact of gamification methods on student engagement in the educational process

The article presents the factors influencing the learning outcomes in distance format, modern types of e-learning and factors that affect the educational motivation of users in online courses. The features of educational motivation are considered and the process of motivational design in e-learning is presented. As a key method of increasing motivation and involvement in the educational process of online courses, the article describes the technology of gamification. The paper also presents the «Octalize» method, developed and applied for the balanced and effective use of gamification techniques, assessment of the main motivational factors, as well as for the analysis and design of optimal conditions for the presence of game elements in the educational process.
A Method of Prediction for Transformer Malfunction Based on Oil Chromatography

Electric power transformer is one of the most necessary part in power system, and hence, it's significant to diagnose the transformer malfunction in advance; A methods of prediction for transformer malfunction based on oil chromatography is described; 4 models for time series prediction are illustrated, and the specific methods for model identification and ordering are explained; The time series model was applied to predict transformer malfunction in the oil chromatography analysis example, and accurate results were obtained, which shows that the method described in this paper can effectively predict the concentration of dissolved gas in transformer oil in future, and diagnose the types of malfunctions so that meet the actual need of projects.
Incremental and Parallel Computation of Structural Graph Summaries for Evolving Graphs

Graph summarization is the task of finding condensed representations of graphs such that a chosen set of (structural) subgraph features in the graph summary are equivalent to the input graph. Existing graph summarization algorithms are tailored to specific graph summary models, only support one-time batch computation, are designed and implemented for a specific task, or evaluated using static graphs. Our novel, incremental, parallel algorithm addresses all these shortcomings. We support various structural graph summary models defined in our formal language FLUID. All graph summaries defined with FLUID can be updated in time O(Δ · dk), where Δ is the number of additions, deletions, and modifications to the input graph, d is its maximum degree, and k is the maximum distance in the subgraphs considered. We empirically evaluate the performance of our algorithm on benchmark and real-world datasets. Our experiments show that, for commonly used summary models and datasets, the incremental summarization algorithm almost always outperforms their batch counterpart, even when about $50%$ of the graph database changes. The source code and the experimental results are openly available for reproducibility and extensibility.
Quantum based Support Vector Machine Identical to Classical Model

The paper presents an influence of machine learning techniques that can be efficiently solved using quantum computer. Machine learning is considered as a core concept of modeling and mining large dimensionality data and visualizes data instances as a vector. Support vector is one of the trending methods that use supervised learning for classifying the data efficiently irrespective of linearity or non-linearity in data. As the quantum computations already proved to be inherently parallel and can become one of the key factors in order to reduce the complexities while dealing with the classical machine learning model. The quantum machine learning takes advantage of parallelism and can explore the number of vectors and their dimensions in logarithmic time, and hence it can achieve the computational speedup. Now, the main contribution of the paper is to discuss quantum fundamental, quantum algorithm and then to review over classical equivalent quantum support vector machine along with their training algorithm and the comparative analysis between algorithmic growths. At last, the paper justifies the quantum support vector as a highly efficient model that gains the computational speedup and also shows the possibility of further improvement.
ABLA: An Algorithm for Repairing Structure-Based Locators Through Attribute Annotations

The growth of the web has been unstoppable in the last decade, which leads to an increasing demand for extracting information from it. Apart from the need to extract information, this growth also has brought the necessity to adapt web pages to user requirements, create annotations or test web applications. Due to the evolution of web pages, the complexity of the implementation of these techniques has increased. Being able to test, annotate, adapt and extract information from web pages correctly and efficiently has become a primary task. In order to perform all these tasks, it is mandatory to have the best mechanisms to effectively and unequivocally locate the desired elements throughout the web page life cycle, especially when a web page evolves. Different mechanisms are used to find web nodes. These mechanisms, called locators, are prone to fail over time owing to changes on websites. Many authors improve life expectancy of locators developing algorithms that use different types of locators. Some others have created algorithms that regenerate locators by saving extra information from the previous structure of the website. These algorithms extend the useful life of locators but their computational and storage cost is much higher. To avoid these problems, we have designed an algorithm that employs an attribute system embedded in the HTML code. The algorithm is able to regenerate the locators based on these attributes every time a single change takes place in a given element attribute. The evaluation of the proposal shows a much lower computational cost than in previous works.
A Taste of the Software Industry Perception of Technical Debt and its Management in Uruguay

Background: Technical debt (TD) has been an important focus of attention in recent years by the scientific community and the software industry. TD is a concept for expressing the lack of internal software quality that directly affects its capacity to evolve. Some studies have focused on the TD industry perspective. Aims: To characterize how the software industry professionals in Uruguay understand, perceive, and adopt technical debt management (TDM) activities. Method: To replicate a Brazilian survey with the Uruguayan software industry and compare their findings. Results: From 259 respondents, many indicated any awareness of the TD concept due to the faced difficult to realize how to associate such a concept with actual software issues. Therefore, it is possible to observe a considerable variability in the importance of TDM among the respondents. However, a small part of the respondents declares to carry out TDM activities in their organizations. A list of software technologies declared as used by practitioners was produced and can be useful to support TDM activities. Conclusions: The TD concept and its management are not common yet in Uruguay. There are indications of TD unawareness and difficulties in the conduction of some TDM activities considered as very important by the practitioners. There is a need for more effort aiming to disseminate the TD knowledge and to provide software technologies to support the adoption of TDM in Uruguay. It is likely other software engineering communities face similar issues. Therefore, further investigations in these communities can be of interest.