Reviewerly/exHarmony huggingface dataset:
RASWNet: An Algorithm That Can Remove All Severe Weather Features from a Degraded Image

The advanced driving assistant system (ADAS) is an important vehicle safety technology that can effectively reduce traffic accidents. This system can perceive information about the surrounding environment through in-vehicle cameras. However, these cameras are easily affected by severe weather conditions, such as those involving fog, rain, and snow. The quality of the images acquired by the system is degraded, and the function of the ADAS is thus weakened. In response to this problem, we propose a comprehensive imaging model that can represent the features of fog, rain streaks, raindrops and snowflakes in an image. Subsequently, an algorithm called RASWNet is proposed, which can remove all severe weather features from a degraded image. Based on the generative adversarial network, RASWNet combines the focus capture ability of a visual attention mechanism, the memory ability of the recurrent neural network and the feature extraction ability of the dense blocks approach. We verify the network structure through several ablation studies and use various synthetic and real images to test it. The results of these experiments show that our algorithm is not only better than the commonly used algorithms in terms of its clarity enhancement capacity but is also suitable for all severe weather conditions.
A FLCPID RPM Hybrid Strategy for Speed Control — Case Study and Performance Comparison

The research paper is focused to implement in real time MATLAB/SIMULINK software environment two rpm control speed strategies for the case study of a permanent magnet DC motor (PMDCM) integrated in a single zone variable volume air (VAV) control system, as one of the most spread in heating ventilation and air conditioning (HVAC) control applications. The first control strategy is a non-fuzzy proportional integral derivative (PID), and the second is a fuzzy logic PID control (FLCPID) approach. The aim is to reveal the strengths and weaknesses of both control strategies, so that in the end we select the most appropriate control strategy for this type of application. The selection criterion is based by a rigorous comparison of the performance of both control strategies. The novelty of the actual research paper is a new modelling approach by using a SIMSCAPE model for PMDCM, instead to use an analytical model. The new development has a great potential to be extended to any nonlinear control systems.
Research and Exploration on the Construction Method of Knowledge Graph of Water Field Based on Text

With the development of the Internet age, collected data have become an important source of knowledge. The field of unstructured text contains many named entities, but includes very little detailed information about those entities. However, the Baidu encyclopedia website is a type of semistructured data that in many cases includes a detailed introduction of entities. By combining the advantages of these two kinds of data, we can enrich the knowledge base of a knowledge graph. This paper aims to extract semistructured data consisting of named entities starting from raw text data. On one hand, this paper extracts named entities with the help of the Harbin Institute of Technology model, parses semistructured content about the named entities using the Octopus tool, constructs a local ontology, and merges the ontology using Python's built-in difflib. SequenceMatcher tool and the Deckard similarity algorithm. On the other hand, we create an XPath-based wrapper to extract the attributes and attribute values of named entities from semistructured data. The experimental results show that this approach can extract information related to named entities from the Baidu encyclopedia automatically to supplement the knowledge base of a water domain knowledge graph. This article can also serve as a reference for constructing domain knowledge graphs in other fields.
Tourism Recommendation System based on Knowledge Graph Feature Learning

Tourism recommendation system based on the knowledge graph feature learning is proposed and designed in this paper. The primary task for implementing a travel recommendation system is data collection, including user information, integrated user interaction records, tourist attraction information, and also contextual information. Among them, the user information primarily originates from the information entered by user in the registration process. The interaction record between the user and the system can be obtained from the system log, while the contextual information is entered by the user autonomously or obtained through various sensors. In this paper, a data processing and analytic framework is integrated to construct the novel scenario used for the recommendation. When compared the proposed model with the state-of-the-art research works, it has been proven that the proposed model can obtain the higher recommendation accuracy.
Memory level neural network: A time-varying neural network for memory input processing

Affective computing is an important foundation for implementing brain-like computing and advanced machine intelligence. However, the instantaneous and memory fusion input characteristic makes current neural networks not suitable for affective computing. In this paper, we propose an affective computing oriented memory level neural network. A “switch” has been added to the memory level neurons, which will achieve a transition from the instantaneous input to the memory input when the temporal integration of inputs above a certain threshold. Then, the “switch” is continualized by an adjustable sigmoid function whose parameters are tuned to adjust the speed of the transition and the mixing ratio of the two inputs. Multiple memory level neurons form a deep time-varying neural network capable of handling fusional inputs. We demonstrate on both process datasets and static datasets that the memory level neural network successfully converges on both datasets and meets the error accuracy requirements.
Importance of Data Distribution on Hive-Based Systems for Query Performance: An Experimental Study

