Reviewerly/exHarmony huggingface dataset:
Human-agent Explainability: An Experimental Case Study on the Filtering of Explanations

The communication between robots/agents and humans is a challenge, since humans are typically not capable of understanding the agent's state of mind. To overcome this challenge, this paper relies o ...
Advanced Twitter Sentiment Analysis Using Supervised Techniques and Minimalistic Features

Social media along with different web forums contribute to a huge amount of data, i.e. opinions, feedbacks, reviews generated on a daily basis. Sentiment analysis is the identification of polarity (positive, negative or neutral) of the data to analyse the opinion of users on various platforms on a range of topics including those that have real business value, i.e. polarity of users on a product. With the advent of platforms like Twitter, people freely express their opinion on almost everything and that generates a huge amount of data which cannot be processed or analysed manually; therefore, we have various NLP and machine learning techniques which can effectively analyse and predict the polarity of the data which enables us to capture the sentiment of the people regarding a particular issue. In this paper, we intend to analyse the different machine learning algorithms for sentiment analysis of Twitter data and compare the algorithms’ performance on different datasets with the help of metrics like precision, accuracy, F-measure and recall.
A Novel Visual Similarity-based Phishing Detection Scheme using Hue Information with Auto Updating Database

In this paper, we propose a novel visual similarity-based phishing detection scheme using hue information with auto updating database. Since a PWS (Phishing Website) is created based on targeted legitimate website or other subspecies whose hue information is similar each other, many PWSs can be exhaustively detected by tracing similar colored subspecies. Based on this notion, the proposed scheme detects a new PWS which has similar hue information to already detected PWSs. By repeating this procedure, the detection scope can be effectively expanded. In order to avoid the misdetection of legitimate websites which have similar hue information to database's ones, the proposed scheme utilizes the fact that the combination of used colors is hard to be similar among legitimate websites and PWSs. By the computer simulation with real dataset, we demonstrate that the proposed scheme improves the detection performance as the number of detected PWSs increases.
Adversarial Texture Optimization from RGB-D Scans

Realistic color texture generation is an important step in RGB-D surface reconstruction, but remains challenging in practice due to inaccuracies in reconstructed geometry, misaligned camera poses, and view-dependent imaging artifacts. In this work, we present a novel approach for color texture generation using a conditional adversarial loss obtained from weakly-supervised views. Specifically, we propose an approach to produce photorealistic textures for approximate surfaces, even from misaligned images, by learning an objective function that is robust to these errors. The key idea of our approach is to learn a patch-based conditional discriminator which guides the texture optimization to be tolerant to misalignments. Our discriminator takes a synthesized view and a real image, and evaluates whether the synthesized one is realistic, under a broadened definition of realism. We train the discriminator by providing as `real' examples pairs of input views and their misaligned versions -- so that the learned adversarial loss will tolerate errors from the scans. Experiments on synthetic and real data under quantitative or qualitative evaluation demonstrate the advantage of our approach in comparison to state of the art. Our code is publicly available with video demonstration.
Information and Computer Support for Adaptability of Learning in Higher Education Institutions

The paper substantiates the problem of timely determining the need for specific educational services and competent and instant provision of these services in the context of the requirements of relevance, variability, and attractiveness in the higher education system. It was noted that in order to provide higher education of the appropriate degree in specific specialties, postgraduate education, and additional educational services, educational and professional programs, curricula, work programs in academic disciplines, didactic training projects, diagnostic tools require the development. In accordance with the types of educational services of higher educational institutions and educational documentation, which reflects the characteristics of the provision of these educational services, factors that influence the type and nature of the developments have been established. The tasks that can be assigned to the developer of educational documentation have been formulated. The types of information and computer operations the execution of which is relevant to the performance of each of the tasks and sets of tasks have been justified. On the example of the curriculum, the work of the computer system supporting its compilation has been demonstrated.
Counterfighting Counterfeit: Detecting and Taking down Fraudulent Webshops at a ccTLD

Luxury goods such as sneakers and bags are in high demand. Many websites offer them at high discounts, which, in many cases, are simply cheap counterfeit versions of the original product. Online shoppers, however, may be unaware they are buying a counterfeit product and end up being scammed and having to deal with financial losses, as has been widely reported by various news outlets. This work presents a multiyear effort of The Netherlands’ .nl country-code top-level domain (ccTLD) in detecting and removing counterfeit online shops from the .nl DNS zone. We have developed two detection systems and partnered with registrars and a large credit card issuer, which ultimately led to more than 4,400 counterfeit online shops being taken down.
Deep Learning from Heterogeneous Sequences of Sparse Medical Data for Early Prediction of Sepsis

