Reviewerly/exHarmony huggingface dataset:
Locally Private Distributed Reinforcement Learning

We study locally differentially private algorithms for reinforcement learning to obtain a robust policy that performs well across distributed private environments. Our algorithm protects the information of local agents' models from being exploited by adversarial reverse engineering. Since a local policy is strongly being affected by the individual environment, the output of the agent may release the private information unconsciously. In our proposed algorithm, local agents update the model in their environments and report noisy gradients designed to satisfy local differential privacy (LDP) that gives a rigorous local privacy guarantee. By utilizing a set of reported noisy gradients, a central aggregator updates its model and delivers it to different local agents. In our empirical evaluation, we demonstrate how our method performs well under LDP. To the best of our knowledge, this is the first work that actualizes distributed reinforcement learning under LDP. This work enables us to obtain a robust agent that performs well across distributed private environments.
Rating the performance of noisy teleportation using fluctuations in fidelity

Quantum teleportation is one of the most pioneering features of the quantum world. Typically, the quality of a teleportation protocol is solely judged by its average fidelity. In this work, we analyze the performance of teleportation in terms of both fidelity and the deviation in fidelity. Specifically, we define a quantity called teleportability score, which incorporates contributions from both the fidelity and its deviation. It also takes into account the sensitivity one requires for a protocol in which the teleportation of a quantum state is required in one or many intermediate steps. We compute the teleportability score in the noiseless scenario and find that it increases monotonically with the entanglement content of the resource state. The result remains same even if we consider an n-chain repeater-like configuration. However, in the presence of noise, the teleportability score, can sometime display a nonmonotonic behaviour with respect to the entanglement content of the initially shared resource state. Specifically, under local bit-flip and bit-phase-flip noise, lesser entangled states can have higher teleportability score for certain choice of system parameters. In the presence of global depolarizing noise, for low entangled resource states and high sensitivity requirements, the noisy states can have better a teleportability score in comparison to the noiseless scenario.
Human-like Decision Making for Autonomous Driving via Adversarial Inverse Reinforcement Learning

To make human-like decisions under complex driving environment is a challenging task for autonomous agents. Imitation Learning or learning-from-demonstration methods have seen great potential for achieving such a goal. Some state-of-the-art studies apply Generative Adversarial Imitation Learning (GAIL) to learn sequential decision-making and control policies. While GAIL can directly learn a policy, it lacks the ability to recover a reward function, which is considered robust and adaptable to environments changes. Adversarial Inverse Reinforcement Learning (AIRL) is another learning-from-demonstration method that can achieve similar benefits as GAIL but also learns the reward function with the policy simultaneously. In the original work of AIRL, it has been demonstrated in single-agent environments such as maze navigation and ant running tasks in OpenAI Gyms. In this paper, we augment AIRL by concatenating semantic reward terms in the learning framework to improve and stabilize its performance, and then extend it to a more practical but challenging situation, i.e. decision-making scenario in highly interactive driving environment. Four performance evaluation metrics are proposed and compared with some Imitation Learning based methods and Reinforcement Learning based methods. Simulation results show that the augmented AIRL outperforms all the other methods, and the trained vehicle agent can perform decision-making behaviors comparable with that of the experts.
The index of small length sequences

Let [Formula: see text] be a fixed integer. Define [Formula: see text] to be the unique integer in the range [Formula: see text] which is congruent to [Formula: see text] modulo [Formula: see text]. Given [Formula: see text], let [Formula: see text] and define [Formula: see text] to be the index of the sequence [Formula: see text]. If [Formula: see text] have [Formula: see text] but [Formula: see text] for all proper, non-empty subsets [Formula: see text], then a still open conjecture asserts that [Formula: see text] provided that [Formula: see text]. We give an alternative proof, that does not rely on computer calculations, verifying this conjecture when [Formula: see text] is a product of two prime powers.
Gaussian Embedding of Large-Scale Attributed Graphs

