Reviewerly/exHarmony huggingface dataset:
Substitution Rules with Respect to a Context

In this paper, we deal with Leibniz’s rule of substitution of identicals, and describe how the rule can be applied in the TIL-Script language. The main goal is to introduce the algorithm of valid application of the substitution rules in all the three kinds of context that we distinguish in the TIL-Script language. The language is a computational variant of TIL, which is a hyperintensional, partial typed -calculus. Hyperintensional, because the meaning of TIL -terms are procedures producing functions rather than the denoted functions themselves. Partial, because TIL is a logic of partial functions, and typed, because all the entities of TIL ontology receive a type. Based on the results of context recognition the algorithm makes it possible to validly apply the substitution rules and derive relevant new pieces of analytic information.
Security, privacy, and ethical concerns on human radio-frequency identification (RFID) implants

This report discusses the security, privacy, and ethical concerns on human RFID-implants. Emerging technologies are being developed to help with convenience for everyday people, but these conveniences can pose security and privacy threats. The uses of technology being implanted in humans without the proper assessment could cause a new wave of security flaws. This study will review how human RFID microchip implants will impact and effect security, privacy, and ethical concerns associated with the new initiative for RFID implants to be used on human beings in everyday activities.
Information Extraction from Natural Language Using Universal Networking Language

Contemporaneous research has strongly indicated that most of the data on the Internet are unstructured data due to the phenomenon that during the input, processing of data and collection and storage of data by almost all the entities involved do not keep the data in a format that complies with a certain structure; this scenario has a domino effect on retrieving information should there be any inquiry. A part and parcel of semantic web area is data extraction and crucial for linking question and answer in the web. Should a question is pitched, it requires semantic analysis of data—both, structured and unstructured, map each part of the answer to the relevance of the question. Information extraction entails a crucial area of natural language processing and without the proper application of data acquisition from really large data set, for instance billions of alphanumeric words—the required data are hardly ever on the receiving end. The practical application, however, certainly needs answers that are succinct, correct and to the point; often times, the readers would skim-read through each answer as they themselves have to decide on which is more accurate to their question. This poses a unique challenge, a scenario where the question is incomplete; the answer is hidden under layers of data, and to make the query even more complex, researchers add the languages that are available. For English, a lot of researches have been conducted and due to the exceptional amount of usage among all the entities alike, English language has passed the initial issues and has been producing nearly ninety-nine percent accurate data. That is not the case for Bengali semantic analysis, and deriving meaningful information has been a challenge. This paper proposes a decisive algorithm to acquire meaningful and relevant data from unstructured data. The exactitude and efficiency of target data extraction depend on reasoning and analysis of unstructured data. Here, Universal Networking Language (UNL) has been applied to the proposed method to bring out the desired output. In this method, exceptionally large data sets that are unstructured have been categorized in prespecified relation with the help of UNL, and on these relations, every word of a sentence has been compared in binary relation. Finally, the proposed method extracts information from these binary relations.
Detection and correction of subtle context-dependent robot model inaccuracies using parametric regions

Autonomous robots frequently rely on models of their sensing and actions for intelligent decision making. Unfortunately, in complex environments, robots are bound to encounter situations in which their models do not accurately represent the world. Furthermore, these context-dependent model inaccuracies may be subtle, such that multiple observations may be necessary to distinguish them from noise. This paper formalizes the problem of detection and correction of such subtle contextual model inaccuracies in autonomous robots, and presents an algorithm to address this problem. The solution relies on reasoning about these contextual inaccuracies as parametric regions of inaccurate modeling (RIMs) in the robot’s planning space. Empirical results from various real robot domains demonstrate that, by explicitly searching for RIMs, robots are capable of efficiently detecting subtle contextual model inaccuracies, which in turn can lead to task performance improvement.
End-to-End Emotion Recognition From Speech With Deep Frame Embeddings And Neutral Speech Handling

In this paper we present a novel approach to improve machine learning techniques in emotion recognition from speech. The core idea is based on the fact that not all parts of the utterance convey emotion information. Thus, we propose to separate a given utterance into emotional and neutral parts and clean up the database to make it more univocal. Then we estimate short speech interval embeddings using speaker recognition convolutional neural network trained on the VoxCeleb2 dataset with the triplet loss. Sequences of these features are processed with a recurrent neural network to get an emotion label for the considered utterance. This stage consists of two sub-stages. At the first one we train a model to recognize neutral frames in a given utterance. Next we separate a corpus into emotional and neutral parts and train an improved model. Our experiments on the IEMOCAP corpus show that the final model achieves 66% of unweighted accuracy (UA) on four emotions and outperforms other known approaches like out-of-the-box Connectionist Temporal Classification (CTC) and local attention by more than 4%.
Low-Level Human Action Change Detection Using the Motion History Image

