Reviewerly/exHarmony huggingface dataset:
Emotional Dialogue Generation with Generative Adversarial Networks

Dialogue generation is getting more and more attention in the area of natural language generation, while existing dialogue models focus on the fluency and content-related for reply primarily and ignore its emotional factor. In recent years, Generative Adversarial Networks (GAN) has shown satisfactory results in text generation instead of dialogue generation. In this paper, we propose a dialogue generation framework - EDGAN, which has multiple generators and one multi-class discriminator. Multiple generators are trained to generate response with different emotion labels respectively. The human-generated dialogues and the machine-generated ones are fed to the discriminator. The output about emotion types from the discriminator are used as penalty for each generator to focus on generating its own examples of a specific emotion type accurately. The experimental results on automatic evaluation and manual evaluation demonstrate that our model can generate more high-quality emotional responses than previous baselines.
Parallel Association Rules Pruning Algorithm on Hadoop MapReduce

Mining association rules is essential in the discovery of knowledge hidden in datasets. There are many efficient association rule mining algorithms. The problem is with the large number of rules they often discover. Large number of rules makes the discovery of knowledge very challenging because too many rules are difficult to understand, interpret, or visualize. To reduce the number of discovered rules, researchers proposed a number of solutions. However, these solutions are limited to the rules generated from traditional datasets and are incapable of handling rules generated from big datasets. To solve this problem, this paper proposes a Hadoop MapReduce-based parallel association rule pruning algorithm, named PPrune. Experimental results show that PPrune to be efficient and has good speedup, scaleup, and sizeup.
On How Students of Humanitarian and Engineering Specialties Perceive Their Educational and Professional Activities: Psycho-Semantic Analysis

This paper aims at determining the specific features of the way future specialists in sciences and humanities perceive their educational and professional activities. For this purposes we compiled a list of most relevant notions connected to the concepts of “education” and “profession” to carry out a psycholinguistic experiment. We applied the psychological testing procedure known as “colored metaphors” to ensure higher reliability of the findings. Thus, the fact that all the respondents consider “self-education” to be of the first magnitude while neglecting such notions as “my teachers”, “homework” and “humanities” attests to particular significance of applying interactive and creative types of tasks in modern teaching. The notion “success” (correlated with “my university”, “sciences” and “foreign languages”) is of highest importance among future engineers. Future psychologists, on the contrary, concentrate more on professional self-actualization (prioritizing such notions as “my studies”, “my profession”, “my career” and “psychology”) rather than prestige. This sharp distinction emphasizes the value of differentiated approach in teaching.
Differentially Private Stream Processing for the Semantic Web

Data often contains sensitive information, which poses a major obstacle to publishing it. Some suggest to obfuscate the data or only releasing some data statistics. These approaches have, however, been shown to provide insufficient safeguards against de-anonymisation. Recently, differential privacy (DP), an approach that injects noise into the query answers to provide statistical privacy guarantees, has emerged as a solution to release sensitive data. This study investigates how to continuously release privacy-preserving histograms (or distributions) from online streams of sensitive data by combining DP and semantic web technologies. We focus on distributions, as they are the basis for many analytic applications. Specifically, we propose SihlQL, a query language that processes RDF streams in a privacy-preserving fashion. SihlQL builds on top of SPARQL and the w-event DP framework. We show how some peculiarities of w-event privacy constrain the expressiveness of SihlQL queries. Addressing these constraints, we propose an extension of w-event privacy that provides answers to a larger class of queries while preserving their privacy. To evaluate SihlQL, we implemented a prototype engine that compiles queries to Apache Flink topologies and studied its privacy properties using real-world data from an IPTV provider and an online e-commerce web site.
A Survey on Visualization Techniques to Narrate Interpersonal Interactions between Sportsmen

Technological advances have resulted in rapid growth in personal activity data documentation. Chronological activity data flowing can narrate a story. Writing a story needs the means to visualize interactions to know the relationships between characters. This article explores time-oriented data visualization techniques. Exploration aims to investigate visualizations that might be used to narrate activities about interpersonal interactions. We map data visualizations based on completeness of story elements and shape flexibility. Based on the analysis of visualization techniques, we are looking for a flow visualization technique from events that can describe the event in detail.
Partial Label Learning by Entropy Minimization

Partial label learning deals with the problem where each training example is associated with a set of candidate labels, only one of which is assumed to be valid. To learn from such ambiguous labeling information, the critical point is to disambiguate the set of candidate labels, thereby targeting the ground-truth label. By utilizing the nature that only one of the candidate labels is correct, we employ the entropy minimization strategy to force the model making confident predictions of the training data. By doing this, the ground-truth labels are likely to make more contributions to the model training. Finally, comparative experiments on a number of real-world datasets are conducted, demonstrating the effectiveness of the proposed approach.
A Human Gait Sequence Merging Method For Multi-kinect

