Reviewerly/exHarmony huggingface dataset:
Survey on streaming data computing system

In the era of big data, as the data has been developed to be more and more streaming, the value produced by that has increased considerably, therefore contributing to the creation of a large number of computing platforms. Besides, because the traditional big data computing technology is unable to meet the needs of this innovation, a great deal of new technologies have been created rapidly. Based on that background, this paper analyzes the distinctive characteristics of streaming data, and enumerates and compares five widely used technology platforms based on their system architectures, usage scenarios, advantages and drawbacks.
Applying GA as Autonomous Landing Methodology to a Computer-Simulated UAV

The ultimate purpose of this study is to truly make unmanned aerial vehicles to be autonomous. As the starting point, in this paper, we have selected genetic algorithm as the method to achieve autonomy and will check the possibility of self-regulated autonomous unmanned aerial vehicle by applying the genetic algorithm. Landing has always been one of the most important tasks for aerial vehicles. Particularly, self-regulated autonomous landing is essential when it comes to unmanned aerial navigation. Since researching the autonomy of landing through falling body installed with a lunar Lander-like propulsion system would be more efficient for attaining the generalization of autonomous landing, it is applied on the simulation of computer-simulated falling body. When applying genetic algorithm, by first encoding genome into only 4 types of actions(left turn, right turn, thrust, free fall) then applying it on unmanned falling body, and finally combining the major computations of genetic algorithm to the unmanned falling body, we have made a successful progress in experiments. Meanwhile, previous studies have relied on various sensors to correct vertical, rotational, and horizontal errors. However, the use of measurement sensors has to be minimized in order to achieve true autonomy. The greatest accomplishment in this study was implemented a computer-simulated unmanned aerial vehicle, in order to minimize reliance of sensors, which can achieve the true meaning of autonomy of unmanned aerial vehicle and establishing a test bed for verifying the possibility by using genetic algorithm.
Disparity Optimization Algorithm for Stereo Matching Using Improved Guided Filter

A disparity optimization algorithm based on an improved guided filter is proposed to smooth the disparity image. A well-known problem to local stereo matching is the low matching accuracy and staircase effect in regions with weak texture and slope. Our disparity optimization method solves this problem and achieve a smooth disparity. First, the initial disparity image is obtained by a local stereo matching algorithm using segment tree. Then, the guided filter is improved by using gradient domain information. Lastly, the improved guided filter is adopted as the disparity optimization method to smooth the disparity image. Experiments conducted on the Middlebury data sets demonstrate that by using the proposed algorithm in this paper, the smoothness of the disparity map in slope regions is improved, and a higher precision of dense disparity is obtained.
Exploiting Search Logs to Aid in Training and Automating Infrastructure for Question Answering in Professional Domains

Developing an AI question answering system for the legal and regulatory domain requires significant ground truth annotations for what constitutes a good answer for a given question. Collecting these annotations from qualified legal and regulatory professionals is time consuming and expensive. By making use of user activity data from the query logs of existing legal and regulatory search engines, it is possible to speed up the annotation collection process as well as supplement annotations with imputed labels. We used signals from user activity logs indicating that a user affirmatively engaged with an answer after entering a query. We leveraged these signals to infer suitable answers to questions without needing to rely on annotators. In previous research efforts, such identification was known as Implicit Relevance Feedback (IRF). Our investigations have determined that 90% of our IRF candidates contain either a complete or partial answer. Given such an elevated baseline, the next phase of this project involved harvesting data derived from such IRF (we've termed it "silver data" in contrast to expert-annotated "gold data") and extending the process to significantly larger sets of data. We examine how the approach affects performance ranging from zero, and very low amounts of gold data to substantially higher amounts of gold data. Such efforts can result in producing appreciably more reliable amounts of training data for next generation QA systems as well as establishing the means to automate the infrastructure that supports such systems. We investigate the impact of including silver data alongside gold data on the performance of a QA system. Specifically: how does silver data impact the cold start challenge (when no gold data exists initially), how much gold data is needed to achieve comparable performance to a model trained on a given amount of silver data, and what performance gains can be realized by introducing silver data to graduated amounts of gold data? We show that leveraging silver data can establish a preliminary QA system in the absence of gold data, and boost the system's performance once the gold data workstream is in place. We further show the relative efficacy of silver data to gold data by conducting performance comparisons for models trained on varying ratios of each type of data.
Task Identification Framework to Automatically Detect Anomalies in Users’ Interactions with Mobile Application to Support Usability Evaluation

