Reviewerly/exHarmony huggingface dataset:
Optimizing High-dimensional Learner with Low-Dimension Action Features

Model-free reinforcement learning is capable of learning high-dimensional robotic tasks, but the requirement of large-scale training data makes it hard to reach better performance in limited time. On the contrary, model-based methods are capable of learning low-dimensional tasks efficiently, but lack of extensibility for complex robotic tasks. It is an instinct that, combining the advantage of both, transferring knowledge to higher dimension may benefit in sample efficiency and model accuracy. In the thesis, we present a hybrid framework that transfer low-dimensional action features to high-dimensional deep reinforcement learning model through imitation learning, in order to decrease the training data needed to reach practical performance. In this work, the hybrid framework is experimented on the simulated locomotion tasks, showing that our framework can improve model-free learning process. Our hybrid algorithm outperforms the pure model-free method, utilizing the low-dimensional action features efficiently and being competent in model accuracy.
Kullback-Leibler differential entropy equation based CIMM-PDA for reliable positioning

An interactive multi-model probability data association based on Kullback-Leibler differential entropy for reliable positioning is proposed to stably track noise. When the changes in measurement noise and process noise occurs, the interactive multi-models can fuse the estimated results and by combination of probabilities in models, noise can be dynamically tracked, to improve the reliability and accuracy of positioning procedures. The simulation results show that with the increase of measurement noise, the KL-CIMM-PDA method is more stable and more adaptive to track noise than the CIMM-PDA method and compared with the IMM-UKF, the accuracy of the KL-CIMM-PDA is improved by 26.7%.
Improved algorithm for solving discrete logarithm problem by expanding factor

The discrete logarithm problem (DLP) is to find a solution n such that g <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> = h in a finite cyclic group G = 〈g〉, where h ϵ G. The DLP is the security foundation of many cryptosystems, such as RSA. We propose a method to improve Pollard's kangaroo algorithm, which is the classic algorithm for solving the DLP. In the proposed algorithm, the large integer multiplications are reduced by controlling whether to perform large integer multiplication. To control the process, the tools of expanding factor and jumping distance are introduced. The expanding factor is an indicator used to measure the probability of collision. Large integer multiplication is performed if the value of the expanding factor is greater than the given bound. The improved algorithm requires an average of (1.633 + O (1))√q times of the large integer multiplications. In experiments, the average large integer multiplication times is approximately (1.5 + O (1))√q.
Improving Bug Localization by Mining Crash Reports: An Industrial Study

The information available in crash reports has been used to understand the root cause of bugs and improve the overall quality of systems. Nonetheless, crash reports often lead to a huge amount of information, being necessary to consolidate the crash report data into groups, according to a set of well-defined criteria. Recent research work have proposed different criteria and techniques to group crash report data, making more effective the process of finding the root causes of a bug and showing the performance of the approaches in the context of open source applications (such as IDEs and web browsers). In spite of that, it is still not clear how these approaches perform in other application domains, such as enterprise systems. In this paper, we present an industrial study in this field. We tailor existing approaches to find and group correlated crash reports, and identify buggy files in the domain of web-based systems. We then evaluate the performance of the resulting criteria and technique in industrial settings - identifying and ranking the classes that are more likely to contribute to a crash and thus might need a fix. We also check if the methods changed by the developers to fix a bug are present in the stack traces of the crash report groups used to identify the buggy classes. Our study provides new pieces of evidence of the potential use of crash report groups to indicate buggy classes and methods using stack traces information. For instance, we successfully identify buggy classes with recall varying from 61.4% to 77.3%, considering the top 1, top 3, top 5, and top 10 suspicious buggy files identified and ranked by our approach. We also found that 80% of changed methods from the closed bug fix issues appeared in related stack traces of the crash report groups. Finally, the approach also received positive response from the project leaders of the evaluated projects to help their bug resolution processes.
NEURAL NETWORK OPTIMIZATION WITH PARTICLE SWARM OPTIMIZATION AND BAGGING METHODS ON CLASSIFICATION OF SINGLE PAP SMEAR IMAGE CELLS

