Reviewerly/exHarmony huggingface dataset:
Expert System Diagnosis of Urinary System Diseases using Forward Chaining and Dempster Shafer

Expert system is a computer system that can adopt human knowledge into a computer. Expert system can be used to solve problems commonly performed by experts, one of them is the diagnosis of urinary system diseases. Expert system for the diagnosis of the urinary system disease especially for the inflammation of the bladder and these pyelonephritis using the forward chaining and the dempster shafer method. Forward chaining is used to diagnose disease based on the rules and the dempster shafer is used to determine the value of confidence. The goal is to build an expert system using forward chaining and dempster shafer methods to diagnose early urinary system diseases and to determine the level of accuracy. The data used is the secondary data obtained from the UCI Machine Learning Repository as much as 120 data and 6 attributes. The result of the implementation of the forward chaining and the dempster shafer methods on this expert system of diagnosis of urinary system diseases generates an accuracy value of 87.5%.
Paraconsistent logic approach for active noise reduction

The Active Noise Reduction (ANR) is widely used in aircraft, headsets, telecommunications and medicine systems, to reduce or eliminate noise, while maintaining the characteristics of the desired signal. In this article a network of paraconsistent artificial neural cells (PANC) will be presented, based on the paraconsistent annotated logic by 2 values annotations (PAL2v) that allows to operate as ANR. Simulations indicate that the results presented by ANRPAL are better than those obtained by classic filters.
Real-time discrete Fourier transformer with complex-valued outputs based on the inverse temporal Talbot effect

Discrete Fourier transform (DFT) plays an important role in digital signal processing. In this paper, we present a novel optical real-time discrete Fourier transformer with complex-valued outputs, which is enabled by the inverse temporal Talbot effect. In the system, an input pulse train is first quadratically phase-modulated as in an inverse temporal Talbot system and then split into two channels. In the first channel, the pulse train is further amplitude-modulated pulse-by-pulse by a discrete data sequence to be transformed. In the second channel, a reference signal modulates the pulse train, which is for removing the residual quadratic phase profile in the output pulse train. The pulse trains in the two channels propagate through a shared dispersion medium with a proper dispersion value determined by the inverse temporal Talbot effect. A 90-degree optical hybrid and two balanced photodetectors are employed to retrieve the real and imaginary parts of the DFT results. In this scheme, the pulse repetition rate of the output pulse train is equal to the input one. In addition, we present a full theoretical framework to explain exactly the DFT relationship. We also demonstrate that the input data sequence can be complex-valued with the help of an I/Q modulator.
Soft Regression of Monocular Depth Using Scale-Semantic Exchange Network

This paper focuses on depth estimation from single monocular image. Most of existing methods regress depth values or classify depth labels, based on single scale feature representations. However, neither regression nor classification can avoid their inherent defects. Single scale context and low-level semantic cannot support accurate depth estimations. We innovatively address single monocular depth estimation by performing soft regression on probability distribution of classification generated by our proposed scale-semantic exchange network (SSE-Net). Our network maintains rich high-resolution representations. With adding high-to-low resolution to form more stages, the repeated context fusions guarantee each representation receives scale information from other parallel representations over and over. Mutual channel attention mechanism is proposed for emphasizing specific semantics of feature representation. We allow each depth class to be shifted and scaled adaptively. Depth estimation is calculated as expected value of probability distribution. The experimental results verify the efficiency of every proposed components and show competitive results compared with the recent state-of-the-art methods.
Improving the Community Question Retrieval Performance Using Attention-Based Siamese LSTM

In this paper, we focus on the problem of question retrieval in community Question Answering (cQA) which aims to retrieve from the community archives the previous questions that are semantically equivalent to the new queries. The major challenges in this crucial task are the shortness of the questions as well as the word mismatch problem as users can formulate the same query using different wording. While numerous attempts have been made to address this problem, most existing methods relied on supervised models which significantly depend on large training data sets and manual feature engineering. Such methods are mostly constrained by their specificities that put aside the word order and ignore syntactic and semantic relationships. In this work, we rely on Neural Networks (NNs) which can learn rich dense representations of text data and enable the prediction of the textual similarity between the community questions. We propose a deep learning approach based on a Siamese architecture with LSTM networks, augmented with an attention mechanism. We test different similarity measures to predict the semantic similarity between the community questions. Experiments conducted on real cQA data sets in English and Arabic show that the performance of question retrieval is improved as compared to other competitive methods.
Determination of Temporal Association Rules Pattern Using Apriori Algorithm

