Reviewerly/exHarmony huggingface dataset:
Assessing Multiplayer Level Design Using Deep Learning Techniques

This paper proposes a new framework to measure the fairness of asymmetric level-design in multiplayer games. This work achieves real time prediction of the degree to which asymmetric levels are balanced using deep learning. The proposed framework provides both cost and time savings, by removing the requirement of numerous designed levels and the need to gather player data samples. This advancement with the field is possible through the combination of deep reinforcement learning (made accessible to developers with Unity’s ML-Agents framework), and Procedural Content Generation (PCG). The result of this merger is the acquisition of accelerated training data, which is established using parallel simulations. This paper showcases the proposed approach on a simple two player top-down -shooter game implemented using MoreMountains: Top Down Engine an extension to Unity 3D a popular game engine. Levels are generated using the same PCG approaches found in ’Nuclear Throne’ a popular cross platform Roguelike published by Vlambeer.
Comparison of Collision Avoidance Algorithms for Autonomous Multi-agent Systems

Autonomous multi-agent systems are raising in popularity in recent years. More specifically, Unmanned Aerial Vehicles (UAVs) are involved in modern solutions for surveillance, delivering and film shooting. To carry out these tasks, the avoidance of any possible collision is a crucial matter, mostly when agents need to cooperate. In this paper, different collision avoidance algorithms are compared and analyzed for distributed multi-agent holonomic systems. Our purpose is to identify and clarify the different classes of reciprocal collision avoidance algorithms and then to compare them using meaningful metrics and test for the evaluation.
Skuld

As the development progresses, software projects tend to accumulate Technical Debt and become harder to maintain. Multiple tools exist with the mission to help practitioners to better manage Technical Debt. Despite this progress, there is a lack of tools providing actionable and self-learned suggestions to practitioners aimed at mitigating the impact of Technical Debt in real projects. We aim to create a data-driven, lightweight, and self-learning tool positioning highly impactful refactoring proposals on a Jira backlog. Bearing this goal in mind, the first two authors have founded a startup, called Skuld.ai, with the vision of becoming the go-to software renovation company. In this tool paper, we present the software architecture and demonstrate the main functionalities of our tool. It has been showcased to practitioners, receiving positive feedback. Currently, its release to the market is underway thanks to an industry-research institute collaboration with Fraunhofer IESE to incorporate self-learning technical debt capabilities.
A Method to Anonymize Business Metrics to Publishing Implicit Feedback Datasets

This paper shows a method for building and publishing datasets in commercial services. Datasets contribute to the development of research in machine learning and recommender systems. In particular, because recommender systems play a central role in many commercial services, publishing datasets from the services are in great demand from the recommender system community. However, the publication of datasets by commercial services may have some business risks to those companies. To publish a dataset, this must be approved by a business manager of the service. Because many business managers are not specialists in machine learning or recommender systems, the researchers are responsible for explaining to them the risks and benefits.
ANN-MLP Classifier of Native and Nonnative Speakers Using Speech Rhythm Cues

In this paper, speech rhythm metrics were used in classification of native vs. nonnative speakers. The speech corpus exploited is a part of West Point corpus. Nonnative speakers (14) are English participants who read the same set of Arabic text then their Arabic counterpart (15). Seven rhythm metrics from all vowels and consonants were calculated from 145 sentences using two rhythm models: Interval Measures (IM) and Compensation/Control Index (CCI). Rhythm data were use as input vector of ANN-MLP classifier. The classifier was trained and tested using different configurations of the input vectors. The best accuracy of the engine achieved (80.7%) when we used all speech rhythm input vectors.
Research on Information Security of Large Enterprises

With the rapid development of information technology, the construction of large enterprise information system has entered the fast lane from point to surface and from shallow to deep. Enterprises increasingly rely on information system to play a role in its strategic planning and management. Meanwhile, new technologies and applications such as Cloud Computing, Internet of Things, Big Data and Mobile Internet has been proposed and developed. With the advance of the Internet Plus era, some enterprise information security issues have been discovered, and the security of information system has been challenged by complex network infrastructure, virus, and inefficient management, etc. Therefore, the security of enterprise information system has become an urgent problem for enterprises to solve. Based on several large enterprises as the research background, this paper describes current situation and challenge on large enterprise information system. To propose measures and solutions for the related problems, combined with our practical experience, we point out various information security issues and causes encountered by large group enterprises in the process of information system construction, operation and maintenance. Here, we propose a security strategy that combines security management with advanced technologies to address the above mentioned problems.
Robot Depth Estimation Inspired by Fixational Movements

Distance estimation is a challenge for robots, human beings, and other animals in their adaptation to changing environments. Different approaches have been proposed to tackle this problem based on classical vision algorithms or, more recently, deep learning. We present a novel approach inspired by mechanisms involved in fixational movements to estimate a depth image with a monocular camera. An algorithm based on microsaccades and head movements during visual fixation is presented. It combines the images generated by these micro-movements with the ego-motion signal, to compute the depth map. Systematic experiments using the Baxter robot in the Gazebo/ROS simulator are described to test the approach in two different scenarios and evaluate the influence of its parameters and its robustness in the presence of noise.
Immersive Gameplay via Improved Natural Language Understanding