In this study, an automatic diagnosis analysis of the results of pap smear image extraction using neural network algorithms, the analysis included a review of the results of Herlev pap smear extraction level 7 grade, 2 normal and abnormal classes, 3 classes of normal level dysplasia and 4 classes of abnormal dysplasia levels. The problem is that neural networks are very difficult to designate optimal features in diagnosing and difficult to handle class imbalances. This study proposes a combination of particle swarm optimization (PSO) to optimize the features and bagging methods to deal with class imbalances, with the aim that the results of diagnosis using a neural network can increase its accuracy. The results show that using PSO and bagging methods can improve the accuracy of the algorithm of network balance. At level 7 the buffer class increased by 1.64%, 2 classes increased by 0.44%, 3 classes increased by 2.04%, and at level 4 the class increased by 5.47%In this study, an automatic diagnosis analysis of the results of pap smear image extraction using neural network algorithms, the analysis included a review of the results of Herlev pap smear extraction level 7 grade, 2 normal and abnormal classes, 3 classes of normal level dysplasia and 4 classes of abnormal dysplasia levels. The problem is that neural networks are very difficult to designate optimal features in diagnosing and difficult to handle class imbalances. This study proposes a combination of particle swarm optimization (PSO) to optimize the features and bagging methods to deal with class imbalances, with the aim that the results of diagnosis using a neural network can increase its accuracy. The results show that using PSO and bagging methods can improve the accuracy of the algorithm of network balance. At level 7 the buffer class increased by 1.64%, 2 classes increased by 0.44%, 3 classes increased by 2.04%, and at level 4 the class increased by 5.47%
Approach to Mitigate the Cyber-Environment Risks of a Technology Platform

In recent years the issue of security has been given more important in the professional world, but not enough organizations have forgotten this issue or ignore it because they think it is an unnecessary expense, but it is not so computer security is very important in a world where the internet is available to anyone. The objective of this article is to analyze the different forms of risk prevention to obtain the most efficient measures for a technological platform. Was used the deductive and exploratory research method to analyze the information of available means and scientific article. resulted in a table with different prevention measures that allows us to improve the defense against cyberattacks. it concluded at the end of the study of the different cyber measures we can mitigate risks, but this depends on the type of organization for the small ones, they may only need basic security, but the large organizations need specialized teams.
QoE on H.264 and H.265: Crowdsourcing versus Laboratory Testing

Crowdsourcing as one of the newest approaches in the field of Quality of Experience (QoE) has become more and more popular. Even though many papers deal with exploring the quality, most of them use objective or subjective laboratory methods. Publications comparing the quality of any codecs using crowdsourcing method are very rare. Although some of them utilize this approach, they use one of the web-based crowdsourcing frameworks with paid crowdworkers. In this paper we decided to present our own developed web-based crowdsourcing framework and use it as unpaid approach for evaluating two well-known compression standards - H.264 and H.265 and for comparing the results getting from testing via this crowdsourcing framework with results which we got from laboratory testing. These results showed that the subjective quality testing via unpaid crowdsourcing approach can be used as a replacement to the subjective laboratory testing, especially in cases where not very sophisticated evaluation is required.
Information and communication technology (ICT) in preschool education

The ability of a computer to reproduce information simultaneously in the form of text, graphic images, sound, speech, video, to memorize and process data with great speed allows specialists to create new means of activity for children that are fundamentally different from all existing games and toys. "Information technology is a complex of methods, methods and tools that provide storage, processing, transmission and display of information and aimed at improving the efficiency and productivity of labor." At the present stage, methods, methods and tools are directly interconnected with a computer (computer technology). A computer can enter a child's life through a game. The game is one of the forms of practical thinking. In the game, the child operates with his knowledge, experience, impression, reflected in the social form of game modes of action, game signs that acquire significance in the semantic field of games.
Object tracking algorithm by moving video camera

The algorithm ACT (Adaptive Color Tracker) to track objects by a moving video camera is presented. One of the features of the algorithm is the adaptation of the feature set of the tracked object to the background of the current frame. At each step, the algorithm extracts from the object features those that are more specific to the object and at the same time are at least specific to the current frame background, since the rest of the object features not only do not contribute to the separation of the tracked object from the background, but also impede its correct detection. The features of the object and background are formed based on the color representations of scenes. They can be computed in two ways. The first way is 3D-color vectors of the clustered image of the object and the background by a fast version of the well-known k-means algorithm. The second way consists in simpler and faster partitioning of the RGB-color space into 3D-parallelepipeds and subsequent replacement of the color of each pixel with the average value of all colors belonging to the same parallelepiped as the pixel color. Another specificity of the algorithm is its simplicity, which allows it to be used on small mobile computers, such as the Jetson TXT1 or TXT2. The algorithm was tested on video sequences captured by various camcorders, as well as by using the well-known TV77 data set, containing 77 different tagged video sequences. The tests have shown the efficiency of the algorithm. On the test images, its accuracy and speed overcome the characteristics of the trackers implemented in the computer vision library OpenCV 4.1.
Building a ChatBot Using Emotional Cognitive Conversational Agent Architecture (ECCAA)

