Reviewerly/exHarmony huggingface dataset:
The Mother of All Leakages: How to Simulate Noisy Leakages via Bounded Leakage (Almost) for Free

We show that the most common flavors of noisy leakage can be simulated in the information-theoretic setting using a single query of bounded leakage, up to a small statistical simulation error and a slight loss in the leakage parameter. The latter holds true in particular for one of the most used noisy-leakage models, where the noisiness is measured using the conditional average min-entropy (Naor and Segev, CRYPTO'09 and SICOMP'12). Our reductions between noisy and bounded leakage are achieved in two steps. First, we put forward a new leakage model (dubbed the dense leakage model) and prove that dense leakage can be simulated in the information-theoretic setting using a single query of bounded leakage, up to small statistical distance. Second, we show that the most common noisy-leakage models fall within the class of dense leakage, with good parameters. Third, we prove lower bounds on the amount of bounded leakage required for simulation with sub-constant error, showing that our reductions are nearly optimal. In particular, our results imply that useful general simulation of noisy leakage based on statistical distance and mutual information is impossible. We also provide a complete picture of the relationships between different noisy-leakage models. Our result finds applications to leakage-resilient cryptography, where we are often able to lift security in the presence of bounded leakage to security in the presence of noisy leakage, both in the information-theoretic and in the computational setting. Additionally, we show how to use lower bounds in communication complexity to prove that bounded-collusion protocols (Kumar, Meka, and Sahai, FOCS'19) for certain functions do not only require long transcripts, but also necessarily need to reveal enough information about the inputs.
Explaining Concept Drift by Mean of Direction

The notion of concept drift refers to the phenomenon that the distribution, which is underlying the observed data, changes over time; as a consequence machine learning models may become inaccurate and need adjustment. In this paper we present a novel method to describe concept drift as a whole by means of flows, i.e. the change of direction and magnitude of particles drawn according to the distribution over time. This problem is of importance in the context of monitoring technical devices and systems, since it allows us to adapt models according to the expected drift, and it enables an inspection of the most prominent features where drift manifests itself. The purpose of this paper is to establish a formal definition of this problem and to present a first, yet simple linear method as a proof of concept. Interestingly, we show that a natural choice in terms of normalized expected linear change constitutes the canonical solution for a linear modeling under mild assumptions, which generalizes expected differences on the one hand and expected direction on the other. This first, global linear approach can be extended to a more fine grained method using common localization techniques. We demonstrate the usefulness of our approach by applying it to theoretical and real world data.
webAppOS: Creating the Illusion of a Single Computer for Web Application Developers

Unlike traditional single-PC applications, which have access to directly attached computational resources (CPUs, memory, and I/O devices), web applications have to deal with the resources scattered across the network. Besides, web applications are intended to be accessed by multiple users simultaneously. That not only requires a more sophisticated infrastructure but also brings new challenges to web application developers. The webAppOS platform is an operating system analog for web applications. It factors out the network and provides the illusion of a single computer, the “web computer”. That illusion allows web application developers to focus on business logic and create web applications faster. Besides, webAppOS standardizes many aspects of web applications and has the potential to become a universal environment for them.
Toward a Definition of Cognitive-Driven Development

Software separation into components is a recognition that human work can be improved by focus on a limited set of data. The growing complexity of software has always been a challenge to industry. Several approaches have been proposed to support code design based on architectural styles and code quality metrics. However, most research involving human cognition in software engineering is focused on the evaluation of programs and learning instead of how the source code could be developed under this perspective. This paper presents an approach called Cognitive-Driven Development (CDD) that is based on cognitive complexity measurements and Cognitive Load Theory. This strategy can reduce the cognitive overload of the developers through the limitation of intrinsic complexity points from source code. Some cognitive complexity metrics has been extended and guidelines are presented to calculate the intrinsic complexity points and how their limit can be adapted under certain quality criteria. Experimental studies are currently being conducted to evaluate the CDD. Preliminary results indicate that the approach can reduce the future effort for maintenance and fixing software faults.
Improved Population Control for More Efficient Multimodal Optimizers

Multimodal optimization methods seek multiple optima at several search subspaces in complex (industrial) scenarios. At their forefront, the Hill-Valley Evolutionary Algorithm (HillVallEA) was lately extended as the Hill-ValleyClustering-based Variable Mesh Optimization (HVcMO) approach. This study strives to improve their efficiency, without loss of effectiveness. We conjecture that it occurs if the growth of the population is better controlled. To do that, we implement a very simple but potent strategy, which consists of postponing the increase of the presumed unsuccessful populations. Results demonstrate that such an idea reduces the final population of the two algorithms drastically and therefore, it accelerates their executions, while conserving their multimodal competence.
E-Resources: The Millennium Users' Expectations

