Reviewerly/exHarmony huggingface dataset:
Secure Public Storage Auditing Protocol for Privacy-Preserving Fog-to-Cloud Computing

With the emergence of the Internet of things (IoT) and the rapid development of Mobile Network Devices, data collection and storage become more convenient and fast, especially for the edge computing and development in the cloud. However, the uncertain time-delay, lack of mobile support, location-sensitive and other problems, the efficiency and security of data storage are greatly tested in the process of data storage in cloud computing. In order to solve these problems, fog Computing came into being. In this paper, we use the Internet of things (IoT) equipment to cloud data transmission and security audit in Intelligent Transportation System (ITS), and use the techniques of Bilinear map, homomorphic verifier and CDH difficult problem hypothesis, etc. A secure storage and Public Audit Protocol for step-by-step Storage and signature verification is proposed to improve the storage efficiency and security audit of fog-to-cloud data. And through theoretical analysis and verification, the security and efficiency of the protocol are analyzed, which can achieve the desired effect.
Agents in dynamic contexts, a system for learning plans

Reproducing the human ability to cooperate and collaborate in a dynamic environment is a significant challenge in the field of human-robot teaming interaction. Generally, in this context, a robot has to adapt itself to handle unforeseen situations. The problem is runtime planning when some factors are not known before the execution starts. This work aims to show and discuss a method to handle this kind of situation. Our idea is to use the Belief-Desire-Intention agent paradigm, its the Jason reasoning cycle and a Non-Axiomatic Reasoning System. The result is a novel method that gives the robot the ability to select the best plan.
An Area-Optimized Architecture for LiCi Cipher

With the rise in ubiquitous computing in resource-constrained devices, there is the huge emphasis being laid on minimal area consumption, power utilization, and optimum cost without compromising on the security objectives. Selecting lower area overhead as the target, two architectures for LiCi cipher are proposed, which primarily reduce the LUTs, slices utilized. S-boxes are one of the chief reasons for the increase in area and cost due to the memory requirements. In this chapter, we reduce the number of S-boxes, thereby reducing the overall area footprint. Based on the proposed architecture, power consumption is minimized.
Personalized Recommendation Algorithm Based on User Preference and User Profile

Traditional recommendation algorithms are faced with the problems of low recommendation accuracy and cold start. To solve these problems, a personalized recommendation algorithm model based on user profile and user preference is proposed. It is more effective than the traditional collaborative filtering recommendation algorithm based on users. Firstly, the user model is established based on the user’s historical rating data. When calculating the user similarity, the user profile information is integrated into the user model. Finally, according to the individual profile differences between different users, the user profile parameters are adjusted to further optimize the user model. The experimental results show that the personalized recommendation algorithm based on user profile and user preference model can effectively improve the performance of the recommendation system.
Using K-means Clustering Algorithm with Python Programming for Predicting Breast Cancer

In this paper, we identify the mutated signal transduction pathways in a breast cancerous cell. A simulated model is developed for these pathways. Some of the pathways like PKB (protein kinase B), MAPK (mitogen-activated protein kinase), MTOR (mammalian target of rapamycin), Fas ligand (Type-II transmembrane protein), Notch (single-pass transmembrane receptor), SHH (Sonic Hedgehog), Tnf (tumor necrosis factor), Wnt (wingless/integrated) pathways are simulated. For computational modeling of signal transduction pathways, SBML (Systems Biology Markup Language) is used. Programming is done in SBML and executed in Cell Designer. These simulated models are in the form of XML files. We extracted the information in the XML files into tables, and we applied information processing techniques to it like information cleaning, information integration, information transformation, information reduction and information discretization. K-means clustering algorithm is applied on the extracted data set. Python code is written to implement K-means clustering algorithm. Two clusters are formed after running the code on the data set, one representing benign tumors and the other representing malignant tumors.
A C-LSTM with Attention Mechanism for Question Categorization

