Reviewerly/exHarmony huggingface dataset:
Methods to minimize false detection in accidental fall warning systems

Falls are one of the leading causes of mortality among the elderly. Fast detection of a fall is a key factor in alleviating the main negative health consequences. A current challenge for the scientific and commercial community is to choose the appropriate methods for reducing the false positives in fall detection systems (FDS). It is also very important that the chosen methods be analyzed and applied in order of priority. It has been found that there are different methods of combating erroneous detection of a fall. If these methods are chosen properly, the false positive rate can be greatly diminished. This paper presents some methods that can be used to reduce the false positive rate from falls detection systems.
Toward a pedestrian detection method by various feature combinations

Pedestrian detection has been a crucial issue over the last decades. The existing pedestrian detection methods are still face abrupt illumination, partial occlusion, different poses of humans, and cluttered backgrounds challenges. Consequently, the significance of pedestrian detection systems encou rages us to propose a new method to address some of these challenges and offer higher accuracy rate. Noting that the power of various kinds of features are different and a single type of feature cannot extract the comprehensive information of human shape. Taking this fact into consideration, we combined pragmatic and useful features in order to detect pedestrian more accurate. Indeed, we combine histogram of oriented gradients (HOG), a proposed modified local binary pattern (M-LBP), and a proposed modified Haar-like features (M-Haar) to achieve these goals. By applying the proposed method, it is possible to extract various information on human shapes including the edge information, texture information, and local shape information. After feature extraction, Cascade Adaboost classifier is used to detect pedestrian images from non-pedestrian. In experiments, INRIA dataset, Daimler dataset, and ETH dataset are applied. The extensive experimental results demonstrate that our approach outperforms the traditional methods in terms of the accuracy and robustness.
HIKE: A Step Beyond Data Exchange

The problem of exchanging data, even considering incomplete and heterogeneous data, has been deeply investigated in the last years. The approaches proposed so far are quite rigid as they refer to fixed schema and/or are based on a deductive approach consisting in the use of a fixed set of (mapping) rules. In this paper, we propose HIKE (Highly Intelligent Knowledge Extraction), a framework that addresses this problem. The core of the framework consists of a smart data exchange architecture integrating deductive and inductive techniques to obtain new knowledge. The use of graph-based representation of source and target data, together with the midway relational database and the extraction of new knowledge allow us to manage dynamic databases where also features of data may change over the time. The paper also addresses the problem of computing certain answers in the new setting and reports a precise analysis of its complexity.
Access Service Unbound:

Following a supply chain model, appropriated from the business world, a public four-year institution dismantled its Access Services Department by limiting physical item processing tasks, delinquent account management, and the Circulation Desk, redoubling its efforts to focus on customer service by offering a “roving” model of customer service. This newly reformed Public Services Department offers self-checkout options in addition to these aforementioned changes. Users depend on access to course materials, technological equipment, and ILL/Reserves items. This paper describes how this library managed the transition of changes in responsibilities and offered excellent access despite the new format.
Innovative Architecture for Dynamic Solar Data Acquisition and Processing: A Case for Mauritius and Outer Islands

The aim of the project is to enable sustainable PV electricity generation in Mauritius, Rodrigues and Agalega through the deployment of an online platform for provision of GIS Solar Maps and bankable open data to public. Research grade solar stations have been deployed in 9 sites across Mauritius, 2 Sites in Rodrigues and 1 site in Agalega in order to measure global horizontal irradiance (GHI), plane of array irradiance (PoA), ambient temperature and cell temperature at time steps of one minute. The data are logged and sent to a server located at the University of Mauritius. A platform was developed for quality control and processing of GHI and PoA data and generation of GIS maps. The solarmap.uom.ac.mu website has been developed to publish the solar related data to the public at large. The website provides information about the equipment used and the placement of sensors. It allows access to monthly solar maps, viewing statistics, downloading data, estimating yield using a PV calculator and a gallery section to view pictures of the equipment. Having collected high quality data at time stamp of one minute for a year, several findings have been made and based on those findings also recommendations for prospective PV projects have been made. The results of spatial and temporal variations of global horizontal insolation and plane of array radiation are displayed in the form of GIS solar maps. The solar map and data for the three islands can be reliably used while choosing a site for PV plants, for financial analysis of PV projects and for PV system design. Information on daily solar energy, peak levels of solar power as well as information on temperature variations have been made available through high-precision on-site measurements under the local environmental conditions
Augmented Curiosity: Depth and Optical Flow Prediction for Efficient Exploration

