Reviewerly/exHarmony huggingface dataset:
Enabling Privacy Policies for mHealth Studies

Pervasive sensing has enabled continuous monitoring of user physiological state through mobile and wearable devices, allowing for large scale user studies to be conducted, such as those found in mHealth. However, current mHealth studies are limited in their ability of allowing users to express their privacy preferences on the data they share across multiple entities involved in a research study. In this work, we present mPolicy, a privacy policy language for study participants to express the context-aware and data-handling policies needed for mHealth. In addition, we provide a privacy-adaptive policy creation mechanism for byproduct data (such as motion inferences). Lastly, we create a software library called privLib for implementing parsing, enforcement, and policy creation on byproduct data for mPolicy. We evaluate the latency overhead of these operations, and discuss future improvements for scaling to realistic mHealth scenarios.
A Comprehensive Survey on Content Based Image Processing Techniques

In modern computing world there is rapid growth in image transaction. Most of the digital equipments generate images for different operations. It creates huge repository of images. Accurate Retrieval of desired images from these repositories becomes a challenging task for every researcher. There are tremendous methods of retrieval of images. CBIR is one of the well-liked techniques to pull out low level features of images. Some of the features are Color, shape, texture and Spatial Location. In the paper, we survey perception of CBIR, variety of techniques for retrieval, features of images, standard image datasets.
Ordalia: Deep Learning Hyperparameter Search via Generalization Error Bounds Extrapolation

We introduce Ordalia, a novel approach for speeding up deep learning hyperparameter optimization search through early-pruning of less promising configurations. Our method leverages empirical and theoretical results characterizing the shape of the generalization error curve for increasing training data size and number of epochs. We show that with relatively small computational resources one can estimate the dominant parameters of neural networks' learning curves to obtain consistently good evaluations of their learning process to reliably early-eliminate non-promising configurations. By iterating this process with increasing training resources Ordalia rapidly converges to a small candidate set that includes many of the most promising configurations. We compare the performance of Ordalia with Hyperband, the state-of-the-art model-free hyperparameter optimization algorithm, and show that Ordalia consistently outperforms it on a variety of deep learning tasks. Ordalia conservative use of computational resources and ability to evaluate neural networks learning progress leads to a much better exploration and coverage of the search space, which ultimately produces superior neural network configurations.
Graph matching on social networks without any side information

Graph matching is an important yet difficult task with many applications in bioinformatics, where it uncovers hidden relationships between species, network security, where it can be used for network de-anonymization, and computer vision, where it solves correspondence problems. When no side information is available, any graph matching algorithm must rely only on the structural information of the two input graphs in order to compute a mapping between the two node sets. One of the most scalable approaches for graph matching uses ideas from percolation theory, where already matched pairs ”infect” other neighbouring pairs. In the absence of any side information, such algorithms expect from the user to provide an initial set of pairs, the seeds, from which the percolation can start. In this paper, we propose DiNoiSe, a new distributed percolation graph matching algorithm, which employs the structural information of the network, but no initial seeds or any other node or edge labels. The algorithm takes advantage of the structural features of scale-free graphs (such as the ones behind social networks) and scales well for large networks, without using any additional input. We demonstrate the effectiveness of our algorithm via experiments on synthetic and real-world datasets.
A Cloud Resource Scheduling Framework for big data stream and analytics in Cloud Environment

The volume of data entering into the cloud is rapidly increasing; therefore, on-demand cloud services should be provided to clients at any time, whereas providers must be able to preserve system availability as well as process a large volume of data. It is obligatory for cloud service providers to lessen large volumes of data; thereby they can reduce costs for maintaining large storage systems. Infrastructure level performance is an important problem which directly affects the overall working of cloud computing environment. The objective of our framework is enhancing the performance of cloud infrastructure. Our approach demonstrates high effective in cloud performance enhancement, as it displays enhancement in both the service providers as well as for cloud users.
Flash Crashes in Multi-Agent Systems Using Minority Games And Reinforcement Learning to Test AI Safety

As AI advances and becomes more complicated, it becomes necessary to study the safety implications of its behavior. This paper expands upon prior AI-safety research to create a model to study the harmful outcomes of multi-agent systems. In this paper, we outline previous work that has highlighted multiple aspects of AI-safety research and focus on AI-safety systems in multi-agent systems. After overviewing previous literature, we present a model focused on flash crashes, a concept often found in economics. The model was constructed using an interdisciplinary approach that includes game theory, machine learning, cognitive science and systems theory to study flash crashes in complex human-AI systems. We use the model to study a complex interaction between AI-agents, and our results indicate the multi-agent system in question is prone to cause flash crashes.
Measurement uncertainty of in-plane displacement field distribution using speckle photography with LiNbO3 crystal

