Reviewerly/exHarmony huggingface dataset:
Depth Map Estimation of Dynamic Scenes Using Prior Depth Information

Depth information is useful for many applications. Active depth sensors are appealing because they obtain dense and accurate depth maps. However, due to issues that range from power constraints to multi-sensor interference, these sensors cannot always be continuously used. To overcome this limitation, we propose an algorithm that estimates depth maps using concurrently collected images and a previously measured depth map for dynamic scenes, where both the camera and objects in the scene may be independently moving. To estimate depth in these scenarios, our algorithm models the dynamic scene motion using independent and rigid motions. It then uses the previous depth map to efficiently estimate these rigid motions and obtain a new depth map. Our goal is to balance the acquisition of depth between the active depth sensor and computation, without incurring a large computational cost. Thus, we leverage the prior depth information to avoid computationally expensive operations like dense optical flow estimation or segmentation used in similar approaches. Our approach can obtain dense depth maps at up to real-time (30 FPS) on a standard laptop computer, which is orders of magnitude faster than similar approaches. When evaluated using RGB-D datasets of various dynamic scenes, our approach estimates depth maps with a mean relative error of 2.5% while reducing the active depth sensor usage by over 90%.
Statistical Tests and Confidential Intervals as Thresholds for Quantum Neural Networks

Some basic quantum neural networks were analyzed and constructed in the recent work of the author \cite{dndiep3}, published in International Journal of Theoretical Physics (2020). In particular the Least Quare Problem (LSP) and the Linear Regression Problem (LRP) was discussed. In this second paper we continue to analyze and construct the least square quantum neural network (LS-QNN), the polynomial interpolation quantum neural network (PI-QNN), the polynomial regression quantum neural network (PR-QNN) and chi-squared quantum neural network ($χ^2$-QNN). We use the corresponding solution or tests as the threshold for the corresponding training rules.
CC-Spin: A Micro-architecture design for scalable control of Spin-Qubit Quantum Processor

Quantum Computing is an emerging field of technology with the promise that engineered quantum systems can address hard problems such as, problems with exponential compute complexity in Chemistry, Genomics, Optimization and many more applications. Quantum Computer Architecture is an area of research targeted for the NISQ-era quantum computing and little research has been done for development of a scalable classical control and read-out infrastructure for the quantum processors. The project is aimed at study of SoC-FPGA design methodology and architecture design for control of quantum processor. The targeted quantum hardware is the Spin-Qubit in Semiconductor Quantum Dot Chip. The project is intended for understanding the design and working of a silicon-spin qubit for a computer (architecture) engineer. It further helps identify necessities for an architecture, Instruction Set requirements, bottlenecks and future challenges (specific to Spin-Qubit quantum processor) that would help in better designs for new control architectures. The objective of this thesis is directed towards addressing the architectural challenges for the quantum-classical hardware for controlling the NISQ-era quantum devices and beyond. We analyze the control infrastructure requirements and propose a micro-architecture and waveform generation methodology to integrate the physical device with the quantum compilation tool-chain.
Achieving Confidentiality and Effective Access Control of Cloud Data Using Cipher Text Policy Based Attribute Based Encryption

Today’s era most of the data required for large businesses are stored on cloud. Cloud systems have number of advantages such as it provides space for store large amount of data and shared resources for processing the stored data. Security of the files stored on the cloud is one of the major problems of cloud system. Because data stored on cloud may leaks to unauthorized user or cloud system itself can modify the data or delete the data. So mainly whatever the data stored on cloud by the owner is retrieved by the users who have the permission to access the data which provides the superior access control to the data. Also need to make provision that the data cannot be accessed by cloud itself that is we need to provide integrity auditing of data. This work proposes cipher text-policy ABE scheme with integrity auditing to provide excellent access control and confidentiality of data.
Better Multi-class Probability Estimates for Small Data Sets

Many classification applications require accurate probability estimates in addition to good class separation but often classifiers are designed focusing only on the latter. Calibration is the process of improving probability estimates by post-processing but commonly used calibration algorithms work poorly on small data sets and assume the classification task to be binary. Both of these restrictions limit their real-world applicability. Previously introduced Data Generation and Grouping algorithm alleviates the problem posed by small data sets and in this article, we will demonstrate that its application to multi-class problems is also possible which solves the other limitation. Our experiments show that calibration error can be decreased using the proposed approach and the additional computational cost is acceptable.
SECURITY CONSIDERATIONS REGARDING TERMS AND CONDITIONS OF CLOUD SERVICE PROVIDERS

Security is one of the biggest issues in the cloud-computing world. The customers may keep sensitive information on the cloud services and should trust to the service providers. Cloud service providers should comply with the legislations like data protection laws and should give confidence about their security policy. The terms and conditions document could give some clues about the security policies of the providers. Therefore, in this study we reviewed the T&C documents of some cloud service providers from security aspect in order to provide key measures against breaches in the cloud environment.
PESTO: Proactively Secure Distributed Single Sign-On, or How to Trust a Hacked Server.

