Reviewerly/exHarmony huggingface dataset:
Encrypted Traffic Classification Using eXtreme Gradient Boosting Algorithm

In an organization, encrypted traffic is generated not only from HTTPS, VOIP, and SMTPS protocols but also from private VPNs and TOR browsers. Employees use these tools to access websites restricted by the organization. VPN and TOR traffic consumes important network resources and also violates organizational policies. Hence, encrypted traffic classification is crucial for an organization’s network management process. This paper proposes a novel system to perform encrypted traffic classification. The proposed system uses the eXtreme gradient boosting algorithm to classify the traffic into three classes: normal (non-VPN and non-TOR) traffic, TOR traffic, and VPN traffic. The CIC-Darknet 2020 dataset has been used to train the proposed system as it contains an imbalanced amount of TOR and VPN traffic, which closely resembles the real-world traffic. The proposed system outperforms other machine learning algorithms in terms of recall, precision, F1-score, and accuracy.
Towards User-Centric Text-to-Text Generation: A Survey

Natural Language Generation (NLG) has received much attention with rapidly developing models and ever-more available data. As a result, a growing amount of work attempts to personalize these systems for better human interaction experience. Still, diverse sets of research across multiple dimensions and numerous levels of depth exist and are scattered across various communities. In this work, we survey the ongoing research efforts and introduce a categorization of these under the umbrella user-centric natural language generation. We further discuss some of the challenges and opportunities in NLG personalization.
Monitoring electrical systems data-network equipment by means of Fuzzy and Paraconsistent Annotated Logic

The constant increase in the amount and complexity of information obtained from IT data network elements, for its correct monitoring and management, is a reality. The same happens to data networks in electrical systems that provide effective supervision and control of substations and hydroelectric plants. Contributing to this fact is the growing number of installations and new environments monitored by such data networks and the constant evolution of the technologies involved. This situation potentially leads to incomplete and/or contradictory data, issues that must be addressed in order to maintain a good level of monitoring and, consequently, management of these systems. In this paper, a prototype of an expert system is developed to monitor the equipment status of data networks in electrical systems. This expert system is capable of dealing with inconsistencies without trivialising the inferences. This work was developed in the context of the remote control of hydroelectric plants and substations at a Regional Operation Centre (ROC). The expert system is developed with algorithms defined upon a combination of Fuzzy logic and Paraconsistent Annotated Logic with Annotation of Two Values (PAL2v) in order to analyse uncertain signals and generate the operating conditions (faulty, normal, unstable or inconsistent/indeterminate) of the equipment that are identified as important for the remote control of hydroelectric plants and substations. A prototype of this expert system was installed on a virtualised server with CLP500 software (from the EFACEC manufacturer) that was applied to investigate scenarios consisting of a Regional (Brazilian) Operation Centre, with a Generic Substation and a Generic Hydroelectric Plant, representing a remote control environment.
Multimodal travel‐time maps with formally correct and schematic isochrones

Abstract The automatic generation of travel‐time maps is a prerequisite for many fields of application such as tourist assistance and spatial decision support systems, for example to analyze the accessibility of health and social facilities. The task is to determine outlines of zones that are reachable from a user’s location in a given amount of time. In this work we focus on travel‐time maps with a formally guaranteed SEPARATION PROPERTY in the sense that a zone exactly contains the part of the road network that is reachable within a pre‐defined time from a given starting point and start time. In contrast to other automated methods that create travel‐time maps, our approach generates schematized travel‐time maps that reduce the visual complexity by representing each zone by an octilinear polygon, that is, the edges of the polygons use only eight pre‐defined orientations. We aim for octilinear polygons with a small number of bends to further optimize the legibility of the map. The reachable parts of the road network are determined by the integration of timetable information for different modes of public transportation, for example buses, trains or ferries, and pedestrian walkways based on a multimodal time‐expanded network. Moreover, the travel‐time maps generated visualize multiple travel times using a map overlay of different time zones and taking natural barriers such as rivers into account. In experiments on real‐world data we compare our schematic visualizations to travel‐time maps created with other visualization techniques with respect to simple but robust quality measures such as the number of bends and the perimeter of the zones.
Neuron-level Interpretation of Deep NLP Models: A Survey

