Reviewerly/exHarmony huggingface dataset:
Data Sharing Model of Internet of Things Based on Blockchain

Abstract Blockchain technology and Internet of Things technology are two new technologies formed in the current transmission of information technology. In the implementation of its technical control, it can scientifically control the information sharing work and realize the artificial adjustment of the transmission control of the Internet of Things technology. In this paper, research on the Internet of Things data sharing model based on block chain with a view to provide guidance to the security of Things data sharing technology under the block chain. In this paper, Hyperledger Fabric block chain platform-based platform, proposed a block-based chain of IOT data sharing model, security and data privacy is an enhancement, obtained by the performance of the test model. Throughput is maximized when the write transaction sending frequency is 100 TPS and the query transaction sending frequency is 250 TPS. The maximum write throughput is 60 TPS, which is better than Bitcoin and Ethereum on the public chain, which proves the feasibility of the model implementation. This model can achieve storage and sharing without the help of a third-party centralized organization, and directly establish trust between participants, which can ensure the safe sharing of data.
Community and LBD-Based Clause Sharing Policy for Parallel SAT Solving

Modern parallel SAT solvers rely heavily on effective clause sharing policies for their performance. The core problem being addressed by these policies can be succinctly stated as “the problem of identifying high-quality learnt clauses”. These clauses, when shared between the worker nodes of parallel solvers, should lead to better performance. The term “high-quality clauses” is often defined in terms of metrics that solver designers have identified over years of empirical study. Some of the more well-known metrics to identify high-quality clauses for sharing include clause length, literal block distance (LBD), and clause usage in propagation. In this paper, we propose a new metric aimed at identifying high-quality learnt clauses and a concomitant clause-sharing policy based on a combination of LBD and community structure of Boolean formulas. The concept of community structure has been proposed as a possible explanation for the extraordinary performance of SAT solvers in industrial instances. Hence, it is a natural candidate as a basis for a metric to identify high-quality clauses. To be more precise, our metric identifies clauses that have low LBD and low community number as ones that are high-quality for applications such as verification and testing. The community number of a clause C measures the number of different communities of a formula that the variables in C span. We perform extensive empirical analysis of our metric and clause-sharing policy, and show that our method significantly outperforms state-of-the-art techniques on the benchmark from the parallel track of the last four SAT competitions.
Blockchain Platforms: A Look at the Underbelly of Distributed Platforms

This book introduces all the technical features that make up blockchain technology today. It starts with a thorough explanation of all technological concepts necessary to understand any discussions related to distributed ledgers and a short history of earlier implementations. It then discusses in detail how the Bitcoin network looks and what changes are coming in the near future, together with a range of altcoins that were created on the same base code. To get an even better idea, the book shortly explores how Bitcoin might be forked before going into detail on the Ethereum network and cryptocurrencies running on top of the network, smart contracts, and more. The book introduces the Hyperledger foundation and the tools offered to create private blockchain solutions. For those willing, it investigates directed acyclic graphs (DAGs) and several of its implementations, which could solve several of the problems other blockchain networks are still dealing with to this day. In Chapter 4, readers can find an overview of blockchain networks that can be used to build solutions of their own and the tools that can help them in the process.
A Series of PIN/Password Input Methods Resilient to Shoulder Hacking Based on Cognitive Difficulty of Tracing Multiple Key Movements

This paper presents a series of secure PIN/password input methods resilient to shoulder hacking. When a person inputs a PIN or password to a smartphone, tablet, banking terminal, etc., there is a risk of shoulder hacking of the PIN or the password being stolen. To decrease the risk, we propose a method that erases key-top labels, moves them smoothly and simultaneously, and lets the user touch the target key after they stopped. The user only needs to trace a single key, but peepers have to trace the movements of all the keys at the same time. We extend the method by assigning different colors, shapes, and/or sizes to keys for enhancing distinguishability, which allows all the keys to be moved instantaneously after key-top labels are erased and the user to touch the target key. We also introduce a “move backward/forward” function that allows the user to play back the movements. This series of methods does not have the highest security, but it is easy to use and does not require any changes to the server side. Results of a performance evaluation demonstrate that this method has high resistance to shoulder hacking while providing satisfactory usability without large input errors.
Human-Computer Interaction using hand gestures