Conversational agent popularly known as Chatbot Alexa, Siri, Watson, etc., has become the most happening thing in our day to day lives. This research paper is an attempt to design architecture for Conversational agents based on human cognition. Conversational agents generally use NLP and Machine Learning techniques for programming a ChatBot. To achieve human like conversations we combine those techniques with the ‘Society of Minds’ human cognition layers.
Securing Event Logs with Blockchain for IoT

The Internet of Things (IoT) is growing in popularity in recent years. With the increasing use, security threats are also becoming a bigger concern, especially considering new challenges, like limited computational power, low storage capacity and unprecedented number of independent, uncoordinated hardware device manufacturers. Unfortunately, modern attacks are more and more sophisticated and some attackers may even use anti-forensics techniques to hide any evidence of their malicious activity. As a result, digital forensics will be crucial in investigating crimes committed against IoT devices. One of the challenges is to create secure, lightweight and tamper-proof event log for the IoT system. Proposed solution relies on a blockchain to store event logs, guaranteeing the integrity of data. Event logs from IoT devices are being sent to multiple servers using multiple channels of communication to ensure logs availability and to move most of computational effort from the IoT device to the server. Logs are (optionally) encrypted to provide confidentiality of stored data. A set of security and performance tests were performed to prove effectiveness of proposed solution.
Cross Platform Recommendation System

Mammoth amount of data is scattered across the Internet and surfing in this ocean of data is an endless task. To make user interaction smoother, recommendation systems are used to reduce the overload of information. In single domain recommendation systems, although the item space is humongous, users usually rate only few items and hence these systems focus on users having specific interests rather than relying on the wisdom of the majority. Hence, they suffer from a plethora of problems related to cold start, lack of data for new user/items etc. In cross platform recommender system (CPRS), acquired information from other domains is used to diminish such problems, i.e. the cold start problem faced by the target platform can be alleviated by transferring knowledge available in other platforms (known as the source domain or platform). This not only helps to establish relations between the items but also leads to higher user involvement and better suggestions. In this paper we explore the classic framework of item-item based collaborative filtering using which we propose a CPRS that maps the wisdom from our source platform Netflix to our target platform MovieLens. CPRS aims at achieving better accuracy and adds a new dimension in solving the cold start problem.
Abnormal Human Behavior Detection in Video Using Suspicious Object Detection

Nowadays, there is a large requirement of video surveillance system in security and intelligent system for the public and finding out motion of real time object is a major task in video surveillance system. A single surveillance camera can record all activity happens in day and night. In this paper, we used an approach to find out an abnormal activity based on features of object which is independent of object size, shape and speed. To detect the suspicious moving object of an abnormal behavior, we have used a foreground subtraction approach and features based descriptor. In frame subtraction, we will calculate the difference of active pixel of successive frame in motion and find out the region of interest by setting a fixed value of threshold. We will compare the mean of illumination of frame in motion by threshold value, and locate the suspicious object if its value is more than the threshold value by setting pixel value 1 and non-interest portion with pixel value 0. In our approach, we have only focused on the frame in motion having active pixel, so for that we find out the frame in motion to increase the efficiency. The proposed algorithm requires less computation by using efficient approach and also increase accuracy in video surveillance system to detect abnormal activity in real time environment.
Conservative Extensions in Horn Description Logics with Inverse Roles

We investigate the decidability and computational complexity of conservative extensions and the related notions of inseparability and entailment in Horn description logics (DLs) with inverse roles. We consider both query conservative extensions, defined by requiring that the answers to all conjunctive queries are left unchanged, and deductive conservative extensions, which require that the entailed concept inclusions, role inclusions, and functionality assertions do not change. Upper bounds for query conservative extensions are particularly challenging because characterizations in terms of unbounded homomorphisms between universal models, which are the foundation of the standard approach to establishing decidability, fail in the presence of inverse roles. We resort to a characterization that carefully mixes unbounded and bounded homomorphisms and enables a decision procedure that combines tree automata and a mosaic technique. Our main results are that query conservative extensions are 2ExpTime-complete in all DLs between ELI and Horn-ALCHIF and between Horn-ALC and Horn-ALCHIF, and that deductive conservative extensions are 2ExpTime-complete in all DLs between ELI and ELHIF_bot. The same results hold for inseparability and entailment.
Automatic Myanmar Image Captioning using CNN and LSTM-Based Language Model

An image captioning system involves modules on computer vision as well as natural language processing. Computer vision module is for detecting salient objects or extracting features of images and Natural Language Processing (NLP) module is for generating correct syntactic and semantic image captions. Although many image caption datasets such as Flickr8k, Flickr30k and MSCOCO are publicly available, most of the datasets are captioned in English language. There is no image caption corpus for Myanmar language. Myanmar image caption corpus is manually built as part of the Flickr8k dataset in this current work. Furthermore, a generative merge model based on Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM) is applied especially for Myanmar image captioning. Next, two conventional feature extraction models Visual Geometry Group (VGG) OxfordNet 16-layer and 19-layer are compared. The performance of this system is evaluated on Myanmar image caption corpus using BLEU scores and 10-fold cross validation.
Knowledge Graph Embeddings and Explainable AI.