Sepsis is a life-threatening complication to infections, and early treatment is key for survival.Symptoms of sepsis are difficult to recognize, but prediction models using data from electronic health records (EHRs) can facilitate early detection and intervention.Recently, deep learning architectures have been proposed for the early prediction of sepsis.However, most efforts rely on high-resolution data from intensive care units (ICUs).Prediction of sepsis in the non-ICU setting, where hospitalization periods vary greatly in length and data is more sparse, is not as well studied.It is also not clear how to learn effectively from longitudinal EHR data, which can be represented as a sequence of time windows.In this article, we evaluate the use of an LSTM network for early prediction of sepsis according to Sepsis-3 criteria in a general hospital population.An empirical investigation using six different time window sizes is conducted.The best model uses a two-hour window and assumes data is missing not at random, clearly outperforming scoring systems commonly used in healthcare today.It is concluded that the size of the time window has a considerable impact on predictive performance when learning from heterogeneous sequences of sparse medical data for early prediction of sepsis.
Social Media Data Mining for Proactive Cyber Defense

The Internet is constantly evolving, producing many new data sources that can be used to help us gain insights into the cyber threat landscape and in turn, allow us to better prepare for cyberattacks. With this in mind, we present an end-to-end real-time cyber situational awareness system which aims to retrieve security-relevant information from the social networking site Twitter.com. This system classifies and aggregates the data extracted and provides real-time cyber situational awareness information based on sentiment analysis and data analytics techniques. This research will assist security analysts in rapidly and efficiently evaluating the level of cyber risk in their organization and allow them to proactively take actions to plan and prepare for potential attacks before they happen.
Exploiting De-Noising Convolutional Neural Networks DnCNNs for an Efficient Watermarking Scheme: A Case for Information Retrieval

Digital watermarking has various applications in which it plays an important role; these include document security and multimedia vendor copyrights. This paper presents an efficient watermarking technique called ‘modified selective embedding in low frequency’ (M-SELF) implementing a Fast Fourier Transform (FFT) for frequency domain analysis where FFT shift brings down lower-frequency components to the center of the cover image. Embedding of the watermark is done by optimal radial implementation after coordinates computation via phase and magnitude separation. Furthermore, the watermark is protected by the convolutional code (1/2) and Advanced Encryption Standard (AES-128) algorithm providing a double layer of security. Moreover, the exploitation of de-noising convolutional neural networks (DnCNNs) is another contribution of the proposed work. The network is utilized as a de-noiser in an integrated manner for the proposed watermarking technique on blind level of additive white Gaussian noise (AWGN) to check the robustness of the technique. Performance evaluation of the proposed integrated watermarking technique is done in terms of percentage of bits being retrieved as a success rate at each level of noise and perceived visual quality by using full-reference image quality assessment (IQA) metrics such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM).
Geochemical characteristics and metalogeny of Herzin granitoid complexes (Eastern Kazakhstan)

GeoCheMICAl ChARACteRIStICS AND MetAloGeNy of heRzIN GRANItoID CoMPlexeS (eASteRN KAzAKhStAN
Chance-Constrained Control With Lexicographic Deep Reinforcement Learning

This letter proposes a lexicographic Deep Reinforcement Learning (DeepRL)-based approach to chance-constrained Markov Decision Processes, in which the controller seeks to ensure that the probability of satisfying the constraint is above a given threshold. Standard DeepRL approaches require i) the constraints to be included as additional weighted terms in the cost function, in a multi-objective fashion, and ii) the tuning of the introduced weights during the training phase of the Deep Neural Network (DNN) according to the probability thresholds. The proposed approach, instead, requires to separately train one constraint-free DNN and one DNN associated to each constraint and then, at each time-step, to select which DNN to use depending on the system observed state. The presented solution does not require any hyper-parameter tuning besides the standard DNN ones, even if the probability thresholds changes. A lexicographic version of the well-known DeepRL algorithm DQN is also proposed and validated via simulations.
Effective Data Augmentation Approaches to End-to-End Task-Oriented Dialogue

The training of task-oriented dialogue systems is often confronted with the lack of annotated data. In contrast to previous work which augments training data through expensive crowd-sourcing efforts, we propose four different automatic approaches to data augmentation at both the word and sentence level for end-to-end task-oriented dialogue and conduct an empirical study on their impact. Experimental results on the CamRest676 and KVRET datasets demonstrate that each of the four data augmentation approaches is able to obtain a significant improvement over a strong baseline in terms of Success F 1 score and that the ensemble of the four approaches achieves the state-of-the-art results in the two datasets. In-depth analyses further confirm that our methods adequately increase the diversity of user utterances, which enables the end-to-end model to learn features robustly.
Zero-day Malicious Email Investigation and Detection Using Features with Deep-learning Approach

