Reviewerly/exHarmony huggingface dataset:
Aolah Databases for New Arabic Online Handwriting Recognition Algorithm

Developing an online handwriting recognition system for Arabic script used in pen-based devices plays an important role in making these devices available and usable for Arabic society. This paper is carried out for Arabic script to overcome the difficulties presented in the Arabic language in cursive, overlapping, handwriting variability, different writing styles, delayed strokes, and other challenges. An algorithm for recognizing Arabic strokes written by hand is proposed; since there are some troubles in distinguishing the written stroke for similar characters. The uniqueness of the recommended algorithm is dealing with every stroke in the character separately. Furthermore, in the current research, two novel databases for Arabic characters and Arabic characters’ strokes are generated. The two databases are presented, one for Arabic characters by different writers for the 28 Arabic characters, the other database is extracted from the previous database by taking only the Arabic character strokes. The algorithm used for data collection is distinguished by the ability to deal with each stroke in the written characters separately. The code acts as a simulation of a stylus pen and a touch screen. Stroke capturing is achieved by collecting data points along the path of an input device (stylus pen or mouse) same time those characters are written.
A Reinforcement Learning-based Path Planning Method for Complex Thin-walled Structures in 3D Printing

Path planning is an important part of the 3D printing process. The optimized path planning method can improve not only effect of the molding but also the efficiency of printing process. However, traditional path planning methods are not satisfactory in 3D printing, especially when printing the entities with complex thin-wall structures. We propose an intelligent path planning method named Q-Path, based on reinforcement learning for complex thin-walled structures. We first convert the path planning task to a full-path traversing problem. Then we use the Q-learning algorithm to find the optimal solution with the constraints of 3D printing, such as the minimum number of lifts and turns of the print head. Experimental results show that the proposed methods are superior to the traditional methods in printing complex thin-walled structures.
Graph Theory Compliant Technical Intelligence Implementation in NoSQL Database

Since the introduction of Firebase by Google, the database management was never the same for everyone who used to do database management via SQL. Instead of working primarily on keys, the Firebase worked on the concept of nodes. Then came “Firestore” and everything changed as it introduced us to the ideas of collections and documents. Same is the case with other NoSQL-based databases such as MongoDB which is also working on collection and documents. However, to link the people together in the database itself, we need graph theory as it works on the concepts of vertices and edges. Pictorially representing a graph of two people with more communication or relating in one way or other is one of the understanding but not practical. Because linking two people, we can put their user-ID inside the two vertices and put the jobs, age, and recent transactions on the edges as weights. However, in the database, we do not have connections, edges, and nodes, and we need to find out a way to achieve this concept via two things—collections and documents.
Stereo Video Quality Metric Based on Multi-Dimensional Analysis

Stereo video has been widely applied in various video systems in recent years. Therefore, objective stereo video quality metric (SVQM) is highly necessary for improving the watching experience. However, due to the high dimensional data in stereo video, existing metrics have some defects in accuracy and robustness. Based on the characteristics of stereo video, this paper considers the coexistence and interaction of multi-dimensional information in stereo video and proposes an SVQM based on multi-dimensional analysis (MDA-SVQM). Specifically, a temporal-view joint decomposition (TVJD) model is established by analyzing and comparing correlation in different dimensions and adaptively decomposes stereo group of frames (sGoF) into different subbands. Then, according to the generation mechanism and physical meaning of each subband, histogram-based and LOID-based features are extracted for high and low frequency subband, respectively, and sGoF quality is obtained by regression. Finally, the weight of each sGoF is calculated by spatial-temporal energy weighting (STEW) model, and final stereo video quality is obtained by weighted summation of all sGoF qualities. Experiments on two stereo video databases demonstrate that TVJD and STEW adopted in MDA-SVQM are convincible, and the overall performance of MDA-SVQM is better than several existing SVQMs.
Study on Pre-service Cultivation Mechanism of General Practitioners in Rural Primary Schools in Jiangxi Province

The cultivation of students can not be separated from excellent teachers, Jiangxi rural areas should be underdeveloped economy, life is not perfect and other reasons, resulting in a shortage of excellent teachers. In this context, it is necessary to carry out pre-service education and training for general-practice teachers in rural primary schools. On the basis of analyzing the necessity of pre-service training, this paper fully investigates the present situation of teacher training in rural primary schools, so as to construct the feasible mechanism of pre-service training for general-practice teachers and contribute to the development of education in rural areas of Jiangxi Province.
Automatic Baseball Pitch Overlay

