Reviewerly/exHarmony huggingface dataset:
Towards a Security Reinforcement Mechanism for Social Cyber-Physical Systems

Cyber-physical systems (CPS) are heterogeneous inter-operating parts of different aspects that can be physical, technical, networking, and even social like agent operators in smart grids. The main concerns of CPS are ensuring security and well-functioning against attacks that can either be technical or socio-technical based threats. To detail how well security policies are expressed, integrated, and reinforced within a CPS, we rely on formal methods to develop a sound approach that models CPS entities, especially their demeanor and interactions. Further, the approach proposes to specify formally security requirements and policies in CPS. For security analysis, we propose an algorithm that reinforces the specified security policies and also quantifies the validity of requirements for CPS. Finally, we validate the approach on a real case scenario of CPS in the presence of social and technical treats.
Taylor Expansion Policy Optimization

In this work, we investigate the application of Taylor expansions in reinforcement learning. In particular, we propose Taylor expansion policy optimization , a policy optimization formalism that generalizes prior work (e.g., TRPO) as a first-order special case. We also show that Taylor expansions intimately relate to off-policy evaluation. Finally, we show that this new formulation entails modifications which improve the performance of several state-of-the-art distributed algorithms.
Extracting information from single qubits among multiple observers with optimal weak measurements

In the context of quantum information, major efforts have been made to maximize the mutual information by measuring single copies of signal states. In general, one execution of optimal projective measurement extracts all the accessible mutual information. However, in some scenarios, weak measurements are preferred because of kinds of specific requirements, e.g., to distribute secret keys to multi-observers. In this study, we propose a method to construct optimal weak measurements for multi-party quantum communications. Utilizing the method in [Physical Review Letters 120 , 160501 (2018)] to classify the mutual information, the theoretical study shows that by successively performing this optimal weak measurement, all accessible information can be obtained by multiple observers. This conclusion is experimentally verified by a cascaded measurement apparatus that can perform six successive weak measurements on heralded single photons. The experimental results clearly indicate that almost all accessible mutual information is extracted by this sequence of optimal weak measurements; meanwhile, none of the information is destroyed or residual. Thus, this optimal weak measurement is an efficient and reliable tool for performing quantum communication tasks. The consistence between the experimental and theoretical results verifies that the classifying method in [ Phys. Rev. Lett. 120 , 160501 ( 2018 ) ] can be applied to characterize realistic quantum measurements.
Adopting Tiny Encryption Algorithm for Patient Healthcare Record on Smart Card

Smart Card is something that is used for data transactions quickly and practically. The definition of ”smart” is when a card has a chip implanted. The card is very helpful in the authentication and administration process even during emergencies, such as a patient healthcare record at the hospital. In this case study, patient data should only be accessed and known by people who have rights. So it is necessary for the security of data storage such as cryptography algorithms. The algorithm needed for patient cards is an algorithm that has fast processing capability and a high level of security. So the aim of this study is to implement the Tiny Encryption Algorithm on Smart Cards. Based on the results of testing in this study the time of encrypting patient data with a total of 600 lines of patient history measuring 30.6 Kb produces an average encryption time of 20.17 ms. While the decryption for the same data takes 17.21 ms which shows very little processing time and the tolerance for waiting time for accessing data. Furthermore, the Avalanche Effect test carried out in the TEA encryption process results 77%, where these results indicate that encrypted data has very random cipher-text results so that the data contents are kept confidential with a high level of security. The results of testing the processing time and AE can be concluded that TEA is an algorithm that has a fast data security process with high security and time that can be tolerated by users.
Variance Reduction in Stochastic Particle-Optimization Sampling

Stochastic particle-optimization sampling (SPOS) is a recently-developed scalable Bayesian sampling framework that unifies stochastic gradient MCMC (SG-MCMC) and Stein variational gradient descent (SVGD) algorithms based on Wasserstein gradient flows. With a rigorous non-asymptotic convergence theory developed recently, SPOS avoids the particle-collapsing pitfall of SVGD. Nevertheless, variance reduction in SPOS has never been studied. In this paper, we bridge the gap by presenting several variance-reduction techniques for SPOS. Specifically, we propose three variants of variance-reduced SPOS, called SAGA particle-optimization sampling (SAGA-POS), SVRG particle-optimization sampling (SVRG-POS) and a variant of SVRG-POS which avoids full gradient computations, denoted as SVRG-POS$^+$. Importantly, we provide non-asymptotic convergence guarantees for these algorithms in terms of 2-Wasserstein metric and analyze their complexities. Remarkably, the results show our algorithms yield better convergence rates than existing variance-reduced variants of stochastic Langevin dynamics, even though more space is required to store the particles in training. Our theory well aligns with experimental results on both synthetic and real datasets.
Hiroshima University: KOBELCO Construction Machinery Dream-Driven Co-Creation Research Center