Cyber hackers use email as a tool to trick, inject or drop malicious software into the recipient's device. Everyday users have to face off against, phishing or malicious emails and it would be a huge problem for whole organizations even if only one user clicked on a single link from this malicious email. The difficult issue is how to classify and detect those malicious emails from ordinary, especially spear phishing emails, which are designed for a particular target, or zero-day malicious emails that no one has ever found until now. In this paper, we introduce a way to classify and detect zero-day malicious emails by using deep-learning with data investigated from the email header and body itself, combined with dynamic analysis information as a group of features. Four different language email datasets can be used to train and test the system to simulate real-world diversity and zero-day malicious email attack situations. We succeeded in obtaining a satisfactory accuracy rate for detection results for both zero-day malicious email types and normal spam.
Low Latency ASR for Simultaneous Speech Translation

User studies have shown that reducing the latency of our simultaneous lecture translation system should be the most important goal. We therefore have worked on several techniques for reducing the latency for both components, the automatic speech recognition and the speech translation module. Since the commonly used commitment latency is not appropriate in our case of continuous stream decoding, we focused on word latency. We used it to analyze the performance of our current system and to identify opportunities for improvements. In order to minimize the latency we combined run-on decoding with a technique for identifying stable partial hypotheses when stream decoding and a protocol for dynamic output update that allows to revise the most recent parts of the transcription. This combination reduces the latency at word level, where the words are final and will never be updated again in the future, from 18.1s to 1.1s without sacrificing performance in terms of word error rate.
Causal Discovery Toolbox: Uncovering causal relationships in Python

This paper presents a new open source Python framework for causal discovery from observational data and domain background knowledge, aimed at causal graph and causal mechanism modeling. The Cdt package implements an end-to-end approach, recovering the direct dependencies (the skeleton of the causal graph) and the causal relationships between variables. It includes algorithms from the 'Bnlearn' (Scutari, 2018) and 'Pcalg' (Kalisch et al., 2018) packages, together with algorithms for pairwise causal discovery such as ANM (Hoyer et al., 2009). Cdt is available under the MIT License at https://github.com/FenTechSolutions/CausalDiscoveryToolbox.
Selection criteria for cloud-oriented learning technologies for the formation of professional competencies of bachelors majoring in statistics

This article scientifically substantiates the criteria for the selection of cloud-oriented learning technologies for the formation of professional competencies of bachelors majoring in statistics, as well as presents the results of expert evaluation of existing cloud-oriented learning technologies by defined criteria. The criteria for the selection of cloud-oriented learning technologies for the formation of professional competencies of bachelors majoring in statistics, were determined: information-didactic, functional and technological. To implement the selection of cloud-oriented learning technologies for the formation of professional competencies of bachelors majoring in statistics, and effective application in the process of formation of relevant competencies, the method of expert evaluation was applied. The expert evaluation was carried out in two stages: the first one selected cloud-oriented learning technologies to determine the most appropriate by author’s criteria and indicators, and the second identified those cloud-oriented learning technologies that should be used in the educational process as a means to develop professional skills Bachelor of Statistics. According to the research, the most appropriate, convenient and effective cloud-oriented learning technologies for the formation of professional competencies of future bachelors of statistics by the vmanifestation of all criteria are cloud-oriented learning technologies CoCalc and Wolfram|Alpha.
Modified Image Haze Removal Algorithm Based on Dark Channel Prior

Image defogging/dehazing is an important research hotspot in the field of image processing. The purpose of image defogging is to remove fog and obtain more image details. However, image defogging results based on dark channel prior usually have the problems of color distortion and insufficient brightness, which leads to the loss of partial image information. In order to solve this problem, we proposed a new image defogging algorithm based on dark channel prior. The proposed algorithm first selects the candidate regions of the sky by combining the location priori and brightness characteristics of the image pixel points, and reasonably estimate the atmospheric light value via the pixel points in the candidate regions. Second, the proposed algorithm respectively reduces the RGB (Red, Green, and Blue) channel values of the fog image, and combines each reduced channel and the other two previously unreduced channels to make up three new fog images. Finally, the proposed algorithm uses our image defogging algorithm to get three defogging images, and weights three defogging images to get the restored image. Comparative study and qualitative evaluation indicated that the proposed algorithm can obtain clearer defogging image.
Innovation-based fractional order adaptive Kalman filter