This paper highlights the link between the fifth law of library science "library/information is a growing organism” and the third law" every book/e-resource its reader/user", referring to user expectations. User expectations are growing and the reader/user" is lost between the mountain of information requiring changes in Library services and resources.
Research on Video Detection of Object Intrusion in Substation

Abstract The safe running of substations is an important part of power system security, as well as an important work of the power companies. The intrusion of kites, plastic trash, small animals and other objects will affect the safe running of substations. With the development of video monitoring technology, intelligent recognition technology of images and videos, video monitoring has gradually become the main monitoring means of unattended substations. This paper firstly analyses the research status and main technologies of video object recognition. To achieve the detection of foreign objects in substations, moving object detection and video object detection technologies are studied. And two outlines are proposed: using background subtraction and image classification, using YOLOv3 to detect objects in videos. In the end of the paper, YOLOv3 was tested by training a detector of small animals. The results show that the value of mAP equals to 0.7169 and the detection speed fulfills the request of real-time detection. This method could serve as reference for detecting and tracking intrusion objects in substations.
TKGFrame: A Two-Phase Framework for Temporal-Aware Knowledge Graph Completion

In this paper, we focus on temporal-aware knowledge graph (TKG) completion, which aims to automatically predict missing links in a TKG by making inferences from the existing temporal facts and the temporal information among the facts. Existing methods conducted on this task mainly focus on modeling temporal ordering of relations contained in the temporal facts to learn the low-dimensional vector space of TKG. However, these models either ignore the evolving strength of temporal ordering relations in the structure of relational chain, or discard more consideration to the revision of candidate prediction results produced by the TKG embeddings. To address these two limitations, we propose a novel two-phase framework called TKGFrame to boost the final performance of the task. Specifically, TKGFrame employs two major models. The first one is a relation evolving enhanced model to enhance evolving strength representations of pairwise relations pertaining to the same relational chain, resulting in more accurate TKG embeddings. The second one is a refinement model to revise the candidate predictions from the embeddings and further improve the performance of predicting missing temporal facts via solving a constrained optimization problem. Experiments conducted on three popular datasets for entity prediction and relation prediction demonstrate that TKGFrame achieves more accurate prediction results as compared to several state-of-the-art baselines.
FPGA Based Design of Speech Encryption and Decryption for Secure Communication

Data in any form, such as speech, image, video, text etc. need to be transferred against various threats. Such information need to be modified in such a form that even if someone gets an access to it, he will not be able to understand it i.e. it needs to be encrypted before transmission and must be decrypted at receiver side in order to ensure data security. Breaching of sensitive data by fraudsters may impose a potential cost in terms of money, time and efforts. Many approaches have been taken in this direction to attain such security among which chaos based encryption is relatively newer one. Chaotic systems are highly secure and reliable system for encryption. The chaotic systems are highly sensitive to initial conditions and their values dramatically change with the change in initial conditions. This work intends to improve the existing cryptology methods utilizing chaotic circuits. Also, a standalone hardware design which can be used as a part of portable chips for online encryption and decryption is the main objective of this work. In this paper, we have proposed an FPGA based chaotic system with modified logistic encryption algorithm in order to reduce processing time in comparison to that of software. The synthesis results clearly show that the hardware implementation of logistic encryption outperforms the software based implementation and thus can be used in portable devices as a chip.
Synthesis of Control Systems on the Basis of a Canonical Vector Decomposition of Random Sequences

Method of the modeling of stochastic systems of control for an arbitrary number of input parameters and the duration of the observation process is obtained in the work. The method is based on the canonical vector decomposition of of the random sequence of change of the coordinates of an investigated object. Optimal solutions of the control problems for the case when parameters are measured with errors and, provided that true values of a coordinates are known, are obtained. The schemes reflecting the peculiarities of the determination of algorithm parameters and functioning regularities of a stochastic model are introduced in the work. The proposed approach can be successfully implemented for design and investigations of the control systems for critical applications, which include such important components as MEMS (microelectromechanical systems). Besides, suggested models can be efficiently applied for the development of the strategies of embedded systems design, as well as for testing, reliability investigations.
Improved Metric Factorization Recommendation Algorithm Based on Social Networks and Implicit Feedback