As a motion capture device, Kinect can predict temporal 3D positions of 25 body joints from depth image, so it is widely used in many field such as gait analysis, gait recognition and clinical medicine. However, the collection range of a single Kinect is very limited and it only collect few gait data, which will greatly reduce the accuracy and reliability of the gait analysis and recognition. In order to solve this problem, we propose a new human gait sequence merging method for multi-Kinect. It can not only extend the data acquisition range and increase the length of gait data sequence, but also avoid the bad effects of walking on a treadmill because it is a non-invasive, non-contact data collection method. Firstly, we first introduced the optimal collection range of kinect to improve the accuracy of gait sequence measurements. Secondly, we directly use the depth value of the joint point for coordinate transformation, which not only reduces the conversion error but also make the calculation easy. Finally, when gait sequences are merged, we utilize all the gait sequences to obtain stable, effective and long-distance gait sequences. We designed relevant experiments to compare the merged gait sequence with the gait sequence collected by single Kinect, and the results verified the validity of the method.
Exploiting Deep Neural Networks for Intention Mining

In the current era of digital media, people are greatly interested to express themselves on online interaction which produces a huge amount of data. The user generated content may contain user's emotions, opinions, daily events and specially their intent or motive behind their communication. Intention identification/mining of user's reviews, that is whether a user review contains intent or not, from social media network, is an emerging area and is in great demand in various fields like online advertising, improving customer services and decision making. Until now, a lot of work has been performed by researchers on user intention identification using machine learning approaches. However, it is demanded to focus on deep neural network methods. In this research work, we have conducted experimentation on intention dataset using a deep learning method namely CNN+BILSTM. The results exhibit that the proposed model efficiently performed identification of intention sentences in user generated text with a 90% accuracy.
Application of soft computing techniques in machine reading of Quranic Kufic manuscripts

Research work in the field of offline Arabic handwriting recognition has seen an exponential growth during past decades. Despite much work dedicated to the recognition of handwritten Arabic text, there remains a lot to achieve in this regard. Though there exists a lot of work that is confined to the Arabic text, this paper presents an approach towards classifying and recognizing text written in one of the famous scripts of Arabic language i.e. Kufic script. The approach based on character segmentation does not perform well in recognizing Kufic text due to various complexities. The proposed system is based on word segmentation and employs a Histogram of Oriented Gradient (HOG) and Local Binary Pattern (LBP) feature extraction techniques. Later in the paper a comparison is given between the numerical results of proposed technique with previous Arabic text recognition techniques to show the effectiveness of present work. This approach yields effective results with 97.05% accuracy in recognizing the Arabic text written in Kufic script using Polynomial kernel of SVM classifier. Experimental results show that the proposed system for recognition of Kufic script performs better than the previous recognition systems for Arabic text.
Multi‐touch detector architecture based on efficient buffering of intensities and labels

This Letter presents a multi-touch detector architecture incorporating an efficient buffering scheme. Most of the power consumption in a detector is originated from the intensive use of buffers that necessitate dual-port memories. To alleviate the silicon area and the power consumption, the author scrutinises and manipulates the pattern of requests made to the buffers. More specifically, two consecutive requests are rescheduled to be processed at once, and the buffers and the surrounding circuits are modified accordingly. As a result, only one request is made to a buffer at a time, and the dual-port memories are substituted with the single-port counterparts. Implementation results demonstrate that the proposed architecture can diminish the silicon area by 36% and the power consumption by 17% compared with the state-of-the-art one.
Influence of birefringent fibre joints on the visibility drift in a Mach – Zehnder interferometer

Abstract It is shown that imperfect joints of linear birefringent fibres in a fibre interferometer may result in an uncontrolled visibility drift under varying environmental conditions even with a standard phase matching device. As an example, a double Mach – Zehnder interferometer is considered, which is employed in schemes of quantum key distribution. Results of numerical simulation demonstrate the standard deviation of the quantum bit error rate (QBER), which is comparable to an average QBER value.
Random selective block encryption technique for image cryptography using chaotic cryptography