Single Sign-On (SSO) is becoming an increasingly popular authentication method for users that leverages a trusted Identity Provider (IdP) to bootstrap secure authentication tokens from a single user password. It alleviates some of the worst security issues of passwords, as users no longer need to memorize individual passwords for all service providers, and it removes the burden of these service to properly protect huge password databases. However, SSO also introduces a single point of failure. If compromised, the IdP can impersonate all users and learn their master passwords. To remedy this risk while preserving the advantages of SSO, Agrawal et al. (CCS'18) recently proposed a distributed realization termed PASTA (password-authenticated threshold authentication) which splits the role of the IdP across n servers. While PASTA is a great step forward and guarantees security as long as not all servers are corrupted, it uses a rather inflexible corruption model: servers cannot be corrupted adaptively and - even worse - cannot recover from corruption. The latter is known as proactive security and allows servers to re-share their keys, thereby rendering all previously compromised information useless. In this work, we improve upon the work of PASTA and propose a distributed SSO protocol with proactive and adaptive security (PESTO), guaranteeing security as long as not all servers are compromised at the same time. We prove our scheme secure in the UC framework which is known to provide the best security guarantees for password-based primitives. The core of our protocol are two new primitives we introduce: partially-oblivious distributed PRFs and a class of distributed signature schemes. Both allow for non-interactive refreshing of the secret key material and tolerate adaptive corruptions. We give secure instantiations based on the gap one-more BDH and RSA assumption respectively, leading to a highly efficient 2-round PESTO protocol. We also present an implementation and benchmark of our scheme in Java, realizing OAuth-compatible bearer tokens for SSO, demonstrating the viability of our approach.
Acceleration Methods for Centralized Multiagent Reinforcement Learning

For multiagent environments, a centralized reinforcement learner can find optimal policies, but it is time-consuming. A method is proposed for finding the optimal policies acceleratingly. The method basically uses the centralized learner and supplementarily uses independent learners in the former phase. The independent learners transfer their learning results to the centralized learner, but excessive transfers cause the failure of learning. Therefore the independent learners should stop according to an appropriate condition. However, it is difficult for this method to find optimal policies for environments in which initial states are far from termination states. In order to find the optimal policies acceleratingly for such environments, this paper proposes multiagent reinforcement learning methods introducing new stop conditions.
Graph Embedded Multiple Kernel Extreme Learning Machine for Music Emotion Classification

Music emotion classification is one of the most importance parts of music information retrieval (MIR) because of its potential commercial value and cultural value. However, music emotion classification is still a tough challenge, due to the low representation of music features. In this paper, a novel Extreme Learning Machine (ELM), combining graph regularization term and multiple kernel, is proposed to enhance the accuracy of music emotion classification. We use nonnegative matrix factorization (NMF) to find the optimal weights of combining multiple kernels. Furthermore, the graph regularization term is added to increase the relevance between predictions from the same class. The proposed Graph embedded Multiple Kernel Extreme Learning Machine (GMK-ELM) is tested on three music emotion datasets. Experiment results show that the proposed GMK-ELM outperforms several well-known ELM methods.
Hubungan Pemahaman Gaya Bahasa dalam Cerita Rakyat terhadap Minat Baca Siswa Kelas V Sekoah Dasar Negeri 17 Kota Bengkulu

The aims in this study to determines the correlation between understanding of folklore language style against reading interest on the V grade students in SDN 17 Bengkulu city. Research method using correlational with quantitative. The population are 46 people located in SDN 17 bengkulu city. The sampling technique used saturated side (census. Questionnaire and data is a data collection technique with a questionnaire for assessing reading interest in the home of 20 item statements while the test for understanding the style with 5 questions that serve as benchmark. Data analysis techniques use the correlation statistics "Product Moment". The results showed that there is a correlation between understanding the style of language in folklore against reading interest. It is proved by rcount = 0,600 bigger than rtable that is 0,355 with significant level 5%. Thus, the hypothesis is proved and accepted. It’s means that reading foklore, making someone will increase interest reading, then someone will understand the style of Language by it self.
WELTY System Design Based on Service Oriented Architecture for Smart HealthCare