To provide rich viewing experience and assist pitcher training, we propose an automatic baseball pitch overlay system in this paper. Given multiple pitching video sequences, this system detects and tracks the ball to construct ball trajectories. Because of occlusion, motion blur, and background noise, the ball usually cannot be detected successfully. We propose a series of processes like initial compensation and polynomial fitting to construct complete trajectories. To make the overlay results more appealing, different sequences are weighted differently, and different trajectories are intentionally drawn in different colors. We believe this would be the first fully-automatic pitch overlay system that only takes pitching videos as inputs. Source code is at \\https://github.com/chonyy/ML-auto-baseball-pitching-overlay.
Algorithm of Target Point Assignment for Robot Path Planning Based on Costmap Data

Most mobile robots which use the operating systems (ROS) employ costmap-based path planner for navigation in environment. Besides the systems, in which robotic devices function in completely autonomous manner, also such systems exist, where user specifies in a self-contained interface the target point for robot motion, without using any costmap-related data. In such settings, the robot sometimes cannot reach the user-specified point because it is treated as an obstacle on the costmap. To solve this problem an algorithm was developed, which accepts the coordinates of the target point, specified through user interface, and finds possible target points in vicinity of the specified one, regarding the predefined distance constraints. Conceptually, the search of possible points consists in looping through costmap cells of the global cost planner in orthogonally related directions from the obtained point and in defining of the preferable target points, upon reaching which the task is considered to be completed. In this paper, the algorithm was tested as part of the navigational module of the robot. Experiments were performed, particularly, in the Gazebo simulation environment; robot model TURTLEBOT3 Waffle Pi was also utilized. The experimental results showed that this module can be combined with any planner. When the value of validation step was set at the minimum value of 0.01 m, it was required no more than 55 ms to find a target point. The minimum duration of such search was 1 ms.
A Novel Method for Automated Suggestion of Similar Software Incidents Using 2-Stage Filtering: Findings on Primary Data

Advancements in software technology have resulted in a sharp increase in the complexity of software with an equally increasing number of bugs reported every day. Some of these bugs have a high severity and can often lead to significant business impacts. Thus, they need to be resolved by the developers at the earliest. Many of these reports are similar to the bug reports that were reported and resolved in the past. By suggesting similar incidents, the developers can refer to the troubleshooting information, thus effectively reducing the TTM (Time to Mitigate) of the software bugs. The developers also spend a significant amount of time and effort in triaging the bugs into their respective areas. Previous studies have mainly relied on unsupervised learning techniques for the detection of duplicate reports and ignored some key aspects of the bug reports. We conducted comprehensive research on real bugs reported for Microsoft Dynamics 365 Application Software. Our research presents a novel two-phase approach for suggesting similar incidents. The first phase called Binning involves the creation of a labelled dataset for employing a supervised learning algorithm for triaging the software incidents into multiple categories. Thus, the first phase also presents a solution for automating the process of triaging the incidents in addition to the first stage of filtering. The second phase introduces the use of error execution information and acknowledgment information for the calculation of similarity scores which has largely been ignored in the previous studies. The evaluation results show that the precision rate of our proposed approach reaches up to 95.8% while the model achieves recall rates of 67%–93.5%.
An Efficient and Secure Power Data Trading Scheme Based on Blockchain

The traditional mode of power data transaction is mediated by web pages, which cannot be employed to data explosion as the power trading reform. It is a challenge issue to improve efficiency and privacy under the premise of transaction security and data reliability in power data trading. In this paper, we propose a novelty efficient and secure power data trading scheme (PDTS) based on blockchain and trusted execution environment platform (PDTP). PDTS makes full use of the dispersibility and immutability of blockchain to ensure the reliability of data transaction. It is noteworthy that the power data processing tasks take the form of smart contracts to ensure the efficiency of the transaction process. Meanwhile, trusted execution environment (TEE) is employed to ensure the security of power data processing. Finally, we analyze the security of the scheme and prove that the scheme can resist single point attack, external attack and internal attack. Moreover, the experimental results prove the feasibility and efficiency of the proposed scheme.
Multinational Oil Companies in Nigeria and Corporate Social Responsibility in the HIV/AIDS Response in Host Communities