A supermarket must have good business plan in order to meet customer desires. One way that can be done to meet customer desires is to find out the pattern of shopping purchases resulting from processing sales transaction data. Data processing produces information related to the function of the association between items of goods temporarily. Association rules functions in data mining.Association rule is one of the data mining techniques used to find patterns in combination of transaction data. Apriori algorithm can be used to find association rules. Apriori algorithm is used to find frequent itemset candidates who meet the support count. Frequent itemset that meets the support count is then processed using the temporal association rules method. The function of temporal association rules is as a time limitation in displaying the results of frequent itemsets and association rules. This study aims to produce rules from transaction data, apriori algorithm is used to form temporal association rules. The final results of this research are strong rules, they are rules that always appear in 3 years at certain time intervals with limitation on support and confidence, so that the rules can be used for business plan layout recommendations in Maharani Supermarket Demak.
A New Hypred Improved Method for Measuring Concept Semantic Similarity in WordNet

Computing semantic similarity between concepts is an important issue in natural language processing, artificial intelligence, information retrieval and knowledge management. The measure of computing concept similarity is a fundament of semantic computation. In this paper, we analyze typical semantic similarity measures and note Wu and Palmer’s measure which does not distinguish the similarities between nodes from a node to different nodes of the same level. Then, we synthesize the advantages of measure of path-based and IC-based, and propose a new hybrid method for measuring semantic similarity. By testing on a fragment of WordNet hierarchical tree, the results demonstrate the proposed method accurately distinguishes the similarities between nodes from a node to different nodes of the same level and overcome the shortcoming of the Wu and Palmer’s measure
Applying Semantic Role Labeling and Spreading Activation Techniques for Semantic Information Retrieval

Semantically enhanced information retrieval (IR) is aimed at improving classical IR methods and goes way beyond plain Boolean keyword matching with the main goal of better serving implicit and ambiguous information needs. As a de-facto pre-requisite to semantic IR, different information extraction (IE) techniques are used to mine unstructured text for underlying knowledge. In this paper we present a method that combines both IE and IR to enable semantic search in natural language texts. First, we apply semantic role labeling (SRL) to automatically extract event-oriented information found in natural language texts to an RDF knowledge graph leveraging semantic web technology. Second, we investigate how a custom flavored graph traversal spreading activation algorithm can be employed to interpret user’s information needs on top of the prior-extracted knowledge base. Finally, we present an assessment on the applicability of our method for semantically enhanced IR. An experimental evaluation on partial WikiQA dataset shows the strengths of our approach and also unveils common pitfalls that we use as guidelines to draw further work directions in the open-domain semantic search field.
The Impact of Total Quality Management Multidimensional on Non-Financial Organizational Performance: An Empirical Investigation of Saudi Commission for Tourism and National Heritage Hotels

This examination experimentally explores the most powerful components of all out total quality management (TQM) (top management support, customer focus, employees' participation, training and education, continuous process improvement, strategic planning, effective communication, and decision-making based on data and information ) on the non-financial organizational performance measurements (job satisfaction, organizational commitment, and employee's creativity).The investigation involved 121 employees from different hotels, which are classified within the Saudi Commission for Tourism and national Heritage (SCTH) in Najran Region.Using multiple regression analysis, findings indicated that Effective communication followed by Top management support, Continuous process improvement, Employee participation, Decision-making based on information, Strategic planning, Training and education and finally by customer focus.Thusly, this current investigation's results can be prescribed to expand acknowledgment of the significance of human resource metrics in organizational performance.
A Lightweight Detection Algorithm For Collision-Optimized Divide-and-Conquer Attacks

By introducing collision information into divide-and-conquer attacks, several existing works transform the original candidate space, which may be too large to enumerate, into a significantly smaller collision space, thus making key recovery possible. However, the inefficient collision detection algorithms and fault tolerance mechanisms make them time-consuming and their success rate low. Moreover, they may still leave very huge chain spaces that makes it difficult for key recovery. In this article, we exploit collision attack to optimize Template Attack (TA), and propose a Lightweight Collision Detection (LCD) algorithm. The proposed method exploits a jump detection mechanism to efficiently reduce the repetitive collision detections on chains with the same prefix sub-chains. We then introduce guessing theory to reorder the collision detection of the sub-keys according to their guessing lengths, and provide us with an evaluation tool. Finally, we design a highly efficient fault tolerance mechanism for our LCD to allow flexible thresholds adjustment, and further optimize sieving mechanism to efficiently extract the best chains with the largest number of collisions. Experimental results fully demonstrate LCD's superiority.
QEVC: Quality Enhancement-Oriented Video Coding