Graph embedding methods transform high-dimensional and complex graph contents into low-dimensional representations. They are useful for a wide range of graph analysis tasks including link prediction, node classification, recommendation and visualization. Most existing approaches represent graph nodes as point vectors in a low-dimensional embedding space, ignoring the uncertainty present in the real-world graphs. Furthermore, many real-world graphs are large-scale and rich in content (e.g. node attributes). In this work, we propose GLACE, a novel, scalable graph embedding method that preserves both graph structure and node attributes effectively and efficiently in an end-to-end manner. GLACE effectively models uncertainty through Gaussian embeddings, and supports inductive inference of new nodes based on their attributes. In our comprehensive experiments, we evaluate GLACE on real-world graphs, and the results demonstrate that GLACE significantly outperforms state-of-the-art embedding methods on multiple graph analysis tasks.
Tree-Based Classifiers on Human Locomotion Statistical Parameters

In order to describe the human locomotion types based on the extracted parameters from video sequences, data mining techniques are used to discover classification rules and generating decision trees. Applying J48, JRIP and Random forest classifiers in the space of extracted statistical parameters from video sequences, in the evaluation process, the best result was obtained by the Random forest classifier. The purpose of this research is to represent knowledge obtained from video sequences about human locomotion, in form of ontology, with rules that describe the walking, jogging and running locomotion types.
A Modified Canny Edge Detection Approach to Early Detection of Cancer Cell

Canny Edge Detection technique is considered to be one of the most efficient techniques over other edge detection techniques. However this technique has some limitations; the computation time is very high because of its complexity in processing. When applied on medical images, sometimes it detects edges which are not desired. In this communication we present an edge detection technique which may be considered as a Modified Canny Edge Detection (MCED) technique. This work is developed using MATLAB and analysis is done using medical images where we can detect the malign portions easily through the segmentation portions.
Properties of phonon modes of an ion-trap quantum computer in the Aubry phase

We study analytically and numerically the properties of phonon modes in an ion quantum computer. The ion chain is placed in a harmonic trap with an additional periodic potential of which dimensionless amplitude $K$ determines three main phases available for quantum computations: at zero $K$ we have the case of Cirac-Zoller quantum computer, below a certain critical amplitude $K&lt;{K}_{c}$ the ions are in the Kolmogorov-Arnold-Moser (KAM) phase, with delocalized phonon modes and free chain sliding, and above the critical amplitude $K&gt;{K}_{c}$ ions are in the pinned Aubry phase with a finite frequency gap protecting quantum gates from temperature and other external fluctuations. For the Aubry phase, in contrast to the Cirac-Zoller and KAM phases, the phonon gap remains independent of the number of ions placed in the trap keeping a fixed ion density around the trap center. We show that in the Aubry phase the phonon modes are much better localized compared to the Cirac-Zoller and KAM cases. Thus in the Aubry phase the recoil pulses lead to local oscillations of ions, while in the other two phases they spread rapidly over the whole ion chains, making them rather sensible to external fluctuations. We argue that the properties of localized phonon modes and phonon gap in the Aubry phase provide advantages for the ion quantum computations in this phase with a large number of ions.
Institutional quality and financial development in West Africa Economic and Monetary Union

Having become aware of the financial status of underdeveloped countries of the West African Economic and Monetary Union (WAEMU) and the potential role of institutional factors in the effectiveness of financial development policies,this study proposes an analysis of the impact of institutional quality on the success of their financial development policy.The results of the study show that institutions have a decisive impact on the finance effect on economic growth and development. The study explains that since independence(1960) to the present, various financial development policies have not paid off. The author, therefore, assigns a cause for these,institutional deficiencies, and inconsistencies in the choice of economical and financial policies and shortcomings in the quality of governance. This study emphasizes the role of institutions and a favorable legal and institutional environment for the formation of a foundation for healthy financial development.
A BigData approach for sentiment analysis of twitter data using Naive Bayes and SVM Algorithm

Data mining and sentiment analysis are two most versatile research areas in field of real time knowledge extraction. Real time twitter data analysis can plays very crucial role to observe the thinking and view point of people and users. Nowadays, social networking sites have become centric points to share your thoughts and viewpoints. Analysis of social networking data can help a lot to observe trend of society. It can also help to derive user interest and hidden activities. Sentiment analysis is the approach to determine whether piece of writing is positive, negative or neutral. It also help to derive user opinion and attitude of writer. Sentiment analysis of twitter user can help to track eventual view point of user. Country wise accumulative view point can help to derive overall opinion of country citizens and their thinking criteria. This work has proposed sentiment analysis model to observe positive and negative view point of different countries based on sentiment analysis approach. The complete work will be implemented using Hadoop Ecosystem to perform parallel processing on large data.
PERENCANAAN STRATEGIS SI/TI PADA DOCUMENT MANAGEMENT MENGGUNAKAN WARD AND PEPPARD (STUDI KASUS: PT. VISIONET DATA INTERNATIONAL)