Ferroelectric materials are of great interest because of their rich applications. Several techniques have been used to understand their behaviors. Two-beam coupling configuration using LiNbO3 crystal as a recording mediums has been extensively used for real-time in-plane displacement of a transparent rough object. Low-power He–Ne laser was used to create the index grating of the speckle pattern inside the crystal and to read the grating to give the Young’s fringes. In this technique, for the in-plane displacement measurement, the image of the transmitted object is formed in the crystal. The aim of our work is to extend the applicability of the two-beam coupling configuration using LiNbO3 as a recording medium for the evaluation of in-plane displacement of a transparent rough object using speckle photography method; measurement uncertainty of in-plane displacement was studied on a wide range of displacements using experimental and new theoretical formula for displacement measurement.
Performance Analysis of Jaya Algorithm Using CEC’2013 Benchmark Functions

Jaya algorithm is population-based parameter less heuristic algorithm. The algorithm requires only control parameters like population size and a number of iterations and two stochastic steps (three random number generators). This paper presents an investigation of Jaya algorithm on Congress on Evolutionary Computation 2013 test functions. The experimental results show that Jaya is performing satisfactorily for 28 benchmark functions for lower dimensions and performance degrades for higher dimensions.
Improving the performance of stochastic local search for maximum vertex weight clique problem using programming by optimization

The maximum vertex weight clique problem (MVWCP) is an important generalization of the maximum clique problem (MCP) that has a wide range of real-world applications. In situations where rigorous guarantees regarding the optimality of solutions are not required, MVWCP is usually solved using stochastic local search (SLS) algorithms, which also define the state of the art for solving this problem. However, there is no single SLS algorithm that gives the best performance across all classes of MVWCP instances, and it is challenging to effectively identify the most suitable algorithm for each class of MVWCP instances. In this work, we follow the paradigm of Programming by Optimization (PbO) to develop a new, flexible and highly parametric SLS framework for solving MVWCP, combining, for the first time, a broad range of effective heuristic mechanisms. By automatically configuring this PbO-MWC framework, we achieve substantial advances in the state of the art in solving MVWCP over a broad range of prominent benchmarks, including two derived from real-world applications in transplantation medicine (kidney exchange) and assessment of research excellence.
Empirical Analysis of MapReduce Job Scheduling with respect to Energy Consumption of Clusters

The growing popularity of Cloud Computing has led to an increasing number of applications using MapReduce in cloud data centers. MapReduce workloads are mostly interactive workloads or batch processing workloads that consume an enormous amount of energy when hosted on multiple clusters. Currently, the scheduling of workloads is done with the sole goal of the faster execution time of jobs. However, in doing so, there is wastage of energy as the same jobs could be completed within the stipulated Service Level Agreement (SLA) using fewer resources when hosted on small clusters. Therefore, in order to minimize the energy consumption of MapReduce clusters, workloads could be deployed on a minimum number of clusters depending on the type and size with the goal of minimizing energy consumption and not faster response time. In this work, the three schedulers i.e FIFO, Fair and Capacity schedulers are compared with respect to energy efficiency on small-scale and large-scale workloads. Experiments performed on a small cluster using these workloads show significant energy savings with respect to Capacity scheduler compared to other schedulers.
Machine learning analysis of topic modeling re-ranking of clinical records

Technologies in big data have improved the analysis of clinical information for better understanding diseases in order to provide more efficient diagnoses. An online healthcare system has created huge data by record maintaining, taking into account acceptable requirements and the patient’s care. These clinical records are in files that pose a challenge for data processing and finding relevant documents. In this work, we used a method that combines Statistical Topic Models, Language Models, and Natural Language Processing, in order to retrieve clinical records. On the contrary, for analyzing large clinical records in the form of documents, topic models are used to finding related clusters of disease patterns. Here, it is explored the decomposition of clinical record summaries into topics which enables the effective clustering of relevant documents based on the topic under study. Clinical documents selected in a topic-based approach give proper information to the users for better understanding and derive insights from the related data. In our proposed method, clustering-based semantic similarity topic modeling is used in order to summarize the clinical reports based on latent Dirichlet allocation (LDA) in a MapReduce framework. Automated unsupervised analysis of LDA models is used to identify different disease patterns and to rank topic significance. In this, topic and keyword re-ranking methods assist physicians to get improved information through the LDA-obtained topics. The experimental assessment confirmed the value of the used methods in clinical documents summarization.
The Machine Learning Models for Activity Recognition Applications with Wearable Sensors