Dynamic random growth technique and a hybrid chaotic map which is proposed in this paper are used to perform block-based image encryption. The plaintext attack can easily crack the cat map, as it is periodic, and therefore cat map securely used in which it can eliminate the cyclical occurrence and withstand the plaintext attack's effect. The diffusion process calculates the intermediate parameters according to the image block. For the generation of the random data stream in the chaotic map, we use an intermediate parameter as an initial parameter. In this way, the generated data stream depends on the plain text image that can withstand the attack on plain text. The experimental results of this process prove that the proposed dynamic random growth technique and a hybrid chaotic map for image encryption is a secured one in which it can be used in secured image transmission systems.
ОЦЕНКА «КАЧЕСТВА ОБРАЗОВАНИЯ» В ВЫСШИХ УЧЕБНЫХ ЗАВЕДЕНИЯХ (НА ПРИМЕРЕ БАТУМСКОЙ ГОСУДАРСТВЕННОЙ МОРСКОЙ АКАДЕМИИ)

&#x0D; &#x0D; &#x0D; The article addresses the problem of higher education - improving the quality of graduates' professional training, provides a brief analysis of research results, Recommendations on how to use the results “Quality of education” is a complex characteristic of education, the task of which is to train specialists who are competitive in the world market. It is impossible to develop a single, scientifically based system of indicators for the quality of student training, since the education system is different in all countries of the World, moreover, a different system of training students, which is one of the influencing factors on the quality of Higher Education. It is necessary to take into account the specifics of the university.One of the elements of the quality assessment system is monitoring, which is considered to be a comprehensive system for diagnosing the quantitative and qualitative characteristics of the effectiveness of an educational system. The assessment of the quality of education should be formed as a cumulative indicator of all the qualities that influence the educational process. During the work we used the results of sociological research of the subjects of the educational process: students, teachers, monitoring of the educational process (academic performance of students).&#x0D; &#x0D; &#x0D;
Feature Selection Based on Modified Harmony Search Algorithm

Feature selection is the pre-processing step that is widely used, especially in the field of data mining, to simplify processes that can reduce costs and computing time. Selected features can improve the best classification accuracy. In this work, a wrapper method approach is proposed using a modified harmony search. Modification is to update memory harmony using binary encoding. The coding process is adopted from the coding process of genetic algorithms for feature selection. The process of finding a new solution is done by manipulating each variable of the decision solution based on the harmony memory consideration and pitch adjustment procedures and the non-uniform mutation procedure. Evaluate its features using a support vector machine and is called a modified HS-SVM. The results showed that the proposed method has the same genetic algorithm performance for feature selection with SVM classification (GA-SVM), but has faster access time. This performance will reduce costs and computing time, especially if applied to high dimensional data. Both of these algorithms have 96.6 percent accuracy with one feature selected, and the harmony memory size is 50, and the generation size is 100.
Decomposition and construction of higher-dimensional neighbourhood operations

We prove that the 2n-neighbourhood in an n-dimensional digital space is decomposed into the 2(n−1)-neighbourhoods in the mutually orthogonal (n−1)-dimensional digital spaces. This decomposition and construction relation of the neighbourhoods and objects implies that morphological operations in an n-dimensional digital space can be computed as the union of one- and two-dimensional morphological operations on isothetic digital lines and planes intersecting with the digital object in the digital space.
Investigations into the Production of Broadline Purple-on-red Pottery from the Tucson Basin

Broadline Purple-on-red pottery, dated to the Tortolita Phase (AD 500–700), is notable as one of the earliest decorated wares in the Tucson Basin. This ware is also unique for its sparkly “purple” paint made with ground specular hematite. Such material is available in the Tucson Mountains, but whether it was exploited for the production of Broadline Purple-on-red vessels is unknown. Our experimental work suggested these sources could be used to make paint nearly identical to that on the vessels. Petrographic analysis was employed to confirm production of Broadline Purple-on-red pottery in the Tucson Basin, which was previously lacking. Laser ablation inductively coupled mass spectrometry was utilized to chemically characterize specular hematite rocks and the paint on four sherds of Broadline Purple-on-red. The results indicate compositional discrimination between rock sources, but difficulties in connecting these data to those for the paint, which is often thin and likely combined with a clay binder.
The Evolution of Representations in Genetic Programming Trees

