Reviewerly/exHarmony huggingface dataset:
Distributed and automated machine learning in big data stream analytics

Distribution and automation of machine learning, a relatively new concepts occupying the scene of data science and its applications, in an era of massive data that it is growing by the second requiring complex processing and insight generation to feed the new industry of data-driven applications, utilizing the tools of machine learning and artificial intelligence built over big data infrastructures that support parallel and real-time computing, an approach that has fulfilled the requirements to a certain degree but yet requires much more optimization and a sense of autonomy to accommodate new developments and reduce human expertise dependency to achieve the bigger picture and fulfill the potential it has of revolutionizing the tech industry and overall scene. Here in this work we briefly investigate the components required to build such complex pipelines to fulfill autonomy and distribution requirements previously mentioned and propose some abstract concepts to integrate such components to achieve somewhat parallel machine learning pipeline that requires minimal human involvement.
A Deep Reinforcement Learning based Homeostatic System for Unmanned Position Control

Deep Reinforcement Learning (DRL) has been proven to be capable of designing an optimal control theory by minimising the error in dynamic systems. However, in many of the real-world operations, the exact behaviour of the environment is unknown. In such environments, random changes cause the system to reach different states for the same action. Hence, application of DRL for unpredictable environments is difficult as the states of the world cannot be known for non-stationary transition and reward functions.
Importance of Visual Information at Time of Changing Motion Direction on Depth Perception from Monocular Motion Parallax

We demonstrated the importance of visual information displayed at the time of changing the direction of head and stimulus movements on depth perception from monocular motion parallax. In head-tracking systems, increasing the delay time between head and stimulus movements degrades the depth perception from the monocular motion parallax. Based on this result, we hypothesized that the visual information at the time of the changing direction plays a critical role in the depth perception in motion parallax because the delay is especially noticeable at this timing. To test this hypothesis, we evaluated depth perception from monocular motion parallax under conditions with and without displaying a visual stimulus at the time of the motion direction change. Our experiment clarified that stable and unambiguous depth can be perceived by displaying the change of the stimulus motion direction. We also demonstrated the importance of changing the motion direction itself rather than the temporal stopping sandwiched by the deceleration and the acceleration of the stimulus motion.
ELM-Fuzzy Method for Automated Decision-Making in Price Directed Electricity Markets

Among many domains application of information technologies has also transformed electricity markets. Price directed markets refer to the driving the electricity consumption by controlling the electricity prices in real time. This paper frames itself in such an electricity market, where consumers receive the prices and they respond with their demand for the next hour in real time. Response is performed by a smart meter that is equipped with tailored algorithms that make decisions based on the preferences of the customer. In this paper, a responding method is proposed that is based on Extreme Learning Machine (ELM) and Fuzzy Logic Inference. The synergism of the two tools allows the automated decision making where the interference of the human customer is minimal. The proposed method, called ELM-Fuzzy, is presented and tested on a set of real-world data. Results demonstrate the efficiency of the ELM-Fuzzy method to make fast and optimal decisions aiming at reducing the electricity expenses of the customer.
Augmented Reality Sandtable (ARES) Impacts on Learning

The Army Future Command’s Synthetic Training Environment (STE) is positioned to push innovation in training technologies to provide distributed, digital training experiences where and when needed. This paper supports the future training vision in providing an empirical evaluation of how various display technologies influence spatial knowledge within a battlespace visualization context. Three display media were evaluated: a 3D digital map displayed on a 17” monitor, a 2D map digitally down-projected onto a sand table, and a 3D virtual map of a sand table surface rendered in a Microsoft® HoloLens®. Results showed that media used did not significantly alter the participants’ accuracy in performing the tasks, but did have an effect on their time to complete terrain feature identification and SJT tasks, and on their engagement with the tools. Future research should be conducted to assess the potential for learning, the persistence of learning and the transfer of training to real-world activities as compared to traditional training tools within team, collaborative settings – matching the expected training environment of the STE.
A New Network Traffic Identification Base on Deep Factorization Machine

Effective network traffic identification has important significance for network monitoring and management, network planning and user behavior analysis. In order to select and extract the most effective attribute as well as explore the inherent correlation between the attributes of network traffic. We proposed a new network traffic identification method based on deep factorization machine (DeepFM) which can classify and do correlation analysis simultaneously. Specifically, we first embed the feature vector into a joint space using a low-rank matrix, then followed by a factorization machine (FM) which handle the low-order feature crosses and a neural network which can learn the high- order feature crosses, finally the low-order feature crosses and high-order feature crosses are fused and give the classified result. We validate our method on Moore dataset which is widely used in network traffic research. Our results demonstrate that DeepFM model not only have a strong ability of network traffic identification but also can reveal some inherent correlation between the attributes.
A Mapping Approach to Identify Player Types for Game Recommendations