Many first-person shooters feature non-player characters (NPCs) that work alongside the player. Interfacing with these NPCs can add unnecessary complication to a game and steepen the learning curve for new players. Recent improvements in automated voice recognition and language representation have set the stage for more immersive methods of interfacing with NPCs through player speech. In this paper, we present several promising methods of classifying user utterances to extract predefined commands from unstructured speech. This framework facilitates a more flexible interface than has been used in past speech-controlled games. We also show how our methods effectively leverage small sets of example data to outperform existing industrial utterance classification systems.
Multiple Digital Image Watermarking Using SWT, FWHT and R, G, B Channel

Communication in digital era has made the world too small, but at the same time, it has created the ample of opportunities for digital information attackers. The basic need and importance of communication is security to protect the copyright of the information owner. Digital image is one of the important media in communication. To provide copyright protection for digital image, digital image watermarking is one of the finest ways. The scheme for digital watermarking should take care of the properties like fidelity, capacity and robustness. The proposed scheme is an invisible watermarking scheme, which uses blind watermarking scheme where a watermark (secret binary image) is embedded in all R, G and B planes separately of cover image using stationary wavelet transformation (SWT), fast Walsh--Hadamard transformation (FWHT) and in association with binary digit sequence block to get complete watermarked image. The paper proposes the effect of embedding and retrieval of the watermark into/from R, G and B planes of the cover image separately and in combination. The results obtained are tested and compared with different existing system and are quite encouraging and satisfactory with respect to fragileness, robustness, fidelity and capacity. The experimental results are calculated in the form of peak signal-to-noise ratio and correlation values in all of the cases.
Game Recommendation Based on Dynamic Graph Convolutional Network

Recent years have witnessed the popularity of game recommendation. Different from the other recommendation scenarios, the user and item properties in game recommendation usually exhibit highly dynamic properties, and may influence each other in the user-item interaction process. For taming such characters, so as to design a high quality recommender system tailored for game recommendation, in this paper, we design a dynamic graph convolutional network to highlight the user/item evolutionary features. More specifically, the graph neighbors in our model are not static, they will be adaptively changed at different time. The recently interacted users or items are gradually involved into the aggregation process, which ensures that the user/item embeddings can evolve as the time goes on. In addition, to timely match the changed neighbors, we also update the convolutional weights in a RNN-manner. By these customized strategies, our model is expected to learn more accurate user behavior patterns in the field of game recommendation. We conduct extensive experiments on real-world datasets to demonstrate the superiority of our model.
Local Spatio-Temporal Propagation Based Adaptive Model Generation and Update for High Frame Rate and Ultra-Low Delay Foreground Detection

High frame rate and ultra-low delay matching system plays an increasingly important role in human-machine interactive applications, which demands better experience and higher accuracy. Foreground detection is an indispensable preprocessing step to make the system suitable for complex scenes. Although many foreground detection algorithms have been proposed, few can achieve high speed in hardware due to their high complexity or high consumption. Based on the foreground detection algorithm ViBe, this paper proposes a local spatio-temporal propagation based adaptive model generation and update strategy for high frame rate and ultra-low delay foreground detection. Our algorithm predicts whether a region is a foreground by setting up detecting points, thereby adaptively adjusting the number of pixels that needs to be modeled. Secondly, the local linear illumination correlation is used to update models, which makes the algorithm more robust to illumination changes. The evaluation results show that the proposed algorithm successfully achieves real-time processing on the field-programmable gate array (FPGA) at a resolution of 640×480 pixels, with a delay of 0.908ms/frame.
Two-Level Convolutional Neural Network for Aspect Extraction

Extract product aspect is an important task in fine-grained sentiment analysis. Though many models have been proposed to solve the problem. They either use handcrafted features or complex neural network architectures. In this paper, we propose a simple Two-level CNN model to extract product aspects from customer reviews, namely Char- and Word-level CNN. The Char-level CNN learns char-level representation of each word (also named morphological information), while the Word-level CNN captures features from the concatenation of char-level representations and word embeddings. Compared to previous neural architectures, our model do not use any external resources like dependency parsing tree or lexicons. To the best of our knowledge, this is the first time to couple Char- and Word-level CNN for aspect extraction. We conduct comparison experiments on two product review datasets. Experimental results demonstrate the effectiveness of our proposed model.
Interval regression models of a machine-building enterprise

Abstract The article discusses methods of interval regression analysis and their application in modeling. The object of the study was the reporting of the engineering company LLC Spetsstroymash (in a sixteen-year period). The results obtained are compared with traditional regression analogs such as the least squares method and the least modulus method.
Image Processing Using Case-Based Reasoning: A Survey

Case-based reasoning (CBR) is a technique which solves a problem using past experiences, where a case base stores these past experiences called cases. CBR is used to solve different kinds of problems where past information is available. With the advent of modern and efficient digital tools, huge amount of image data is being captured. Hence, huge amount of image data is stored in different application fields. Different organizations use CBR to find valuable and useful information from this image data. Therefore, in the recent times, there is a huge use of CBR in image data applications. This paper discusses the framework of CBR in image processing and reports different application models in different applications. From literature, it is observed that the performance of CBR in image processing is significant and acceptable.
Real-Time Template Matching Based on Local Stable Pixels