Abstract The proliferation of Deep Neural Networks in various domains has seen an increased need for interpretability of these models. Preliminary work done along this line, and papers that surveyed such, are focused on high-level representation analysis. However, a recent branch of work has concentrated on interpretability at a more granular level of analyzing neurons within these models. In this paper, we survey the work done on neuron analysis including: i) methods to discover and understand neurons in a network; ii) evaluation methods; iii) major findings including cross architectural comparisons that neuron analysis has unraveled; iv) applications of neuron probing such as: controlling the model, domain adaptation, and so forth; and v) a discussion on open issues and future research directions.
Logo Detection and Identification in System for Audio-Visual Broadcast Transcription

We present logo detection and identification based on a single-stage deep convolutional detector, the Scaled YOLOv4. This method is used in our system for audio-visual broadcast transcription and indexing which can be employed mainly for transcription of TV programs, mostly sports and advertising blocks. All transcribed information from audio and video streams together with time boundaries is indexed in the ElasticSearch database which can then be used to search for interesting keywords, entities etc. In this paper we present mainly the development and evaluation of the method for detection and identification of logos from images. We evaluate the logo detector on several of the most popular logo detection benchmarks, namely FlickrLogos-32, Logos-32plus, TopLogo-10 and QMUL-OpenLogo. The detector significantly outperforms the most common approach based on two stage models such as Faster R-CNN in terms of both speed and accuracy, achieving relative improvement up to 46% while running up to 2x faster.
Multi-level retrieval with semantic Axiomatic Fuzzy Set clustering for question answering

This paper considers open-domain and multi-hop reading comprehension tasks that require complex multi-step reasoning processes. The study is particularly challenging because it requires a model to learn to explore "bridge" information to connect text snippets relevant to the answer. Unlike the usual neural-network-based retrieval models, which are difficult to interpret, this paper proposes a coarse-to-fine unsupervised evidence sentences retrieval model based on the Axiomatic Fuzzy Sets clustering with both reasoning ability and interpretability. According to the entities that appeared in a question, a chained inference retrieval was carried out to get the coarser candidate documents from knowledge bases. Then, sentence-level multi-feature scoring rules based on the part of speech and grammar are proposed. The Axiomatic Fuzzy Sets clustering algorithm based on the feature scores selects finer and sentence-level evidence by semantic descriptions. The retrieval process of the candidate sentences is unsupervised and straightforward, which does not require word embedding. Our model achieves state-of-the-art results in three open-domain QA datasets: HotpotQA, SQuAD Open and Natural Questions Open.
On Galois NFSRs with Terminal Bits

Nonlinear feedback shift registers (NFSRs) are generally classified as Fibonacci NFSRs and Galois NFSRs according to their implementation configurations. Some Galois NFSRs can be equivalent to Fibonacci ones in the sense that their sets of output sequences are equal. Finding the characterization of those equivalent NFSRs is helpful to the design of NFSR-based stream ciphers. Moreover, one of their design's security criteria is to assure their used NFSRs are nonsingular. This paper considers the Galois NFSRs with terminal bits, which have the first several bits involved only shifts and have been used in many stream ciphers such as Grain and Trivium. The paper first gives a special class of such Galois NFSRs and reveals its relation with Fibonacci ones with respect to their sets of output sequences. It then presents a necessary and sufficient condition for an n- stage Galois NFSR with terminal bit equivalent to an n-stage Fibonacci NFSR. Based on this condition, the paper enumerates those n-stage Galois NFSRs with the same terminal bit that are equivalent to a given n-stage Fibonacci NFSR. Finally, the paper gives a necessary/sufficient condition for the nonsingularity of Galois NFSRs with terminal bits.
DiagramNet: Hand-Drawn Diagram Recognition Using Visual Arrow-Relation Detection

Offline hand-drawn diagram recognition is concerned with digitizing diagrams sketched on paper or whiteboard to enable further editing. Our proposed DiagramNet model addresses this recognition problem. We combine shape detection and visual arrow-relation detection to recognize arrows between shape pairs. A shape degree predictor predicts the number of in- and outgoing arrows in each direction. An optimization procedure uses the generated predictions to find the set of globally coherent arrows. Previous offline methods focus on clean images from online datasets with nicely layouted diagrams. We show that our approach is effective in the domain of camera-captured diagrams with chaotic layouts and various recognition challenges such as crossing arrows. To that end, we introduce a new dataset of hand-drawn business process diagrams that originate from textual process modeling tasks. Our evaluation shows that DiagramNet considerably outperforms prior state-of-the-art in this challenging domain.
High Performance Urdu and Arabic Video Text Recognition Using Convolutional Recurrent Neural Networks