Abstract Kalman Filter (KF) is the most widely used estimator to estimate and track the states of target. It works well when noise parameters and system models are well defined in advance. Its performance degrades and starts diverging when noise parameters (mainly measurement noise) changes. In the open literature available researchers has used the concept of Fractional Order Kalman Filter (FOKF) to stabilize the KF. However in the practical application there is a variation in the measurement noise, which will leads to divergence and degradation in the FOKF approach. An Innovation Adaptive Estimation (IAE) based FOKF algorithm is presented in this paper. In order to check the stability of the proposed method, Lyapunov theory is used. Position tracking simulation has been performed for performance evaluation, which shows the better result and robustness.
A Dataset of German Legal Documents for Named Entity Recognition

We describe a dataset developed for Named Entity Recognition in German federal court decisions. It consists of approx. 67,000 sentences with over 2 million tokens. The resource contains 54,000 manually annotated entities, mapped to 19 fine-grained semantic classes: person, judge, lawyer, country, city, street, landscape, organization, company, institution, court, brand, law, ordinance, European legal norm, regulation, contract, court decision, and legal literature. The legal documents were, furthermore, automatically annotated with more than 35,000 TimeML-based time expressions. The dataset, which is available under a CC-BY 4.0 license in the CoNNL-2002 format, was developed for training an NER service for German legal documents in the EU project Lynx.
Mutual Learning Network for Multi-Source Domain Adaptation

Early Unsupervised Domain Adaptation (UDA) methods have mostly assumed the setting of a single source domain, where all the labeled source data come from the same distribution. However, in practice the labeled data can come from multiple source domains with different distributions. In such scenarios, the single source domain adaptation methods can fail due to the existence of domain shifts across different source domains and multi-source domain adaptation methods need to be designed. In this paper, we propose a novel multi-source domain adaptation method, Mutual Learning Network for Multiple Source Domain Adaptation (ML-MSDA). Under the framework of mutual learning, the proposed method pairs the target domain with each single source domain to train a conditional adversarial domain adaptation network as a branch network, while taking the pair of the combined multi-source domain and target domain to train a conditional adversarial adaptive network as the guidance network. The multiple branch networks are aligned with the guidance network to achieve mutual learning by enforcing JS-divergence regularization over their prediction probability distributions on the corresponding target data. We conduct extensive experiments on multiple multi-source domain adaptation benchmark datasets. The results show the proposed ML-MSDA method outperforms the comparison methods and achieves the state-of-the-art performance.
Optimizing Performance of Text Searching Using CPU and GPUs

In this work, we are solving the major problem of reducing the time complexity of searching a string in huge corpus by using GPU as our computational environment (utilizing GPGPU and CUDA as programming platform) and Knuth–Morris–Pratt (KMP) and BMH (Boyer–Moore–Horspool) as string matching algorithms. String matching is a widely used technique in current research interest of various application areas such as bioinformatics, network intrusion detection, and computer virus scan. Although data are memorized in various ways, text remains the main form to exchange information. This is particularly evident in literature or linguistics where data are composed of huge corpus and dictionaries. These analytics are required in computer science where a large amount of data is stored in linear files. To search a particular string from these huge corpus takes more time in traditional CPU’s and this is a major problem.
Keystroke biometrics in the encrypted domain: a first study on search suggestion functions of web search engines

Abstract A feature of search engines is prediction and suggestion to complete or extend input query phrases, i.e. search suggestion functions (SSF). Given the immediate temporal nature of this functionality, alongside the character submitted to trigger each suggestion, adequate data is provided to derive keystroke features. The potential of such biometric features to be used in identification and tracking poses risks to user privacy.For our initial experiment, we evaluate SSF traffic with different browsers and search engines on a Linux PC and an Android mobile phone. The keystroke network traffic is captured and decrypted using mitmproxy to verify if expected keystroke information is contained, which we call quality assurance (QA). In our second experiment, we present first results for identification of five subjects searching for up to three different phrases on both PC and phone using naive Bayesian and nearest neighbour classifiers. The third experiment investigates potential for identification and verification by an external observer based purely on the encrypted traffic, thus without QA, using the Euclidean distance. Here, ten subjects search for two phrases across several sessions on a Linux virtual machine, and statistical features are derived for classification. All three test cases show positive tendencies towards the feasibility of distinguishing users within a small group. The results yield lowest equal error rates of 5.11% for the single PC and 11.37% for the mobile device with QA and 23.61% for various PCs without QA. These first tendencies motivate further research in feature analysis of encrypted network traffic and prevention approaches to ensure protection and privacy.