Reviewerly/exHarmony huggingface dataset:
Design and Feasibility Test of an Indigenous Motorized Wheel for Manual Wheelchair

The wheelchair is essential for people with either spinal injury, limb injury, or trauma patients. The need at present is to customize the wheelchair based on the requirements of the disabled person. The maintenance and customization of a manual wheelchair is both simple and cost-effective when compared to powered wheelchairs, which are expensive and difficult to maintain in the long run. Accordingly, in this article, an attempt has been made to bring the facilities available in a powered wheelchair into the manual wheelchair, making it affordable to common people. Feasibility of a distinct manual wheelchair rear wheel rim is examined for various hub motor weights. The rear wheel of the manual wheelchair was replaced with an in-wheel direct drive hub-motor system. The proposed wheel model was designed using CATIA – V5 and an analysis was done using ANSYS software. A structural analysis was carried out to check the reliability and durability of the proposed wheel for different materials by changing hub-motor weights at various loading conditions. The nature of vibrations with respect to natural mode frequencies are found through modal analysis. Finally, the dynamic behavior of the proposed motorized wheel was examined using harmonic response analysis. Simulation results show the robustness of the proposed design and viability for real-time implementation.
A Logic Framework for P2P Deductive Databases

This paper presents a logic framework for modeling the interaction among deductive databases in a P2P (Peer to Peer) environment. Each peer joining a P2P system provides or imports data from its neighbors by using a set of mapping rules, i.e. a set of semantic correspondences to a set of peers belonging to the same environment. Two different types of mapping rules are defined: mapping rules allowing to import a maximal set of atoms not leading to inconsistency (called maximal mapping rules) and mapping rules allowing to import a minimal set of atoms needed to restore consistency (called minimal mapping rules). Implicitly, the use of maximal mapping rules states it is preferable to import as long as no inconsistencies arise; whereas the use of minimal mapping rules states that it is preferable not to import unless a inconsistency exists. The paper presents three different declarative semantics of a P2P system: (i) the Max Weak Model Semantics, in which mapping rules are used to import as much knowledge as possible} from a peer's neighborhood without violating local integrity constraints; (ii) the Min Weak Model Semantics, in which the P2P system can be locally inconsistent and the information provided by the neighbors is used to restore consistency, that is to only integrate the missing portion of a correct, but incomplete database; (iii) the Max-Min Weak Model Semantics that unifies the previous two different perspectives captured by the Max Weak Model Semantics and Min Weak Model Semantics. This last semantics allows to characterize each peer in the neighborhood as a resource used either to enrich (integrate) or to fix (repair) the knowledge, so as to define a kind of integrate-repair strategy for each peer. Under consideration in Theory and Practice of Logic Programming (TPLP).
Fuzzy trees and forests—Review

Abstract Data classification and regression are commonly encountered data analysis problems. Many researchers created multiple tools to deal with these issues. Fuzzy clustering, fuzzy decision trees, and ensemble classifiers such as fuzzy forests are popular tools used for this kind of problems. We would like to describe some interesting, more or less popular, solutions which belong to mentioned areas to show the way they deal with data classification and regression problems. This paper is divided into four parts. In the first part we present the issue of fuzzy clustering, which is one of the most important aspects of fuzzy trees which base on clusters. Some methods of splitting objects into clusters using fuzzy logic are described there. The second part describes different fuzzy decision trees. The way these trees can deal with classification and regression problems is presented. In the third part the issue of forests—ensemble classifiers which consist of fuzzy trees—is described. The last part treats about the way of performing weighted decision making in fuzzy forests. This article is categorized under: Fundamental Concepts of Data and Knowledge &gt; Big Data Mining Technologies &gt; Classification Technologies &gt; Prediction Technologies &gt; Machine Learning
ODToolkit

Recent years have witnessed a steady increase in the number of occupancy detection algorithms and people counting systems designed for residential and commercial buildings, yet comparing the accuracy of existing solutions has been impossible to date due to the lack of publicly available test data sets, open-source implementation of the state-of-the-art algorithms, and consensus on the evaluation metrics. This paper addresses this problem by presenting the design and implementation of an open-source toolkit for occupancy detection. ODToolkit is capable of importing and converting sensor data acquired from various buildings into a common data format, provides implementation of a broad suite of data-driven occupancy detection techniques, and calculates a set of evaluation metrics for each experiment. We present several case studies to show how this toolkit facilitates the development of new occupancy detection algorithms. In particular, we extend this toolkit by implementing novel domain-adaptive occupancy detection algorithms and compare them with the benchmark supervised learning algorithms on multiple data sets. Furthermore, we investigate what sensing modalities and precision are needed to achieve a desired level of accuracy for occupancy estimation through sensor fusion. ODToolkit code and documentation are available at https://odtoolkit.github.io/.
Sampling and Reconstruction of Diffused Sparse Graph Signals From Successive Local Aggregations