The growing number of fitness trackers and dedicated smartphone applications indicate the importance of human activity recognition tasks in everyday life. Wearable devices tend to incorporate a broad spectrum of various sensors, such as an accelerometer, a gyroscope, a heart rate sensor, etc. It is essential to understand what type of data and algorithm contribute to a better overall activity recognition rate, especially for real-time activity recognition based on real-time data streams. In this paper, we focus on comparing and contrasting different machine learning techniques and gauge their performance in terms of human activity recognition accuracy. We investigate and analyze sensor mounting position, sensor types, and data type influence on activity recognition accuracy. Also, we render and analyze the benefits of using a RNN classifier over a more static classifier such as J48 and Naive Bayes algorithms in our empirical study.
Tight lower bound of sparse covariance matrix estimation in the local differential privacy model

In this paper, we study the sparse covariance matrix estimation problem in the local differential privacy model, and give a lower bound of Ω(s2log⁡pnϵ2) on the ϵ non-interactive private minimax risk in the metric of squared spectral norm, where s is the row sparsity of the underlying covariance matrix, n is the sample size, and p is the dimensionality of the data. We show that the lower bound is actually tight, as it matches a previous upper bound. Our main technique for achieving this lower bound is a general framework, called General Private Assouad Lemma, which is a considerable generalization of the previous private Assouad lemma and can be used as a general method for bounding the private minimax risk of matrix-related estimation problems.
Control of the Operation Mode of the Production Facility Based on the Relevant Characteristics of the Technological Process

The article deals with the control of the operation mode of a production facility taking into consideration its actual operation conditions. Control procedure, which is based on the application of Shewhart charts for technological process characteristics, has been proposed. This procedure provides not only identification of the discrepancies in the process, but also the reasons that caused them. Construction of control charts is based on data obtained from the monitoring system and organized taking into consideration cyclic changes in the production process. This allows taking into consideration the actual operation conditions of the production facility and setting the correct standards for controlled parameters. The principles of interpretation of control charts and set of alarms about exceeding the standards, which take into consideration the nature of the influence of the controlled parameters on the production process efficiency, have been proposed. The proposed procedure has been applied to control the operation mode of the water supply pumping station. Standards for controlled technological parameters have been set taking into consideration cyclic changes in the water supply process. The description of alarms, which have been used to notify the operator of the control results, has been performed. The analysis of control charts of technological parameters enabled to determine moments of the discrepancy between the operation mode of the pumping station and the actual needs of the technological process, and to identify its reasons. The obtained results confirm the expediency of using the proposed procedure to take a decision on the need to improve the production process efficiency.
High-fidelity photon-subtraction operation for large-photon-number Fock states

The statistics of photon-subtracted and photon-added states has several important applications in quantum optics and quantum information science. In this study, we propose a scheme for a photon-subtraction operation with SU(1,1) beam splitters. We demonstrate that our scheme overcomes the exponential decaying trend of fidelity and works exceptionally well for quantum states with a large number of photons. Moreover, our scheme is effective even with realistic on-off photon detectors and the heralded single-photon source. Thus, our scheme can serve as a useful tool for manipulating the number of photons in bright coherent states and strongly squeezed states in the future.
Semi-supervised Early Event Detection

Early event detection is among the keys in the field of event detection due to its timeliness in widespread applications. The objective of early detection (ED) is to identify the specified event of the video sequence as early as possible before its ending. This paper introduces semi-supervised learning to ED, which is the first attempt to utilize the domain knowledge in the field of ED. In this setting, some domain knowledge in the form of pairwise constraints is available. Particularly, we treat the segments of complete events as must-link constraints. Furthermore, some segments do not overlap with the event are put together with the complete events as cannot-link constraints. Thus, a new algorithm termed semi-supervised ED (SemiED) is proposed, which could make better early detection for videos. The SemiED algorithm is a convex quadratic programming problem, which could be resolved efficiently. We also discuss the computational complexity of SemiED to evaluate its effectiveness. The superiority of the proposed method is validated on two video-based datasets.
Implementation of MRI Images Reconstruction Using Generative Adversarial Network

This paper focuses on a Magnetic Resonance Imaging (MRI) reconstruction that gives brisk realization. This diminishes the scanning cost and image reconstructed in very fewer time. In this method, Generative Adversarial Network (GAN) designed a generator which gives the better enhancement like texture smoothness, and high resolution. In addition, it also finds the frequency province information to embed resemblance in both the images using parameter Structural Similarity Index (SSIM). Also performed radon transform to find the structural similarity of images with enhancement, accuracy and test whether the images are real or fake. Compared to other methods, the proposed GAN method provides superior reconstruction.
Visualization of Tree-Structured Data Using Web Service Composition