Human action recognition is an active topic in computer vision. In recent years, tangible results have been shown through deep learning methods; however, at a very high computational cost. They may be suitable for video retrieval or video summarization of a movie or drama, but they are not suitable for the visual surveillance of human action, which should be analyzed in real time. In this study, we propose an action change detection method to reduce the computational cost. On the surveillance camera screen, anomalous actions such as movies and sports are frequently not observed, and simple actions are often repeated. Thus, it is very inefficient to continue to apply high cost action recognition methods on repeated actions. The proposed action change detection method decides whether the previous action of the person is the same as the current action. The action recognition method is applied only when it has determined that the action has changed. The action change detection process is as follows. First, extract the motion history image from the input video and create one-dimensional time-series data. Second, determine the action change using the change of time-series data trend by the threshold. Experiments on the proposed method were performed on the KTH dataset.
Modeling SNP-Trait Associations and Realizing Privacy-Utility Tradeoff in Genomic Data Publishing

Genomic data privacy arises as one of the most important concerns facing the wide commoditization of DNA-genotyping. In this paper, we study the problem of privacy preserved kin-genomic data publishing. The major challenge in protecting kin-genomic data privacy is to protect against powerful attackers with abundant background knowledge. We propose a probabilistic model based on factor graph with the knowledge of publicly available GWAS statistics to reveal the dependency relationship between genotypes and phenotypes. Furthermore, a genomic data sanitization method is proposed to protect against optimal inference attacks launched by powerful attackers.
Decision Tree Model to Support the Successful Selection of a Database Engine for Novice Database Administrators

There are currently several types of databases that have different ways of manipulating data that affects the performance of transactions when dealing with the information stored. And it is very important for companies to manage information fast, so they do not lose any operation because of a bad performance of a database, in the same way, they need to operate fast while keeping the integrity of the information. Likewise, every database category's purpose is to serve a specific or specifics use cases to perform fast to manage the information when needed, so in this paper, we study and analyze the SQL, NoSQL and In Memory databases to understand their fit uses cases and make performance tests to build a decision tree that can help to take the decision to choose what database category to use to maintain a good performance. The precision of the tests of relational databases was 96.26% in NoSQL databases was 91.83% and finally in IMDBS was 93.87%.
Counter-mine augmented reality training system (CMARTS)

Advances in training and simulation technologies, particularly in the arena of augmented reality systems, not only enable a more immersive training experience for users, but also provide more opportunities to execute training both within and outside of operational environments. The Counter-Mine Augmented Reality Training System (CMARTS) effort was an investigation of fusion of augmented reality and embedded training capabilities into fielded hand-held mine detectors to support performance training at home stations, as well as in operational environments. The resulting system, including both metal detection and ground penetrating radar (GPR) sensors, provides: (1) real-time operator feedback in the form of augmented visualizations and (2) embedded “anywhere-anytime” mine detection training using simulated targets. The real-time feedback consists of a head-mounted display and tablet visualizations to indicate what areas of ground have already been scanned; markers for where devices have already been located; any problems with user swing; detector height and swing speed; and also device power status. The embedded training capability enables operators to practice mine detection in both indoor and outdoor environments with synthetic mines, to include actual device responses to simulated detection with real-time feedback. CMARTS will provide the basis for further investigation of augmented reality applications to support both real-time operations and training, not only for mine detection but for other sensing modalities and target types as well. This paper will start with a discussion of mine detection training challenges, followed by the design and development of CMARTS, concluding with possible future efforts.
Single point positioning by means of particle filtering on the GPU

High demands on accuracy and reliability of real-time navigation and positioning applications require the exploitation of multiple sensors. However, in many cases, the relations between the state vector and the observation space are of non-linear mathematical nature or observations have stochastic properties which deviate from those of Gaussian normal distributions. Thus, the extended Kalman filter and its variants are not always the most suitable choice. For such problems a particle filter, or more generally, sequential Monte Carlo methods, can increase the reliability of the estimates. Since particle filters are well-suited for parallel data processing, it will be shown how single point positioning GNSS solutions can be obtained when using a graphics processing unit (GPU) as a massive parallel computing device. The implementation of this approach and evaluation of its performance concerning real-time capability will be discussed as well as its precision and accuracy compared to a standard Kalman filter solution.
Hierarchical nonparametric survival modeling for demand forecasting with fragmented categorical covariates