We analyze the sampling and posterior recovery of diffused sparse graph signals from observations gathered at a single node by using an aggregation sampling scheme. Diffused sparse graph signals can be modeled as the output of a linear graph filter to a sparse input and are useful in scenarios where a few seeding (source) nodes generate a non-zero input, which is then diffused according to the network dynamics dictated by the filter. Instead of considering a traditional setup where the observations correspond to the signal values at a subset of nodes, here the observations are obtained locally at a single node via the successive aggregation of its own value and that of its neighbors. Depending on the particular application, the goal is to use the local observations to recover the diffused signal or (the location and values of) the seeds. Different sampling configurations are investigated, including those of known and unknown locations of the sources as well as those of the diffusing filter being unknown.
Text marking approach for data leakage prevention

Printed documents protection problem against leakage is still one of the relevant. Existing security tools allow us to protect electronic text documents, however are ineffective in protecting their printed versions. This research presents the marking approach for text electronic documents invariant to the print-and-scan transformation. During marker embedding source text line spacing values are changing to the specified values within perceptual invisibility. The watermark extraction is carried out from the images containing text and based on normal Radon transform and Gaussian mixture model. This marking approach is robust to various image transformations and distortions. The accuracy of embedded information extraction was more than 0.98 for 200 DPI images and line spacing value change about 490 micrometers.
ADVoIP: Adversarial Detection of Encrypted and Concealed VoIP

A network attacker wants to transmit Voice-over-IP (VoIP) traffic streams covertly. He tries to evade the detection system by manipulating the VoIP streams through padding, shifting, and splitting operations, so as to conceal them amidst the Internet traffic. A defender wants to detect the manipulated VoIP streams. Tackling this problem from an adversarial perspective, we provide two contributions: 1) we obtain a highly stylized representation of VoIP streams in terms of transmission frequency F and packet length L, and characterize the (F, L) region achievable by the attacker's transformation and 2) We formulate the VoIP detection game, and find both theoretical conditions and a practical algorithm to find the Nash equilibrium of the game. As a result, we are able to design an optimal (from the adversarial perspective) algorithm for VoIP detection, which is nicknamed as ADVoIP. Simulations over real network traces, and comparison with existing approaches, show the effectiveness of the proposed approach.
Adversarial data poisoning attacks against the PC learning algorithm

Data integrity is a key component of effective Bayesian network structure learning algorithms, namely PC algorithm, design and use. Given the role that integrity of data plays in these outcomes, this research demonstrates the importance of data integrity as a key component in machine learning tools in order to emphasize the need for carefully considering data integrity during tool development and utilization. To meet this purpose, we study how an adversary could generate a desired network with the PC algorithm. Given a Bayesian network B1 and a database DB1 generated by B1 and a second Bayesian network, B2, which is equal to B1, except for a minor change like a missing link, a reversed link, or an additional link, we explore and analyze what is the minimal number of changes such as additions, deletions, substitutions to DB1 that lead to a database DB2 that, when given as input to PC algorithm, results in B2.
Parallelizing particle filters with butterfly interactions

Abstract The bootstrap particle filter (BPF) is the cornerstone of many algorithms used for solving generally intractable inference problems with hidden Markov models. The long‐term stability of the BPF arises from particle interactions that typically make parallel implementations of the BPF nontrivial. We propose a method whereby particle interaction is done in several stages. With the proposed method, full interaction can be accomplished even if we allow only pairwise communications between processing elements at each stage. We show that our method preserves the consistency and the long‐term stability of the BPF, although our analysis suggests that the constraints on the stagewise interactions introduce errors leading to a lower convergence rate than standard Monte Carlo. The proposed method also suggests a new, more flexible, adaptive resampling scheme, which, according to our numerical experiments, is the method of choice, displaying a notable gain in efficiency in certain parallel computing scenarios.
Raiders of the lost artefacts: Championing the need for digital forensics research