Abstract The Metric Factorization algorithm solves the problem of the suboptimal solution caused by the inner product of the traditional matrix factorization algorithm. Although the basic metric factorization model has achieved good results in rating prediction and item ranking tasks, the algorithm ignores the role of implicit feedback and user social information. Considering the social relationship and implicit feedback information between users, this paper improves the basic metric factor Factorization algorithm, and proposes an improved metric factorization recommendation algorithm based on social networks and implicit feedback. We do rating prediction tasks on the Filmtrust and Last.FM datasets, experimental results show that the improved algorithm can further improve the accuracy of prediction.
Person-Following Shopping Support Robot Using Kinect Depth Camera Based on 3D Skeleton Tracking

The lack of caregivers in an aging society is a major social problem. Without assistance, many of the elderly and disabled are unable to perform daily tasks. One important daily activity is shopping in supermarkets. Pushing a shopping cart and moving it from shelf to shelf is tiring and laborious, especially for customers with certain disabilities or the elderly. To alleviate this problem, we develop a person following shopping support robot using a Kinect camera that can recognize customer shopping actions or activities. Our robot can follow within a certain distance behind the customer. Whenever our robot detects the customer performing a “hand in shelf” action in front of a shelf it positions itself beside the customer with a shopping basket so that the customer can easily put his or her product in the basket. Afterwards, the robot again follows the customer from shelf to shelf until he or she is done with shopping. We conduct our experiments in a real supermarket to evaluate its effectiveness.
Implementation of an Instrumented Crutch with Scalable E-Care Architecture Using IoT

The main objective of this research work is to design a Smart Robotic Crutch with Scalable E-Care Architecture using IoT. The aim of this research is to convert this project into product by which a paralyzed patient can get the comfort feeling while using it and get into this applicable smart feature of this crutch with its scalable e-care architecture. The motive of this work is to support a paralyzed patient to rise up from a wheelchair and give them the opportunity to move without any other help. Many of paralyzed people have a common problem of falling. The aim is to reduce the users falling tendencies with the help of Ultrasonic sensor. The objective of this research work is to help the patient to move freely, actual health monitoring with real time tracking and give them the effective multifunctional smart applications by which they can feel confident while using the crutch. In this paper, the authors are motivated to introduce a crutch that is smart in functionalities. Basically, it is a digital upgrade of manual crutch with its robotics and IoT features. This crutch as a humanitarian project is designed to explore empathy for paralyzed patients in an effective system.
Design and Implementation of “AugMedicine: Lung Cases,” an Augmented Reality Application for the Medical Curriculum on the Presentation of Dyspnea

Introduction: Augmented Reality is a technique that enriches the real-life environment with 3D visuals and audio. It offers possibilities to expose medical students to a variety of clinical cases. It provides unique opportunities for active and collaborative learning in an authentic but safe environment. We developed an Augmented Reality application on the clinical presentation of shortness of breath (dyspnea), grounded on a theoretical instructional design model. Methods: A team of various stakeholders (including medical teachers and students) was formed to design the application and corresponding small group learning session, grounded on principles of instruction as described by Merrill. Evaluation was performed by an explorative questionnaire, consisting of open and closed questions (Likert scales), covering user experience, content and physical discomfort. Results: Multiple interactive cases of dyspnea were designed. The application plays back audio samples of abnormal lung sounds corresponding to a specific clinical case of dyspnea and displays a 3D model of the related pulmonary pathologies. It was implemented in the medical curriculum as an obligatory small group learning session scheduled preceding clinical clerkships. Prior knowledge was activated prior to the learning session. New knowledge was acquired with the application by solving an authentic problem based on a real patient case. In total 110 students participated in the study and 104 completed the questionnaire. 85% of the students indicated that the virtually auscultated lung sounds felt natural. 90% reported that the augmented reality experience helped them to better understand the clinical case. The majority of the students (74%) indicated that the experience improved their insight in the portrayed illness. 94.2% reported limited or no physical discomfort. Discussion: An Augmented Reality application on the presentation of dyspnea was successfully designed and implemented in the medical curriculum. Students confirm the value of the application in terms of content and usability. The extension of this Augmented Reality application for education of other healthcare professionals in currently under consideration.
Eye-head coordination during exploration of 360-degree scenes in virtual reality