Health is a basic need for humans to live. Personal health is obtained by maintaining regular diet and exercise. However, because of the lack of public knowledge about balanced food intake and calorie requirements for daily activities, it is difficult for the community to determine the amount of calories consumed and burned. The author proposes the concept of a system that integrates Service Oriented Architecture (SOA)-based gym and logistics to solve this problem. This research methodology uses a systematic literature review. The results of this study are the big picture of service-based architecture which consists of nine layers, namely profile, order, catalog, payment, shipping, chat, diary, promotion and gym inventory.
A Comparison on Motivation in Learning English between Who Taught by Using Picture Stories and Who Taught by Using Conventional Technique of the Second Year Students’ of Madrasah Tsanawiyah Darul Hikmah Pekanbaru

The research has three formulations of the problem that how students’ motivation in learning English taught by using conventional technique is, how students’ motivation in learning English taught by using picture sories is, and whether or not there is a significant difference of students’ motivation in learning English between students who are taught by using conventional technique and those taught by using picture stories. This research is causal-comperative research. There are two variables in this research. They are x variable (using picture stories) as independent variable and y variable (students’ motivation in learning English) as dependent variable. Based on the writer’ preliminary study of the second year students of Madrasah Tsanawiyah Darul Hikmah Pekanbaru, showed that students’ motivation in learning English was low. The writer had tried to solve this problem by implementing the picture stories as media. The pictures were one of the media in teaching and learning process that could be implemented to the students in learning English. The objective of this research was to find out whether there was a significance difference of using picture stories toward students’ motivation in learning English. The sample of this research was the second year students of Madrasah Tsanawiyah Darul Hikmah Pekanbaru. The writer used observation and questionnaire to collect the data of this research. The data were analyzed by using T-test. Having analyzed the data with independent sample T-test, it can be seen that there is a significance difference between using picture stories and conventional technique toward motivation in learning English of the second year students’ of Madrasah Tsanawiyah Pekanbaru. It was provided by finding t0 (3.510), which is greater than t-table at 5% grade of significance (2.04) and at 1% grade significance (2.76). And to = 3.510 is higher than ttabel from sig. 5% and 1% (2.04 &lt; 3.510 &gt; 2.76). It means there is significance difference between students’ motivation by using picture stories and conventional technique.
PENGOPTIMALAN PORTOFOLIO SAHAM PADA MODEL RISIKO MEAN- SEMI VARIANCE MENGGUNAKAN ALGORITMA GENETIKA

Genetic algorithm is a metaheuristic search algorithm based on searching natural selection and genetic operations to find a solution. The solution obtained from the genetic algorithm is determined by the operator and the parameters used. One that can be discussed using genetic algorithms is an optimal portfolio. In the stock portfolio also used a portfolio model that is the average semi variance to calculate the level of profit and loss levels that exist in the period May 2017 - December 2018. The shares used are LQ 45 shares with each share grouped in each share. The shares are then searched for the optimal weight of each stock using genetic algorithms. From the calculation results, get the optimal solution. The results obtained using a crossover operator consisting of advanced intermediate crossovers, mutations using random mutations, and selection using elistism selection. From this operator, optimal results were obtained with a population of 100, 275 generation, and a 0.6 crossover rate of 0.4 mutation rates.
A Multi-Attention Matching Model for Multiple-Choice Reading Comprehension

The Multi-choice machine reading comprehension, selecting the correct answer in the candidate answers, requires obtaining the interaction semantics between the given passage and the question. In this paper, we propose an end-to-end deep learning model. It employs Bi-GRU to contextually encode passages and question, and specifically models complex interactions between the given passage and the question by six kinds of attention functions, including the concatenated attention, the bilinear attention, the element-wise dot attention, minus attention and bi-directional attentions of Query2Context, Context2Query. Then, we use the multi-level attention transfer reasoning mechanism to focus on further obtaining more accurate comprehensive semantics. To demonstrate the validity of our model, we performed experiments on the large reading comprehension data set RACE. The experimental results show that our model surpasses many state-of-the-art systems on the RACE data set and has good reasoning ability.
Increasing Understanding Of Reading Through Sustained Silent Reading (SSR) At The Second Grade Students Of English Department Of STKIP PGRI Bangkalan