SQL-on-Hadoop systems have been gaining popularity in recent years. One popular example of SQL-on-Hadoop systems is Apache Hive; the pioneer of SQL-on-Hadoop systems. Hive is located on the top of big data stack as an application layer. Besides the application layer, the Hadoop Ecosystem is composed of 3 different main layers: storage, the resource manager and processing engine. The demand from industry has led to the development of new efficient components for each layer. As the ecosystem evolves over time, Hive employed different execution engines too. Understanding the strengths of components is very important in order to exploit the full performance of the Hadoop Ecosystem. Therefore, recent works in the literature study the importance of each layer separately. To the best of our knowledge, the present work is the first work that focuses on the performance of the combination of both the storage layer and the execution engine. In this work, we compare the Hive's query performance by using three different execution engines: MR, Tez and Spark on the skewed/well-balanced data distribution through the full TPC-H benchmark. Our results show the importance of data distribution on the storage layer for overall job performance of SQL-on-Hadoop systems and empirically showed even distribution improves performance up to 48% compared to skewed distribution. Moreover, the present study provides insightful findings by identifying particular SQL query cases that the certain processing engine deals exceptionally well.
Hierarchical Text Clustering and Categorisation Using a Semi-Supervised Framework

Several steps need to be considered when conducting a data mining study on a text dataset, this will also involve the use of multiple techniques before achieving any results or extracting any hidden knowledge. Hence, the complexity of working with text data. One of the main steps would be the pre-processing of the text dataset, and this might include multiple techniques such as tokenisation, word stemming, stop words removal, and text vectorisation. To extract knowledge from the text data after preprocessing, depending on the use case or end goal, the application steps might include clustering of text documents, classification of the text documents, and the extraction of document topics and entities. For each of these steps there are several methods and techniques being presented in different research studies. In this paper we present a framework that would categorise, cluster and classify a corpus of unclassified documents. The framework extracts named entities and uses a linked data Knowledge Graph to assign several topics and categories to each document. Then automatically cluster the documents into groups using the K-Mean model with the Elbow and Silhouette methods. Each cluster then gets assigned to a readable name from the extracted linked data based on word frequency in each cluster.
A Survey on Video-based Evidence Analysis and Digital Forensic

In the past couple of decades, the most emerging domain across the globe is Information Technology [IT]. It has made a significant contribution in cracking the criminal case and preparing the strong cases in the court of justice. IT technologies have become a vital tool for examination and gathering of the digital evidences. As crime rate is increasing day-by-day, in order to reduce the occurrence of crime and bring the culprit of the crime to justice, the computer forensics plays an indispensable role. With the recent technological developments, it has become more powerful and portable. Large amount of data has created stored and accessed everyday. Many technologically advanced devices are acting as repositories for the collected personal information. Such devices remain portable and accessible with a single click or even to the voice command. To have such large-scale information is important to get the conviction for the culprits but at the same time, investigating agencies has to maintain the privacy of the recovered data to make it admissible in the court. This gives rise to the security mechanism for such data. Collection, analysis, investigation and reporting of the digital data come under forensic science, which is a part of digital forensics domain. In this paper, we have presented a review on the existing researches available in the field of video forensic analysis.
A Demonstration System for the Generation and Interaction of Battlefield Situation Based on Hololens

Aiming at the current battlefield situation visualization requirements and combining the advantages of augmented reality technology(AR). In this paper, we propose a design idea to develop a demonstration system for the generation and interaction of battlefield situation based on Hololens, and develop a battlefield situation demonstration prototype system with good three-dimensional display effect, model versatility and natural interaction. The system uses Unity3D as a virtual reality engine to develop Hololens. At first, using real terrain data to render the battlefield environment through Unity3D. Then, creating the battlefield entity model and add special effects through three-dimensional modeling software. Finally, publishing the system to Hololens through LAN. Users can realize the entity deployment and situation generation by wearing Hololens helmet to make gesture operations or voice commands and the test results show that the application effect is good.
Towards a Conceptual Model to Understand Software Ecosystems Emerging from Systems-of-Information Systems