Text extraction from videos is an emerging research field in the document analysis community. We propose a simple Convolutional Recurrent Neural Network to perform text recognition on both Arabic and Urdu scripts. We use a large variety of data augmentation techniques to generalize the model and prevent over-fitting. We also use a slightly improved loss function that helps the model converge faster. Using the proposed method we achieved 99.73% CRR, 88.37% WRR and 89.92% LRR on the Urdu Ticker Text dataset and 96.82% CRR, 90.41% WRR and 76.78% LRR on the AcTiVComp20 dataset. The proposed method has significantly outperformed Google Vision API on both of the datasets.
Clinical Named Entity Recognition Methods: An Overview

Clinical named entity recognition plays an important role in the field of clinical research based on clinical information mining. The objective of clinical named entity recognition is to analyze and categorize medical conditions, namely symptoms, treatments, diseases, and body conditions in the Electronic Medical Records (EMRs). In recent years, deep neural networks have gained considerable achievement in several languages handling tasks and named entity recognition. Many algorithms are trained to learn the text features from the big scale labeled datasets. However, these data-driven techniques do not handle rare and unseen cases. Most of the existing methods have shown that human knowledge offered important information for managing rare and unobserved entities. However, there exist many issues in the medical records based on the clinical named entity recognition because of the various natural language text features and the exceptional clinical conditions in EMRs. Therefore, it is necessary to enhance the natural language text features of the model. Hence, this survey analyzes various methods of clinical named entity recognition. The main aim of this survey is to study the existing clinical named entity recognition techniques and classifies them under various categories. Accordingly, this paper gives a detailed survey of 25 research papers and classifies them under different categories, such as machine-learning-based methods, deep-learning-based methods, namely Neural Networks (NN), Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and active learning methods. Also, the analysis is promoted in the survey based on the publication year, research techniques, performance measures, and achievement of the research methodologies. Moreover, the problems in the methods are explained in the research gaps and issues. Furthermore, the future extent of this research work is provided based on the limitations identified from the existing research methods.
Multiscale Visualization: A Structured Literature Analysis

Multiscale visualizations are typically used to analyze multiscale processes and data in various application domains, such as the visual exploration of hierarchical genome structures in molecular biology. However, creating such multiscale visualizations remains challenging due to the plethora of existing work and the expression ambiguity in visualization research. Up to today, there has been little work to compare and categorize multiscale visualizations to understand their design practices. In this article, we present a structured literature analysis to provide an overview of common design practices in multiscale visualization research. We systematically reviewed and categorized 122 published journal or conference articles between 1995 and 2020. We organized the reviewed articles in a taxonomy that reveals common design factors. Researchers and practitioners can use our taxonomy to explore existing work to create new multiscale navigation and visualization techniques. Based on the reviewed articles, we examine research trends and highlight open research challenges.
A class-specific metaheuristic technique for explainable relevant feature selection

A significant amount of previous research into feature selection has been aimed at developing methods that can derive variables that are relevant to an entire dataset. Although these approaches have revealed substantial improvements in classification accuracy, they have failed to address the problem of explainability of outputs. This paper seeks to address this problem of identifying explainable features using a class-specific feature selection method based on genetic algorithms and the one-vs-all strategy. Our proposed method finds relevant features for each class in the dataset and uses these features to enable more accurate classification, and also interpretation of the outputs. The results of our experiments demonstrate that the proposed method provides descriptive insights into prediction outputs, and also outperforms popular global feature selection techniques in the classifications of high dimensional and noisy datasets. Since there are no known challenging benchmark datasets for evaluating class-specific feature selection algorithms, this paper also recommends an approach for combining disparate datasets for this purpose.
Mining Partially-Ordered Episode Rules with the Head Support

Episode rule mining is a popular data analysis task that aims at finding rules describing strong relationships between events (or symbols) in a sequence. Finding episode rules can help understanding the data or making predictions. However, traditional episode rule mining algorithms find rules that require a very strict ordering between events. To loosen this ordering constraints and find more general and flexible rules, this paper presents an algorithm named POERMH (Partially-Ordered Episode Rule Mining with Head Support). Unlike previous algorithms, the head support frequency measure is used to select interesting episode rules. Experiments on real data show that POERMH can find interesting rules that also provides a good accuracy for sequence prediction.
A Frictionless and Secure User Authentication in Web-Based Premium Applications