Abstract This paper addresses the problem of data fragmentation when incorporating imbalanced categorical covariates in nonparametric survival models. The problem arises in an application of demand forecasting where certain categorical covariates are important explanatory factors for the diversity of survival patterns but are severely imbalanced in the sense that a large percentage of data segments defined by these covariates have very small sample sizes. Two general approaches, called the class‐based approach and the fusion‐based approach, are proposed to handle the problem. Both reply on judicious utilization of a data segment hierarchy defined by the covariates. The class‐based approach allows certain segments in the hierarchy to have their private survival functions and aggregates the others to share a common survival function. The fusion‐based approach allows all survival functions to borrow and share information from all segments based on their positions in the hierarchy. A nonparametric Bayesian estimator with Dirichlet process priors provides the data‐sharing mechanism in the fusion‐based approach. The hyperparameters in the priors are treated as fixed quantities and learned from data by taking advantage of the data segment hierarchy. The proposed methods are motivated and validated by a case study with real‐world data from an operation of software development service.
Distance bounding under different assumptions

Distance-bounding protocols were introduced in 1993 as a countermeasure to relay attacks, in which an adversary fraudulently forwards the communication between a verifier and a distant prover. In the more than 40 different protocols that followed, assumptions were taken on the structure of distance-bounding protocols and their threat models. In this paper, we survey works disrupting these assumptions, and discuss the remaining challenges.
Data Security and Trustworthiness in Online Public Services

Providing public services through the internet is an effective approach towards an encompassing number of citizens being covered by them and for cost reduction. However, the fast development of this area has fostered discussion and legislation regarding information security and trustworthiness. In addition to security mechanisms for data processed and stored internally, service providers must ensure that data exchanged between their servers and citizens are not intercepted or modified when traversing heterogeneous and uncontrolled networks. Moreover, such institutions should provide means enabling the citizen to verify the authenticity of the services offered. In this way, the present work provides a comprehensive overview regarding the security posture of Portuguese public institutions in their online services. It consists of non-invasive robustness evaluation of the deployed solutions for end-to-end data encryption and the correct use of digital certificates. As a result, we provide some recommendations aiming to enhance the current panorama in the majority of the 111 online services considered in this study.
Monitoring as a Method of Actualization of Education Development in Russia and Abroad

The article presents a modern understanding of the nature and purpose of monitoring in the educational system, lists its functions, stages and forms, allowing to evaluate the success and relevance of monitoring development. The author states that the origins of the formation of monitoring lie in the theory of education management. The paper presents the main milestones of the education management development both at the domestic and foreign levels. In particular, it considers the structure of education management in France, which is distinguished by strict regulation, and Denmark, which is in some way an antagonist of France in the education management area. A review of the goals and objectives of monitoring in the system of education allows the author to conclude about the contribution of monitoring to the development of education, in particular, by obtaining operational and reliable data on quality of education and other studied structural parameters.
SOLEIL Data Management Policy

In October 2018, the directors of SOLEIL approved the implementation of a SOLEIL data management policy for experimental data, and associated metadata, which are collected, reduced, processed, and/...
Adaptive Channel Access Control Solving Compound Problem of Hidden Nodes and Continuous Collisions among Periodic Data Flows

With the rapid increase in IoT (Internet of Things) applications, more sensor devices, generating periodic data flows whose packets are transmitted at regular intervals, are being incorporated into WSNs (Wireless Sensor Networks). However, packet collision caused by the hidden node problem is becoming serious, particularly in large-scale multi-hop WSNs. Moreover, focusing on periodic data flows, continuous packet collisions among periodic data flows occur if the periodic packet transmission phases become synchronized. In this paper, we tackle the compounded negative effect of the hidden node problem and the continuous collision problem among periodic data flows. As this is a complex variant of the hidden node problem, there is no simple and well-studied solution. To solve this problem, we propose a new MAC layer mechanism. The proposed method predicts a future risky duration during which a collision can be caused by hidden nodes by taking into account the periodic characteristics of data packet generation. In the risky duration, each sensor node stops transmitting data packets in order to avoid collisions. To the best of our knowledge, this is the first paper that considers the compounded effect of hidden nodes and continuous collisions among periodic data flows. Other advantages of the proposed method include eliminating the need for any new control packets and it can be implemented in widely-diffused IEEE 802.11 and IEEE 802.15.4 devices.
Bringing transparency and trust to elections