Nowadays usability is a crucial factor for mobile applications to retain users. However, its traditional methods are still expensive and time-consuming. To overcome these disadvantages and support the automated approach to usability evaluation, current research offers a framework for automated detection of anomalies in users’ interactions with mobile applications. It first uses sequence detection with regular expressions to perform the correct user’s task identification and a Long Common Subsequence algorithm to identify tasks with anomalies and locate them in the user’s interaction sequences. For the framework evaluation, the usage data from the trade fair “DOMOTEX” was taken. It consists of user interface interaction sequences of 24 unique users (163 sessions). The results indicate that by considering the main constraint of the framework: its dependability on the initial baseline, the system can show high accuracy on task and anomaly detection and deliver detailed insight on users’ behavior with the application.
VScan

Identifying key scenes in massive surveillance videos is extremely challenging because these scenes occur rarely while automotive identification using full-feature neural network (NN) models consumes immense computational resources. This paper proposes VScan, an efficient model-joint mechanism that adaptively schedules streams on a light-weight NN model and a full-feature NN model for analyzing videos concurrently. These two combined models with overlapped detectable objects are generic and well-developed. The former model fast scans videos to seek potential interest scenes. Only the streams with identified scenes are further analyzed by the latter model. We provide a model selection approach to select a light-weight model with an appropriate accuracy and high throughput. VScan further determines key parameters to correct predictions at runtime, thus guaranteeing the recall of target scenes. The full-feature model is responsible for ensuring output precision. To maintain a high hardware efficiency and utilization dynamically, VScan uses automatic sampling to reduce unnecessary computations, proposes stream scheduling to maximize hardware usage, and designs GPU scheduling to optimize the data processing flow. Experimental results show that benefitting from the model-joint mechanism and runtime scheduling optimizations, VScan significantly boosts the video processing throughput by up to 15x without key scene loss.
Process convergence for the complexity of Radix Selection on Markov sources

A fundamental algorithm for selecting ranks from a finite subset of an ordered set is Radix Selection. This algorithm requires the data to be given as strings of symbols over an ordered alphabet, e.g., binary expansions of real numbers. Its complexity is measured by the number of symbols that have to be read. In this paper the model of independent data identically generated from a Markov chain is considered. The complexity is studied as a stochastic process indexed by the set of infinite strings over the given alphabet. The orders of mean and variance of the complexity and, after normalization, a limit theorem with a centered Gaussian process as limit are derived. This implies an analysis for two standard models for the ranks: uniformly chosen ranks, also called grand averages, and the worst case rank complexities which are of interest in computer science. For uniform data and the asymmetric Bernoulli model (i.e. memoryless sources), we also find weak convergence for the normalized process of complexities when indexed by the ranks while for more general Markov sources these processes are not tight under the standard normalizations.
Traffic Regulation Velocity Obstacles method

The goal of this paper is to show a new concept of the reactive motion planning algorithms for mobile robots in dynamic environment using the main rules of the Traffic Regulation. At the presented solution the algorithm uses the velocity vectors and positions of the obstacles and the robot so the Velocity Obstacles (VO) method seems to be a good choice as a basis for the motion planning. As an assumption the obstacles do not change their velocities during the sampling time. In that case the method can plan evasive maneuvers between the robot and the obstacles. In this work, a new motion planning method is introduced that plans the velocities and the path for the mobile robot using the basic Traffic Regulations. The solution method was extended with emergency vehicles that changes the use of the Traffic Regulations during the motion planning. This method provides an appropriate solution also if there are more obstacles in the workspace. The results of the motion of the obstacles and the robot were tested in simulations.
Experiential Learning in Digital Forensics

In this chapter, we introduce the concepts of digital forensics and experiential learning, and describe how we implement experiential learning in an undergraduate level digital forensic course and a graduate level digital forensic course. The students were given the option of working on either a digital forensic research topic or the Digital Forensic Research Workshop (DFRWS) IoT Forensic Challenge (2018–2019). We also report on the artifacts generated by the students working on both types of final projects.
PRBL

In recent years complex network has become a research hot spot for the large-scale systems. The bipartite network, a particular format of the complex network, can be used to describe the personalized recommendation systems that recommend the interesting items to users based on their own interests. In this paper, we propose a personalized recommendation systems based on the bipartite network projection and link community detection. The preference from users to items is abstracted as the edges in the bipartite user-item network. To get the relationship between the items, we make the one-mode projection to generate the item-item network. The link community detection is used to cluster the items to recommend to preferred users. Our systems could be used for any area with large-scale datasets. The experimental results show that our system could efficiently recommend commodities to the consumers based on the big data from the e-commerce.
Auto Semi-supervised Outlier Detection for Malicious Authentication Events