Quality enhancement (QE) is an important post-processing technology for high-resolution video services at low bit rates, which can effectively improve the quality of compressed video. The application of deep learning methods to the quality enhancement task has achieved great success in the past few years. However, the existing schemes are usually coding-independent, which still leaves room for further development of related technologies. Therefore, in this paper, we propose a quality enhancement-oriented video coding scheme. By analyzing the features of different video regions, a deep reinforcement learning model is used to determine the distortions of regions. Then during the video reconstruction, convolutional neural network (CNN)-based quality enhancement networks with different scales are selected to improve the video quality according to the distortion of different regions. Experimental results show that the proposed scheme outperforms the HEVC anchor in case of bits saving, bits allocation, and shows good visual quality especially at low bit rates.
An IoT and Blockchain based Electronic Voting System

The objective of voting is to permit voters to exercise their right to precise their choices regarding specific issues, pieces of legislation, citizen initiatives, constitutional amendments, recalls and/or to settle on their government and political representatives.It has always been an onerous task for the election commission to conduct free and fair polls in our country, the largest democracy in the world.A lot of cash has been spent on this to form sure that the elections are rampage free. But, now-a -days it's become very usual for a few forces to enjoys rigging which can eventually cause a result contrary to the particular verdict given by the people. In order to provide inexpensive solutions to the above, this project will be implemented with biometric system i.e. finger print scanning. This is wont to make sure the security to avoid fake, repeated voting etc. It also enhances the accuracy and speed of the method . The system uses thumb impression for voter identification as we all know that the thumb impression of each person features a unique pattern. Thus it might have a foothold over this day voting systems. Nowadays, crypto currency has become a trending theme within the software package globe. Crypto currency may be a digital quality that's meant to operate as a dealings medium that utilizes sturdy cryptography to secure asset exchange and make sure plus transfer. Crypto currency is in addition spoken as digital suburbanized cash. Block chain stores knowledge concerning dealings which will be accustomed assess transaction trait. This voting system deals with multi chain block chain technology. We can define block chain as a digital transaction which is used to record financial transactions also as totally different transactions. As a result of the knowledge keep within the block chain isn't related to personal identifiable information, it's such a anonymity attribute. Blockchain allows dealings and verification to be clear. The options of this block chain technology are useful in powerful voting system, robustness, obscurity and transparency. The electoral system is our country's core. Verification of fingerprints employed in this theme to authenticate identity of electors.
An Integration between Information Systems Engineering and Software Engineering Theories towards Engineering a Novel Instrument and Framework of Web-Based Systems Success: Hypothesis Testing

Abstract Systems and its success evaluation are an important prospect whereby, several studies can be traced, outlining its importance and vitality. However, there is a need to identify responsive specific factors to help better understand the systems determinants and its success evaluation. In the academia, systems are used to facilitate students, human resource, e-commerce activities any much more. Hence, examining the systems and evaluating as to what length it is successful is essential. This study aims to provide the results of the hypothesis testing. There have been 7 accepted hypothesis and 3 rejected hypotheses. Hypothesis results showed that ISO 25010 works better than TAM and DM Models in systems success measurement.
Approach of spectral information-based image registration similarity

For local intensity-based image registration methods, a template of a predefined size is adopted. Some measure of similarity between the images is used to determine when the optimal alignment has occurred. Most intensity-based similarity utilizes the statistical and spatial information of an image. However, there are significant nonlinear radiometric differences between infrared and visible data. Images of natural scenes usually do not have enough spatial features within the template. Hence, many similarities will be useless when dealing with infrared and visible data. Minor eigenvalues (ME) image registration similarity is presented with the exploitation the spectral properties of remote sensing images. ME similarity is based on the linear spectral mixture model and detects control points through searching the minimum of ME of the covariance matrix. Experiments on Landsat-7 satellite Enhanced Thematic Mapper Plus data are performed to verify and evaluate the effectiveness. Transformation performance curves, correct match ratio (CMR), and registration accuracy are also discussed. According to the data, root-mean-square error of phase correlations is 0.0722 pixels and the CMR of ME similarity is nearly 100%. The results on the basis of TM1, TM2, TM3, and TM4 band images indicate that the proposed similarity holds promise for infrared and color image registration in natural scenes, with advantages over previous normal mutual information and gradient mutual information similarities.
Using Learning Classifier Systems for the DSE of Adaptive Embedded Systems

