Reviewerly/exHarmony huggingface dataset:
An Object-Pose Estimation Acceleration Technique for Picking Robot Applications by Using Graph-Reusing k-NN Search

An object-pose estimation acceleration technique for picking robot applications by using hierarchical-graph-reusing k-nearest-neighbor search (k-NN) has been developed. The conventional picking robots suffered from low picking throughput due to a large amount of computation of the object-pose estimation, especially the one for k-NN search, which determines plural neighboring points for every data point. To accelerate the k-NN search, this work introduces a hierarchical graph to the object-pose estimation for the first time instead of a conventional K-D tree since the former enables simultaneous acquisition of plural neighboring points. To save generation time of the hierarchical graph, a reuse of the generated graph is also proposed. Experiments of the proposed accelerating technique using Amazon Picking Contest data sets and Arm Cortex-A53 CPU have confirmed that the object-pose estimation takes 1.1 seconds (improved by a factor of 2.6), and the entire picking process (image recognition, object-pose estimation, and motion planning) takes 2.5 seconds (improved by a factor of 1.7).
Applying Johansen VECM cointegration approach to propose a forecast model of photovoltaic power output plant in Reunion Island

Since 2007 Reunion Island, a French overseas region located in the Indian Ocean, aims to achieve energy self-sufficiency by 2030. The French government has made this insular zone an experimental territory for renewable energy resources (RES) by implementing great powers photovoltaic (PV) plants. However, the performance of PV conversion is highly climate dependent, and there have been many research contributions to show that the two main factors that influence PV cell efficiency are solar radiation and cell temperature. Moreover, considering the high variability of environmental factors on PV plants, the high penetration of PV in electric systems may threaten the stability and reliability of the electrical power grid. In this study, a linear relation analysis of time series data collected over one year is performed in order to investigate the dependent variable of PV power output from explanatory variables such as solar irradiance, cell temperature, wind speed and humidity. The originality of this paper is to apply cointegration methods, usual tools of econometrics, to PV systems. More precisely, this research work lies in the use a robust statistical method to model a vector cointegrating relationship linking the PV power output and the four environmental parameters mentioned above, to make accurate forecasts in a tropical area. The Johansen vector error correction model (VECM) cointegration approach is used to determine the most appropriate PV power output forecasting when the desired model is concerned with N explanatory variables and for N &gt; 2. This long run equilibrium relationship has been tested over many years of data and the outcome is more than reliable when comparing the model to measured data.
Light Weight Secure Data Sharing Scheme for Mobile Cloud Computing

Nowadays more and more data is stored and retrieved through Cloud Computing. With advancement there arises a problem in security. This means the data can be decrypted easily and the content being accessed by strangers and the privacy of the data will be lost. We have introduced a new algorithm known as “Cipher Attribute Based Encryption Algorithm” with symmetric key in our newly proposed light weight data sharing scheme for mobile cloud computing. Light weight in the sense, data with a fairly light storage capacity like files, audio clips etc. will be secured based on our proposed concept LDSS. The LDSS structure is modified and used as an access control in Cipher Attribute Based Encryption (CP-ABE). To reduce the user cost, it introduced attribute description fields to implement lazy revocation which is difficult in CP-ABE working systems. Everything in this operation might not be applicable in all mobile devices because the components are small and flexibility is less. The results from this paper show the issues related to data privacy have been solved in most cases for light weight data sharing scheme.
Using Jazz Chants to Increase Vocabulary Power among ESL Young Learners

The foundation of learning starts for children from pre-school and progresses to young learners in school.They come from various backgrounds with abilities to use home languages and mother tongue.However, their exposure to English Language is very limited due to the importance of this language is neglected as it is seen as impossible to master.Therefore, this paper investigates the effects of jazz chants approach to teach English Language to 30 pupils who are 10-year-old that have low proficiency level in a primary school located in a state in Malaysia.To improve teacher support and student involvement, it is crucial to have suitable strategies in educational contexts that would be able to develop a positive attitude among the pupils and improve their academic performance in English Language.The objective of this paper is to propose language learning strategies for low performance ESL pupils.It will highlight a direction which might improve the second language learning pedagogy in classrooms.Data was collected through pre-test and post-test, and satisfaction questionnaire which was given to the pupils (purposive sampling).The findings showed a marked increase in scores involving 15 students who achieved 100% scores.As a conclusion, they had motivating fun learning experience while they progressed in their learning activities.
Analysis of Cyberattacks in Public Organizations in Latin America