Software vendors are currently concerned to the development of software-intensive Information Systems (IS) that interoperate among themselves using web and/or mobile platforms. This phenomenon has risen the concept of System-of-Information Systems (SoIS), which is a set of interoperable and independent IS that exchange data and combine synergistic services to achieve broader business goals. Since new types of sociotechnical relations can be established to increase gains and productivity in this context, considering technical, business and social dimensions, Software Ecosystems (SECO) emerge as a theoretical lens to study SoIS. We claim that the software ecosystem perspective can foster the comprehension about SoIS by exploring the existing relations among constituent ISs within a SoIS as well as the nature of such relations. The main contribution of this work is to offer a conceptual model to support SoIS analysis under a SECO perspective, rising the idea of EcoSoIS. Coding analysis was applied to extract the main concepts of SoIS and SECO from experts and to link these concepts to compose the conceptual model. Smart city was used as an example to illustrate how the model could be instantiated. Once the model enables a conformant representation of the smart city SoIS under a SECO lens, we believe that the model can be used for other SoIS.
A Smart Card Oriented Secure Electronic Voting Machine Built on NTRU

Free and fair elections are indispensable to quantify the sentiments of the populace for forming the government of representatives in democratic countries. Due to its procedural variation from country to country and complexity, to maneuverer, it is a challenging task. Since the Orthodox paper-based electoral systems are slow and error-prone, therefore, a secure and efficient electoral system always remained a key area of research. Although a lot of literature is available on this topic. However, due to reported anomalies and weaknesses in American and France election in 2016, it once again has become a pivotal subject of research. In this article, we proposed a new secure and efficient electronic voting scheme based on public key cryptosystem dubbed as Number Theory Research Unit (NTRU). Furthermore, an efficient and robust three factors authentication protocol based on a personalized memorable password, a smartcard, and bioHash is proposed to validate the legitimacy of a voter for casting a legal vote. NTRU based blind signatures are used to preserve the anonymity and privacy of vote and voters, whereas the proficiency of secure and efficient counting of votes is achieved through NTRU based homomorphic tally. Non-coercibility and individual verifiability are attained through Mark Pledge scheme. The proposed applied electronic voting scheme is, secure, transparent and efficient for large scale elections.
The role of knowledge in determining identity of long-tail entities

Identifying entities in text is an important step of semantic analysis. Some entity mentions comprise a name or description, but many include no information that identifies them in the system’s knowledge resources, which means that their identity cannot be established through traditional disambiguation. Consequently, such NIL (not in lexicon) entities have received little attention in entity linking systems and tasks so far. However, given the non-redundancy of knowledge on NIL entities, their lack of frequency priors, their potentially extreme ambiguity, and their numerousness, they constitute an important class of long-tail entities and pose a great challenge for state-of-the-art systems. In this paper, we describe a method for imputing identifying knowledge to NILs from generalized characteristics. We enrich the locally extracted information with profile models that rely on background knowledge in Wikidata. We describe and implement two profiling machines using state-of-the-art neural models. We evaluate their intrinsic behavior and their impact on the task of determining the identity of NIL entities.
Fvsoomm a Fuzzy Vectorial Space Model and Method of Personality, Cognitive Dissonance and Emotion in Decision Making

The purpose of this extension of the ESM’2019 conference paper is to propose some means to implement an artificial thinking model that simulates human psychological behavior. The first necessary model is the time fuzzy vector space model (TFVS). Traditional fuzzy logic uses fuzzification/defuzzification, fuzzy rules and implication to assess and combine several significant attributes to make deductions. The originality of TFVS is not to be another fuzzy logic model but rather a fuzzy object-oriented model which implements a dynamic object structural, behavior analogy and which encapsulates time fuzzy vectors in the object components and their attributes. The second model is a fuzzy vector space object oriented model and method (FVSOOMM) that describes how-to realize step by step the appropriate TFVS from the ontology class diagram designed with the Unified Modeling Language (UML). The third contribution concerns the cognitive model (Emotion, Personality, Interactions, Knowledge (Connaissance) and Experience) EPICE the layers of which are necessary to design the features of the artificial thinking model (ATM). The findings are that the TFVS model provides the appropriate time modelling tools to design and implement the layers of the EPICE model and thus the cognitive pyramids of the ATM. In practice, the emotion of cognitive dissonance during buying decisions is proposed and a game addiction application depicts the gamer decision process implementation with TFVS and finite state automata. Future works propose a platform to automate the implementation of TFVS according to the steps of the FVSOOMM method. An application is a case-based reasoning temporal approach based on TFVS and on dynamic distances computing between time resultant vectors in order to assess and compare similar objects’ evolution. The originality of this work is to provide models, tools and a method to design and implement some features of an artificial thinking model.
A Velocity Filtering Method for Track - Before- Detect with Multiple Sensors