Cyber-attacks become more sophisticated and complex especially when adversaries steal user credentials to traverse the network of an organization. Detecting a breach is extremely difficult and this is confirmed by the findings of studies related to cyber-attacks on organizations. A study conducted last year by IBM found that it takes 206 days on average to US companies to detect a data breach. As a consequence, the effectiveness of existing defensive tools is in question. In this work we deal with the detection of malicious authentication events, which are responsible for effective execution of the stealthy attack, called lateral movement. Authentication event logs produce a pure categorical feature space which creates methodological challenges for developing outlier detection algorithms. We propose an auto semi-supervised outlier ensemble detector that does not leverage the ground truth to learn the normal behavior. The automatic nature of our methodology is supported by established unsupervised outlier ensemble theory. We test the performance of our detector on a real-world cyber security dataset provided publicly by the Los Alamos National Lab. Overall, our experiments show that our proposed detector outperforms existing algorithms and produces a 0 False Negative Rate without missing any malicious login event and a False Positive Rate which improves the state-of-the-art. In addition, by detecting malicious authentication events, compared to the majority of the existing works which focus solely on detecting malicious users or computers, we are able to provide insights regarding when and at which systems malicious login events happened. Beyond the application on a public dataset we are working with our industry partner, POST Luxembourg, to employ the proposed detector on their network.
The difficulty of Monte Carlo approximation of multivariate monotone functions

We study the L1-approximation of d-variate monotone functions based on information from n function evaluations. It is known that this problem suffers from the curse of dimensionality in the deterministic setting, that is, the number n(ε,d) of function evaluations needed in order to approximate an unknown monotone function within a given error threshold ε grows at least exponentially in d. In the randomized setting (Monte Carlo setting) the complexity n(ε,d) grows exponentially in d (modulo logarithmic terms) only. An algorithm exhibiting this complexity is presented. The problem remains difficult as best methods known are deterministic if ε is comparably small, namely ε⪯1∕d. This inherent difficulty is confirmed by lower complexity bounds which reveal a joint (ε,d)-dependence and from which we deduce that the problem is not weakly tractable. The lower bound proof also has implications on the complexity of learning Boolean monotone functions.
Direction Finding and Signal Characterization of Unknown Emitters

Under guidance of the NATO HQ C3 Staff and NATO Capability Panel 2 (CaP2) on Navigation and Identification, an experimental prototype of a GPS jammer direction finding and signal classification system was constructed using Software Defined Radio technology. The system is based on phased array processing of signals from a 6-element uniform linear antenna array, sampled with a multi-channel phase coherent Radio Frequency receiver. The prototype system was tested against realistic jammers and spoofers and demonstrated to have an accuracy of 2 degrees and a resolution around 10 degrees using the MUSIC algorithm. Signal classification features were proven to be very useful tools to support the operator in assessment of the jamming and spoofing situations. This report describes the design, implementation and extensive test results.
A Combinatorial Fair Economical Double Auction Resource Allocation Model

Role of cloud computing is emerging extensively as it not only reduces hardware maintenance cost, but high end servers provide all the facilities with very high speed and versatile resources required to run an application. Pricing and fairness play very important role in the market as marketing is done online and everything is handled virtually without physical presence. Role of cloud providers become very significant as they are controlling the competitive market by providing the required resources within the budget of a customer. At the same time importance of customers can't be neglected as market can only flourish with the customer's satisfaction. In online market fair distribution of resources is also a necessity for sustainability. The proposed model maintains fairness by giving priority to the genuine providers over the providers who want to monopolies or spoil the market. It also encourages customers who want to use resources for more time with fair bid values. Instead of rejecting non-genuine providers having bid very close to genuine providers, they are allowed to participate after genuine providers according to their degree of closeness with genuine providers. It will lead to increased resource availability and hence increased customers and provider's participation ratio. The proposed model is implemented in CloudSim environment and compared with two popular existing models. The evaluation of proposed model has clearly shown improved utilization of resources and fair allocation with increased participation of customers and providers both.
Vision-Based Virtual Joystick Interface

