Reviewerly/exHarmony huggingface dataset:
Lexical Basis of Causal Attribution and Explanation

This chapter explains how laypeople generate and evaluate explanations. Traditionally, deliberate processing is assumed to be involved in generating and evaluating explanations. However, the author proposes two stages account for causal attribution and explanation to explain how laypeople generate and evaluate explanations quickly: a semi-autonomous processing stage which is primarily dependent on the lexical information of the verb, and a deliberate processing stage that takes many factors into account. The author proposes that verb types play an important role in determining the type of explanation and calls it verb cue hypothesis. In addition, the author proposes that verb cue hypothesis works as a cognitive shortcut that comprises the first stage of the two-stages account. Empirical evidence for the verb cue hypothesis was found in studies on causal attribution and explanation type preference.
Multi-Stage Algorithms of Localization of a Random Pulsed Source with a Two-Step Probability Density Function in the Search Interval

Time-optimal algorithms of spatial localization of random pulsed-point sources are proposed, and their parameters are calculated. These sources have a two-step probability density function in the search interval and are detected due to generation of instantaneous pulses at random time instants (delta functions). The advantage of the developed optimal localization algorithms over the search procedures that ignore specific features of the spatial distribution of the detected pulsed-point objects is demonstrated.
SAD Generator: Eating Our Own Dog Food to Generate KGs and Websites for Academic Events

Nowadays, a website is used to disseminate information about an event (e.g., location, dates, time). In the academic world, it is common to develop a website for an event, such as workshops or conferences. Aligning with the “Web of data”, its dissemination should also happen by publishing the information of the event as a knowledge graph, e.g., via RDF that is available through a SPARQL endpoint or a Triple Patterns Fragment server. However, the RDF generation and website development is not always straightforward and can be time-consuming. In this demo, we present the Semantic Academic-event Dissemination (SAD) Generator for generating RDF and websites for academic events. The generator allows to (i) annotate CSV files that contain academic event data and use the annotations to generate a knowledge graph and (ii) generate a website with the information for the event querying the knowledge graph. We used our generator to generate the RDF and website of a real workshop, the KGB workshop. It can be easily reused by organizers of other academic events by simply providing the event’s information in CSV files.
Cancer Image Quantification With And Without, Expensive Whole Slide Imaging Scanners

Automated analysis of digitized pathology images in tele-health applications can have a transformative impact on under-served communities in the developing world. However, the vast majority of existing image analysis algorithms are trained on slide images acquired via expensive Whole-Slide-Imaging (WSI) scanners. High scanner cost is a key bottleneck preventing large-scale adoption of digital pathology in developing countries. In this work, we investigate the viability of automated analysis of slide images captured from the eyepiece of a microscope via a smart phone. The mitosis detection application is considered as a use case.Results indicate performance degradation when using (lower-quality) smartphone images; as expected. However, the performance gap is not too wide (F1-score smartphone=0.65, F1-score WSI=0.70) demonstrating that smartphones could potentially be employed as image acquisition devices for digital pathology at locations where expensive scanners are not available.
Modification of Vigenere Algorithm and One Time Pad Using Rivest Code 6 (RC6) Key Expansion

Information technology that is increasingly developing today must be balanced with the similarity of information data. Information security is obtained by applying cryptographic techniques, Vigenere Cipher is a key symmetry algorithm that uses a substitution technique that uses two letters encoding the original message. Vigenere cipher has a weakness because it uses short keywords and its use will be repeated. In this study, we will discuss the modification of the vigenere cipher algorithm and one time pad by adding a key elongation process as done in the RC6 algorithm. The results of the study will produce new variations in data security by combining the vigenere cipher algorithm and vernam cipher with key expansion algorithm RC6
Correspondence-Steered Volumetric Descriptor Learning Using Deep Functional Maps

In this paper, we consider the dense correspondence of volumetric images and propose a convolutional network-based descriptor learning framework using the functional map representation. Our main observation is that the correspondence-steered descriptor learning improves dense volumetric mapping compared with the hand-crafted descriptors. We present an unsupervised way to find the optimal network parameters by aligning volumetric probe functions and the enforcement of invertible coupled maps. The proposed framework takes the one-channel volume as input and outputs the multi-channel volumetric descriptors using the cascaded convolutional operators, which are faster than the conventional descriptor computations. We follow the deep functional map framework and represent the dense correspondence by the low-dimensional spectral mapping for the functional transfer and dense correspondence using the linear algebra. We demonstrate that by using the correspondence-steered deep descriptor learning, the quality of both the dense correspondence and attribute transfer are improved in the extensive experiments.
A comparison study of deep visual tracking on infrared imagery in a maritime environment