Exploring novel environments for a specific target poses the challenge of how to adequately provide positive external rewards to an artificial agent. In scenarios with sparse external rewards, a reinforcement learning algorithm often cannot develop a successful policy function to govern an agent’s behavior. However, intrinsic rewards can provide feedback on an agent’s actions and enable updates towards a proper policy function in sparse scenarios. Our approaches called the Optical Flow-Augmented Curiosity Module (OF-ACM) and Depth-Augmented Curiosity Module (D-ACM) extend the Intrinsic Curiosity Model (ICM) by Pathak et al. The ICM forms an intrinsic reward signal from the error between a prediction and the ground truth of the next state. Shown with experiments in visually rich and sparse feature scenarios in ViZDoom, our predictive modules exhibit improved exploration capabilities and learning of an ideal policy function. Our modules leverage additional sources of information, such as depth images and optical flow, to generate superior embeddings that serve as inputs for next state prediction. With D-ACM we show a 63.3% average improvement in time to convergence of a policy over ICM in “My Way Home” scenarios.
Fusing Multimodal features for Recognizing Hand Gestures

Human computer interaction systems based on hand gestures are getting much attention from the research community for establishing realistic communication between human and machines. However, different persons perform the same gestures in different manner in terms of velocity and motion scale. This poses a challenging problem to reduce the variations between different persons and maximize the coherence of same gestures. In this paper, the original pyramid histogram of gradients in three orthogonal planes combining with optical flow to build dynamic descriptor is explored to discriminate features for recognition of hand gestures. The shape and motion features of images in a video sequence are captured to obtain the geometric and illumination invariant dynamic feature vector for classification. A multiclass support vector machine classifier is utilized here to detect the hand gestures. The proposed method gives excellent recognition rate and outperformed the existing approaches.
A Cost-efficient Protocol for Open Blockchains

Current proof-of-work blockchains are not sustainable in terms of energy needed to run them. In this paper we propose a new scheme that avoids wasted proof-of-work by a dynamic probabilistic method, where the consensus algorithm can be adjusted according to the parties' required assurance levels.
Commercial Security Scanning: Point-on-Sale (POS) Vulnerability and Mitigation Techniques

Point of Sale (POS) systems has become the technology of choice for most businesses and offering number of advantages over traditional cash registers. They manage staffs, customers, transaction, inventory, sale and labor reporting, price adjustment, as well as keeping track of cash flow, expense management, reducing human errors and more. Whether traditional on-premise POS, or Cloud-Bases POS, they help businesses to run more efficiently. However, despite all these advantages, POS systems are becoming targets of a number of cyber-attacks. Security of a POS system is a key requirement of the Payment Card Industry Data Security Standard (PCI DSS). This paper undertakes research into the PCI DSS and its accompanying standards, in an attempt to break or bypass security measures using varying degrees of vulnerability and penetration attacks in a methodological format. The resounding goal of this experimentation is to achieve a basis from which attacks can be made against a realistic networking environment from whence an intruder can bypass security measures thus exposing a vulnerability in the PCI DSS and potentially exposing confidential customer payment information.
Long-Term Background Redundancy Reduction for Earth Observatory Video Coding

Huge earth observatory video data (EOVD) and the limited transmission bandwidth from satellites to terrestrial devices pose serious challenges to compression efficiency for satellite video. In this work, we deeply explore long-term background redundancy caused by periodical satellite revisit, and make utilization of long-term background reference (LTBR) to design a high-efficiency coding method specific for EOVD. Firstly, we use data of Google Earth as prior background knowledge and construct LTBR from it. Then, two novel prediction methods are proposed to make full use of prior information of LTBR, including color and definition correction based inter prediction with LTBR (CDIP-LTBR), structure and texture constrained intra prediction with LTBR (STIP-LTBR). Lastly, the proposed two new prediction schemes are integrated into one unified coding framework along with HEVC to achieve improved coding performance, and an improved RDO method is designed for additional prediction modes selection problem in the framework to obtain a higher prediction efficiency. Extensive experiments on real-world EOVD show that the proposed coding scheme exhibit significant improvement over HEVC and H.264, in terms of BD-Rate, BD-PSNR and rate distortion comparison.
Word Sense Disambiguation Based on Semantic Knowledge

Word sense disambiguation (WSD) is an important and hot research topic in machine translation and information retrieval. A new WSD method is proposed in this paper. Chinese sentence containing ambiguous word is segmented into words. Its left and right words’ semantic categories are used as disambiguation features after Tongyici Cilin is consulted. Based on discriminative features, bayesian model is applied to select correct semantic categories for ambiguous words. Training data set is used to optimize bayesian model and test data set is utilized to test the performance of WSD classifier. Experiments show that accuracy of WSD classifier is improved.
An adaptive system for quality improved digitalization of medical images