The transmission and tabulation of results are critical steps in the election process. If election results are provided quickly and transparently, they may inspire trust and confidence in the overall management of the contest. On the contrary, the late and questionable delivery of results might raise concerns and suspicion. In some cases, improper counting and tabulation procedures have brought candidates to question election results and even spurred long periods of violence. In this paper, we explore the potential of blockchain technology to enhance the counting and tabulation procedures during elections. Blockchains are distributed ledgers technologies whose transactions are protected cryptographically. It means that their contents cannot be tampered with nor modified in the long term. We argue that blockchain technology meets the requirements for electronic transmission and consolidation of election results. To prove so, we have implemented a proof of concept with a smart contract running on an Ethereum blockchain that registers the address of several polling stations and records the tally sheets that these submit at the end of the election. We also resort to the smart contract for the automatic and accurate consolidation of the election results once they have been submitted.
A Study on Influencing Factors of International Applied Talent Cultivation in Higher Vocational Education

With the tide of global economic integration, international exchanges and cooperation in higher education have become more and more frequent and deepening. The internationalization of higher education has become a hot issue and new trend in the reform and development of higher education institutions in the world. This paper uses Analytic Hierarchy Process, combining qualitative and quantitative analysis, to explore the problems, influencing factors and countermeasures in the training of internationalized applied talents in higher vocational education, and expounds the connotation, standards, evaluation methods and evaluation steps of internationalized applied talents in higher vocational education.
Defect detection based on monogenic signal processing

Using an infrared image sequence, how can one make the inner structure of a sample more visible without human supervision nor understanding of the context? This task is well known as a challenging task. One of the reasons is due to the great number of external events and factors that can influence the acquisition. This paper introduces a solution to this question. The sequence of infrared images is processed using the monogenic signal theory in order to extract the phase congruency. The Fourier Transform must respect the Hermitian property and it does thank to the Hilbert Transform in the 1D case, however this property is not respected in 2D. It does thanks to some approximation made in the analytic signal. The monogenic signal theory consists in reprocessing the Fourier Transform by replacing the Hilbert Transform by a Riesz Transform in order to maintain the Hermitian symmetry. In other words the phase congruence can be described as a feature detection approach. Using the assumption that the symmetry, or asymmetry of the phase does represent the similarity of the features at one scale, then the phase congruency represents how similar the phase values are at different scales. The proposed approach is invariant to image contrast which makes it suitable for applications. It can also give valuable results even with very noisy sequences. The proposed approach has been evaluated by using referenced Carbon Fiber Reinforced Plastic sample.
Overlay Security: Quantum-Safe Communication over the Internet Infrastructure

The need for a quantum-safe Internet is emerging, and this is a great opportunity to re-examine the legacy of public key infrastructure. There is a need for perspective on the evolution of cryptography over the years, including the perfect information-theoretical secure schemes and the computationally secure schemes, in particular. There is also a need to examine the evolving Internet infrastructure to identify efficient design and secure cryptographic schemes over the existing Internet infrastructure. A combination of overlay security, blockchain, and Merkle trees with Lamport’s signatures offers just such an easily implementable quantum-safe Internet.
Optimization of gas injection allocation to increase oil production using Gbest-guided artificial bee colony algorithm

Oil comes from fossils thousands of years ago and it is natural resource that can't be renewed. If it used continuously, the oil supplies will eventually run out. One way to optimize an oil production is by using gas lift. When the supply of the gas injection is limited, the total available gas should optimally distributed among the oil wells of the field such that the total production of oil is maximized. In this paper, Gbest-guided artificial bee colony (GABC) will became the optimization techniques while Least square is used to make the mathematical model from the correlation between gas injection and oil production. Contribution of this research is to find the fittest mathematical model and implement GABC to find the maximum value of mathematical model. The selected model is 10th order polynomial with R-square value of 1 and Sum of squares error (SSE) is 34.091. GABC will optimize the model and it will be compared to another optimization method such as Artificial bee colony (ABC). The results are GABC is better than ABC for each case with 4812.877 barrel oil per day (Bopd), 5107.873 Bopd, and 5142.156 Bopd respectively, while the best C values in GABC is 2.