Over the past years, tracking in the visible domain has seen rapid growth by exploiting Deep Neural Network (DNN) based methods, whereas, tracking in the Thermal Infrared (TIR) domain has seen a small interest. In this comparative study, we address tracking in a TIR maritime context for surveillance applications. Towards this end, we first compare the performances of traditional Single Object Trackers (SOTs) and recent DNN-based SOTs on a TIR maritime data set. Following this, we examine the sequences of the TIR data set causing difficulties for trackers and identify problematic attributes. Firstly, We use a group constituted of recent state-of-the-art DNN-based trackers and another group constituted of traditional trackers not employing DNN-based methods, and measure performance using the following metrics: Intersection over Union (IoU), center error, success rate, and robustness. Furthermore, we rank the trackers by taking into account their scores on IoU and robustness. The presented study shows that recent trackers exploiting DNNs methods for tracking perform on average better: over 24% on IoU and over 14% on robustness than their counterparts not utilizing DNN in their tracking process. Moreover, despite the provided improvement by using DNN-based trackers, a failure case analysis shows that clutter, occlusion handling, low-resolution and scale change of the target, are visual attributes that still remain challenging, requiring further improvement.
The Design Control of Airplane�s Movement by the Influence of Leader for Tracking a Desired Path

Airplane should flight in compliance with the predetermined flight track to avoid the accident.This paper discusses how to design the optimal tracking control from airplanes with the influence of leader.There are four steps in designing the control of airplane's movement by leader influence.Firstly, we will modify the airplane model by adopting Fossen model 1 .Secondly, we determine the trajectory that will be tracked by the movement of airplanes.Thirdly, we design the control of airplane's movement by using tracking error dynamics methods to the leader and the agents.Finally, the numerical simulation output is shown by error tracking of airplane's movement towards desired path followed by agents and leader of the planes.
Software Integrity Control

In this article the tool for software integrity control is introduced. With its help the risk of violation of enterprise's information system's software integrity can be reduced. The tool implements the algorithm of integrity control based on the Russian standard of hashing function “GOST R 34.11-2012”. The functionality of developed tool allows to create the initial state of the enterprise's information system by obtaining a set of hash values for each program. These values are saved in the database of initial hashes. The tool carries out periodic integrity check, firstly by calculating the current hash values for software based on the algorithm of the hashing function “GOST R 34.11-2012”, and then by comparing the current hash values with the initial ones. If modification of the program is authorized, its hash value is modified. The tool provides report of the performed software integrity check. The experimental studies are carried out. To confirm the functionality of developed tool, the malware “12Trojan.Win32.Krepper.ab” is modeled. It is shown that the use of the developed tool reduces information security risk by 5%.
Action Recognition by Weighted Averaged Hidden Markov Models

In the paper the problem of action recognition with the help of Markov models is considered. We propose an algorithm aiming at reduction of the false positive rate. We state a hypothesis that these errors are caused by learning gesture sequences representing rare movements and not that containing popular ones. Our algorithm translates the sequence of gestures into the corresponding Markov models which are used for a preliminary classification. The obtained evaluation coefficients (numbers of proper classifications) are then used to determine the best weights in a combined model composed of these models. In order to find a compromise between the number of true positives and the number of false positives, the power function of weights is examined.
A Framework for Data and Concept Interoperability in Smart Mobility Systems

Transport systems need to represent and analyze data from multiple sources and networks to dynamically respond to demands and operate more efficiently. But deploying ad-hoc platforms could be very expensive and ineffective in the long run. It is preferable to focus on data availability and smart data analysis and to develop interoperability strategies suitable for smart mobility based on open standards and allowing seamless integration of third-party systems and custom applications. This paper highlights the opportunity to take advantage of emerging technologies, like the open-source data platforms and to use services for data linked queries in cities, and especially in the domain of smart mobility systems.
Aequilibrium

In recent years, with popularization of video streaming service new video distribution technologies have been created. Currently, one of the most promising ones is the Moving Picture Expert Group Dynamic Adaptive Streaming over HTTP or MPEG-DASH. Even so, with the limitation of the TCP/IP network structure, the end user quality of experience (QoE) may be affected. One issue that can affect user QoE is the workload of content distribution servers. Thus, the imbalancing of server's workload comprising user's attendance can lead to a content server provider non optimized choice. This work presents two load-balancing solutions between MPEG-DASH video servers based on Software-Defined Networks, using as a balancing workload metric the content servers throughput as well as the CPU load in order to provide QoE to the end user during the server's transitioning balance.
Image Retrieval Based on LDA and SVM

