Reviewerly/exHarmony huggingface dataset:
Associative Graphs for Fine-Grained Text Sentiment Analysis

Due to social media's ubiquitousness, most advertising campaigns take place on such platforms as Facebook, Twitter, or Instagram. As a result, Natural Language Processing has become an essential tool to extract information about the users: their personality traits, brand preferences, distinctive vocabulary, etc. Such data can be further used to create text adverts profiled to engage with users who share a certain set of features. While most of the algorithms capable of processing the text are neural network-driven, associative graphs serve the same purpose, attaining usually similar or better accuracy, but being more explainable than black box-like models based on neural networks. This paper presents an associative graph for natural language processing and fine-grained sentiment analysis. The ability of associative graphs to represent complex relations between phrases can be used to create a model capable of classifying the input data into many categories simultaneously with high accuracy and efficiency. This approach enabled us to acquire a model performing similarly or better than the state-of-the-art solutions while being more explicit and easier to create and explain.
On Geometric Properties of Adversarial Examples

Majority of widely used machine learning models are vulnerable to adversarial attacks, which are slight modifications of inputs that change the model's prediction with high confidence. The detection of such attacks and adversarial samples is still an open problem. In this work, we continue exploration of a novel method for adversarial image detection by means of linear algebra approach. The method is built on a comparison of distances to the centroids for a given point and its neighbours. The method of adversarial examples detection is considered theoretically, and the numerical experiments are done to illustrate the approach. A number of methods of adversarial examples generation are considered. It is shown, that the method is prone to error in case of certain adversarial attacks and training sets.
CARE

Top-N sequential recommendation is to predict the next few items based on user's sequential interactions with past items. This paper aims at boosting the performance of top-N sequential recommendation based on a state-of-the-art model, Caser. We point out three insufficiencies of Caser - do not model variant-sized sequential patterns, treating the impact of each past time step equally, and cannot learn cumulative features. Then we propose a novel Convolutional Attentional Recurrent Embedding (CARE) learning model. Experiments conducted on a large-scale user-location check-in dataset exhibit promising performance, comparing to Caser.
Permutation binomials over finite fields

Let Fq denote the finite field with q elements. In this paper we use the relation between suitable polynomials and number of rational points on algebraic curves to give the exact number of elements a∈Fq for which the binomial xn(xq−13+a) is a permutation polynomial. In order to do this, we employ results on the Cartin-Manin operator and the Riemann Hypothesis for elliptic curves to present the exact number of points on a suitable elliptic curve.
Toward Faster Parameter-Tuning of Sampling-Based Motion Planners

This paper provides an alternative parametertuning method for sampling-based motion planners, while handling case studies of global path planning for multi–Degrees Of-Freedom DOF manipulators. Observations of the behavior of average-planning-time as function to parameters’ values are presented for the Open Motion Planning Library OMPL bidirectional planners. Based on the presented observations, recommendations are made of which parameters are worth to be tuned. It is also discussed how time-efficient is to move the parameter tuning problem from searching for multiple parameters together, to be searching for a single parameter at a time. Moreover, an alternative single-parameter-tuning method is proposed depending on polynomial interpolation of a pre-chosen performance-estimation function. The proposed method has been formulated using average-planning-time as a performance-estimation function, and has been tested in tuning bidirectional planners’ parameters in three different scenarios equipped with three different manipulators (LBR IIWA 14 R820, KUKA KR3 R540, and Kinova JACO). The proposed method was compared and outperformed tuning results of the Sequential Model-based Algorithm Configuration SMAC where it is used as a multiple-parameter- tuning method.
Intelligent Video Surveillance Method for Digital Substation Based on Background Difference

Aiming at the low efficiency of video surveillance in digital substation, this paper presents an intelligent video surveillance method based on background difference for digital substation. In the aspect of hardware, according to the object and working condition of substation video monitoring, the module of video monitoring and infrared sensor is designed. In the aspect of system software, background subtraction is used to extract the target object, and the binary images of the two objects are matched together to get the relatively accurate position information. System testing shows that the average implementation time of software is 68.40ms, the average implementation time of hardware is 11.40ms, improving the efficiency of digital substation video monitoring.
Dialogue emotion recognition method based on dynamic graph network