By and large authentication systems employed for web-based applications primarily utilize a conventional username and password-based schemes, which can be compromised easily. Currently, there is an evolution of various complex user authentication schemes based on the sophisticated methodology of encryption. However, many of these schemes suffer from either low impact full consequences or offer security at higher resource dependence. Furthermore, the majority of these schemes don’t consider dynamic threat and attack strategies when the clients are exposed to unidentified attack environments. Hence, this paper proposes a secure user authentication mechanism for web applications with a frictionless experience. An automated authentication scheme is designed based on user behaviour login events. The uniqueness of user identity is validated in the proposed system at the login interface, followed by implying an appropriate user authentication process. The authentication process is executed under four different login mechanisms, which depend on the profiler and the authenticator function. The profiler uses user behavioural data, including login session time, device location, browser, and details of accessed web services. The system processes these data and generates a user profile via a profiler using the authenticator function. The authenticator provides a login mechanism to the user to perform the authentication process. After successful login attempts, the proposed system updates database for future evaluation in the authentication process. The study outcome shows that the proposed system excels to other authentication schemes for an existing web-based application. The proposed method, when comparatively examined, is found to offer approximately a 10% reduction in delay, 7% faster response time, and 11% minimized memory usage compared with existing authentication schemes for premium web-based applications.
Unearthing malicious campaigns and actors from the blockchain DNS ecosystem

Blockchain DNS has emerged as an alternative solution to traditional DNS to address many of its inherent drawbacks. In this regard, a blockchain DNS approach is decentralised, resilient, provides high availability, and prevents censorship. Unfortunately, despite these desirable features, the major blockchain DNS solutions to date, Namecoin and Emercoin have been repeatedly reported for malicious abuse, ranging from malware distribution to phishing. In this work, we perform a longitudinal analysis of both these chains trying to identify and quantify the penetration of malicious actors in their ecosystems. To this end, we apply a haircut blacklisting policy and the intelligence collected from various engines to perform a taint analysis on the metadata existing in these blockchains, aiming to identify malicious acts through the merge of identifying information. Our analysis provides an automated validation methodology that supports the various reports about the wide-scale abuse of these solutions showing that malicious actors have already obtained an alarming and extensive share of these platforms.
The impact of traceability on software maintenance and evolution: A mapping study

Abstract Software traceability plays a critical role in software maintenance and evolution. We conducted a systematic mapping study with six research questions to understand the benefits, costs, and challenges of using traceability in maintenance and evolution. We systematically selected, analyzed, and synthesized 63 studies published between January 2000 and May 2020, and the results show that traceability supports 11 maintenance and evolution activities, among which change management is the most frequently supported activity; strong empirical evidence from industry is needed to validate the impact of traceability on maintenance and evolution; easing the process of change management is the main benefit of deploying traceability practices; establishing and maintaining traceability links is the main cost of deploying traceability practices; 13 approaches and 32 tools that support traceability in maintenance and evolution were identified; improving the quality of traceability links , the performance of using traceability approaches , and tools are the main traceability challenges in maintenance and evolution. The findings of this study provide a comprehensive understanding of deploying traceability practices in software maintenance and evolution phase and can be used by researchers for future directions and practitioners for making informed decisions while using traceability in maintenance and evolution.
Federated Learning With Heterogeneity-Aware Probabilistic Synchronous Parallel on Edge

With the massive amount of data generated from mobile devices and the increase of computing power of edge devices, the paradigm of Federated Learning has attracted great momentum. In federated learning, distributed and heterogeneous nodes collaborate to learn model parameters. However, while providing benefits such as privacy by design and reduced latency, the heterogeneous network present challenges to the synchronisation methods, or barrier control methods, used in training, regarding system progress and model convergence etc. The design of these barrier mechanisms is critical for the performance and scalability of federated learning systems. We propose a new barrier control technique called Probabilistic Synchronous Parallel (PSP). In contrast to existing mechanisms, it introduces a sampling primitive that composes with existing barrier control mechanisms to produce a family of mechanisms with improved convergence speed and scalability. Our proposal is supported with a convergence analysis of PSP-based SGD algorithm. In practice, we also propose heuristic techniques that further improve the efficiency of PSP. We evaluate the performance of proposed methods using the federated learning specific FEMNSIT dataset. The evaluation results show that PSP can effectively achieve good balance between system efficiency and model accuracy, mitigating the challenge of heterogeneity in federated learning.