PT. Visionet Data International is a company engaged in the field of IT that serves Total IT Managed services. Because the development of Information Systems and Information Technology (IS / IT) in fulfilling the company's vision and mission, a good plan is needed. In this research focuses on the documentation management system in which there is no good company documentation management in accordance with the provisions in force. Regarding data collection methods, this research uses interviews with company’s employees. This research is a qualitative descriptive study using the Ward and Peppard method. Which includes SWOT analysis, Value Chain analysis, PEST analysis, application mapping analysis using McFarlan Strategic Grid and analysis of Proposed Information Systems. This research results in the form of application portfolio recommendations such as the development of Document Management in the form of Web-based applications in which all processes are integrated into one. So in the process of submitting new documents to the process of issuing documents can be done in an application which can support the reduction in the use of paper in the company.
Didactic Potential of Using Mobile Technologies in the Development of Mathematical Thinking

The relevance of the research is due to additional opportunities to improve the quality of teaching mathematics in a digital school, support professional orientation and self-determination of young people through the inclusion of mobile services and platforms in mathematical activities. The research problem is determined by the following contradictions: 1) between the capabilities of modern digital technologies in terms of improving the quality of mathematical training, the formation of universal skills required by the economy of the future, and the model implemented by educational institutions; 2) between the need to provide resources for solving problems of a socio-economic nature in the conditions of the fourth industrial revolution and the insufficient level of mathematical literacy of graduates. The purpose of this research is to study the didactic potential of mobile technologies to support mathematical education in a digital school while preparing specialists of the future. Moreover, the research offers a methodological approach to organize project activities of developing applications in terms of increasing the effectiveness of teaching mathematics and vocational guidance. The article clarifies the concepts “mathematical thinking”, “mathematical literacy” for a digital school and describes the didactic principles of incorporating mobile resources into the educational space which increase the efficiency of training engineers of the future, support vocational guidance and self-determination. The author offers methodological methods and recommendations for organizing creative interdisciplinary project activities in order to develop mobile applications that contribute to the formation of qualities and skills forming the basis of mathematical thinking. The materials of the article can be used: firstly, to ensure the personalization of mathematical training in the course of creative, interdisciplinary, cognitive research activities of students while developing mobile applications, social integration and vocational guidance; secondly, to change the concept of mathematical education at school; thirdly, to improve the implemented model of education based on the traditional trajectory
Pengembangan Sistem Informasi Pelayanan Desa Berbasis Web Dengan Metode Prototyping Pada Desa Leran

Current web-based administrative services are not only for government at the city or regional level, but now the village also needs it to support its government activities. According to Law No. 6 of 2014 the village is a legal community unit that has the territorial boundaries that are authorized to regulate and administer government affairs, the interests of the local community based on community initiatives, original rights, and / or traditional rights that are recognized and respected in the Republic of the Republic of Republic's government system. Indonesia. In the current technological era, there are many services for villages that use and utilize information technology so that the service process becomes easier, more transparent and faster. For this reason, in this study we had the opportunity to develop a web-based Village Service Information System in Leran village, in developing the system this time we used a prototyping method. This system will have several features, including: making independent reports, submitting a letter of introduction from the village and online, making a biodata of the village community, and also containing the profile of the village of Leran. The results of this research are a village service information system that can help the work of government officials in the village of Leran, and also facilitate the villagers in obtaining information about services or other information in the village of Leran.
Adaptive Estimator Selection for Off-Policy Evaluation

We develop a generic data-driven method for estimator selection in off-policy policy evaluation settings. We establish a strong performance guarantee for the method, showing that it is competitive with the oracle estimator, up to a constant factor. Via in-depth case studies in contextual bandits and reinforcement learning, we demonstrate the generality and applicability of the method. We also perform comprehensive experiments, demonstrating the empirical efficacy of our approach and comparing with related approaches. In both case studies, our method compares favorably with existing methods.
Coherent track‐before‐detect with micro‐Doppler signature estimation in array radars