Text categorization plays a vital role in text mining applications such as customer care service, business intelligence, and records management. Specifically, text categorization assigns content into a set of predefined aspects or categories. In this paper, we propose a convolutional long short-term memory (C-LSTM) with an attention mechanism for question categorization. The convolutional neural network (CNN) layer extracts higher-level feature representation on input data. These features fed into an LSTM network for creating a document or sentence context representation sequentially. Attention mechanism employed to pay selective attention to the output of C-LSTM. Also, a fully connected layer and output layer added on the top of the attention layer. We evaluate the proposed model on the question classification dataset. The results show that the proposed C-LSTM with attention mechanism outperforms.
ReLUSyn: Synthesizing stealthy attacks for deep neural network-based safety-critical cyber-physical systems

Safety-critical cyber-physical systems have become an important part of our society. The controllers for safety-critical systems have recently been leveraging the research progress in Deep Neural Networks (DNNs) in order to construct data-driven models with high safety and reliability properties. There have been multiple approaches that are being used to enforce properties such as safety and stability on the models obtained after training in order to obtain robust neural networks.
Parallel implementation of genetic algorithm on FPGA using Vivado high level synthesis

Genetic algorithm (GA) is one of most popular evolutionary search algorithms that simulates natural selection of genetic evolution for searching solution to arbitrary engineering problems. However, it is computationally intensive and will become a limiting factor for evolving solution to most of the real life problems as it involves large number of parameters that needs to be determined. Fortunately, there are some parallel platforms such as field programmable gate array (FPGA) that can be adopted to overcome this constrains by improving its latency. So, efficient parallel implementation of GA was proposed where each step of GA was exploited to improve its computational task. Moreover, many optimization and parallelisation techniques were adopted and applied to achieve high speed up. The results show that 43 speed up is achieved compared with the typical one. Moreover, higher speed up can be achieved with larger input size.
Learning from Small Data Through Sampling an Implicit Conditional Generative Latent Optimization Model.

We revisit the long-standing problem of learning from a small sample, to which end we propose a novel method called GLICO (Generative Latent Implicit Conditional Optimization). GLICO learns a mapping from the training examples to a latent space and a generator that generates images from vectors in the latent space. Unlike most recent works, which rely on access to large amounts of unlabeled data, GLICO does not require access to any additional data other than the small set of labeled points. In fact, GLICO learns to synthesize completely new samples for every class using as little as 5 or 10 examples per class, with as few as 10 such classes without imposing any prior. GLICO is then used to augment the small training set while training a classifier on the small sample. To this end, our proposed method samples the learned latent space using spherical interpolation, and generates new examples using the trained generator. Empirical results show that the new sampled set is diverse enough, leading to improvement in image classification in comparison with the state of the art, when trained on small samples obtained from CIFAR-10, CIFAR-100, and CUB-200.
Fractional Complex Dynamical Systems for Trajectory Tracking using Fractional Neural Network via Fractional Order PID Control Law

In this paper the problem of trajectory tracking is studied.Based on the Lyapunov theory, a Fractional Order PIDcontrol lawthat achieves the global asymptotic stability of the trackingerror between a fractional order recurrent neural network anda fractional order complex dynamical network is obtained. Toillustrate the analytic results we present a tracking simulationof a dynamical network with each node being just one frac-tional order Lorenz´ s dynamical system and three identicalfractional order Chens dynamical systems.
Your Neighbor Knows What You’re Doing: Defending Smart Home IoT Device Traffic from Privacy LAN Attacks

Internet of Things (IoT) devices introduce unprecedented security challenges for protecting the privacy of users inside the home. Despite encrypting wireless communication traffic by standard security protocols (e.g., WPA2), an attacker near the smart home can still extract packet header information (e.g., MAC address, packet length) from the available unencrypted contents to make predictions about the user’s behavior. To prevent this severe breach on privacy, in this paper, we propose a bandwidth efficient defense method through the introduction of changing padding durations for traffic shaping to reduce the confidence of a nearby attacker in the LAN from identifying genuine user activities for WiFi-enabled IoT devices. From our performance evaluation, we decreased bandwidth usage by over 20% at low attacker confidence with our proposal compared to the conventional method of fixed padding.
A Novel Hardware Architecture for Rectangle Block Cipher

