Reviewerly/exHarmony huggingface dataset:
Surface water quality classification using data mining approaches: Irrigation along the Aladag River*

Abstract Knowledge of water quality is an important requirement in planning, developing, and protecting water resources. Therefore, it is essential to determine the quality of water for various uses, including irrigation in different areas. The aim of this study is to evaluate the performance of different data mining methods using water quality parameters measured in Aladag River. At the first stage, the water quality in the Aladag River in Turkey was classified by United States Salinity Laboratory (USSL) diagrams and the Schofield 1933, Schofield 1935, and Wilcox–Magistad methods. Support vector classifier (four different kernels), k‐nearest neighbour, and decision tree methods—including Hoeffding tree, random forest, random tree, and REP tree methods were used to classify the water quality. Hydro‐chemical and hydrological parameters were used in the analysis, based on alternative scenarios. The methods were compared for accuracy based on kappa statistics and error rates. Evaluation of the data mining methods reflected the high accuracy and performance of these methods in classifying the water quality. In general, from the kernels used for the support vector classifier method, the Pearson universal kernel and from the decision trees, the REP tree provided the best results for Aladag River water quality classification. For the k‐nearest neighbour method, the best results were obtained using the four nearest neighbours and Euclidean distances.
About One Property of Number Rank in RNS

An approximate value of the number rank is used to increase the speed of homomorphic encryption algorithms. The use of an approximate rank of a number allows reducing the computational complexity of the data encryption algorithm, but at the same time leads to a loss of accuracy. In this paper, we investigate the properties of the number rank. We have shown that it is possible to reduce the accuracy of calculating the approximate rank of a number, but the homomorphic encryption scheme will have the same properties.
Increasing the Understandability and Explainability of Machine Learning and Artificial Intelligence Solutions: A Design Thinking Approach

Nowadays, Artificial Intelligence (AI) is proving to be successful for solving complex problems in various application domains. However, despite the numerous success stories of AI-systems, one challenge that characterizes these systems is that they often lack transparency in terms of understandability and explainability. In this study, we propose to address this challenge from the design thinking lens as a way to amplify human understanding of ML (Machine Learning) and AI algorithms. We exemplify our proposed approach by depicting a case based on a conventional ML algorithm applied on sentiment analysis of students’ feedback. This paper aims to contribute to the overall discourse of a need of innovation when it comes to the understandability and explainability of ML and AI solutions, especially since innovation is an inherent feature of design thinking.
Who Is Authenticating My E-Commerce Logins?

Multifactor authentication has inherent security flaws, including its reliance on passwords. A generic solution is proposed for authentication based on a hybrid of biometric-authentication artifacts, digital certificates, and error correction.
Path planning of mobile Robot based on improved ant colony algorithm based on Honeycomb grid

Based on the problems of traditional ant colony algorithm in complex environment such as slow convergence speed and easily falling into local optimal value, this paper proposes an improved ant colony algorithm based on honeycomb grid. Firstly, a grid map based on honeycomb grid was established to solve the problem of non-uniformity of traditional grid step size. Secondly, according to the traditional ant colony algorithm blindly search for the path in the early stage, centering on the straight line from the starting point to the end point, the design of uneven distribution of initial pheromone concentration was carried out by integrating three factors, namely the length from the ideal line, the number of obstacle grids and the target deviation Angle, to avoid the blind search in the early planning, improve the search efficiency in the early stage, and alleviate the problem of ant death. Thirdly, an adaptive attenuation coefficient is introduced to improve the heuristic function to strengthen the guiding role of pheromones in the later period and accelerate the convergence speed. The safety factor is introduced into the transfer probability to reduce the ant "deadlock" problem. Then, the updating rules of pheromones are improved by combining ant colony sequencing model, and the critical range of pheromones is set to improve the global optimization ability. The simulation results show that the proposed algorithm can improve the global search capability, and the path length and turning point can be reduced by 17.12% and 55.56% respectively compared with the traditional algorithm. The effectiveness of the proposed algorithm is verified.
Investigation of Stereo Camera Calibration Based on Python

Camera calibration is a crucial process in the field of computer vision. In this paper, describing the pinhole camera model is introduced. A calibration process of two similar cameras using a 2D pattern is then described. A Python program is developed to extract the cameras intrinsic and extrinsic parameters, and save them for a further use. The OpenCV library based the proposed program takes in consideration the radial and tangential distortion of the camera. To evaluate the performance of the proposed program, real data experiment has been performed. The results show the effectiveness of the proposed program to calibrate the cameras and form the basis for the next stage of the research. The proposed program can be used as first step in real time applications such as obstacle detection and navigation systems.
GP3: A Sampling-based Analysis Framework for Gaussian Processes