Medical images are an important part of the modern diagnostic process of many diseases. The low quality of images is one of the reasons for hypo- or hyper-diagnosis as a result of incorrect deciphering. One of the image applications is to track changes in patients' organs and tissues over time. Screen-films and direct exposure films are part of medical image sources. The proposed paper presents a prototype of a new generation of medical image fil digitizer. The characteristics of such an apparatus and the necessary technologies for its implementation are considered.
Development of an ECMO Remote Monitoring System

Extracorporeal membrane oxygenation (ECMO) is a technique that provides cardiac and respiratory support for patients with severe heart failure. Remote monitoring of the ECMO device is very important, but there have been few reports on remote monitoring, and management of ECMO devices in use depends on medical staff. Therefore, we have developed a simple and practical system for remote monitoring of the ECMO device. By connecting a small PC to the ECMO device and using a custom-built application and MySQL server, we were able to monitor the operation status of the ECMO device from various remotely located communication terminals via a web browser. Because the remote monitoring procedure involves only a few steps and does not require large equipment or numerous wired connections around the ECMO device, it does not significantly burden medical staff. Using this approach, it is possible to monitor the operation status of the ECMO device from a remote location using a very simple system configuration.
Verification of two averaged models of three-phase filtration in inhomogeneous layers of oil reservoirs, obeying uniform distribution

Abstract This paper studies the possibility of using two averaged models, including models with modified relative phase permeabilities for solving filtration problems. Furthermore, a method of averaging the relative phase permeabilities for the three-phase filtration problem was created. The problem of three-phase isothermal displacement of oil and gas with water was solved during areal flooding in a stratified reservoir. Five-point and nine-point waterflood systems were considered. In addition, a comparative analysis of the calculation results based on three-dimensional and two-dimensional three-phase filtration models is performed.
Effectiveness in the Realisation of Speaker Authentication

An important consideration for the deployment of speaker recognition in authentication applications is the approach to the formation of training and testing utterances. Whilst defining this for a specific scenario is influenced by the associated requirements and conditions, the process can be further guided through the establishment of the relative usefulness of alternative frameworks for composing the training and testing material. In this regard, the present paper provides an analysis of the effects, on the speaker recognition accuracy, of various bases for the formation of the training and testing data. The experimental investigations are conducted based on the use of digit utterances taken from the XM2VTS database. The paper presents a detailed description of the individual approaches considered and discusses the experimental results obtained in different cases.
Human Resource Management in the Educational Institution – Big Changes Ahead

The article was devoted to improving the management of human resources in the educational institution. On the basis of Russian and international experience, the main tasks of personnel management in a modern university were considered. Key factors determining peculiarities of personnel work in the university depending on the development strategy, life cycle, priority areas of activity have been identified. Separate proposals have been formulated to strengthen the role of the personnel service in increasing the competitiveness of Russian universities in the international arena. The main reserves of efficiency of personnel work in the university were analyzed. Challenges and opportunities were identified to ensure staff involvement in solving the strategic tasks of the educational institution on the basis of increasing motivation of employees and taking into account their labour contribution to the progressive development of the university. Existing contradictions and problems in assessing the activities of scientific and pedagogical workers and communicating the results of the assessment to the performers were identified. The system of com-plex evaluation of personnel of educational institution is proposed, its goals and tasks are defined. Classifications of objects, subjects and methods of assessment are proposed. The need to apply progressive methods of assessing university personnel on the basis of MBO (management by objectives) using KPI, 360-degree approach, etc., was justified. Directions of personnel evaluation results use in personnel decision-making are defined.
A Combined Approach to Automatic Taxonomy Extraction

We propose a method for taxonomic relationships extraction from text based on morpho-syntactic and pattern-based approach combined with utilization of distributional vectors and application of graph algorithms. We evaluated our method on the datasets from popular SemEval Workshop series. In addition to the standard evaluation measures, we explored also taxonomic measures, which can show other aspects of hierarchy quality. We also conducted an experiment for manual evaluation, which employed participants to evaluate outputs of our method. In most observed measures we obtained better results in comparison with methods competing at the workshops. We show that utilization of vector space and additional external semantic knowledge can significantly improve overall quality of extracted taxonomic hierarchy.
Teaching French to young learners through DDL