It was analyzed certain information about cyberattacks in Latin America and methods to counteract the aggressions that affect services, data and infrastructure.The problem is the cyberattack on information networks where there is interdependence between processes, people and devices within public organizations with the negative consequence of denial of services.The objective is to propose a protection model against cyberattacks in public organizations in Latin America to minimize the denial of public services.It was applied the deductive method and exploration to examine the information of cited articles.It resulted a General Model of Protection against Attacks, a Prototype of a network, a Algorithm in flowchart to minimize the attack arrival and a Formula of probability of attack arrival.It was concluded that in order to maintain the continuity of services and activities of public organizations, secure platforms must be in place to monitor and minimize possible attacks; our proposal has an attack detection accuracy of 87.49%.
A hybrid multiverse optimisation algorithm based on differential evolution and adaptive mutation

Multiverse optimisation (MVO) algorithm is an excellent meta-heuristic algorithm based on laws of physics. However, it is easy to fall into local optimum when solving complex multimodal optimisation problems with high dimensions. The global optimisation performance of the algorithm is still unsatisfactory. In this paper, we propose a hybrid multiverse optimisation (DE-SMVO) algorithm. First, in order to increase the ability of information exchange and global exploration, a differential mutation strategy is introduced to improve the search equations of MVO based on the differential evolution (DE) theory, enabling the algorithm to explore more unknown space. In addition, the adaptive mutation is carried out on the current global optimal universe in each iteration process, and excellent mutation universe is retained, which enhance the exploitation ability and improve the convergence accuracy of MVO algorithm. In order to investigate the performance of the proposed DE-SMVO algorithm, it is evaluated on 23 benchmark functions in this paper. The experimental results prove that the optimisation performance is significantly better than that of the MVO algorithm, and its performance is also superior compared with nine state-of-the-art meta-heuristic optimisation algorithms.
Image Detail Enhancement via Constant-Time Unsharp Masking

Image degradation due to weather phenomena and poor lighting conditions is inevitable in photography and computer vision applications. For example, fine details like distant objects or traffic signs are easily obscured by haze, mist, or dust in the atmosphere. Thus, the proposed algorithm is designed to address this issue by means of unsharp masking technique. Our approach differs from other unsharp masking methods in: i) the ability to control the contribution of sharpness enhancement according to the local variance of the image, and ii) the constant-time algorithmic complexity by virtue of the constant-time O(1) box filter. The use of the image's local statistics allows the proposed method to add more details in the heavily-degraded areas and little or no details in the smooth areas. In addition, the advantage of speed facilitates the integration into real-time processing applications. A comparative study and quantitative evaluation are conducted with a state-of-the-art algorithm to demonstrate the performance and efficiency of the proposed approach.
Collaborative Video Object Segmentation by Foreground-Background Integration

This paper investigates the principles of embedding learning to tackle the challenging semi-supervised video object segmentation. Different from previous practices that only explore the embedding learning using pixels from foreground object (s), we consider background should be equally treated and thus propose Collaborative video object segmentation by Foreground-Background Integration (CFBI) approach. Our CFBI implicitly imposes the feature embedding from the target foreground object and its corresponding background to be contrastive, promoting the segmentation results accordingly. With the feature embedding from both foreground and background, our CFBI performs the matching process between the reference and the predicted sequence from both pixel and instance levels, making the CFBI be robust to various object scales. We conduct extensive experiments on three popular benchmarks, i.e., DAVIS 2016, DAVIS 2017, and YouTube-VOS. Our CFBI achieves the performance (J$F) of 89.4%, 81.9%, and 81.4%, respectively, outperforming all the other state-of-the-art methods. Code: https://github.com/z-x-yang/CFBI.
Entanglement witness and entropy uncertainty of an open quantum system under the Zeno effect

In this paper, a two-level atom coupled with a double Lorentzian spectrum is solved by pseudomode theory, and an analytic representation of the density operator is obtained. Second, the paper investigates the entanglement witness and entropy uncertainty, and gets the analytical representation of entanglement, uncertainty, and their relationship. The environmental effects of the double Lorentzian spectrum are explained by non-Markovianity. In addition, this paper studies the influence of the Zeno effect on entanglement witness and uncertainty. The results show that the Zeno effect not only can effectively prolong the time of entanglement witness and reduce the lower bound of the entropy uncertainty but also can greatly enhance the time of entanglement witness and reduce the entanglement value of witness.
Finite-Time Analysis of Stochastic Gradient Descent under Markov Randomness