Although machine learning is increasingly applied in control approaches, only few methods guarantee certifiable safety, which is necessary for real world applications. These approaches typically rely on well-understood learning algorithms, which allow formal theoretical analysis. Gaussian process regression is a prominent example among those methods, which attracts growing attention due to its strong Bayesian foundations. Even though many problems regarding the analysis of Gaussian processes have a similar structure, specific approaches are typically tailored for them individually, without strong focus on computational efficiency. Thereby, the practical applicability and performance of these approaches is limited. In order to overcome this issue, we propose a novel framework called GP3, general purpose computation on graphics processing units for Gaussian processes, which allows to solve many of the existing problems efficiently. By employing interval analysis, local Lipschitz constants are computed in order to extend properties verified on a grid to continuous state spaces. Since the computation is completely parallelizable, the computational benefits of GPU processing are exploited in combination with multi-resolution sampling in order to allow high resolution analysis.
Feature Extraction and Selection in Archaeological Images for Automatic Annotation

In this paper, we present two steps in the process of automatic annotation in archeological images. These steps are feature extraction and feature selection. We focus our research on archeological images which are very much studied in our days. It presents the most important steps in the process of automatic annotation in an image. Feature extraction techniques are applied to get the feature that will be used in classifying and recognizing the images. Also, the selection of characteristics reduces the number of unattractive characteristics. However, we reviewed various images of feature extraction techniques to analyze the archaeological images. Each feature represents one or more feature descriptors in the archeological images. We focus on the descriptor shape of the archaeological objects extraction in the images using contour method-based shape recognition of the monuments. So, the feature selection stage serves to acquire the most interesting characteristics to improve the accuracy of the classification. In the feature selection section, we present a comparative study between feature selection techniques. Then we give our proposal of application of methods of selection of the characteristics of the archaeological images. Finally, we calculate the performance of two steps already mentioned: the extraction of characteristics and the selection of characteristics.
Rulebase construction using variables with data-dependent domains

Fuzzy inference systems are used to determine output values based on input values, with the connection between output and input determined through a training phase. This approach lends itself when this connection is not easily modelled. Here, such a system is considered for spatial data, where an underlying (spatial) distribution is not known but can be estimated from other available data to perform a regridding. The spatial context provides interesting constraints, justifying modifications to the traditional approach in order to improve its performance for this problem. We will present a methodology where the most suitable domain of the variables of the rulebase is dependent on the datapair; this dependency is dealt with using two different approaches: one where the values are scaled to a common domain and one where the fuzzy sets are redefined on the fly. The spatial data problem serves as a test case to illustrate the performance, using the basic Mamdani rulebase construction algorithm as a benchmark to verify and assess the methodology. The presented methodology is not limited to spatial data processing and generally is applicable when subsets of the dataset behave similarly if the domain is appropriately reconsidered.
GAMES AND CARDINALITIES IN INQUISITIVE FIRST-ORDER LOGIC

Abstract Inquisitive first-order logic, InqBQ , is a system which extends classical first-order logic with formulas expressing questions. From a mathematical point of view, formulas in this logic express properties of sets of relational structures. This paper makes two contributions to the study of this logic. First, we describe an Ehrenfeucht–Fraïssé game for InqBQ and show that it characterizes the distinguishing power of the logic. Second, we use the game to study cardinality quantifiers in the inquisitive setting. That is, we study what statements and questions can be expressed in InqBQ about the number of individuals satisfying a given predicate. As special cases, we show that several variants of the question how many individuals satisfy $\alpha (x)$ are not expressible in InqBQ , both in the general case and in restriction to finite models.
Joint Face Retrieval System Based On a New Quadruplet Network in Videos of Multi-Camera

At present, a large number of off-line videos is stored in the server of surveillance network. In order to retrieve the target face in these massive videos frames, the face retrieval system is designed. A new Quadruplet Network is constructed by changing the RELU structure of CNN network and training the new Quadruplet Network to acquire the depth features. Join with the online fugitive face picture that launched online to initiate the wanted, with the help of the depth feature contrast to launch the Content-Based Image Retrieval (CBIR). The new Quadruplet Network converges faster than familiar networks such as Alexnet, Googlenet, VGGNet and ResNet. Because of the shared weight design of the network, the retrieval has a high precision, recall and the retrieval rate. Image depth features can be shared quickly online between the cameras. The experimental results show that the proposed method is effective, with an accuracy of 98.74% and a precision of 99.54%, and a frame rate of 28 FPS.
Evaluating Answer Qualities on Q&amp;amp;A Community Sites (StackOverFlow)