As the size of the domestic and international gaming industry gradually grows, various games are undergoing rapid development cycles to compete in the current market. However, selecting and recommending suitable games for users continues to be a challenging problem. Although game recommendation systems based on the prior gaming experience of users exist, they are limited owing to the cold start problem. Unlike existing approaches, the current study addressed existing problems by identifying the personality of the user through a personality diagnostic test and mapping the personality to the player type. In addition, an Android app-based prototype was developed that recommends games by mapping tag information about the user’s personality and the game. A set of user experiments were conducted to verify the feasibility of the proposed mapping model and the recommendation prototype.
UC-Secure Multiparty Computation from One-Way Functions Using Stateless Tokens

We revisit the problem of universally composable (UC) secure multiparty computation in the stateless hardware token model. All prior constructions required either stronger complexity assumptions, or larger number of rounds, or a larger number of tokens.
FlowSpec: A declarative specification language for intra-procedural flow-Sensitive data-flow analysis

Data-flow analysis is the static analysis of programs to estimate their approximate run-time behavior or approximate intermediate run-time values. It is an integral part of modern language specifications and compilers. In the specification of static semantics of programming languages, the concept of data-flow allows the description of well-formedness such as definite assignment of a local variable before its first use. In the implementation of compiler back-ends, data-flow analyses inform optimizations. Data-flow analysis has an established theoretical foundation. What lags behind is implementations of data-flow analysis in compilers, which are usually ad-hoc. This makes such implementations difficult to extend and maintain. In previous work researchers have proposed higher-level formalisms suitable for whole-program analysis in a separate tool, incremental analysis within editors, or bound to a specific intermediate representation. In this paper, we present FlowSpec, an executable formalism for specification of data-flow analysis. FlowSpec is a domain-specific language that enables direct and concise specification of data-flow analysis for programming languages, designed to express flow-sensitive, intra-procedural analyses. We define the formal semantics of FlowSpec in terms of monotone frameworks. We describe the design of FlowSpec using examples of standard analyses. We also include a description of our implementation of FlowSpec. In a case study we evaluate FlowSpec with the static analyses for Green-Marl, a domain-specific programming language for graph analytics.
Learning Deep Representations from Clinical Data for Chronic Kidney Disease

We study the behavior of a Time-Aware Long Short-Term Memory Autoencoder, a state-of-the-art method, in the context of learning latent representations from irregularly sampled patient data. We identify a key issue in the way such recurrent neural network models are being currently used and show that the solution of the issue leads to significant improvements in the learnt representations on both synthetic and real datasets. A detailed analysis of the improved methodology for representing patients suffering from Chronic Kidney Disease (CKD) using clinical data is provided. Experimental results show that the proposed T-LSTM model is able to capture the long-term trends in the data, while effectively handling the noise in the signal. Finally, we show that by using the latent representations of the CKD patients obtained from the T-LSTM autoencoder, one can identify unusual patient profiles from the target population.
A novel image encryption algorithm based on chaotic billiards

In this paper, a novel symmetric image encryption algorithm is proposed on the basis of the chaotic billiard (Sinai billiard). These chaotic systems, characterized by the highest degree of chaos, have been recently introduced in cryptography. The design of the algorithm relies on the random walk of three-point particles that move freely in the billiard. It includes several rounds of the confusion and diffusion processes. In each ciphering round, the permutation of the image pixels is performed according to the random numbers obtained from the output of a particle system, by using a new calculation technique. Thus, the pixel values are hidden by the pseudo-random sequences generated by the systems of the other two particles. The simulation results demonstrate that the algorithm is simple to implement and is highly secure. It has successfully passed all security tests and has shown the desirable properties in a good secure cryptosystem.
Anti-clutter Gaussian Inverse Wishart PHD Filter for Extended Target Tracking