We assess the impact of corporate social responsibility (CSR) of multinational oil companies (MOCs) on HIV/AIDS prevalence in Nigeria’s oil producing communities. One thousand, two hundred households were sampled across the rural communities of Niger Delta. Using logit model, the main result indicates that General Memorandum of Understandings (GMoUs) have not significantly impacted on factors behind the spread of HIV/AIDS in rural communities. This implies that the impact of the disease on MOCs business, employees and their families, contractors, business partners and the oil communities has not inclined downward. The findings suggest that CSR offers an opportunity for MOCs to help address HIV/AIDS prevalence through a business case for stakeholders’ health in the region. It calls for MOCs to improve GMoUs health intervention on sensitization campaigns, funding testing and counselling centers, subsidizing anti-retroviral drugs, prevention of mother-to-child transmission, rehabilitation of orphaned and vulnerable children and other cares for people living with AIDS.
Proposed Ontology to Intelligent Road Network

A road network is a system of interconnecting lines and points that represents a system of roads for a given area. Some sections of the roads network have a special properties and they are usable by all types of vehicles. Others road are reserved for special vehicles. The ontology has been used for an explanation of any term and helps to value during the analysis of a new or unknown information. This paper proposes an ontology for taxonomy of a road network classes and their relationships taking into account the semantics of its segment (Board, Way, Pavement, Bridge, Subway or Tunnel, …) and their technical characteristics (Congestion, Validity, Security, …) in order to give appropriate solutions of some problems such as the travelling salesman problem (TSP). For this aim, we use the OWL (Ontology Web Language) for modeling our specific lightweight ontology. This ontology is supported by a complementary language SWRL (Semantic Web-enhanced Rule Language) and SQWRL (Semantic Query Web-enhanced Rule Language) to retrieve implicit road knowledge.
Frequent Itemset Mining Using Genetic Approach

Amballoor, Renji George Naik, Shankar B.There is a realization in the industry to use business analytics for extracting knowledge from data for better decision-making. This involves the use of data mining techniques, like frequent itemset mining for discovering patterns of interest. The process of mining frequent itemsets is challenging due to the large number of itemsets generated in the process and presented to the user. Use of genetic algorithms in frequent itemset mining can help address this issue. A genetic algorithm to mine frequent itemsets called FIMGA has been proposed in this paper. FIMGA employs the features of genetic approaches in mining frequent itemsets. FIMGA does not consider the itemsets which are sure of not generating any frequent itemset for crossover. This reduces the overall search time of itemsets making FIMGA time efficient. Experiments have shown that FIMGA is efficient in generating frequent itemsets.
A Novel Surgeon Training System for the Vascular Interventional Surgery based on Augmented Reality

At present, cerebrovascular disease has become the number one killer of human health, and minimally invasive interventional surgery is the main treatment. In order to solve the problem of radiation to doctors, master-slave surgical robot is favored. Even so, vascular interventional surgery has high requirements on the operation of doctors, and the actual modeling and training costs are high. The research of vascular interventional surgeon training system based on virtual reality technology is becoming more and more important. The quality of a training system depends on whether it can provide a realistic training environment. In the past, most of the researches mainly focused on the reality of touch, such as the simulation of the force on the guide wire in the blood vessel, but less on the human-computer interaction. A good human-computer interaction can also improve the doctor's operation experience and effect. To solve this problem, this paper built a training system based on augmented reality technology, and proposed gesture interaction to replace the traditional interaction. Through experiments, the system can satisfy the greater operational needs of doctors and improve the training effect.
Query Expansion for Transliterated Text Retrieval

With Web 2.0, there has been exponential growth in the number of Web users and the volume of Web content. Most of these users are not only consumers of the information but also generators of it. People express themselves here in colloquial languages, but using Roman script (transliteration). These texts are mostly informal and casual, and therefore seldom follow grammar rules. Also, there does not exist any prescribed set of spelling rules in transliterated text. This freedom leads to large-scale spelling variations, which is a major challenge in mixed script information processing. This article studies different existing phonetic algorithms to handle the issue of spelling variation, points out the limitations of them, and proposes a novel phonetic encoding approach with two different flavors in the light of Hindi transliteration. Experiments performed over Hindi song lyrics retrieval in mixed script domain with three different retrieval models show that proposed approaches outperform the existing techniques in a majority of the cases (sometimes statistically significantly) for a number of metrics like nDCG@1, nDCG@5, nDCG@10, MAP, MRR, and Recall.
Efficient implementation of Background Subtraction GMM method on Digital Signal Processor