Emotion Recognition in Conversation has attracted widespread attention from researchers due to its wide application prospects, but most of the existing methods ignore that the dialogue structure will change as the dialogue progresses. To solve this problem, we propose a dialogue sentiment analysis method based on Dynamic Graph Neural Networks (DyGNN). We conduct experiments on two sentiment classification datasets thereby demonstrating that our model is superior to the state of the art in most cases.
Contingent Planning for Robust Multi-Agent Path Finding

Multi-agent Path Finding deals with finding collision-free paths for a set of agents moving in a shared environment. Due to uncertainty during execution, agents might be delayed, which may bring collisions among them. In the paper, we propose using contingent planning to generate plans robust to delays. The initial plan is analyzed to find locations for possible collisions, and alternative paths are planned to divert delayed agents before the collision occurs. This novel concept of robustness guarantees no collisions (until some maximum delay), it does not prolong the execution of plans if the delay does not occur, and it does not significantly extend the planning time.
Medical Scene Graphs and Reasoning

Medical scene graph contributes to cognitive tasks such as question answering. An automatic medical scene graph generator can annotate medical images with scene graphs conveniently. An end-to-end model is proposed, which contains an automatic medical scene graph generator based on medical images and surgery records. The novel model can extract symptom information of the images from surgery records via name entity recognition then convert it into scene graphs. The content and format of the scene graph are pre-defined according to the features of the images. A medical scene graph (MSG) dataset is also produced. The dataset contains 116 scene graphs where each graph includes 6 to 30 entities. Experiments show that our model performs well on making scene graphs of CT coronary angiography. Significantly, the scene graphs have the type, state, location, and index of arteries. To verify the practicability of the MSG dataset, We construct an intelligent model that can generate key diagnostic results. In addition, our medical scene graph generator can be applied to other medical areas to annotate different kinds of medical images intelligently. And the MSG dataset can directly be used in other cognitive visual tasks.
Epistemic, Default and Dynamic Logics

In this chapter, we propose a discussion of epistemic logics, a clone of modal logics in which operators of necessity and possibility appear as operators of knowledge and belief. Yet, it is not any direct translation consisting in mere symbol changing as notions of knowledge and belief have a wide spectre of applications, for instance, in reasoning by teams of agents. Next, we insert an interlude on default logics, invented with the aim of catching basic aspects of incomplete knowledge and its updating. Finally, we are concerned with dynamic propositional logic, the logic of actions and programs. We begin with epistemic logics for a single reasoning agent.
The Cultural Foundation of Information Security Behavior

Past behavior research overwhelmingly focused on information security policy compliance and under explored the role of organizational context in shaping information security behaviors. To address this research gap, this study integrated two threads of literature: organizational culture, and information security behavior control, and proposed a framework that integrates mid-range theories used in empirical research, connects them to organizational culture, and predicts its role in information security behavior control. Consistent with the cultural-fit perspective, this framework shows that information security policy compliance fits hierarchical culture and the approach of promoting positive, proactive, and emerging information security behaviors fits participative culture. Contributions and practical implications of this framework, together with future research directions, are discussed.
Comparative Analysis Between Gaussian Filter &amp; Its Modified Version for High-Resolution Image Compression

Compressing any digital image without discarding a significant portion information is of tremendous importance due to the perceived higher value of portable and limited digital storage. This study proposes an alternative to traditional Gaussian filtering scheme by including upsampling and downsampling in the process. Higher compression ratio is always desirable if the signal-to-noise ratio (SNR) is above an acceptable margin. The filtering scheme becomes more attractive for high-resolution images, since all modern devices boast in high-resolution and those files use up most of our portable storage. In this study, better compression ratio is achieved using the proposed modified Gaussian filter against a generic version of that. This study also shows tolerable SNR against the generic one, and the reduction of SNR difference between the two methods when applied to higher-resolution images.
Generative Adversarial Negative Imitation Learning from Noisy Demonstrations