In this work, the authors consider joint micro-Doppler signature estimation and track-before-detect for detecting and classifying manoeuvring and small rotary-wing aircraft with a phased array radar receiver. Such aircraft induce low signal-to-noise ratio reflections which complicates their detection. These difficulties are exacerbated by the high manoeuvrability of such aircraft which results with resolution cell migrations in conventional detectors. The authors propose a novel track-before-detect algorithm which jointly estimates the micro-Doppler signature, dominant reflectivity and kinematic trajectory of an object before the decision on its existence is made. These quantities are used in a Neyman-Pearson test against a constant false alarm rate threshold which is equivalent to evaluating a coherent long time integration value. The authors demonstrate the efficacy of the proposed scheme in a real data experiment and compare it with conventional techniques.
Computer-based solar tracking system for PV energy yield improvement

&lt;p&gt;Electric energy is the main driver of various daily activities, both to increase productivity as well as to improve life quality. Energy demand is continuously increasing in parallel to the progress in technology and population growth. Depletion of fossil energy sources and awareness of environmental protection make people resort to renewable energy sources like solar energy. Low efficiency and intermittent characteristic of solar energy may originate from the sun movement along the day as well as the year. Efforts have always been tried to deal with the drawbacks. This paper proposes a computer-based tracking system to fully monitor and control a solar panel movement. The designed system has proven a good performance of working at certain time intervals, either hourly, or even continuously, as desired. Energy yield improvement has been achieved by processing the tracking results with the help of a microcontroller to optimize the power generation of the solar panel.&lt;/p&gt;
A BLAST-based, Language-agnostic Text Reuse Algorithm with a MARKUS Implementation and Sequence Alignment Optimized for Large Chinese Corpora

Until relatively recently, following the life of a phrase or passage from its origin as it is quoted, reused, and remixed through a large corpus of Chinese writing was extremely difficult. Yet identifying how and when a document is appropriating materials from earlier works is critical in many domains.
Improved quality of service-based cloud service ranking and recommendation model

One of the ongoing technologies which are used by large number of companies and users is cloud computing environment. This computing technology has proved that it provides certainly a different level of efficiency, security, privacy, flexibility and availability to its users. Cloud computing delivers on demand services to the users by using various service-based models. All these models work on utility-based computing such that users pay for their used services. Along with the various advantages of the cloud computing environment, it has its own limitations and problems such as efficient resource identification or discovery, security, task scheduling, compliance and sustainability. Among these resource identification and scheduling plays an important role because users always submits their jobs and expects responses in least possible time. Research is happening all around the world to optimize the response time, make span so as to reduce the burden on the cloud resources. In this paper, QoS based service ranking model is proposed for cloud computing environment to find the essential top ranked services. Proposed model is implemented in two phases. In the first phase, similarity computation between the users and their services is considered. In the second phase, computing the missing values based on the computed similarity measures is calculated. The efficiency of the proposed ranking is measured and the average precision correlation of the proposed ranking measure is showing better results than the existing measures.
Local Propagation in Constraint-based Neural Network

In this paper we study a constraint-based representation of neural network architectures. We cast the learning problem in the Lagrangian framework and we investigate a simple optimization procedure that is well suited to fulfil the so-called architectural constraints, learning from the available supervisions. The computational structure of the proposed Local Propagation (LP) algorithm is based on the search for saddle points in the adjoint space composed of weights, neural outputs, and Lagrange multipliers. All the updates of the model variables are locally performed, so that LP is fully parallelizable over the neural units, circumventing the classic problem of gradient vanishing in deep networks. The implementation of popular neural models is described in the context of LP, together with those conditions that trace a natural connection with Backpropagation. We also investigate the setting in which we tolerate bounded violations of the architectural constraints, and we provide experimental evidence that LP is a feasible approach to train shallow and deep networks, opening the road to further investigations on more complex architectures, easily describable by constraints.
On the Influence of the FPGA Compiler Optimization Options on the Success of the Horizontal Attack

This paper reports about the impact of compiler options on the resistance of cryptographic implementations against side channel analysis attacks. We evaluated four compiler option for six different FPGAs from Intel and Xilinx. In order to ensure fair assessment we synthesized always the same VHDL code, kept the measurement setup and statistical analysis method etc. constant. Our analysis clearly shows that the compiler options have an impact on the success of attacks but also that the impact is unpredictable not only between different FPGAs but also for an individual FPGA.