Detecting and tracking weak targets with multiple sensors performs superiorly to that with a single sensor. In this paper, a multi-sensor velocity filter based track-before-detect (MS-VF-TBD) method in mixed coordinates is proposed for weak target detection. First, the predicted position in the global Cartesian coordinates of each cell of each sensor is achieved in terms of the assumed velocity and the position in Cartesian coordinates which is converted by the cell in sensor coordinates. Then the measurement of the cell is added onto the cell closest to the predicted position converting back to the global sensor coordinates to realize the process of target energy integration within multiple sensors. The energy accumulation procedure of multiple sensors is derived in detail. Simulation results demonstrate the superiority of the proposed method compared with other MS- TBD methods in terms of detection probability and estimation accuracy.
The new 3D facial expression recognition method based on semantic knowledge of Gaussian mixture model

Firstly, 2D images is susceptible to the face-pose, illumination etc. Secondly, image recognition are mostly based on image low-level visual features, while human perception of images are based on high-level semantic knowledge, which results in the "semantic gap" between them. For this reason, a new 3D facial expression recognition method is proposed based on semantic knowledge of Gaussian mixture model. The method uses Gaussian curvature and mean curvature to extract several key points of low-level visual features of 3D facial expressions, and uses European-style distance to form several key points into a set of low-level visual feature vectors. Then the Gaussian mixture model and the AHP hierarchical model are combined to calculate the high-level semantic feature vector, which solves the "semantic gap" between the low-level visual features and the high-level semantic knowledge of facial expression images, and improve the robustness and recognition rate of 3D facial expression recognition.
Integrated Circuit Security Risk Management Framework in Government Agencies

The security risk management framework is an essential part of strategic management for government agencies. It allows a government to systematically identify and address the risks associated with its activities to achieve sustainability for different activities of security risk management. The goal of security risk management is to add sustainable value to government activities and reduce the chance of security breaches. Applying security risk management techniques used to government projects can increase the chances of success, help achieve objectives, and assist in finding preventive solutions for future projects. The application of security risk management is profitable for government agencies because it sets specific risk management objectives that are based on the broader overall strategy. It contributes to the achievement of strategic objectives with mechanisms like Spearman's rank correlation coefficient and simple linear regression. These techniques can improve decision-making, planning and implementation of government activities, as well as reduce the negative consequences of present threats. It is recommended to apply the integrated security risk management framework proposed in this paper to increase the effectiveness of security risk management in government agencies. Also using quantitative and intelligent techniques in the analysis and estimation of security risks can help managers to make decisions regarding security issues in government agencies.
Multi-Objective Path Planning for Autonomous Robots Using Reconfigurable Analog VLSI

This paper presents a Multi-Objective path planning approach using reconfigurable Analog-Very-Large-Scale-Integrated (AVLSI) circuits. It is significant because it is the first example of floating-gate based analog resistive grid circuits used for Multi-Objective path planning. The two path planning objectives are 1) minimizing path length and 2) minimizing path cost. Three hardware experimental results are presented that implement the approach using a Field Programmable Analog Array (FPAA) circuit. First, an example demonstrates a simple proof-of-concept. Second, an example shows how the FPAA solution compares to an entire solution set for a specific Start and Goal path planning problem. Third, an example shows how the FPAA solution compares to two edge-cases. The edge-cases are the two ideals: ideal lowest cost path, and ideal shortest distance path. Based on these foundational proof-of-concept hardware results, larger environment grids than are currently implementable on the FPAA hardware were simulated to predict performance if a custom FPAA application specific integrated circuit (ASIC) was built for this Multi-Objective path planning purpose. Finally, analysis is presented to address this method's computational complexity.
eMap: Mobile App : Map Directory of EVSU Main Campus with Web-Based Management Panel

The Eastern Visayas State University - Tacloban Campus is a vast campus - which suggests that it is more confusing and complicated with regards to locating the rooms, buildings, etc. inside the university. Through the "eMap: Mobile App Map Directory of EVSU Main Campus with Web-Based Management Panel," we can lessen or even give an end to this problem. This mobile app is a map wherein you can track your current location, browse over the directories of the campus and trace the shortest and possible routes to your destination inside the university[1]. And to satisfy the users more, we added the News and Updates feature, to keep them updated on the latest events and happenings in the university [2]. The application is a navigation app, so we apply GPS to acquire location data. Since the map data from Mapbox is outdated and not detailed, we integrated an OpenStreetMap to enhance the map layout. Then through a local database, we stored the latitude, longitude, zoom, and bearing of each building to place a marker on their position. Just like most of the other mobile applications, the Android Studio is the development tool of the project.