The human-computer interaction became an instrument of maximum importance in bringing the idea that the link between a user and a computer should look more and more like one between two human beings. Later becoming available for the common consumers, the idea attracted many additional fields. Human-computer interaction is now defined as a sum of other related fields named: computer science, cognitive science, and human factors engineering, which ease the communication between users and computers. Research in the human interaction field is done by innovation in the field of software tools, studies of computer-supported work, or intelligent agents. Cognitive models and gesture recognition are also part of this multidisciplinary initiative. The researchers design, construct and inquire new ways and tools to support human activity concerning computers. In the past few years, a big focus of human-computer interaction was the vocal part such as Microsoft's Cortana, Apple's Siri, or Amazon's Alexa. Also, the current trend includes more and more advancements in the fields of augmented reality and virtual reality. The paper is focusing on the interaction between the users' hands and computer programs. The starting point is the given device, a “LEAP MOTION” controller, which can pick up the gestures made by the user and transform them into signals that can be interpreted by the computer, through custom-made software.
Employment Analysis of University Students Majoring in Environmental Materials Based on Human Resources Information System

Abstract In modern business activities, information has become one of the important resources. Whoever obtains the latest information and pays attention to the development and utilization of information resources may obtain huge economic benefits. The intensification of talent competition is both a challenge and an opportunity for colleges and universities. The human resources management department can no longer be the role of the policy executor in the traditional planned economy era, but should change into a policy advisor while understanding the superior policies. An analysis of the existing contingent of environmental materials professionals requires a powerful platform, namely, the human resources management information system. Human resource management department can no longer be the role of policy executor in the era of traditional planned economy, but should be transformed into policy proponent while understanding the superior policy. Based on the human resource information system, this paper attempts to analyze the employment of environmental materials majors in China from the perspective of human resource development, using the relevant theories of human resource development, and puts forward relevant suggestions.
Denoising Algorithm for CT oral Image Based on Bayesian Threshold and Non-Local Mean

Wavelet transform is widely used in speech and image denoising. In the process of oral CT image acquisition, because of the noise caused by imaging principle, human environment and transmission process, it will affect the accuracy of later CT image processing and reconstruction. Therefore, image denoising is an essential part of the CT image preprocessing stage. This paper presents a new Bayesian multi-threshold wavelet transform denoising algorithm. In this method, different thresholds are selected in different generations and directions, and the generated thresholds are used to process the high-frequency coefficients. At the same time, the low-frequency coefficients after wavelet transform are processed with a non-local mean algorithm. Finally, in view of the disadvantages of the traditional soft and hard threshold functions, an improved threshold function is adopted. Compared with bilateral filtering, non-local mean filtering and bilateral filtering algorithms combined with wavelet changes, this method not only improves the peak signal-to-noise ratio and structural similarity, but also makes the image clearer.
PERSONAL AND PROFESSIONAL DEVELOPMENT OF FUTURE EDUCATORS IN THE PROCESS OF EDUCATIONAL PRACTICES

The article highlights the features of content and technology support of personal and professional development of future teachers in the process of educational practices. Abovementioned ideas are presented here in an anthropological dimension. Personal and professional development of students is shown as a process aimed at modelling the personal and professional qualities of future teachers, ability and willingness for development and self-development. Specifics that have been named are for producing new ideas and practices based on acquired knowledges for creation own trajectory of professional development. The value-personal, search and research, reflective components and the 21st century skills are defined as important components of personal and professional development of students. The exploration describes empirical results of research on the formation of abovementioned components in students of specialties «Preschool education» and «Primary education» before their practices. It has been found that future teachers strive for personal and professional development however they don’t have enough skills to plan their actions of self-development. According to the empirical results of this study the scientific and pedagogical staff of Department of Pedagogy and Psychology of Pedagogical institute of Borys Grinchenko Kyiv University developed programs and recommendations for the organization of educational practices. The article describes the content, forms, methods of development of components of personal and professional development of students in the course of educational practices conducted in the preschool and primary school institutions in Kyiv. The essence of such interactive forms as: a roadmap, reflection, a blog, an individual portfolio of personal and professional growth is revealed. The proposed forms of work contribute to the students’ willingness to evaluate the results of their professional activity, to form the ability to work as a team and alone, to be openminded to the opinions of others in educational space, to find new ways of solving pedagogical goals, etc. The empirical results of the study show the positive dynamics of students’ development in value-personal, search and research, reflective components and 21st century skills in the course of proposed forms of work. This testifies to the effectiveness of the created content and technology support for the development of components of personal and professional development of students in the process of educational practices.
Adaptively Simulation-Secure Attribute-Hiding Predicate Encryption