Motivated by broad applications in reinforcement learning and machine learning, this paper considers the popular stochastic gradient descent (SGD) when the gradients of the underlying objective function are sampled from Markov processes. This Markov sampling leads to the gradient samples being biased and not independent. The existing results for the convergence of SGD under Markov randomness are often established under the assumptions on the boundedness of either the iterates or the gradient samples. Our main focus is to study the finite-time convergence of SGD for different types of objective functions, without requiring these assumptions. We show that SGD converges nearly at the same rate with Markovian gradient samples as with independent gradient samples. The only difference is a logarithmic factor that accounts for the mixing time of the Markov chain.
Encryption Schemes Using Random Oracles: From Classical to Post-Quantum Security

The security proofs of post-quantum cryptographic schemes often consider only classical adversaries. Therefore, whether such schemes are really post-quantum secure remains unknown until the proofs take quantum adversaries into account. Switching to a quantum adversary might require to adapt the security notion. In particular, post-quantum security proofs for schemes which use random oracles have to be in the quantum random oracle model ($$\mathrm {QROM}$$), while classical security proofs are in the random oracle model ($$\mathrm {ROM}$$). We remedy this state of affairs by introducing a framework to obtain post-quantum security of public key encryption schemes which use random oracles. We define a class of encryption schemes, called oracle-simple, and identify game hops which are used to prove such schemes secure in the $$\mathrm {ROM}$$. For these game hops, we state both simple and sufficient conditions to validate that a proof also holds in the $$\mathrm {QROM}$$. The strength of our framework lies in its simplicity, its generality, and its applicability. We demonstrate this by applying it to the code-based encryption scheme $$\mathrm {ROLLO{\hbox {-}}II}$$ (Round 2 NIST candidate) and the lattice-based encryption scheme $$\mathrm {LARA}$$ (FC 2019). Thereby we prove that both schemes are post-quantum secure, which had not been shown before.
Semi-structured data protection scheme based on robust watermarking

Abstract Semi-structured data is a widely used text format for data interchange and storage. This paper proposes a robust watermarking scheme of data protection for semi-structured data, which uses JSON format as an example for illustration. We first parse JSON file into a data structure of distinct pairs. Afterwards, we generate a transfer matrix to get the intermediate sequences, which are then encoded using error-correction codes and embedded into the pairs. A private key is shared by the data hider and the recipient to resist collusion attacks. On the recipient’s side, data extraction can be successfully carried out even the received stego data are tampered. The imperceptibility is realized by embedding data into the less significant digits of numeric data in the cover file. The proposed scheme can be extended on several other formats. The experimental results show that the proposed scheme is robust to various kinds of typical attacks such as contextual truncating, modification, and redundancy addition.
Prediction Model for Amphetamine Behaviors Based on Bayes Network Classifier

This paper focus to present a prediction model for drug addiction of the accused in type 1 drug abuse case as amphetamine. Case studies in the Suan Phueng police station area Ratchaburi province. The data set that used for modeling is obtained from the collection from Suan Phueng police station from 2016-2018. The data set 1,598 items consist of gender, age, number of offenses, education status, nationality, occupation, and non-drug abuse. For our contribute a prediction model into two classes as “take” and “untake” by using data mining techniques namely Bayes Network classifier. In our experimental, a group of bayes are used to comparison such as Bayes Network, Naive Bayes and Naive Bayes Updateable. The results displayed the Bayes Network classifier shown the highest accuracy rate, Naive Bayes and Naive Bayes Updateable with 81.53, 80.85 and 80.85 respectively.
What do you really want to do? Towards a Theory of Intentions for Human-Robot Collaboration

