Reviewerly/exHarmony huggingface dataset:
Łukasiewicz Logic and Artificial Neural Networks

In this paper we analyze connections between artificial neural networks, in particular multilayer perceptrons, and Łukasiewicz fuzzy logic. Theoretical results lead us to: connect Polynomial completeness and the study of input selection; use normal form of Łukasiewicz formulas to describe the structure of these multilayer perceptrons.
UCT-ADP Progressive Bias Algorithm for Solving Gomoku

We combine Adaptive Dynamic Programming (ADP), a reinforcement learning method and UCB applied to trees (UCT) algorithm with a more powerful heuristic function based on Progressive Bias method and two pruning strategies for a traditional board game Gomoku. For the Adaptive Dynamic Programming part, we train a shallow forward neural network to give a quick evaluation of Gomoku board situations. UCT is a general approach in MCTS as a tree policy. Our framework use UCT to balance the exploration and exploitation of Gomoku game trees while we also apply powerful pruning strategies and heuristic function to re-select the available 2-adjacent grids of the state and use ADP instead of simulation to give estimated values of expanded nodes. Experiment result shows that this method can eliminate the search depth defect of the simulation process and converge to the correct value faster than single UCT. This approach can be applied to design new Gomoku AI and solve other Gomoku-like board game.
Attack on a Microcomputer-Based Random Number Generator Using Auto-synchronization

A novel attack system is proposed to reveal the security weaknesses of a microcomputer-based random number generator (RNG). Convergence of the attack system is proved using auto-synchronization. Secret parameters of the microcomputer-based RNG are revealed where the available information are the structure of the RNG and a scalar time series observed from the chaotic system used as the seed of the RNG. Simulation results verifying the feasibility of the attack system are given such that, next bit can be predicted while the same output sequence of the RNG can be generated.
Customized data extraction and processing for the prediction of Baby Blues from social media

With the explosion of web 2.0, we are witnessing a sharp increase in Internet users such as a vertiginous evolution of social media. These Media constitute a source of rich and varied information for researchers in sentiment analysis. In this paper, we introduce a method to build a Dataset by extracting and processing Data from Twitter. In fact, we present in this paper, a novel approach to represent our Dataset the way to improve the prediction of profiles with depression by considering social media factors as analysis parameters.
Sentiment Analysis of IPL Teams Using Supervised Machine Learning Approach

Indian Premier League (IPL) is a high-profile T20-type cricket game in India involving top celebrities who finance teams of finest players in the country for usually an interstate championship. Since high-profile persons buy or sell teams with huge financial resources, every one often needs to watch over or predict the public opinion about various teams involved. Public opinions usually come in the form of comments and posts on social media platforms. In this paper, Twitter posts (tweets) related to IPL teams have been analysed to classify them in positive or negative sentiments for facilitating trends understanding in this new and promising type of game.
Shallow Network Training With Dynamic Sample Weights Decay - a Potential Function Approximator for Reinforcement Learning

Neural Networks are commonly used as function approximators in Reinforcement Learning, and the Extreme Learning Machine is one of the best algorithms to quickly train a shallow network. The online and sequential version OS-ELM could be a great candidate to quickly train a network to be a function approximator for Reinforcement Learning, but due to its non-forgetting properties it is actually not suitable for direct use with value estimations that improve in accuracy over time. This paper presents an alternative Neural Network training algorithm based on OS-ELM, which is able to perform learning online while dynamically modifying the weights of previously learned samples in order to decrease the importance of old samples learned over time. A mathematical derivation of the formulas used is presented, along with results of experiments showing equivalence of our algorithm to ELM when learning classic datasets and the advantage provided when dealing with Reinforcement Learning data.
Refining Image Search Results using Multiple Attributes

This paper presents a method of refining image search results using multiple attributes. The explosive growth of multimedia contents such as images and videos increases the difficulty of finding pieces of information from such a huge amount of contents. Users who only have a few or imprecise search conditions will be overwhelmed by the large number of search results with many of them are similar and redundant, especially when searching video frames. In this work, we utilize multiple attributes to refine search results and reduce their redundancy. We propose a technique of attribute ranking and selection in order to refine search results efficiently and effectively. In this way, we need to only use a small number of attributes that will contribute to the refinement. We demonstrate the performance of our method using real-world surveillance videos. Our proposed method can reduce up to 60% of result computation time compared to the existing method. Furthermore, our method constantly outperforms the existing method in terms of both MAP (Mean Average Precision) and precision versus recall.
Ranking potentially harmful Tor hidden services: Illicit drugs perspective