In digital forensics, the concept of a ‘digital artefact’ exists; coined here as ‘a digital object containing data which may describe the past, present or future use or function of a piece of software, application or device for which it is attributable to’. In almost all digital investigations, a practitioner will query any digital artefacts resident on any device subject to examination in order to establish the presence of potentially evidential information. Whilst on face value this task appears straightforward, in reality, the pace of change within technology can lead to a practitioner encountering many unknown or previously unseen artefacts with undocumented functionalities. This creates state of ‘catch-up’ in regards to investigatory techniques and knowledge as practitioners must seek to ascertain the relevance of such data through additional research and testing. Yet, the demands placed upon the role of the practitioner may prevent engagement in the testing and evaluation of new digital artefacts, leaving them reliant on the timely publication and dissemination of forensic research (whether academic, industry or vendor produced) as a support mechanism. Whilst digital artefact research has a clear applied value, the difficulty of measuring its impact means that it may not always be considered of worth by academic communities and their publication platforms. As a result, this work champions the need for ‘digital artefact’ research, calling for increased engagement in this form of research to support the forensic community.
Optimized Focused Web Crawler with Natural Language Processing Based Relevance Measure in Bioinformatics Web Sources

Abstract In the fast growing of digital technologies, crawlers and search engines face unpredictable challenges. Focused web-crawlers are essential for mining the boundless data available on the internet. Web-Crawlers face indeterminate latency problem due to differences in their response time. The proposed work attempts to optimize the designing and implementation of Focused Web-Crawlers using Master-Slave architecture for Bioinformatics web sources. Focused Crawlers ideally should crawl only relevant pages, but the relevance of the page can only be estimated after crawling the genomics pages. A solution for predicting the page relevance, which is based on Natural Language Processing, is proposed in the paper. The frequency of the keywords on the top ranked sentences of the page determines the relevance of the pages within genomics sources. The proposed solution uses a TextRank algorithm to rank the sentences, as well as ensuring the correct classification of Bioinformatics web page. Finally, the model is validated by being compared with a breadth first search web-crawler. The comparison shows significant reduction in run time for the same harvest rate.
Using artificial intelligence for modeling of the realistic animal behaviors in a virtual island

The remarkable development of the computer graphic techniques enables the creation and management of more realistic games and virtual environments. However, placing the Artificial Intelligence (AI) in a virtual world get making these environments both more interactive and more believable. One of the most ambitious goals of the AI is to create virtual worlds in which a big number of virtual characters or humans being interacted and behave in an autonomous way. For this purpose, placing embodied intelligence characters in a virtual world offers a unique opportunity to evaluate the AI concept. This paper introduces a Virtual Island developed in an innovative way based on fuzzy rules at the user interaction mechanism. We have used fuzzy tactics to create AI-based animals having various behavior types from an eagles' perspective which called 'Flight Simulation'. The simulation utilizes an eagle flying over the airspace of the Island of Chios. The AI on the ground is triggered by other animals when they enter a radius area with a certain speed defined in the software. It then decides how to behave according to health, behavior type and confidence level. Also, there is non-AI sparrow herd placed over the island to make user understand how fast he is and give the user sense of speed what can be called as an in-project feature. Consequently, the used Fuzzy Tactics have been tested in realized Unity 3D simulation. The results of the study have proven that the virtual environment consisting AI-based animals has a good performance in terms of animal-user interactions and provided satisfactory results in run time.
A Deep Generative Approach to Search Extrapolation and Recommendation

Related search query recommendation is a standard feature in many modern search engines. Interesting and relevant queries often increase the active time of users and improve the overall search experience. However, conventional approaches based on tag extraction, keywords matching or click graph link analysis suffer from the common problem of limited coverage and generalizability, which means the system could only make suggestions for a small portion of well-formed search queries.
An Adaptive Acquisition Approach to Localize Electromagnetic Information Leakage from Cryptographic Modules

An adaptive acquisition protocol is presented to rapidly identify optimal near-field measurement configurations for performing electromagnetic side-channel analysis attacks on physical implementations of cryptographic modules. Because measured fields vary with probe parameters as well as the data being encrypted, identifying the optimal configurations requires searching among a large number of possible configurations. The proposed protocol is a multi-step acquisition that corresponds to a greedy search in a 4-D configuration space consisting of probe coordinates, orientation, and number of signals acquired. To demonstrate its feasibility, the protocol is tested by identifying the best attack configuration for a probe just above an 18mm × 18mm FPGA realization of the advanced encryption standard (AES), subject to the precision of the equipment. It is found that the protocol requires ~35× less acquisition time compared to an exhaustive search for the optimal configuration.
The use of a blockchain-based smart import declaration to reduce the need for manual cross-validation by customs authorities