On April 1, 2018, Hiroshima University instituted the “Joint Research with External Organizations (Private Sector, etc.).” The first project was the “KOBELCO Construction Machinery Dream-Driven Co-Creation Research Center (DDCC Research Center).” This research center has three collaborative research laboratories, and eight joint-research studies are in progress. With the aim of developing a hydraulic excavator that is human-friendly and human-nurturing, the research and development focuses on controls that (i) work as the operator expects and (ii) create value that facilitates growth of the operator. This article gives an overview of how the research center was established and what the research center studies and develops.
Novel Design of the Wheel-footed obstacle-surmounting Robot

Abstract In order to improve the mobile robot’s movement efficiency, obstacle surmounting ability and stability in complex environments, a new wheel-footed obstacle surmounting robot is proposed. The robot wheels include semicircle arc and involute arc. Each group of wheels is complementary in phase to ensure that the contact point between the wheel and the ground is always the circumference of the wheel. This not only continues the high efficiency of the round wheel on the flat ground, but also enhances the ability of the wheel to cross obstacles. This paper studies the structure design and motion analysis of the wheel-footed obstacle-surmounting robot, and completes the robot of the physical prototype. At last, the experiment proves that the robot not only has simple structure, high moving efficiency, but also has good obstacle-surmounting ability, passing ability and stability.
Research on the Design and Construction of University Informationized Learning Environment under the TPACK Framework

Abstract The rapid development of information technology has broken the traditional teaching methods and learning environment, and the traditional teaching environment is difficult to meet the needs of information-based teaching. Therefore, based on the TPACK theory, this paper integrates the teaching method, information technology and subject content, and uses cloud computing technology to design the information-based learning environment model and framework in line with the teaching characteristics of colleges and universities. finally, from the theoretical and technical aspects, a learning environment based on cloud computing technology has been established, which provides support and guarantee for the reform of basic information teaching in colleges and universities.
Studying Test Flakiness in Python Projects : Original Findings for Machine Learning

Flaky tests can fail or pass without any changes to the code under test. They can destroy developers trust in a test suite and if ignored lead to bugs in released code. This thesis performs an empi ...
A Comprehensive Review on State-of-the-Art Image Inpainting Techniques

Image inpainting is the process of restoring missing pixels in digital images in a plausible way. A study on image inpainting technique has acquired a significant consideration in various regions, i.e. restoring the damaged and old documents, elimination of unwanted objects, cinematography, retouch applications, etc. Even though, limitations exist in the recovery process due to the establishment of certain artifacts in the restored image areas. To rectify these issues, more and more techniques have been established by different authors. This survey makes a critical analysis of diverse techniques regarding various image inpainting schemes. This paper goes under (i) Analyzing various image inpainting techniques that are contributed in different papers; (ii) Makes the comprehensive study regarding the performance measures and the corresponding maximum achievements in each contribution; (iii) Analytical review concerning the chronological review and various tools exploited in each of the reviewed works. Finally, the survey extends with the determination of various research issues and gaps that might be useful for the researchers to promote improved future works on image inpainting schemes.
Using Pulse 2 to Teach and Assess Malaysian Pupils Speaking Skill

Textbook plays an important role in ESL class worldwide due to its convenience of providing ready-made language activities. In Malaysian ESL class, locally produced English textbooks have always been the core teaching and learning material but in the new CEFR-aligned curriculum, Pulse 2, a foreign English textbook was introduced to the local secondary school ESL class. Hence, the use of Pulse 2 in teaching and assessing the pupils’ language skills is still considered new to many teachers and its practicality in the local ESL class is yet to be discovered. This study attempts to explore the secondary school teachers’ perception on the speaking activities in this foreign English textbook to teach and assess their pupils’ speaking skill particularly. Survey was employed for teachers to rate the practicality of various aspects of the speaking activities. The overall findings indicated that the speaking activities were mainly rated practical to be used to teach speaking skill and easy to be utilised for speaking assessment purpose. In conclusion, it is evident that the speaking activities in Pulse 2 are widely considered useful in assisting the local teachers in the local ESL classes despite being authored by foreign textbook writer.
Adaptive Transformers for Learning Multimodal Representations

The usage of transformers has grown from learning about language semantics to forming meaningful visiolinguistic representations. These architectures are often over-parametrized, requiring large amounts of computation. In this work, we extend adaptive approaches to learn more about model interpretability and computational efficiency. Specifically, we study attention spans, sparse, and structured dropout methods to help understand how their attention mechanism extends for vision and language tasks. We further show that these approaches can help us learn more about how the network perceives the complexity of input sequences, sparsity preferences for different modalities, and other related phenomena.
Qualimetric monitoring of professional competences of a teacher in the conditions of digitalization of education: problems and solutions