Social Networking sites have gained much popularity in the recent years due to the widespread use of internet and availability of huge amounts of information. One kind of social network is Question Answering Community sites like StackOverflow, Quora and many more. These sites facilitate the user to ask their query on such sites using natural language and get the answers to their queries in natural language. There are many challenges to face when achieving above goals. One of the goals is to find the valid answers out of available answers and present it to the user, so as to save the user from going through multiple answers for a particular question and selecting the valid or appropriate answer. In this paper, we propose a system that finds the best answer from all the available answers based on answer content and the relationship between question and answer. We trained the system using three different classifiers and noted the accuracy results of each; the classifier which gave maximum accuracy was selected.
Single landmark feedback-based time optimal navigation for a differential drive robot

In this paper, we propose a feedback-based control approach to execute the time optimal motion trajectories for a differential drive robot. These trajectories are composed of straight lines and rotations in place. We show that the evolution of the position of a single landmark over time, in a local reference frame, makes it possible to track a prescribed time-optimal robot’s trajectory, based on feedback of the landmark’s position. We also show that the closed-loop system is an exponentially stable one with a nonvanishing perturbation, and that globally uniformly ultimately boundedness of the tracking errors can be achieved. The two main results of this work are: 1) Our approach leverages visual servo control type of methods with tools from optimal control for executing time-optimal trajectories in the state space based on feedback information. 2) The approach is able to work with the minimum number of landmarks–only one–this represents a necessary and sufficientcondition for landmark-based navigation. Experiments in a physical robot, a nonholonomic differential drive system equipped with an omnidirectional laser sensor, are shown, which validate the proposed theoretical modelling.
An Supervisable Anonymous Authentication Scheme Based on Master-slave Certificate

The development of blockchain has put forward higher requirements for authentication technology. In order to solve the current blockchain authentication problem and enhance the supervision and anonymity, we propose an authentication scheme based on master-slave certificate and zero-knowledge proof. Users can hide their real identity information through the anonymity of the slave certificate. When necessary, regulators can track the malicious users through smart contracts. Through security analysis, it is verified that there is no linkability between the master and slave certificates of this scheme, which can efficiently achieve supervisable anonymous authentication. The simulation experiment results show that the scheme is stable and scalable. The results also show that, compared with the traditional authentication, this scheme reduces the computational overhead of certificate issuers and is suitable for adoption in blockchain systems.
Robust Service Mapping in Multi-Tenant Clouds

In a multi-tenant cloud, cloud vendors provide services (e.g., elastic load-balancing, virtual private networks) on service nodes for tenants. Thus, the mapping of tenants' traffic and service nodes is an important issue in multi-tenant clouds. In practice, unreliability of service nodes and uncertainty/dynamics of tenants' traffic are two critical challenges that affect the tenants' QoS. However, previous works often ignore the impact of these two challenges, leading to poor system robustness when encountering system accidents. To bridge the gap, this paper studies the problem of robust service mapping in multi-tenant clouds (RSMP). Due to traffic dynamics, we take a two-step approach: service node assignment and tenant traffic scheduling. For service node assignment, we prove its NP-Hardness and analyze its problem difficulty. Then, we propose an efficient algorithm with bounded approximation factors based on randomized rounding and knapsack. For tenant traffic scheduling, we design an approximation algorithm based on fully polynomial time approximation scheme (FPTAS). The proposed algorithm achieves the approximation factor of 2+ ε , where ε is an arbitrarily small value. Both small-scale experimental results and large-scale simulation results show the superior performance of our proposed algorithms compared with other alternatives.
Trace Elements in Sulfides and Gold of the Olimpiada Deposit (Yenisei Ridge): Ore Substance Sources and Fluid Parameters

Abstract —We consider the distribution of trace elements and precious metals in sulfides and native gold of the Olimpiada deposit. Analysis of the obtained data provided conclusions about the source of ore substance and the parameters of the ore-forming fluid. We think that the deposit was formed by a medium- to high-temperature fluid with variable salinity and acidity/alkalinity, with chloride complexes dominating. The redox potential changed in the course of ore formation, but the ore mineralization formed under reducing conditions. The early Au–As mineralization might have formed with the major participation of crustal substance, but the supply of substance from another, probably deep-seated, source was also possible. The Au–Sb paragenesis differs significantly in REE and precious-metal (primarily PGE) distribution, which might indicate a different source of substance (with a higher portion of a deep-seated component) during the formation of such parageneses. The Au/Ag ratios in sulfides, gold, and ores of the Olimpiada deposit point to the presence of Au and Ag sulfide minerals.
Weakly-Supervised Question Answering with Effective Rank and Weighted Loss over Candidates