This paper demonstrates how to achieve simulation-based strong attribute hiding against adaptive adversaries for predicate encryption (PE) schemes supporting expressive predicate families under standard computational assumptions in bilinear groups. Our main result is a simulation-based adaptively strongly partially-hiding PE (PHPE) scheme for predicates computing arithmetic branching programs (ABP) on public attributes, followed by an inner-product predicate on private attributes. This simultaneously generalizes attribute-based encryption (ABE) for boolean formulas and ABP's as well as strongly attribute-hiding PE schemes for inner products. The proposed scheme is proven secure for any a priori bounded number of ciphertexts and an unbounded (polynomial) number of decryption keys, which is the best possible in the simulation-based adaptive security framework. This directly implies that our construction also achieves indistinguishability-based strongly partially-hiding security against adversaries requesting an unbounded (polynomial) number of ciphertexts and decryption keys. The security of the proposed scheme is derived under (asymmetric version of) the well-studied decisional linear (DLIN) assumption. Our work resolves an open problem posed by Wee in TCC 2017, where his result was limited to the semi-adaptive setting. Moreover, our result advances the current state of the art in both the fields of simulation-based and indistinguishability-based strongly attribute-hiding PE schemes. Our main technical contribution lies in extending the strong attribute hiding methodology of Okamoto and Takashima [EUROCRYPT 2012, ASIACRYPT 2012] to the framework of simulation-based security and beyond inner products.
OccInpFlow: Occlusion-Inpainting Optical Flow Estimation by Unsupervised Learning

Occlusion is an inevitable and critical problem in unsupervised optical flow learning. Existing methods either treat occlusions equally as non-occluded regions or simply remove them to avoid incorrectness. However, the occlusion regions can provide effective information for optical flow learning. In this paper, we present OccInpFlow, an occlusion-inpainting framework to make full use of occlusion regions. Specifically, a new appearance-flow network is proposed to inpaint occluded flows based on the image content. Moreover, a boundary warp is proposed to deal with occlusions caused by displacement beyond image border. We conduct experiments on multiple leading flow benchmark data sets such as Flying Chairs, KITTI and MPI-Sintel, which demonstrate that the performance is significantly improved by our proposed occlusion handling framework.
OptiShard: An Optimized and Secured Hierarchical Blockchain Architecture

Blockchain has become an emerging decentralized computing technology for transaction-based systems due to its peer-to-peer consensus protocol over an open network consisting of untrusted parties. Bitcoin and other major alt-coins based on monolithic blockchain architecture exhibit significant performance overhead which in turn make them highly non-scalable. Imposing hierarchy in blockchain can improve performance, however it adds on additional security and fault-tolerance measures necessary for correctness of transaction validation. This paper presents a hierarchical blockchain architecture named OptiShard, which addresses the issues of performance, fault-tolerance, and security in the presence of faulty and malicious nodes. The hierarchy comes as a result of dividing the network nodes into multiple disjoint shards. Majority of transactions are distributed among these shards in non-overlapped fashion (i.e., one-to-one mapping of transactions to shards). The model of OptiShard provides a theoretical measure to determine optimal shard size based on two parameters: performance and correctness of transaction validation in the presence of malicious or faulty nodes. The theoretical measure provides guaranteed majority of good shards by allowing to choose the right shard size and forms the basis of network and workload sharding protocols discussed in this paper. OptiShard also provides a mechanism for identifying faulty shards through the overlapping of a small fraction of transactions across all the shards so that all faulty transactions can be discarded. Experimental results exhibit the impact of sharding the network on performance and conform to the theoretical results.
Strong Uniform Consistency with Rates for Kernel Density Estimators with General Kernels on Manifolds

When analyzing modern machine learning algorithms, we may need to handle kernel density estimation (KDE) with intricate kernels that are not designed by the user and might even be irregular and asymmetric. To handle this emerging challenge, we provide a strong uniform consistency result with the $L^\infty$ convergence rate for KDE on Riemannian manifolds with Riemann integrable kernels (in the ambient Euclidean space). We also provide an $L^1$ consistency result for kernel density estimation on Riemannian manifolds with Lebesgue integrable kernels. The isotropic kernels considered in this paper are different from the kernels in the Vapnik-Chervonenkis class that are frequently considered in statistics society. We illustrate the difference when we apply them to estimate the probability density function. Moreover, we elaborate the delicate difference when the kernel is designed on the intrinsic manifold and on the ambient Euclidian space, both might be encountered in practice. At last, we prove the necessary and sufficient condition for an isotropic kernel to be Riemann integrable on a submanifold in the Euclidean space.
Trajectory reconfiguration for time delay reduction in the case of unexpected obstacles: application to 4-mecanum wheeled mobile robots (4-MWMR) for industrial purposes