In imitation learning, we aim to learn a good policy from demonstrations directly. A limitation of previous imitation learning algorithms is that the demonstrations are required to be optimal, but collecting expert demonstrations is costly. Existing few robust imitation learning algorithms need additional annotations or strict noise distribution assumptions, which is usually impractical. In this work, we propose a novel generative adversarial negative imitation learning method to learn from noise demonstrations by training a classifier simultaneously exploiting the difference in the intrinsic attributes of the trajectories. Meanwhile, we derive a stopping criterion for the classifier for more stable performance. In particular, our method can be easily integrated into other adversarial imitation learning algorithms to improve their robustness. Experimental results in several continuous-control tasks and noise-rate demonstrations show that our method achieves state-of-the-art performance with only unlabeled trajectories.
Data Mining Applications in Education

We are witnessing the changes in Education by time. Databases on technology developments and especially storage systems grow, Databases need more memory. The discovery of correlations and relationships within the rapidly increasing size of existing data and the finding of valid predictions based on them have become an important concept in the computer age we are in. In many countries' education system has used electronic logging software. WEB software has provided a great contribution especially in the processing of training data, in the determination of training management strategies, self assessment and in the development plans. Although in some countries university department preferences are free, a good orientation is always needed. While the university department or profession is preferred, it is a good choice to process the data in the electronic daily with data mining techniques. In our research we have used the data mining prediction tree, the WEKA open-code software and the Apriori algorithm. It is a fact that data mining methods give very effective results in determining the factors that affect the educational success of the students.
Computer and Physical Modeling for Verification of the Stereo System Calibration Parameters Optimization Results

Close-range photogrammetry is widely used to measure surface shapes and diagnose deformation. Usually, a stereo system of video cameras is used to register images of the measured object from several different angles. The surface shape is determined by triangulating a set of 2D points from these images. Triangulation uses the stereo system calibration parameters, which are determined before the experiment. Measurements during conditions with increased vibration loads can lead to a change in the relative position of the cameras of the stereo system (decalibration). This leads to a change in the actual calibration parameters and an increase in the measurement error. The decalibration problem can be solved using multidimensional optimization algorithms. To verify their calculation's results it is proposed to use a computer and physical modeling of decalibration of a video camera stereo system in laboratory conditions. The paper presents the implementation of the optimizing algorithm for the external parameters of a stereo system and the results of its performance during the experimental investigations.
Generating Big Numbers for Testing Multi-Digit Arithmetic Algorithms

Introduction. The emergence of new parallel computational systems, such as multi-core processors, clusters, distributed systems, is due to the solution of various applied problems in various fields. The difference between devices for which parallel algorithms are implemented causes a variety of existing methods for parallelizing the calculation of multi-digit arithmetic operations. There is a problem of developing universal algorithms for implementing multi-digit arithmetic operations that are efficiently performed on various devices and on various systems. Very often it is not possible to develop a new algorithm, since at this stage there is still no test data with which it is possible to analyze the result of calculation. Therefore, the task of preparing test data and results is no less important than the development of the algorithm itself. The quality of the prepared data determines the quality of the implemented algorithm and the time required to find and eliminate errors in the algorithm-program and its implementation. In this paper, some simple dependencies are given, using which you can visually check the correctness of the calculation of multi-digit operations of addition, subtraction, multiplication, multiplication by modulo and exponentiation by modulo. Simple algorithms for generating input and output multi-digit data are presented. Using dependencies allows to check the integrity of the output when delegating computations to distributed systems such as cloud computing. The purpose of the article is to show simple dependencies between the input data and the results of performing multi-digit operations of addition, subtraction, multiplication, multiplication by modulo and exponentiation by modulo. For the given dependencies, methods for generating input and output multi-digit numbers are shown, which can be used to check the correctness of the calculation of multi-digit operations, which significantly saves the time required for preparing test data. Dependencies are provided in a generic way, which allows you to generate input data and results for devices that operate on words of different lengths (8, 16, 32, 64, 128, 256, etc. bits). Results. The dependences between the input data and the results of performing multi-digit operations are analyzed. The provided dependencies are proved in the form of lemmas. The dependencies are presented in a general form, since to generate multi-digit sequences, it is needed to set two parameters: N – the number of digits in the multi-digit value and n – the length of the digits in bits. The examples show the generation of input data and results for various multi-digit operations. Conclusions. The paper presents dependencies that are easy to remember and use for visual verification of the results of multi-digit calculations without using additional or special software or hardware, which allows to devote the saved time to developing new or more efficient modifications of multi-digit algorithms. Keywords: multi-digit arithmetic, parallel computational model.
Propositional Kernels