This article reiterates on the recently presented hierarchy visualization service HiViSer and its API [51]. It illustrates its decomposition into modular services for data processing and visualization of tree-structured data. The decomposition is aligned to the common structure of visualization pipelines [48] and, in this way, facilitates attribution of the services’ capabilities. Suitable base resource types are proposed and their structure and relations as well as a subtyping concept for specifics in hierarchy visualization implementations are detailed. Moreover, state-of-the-art quality standards and techniques for self-documentation and discovery of components are incorporated. As a result, a blueprint for Web service design, architecture, modularization, and composition is presented, targeting fundamental visualization tasks of tree-structured data, i.e., gathering, processing, rendering, and provisioning. Finally, the applicability of the service components and the API is evaluated in the context of exemplary applications.
Augmented reality and human factors regarding the neurosurgical operating room workflow

Augmented reality (AR) continues to be heavily studied as a research topic for potential medical use. The goal of seeing the patient’s anatomy below the surface of the human body has always been thought of as the ideal surgical navigation tool. Rather than observing medical imaging, such as computed tomography (CT) or magnetic Resonance (MR) images on a monitor, hospital personnel would be able to see patient specific pathologies through Augmented Reality (AR) glasses. Neurosurgery has commonly been a field of choice for AR integration because of the many needs that can potentially be met. Understanding AR in the neurosurgical Operating Room (OR) does pose some benefits well as concern in terms of human computer interaction (HCI). One of the core concepts of HCI is the idea of user-centered design. While one aims to create an intuitive interface for the user-group, introducing AR into the OR can increase cognitive overload and inattentional blindness if executed improperly without considering the full use-case and all stakeholders. A common application of neuro-navigation is in spinal surgery, which, while incredibly accurate, disrupts OR workflow. These devices drastically improve patient outcomes yet are seldom employed because of these disruptions. HCI concepts can better integrate AR into the OR to solve pitfalls observed in modern neuro-navigation, and gives designers, engineers and surgeons the necessary tools to develop AR solutions. Our goal is to thoroughly analyze the OR workflow such that AR can be effectively incorporated.
A Faster Fuzzy Clustering Approach for Recommender Systems

A recommender system has very important role nowadays, be it business, e-commerce, search engines, entertainment, etc. The need for faster, dynamic, and efficient recommender system arises with huge data on the Internet or website platform. A movie or anime recommender system has a major role in delivering improved entertainment. These recommender systems can provide much personalized recommendations, suggestion to a particular user based on one’s watching habits, or other similar user’s interests, ratings. Plentiful of recommendation techniques has been proposed but most are not able to provide useful recommendation within a very short span of time. In this paper, we aim to propose a fuzzy-clustering-based recommender system which is almost quite efficient and accurate as collaborative filtering (CF) technique but much faster than CF. We have achieved an improvement of approximately 4 s faster than CF techniques. Experimental data justifies the efficiency of the system.
Efficient Practice for Deep Reinforcement Learning

Deep reinforcement learning has demonstrated its ability to solve diverse hard problems, which were not able to be solved previously. However, one of the main drawbacks is the required long training time for a task. Furthermore, trial-and-error in reinforcement learning is often inappropriate in most real-world tasks. There were some discussions on transfer learning, specifically sim-to-real transfer, but the design of new environments or defining an environment with similar or relevant goals require tedious human efforts. This research aims to leverage practice approaches, which do not require a new environmental design for transfer learning, to shorten the time for training a deep reinforcement learning agent and to achieve an optimal policy for the maximum expected rewards. We verify the efficacy of existing practice approach with Deep Q networks (DQN) in Atari games and also introduce a novel practice approach involving iterations of short periods of practice and reinforcement learning, to further improve the performance of an agent.
Human Resource Management Under the Impact of Artificial Intelligence

Artificial intelligence drives the fourth industrial revolution and becomes the focus of global commercial capital competition. Artificial intelligence will affect the development of the whole society, but also affect the development of human resources management industry. The human resources industry will be reshaped, human resources services will be highly intelligent, and the labor supply side will need structural reforms. Artificial intelligence has been deeply applied to the “six modules” of human resource management. As a human resources practitioner, it is necessary to re-establish the mission of HR with an open mind, learn human-machine cooperation, and make full use of artificial intelligence technology to improve the efficiency and quality of human resources services. Artificial intelligence and machine learning will provide enterprises with more humanized and valued human resources services.