This era of constrained computing devices require proper security in the limited available area. Lightweight cipher RECTANGLE is appropriate for numerous platforms and resource-limited environment. In this chapter, we present novel hardware architecture of RECTANGLE block cipher having reduced datapath width. The proposed design is evaluated and compared in different field programmable gate arrays (FPGA) based on various metrics like area, power, and latency. This chapter provides area-optimization to the hardware in terms of reduced number of slices, LUTs, and flip-flops. Moreover, architecture gives the same or improved results for power consumption depending on the platform used for implementation purpose.
Penerapan Sistem Informasi Data Iuran di Sekretariat Warga Tutuan Gunaksa

Maha Semaya Ki Mantri Tutuan residents are Ki Mantri Tutuan’s descent. . Ki Mantri Tutuan is Dalem Mangori’s descent who The King of Kelingga East Java. Based on data from the Ki Mantri Tutuan secretariat, it has 6.150 families total population. The residents are scattered around Bali and outside of Bali. Deployment of the residents in various regions, requires the secretariat to be able to utilize information technology to spread information. The technology currently applied cannot disseminate information widely and has weaknesses in data validity. Community service activities aim to implement information technology in managing resident contributions data. The final result of PKM activities is the development and application of website-based information systems in the management of residents contributions data. The data management information system is used to manage residents data, contributions data, and financial organization of Ki Mantri Tutuan. Keywords — Contributions Data Management, Maha Semaya Ki Mantri Tutuan, Information Systems
Information Governance for the Implementation of Cloud Computing

Information Governance has become gradually an important area due to the explosion of data in numerous formats at diverse levels of any organization. An Information Governance platform may have the capability to improve the data quality and may also support in leveraging strategic decision making. There is a growing trend to move the data from on-premise software to the new on-demand cloud-based solutions to support the growing needs of the business computing services which has the potential to lower the costs and mitigating the risks. Cloud computing is considered as a utility that is massively scalable and can be readily modified, the way we may control the temperature in the furnace using the thermostat which has the potential to save energy. There are many organizations that are investigating the Cloud to see if the application can meet their information Governance needs such as eDiscovery, compliance, law, security, risk management, records management, and business operations. Information Governance is a super-discipline that embraces components of several key fields as mentioned above (Robert F. Smallwood, 2014).
Numerical analysis of fractional differential equation by TSI-wavelet method

In this paper, we propose a new numerical algorithm for the approximate solution of non-homogeneous fractional differential equation. Using this algorithm the fractional differential equations are transformed into a system of algebraic linear equations by operational matrices of block-pulse and hybrid functions. Based on our new algorithm, this system of algebraic linear equations can be solved by a proposed (TSI) method. Further, some numerical examples are given to illustrate and establish the accuracy and reliability of the proposed algorithm.
Design and Implementation of Second-hand goods renting System Based On Ethereum Smart Contract

Ethereum blockchain is a revolutionary decentralized database technology on the Internet, with the characteristics of data tamperability, disintermediation, autonomy, traceability, point-to-point transaction, programmable and so on. Its emergence has an important impact on the development of many traditional industries. This paper analyzes the shortcomings of the traditional second-hand goods rental system and uses the blockchain technology to design the second-hand goods renting system based on ethereum smart contract. It introduces the background of blockchain and Ethereum smart contract, gives the overall framework of system design, and specifically describes the three parts of the system: blockchain module, smart contract module and the front end of DAPP, and the problems of combining blockchain and rental system are analyzed to provide a reference for better applications in the future.
Training Neural Networks to Produce Compatible Features