There are five skills in learning English, namely reading, speaking, listening and writing. The five skills are related to one another. This has been taught from elementary to high school so all students must understand it. In reading skills (Reading), students must understand a reading technique because without understanding they will not understand the contents of the reading or the information contained therein. This was experienced by students in the second semester, especially the English department who was in STKIP PGRI Bangkalan. In solving the problem above, the researcher aims to introduce the method used by the lecturer, the Sustained Silent Reading (SSR) Method. This method can help lecturers to invite students to find information and express ideas or ideas. This research method is quantitative in the experimental study where there is only one class of 30 students in one group pre-test and post-test. From these results it can be concluded that there is significant progress between the mean score 34 in the pre-test and the post-test score 73
Bayesian Reinforcement Learning via Deep, Sparse Sampling

We address the problem of Bayesian reinforcement learning using efficient model-based online planning. We propose an optimism-free Bayes-adaptive algorithm to induce deeper and sparser exploration with a theoretical bound on its performance relative to the Bayes optimal policy, with a lower computational complexity. The main novelty is the use of a candidate policy generator, to generate long-term options in the planning tree (over beliefs), which allows us to create much sparser and deeper trees. Experimental results on different environments show that in comparison to the state-of-the-art, our algorithm is both computationally more efficient, and obtains significantly higher reward in discrete environments.
Pengaruh Media Audio Visual terhadap Kemampuan Menyimak Cerita Kelas V SD Negeri 68 Kota Bengkulu

This research aims to determine the effect of the use crossword puzzle media on the ability of vocabulary mastery of second grade students SDN 68 City of Bengkulu. This research is a quantitative research. The research method used is Quasi Experimental Design with The Matching Only Pretest-Posttest Control Group Design. The population in this study is all students of class II SDN in Bengkulu City that accredited A and use curriculum KTS. Sampling technique with Cluster Random Sampling. The sample obtained is SDN 68 Kota Bengkulu with the number of samples consisting of class V A and V B . Data of this research are analyzed by using inferential analysis that is uji t. From the result of the research, the result of t with thitung is 2,43 and t tabel value with significant level 5% equal to 2,04. So with thitung (2,43)&gt; ttabel (2,04). The calculated value obtained is greater than the value of ttabel so that H0 rejected and Ha accepted. The value of thitung obtained is greater than the ttabel value then it can be concluded that there is influence of media use of audio visual to the ability to listen the story on the student of class V in SDN 68 city of Bengkulu.
Interpretability of Blackbox Machine Learning Models through Dataview Extraction and Shadow Model creation

Deep learning models trained using massive amounts of data tend to capture one view of the data and its associated mapping. Different deep learning models built on the same training data may capture different views of the data based on the underlying techniques used. For explaining the decisions arrived by blackbox deep learning models, we argue that it is essential to reproduce that model's view of the training data faithfully. This faithful reproduction can then be used for explanation generation. We investigate two methods for data view extraction: hill-climbing approach and a GAN-driven approach. We then use this synthesized data for creating shadow models for explanation generation: Decision-Tree model and Formal Concept Analysis based model. We evaluate these approaches on a Blackbox model trained on public datasets and show its usefulness in explanation generation.
EVALUASI PENERAPAN KELAS DIGITAL PADA SMP AL AZHAR 21 SUKOHARJO

The development of technology, information, and communication create new era that called digital era. This era bring a big transformation for human activities, one of them is in educational sector. Digital class is developed in educational sector, now, some of scholls use digital class for their learning program. This issue encourages the researcher to investigate about learning process, strength, and weakness in digital class. This study was conducted in 7C of SMP Islam Al Azhar 21 Sukoharjo because this school have been developing digital class since two years ago. This study is qualitative research. The data collection use interview and observation. The observation was conducted for two weeks. The result of study shows that the learning process in digital class is to be more interserting because using any softwares and applications. The strength of digital class also making students do not bring many books to the school. In other hand, the weakness of digital class is the interaction between the teacher and the students to be limited therefore, the class situation is not condusive yet.Keywords: Technology; Digital Class; Learning; Education.
Determination of the baseline value of the target of the national project «Science» related to the publication activity of the Russian Federation in priority areas [Определение Базового Значения Целевого Показателя Национального Проекта «Наука», Связанного С Публикационной Активностью Российской Федерации В Приоритетных Областях]

One of the seven target indicators of the National Science Project (NSP) is “The place of the Russian Federation by share in the total number of articles in the areas defined by the priorities of scientific and technological development in publications indexed in international databases”. The purpose of this study was to assess the basic value of this indicator for disciplines related to priority areas, according to the Strategy of scientific and technological development of the Russian Federation. The volumes of publication flows of the Russian Federation in 112 disciplines are compared with a similar indicator of countries ranked fifth in the number of publications in the subject area under consideration, indexed in the Web of Science Core Collection (WoS CC). A 5–10 fold lag of the Russian Federation was recorded for the most part of the priority areas identified by the passport of the NSP.