In recent years, large-scale image retrieval shows significant potential in many fields. The Bag-of-Visual-Words (BoVW) has been frequently used in the classification of image data and image retrieval. However, this modeling approach lacks semantic description from low-level semantics to high-level semantics. In this article, Latent Dirichlet Allocation (LDA) algorithm is introduced to extract the features of image semantics, and describe the "topic words" by probabilistic characteristics. This paper proposes a new image retrieval method based on the combination of "Topic Word" probability representation and SVM algorithm. The parameters of SVM are optimized through experiments. Experimental results show that the method has certain availability.
Succinct enumeration of distant vertex pairs

The fastest known algorithms for finding the exact value of the diameter of general graphs are no faster than the algorithms that compute all-pairs shortest paths. An extension of the problem of computing graph diameter is enumerating pairs of vertices in a graph, ordered decreasingly by their distance. In this paper, we investigate this problem with the presence of memory constraints. We also show how our result can help the computation of graph Hyperbolicity, by lowering the memory complexity of computing the ordered list of far-apart vertex pairs.
Quantum Communications

In this work we review recent research in some select topics in quantum communications. In each of the topics chosen we highlight how the techniques presented are of direct interest to quantum protocols operating over macro-scales (inter-continental ranges) as well as to quantum protocols operating over nano-scales (inter-chip ranges).
3D Character Generation from Images using Convolutional Neural Networks and 3D-Character Factory

Face recognition using convolutional neural networks can be utilised for constructing 3D characters from a photograph or from a live person. By using convolutional neural networks human attributes like the color of the eyes, skin tone, hair color, presence or absence of facial hair, body type and so on can be identified and provided as a response to a 3D-Character in a game engine like Unity3D using factory pattern approach to make the 3D Character look like the subject.
Environment and Location Aware Drone Services Corresponding to Green Energy Charging Station

Due to the battery capability of drone or unmanned aerial vehicle, the ability of services and flying distance provided by drone are limited. Considering the frequent monitoring of the agricultural disease, the power recharging station for unmanned aerial vehicle without manual operation is important issue. Based on the proposed Environment and Location Aware Drone Services Corresponding to Green Energy Charging Station, the drone services can be started at one green energy charging station and ended at another. According to the complete-coverage path planning algorithm, the drone service such as environment monitoring or pesticide spraying can fully cover the farm. In addition, the implementation shows that suitable green energy power source can be dynamically embedded into power recharging station. The feasibility of the proposed system is verified.
Analysis and modeling of the governance in general programming languages

General Programming Languages (GPLs) continuously evolve to adapt to the ever changing technology landscape. The evolution is rooted on technical aspects but it is ultimately decided by the group of people governing the language and working together to solve, vote and approve the new language extensions and modifications. As in any community, governance rules are used to manage the community, help to prioritize their tasks and come to a decision. Typically, these rules specify the decision-making mechanism used in the project, thus contributing to its long-term sustainability by clarifying how core language developers (external contributors and even end-users of the language) can work together. Despite their importance, this core topic has been largely ignored in the study of GPLs. In this paper we study eight well-known GPLs and analyze how they govern their evolution. We believe this study helps to clarify the different approaches GPLs use in this regard. These governance models, depicted as a feature model, can then be reused and mixed by developers of new languages to define their own governance.
Open Cultural Heritage Data in University Programming Courses

Cultural heritage data are not only an important research subject for the semantic web community, but also provide interesting material for practical programming courses in universities. In this paper, four projects created by master students at Karlsruhe Institute of Technology (KIT) show how open cultural heritage data can be used to develop creative and ambitious applications which improve the students’ knowledge and experience working with semantic web technologies, linked data, natural language processing techniques and machine learning. Furthermore, challenges and lessons learned are discussed.
An Improved Particle Filter Based Track-Before-Detect Method for Underwater Target Bearing Tracking