People naturally move both their head and eyes to attend to information. Yet, studies of attentional orienting normally immobilize the head in a chin rest in order to focus on eye movements, thus questioning their ecological validity. Here, using a virtual reality headset with built-in eye tracking, participants were asked to view indoor and outdoor, fully immersive, 360-degree scenes while their head and eyes were tracked. In order to investigate eye-head coordination, participants viewed the scenes through a small moving window that was yoked either to their head or eye movements. We found that perturbations induced by the head- or gaze-contingent windows affected head and eye movements differentially, in line with their distinct roles in head-eye coordination. Compared with windowless viewing, gaze-contingent viewing was more disruptive than head-contingent viewing, indicating a functional separation between head and eye. Indeed, gaze-contingency actually decreased the coupling between head and gaze movements in 360-degree scene exploration, while head-contingent viewing looked more like windowless viewing. These data dovetail with the nested effectors hypothesis, which proposes that the head prefers exploration into non-visible space while the eyes prefer to exploit visible areas delivered by the head. It also suggests that real-world orienting may be much more head-based than previously thought. We discuss our findings in relation to the cognitive repercussions of eye vs. head movements, as well as highlighting the utility and ecological validity of unconstrained eye and head tracking in virtual reality.
Object Detection Technology of Substation Patrol Robot Based on mYOLO Algorithms

In this paper, a m-yolo algorithm is proposed, which combines multiple groups of camera image data to realize the fast recognition and accurate positioning of the target in the substation. Inspection of equipment in substation mainly depends on manual work, which is labor intensive, inefficient and prone to negligence. Substation inspection robot has unique advantages in inspecting equipment in substation. Rapid detection and accurate identification of targets are the basis of normal operation of substation inspection robot. Based on the deep neural network, this paper realizes the YOLO detection algorithm in substation scenario, which provides technical support for the smooth operation of substation inspection robot.
Joint Reasoning of Events, Participants and Locations for Plot Relation Recognition

Event information is of great value, but the exploitation of it generally relies on not only extracting events from the text, but also figuring out the relations among events and organizing them accordingly. In this paper, based on a more flexible and practical type of event relation called the plot relation, we study the method of automatic event relation recognition. Specifically, we propose a local prediction method by using diversified linguistic and temporal features. Furthermore, we design a joint reasoning framework, in which we leverage the information of participants and locations, and add global constraints to further improve the performance. Finally, we transform the proposed model into integer linear programming (ILP) to obtain the global optimum. Our experiments demonstrate that our method significantly outperforms all the existing methods.
On the Use of Decision Diagrams for Finding Repetition-Free Longest Common Subsequences

We consider the repetition-free longest common subsequence (RFLCS) problem, where the goal is to find a longest sequence that appears as subsequence in two input strings and in which each character appears at most once. Our approach is to transform a RFLCS instance to an instance of the maximum independent set (MIS) problem which is subsequently solved by a mixed integer linear programming solver. To reduce the size of the underlying conflict graph of the MIS problem, a relaxed decision diagram is utilized. An experimental evaluation on two benchmark instance sets shows the advantages of the reduction of the conflict graphs in terms of shorter total computation times and the number of instances solved to proven optimality. A further advantage of the created relaxed decision diagrams is that heuristic solutions can be effectively derived. For some instances that could not be solved to proven optimality, new state-of-the-art results were obtained in this way.
Pearson Correlation Method and Web Scraping for Analysis of Islamic Content on Instagram Videos

The Indonesian Internet Service Providers Association (APJII) revealed that more than half of Indonesia's population is now connected to the internet. One of the social media that can be accessed using is Instagram. Instagram is a photo and video sharing application that allows users to take photos, videos and apply digital filters. In the video post on Instagram, user can see the number of videos displayed especially for Islamic content. But if more video posts are displayed it, are the user get a lot of likes. In this study used web scraping technology which is useful for retrieving data on Instagram. Then the data were calculated using the Pearson correlation method to determine whether or not the relationship between like variables and the number of likes being strong. So, it can be a benchmark for preachers to know what kind of posts are preferred by Instagram users.
Benefit Recommendation System: A Use Case for Pension Fund Data Analytics

To promote the usage of a mobile application and its benefit offers, a benefit recommendation system was proposed, in which different models were combined to improve performance and robustness. First, high-potential users were identified with a classification model based on users' demographics. Then, a recommendation model proposed personalized benefits based on their demographics and overall usage trends of the userbase. This model aimed for new and returning users whose personal historical usage data was absent. The two models could be used together to attract new users as well as to encourage their benefit usage. Finally, for existing active users, a recommendation model that utilized users' behavioral and preference data from their usage records was designed to further improve users' engagement. Overall, our system was able to achieve high prediction performance, up to 89.15% when recommending benefit categories to users.