Cryptomarkets on the dark web have emerged as a hub for the sale of illicit drugs. They have made it easier for the customers to get access to illicit drugs online while ensuring their anonymity. The easy availability of potentially harmful drugs has resulted in a significant impact on public health. Consequently, law enforcement agencies put a lot of effort and resources into shutting down online markets on the dark web. A lot of research work has also been conducted to understand the working of customers and vendors involved in the cryptomarkets that may help the law enforcement agencies. In this research, we present a ranking methodology to identify and rank top markets dealing in harmful illicit drugs. Using named entity recognition, a harm score of a drug market is calculated to indicate the degree of threat followed by the ranking of drug markets. The top-ranked markets are the ones selling the most harmful drugs. The rankings thus obtained can be helpful to law enforcement agencies by locating specific markets selling harmful illicit drugs and their further monitoring.
Towards Design Principles for Visualizing Business Ecosystems

Business ecosystems have recently gained relevance as a reference frame in which firms entertain diverse relationships to develop, produce, and distribute services and products. Only little research however has looked at how to visualize business ecosystems—although visualizations might provide a helpful instrument for firms to position themselves and manage their interactions within their ecosystem. We report from a systematic mapping study that identified 17 types of visualizations used in the business ecosystem context. On basis of this study, we derive requirements and design principles for Visual Analytic Systems (VAS). We discuss some limitations of current VAS with respect to the question how VAS can support management tasks related to business ecosystems, and we provide an outlook on the role of VAS in supporting business ecosystem governance.
Artificial Intelligence: Problems and Prospects of Development

The purpose of this article is to identify main problems and prospects of artificial systems advance and study algorithmic methods simulating human brain and overall structure in this type of systems. As methodology for solving these problems we have chosen the systematic approach and the expert analysis methods to determine the probability of having options for artificial intelligence systems development implemented.
Human Pose Estimation of Diver Based on Improved Stacked Hourglass Model

In this paper, a network structure is proposed for the task of single person pose estimation in a complex environment. This method improves the stacked hourglass model, achieves the feature extraction on most scales, and raises the detection accuracy of human key points. In the hourglass module, we use convolution operation to complete the upsampling to get more semantic information. When the responses of the two residual elements are added, we replace the identity mapping in the residual element with the 1×1 convolution element module to improve the phenomenon of variance explosion. We conducted model evaluation experiments on MPII and LSP data sets, and the results showed that the average detection accuracy of key points was improved by 0.2% and 0.8% respectively through our improvement on the stacked hourglass model.
Steganalysis of Minor Embedded JPEG Image in Transform and Spatial Domain System using SVM-PSO

Steganalysis recognizes an artifact's expression of a hidden message. The assessment is performed statistically in this paper by extracting the characteristics that show a shift during an embedding. The machine learning technique has been used to classify the stego image and cover image. This paper uses Support Vector Machine with Particle Swarm Optimization (SVM-PSO) as a classifier. A comparative research is carried out using spatial and transform domain steganographic systems. In contrast to the kernel functions, the steganographic systems are Least Significant Bit (LSB) Matching and F5 algorithms. Six various kernel functions and four different sampling methods have been used for evaluation. 10 percent of embedding proportion has been considered for analysis.
A Structural Optimization Algorithm for Complex-Valued Neural Networks

A suitable network structure can not only save the computing resources but also enhance the generalization ability of the constructed neural network. A pruning algorithm with group lasso regularization is proposed in this paper, where both the superfluous hidden and input neurons can be efficiently removed. Based on this algorithm, appropriate complex-valued neural networks (CVNNs) are designed for practical applications. It is noted that the numerical ill-posed problem would be caused by the nondifferentiability of the group lasso regularization at the origin. To overcome it, a smooth function is introduced to approximate the regularization term. Then, CVNNs with good generalization capability and reasonable network structure are obtained. Experimental results on some benchmark classification problems are provided to show the performance of the proposed pruning algorithm.
The Role of AI in the Transformation of Mobile Operators

Today, telco operators have vast amounts of data collected from numerous data sources. To prepare themselves for the future, to be able to operate and maintain the upcoming 5G network and to meet the expectations of the new generation of the customers, operators must use these data in the best possible way. Due to the complexity, variety, and volume of the data, the artificial intelligence is inevitable in this process. In the first part, this paper portrays the possible applications of artificial intelligence in the telecommunications and the relevant engagements of mobile operators worldwide. The second part of this paper outlines a possible application of AI which should boost customer satisfaction and improve telco assets utilization.
DAFA: Dialog System Domain Adaptation With a Filter and an Amplifier