The pervasive presence of artificial intelligence (AI) in our everyday life has nourished the pursuit of explainable AI. Since the dawn of AI, logic has been widely used to express, in a human-friendly fashion, the internal process that led an (intelligent) system to deliver a specific output. In this paper, we take a step forward in this direction by introducing a novel family of kernels, called Propositional kernels, that construct feature spaces that are easy to interpret. Specifically, Propositional Kernel functions compute the similarity between two binary vectors in a feature space composed of logical propositions of a fixed form. The Propositional kernel framework improves upon the recent Boolean kernel framework by providing more expressive kernels. In addition to the theoretical definitions, we also provide an algorithm (and the source code) to efficiently construct any propositional kernel. An extensive empirical evaluation shows the effectiveness of Propositional kernels on several artificial and benchmark categorical data sets.
A Model of a Distributed Information System with the Possibility of Dynamic Distribution of the Functions Performed

This article proposes a model of a distributed information system with the possibility of dynamically distributing the functions performed across the physical nodes of the system by changing its structure. The model is based on a matrix representation of the physical and logical structures of the system and the procedure for establishing correspondence between them. Restrictions are introduced, the observance of which is necessary for the formation of a set of equivalent transformations. The implementation of the proposed model is based on the theory of multi-agent systems. The main provisions of this theory, the types of agents used, and their structure are described. The properties of mobile agents as the main tool for implementing the model are described. A fuzzy relational model is proposed as the basis of the implementing agent. It is proposed to use a vertically organized architecture of a multi-agent system. The application of the proposed model in the construction of distributed information systems for various purposes, in particular, systems for monitoring the state of industrial facilities, will increase their reliability and safety.
Preventing Attribute and Entity Disclosures: Combining k-anonymity and Anatomy over RDF Graphs

In this paper, we are considering the privacy/utility trade-off in privacy-preserving RDF data publishing. Starting from our recent utility-focused work on semantic anatomy, which prevents the disclosure of new information about groups of individuals, we are enriching the framework with the well-established k-anonymity approach. We propose two algorithms which differ on the set of individuals used to perform the k-anonymity. The integration of this privacy method allows us to study the interaction between privacy and utility. Our evaluation emphasizes that the combination of anatomy and k-anonymity preserves the utility qualities of our previous solution and increases the privacy of released data sets.
An Analysis of Indian Election Outcomes using Machine Learning

What other people think" has always been makes us curious while indulge in decision making process. Earlier, many of us asked our personal acquaintances to recommend a political candidate or political party to whom we can vote or consult a broker to decide in which share we should invest our money. With the invent of Internet and world wide web have now let it happen to find out large pool of online users to share recommendation, opinion, views about an event. Although these online users are never our personal acquaintance nor professional critics. An irony of situation is many people are sharing opinions available to completely unknown person via Internet. In the beginning of 21st century, social networking websites taken exponential growth in popularity among online users by providing them a platform for discussion and exchange information. Apart from posting photos and videos for event, now online users are posting their comments, views, opinion about event. With this platform, most researchers are analyzing posts or opinion, performing computational methods to come to conclusion about popularity of event. This analysis part is becoming more powerful day by day to gauge user’s opinion and would play a significant role for someone to make major decision in future. The social microblogging sites such as Pinterest, Tumblr, Apsense, Scoop and specially Twitter are leading pools of online users and contains massive information that can be useful by extracting knowledge from information and that knowledge become asset for business specialist, political specialist, marketing specialist etc.
A Static and Dynamic Co-attention Network for Social Recommendation

Social recommendation is based on traditional collaborative filtering recommendation and chooses the social information among users to alleviate the sparseness of users&#x0027; data and improve the prediction accuracy. However, it is hard to obtain all users&#x0027; behavior characteristics in a direct way as the preference behavior of user is complicated and secretive. In order to model users&#x0027; behavior more accurately, we propose a mixed model that combine user static behavior preferences and dynamic behavior preferences. The static part captures the users&#x0027; static preferences, and the dynamic part captures the users&#x0027; dynamic preferences by Temporal Convolution Network (TCN). These modules are integrated through the co-attention layer to get the final value. Compared with the best baseline on two datasets, the experimental results of our model get higher prediction accuracy.