Modern embedded systems are not only becoming more and more complex but are also often exposed to dynamically changing run-time conditions such as resource availability or processing power requirements. This trend has led to the emergence of adaptive systems which are designed using novel approaches that combine a static off-line Design Space Exploration (DSE) with the consideration of the dynamic run-time behavior of the system under design. In contrast to a static design approach, which provides a single design solution as a compromise between the possible run-time situations, the off-line DSE of these so-called hybrid design approaches yields a set of configuration alternatives, so that at run time, it becomes possible to dynamically choose the option most suited for the current situation. However, most of these approaches still use optimizers which were primarily developed for static design. Consequently, modeling complex dynamic environments or run-time requirements is either not possible or comes at the cost of a significant computation overhead or results of poor quality. As a remedy, this paper introduces Learning Optimizer Constrained by ALtering conditions (LOCAL), a novel optimization framework for the DSE of adaptive embedded systems. Following the structure of Learning Classifier System (LCS) optimizers, the proposed framework optimizes a strategy, i.e., a set of conditionally applicable solutions for the problem at hand, instead of a set of independent solutions. We show how the proposed framework-which can be used for the optimization of any adaptive system-is used for the optimization of dynamically reconfigurable many-core systems and provide experimental evidence that the hereby obtained strategy offers superior embeddability compared to the solutions provided by a s.o.t.a. hybrid approach which uses an evolutionary algorithm.
Citation databases and researcher identifiers

Nowadays, there exist many resources available to find the scientific information, both commercial and free. Within the search and analysis of scientific publications, the intensive development of information technologies has led to the qualitative and even revolutionary changes. Despite the saturation of the market with technological tools, software, the tools for the information technologies and communications that surround the scientists, many of them are not used or used not enough because they are little known to the academic community. There is also no unique tool that can meet all the needs of scientists in the process of preparing and reviewing the articles. The main subject of this paper is the review and brief analysis of information resources and systems, identifiers and profiles of scientists. The basic search engines, citation databases, library information retrieval systems are described which can and should be used by a Ukrainian scientist to search for scientific literature. The differences between the search for information in commercial databases and the standard search on the Internet are considered. Examples of practical use of citation databases are given. Results of the research profiles of researchers at the Institute of Geophysics named after S.I. Subbotin testify to the lack of usage of Google Scholar services by scientists and the presence of considerable potential to increase the share of their visibility in world scientific and information flows. Only a third of all members of the Institute have a Google Scholar profile. It is shown that high citation statistics are based not only on the quality and relevance of scientific works, but also on the activity of the researcher in the modern media space. Recommendations are provided for registering and using the international ORCID Researcher (Open Researcher and Contributor ID).
Collision-Free Trajectory Planning With Deadlock Prevention: An Adaptive Virtual Target Approach

Most human-centred robotic applications require robots to follow a certain pre-defined path. This makes the robot's autonomous movements acceptable and predictable for humans. Planning a trajectory for the robot thus involves guiding it along this desired path. The classical approach of segmenting a path into multiple waypoints and tracking them only works well in environments which are obstacle-free or contain fixed stationary obstacles. Movable or dynamic obstacles that can potentially lie directly on waypoints result in deadlock situations causing the robot to oscillate around the desired waypoint without moving forward. This chapter presents a novel approach for trajectory planning in which an Adaptive Virtual Target (AVT) is formulated that follows the desired path irrespective of surrounding obstacles. The AVT essentially plays the role of a moving reference for the trajectory planner to track. Additionally, the AVT velocity can be adapted such that the robot can catch up in case of deviations from the path due to obstacle avoidance manoeuvres. A model predictive control (MPC) based trajectory planner tracks the AVT and accounts for obstacle avoidance. The proposed approach allows the robot to keep moving towards the goal by preventing deadlocks while simultaneously minimizing deviation from the desired path. Simulations based on a medical X-ray robot are provided to validate the approach.
Who Are Controlled by The Same User? Multiple Identities Deception Detection via Social Interaction Activity (Student Abstract)

Social media has become a preferential place for sharing information. However, some users may create multiple accounts and manipulate them to deceive legitimate users. Most previous studies utilize verbal or behavior features based methods to solve this problem, but they are only designed for some particular platforms, leading to low universalness.In this paper, to support multiple platforms, we construct interaction tree for each account based on their social interactions which is common characteristic of social platforms. Then we propose a new method to calculate the social interaction entropy of each account and detect the accounts which are controlled by the same user. Experimental results on two real-world datasets show that the method has robust superiority over state-of-the-art methods.