End-to-end task-oriented dialog systems have attracted vast amounts of attention in recent years, mainly because of their ease of training. However, such an end-to-end model requires a large number of labeled dialogs to train. Labeled dialogs are always difficult to obtain in real-world settings. We propose a domain adaptive end-to-end task-oriented dialog model that transfers knowledge in source domains to a target domain with limited training samples. Specifically, we design a domain adaptive filter in the encoder-decoder model to reduce useless features in source domains and preserve common features. A domain adaptive amplifier is designed to enhance the target domain impact. We evaluate our method on both synthetic dialog and human-human dialog datasets and achieve state-of-the-art results.
Multiplex graph matching matched filters

We consider the problem of detecting a noisy induced multiplex template network in a larger multiplex background network. Our approach, which extends the framework of [14] to the multiplex setting, leverages a multiplex analogue of the classical graph matching problem to use the template as a matched filter for efficiently searching the background for candidate template matches. The effectiveness of our approach is demonstrated both theoretically and empirically, with particular attention paid to the potential benefits of considering multiple channels.
Chapter 7. Some notes on the Hearts and Navy excerpts according to the Language into Act Theory

The paper sketches the Language into Act Theory and how it catches the difference between the Navy monologue and the Hearts dialogue. According to L-AcT, two types of reference units, both ending with a prosodic terminal break are identified: utterance matching with a single speech act and stanza expressing a flow of thought through an adjunction process. Navy is a sequence of two narrative stanzas with a complex informational organization, while Hearts is organized in 11 utterances showing high illocutionary variation. The core of the information pattern is the Comment accomplishing the illocutionary force. The information structure, expressing a closed set of functions, is in one-to-one correspondence with the prosodic structure. The linguistic content is not compositional across information units.
Constrained Hopping Traversability Analysis on Non-Uniform Polygonal Chains

We analyze the existence of constrained ballistic hopping paths on a nonuniform polygonal chain. This analysis has practical significance in constrained path planning applications for jumping robots where robot dynamic constraints, uneven surface structure, and non-uniform surface properties are considered. We derive closed-form conditions to satisfy <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i)</i> damage-free robot landing <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ii)</i> non-sliding of the robot <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iii)</i> actuator saturation, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iv)</i> intermediate terrain avoidance constraints. Using the closed-form conditions, we propose a traversability algorithm to determine the path existence between two given points on the polygonal chain. Correspondingly, a path generation approach is discussed to generate an optimal constrained hopping path with minimum number of intermediate hops and least take-off speed per hop. Applicability and viability of the proposed algorithms are demonstrated through computer simulations in a realistic terrain scenario with robot jumping motion uncertainties and disturbances.
Toward Automatic Recognition of Cursive Chinese Calligraphy : An Open Dataset For Cursive Chinese Calligraphy Text*

Calligraphy is one of the most important writing tools as well as cultural heritage in ancient China. Compared with other calligraphy styles, the cursive script is least restricted and oftentimes exhibits the personality of calligraphers. However, this style-oriented expression makes the cursive script hard to recognize even for trained experts. The call for auxiliary tools for cursive Chinese calligraphy text recognition has thus arisen.Data play a key role in the era of deep learning, yet there is a lack of open databases for the cursive Chinese calligraphy. In this paper, we address this discrepancy by collecting 43000 images consisting of 5301 different cursive Chinese calligraphy text. We have augmented the database with basic image processing operations to obtain a training set containing a total of 656K images. After experimenting with several deep neural architectures, we provided a baseline model Enhanced M6 (EM6) as a proof-of-concept to tackle the classification task. The proposed EM6 model achieved 60.3% top-1 accuracy and 80.8% top-5 accuracy on the evaluation data set, an indication that deep neural network has the potential to undertake the mission of cursive calligraphy recognition.
Generic Construction of Anonymous Deniable Predicate Authentication Scheme with Revocability

We propose a syntax and security definitions of an anonymous deniable predicate authentication scheme with revocability (rADPA). This new cryptographic primitive is to attain revocation function as well as strong privacy guarantee concerning authentication. Anonymity is for privacy in the authentication protocol, while deniability is for anti-forensics after completion of the protocol. Then, we give a generic construction of our rADPA scheme. Our approach is to build-in the revocable attribute-based encryption scheme proposed by K. Yamada et al. (ESORICS2017) into the anonymous deniable predicate authentication scheme proposed by S. Yamada et al. (PKC2012). Finally, we discuss how our rADPA scheme can be instantiated by employing concrete building blocks in our generic construction.
Hybrid Machine Learning-Based Intelligent Technique for Improved Big Data Analytics