This paper proposes to make a first step towards compatible and hence reusable network components. Rather than training networks for different tasks independently, we adapt the training process to produce network components that are compatible across tasks. In particular, we split a network into two components, a features extractor and a target task head, and propose various approaches to accomplish compatibility between them. We systematically analyse these approaches on the task of image classification on standard datasets. We demonstrate that we can produce components which are directly compatible without any fine-tuning or compromising accuracy on the original tasks. Afterwards, we demonstrate the use of compatible components on three applications: Unsupervised domain adaptation, transferring classifiers across feature extractors with different architectures, and increasing the computational efficiency of transfer learning.
New Directions of International Development Cooperation for Korea

연구목적: 우리나라는 2010년 OECD/DAC가입 이후 국제개발협력 분야에서 많은 개선을 이루었지만, 제도·정책 측면에서 원조의 분절화, 개발효과성 실현 및 성과평가의 체계화 등 여러 취약점을 노정하고 있다. 본 연구는 한국의 국제개발협력정책이 가지고 있는 문제점과 그 원인을 파악하고 개선방안을 제시한다. 연구의 중요성: 한국의 국제개발협력정책이 가지고 있는 취약점과 한계를 합리적 정책모형에 따라 정책목적, 이행체제, 성과평가를 분석한다. 이를 통해 향후 한국 정부가 3차 기본계획을 수립함에 있어 일관성 있는 정책으로 개발효과성을 제고할 수 있도록 제도개선 방향을 제시하는 실천적 중요성을 가진다. 연구방법론: 본 연구는 2010년 OECD/DAC에 가입 후 지난 10년간의 대한민국정부의 공식문서와 간행물 그리고 공식적인 통계자료를 분석하였으며, 이와 더불어 정부, 학계 그리고 현장 전문가들과 심층인터뷰를 실시하였다. 연구결과: ODA 시행기관이 나뉘어 있는 현재의 분절화된 추진체계가 유지되면서 기관 간 원조중복, 전달체계 혼선, 사업부실화 등 여러 부작용이 발생하였다. 보다 근본적인 취약성은 한국만의 국제개발협력의 철학과 가치가 불분명한 것으로, 이로 인해 시행목표, 추진방향, 주요정책과제 선정 등 하위단계에 대한 명확한 방향성과 체계가 마련되지 못하고 있다. 이는 한국의 개발효과성, 투명성, 책무성을 제고하는데 한계로 작용한다. 결론 및 시사점: 제3차 기본계획을 수립함에 있어 국제개발협력의 보편적 가치를 설정함과 동시에 한국적 정체성이 조화되어야 함을 강조한다. 정책과 집행의 과정에서 연계성과 정합성을 제고하는 것의 필요함을 제시하고, 정부 이외의 민간부분, 시민사회 등 다양한 개발주체의 역할과 참여를 통해 개발 파트너십을 구축하며, 이를 통해 정책의 효과성을 제고해야 함을 강조한다.Purpose: Despite significant improvements in terms of ODA institutions and policies since Korea’s accession to the OECD/DAC in 2010, there have emerged various weaknesses and shortcomings in Korea’s international development cooperation system such as fragmentation, ineffectiveness and low transparency. The study aims to analyze those weaknesses and shortcomings and provide policy recommendations for improvement. Originality: From the analytical perspective of the rational policy model, this study examines policy goals, implementation system, and performance evaluation of Korea’s international development cooperation system. The findings will be important contributions to the upcoming discussions on the 3rd Mid-term Strategy for Development Cooperation. Methodology: The study analyzes official documents, publications, and statistics of the Korean government over the past decade, and in-depth interviews conducted with experts from government, academic and civil society in the field. Result: Due to the fragmented structure of Korea’s ODA policy and implementation, some negative outcomes such as project overlaps and confusions and insolvency have occurred in the ODA delivery process. The more fundamental problem arises from the ambiguity embedded within the basic ideology and goals of international development cooperation of Korea. Because of such ambiguity the multiple ODA implementing agencies have difficulties of aligning their programs with the overall policy goals, resulting in low degrees of effectiveness, transparency and accountability. Conclusion and Implication: The study suggests that the Korean government should pursue universal values of human freedom, economic/social development and global sustainability in international development policy while also maintaining her comparative advantages such as rapid economic development and democratization. It also suggests that the Korean government should enhance the policy rationality and coherence when establishing the 3rd Mid-term Strategy for Development Cooperation. Lastly, the study emphasizes the importance of building partnership with the private sector and civil society in order to enhance development effectiveness.
Budget Learning via Bracketing