Nowadays, wheeled mobile robots have a very important role in industrial applications, namely in transportation tasks thanks to their accuracy and rapidity. However, meeting obstacles while executing a mission can cause an important time delay, which is not appreciable in industry where production must be optimal. This paper proposes a new trajectory reconfiguration approach dealing with obstacle generated time delay, applied on four wheeled omnidirectional mobile robots. A strategy is proposed to compensate or minimize the time delay caused by unexpected obstacles, allowing the robot to respect as well as possible its mission planed duration. This strategy is based on updating the velocity reference profile in real time with respect to the environment changing. The aim is to provide to the industrial a support for the robot missions planing and managing, in order to optimize the production.
Novel hands-free interaction techniques based on the software switch approach for computer access with head movements

Abstract Head-operated computer accessibility tools (CATs) are useful solutions for the ones with complete head control; but when it comes to people with only reduced head control, computer access becomes a very challenging task since the users depend on a single head-gesture like a head nod or a head tilt to interact with a computer. It is obvious that any new interaction technique based on a single head-gesture will play an important role to develop better CATs to enhance the users’ self-sufficiency and the quality of life. Therefore, we proposed two novel interaction techniques namely HeadCam and HeadGyro within this study. In a nutshell, both interaction techniques are based on our software switch approach and can serve like traditional switches by recognizing head movements via a standard camera or a gyroscope sensor of a smartphone to translate them into virtual switch presses. A usability study with 36 participants (18 motor-impaired, 18 able-bodied) was also conducted to collect both objective and subjective evaluation data in this study. While HeadGyro software switch exhibited slightly higher performance than HeadCam for each objective evaluation metrics, HeadCam was rated better in subjective evaluation. All participants agreed that the proposed interaction techniques are promising solutions for computer access task.
Topic Modeling for Twitter Users Regarding the "Ruanggguru" Application

PT Ruang Raya Indonesia ("Ruangguru") is the largest and most comprehensive technology company in Indonesia that focuses on education-based services. In 2019 there were 15 million Ruangguru users and 300.00 teachers who had joined and were present in 32 provinces in Indonesia. It prepared a number of expansion strategies to become a company valued at more than US $ 1 billion in the next year or two. The purpose of this research is to classify the opinions of Ruangguru users about the services provided so that it can be an evaluation material in improving their services using the latent direchlet allocation method. The data used comes from a collection of tweets of Twitter users in Indonesia using the Twitter API. The Twitter account used in this study is @ruangguru. The results of the analysis showed that the public perception of Twitter users by using latent dirichlet allocation was formed into 28 topics.Keywords: latent dirichlet allocation, ruangguru, twitter.
“FINANCE AND GROWTH” RE-VISITED

We test whether the relationship between finance and growth is present in 48 countries over 20 different periods of an equal length of 15 years, starting in 1980 (to 1995) and ending in 1999 (to 2014). We estimate growth regressions using an IV approach and we find that (1) overall financial development had a positive effect on economic growth for almost all our studied periods, (2) the legal system is the primary determinant of the effectiveness of the overall financial system, and (3) financial services were relevant for economic growth even during the financial crisis of 2008. This research is part of a research agenda revisiting the finance–growth nexus using up-to-date empirical methodologies.
Development of software defect prediction system using artificial neural network

&lt;span&gt;Software testing is an activity to enable a system is bug free during execution process. The software bug prediction is one of the most encouraging exercises of the testing phase of the software improvement life cycle. In any case, in this paper, a framework was created to anticipate the modules that deformity inclined in order to be utilized to all the more likely organize software quality affirmation exertion. Genetic Algorithm was used to extract relevant features from the acquired datasets to eliminate the possibility of overfitting and the relevant features were classified to defective or otherwise modules using the Artificial Neural Network. The system was executed in MATLAB (R2018a) Runtime environment utilizing a statistical toolkit and the performance of the system was assessed dependent on the accuracy, precision, recall, and the f-score to check the effectiveness of the system. In the finish of the led explores, the outcome indicated that ECLIPSE JDT CORE, ECLIPSE PDE UI, EQUINOX FRAMEWORK and LUCENE has the accuracy, precision, recall and the f-score of 86.93, 53.49, 79.31 and 63.89% respectively, 83.28, 31.91, 45.45 and 37.50% respectively, 83.43, 57.69, 45.45 and 50.84% respectively and 91.30, 33.33, 50.00 and 40.00% respectively. This paper presents an improved software predictive system for the software defect detections.&lt;/span&gt;