The extended target Gaussian inverse Wishart probability hypothesis density (ET-GIW-PHD) filter overestimates the number of targets under high clutter density. The reason for this is that the source of measurements cannot be determined correctly if only the number of measurements is used. To address this problem, we proposed an anti-clutter filter with hypothesis testing, we take into account the number of measurements in cells, the target state and spatial distribution of clutter to decide whether the measurements in cell are clutter. Specifically, the hypothesis testing method is adopted to determine the origination of the measurements. Then, the likelihood functions of targets and clutter are deduced based on the information mentioned above, resulting in the likelihood ratio test statistic. Next, the likelihood ratio test statistic is proved to be subject to a chi-square distribution and a threshold corresponding to the confidence coefficient is introduced and the measurements below this threshold are considered as clutter. Then the correction step of ET-GIW-PHD is revised based on hypothesis testing results. Extensive experiments have demonstrated the significant performance improvement of our proposed method.
Internal Motivators for the Protection of Organizational Data

ABSTRACT The purpose of this paper is to investigate the effect of internal motivators (psychological ownership, perceived control, and self-efficacy) on employees' intention to protect organizational data. The threat of cybersecurity breaches is increasing, as are the realization of cybersecurity breaches and the subsequent loss of data. While information security policies increase employees' intentions to protect organizational data, they do not guarantee employee compliance. Results indicate that self-efficacy is associated with perceived control, which, in turn, is associated with perceived psychological ownership of organizational data. Furthermore, self-efficacy and psychological ownership are both associated with an increase in intention to protect organizational data.
Identity-Based Signature Scheme Secure in Ephemeral Setup and Leakage Scenarios

We propose the identity-based signature (IBS) scheme resilient to ephemerals leakage and setup. The scheme is applicable to scenarios, where signers can not trust thoroughly the signing devices, and doubts about the fairness of randomness the hardware and the operating system generate are justified. Our construction is based on the lightweight IBS by Galindo and Garcia. We present a formal security model for IBS in which all values coming from randomness source in signing procedure are leaked or set by adversary. We argue that the original scheme is vulnerable to universal forgery in our security model. We give details on our modified construction and provide a formal security proof in Random Oracle Model, claiming that even such a strong adversary cannot forge a signature in our scheme.
BPNet: Branch-pruned conditional neural network for systematic time-accuracy tradeoff in DNN inference

Recently, there have been attempts to execute the neural network conditionally with auxiliary classifiers allowing early termination depending on the difficulty of the input, which can reduce the execution time or energy consumption without any or with negligible accuracy decrease. However, these studies do not consider how many or where the auxiliary classifiers, or branches, should be added in a systematic fashion. In this paper, we propose Branch-pruned Conditional Neural Network (BPNet) and its methodology in which the time-accuracy tradeoff for the conditional neural network can be found systematically. We applied BPNet to SqueezeNet, ResNet-20, and VGG-16 with CIFAR-10 and 100. BPNet achieves on average 2.0X of speedups without any accuracy drop on average compared to the base network.
Keywords Search Correction Using Damerau Levenshtein Distance Algorithm

Searching is one of the important features on the website, but it is not uncommon for users to make typos when typing keywords. Typing errors of these keywords is usually referred to as typo. This study aims to build a system by providing suggestions for correcting typos in the search feature. Keywords search correction are obtained using the Damerau-Levenshtein Distance Approximate String Matching algorithm by to calculate the editing distance of each word in a keywords with each word in the Indonesian word dictionary. Testing was carried out as many as 40 experiments, with 10 keywords and 250 articles taken randomly. The test results show the Damerau-Levenshtein Distance algorithm is able to provide precision and recall values of 91.24% and 89.58% in providing keyword improvement suggestions. With the improvement of the system, each trial increases with precision value of 0.80 and recall value of 0.98
Color-Guided Adaptive Support Weights for Active Stereo Systems

In this paper we present a color-guided adaptive support weight scheme for the cost aggregation of active stereo matching systems. These systems work by stereo matching two images using the texture provided by infrared pseudo-random dot pattern projectors. This method might fail in regions where the pattern is absent, due to the geometry of the scene and/or the reflectivity properties of the test material. However, leveraging the texture information provided by a separate color sensor might uncover details otherwise unseen by the infrared sensors. We propose a cost aggregation method that utilizes both an infrared and a color image of the scene, making smart aggregation choices depending on the underlying texture information provided by the two separate images. We use our cost aggregation method with the fully self-supervised real-time architecture presented in [14], having in mind the usage of low-cost commercial active stereo matching sensors, like the Intel Realsense D435 sensor, in industrial applications demanding high-quality depth maps. We evaluate our results on our own dataset comprised by vehicle surface data, and give qualitative evidence of the disparity estimation improvements.
Video analytics from edge to server