We study the weakly supervised question answering problem. Weak-ly supervised question answering aims to learn how the questions should be answered directly from the <question, answer> pairs without golden solutions/evidences, which makes question answering models much easier to scale to many domains. However, in weak supervision setup, a question typically involves many candidate solutions and the spuriousness of candidate solutions will hurt the performance of the question answering models. In this paper, we present an effective method to learn a question answering model in a weak supervision way. Specifically, in order to reduce the spuriousness of candidate solutions used for training, we design several simple yet effective scoring functions to rank the candidate solutions. Despite its simplicity, this ranking process can improve the quality of the training data significantly with fewer spurious candidates left. Then, different from previous approaches that either treat all candidates equally for training or only select the candidate with the largest likelihood in each iteration, we formulate this problem as a multi-task learning problem by weighing the losses computed from top-k candidates. Experimental results show that, our method1 can outperform previous approaches on both semantic parsing and machine reading comprehension tasks.
Research and analysis of the control system based on sports running device to assist coaches to adjust speed adaptively

Abstract In this paper, an algorithm of adaptive speed control based on Sports runner assisted coach is proposed, and the control system based on Sports runner assisted coach adaptive speed regulation is established and simulated with MATLAB. The simulation results show that the test curve of the self-adaptive speed regulation of the sports running device is basically consistent with the actual situation of the auxiliary training device, with good stability and strong usability, which meets the requirements of sports running. At the same time, it also has a strong reference significance for the physical education teaching and training in other places.
Efficient Detection Of SQL Injection Attack(SQLIA) Using Pattern-based Neural Network Model

Web application vulnerability is one of the major causes of cyber attacks. Cyber criminals exploit these vulnerabilities to inject malicious commands to the unsanitized user input in order to bypass authentication of the database through some cyber-attack techniques like cross site scripting (XSS), phishing, Structured Query Language Injection Attack (SQLIA), malware etc., Although many research works have been conducted to resolve the above mentioned attacks, only few challenges with respect to SQLIA could be resolved. Ensuring security against complete set of malicious payloads are extremely complicated and demanding. It requires appropriate classification of legitimate and injected SQL commands. The existing approaches dealt with limited set of signatures, keywords and symbols of SQL queries to identify the injected queries. This work focuses on extracting SQL injection patterns with the help of existing parsing and tagging techniques. Pattern-based tags are trained and modeled using Multi-layer Perceptron which significantly performs well in classification of queries with accuracy of 94.4% which is better than the existing approaches.
Research Approach of Hand Gesture Recognition based on Improved YOLOV3 network and Bayes classifier

The study of gesture recognition is closely related to the harmonious development of human-computer interaction, which has important research significance. Aiming at the problems of long detection time and low recognition efficiency of traditional gesture detection algorithm, this paper proposes a gesture recognition model based on the combination of improved the YOLOV3 network and the Bayes classifier. The spatial transformer network is used to improve the YOLOV3 network for processing gesture information and extract key gesture features, so as to solve the problem of data vulnerability while maintaining the depth extraction of feature information. Then, the features are input into the combined model of PCA network and Bayes classifier for predicting gesture categories with reducing the dimension of data and improve the classification accuracy. Finally, the comparison test is performed using the public and self-made dataset, and experimental result illustrate that the propose algorithm can improve detection accuracy with higher effectiveness.
Launch point estimation with a single fixed passive sensor without trajectory state estimation

This paper considers the problem of estimating the launch point (LP) of a thrusting object from a single fixed sensor’s 2-D angle-only measurements (azimuth and elevation). It is assumed that the target follows a mass ejection model and the measurements obtained are available starting a few seconds after the launch time due to limited visibility. Previous works on this problem estimate the target’s state, which, for a passive sensor, requires a long batch of measurements, is sensitive to noise and ill-conditioned. In this paper, a polynomial fitting with the least squares approach is presented to estimate the LP without motion state estimation. We provide a statistical analysis to choose the optimal polynomial order, including overfitting and underfitting evaluation. Next, we present Monte Carlo simulations to show the performance of the proposed approach and compare it to the much more complicated state of the art technique that relies on state estimation. It is shown that the proposed method provides a much simpler and effective way than the state estimation methods to implement in a real-time system.