Knowledge graph embeddings are now a widely adopted approach to knowledge representation in which entities and relationships are embedded in vector spaces. In this chapter, we introduce the reader to the concept of knowledge graph embeddings by explaining what they are, how they can be generated and how they can be evaluated. We summarize the state-of-the-art in this field by describing the approaches that have been introduced to represent knowledge in the vector space. In relation to knowledge representation, we consider the problem of explainability, and discuss models and methods for explaining predictions obtained via knowledge graph embeddings.
Bayesian sparse factor analysis with kernelized observations

Multi-view problems can benefit from latent representations since they find low-dimensional projections that fairly capture the correlations among the multiple views that characterise the data. On the other hand, high-dimensionality and non-linear issues are traditionally handled by kernel methods, inducing a (non)-linear function between the latent projection and the data itself. However, they usually come with exposition to overfitting. Here, we combine Bayesian factor analysis with what we refer to as kernelized observations, in which the proposed model focuses on reconstructing not the data itself, but its relationship with other data points measured by a kernel function. In turn, we extend previous Bayesian FA formulations to be able to model non-linear data relationships by means of kernelized data representations and, at the same time, include additional facilities to obtain compact kernel representations by means of an automatic selection of Bayesian Relevance Vectors (RVs), feature relevance analysis and, even, obtain an automatic multiple kernel learning approach. Besides, this is flexibly included into a modular framework where we can easily adapt the model capabilities to the data needs and, even, combine them with previous FA functionalities such as heterogeneous data representations or semi-supervised learning. Using several public databases, we demonstrate the potential of the approach (and its extensions) w.r.t. common multi-view learning models such as kernel canonical correlation analysis, heterogeneous incomplete – variational autoenconder or manifold relevance determination, where the proposed model shows its ability to outperform the baselines while indistinctly combining model extensions.
Applying an Intelligent Personal Agent on a Smart Home Using a Novel Dialogue Generator

Nowadays, Intelligent Personal Agents include Natural Language Understanding (NLU) modules, that utilize Machine Learning (ML), which can be included in different kind of applications in order to enable the translation of users' input into different kinds of actions, as well as ML modules that handle dialogue. This translation is attained by the matching of a user's sentence with an intent contained in an Agent. This paper introduces the first generation of the CERTH Intelligent Personal Agent (CIPA) which is based on the RASA ( https://rasa.com/ ) framework and utilizes two machine learning models for NLU and dialogue flow classification. Besides the architecture of CIPA—Generation A, a novel dialogue-story generator that is based on the idea of adjacency pairs is introduced. By utilizing on this novel-generator, the agent is able to create all the possible dialog trees in order to handle conversations without training on existing data in contrast with the majority of the current alternative solutions. CIPA supports multiple intents and it is capable of classifying complex sentences consisting of two user's intents into two automatic operations from the part of the agent. The introduced CIPA—Generation A has been deployed and tested in a real-world scenario at Centre's of Research & Technology Hellas (CERTH) nZEB Smart Home ( https://smarthome.iti.gr/ ) in two different domains, energy and health domain.
An Algorithm for Digital Image Watermarking using 2-Level DWT, DCT and QR Decomposition based on Optimal Blocks Selection

Digital image Watermarking is a way to achieve information security. In this paper, we present an algorithm for digital image watermarking based on 2-level Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and QR decomposition. The watermark embedding process is performed on specific blocks of the host image according to its entropy values. The experimental results show that this algorithm has good features of imperceptibility and it is robust against different attacks.
A big step from finite to infinite computations

We provide a construction that, given a big-step semantics describing finite computations and their observations, extends it to include infinite computations as well. The basic idea is that the finite behavior uniquely determines the infinite behavior once observations and their composition operators are fixed. Technically, the construction relies on the framework of inference systems with corules. The effectiveness and scope of the approach are illustrated by several examples. The correctness is formally justified by proving that, starting from a big-step semantics equivalent to a reference small-step semantics, this equivalence is preserved by the construction.