Deep learning algorithms are an essential component of video analytics systems, in which the content of a video stream is analyzed. Although numerous studies target optimizing server-based video analysis, partially processing videos on edge devices is beneficial. Since edge devices are closer to data, they deliver initial insights on data before sending it to cloud. In this paper, we present an edge-tailored video analytics system by using a multi-stage network designed to run on heterogeneous computing resources.
Biomedical Semantic Embeddings: Using hybrid sentences to construct biomedical word embeddings and its applications

word embeddings is a useful method that has shown enormous success in various NLP tasks, not only in open domain but also in biomedical domain. The biomedical domain provides various domain specific resources and tools that can be exploited to improve performance of these word embeddings. However, most of the research related to word embeddings in biomedical domain focuses on analysis of model architecture, hyper-parameters and input text. In this paper, we use SemMedDB to design new sentences called `Semantic Sentences'. Then we use these sentences in addition to biomedical text as inputs to the word embedding model. This approach aims at introducing biomedical semantic types defined by UMLS, into the vector space of word embeddings. The semantically rich word embeddings presented here rivals state of the art biomedical word embedding in both semantic similarity and relatedness metrics up to 11%. We also demonstrate how these semantic types in word embeddings can be utilized.
Approaches to Enhancing Transfer of Training using Adaptive Instructional Systems

This paper examines approaches to improve training technology design with the goal of enhancing transfer of training, the effect that knowledge and skills acquired during training have on problem solving or knowledge acquisition in work or operational environments. Specifically, we address the design of adaptive instructional systems (AISs), a category of learning technology that includes intelligent tutoring systems (ITSs), intelligent mentors and other recommender systems that tailor instruction to more efficiently meet the needs of individual learners and teams. Since tailored training requires more content and instructional decisions and therefore more authoring time than non-tailored computer-based training, we also examine how AIS design and transfer of training might be influenced by AIS authoring processes and interfaces. Design challenges are also discussed and include factors such as the ease of authoring that influence transfer of training.
Evaluating Network Embedding Models for Machine Learning Tasks

Network embedding is a representation learning paradigm that seeks to learn a compact low-dimensional distributed vector representation for each vertex in the network; this learned low-dimensional vector representation can thus be used for different machine learning tasks. Over the years, so many network embedding models have been worked upon based on several approaches. In this paper, we study vector embeddings of 10 different representation learning models, with the sole aim of carrying out two machine learning tasks on these learned representations – unsupervised community clustering and link prediction analysis. The goal is to compare the output of these tasks using the 10 models, and draw inference based on the obtained results. We analyze the results using 4 link prediction baseline heuristic measures for the link prediction analysis; and a combination of silhouette score analysis and dissimilarity metric index for the community analysis.
A Utility-Based Path Planning for Safe UAS Operations with a Task-Level Decision-Making Capability

Unmanned aircraft systems (UAS) are being used more and more every day in almost any area to solve challenging real-life problems. Increased autonomy and advancements in low-cost high-computing technologies made these compact autonomous solutions accessible to any party with ease. However, this ease of use brings its own challenges that need to be addressed. In an autonomous flight scenario over a public space, an autonomous operation plan has to consider the public safety and regulations as well as the task specific objectives. In this work, we propose a generic utility function for the path planning of UAS operations that includes the benefits of accomplishing the goals as well as the safety risks incurred along the flight trajectories, with the purpose of making task-level decisions through the optimization of the carefully constructed utility function for a given scenario. As an optimizer, we benefited from a multi-tree variant of the optimal T-RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> (Multi-T-RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> path planning algorithm. To illustrate its operation, results of simulation of a UAS scenario are presented.
A Grammar-Driven Approach for Parsing Manipuri Language

This paper presents attempts to parse Manipuri language using a context-free grammar (CFG) and Earley’s parsing algorithm. For a low resource language like Manipuri where treebanks are not available, following a grammar-driven approach is the only viable solution as of now. The work covers simple, compound and complex Manipuri sentences. The system reports a Recall of 81.71%, Precision of 72.38%, and F-measure of 76.76%. The CFG and the parsing system we have created can be very useful in building a treebank for Manipuri.
Real-time Obstacle Avoidance using Supervised Recurrent Neural Network with Automatic Data Collection and Labeling

The following topics are dealt with: learning (artificial intelligence); medical signal processing; neurophysiology; electroencephalography; mobile robots; feature extraction; brain-computer interfaces; neural nets; convolutional neural nets; production engineering computing.