The average volume of data produced daily is estimated to be over 2.5 quintillion byte. Moreover, by year 2020, it is estimated that 1.79MB of data will be created every second by each person in the world. Apparently, big datasets contain tremendous amount of valuable information that can be used for improved decision making. However, big data requires incredible amount of storage and computational resources for effective processing. Machine Learning (ML) algorithms are effective tools popularly used to analyze and extract concealed insights from datasets. However, some ML algorithms were not originally designed to handle big datasets, hence their computational complexity decreases with increase in data size. Consequently, this makes big data analytics extremely slow or unrealistic. Therefore, there is an obvious need for fast and effective techniques for big data analytics. This paper introduces an intelligent hybrid ML-based technique suitable for big data analytics (called EDISA_ML). EDISA_ML is a boundary detection and instance selection algorithm, inspired by edge detection in image processing. It was evaluated on four ML algorithms and big datasets, and the results show that it achieved a storage reduction of over 50% and simultaneously improved the training speed of the evaluated ML algorithms by over 93% (in some cases), without meaningfully affecting their prediction accuracy.
Investigating the Effectiveness of Solar Tracking for PV Facility in Chelyabinsk

For the last decade energy production of PV facilities has greatly increased. Due to the problem of the low actual efficiency of PV panels, many researchers conduct experiments with the sun trackers at different locations to obtain the maximum and actual efficiency gain. The purpose of our study is to investigate the effectiveness of one-axis sun tracker for a PV panel located in Chelyabinsk. The four experimental sets are compared to obtain the efficiency gain during the period from May to September of 2016. The experimental sets comprise identical PV panels (Topray solar) and differ with the mount angle of inclination (90o, 0o, 55o, and 55o with the one-axis sun tracker). An average monthly and total solar irradiation is calculated for the collected data. The main result of the study shows that the usage of the one-axis solar tracking system can increase the efficiency of the PV panel up to 33% during a season compared to the fixed at the optimal angle PV panel. Further research is surely perspective in Chelyabinsk, in particularly, for investigating the maximum efficiency gain for a dual-axis sun tracker and designing a sun tracker with the low energy consumption.
Deep Learning Based Hazard Label Object Detection for Lithium-ion Batteries Using Synthetic and Real Data

Changing transportSchweigert, Anneliese regulations for intralogistics tasks leads to the need for object detection of hazard labels on parcels in high-resolution grayscale images. For this reason, this paper compares different Convolutional Neural Network (CNN) basedBlesing, Christian object detection systems. Specifically, a YOLO implementation known as Darkflow as well as a self-developed Object Detection Pipeline (ODP) based on the Inception V3 model is considered. DifferentFriedrich, Christoph M. datasets consisting of synthetic and real images are created to set up the necessary training and evaluation environments. To check the robustness of the systems under real operation conditions, they are assessed by the mean Average Precision (mAP) metric. Moreover, results are evaluated to answer various questions like the impact of synthetic data during training, or the highest quality level of the systems. The YOLO models showed a higher mAP and a much higher detection speed than the MSER Object Detection Pipeline at the cost of higher training times. The mixed training data set with synthetic and real data showed a slightly reduced mAP on the validation set compared to just real data.
Inference-Based Statistical Analysis for Suspicious Activity Detection Using Facial Analysis

This paper describes a suspicious activity detection using facial analysis. Suspicious activities are the actions of an individual or group that is outside the normal acceptable standards for those people or that particular area. Products that are already in the market have been developed on individuals modules. And nowadays CCTV surveillance is commonly used in order to detect suspicious activity which requires human interference and the information about these types of activities reaches the police authorities after the event has occurred. Currently, there is no such intelligent device in the market in India which can ensure the safety of citizens. We aim to develop a system which can detect suspicious activities using facial analysis it will be developed by integrating following modules facial expression recognition, face detection, speech recognition and object recognition. In this paper, we proposed a novel and cost-effective framework designed for suspicious activity detection using facial expression analysis or emotions detection analysis in law enforcement.
The design of an intelligent monitoring system for human hand behaviors

Intelligent monitoring to observe human behaviors and report anomaly activities is a common application of computer vision technologies. However, to the authors knowledge, there has not been a widely accepted structure of building such a system with the fast-developing deep learning method. Within the author's knowledge, current works focus on industrial and traffic conditions, such as measuring the speeds of vehicles in highways, or to help arrange agriculture productions. This paper presents an efficient approach to applying deep learning techniques in such systems to help analyze human behaviors. Specifically, we combined the advancing object detection, pose estimation and image classification methods in our work to recognize some anomaly or special behaviors in determined scenes.