In recent years, the potential of the DDL approach is being increasingly considered in the teaching of French as a foreign language (FLE). While most studies published are mainly concerned with the use of concordances and corpora in the teaching of FLE at university level, this chapter reports on the potential of concordances with an audience of young French L1 students. In this chapter, I describe lessons for younger learners of French incorporating activities involving concordance analysis for the study of certain morphological and syntactic phenomena, including the use of demonstrative adjectives and interrogative determiners, verb conjugation, and expressions of quantity. This research highlights the many advantages of using the DDL approach for materials preparation in FLE teaching and for providing young learners the opportunity to experiment with new learning methods.
Privacy-Preserving Top-k Query Processing in Distributed Systems

We consider a distributed system that stores user sensitive data across multiple nodes. In this context, we address the problem of privacy-preserving top-k query processing. We propose a novel system, called SD-TOPK, which is able to evaluate top-k queries over encrypted distributed data without needing to decrypt the data in the nodes where they are stored. We implemented and evaluated our system over synthetic and real databases. The results show excellent performance for SD-TOPK compared to baseline approaches.
Automated Nonlinear Control Structure Design by Domain of Attraction Maximization with Eigenvalue and Frequency Domain Specifications

This work proposes a new method for nonlinear control structure design for nonlinear dynamical systems by grammatical evolution. The optimization is based on a novel fitness function which implements three different design objectives. First, the closed loop eigenvalues of the linearized system dynamics are restricted to be located in a predefined region of the complex plane. Second, design specifications on the frequency magnitude of the sensitivity transfer functions of the linearized system dynamics are imposed. Third, the estimated domain of attraction of the nonlinear closed loop system dynamics is maximized by evaluating a quadratic Lyapunov function, obtained by the solution of the Lyapunov equation, with a Monte-Carlo sampling algorithm. Additionally, a general formula for the centroid of a design region for pole placement is derived analytically and embedded in the optimization framework. The generated controllers are evaluated on a common, nonlinear benchmark system. It is shown that the proposed method can efficiently generate control laws which meet the imposed design specifications on the closed loop system while maximizing the domain of attraction.
Measuring Trustworthiness of IoT Image Sensor Data Using Other Sensors’ Complementary Multimodal Data

Trust of image sensor data is becoming increasingly important as the Internet of Things (IoT) applications grow from home appliances to surveillance. Up to our knowledge, there exists only one work in literature that estimates trustworthiness of digital images applied to forensic applications, based on a machine learning technique. The efficacy of this technique is heavily dependent on availability of an appropriate training set and adequate variation of IoT sensor data with noise, interference and environmental condition, but availability of such data cannot be assured always. Therefore, to overcome this limitation, a robust method capable of estimating trustworthy measure with high accuracy is needed. Lowering cost of sensors allow many IoT applications to use multiple types of sensors to observe the same event. In such cases, complementary multimodal data of one sensor can be exploited to measure trust level of another sensor data. In this paper, for the first time, we introduce a completely new approach to estimate the trustworthiness of an image sensor data using another sensor's numerical data. We develop a theoretical model using the Dempster-Shafer theory (DST) framework. The efficacy of the proposed model in estimating trust level of an image sensor data is analyzed by observing a fire event using IoT image and temperature sensor data in a residential setup under different scenarios. The proposed model produces highly accurate trust level in all scenarios with authentic and forged image data.
Query Reduction for an Effective Japanese Statute Law Retrieval

In this study we investigate whether reducing very long Japanese Statute Law queries can improve the retrieval effectiveness of the Japanese civil law articles. In particular, we deploy two different query reduction techniques. In the first approach, we generate query terms by extracting key concepts from the original query using TagCrowd to create tag clouds. In order to test the generality of our approach, we deploy the Simplified Clarity Score query performance predictor to select the top 20 terms that are predicted to perform better during the retrieval. The results of this study suggest that reducing the original query can help improve the retrieval effectiveness of the Japanese civil law articles. In addition, expanding these reduced queries with more informative terms from the Japanese civil law articles result in the query drift problem and it degrades the retrieval performance.
Impact of Pulse Coupled Neural Network Parameters on Image Steganography

The paper deals with impact verification of Optimized Model of Pulse Coupled Neural Network (OM- PCNN) parameters on spatial domain image steganography. The main advantage of OM-PCNN is a reduced number of parameters by preserving the robustness against a noise and geometrical transformations of an image. The OM-PCNN robustness against noise is a key attribute in process of embedding the hidden message to a cover image. The search for suitable key parameters and the minimization of the required time of searching is the main goal of our research. Presented results of optimization claim that maintain the robustness of generated binary matrices is mainly affected by only one key parameter. Other parameters can be set by the calculation or by the qualified estimation.