Abstract The architecture described in this paper encodes a theory of intentions based on the key principles of non-procrastination, persistence, and automatically limiting reasoning to relevant knowledge and observations. The architecture reasons with transition diagrams of any given domain at two different resolutions, with the fine-resolution description defined as a refinement of, and hence tightly-coupled to, a coarse-resolution description. For any given goal, nonmonotonic logical reasoning with the coarse-resolution description computes an activity, i.e., a plan, comprising a sequence of abstract actions to be executed to achieve the goal. Each abstract action is implemented as a sequence of concrete actions by automatically zooming to and reasoning with the part of the fine-resolution transition diagram relevant to the current coarse-resolution transition and the goal. Each concrete action in this sequence is executed using probabilistic models of the uncertainty in sensing and actuation, and the corresponding fine-resolution outcomes are used to infer coarse-resolution observations that are added to the coarse-resolution history. The architecture’s capabilities are evaluated in the context of a simulated robot assisting humans in an office domain, on a physical robot (Baxter) manipulating tabletop objects, and on a wheeled robot (Turtlebot) moving objects to particular places or people. The experimental results indicate improvements in reliability and computational efficiency compared with an architecture that does not include the theory of intentions, and an architecture that does not include zooming for fine-resolution reasoning.
Data Preprocessing and Dynamic Ensemble Selection for Imbalanced Data Stream Classification

Learning from the non-stationary imbalanced data stream is a serious challenge to the machine learning community. There is a significant number of works addressing the issue of classifying non-stationary data stream, but most of them do not take into consideration that the real-life data streams may exhibit high and changing class imbalance ratio, which may complicate the classification task. This work attempts to connect two important, yet rarely combined, research trends in data analysis, i.e., non-stationary data stream classification and imbalanced data classification. We propose a novel framework for training base classifiers and preparing the dynamic selection dataset (DSEL) to integrate data preprocessing and dynamic ensemble selection (DES) methods for imbalanced data stream classification. The proposed approach has been evaluated on the basis of computer experiments carried out on 72 artificially generated data streams with various imbalance ratios, levels of label noise and types of concept drift. In addition, we consider six variations of preprocessing methods and four DES methods. Experimentation results showed that dynamic ensemble selection, even without the use of any data preprocessing, can outperform a naive combination of the whole pool generated with the use of preprocessing methods. Combining DES with preprocessing further improves the obtained results.
Explain Your Move: Understanding Agent Actions Using Specific and Relevant Feature Attribution.

As deep reinforcement learning (RL) is applied to more tasks, there is a need to visualize and understand the behavior of learned agents. Saliency maps explain agent behavior by highlighting the features of the input state that are most relevant for the agent in taking an action. Existing perturbation-based approaches to compute saliency often highlight regions of the input that are not relevant to the action taken by the agent. Our proposed approach, SARFA (Specific and Relevant Feature Attribution), generates more focused saliency maps by balancing two aspects (specificity and relevance) that capture different desiderata of saliency. The first captures the impact of perturbation on the relative expected reward of the action to be explained. The second downweighs irrelevant features that alter the relative expected rewards of actions other than the action to be explained. We compare SARFA with existing approaches on agents trained to play board games (Chess and Go) and Atari games (Breakout, Pong and Space Invaders). We show through illustrative examples (Chess, Atari, Go), human studies (Chess), and automated evaluation methods (Chess) that SARFA generates saliency maps that are more interpretable for humans than existing approaches. For the code release and demo videos, see this https URL.
HyperPubSub: Blockchain Based Publish/Subscribe

In this paper we describe the architecture and the implementation of a broker based publish/subscribe system where the broker role is played by a private blockchain, Hyperledger Fabric. We show the effectiveness of our architecture by implementing and deploying a photo trading platform. Interestingly, our architecture is generic enough to be adapted to any digital asset trading.
Statistical study of students’ digital competencies

Currently, there is a tendency of rapid development of information and communication technologies and their implementation into the сountry`s economy and life of the society. The purpose of this article was to study the digital competencies of students at different levels of education, as well as their comparison with digital competencies of employed in the economy. The article considers the projects and strategies developed and implemented by the European Union and the Russian Federation in order to reform the educational environment and scale up the implementation of digital technologies. The objectives of the information society development Strategy in the Russian Federation for 2017-2030 and the Federal project “Digital educational environment” have been characterized. The advantages of education with the use of digital technologies over classical education have been adduced. The analysis of the results of a sample survey of the population on the use of information technologies and information and communication networks, as well as a sample survey of the labor force has been made. Special attention has been paid to the study of students ‘ skills in working with applied software, which allowed us to identify the most and least popular programs. The ability of students to work with digital equipment has been analyzed. The communication skills of students in the digital environment have been analyzed in detail. All digital competencies have been considered among students of both higher and secondary vocational education. This circumstance have allowed us to reveal the differences between these categories in the context of their participation in the digital economy. A comparative analysis of digital competencies of students of secondary vocational and higher education programs and employed in the economy of the Russian Federation, as well as a comparative analysis of digital competencies of students of our country and the European Union has been made.