This article is supported by the Russian Foundation for Basic Research (project No. 19-013-00163 А &amp;ldquo;Methodology and Technology of Socio-Pedagogical Research of Family as a Subject of Creation of Individual Educational Trajectory of a Child&amp;rdquo;). The author examines the problem of monitoring of professional competences of a teacher in the conditions of digital environment. The authors propose a new technique for development of a method for assessing professional competences of a teacher based on latent variables theory, where private criteria manifest as integral with regards to indexes and indicators of professional competencies of a teacher. Qualimetric monitoring of professional competences contributes to the growth of personal growth of a teacher and improvement of the quality of his work in digital environment. The authors outline the scientific basis for qualimetric monitoring of professional competences of a teacher using the RUUM 2020 software (Rasch Unidimensional Measurement Models). The suggested approach proves that in the school digital environment can be resolved the issue of accessibility and automation of assessing professional competences of a teachers, apply Qualimetric monitoring for motivation of personal growth and self-improvement of a teacher. The new approach allows presenting the qualitative indicators in quantitative dimension. Such solution of the problem corresponds to the current level of requirements of digital environment for monitoring and scientific organization aimed at improvement of teacher&amp;rsquo;s competences.
THE USE OF DIGITAL LITERACY IN HIGHER EDUCATION

The study aims to determine the use of digital literacy in the students of FKIP Universitas Halu Oleo. This type of research is quantitative research conducted in the Faculty of teacher training and education Universitas Halu Oleo Kendari in the academic period of the odd semester year 2019/2020. The results of this study indicated that in the dimension of access to digital literacy use in the students of FKIP Universitas Halu Oleo with a good category of 54.83%, in the dimension of digital literacy competency in the students of FKIP Universitas Halu Oleo has a good category of 40.11%, and there are differences in the use of digital literacy in the students FKIP Universitas Halu Oleo reviewed from its dimensional. Conclusion of this research is generally a portrait of the use of digital literacy in the students of FKIP Universitas Halu Oleo categorized quite well with the dimension of access strongly affect the ability of digital literacy students FKIP Universitas Halu Oleo
Clifford+T-based Fault-Tolerant Quantum Implementation of Code Converter Circuit

Biswal, Laxmidhar Bandyopadhyay, Chandan Rahaman, HafizurDesign of high-scalable quantum information processor (QIP) towards achieving quantum supremacy is still now in infancy due to catastrophic obstacles from decoherence. In way to address this problem, the use of quantum error correction code (QECC) and fault-tolerant circuit is highly enticieable, which further promises not only to protect extreme fragile quantum state from decoherence but also from other noises. It is seen that the fault-tolerant property can be achieved by the use of transversal primitive unitary operators. Here, we show the design of fault-tolerant implementation of BCD-to-Excess-3 and 2’s complement code converter which is pivotal in the design of high-scalable QIP. In this transformation process, first we transform the input circuit to an intermediate form where we obtain its NCV-based representation. In the second phase, this design is extensively used to form the fault-tolerant design. We also have compared our design with some of the existing works and have registered 39% improvement in design cost. In terms of T-count and T-depth metrics, our proposed designs also provide near-optimal solution.
Uncertainty Evaluation Metric for Brain Tumour Segmentation.

In this paper, we develop a metric designed to assess and rank uncertainty measures for the task of brain tumour sub-tissue segmentation in the BraTS 2019 sub-challenge on uncertainty quantification. The metric is designed to: (1) reward uncertainty measures where high confidence is assigned to correct assertions, and where incorrect assertions are assigned low confidence and (2) penalize measures that have higher percentages of under-confident correct assertions. Here, the workings of the components of the metric are explored based on a number of popular uncertainty measures evaluated on the BraTS 2019 dataset.
Neural side effect discovery from user credibility and experience-assessed online health discussions