Track-before-detect (TBD) methods have been shown to greatly abate measurement-to-track association (MTA) challenges which could cost plenty of operator workload in many detection systems. In the field of underwater acoustic signal processing, the low signal-to-noise ratio, random missing measurements, multiple interference scenarios, and merging-splitting contacts in measurement space are challenging for common target tracking algorithms. As a result, particle filter (PF) based track-before-detect methods that compute posterior density distribution directly using beamformer output instead of bearing measurements are effective in these cases. However, the general PF always suffers from the particle impoverishment problem which can lead to the misleading state estimation results due to system process error. To cope with above problems, an improved particle filter based track-before-detect method is proposed in this paper. The proposed PF-TBD method adopts crossover and mutation operators from genetic algorithm to evolve particles with small weight. And the quasi-Monte Carlo method is applied into resampling procedure of particle filter. The effectiveness of the method is verified by using experimental data obtained at sea, which can continuously and accurately track target bearings in the case of temporary signal disappearance and multi-target intersection. The estimated result is close to the real value even when the motion model suffers mismatch.
Tighter Security Proofs for Post-quantum Key Encapsulation Mechanism in the Multi-challenge Setting

Due to the threat posed by quantum computers, a series of works investigate the security of cryptographic schemes in the quantum-accessible random oracle model (QROM) where the adversary can query the random oracle in superposition. In this paper, we present tighter security proofs of a generic transformations for key encapsulation mechanism (KEM) in the QROM in the multi-challenge setting, where the reduction loss is independent of the number of challenge ciphertexts. In particular, we introduce the notion of multi-challenge OW-CPA (mOW-CPA) security, which captures the one-wayness of the underlying public key encryption (PKE) under chosen plaintext attack in the multi-challenge setting. We show that the multi-challenge IND-CCA (mIND-CCA) security of KEM can be reduced to the mOW-CPA security of the underlying PKE scheme (with $$\delta $$ -correctness) using transformation. Then we prove that the mOW-CPA security can be tightly reduced to the underlying post-quantum assumptions by showing the tight mOW-CPA security of two concrete PKE schemes based on LWE, where one is the Regev's PKE scheme and the other is a variant of Frodo.
Image Encryption Technique Using Huffman Coding and Spatial Transformation

Security of data or image is one of the important steps in the enormous domain of multimedia. Encryption of images is well known technique to protect the information present in the image in order to maintain the confidentiality, authenticity and integrity of images. This paper proposed a method for image encryption based on the Huffman coding and homogeneous transformation of pixels. Huffman coding is used to protect the huge amount of data with good visibility and no loss of information. Encrypted image generated by the proposed work is quite different from the original image.
Multi-target tracking of birds in complex low-altitude airspace based on GM_PHD filter

GM_PHD (Gaussian mixture of probability hypothesis density) cannot completely track multiple targets, such as the flying birds in the complex low-altitude airspace near the airport, due to the lack of the steps of birth detection, track extraction and death detection. A new algorithm is proposed to solve this problem, which mainly contributes to the following three aspects. First, the k-nearest neighbour algorithm is used to detect the birth of bird targets from measurements which is necessary to construct the birth intensity function. Second, the clustering algorithm is introduced into the probability hypothesis density filter framework to extract the bird targets’ tracks from the filtering results. Third, an algorithm is added to detect the death of bird targets for better tracking. The Gaussian mixture implementation of the algorithm denoted as BT_GM_PHD (Bird Tracking GM_PHD) is presented. The test results on simulation and ground-truth data show that the proposed BT_GM_PHD algorithm can effectively track the multiple flying bird targets in the complex low-altitude airspace near the airport, outperforming the GM_PHD filter.
Simulation platform for wireless data communication using a new signal reconstruction method

A platform for simulation of MIMO wireless data transmission processes is presented in this paper. The innovative tool implements authors' solution based on the polynomial approach related to the nonsquare matrices. Through an application of some nonunique inverses of nonsquare propagation matrices it is possible to increase an efficiency of signal recovery, which is confirmed by the theoretical background and a number of simulation instances. Thus, the useful environment can compete with other platforms employing different techniques. This statement is discussed in this paper.
Kecenderungan Tanggapan Masyarakat terhadap Ekonomi Indonesia berbasis Lexicon Based Sentiment Analysis

Sentiment analysis aims to find opinions, identify sentiments expressed, and then classify their polarity values. One method of sentiment analysis is Lexicon-based. This study implements the Lexicon based sentiment analysis to analyze the polarity of public responses to the topic of the development of "the Indonesian economy". The dataset is collected from social media from 2017 to 2019. Preprocessing used is folding cases, deleting newline characters, changing non-standard words, deleting mentions, deleting hashtags, removing URL strings, changing word negation, and translating text into English with TextBlob library. Then extract the sentiment values from adjectives, adverbs, nouns, and verbs found in the text. Based on the results of sentiment analysis, it can be seen that there are 63.6% positive responses from the public to the development of the Indonesian economy, 7.4% negative responses, and 29% neutral.