Detecting moving objects based on real-time video processing is considered as a challenging task. The background subtraction using the Gaussian Mixture Model (GMM) is one of the widely used technique. However, it requires high-computing power to meet real-time processing constraints. An efficient implementation of GMM algorithm on an embedded platform based on the C6678 digital signal processor (DSP) was proposed in this paper. First, an optimized GMM code has been developed for the proposed embedded platform. Then, an efficient memory management strategy has been implemented to ensure contiguous memory access. In addition, the compiler options have been enabled to take profit from loops unrolling and software pipelining to improve the computing performances. In order to evaluate the quality of our implementation, a metrics comparison have been performed based on the background truth images provided by the literature dataset references. Experimental results show important improvements over the optimized implementation of the state-of-the-art.
The Complexity of Counting Problems Over Incomplete Databases

We study the complexity of various fundamental counting problems that arise in the context of incomplete databases, i.e., relational databases that can contain unknown values in the form of labeled nulls. Specifically, we assume that the domains of these unknown values are finite and, for a Boolean query q , we consider the following two problems: Given as input an incomplete database D , (a) return the number of completions of D that satisfy q ; or (b) return the number of valuations of the nulls of D yielding a completion that satisfies q . We obtain dichotomies between #P-hardness and polynomial-time computability for these problems when q is a self-join–free conjunctive query and study the impact on the complexity of the following two restrictions: (1) every null occurs at most once in D (what is called Codd tables ); and (2) the domain of each null is the same. Roughly speaking, we show that counting completions is much harder than counting valuations: For instance, while the latter is always in #P, we prove that the former is not in #P under some widely believed theoretical complexity assumption. Moreover, we find that both (1) and (2) can reduce the complexity of our problems. We also study the approximability of these problems and show that, while counting valuations always has a fully polynomial-time randomized approximation scheme (FPRAS), in most cases counting completions does not. Finally, we consider more expressive query languages and situate our problems with respect to known complexity classes.
Multi-resolution SVD, Linear Regression, and Extreme Learning Machine for Traffic Accidents Forecasting with Climatic Variable

The purpose of this work is to forecast variables related to traffic accidents in Ecuador since 2015 to 2019. Traffic accidents lead severe injuries and fatalities in Ecuador with 4925 deaths and 20794 injured in the period of analysis. Models based on Multiresolution Singular Value Decomposition (MSVD) and Extreme Learning Machine (ELM) are proposed to improve the accuracy for multi-week ahead forecasting. This study adds a climatic variable for enhancing the effectiveness of both type of models. The performance of MSVD+ELM based is compared with a conventional Linear Regression Model (LRM) joint with MSVD. To assess the forecasting accuracy, three metrics were used, Root Mean Squared Error (RMSE), Index of Agreement modified (IoAm), and Nash-Suctlife Efficiency modified (NSEm). Models based on Linear Regression (SVD+LRM) without climatic variable present the lowest accuracy, with an average Nash-Suctlife Efficiency of 65.4% for 12-weeks ahead forecasting, whereas models that integrate climatic variable at input, present gains in prediction accuracies, with an average Nash-Suctlife Efficiency of 94.6% for Linear Regression - based models, and 95.9%. for ELM -based models. The implementation of the proposed models will help to guide the planning of government institutions and decision-making, in face of complex problem of traffic accidents addressed in this work.
Blind CT Image Quality Assessment Model Based on CT Image Statistics

Image quality assessment is widely used in many image processing tasks, which can help researchers adjust image processing algorithms, design imaging systems, and evaluate image processing systems. Generally, CT image quality assessment can be categorized into task-specific and general image quality evaluation. Task-specific image quality assessment evaluates the performance of the imaging system or the detectability of the tumor. These IQA index, for example, are modulation transfer function (MTF), Signal-to-Noise Ratio (SNR), observer model, etc. General image quality assessment measures the general reconstruction image quality under different reconstruction algorithms. SSIM (Structural Similarity), Mean Squared Error (MSE), etc. are the traditional general image quality assessment indexes widely used in nowadays CT image quality assessment. The drawback of these indexes is the demand for reference images, which is not practical in the real CT system. In this paper, we design a CT image dataset, and by using this dataset, and we propose a blind image quality assessment (BIQA) model based on CT image statistics, which can be employed to measure the algorithms under no reference image situation. Different from other image datasets, we recruited no-converged images of the reconstruction process in designing datasets, which enables our BIQA model to evaluate non-converged images during the iterations. Hence, the BIQA model can be embedded in the reconstruction process to monitor reconstructed image quality during iterations.