Health 2.0 allows patients and caregivers to conveniently seek medical information and advice via e-portals and online discussion forums, especially regarding potential drug side effects. Although online health communities are helpful platforms for obtaining non-professional opinions, they pose risks in communicating unreliable and insufficient information in terms of quality and quantity. Existing methods in extracting user-reported adverse drug reactions (ADRs) in online health forums are not only insufficiently accurate as they disregard user credibility and drug experience, but are also expensive as they rely on supervised ground truth annotation of individual statement. We propose a NEural ArchiTecture for Drug side effect prediction (NEAT), which is optimized on the task of drug side effect discovery based on a complete discussion while being attentive to user credibility and experience, thus, addressing the mentioned shortcomings. We train our neural model in a self-supervised fashion using ground truth drug side effects from mayoclinic.org. NEAT learns to assign each user a score that is descriptive of their credibility and highlights the critical textual segments of their post.Experiments show that NEAT improves drug side effect discovery from online health discussion by 3.04% from user-credibility agnostic baselines, and by 9.94% from non-neural baselines in term of F1. Additionally, the latent credibility scores learned by the model correlate well with trustworthiness signals, such as the number of "thanks" received by other forum members, and improve credibility heuristics such as number of posts by 0.113 in term of Spearman's rank correlation coefficient. Experience-based self-supervised attention highlights critical phrases such as mentioned side effects, and enhances fully supervised ADR extraction models based on sequence labelling by 5.502% in terms of precision.NEAT considers both user credibility and experience in online health forums, making feasible a self-supervised approach to side effect prediction for mentioned drugs. The derived user credibility and attention mechanism are transferable and improve downstream ADR extraction models. Our approach enhances automatic drug side effect discovery and fosters research in several domains including pharmacovigilance and clinical studies.
Quantum error-correcting codes and their geometries

This is an expository article aiming to introduce the reader to the underlying mathematics and geometry of quantum error correction. Information stored on quantum particles is subject to noise and interference from the environment. Quantum error-correcting codes allow the negation of these effects in order to successfully restore the original quantum information. We briefly describe the necessary quantum-mechanical background to be able to understand how quantum error correction works. We go on to construct quantum codes: firstly qubit stabilizer codes, then qubit non-stabilizer codes, and finally codes with a higher local dimension. We will delve into the geometry of these codes. This allows one to deduce the parameters of the code efficiently, deduce the inequivalence between codes that have the same parameters, and presents a useful tool in deducing the feasibility of certain parameters. We also include sections on quantum maximum distance separable codes and the quantum MacWilliams identities.
Optimum Cost Design of Reinforced Concrete Slabs Using Mouth Brooding Fish Algorithm

Abstract In this article, the optimum design of a reinforced concrete solid slab is presented via the Mouth Brooding Fish (MBF) algorithm that is recently developed. It is based on mouth brooding fish life cycle. This algorithm utilizes the movements of the mouth brooding fish and their children’s struggle for survival as a pattern to find the best possible answer. The cost of the solid slab is considered to be the objective function, and the design is based on the ACI code. The efficiency of this algorithm is compared with Neural Dynamic (ND) and Particle Swarm Optimization (PSO). The numerical results indicate that the MBF algorithm can to construct very promising results and has merits in solving challenging optimization problems.
Dual context prior and refined prediction for semantic segmentation

Recently, the focus of semantic segmentation research has shifted to the aggregation of context prior and refined boundary. A typical network adopts context aggregation modules to extract rich semantic features. It also utilizes top-down connection and skips connections for refining boundary details. But it still remains disadvantage, an obvious fact is that the problem of false segmentation occurs as the object has very different textures. The fusion of weak semantic and low-level features leads to context prior degradation. To tackle the issue, we propose a simple yet effective network, which integrates dual context prior and spatial propagation-dubbed DSPNet. It extends two mainstreams of current segmentation researches: (1) Designing a dual context prior module, which pays attention to context prior again with a shortcut connection. (2) The network can inherently learn semantic aware affinity values for each pixel and refine the segmentation. We will present detailed comparisons, which perform on PASCAL VOC 2012 and Cityscapes. The result demonstrates the validation of our approach.
Variable Splitting Methods for Constrained State Estimation in Partially Observed Markov Processes

In this paper, we propose a class of efficient, accurate, and general methods for solving state-estimation problems with equality and inequality constraints. The methods are based on recent developments in variable splitting and partially observed Markov processes. We first present the generalized framework based on variable splitting, then develop efficient methods to solve the state-estimation subproblems arising in the framework. The solutions to these subproblems can be made efficient by leveraging the Markovian structure of the model as is classically done in so-called Bayesian filtering and smoothing methods. The numerical experiments demonstrate that our methods outperform conventional optimization methods in computation cost as well as the estimation performance.
A Context-Aware Personalized Hybrid Book Recommender System

Contextual information such as emotion, location and time can effectively improve product or service recommendations, however, studies incorporating them are lacking. This paper presents a context-aware recommender system, personalized based on several user characteristics and product features. The recommender system which was customized to recommend books, was aptly named as a Context-Aware Personalized Hybrid Book Recommender System, which utilized users’ personality traits, demographic details, location, review sentiments and purchase reasons to generate personalized recommendations. Users’ personality traits were determined using the Ten Item Personality Inventory. The results show an improved recommendation accuracy compared to the existing algorithms, and thus indicating that the integration of several filtering techniques along with specific contextual information greatly improves recommendations.