For determining the correctness of an import declaration lodged by a declarant within international shipping, customs authorities rely on manual cross-validation of the declaration with other trade documents. However, the sheer volume of import declarations lodged annually makes it impossible to manually cross-validate each declaration. Smart contracts have the potential to automatically aggregate relevant information from blockchain-based ledgers for lodging and cross-validation. In this paper we explore how such smart contract could be implemented. We demonstrate that a smart import declaration can be used to automatically aggregate information from different sources (e.g., pro forma invoice data elements stored on the blockchain ledger). However, technical and organizational considerations that are inherent to blockchain technology need to be addressed for the smart import declaration to be acceptable for both customs authorities and declarants. It is therefore suggested that future work focusses on data standards required to enable automatic aggregation of relevant trade information and the generation of the import declaration and governance of the smart import declaration by customs authorities to assure correctness.
A hybrid GIS-assisted framework to integrate Dempster–Shafer theory of evidence and fuzzy sets in risk analysis: an application in hydrocarbon exploration

One of the most important reasons for the existence of geologic risk during the hydrocarbon exploration process is related to uncertainties in geospatial data and models employed for data fusion. This study proposes a geospatial information system-assisted approach integrated with soft computing methods to manage spatial uncertainties during the hydrocarbon exploration process. A framework was designed to illustrate the process of calculating the geologic risk interval of each hydrocarbon structure and its estimation of uncertainties. The model enhances the geologic risk analysis of a Dempster–Shafer data-driven method by a fuzzy logic approach. The resultant hybrid method showed high predictive power with the area under the success and predictive curves being 82.2 and 75.9%, respectively. According to the results, the proposed hybrid method has improved the quality of risk analysis.
VCIPR: Vulnerable Code is Identifiable When a Patch is Released (Hacker's Perspective)

Vulnerable source code fragments remain unfixed for many years and they always propagate to other systems. Unfortunately, this happens often, when patch files are not propagated to all vulnerable code clones. An unpatched bug is a critical security problem, which should be detected and repaired as early as possible. In this paper, we present VCIPR, a scalable system for vulnerability detection in unpatched source code. We present a unique way, that uses a fast, token-based approach to detect vulnerabilities at function level granularity. This approach is language independent, which supports multiple programming languages including Java, C/C++, JavaScript. VCIPR detects most common repair patterns in patch files for the vulnerability code evaluation. We build fingerprint index of top critical CVE's source code, which were retrieved from a reliable source. Then we detect unpatched (vulnerable/non-vulnerable) code fragments in common open source software with high accuracy. A comparison with the state-of-the-art tools proves the effectiveness, efficiency and scalability of our approach. Furthermore, this paper shows that how the hackers can easily identify the vulnerable software whenever a patch file is released.
Simsax: A measure of project similarity based on symbolic approximation method and software defect inflow

Profiling software development projects, in order to compare them, find similar sub-projects or sets of activities, helps to monitor changes in software processes. Since we lack objective measures for profiling or hashing, researchers often fall back on manual assessments. The goal of our study is to define an objective and intuitive measure of similarity between software development projects based on software defect-inflow profiles. We defined a measure of project similarity called SimSAX which is based on segmentation of defect-inflow profiles, coding them into strings (sequences of symbols) and comparing these strings to find so-called motifs. We use simulations to find and calibrate the parameters of the measure. The objects in the simulations are two different large industry projects for which we know the similarity a priori, based on the input from industry experts. Finally, we apply the measure to find similarities between five industrial and six open source projects. Our results show that the measure provides the most accurate simulated results when the compared motifs are long (32 or more weeks) and we use an alphabet of 5 or more symbols. The measure provides the possibility to calibrate for each industrial case, thus allowing to optimize the method for finding specific patterns in project similarity. We conclude that our proposed measure provides a good approximation for project similarity. The industrial evaluation showed that it can provide a good starting point for finding similar periods in software development projects.
Tracking the Evolution of Literary Style via Dirichlet–Multinomial Change Point Regression

Summary It is typical in stylometry to assume that authors have a unique writing style which is common to all their published writings and is constant over time. Based on this assumption, statistical techniques can be used to answer literary questions, such as authorship attribution, in a quantitative manner. However, the claim that authors have a constant literary style has not received much investigation or validation. We propose a collection of statistical models based on Dirichlet–multinomial change point regression which can capture the evolution of writing style over time, including both gradual changes in style as the author matures, and abrupt changes which can be caused by extreme events in the author’s life. To illustrate our framework, we study the literary output of the celebrated British author Sir Terry Pratchett, who was tragically diagnosed with Alzheimer’s disease during the last years of his life. Contrary to the usual assumptions made in stylometry, we find evidence of both gradual changes in style over his lifetime, and an abrupt change which corresponds to his Alzheimer’s diagnosis. We also investigate the published writings of Agatha Christie, who is also rumoured to have suffered from Alheizmer’s disease towards the end of her life, and find evidence of gradual drift, but no corresponding abrupt change. The implications for stylometry and authorship attribution are discussed.