Input devices used nowadays are typically expensive, lack portability, and require a substantial amount of space. However, these limitations can be overcome by using virtual input devices. In this study, we propose a virtual joystick system, which is a type of virtual input device. Our system detects a handheld stick and computes the direction in which the user’s hand moves relative to a user-defined center. The process of the proposed system can be divided into three stages: preprocessing, handheld stick detection, and direction calculation. In each stage, simple operations are performed and a color camera is used. Therefore, users do not need to purchase a high-performance computer or auxiliary devices to use the proposed system. We compare our system’s performance, gauging accuracy, and speed with those produced using a real joystick. The proposed system’s accuracy is competitive and has real-time speed in the laptop environment.
Adapting the IT2017 Curricula for Two Year Transfer Programs

After the Association for Computing Machinery (ACM) and the IEEE Computer Society endorsed the recently completed Information Technology Curricula 2017 Curriculum Guidelines (IT2017) for Baccalaureate Degree Programs in Information Technology (IT) [1], the ACM Committee for Computing Education in Community Colleges (CCECC) at the direction of the ACM Education Board joined with the chair of the IT2017 task group to create corresponding guidance for two-year colleges offering IT transfer programs. In some countries, students can complete the first two years of a Bachelor's degree at a two-year school (which may be known as a community college, technical college, or junior college), and then transfer to a university to complete the remaining two years of their course of study [3]. The CCECC formed the IT Transfer Task Force (ITTTF), consisting of educators from both two-year colleges and four-year universities, to provide guidance for IT programs in such two-year schools. The guidance includes the competencies students should achieve during the first two years of an IT Bachelor's degree. The ITTTF is currently seeking public feedback from educators and IT industry representatives on an initial set of competencies to guide how the ITTTF should proceed to build IT Transfer Curriculum Guidelines. This poster will outline the initial selection of competencies to solicit feedback and input on the guidelines, including best practices for communication between two year IT Transfer programs and their four year university partners.
Video Annotation and Player Classification Using Hough-Grid Transformation (HGT) Method

Video processing is a most challenging research area among image processing. It has made its mark over the world by the rapid development of technology in various fields. Extending the study in the area of sports video digitization, proposed methodology initiated the novel approach on Asian game that is 'Kabaddi'. It is a team game, in which we emphasize on half of the court. This paper proposes a content analysis of kabaddi game includes both foreground and back ground annotation. Player classification, detection and tracking by dominant Color-based Feature Extraction (CFE). Blob generation for each player with consequences of team has performed using accumulation array with grid based transaction and Centroid Region Of Interest (CROI). Then by applying the grids over the play court, line detection and labeling is done by Hough Grid Transformation (HGT) method. Algorithms are implemented on MATALAB tool and tested on self developed videos having 535 frame set videos clips for privileged accuracy in the obliged aspects. By examining the results, proposed methodology can be referred in the Kabaddi tournaments for game annotation and player performance analysis.
Structural conditions for projection-cost preservation via randomized matrix multiplication

Projection-cost preservation is a low-rank approximation guarantee which ensures that the cost of any rank-k projection can be preserved using a smaller sketch of the original data matrix. We present a general structural result outlining four sufficient conditions to achieve projection-cost preservation. These conditions can be satisfied using tools from the Randomized Linear Algebra literature.
Augmented Reality Space Informatics System

The Augmented Reality Space Informatics System (ARSIS) is a Microsoft HoloLens application developed for the NASA SUITS (Spacesuit User Interfaces for Students) challenge that provides an augmented reality (AR) interface for information display and communication to be used by astronauts during extravehicular activity (EVA) procedures. Astronauts are required to deal with complex and changing circumstances on EVA. The current status quo requires heavy reliance on voice communication with Mission Control Centers (MCCs) for everything from biometric data to assistance with procedures. ARSIS is designed with the dual purpose of increasing astronaut autonomy and making communication with MCCs more effective. Features of ARSIS include voice and gaze user interface (UI) navigation, anchored menus, planetary navigation aids, biometric information display, procedure instructions, and a suite of tools for enhanced communication with Mission Control including spatial telestration. User experience (UX) testing results for ARSIS have been generally positive overall, with the biggest takeaway being that users would prefer alternative modes of UI navigation to voice control. Eye tracking for UI navigation is a promising area of exploration in the future.
Frequency Filtering of Source Images for LASER Engravers

This paper deals with image filtering in frequency domain and its effects in area of laser engraving. Current consumer cameras provide high resolution photos which contains dozens megabyte of data. It is not easy task to engrave all the data on wooden materials. Optimal image filtering can decrease production code size up to 30% while preserving quality of the final product. Modular script written in Matlab environment is used for multiple filter types comparison. Effects of frequency filtering to engraving quality and G-Code file size are described.