Conventional machine learning applications in the mobile/IoT setting transmit data to a cloud-server for predictions. Due to cost considerations (power, latency, monetary), it is desirable to minimise device-to-server transmissions. The budget learning (BL) problem poses the learner's goal as minimising use of the cloud while suffering no discernible loss in accuracy, under the constraint that the methods employed be edge-implementable. We propose a new formulation for the BL problem via the concept of bracketings. Concretely, we propose to sandwich the cloud's prediction, $g,$ via functions $h^-, h^+$ from a `simple' class so that $h^- \le g \le h^+$ nearly always. On an instance $x$, if $h^+(x)=h^-(x)$, we leverage local processing, and bypass the cloud. We explore theoretical aspects of this formulation, providing PAC-style learnability definitions; associating the notion of budget learnability to approximability via brackets; and giving VC-theoretic analyses of their properties. We empirically validate our theory on real-world datasets, demonstrating improved performance over prior gating based methods.
Bayesian inference based high framerate stereo matching and its application in robot manipulation

In stereo matching, the trade-off between the matching accuracy and computation complexity is still an important issue. This paper presents a Bayesian inference based multi-scale weighted voting framework to address the problem by enhancing the accuracy of local stereo matching. The method utilizes the rapidity of the local methods to construct a disparity space with scale information and makes use of the complementarity of the disparity in different scales to find the best disparity distribution by Bayesian inference based weighted voting. The method is generic that can be utilized to arbitrary local stereo matching algorithms and is capable of dramatically improving the matching accuracy without much scarification of the computational efficiency. It is shown in a series of comparison tests that, with the use of the proposed method, the simplest block matching method can be optimized to reach both the accuracy and the efficiency better than several the state-of-the-art real-time stereo matching methods. The proposed method is then integrated into a robot manipulating system to validate its effectiveness.
Information Audit: Langkah Awal Menuju Layanan Prima pada Perpustakaan Badan Lit- bang dan Diklat Kementerian Agama

Information audit provides a frame of checking for information activity to be created, collected, shared and saved. As that activity is being seen and proceed now and how it should be in the future where the changes from as is into as to be is expected to happen. The purpose of audit is to understand which information is needed to support institution’s activities, what kind of information do people use now, where do people gain information and is there a gap in accessing information. Audit may be used to analyze the information that people need, the resources that are being used to gain information and the challenges to access information. As a librarian it is important to give a service in accordance with the need of users and working in cooperation in organization to provide the needs of users. Information audit is one of the tools to improve the service for the users, and librarians should see this as an additional value from organization’s aspect activity.
Datasets for Data-Driven Reinforcement Learning

The offline reinforcement learning (RL) setting (also known as full batch RL), where a policy is learned from a static dataset, is compelling as progress enables RL methods to take advantage of large, previously-collected datasets, much like how the rise of large datasets has fueled results in supervised learning. However, existing online RL benchmarks are not tailored towards the offline setting and existing offline RL benchmarks are restricted to data generated by partially-trained agents, making progress in offline RL difficult to measure. In this work, we introduce benchmarks specifically designed for the offline setting, guided by key properties of datasets relevant to real-world applications of offline RL. With a focus on dataset collection, examples of such properties include: datasets generated via hand-designed controllers and human demonstrators, multitask datasets where an agent performs different tasks in the same environment, and datasets collected with mixtures of policies. By moving beyond simple benchmark tasks and data collected by partially-trained RL agents, we reveal important and unappreciated deficiencies of existing algorithms. To facilitate research, we have released our benchmark tasks and datasets with a comprehensive evaluation of existing algorithms, an evaluation protocol, and open-source examples. This serves as a common starting point for the community to identify shortcomings in existing offline RL methods and a collaborative route for progress in this emerging area.