Artificially intelligent machines have to explore their environment, store information about it, and use this information to improve future decision making. As such, the quest is to either provide these systems with internal models about their environment or to imbue machines with the ability to create their own models—ideally the later. These models are mental representations of the environment, and we have previously shown that neuroevolution is a powerful method to create artificially intelligent machines (also referred to as agents) that can form said representations. Furthermore, we have shown that one can quantify representations and use that quantity to augment the performance of a genetic algorithm. Instead of just optimizing for performance, one can also positively select for agents that have better representations. The neuroevolutionary approach, that improves performance and lets these agents develop representations, works well for Markov Brains, which are a form of Cartesian Genetic Programming network. Conventional artificial neural networks and their recurrent counterparts, RNNs and LSTMs, are however primarily trained by backpropagation and not evolved, and they behave differently with respect to their ability to form representations. When evolved, RNNs and LSTMs do not form sparse and distinct representations, they “smear” the information about individual concepts of the environment over all nodes in the system. This ultimately makes these systems more brittle and less capable. The question we seek to address, now, is how can we create systems that evolve to have meaningful representations while preventing them from smearing these representations? We look at genetic programming trees as an interesting computational paradigm, as they can take a lot of information in through their various leaves, but at the same time condense that computation into a single node in the end. We hypothesize that this computational condensation could also prevent the smearing of information. Here, we explore how these tree structures evolve and form representations, and we test to what degree these systems either “smear” or condense information.
Solving recursive problems by definition

The article describes the methodology for studying the theme "Solving recursive problems by definition" in preparing students for Olympiads in informatics. Distance learning system DL.GSU.BY is the effective technical base for teaching.
Optimization of the Tilt Angle of the Photo-voltaic Module in Kuwait

This paper concerns to study the performance of the photovoltaic with different tilt angle and optimize the best tilt angle in Kuwait. Firstly, the geographical location of the house on which the proposed PV system installed. The total area of the house and the effective area of the house roof are given. Secondly, numerical data describing the physical and the electrical characteristics of the PV module used in the construction of the PV system are provided. The data includes the dimensions, fabrication type, thermal rating and the electrical characteristics of the PV module. Also, the design equation relating the tilt angle and the PV system pitch size are provided and illustrated. Thirdly, the PV system is designed from different tilt angles and the corresponding number of PV panels used in covering the effective area of the roof house. Finally, the designed PV system has been modeled as a 3D structure and the required number of PV panels to cover an effective area of the house for maximum power production and optimize the best tilt angle in Kuwait has been presented. The results show that the tilt angle 20 degrees has the highest energy output compared with the tilt angle 30 degrees. Moreover, the energy generation using the tilt angle 20 degrees is 728 KWh and the energy generation using the tilt angle 30 degrees is 717 KWh.
A study on the emotional state of a speaker in voice bio-metrics

In building smart cities, there is a need for voice bio-metrics, through which we can validate a person's identity over a wireless medium like mobile. Voice bio-metrics have many advantages over other bio-metrics like retinal scans and fingerprints because in the case of voice bio-metrics there is no need for physical presence. Speaker recognition (SR) is a process of identifying a person from his or her unique voice. In order to build a robust SR system, it has to take care of background noise, channel effect, speaker health, and emotional state of the speaker. The present study focuses on SR in emotional conditions. In this study, the performance of the Gaussian Mixture Model-Universal Background Model (GMM-UBM) is studied in emotional conditions. The experiments are performed on the Amritha emotional database. The emotions considered in the present study are neutral, anger, happiness, and sadness. The results show that the performance of an SR system trained with clean speech is degrading while testing with emotional data. The accuracies are high when training and testing are done on the same emotion. The reasons for the performance aggravation are analyzed using parameters like strength of excitation, energy of excitation, fundamental frequency (F0), and duration. Further analysis is done using the distance between the GMMs employing Kullback-Leibler divergence. The importance of having emotional data in training is highlighted in several experiments. A two-stage emotional SR system is proposed to enhance the recognition accuracy; in the first step, emotional state of the speaker is detected using an emotion recognition system, and then the utterance is validated using the speaker models of recognized emotion.
Classification of multivariate skew polynomial rings over finite fields via affine transformations of variables

In this work, free multivariate skew polynomial rings are considered, together with their quotients over ideals of skew polynomials that vanish at every point (which includes minimal multivariate skew polynomial rings). We provide a full classification of such multivariate skew polynomial rings (free or not) over finite fields. To that end, we first show that all ring morphisms from the field to the ring of square matrices are diagonalizable, and that the corresponding derivations are all inner derivations. Secondly, we show that all such multivariate skew polynomial rings over finite fields are isomorphic as algebras to a multivariate skew polynomial ring whose ring morphism from the field to the ring of square matrices is diagonal, and whose derivation is the zero derivation. Furthermore, we prove that two such representations only differ in a permutation on the field automorphisms appearing in the corresponding diagonal. The algebra isomorphisms are given by affine transformations of variables and preserve evaluations and degrees. In addition, ours proofs show that the simplified form of multivariate skew polynomial rings can be found computationally and explicitly.