In this paper, a template matching method based on local stable pixels is proposed. First, gradient magnitude and orientation difference are utilized to evaluate the local pixel information for local stable pixel detection. Second, an updated strategy based on gradient magnitude standard deviation is designed to detect local stable pixels. Finally, a coarse-to-fine search approach based on the detected stable pixels is employed to acquire rotation angle. Experimental results demonstrate that the proposed method is robust to arbitrary rotation, occlusion, complex background clutter, blur, noise and it is able to meet the demand of real-time processing for visual positioning project.
Single level Discrete Wavelet Transform based Video Steganography on Horizontal and Vertical coefficients

This paper proposes a single-level discrete wavelet transform based novel video steganography algorithm. Initially ` l' number of carrier frames are chosen to hide the data in this method. After estimating carrier frames, every frame is separated into each R, G and B components which are decomposed using single-level discrete wavelet transform (DWT). For embedding the watermark information the horizontal and vertical coefficients are selected as a small change in these coefficients has negligible effect on the quality of the video frame. The watermark image pixels is shuffled before embedding for which a key is required. The shuffled pixels are grouped in three data matrices. Each data matrix is embedded in the horizontal and vertical coefficients of RGB frames. The original diagonal coefficients, embedded coefficients and original approximation coefficients are used to reconstruct the stego video after embedding. During the extraction process the watermark is extracted from the horizontal and vertical coefficients of stego-video. PSNR value of 57 dB with an embedding capacity of 60% is achieved in the method proposed. The performance of the method proposed in view of video quality as well as embedding capacity outperforms other methods and is justified by the experimental results.
Software Product Line Configuration and Traceability: An Empirical Study on SMarty Class and Component Diagrams

A Software Product Line (SPL) represents a set of systems sharing common and variable features. The varying features and respective elements (variability) enable differentiating products of a certain domain. Thus, managing variability is a crucial activity for the success of SPL engineering, especially those based on UML due to a large amount of variability representation in different diagrams. There are few experiments in the literature to evaluate and to compare UML-based variability management approaches. In this paper, we analyze a subset of such approaches: SMarty (our approach), PLUS, and Razavian and Khosravi. We empirically compared them by conducting an experiment with more than 50 participants in terms of configuring SPL products and variability traceability among class and component diagram variable elements. We also analyzed the influence of the participants knowledge on the use of each approach and the amount of material consultation required for each variability management approach. In addition, we checked whether participants comprehend traceability capabilities of approaches. Results pointed out: SMarty is as effective as other studied approaches to configuring SPL specific products; the number of consultations on each approach instructional material did not influence effectiveness; and SMarty needs more participants previous knowledge on UML to configure SPL products.
Application of LP Structures Theory to Intelligent Attribute Merger Refactoring

An approach to automatized object-oriented code refactoring is described that applies LP structures theory to type hierarchy transformations, which merge attributes sharing common subclasses. A distinctive feature of these algebraic structures is their ability to model aggregation not as a relation between independent sets of types and attributes, but as a relation between specific types. The property enables a more adequate modeling of type hierarchies. The described approach is dual to the “Pull Up Field” refactoring method that was considered in the previous works related to the applications of LP structures theory. In this paper, LP structures on type lattices are extended to be able to model a wider range of type hierarchies and to accommodate external constraints on the refactoring process. Also, the paper details the process of constructing and applying the model.
Network Visualization for Optical Transport Networks

Human interpretation of the network condition plays a key role in managing the network and services; information visualization allows network administrators to understand the behaviour of the managed network. Optical transport networks are inherently multivendor and multidomain networks, and the associated network management functions often make use of different databases and ways of information representation. In this paper we propose a method to optimize and enhance the information visualization, which allows for a unified and common information representation for the network management.
Image Noise Level Classification Technique Based on Image Quality Assessment

Image noise plays a vital role in digital image processing. However, in some specific application scenarios, random noise has an uncontrollable effect on digital image processing. Besides, a large number of hyper parameters which need to be fine-tuned can lead to inefficient projects. Therefore, we propose a Image Noise Level Classification(INLC) technique for specific application scenarios by comparing image quality assessment(IQA) methods, fitting curves and designing two neural networks. For low-accuracy, we come up with a soft way by setting a tolerance rate to achieve a higher acceptable accuracy. Experiments show that our INLC is more accurate and efficient.
Crowd Abnormal Behavior Detection Combining Movement and Emotion Descriptors

At present, the detection of crowd abnormal behavior has the problem of unclear definition of anomalies and mutual occlusion. Existing researches mostly detect the appearance and behavior characteristics of individuals, extract and analyze the movement characteristics of groups. Anomaly detection is achieved through comparison of parameters and thresholds. However, the context semantics cannot be effectively utilized and the definition of anomalies cannot be consistent with the actual situation. There is a problem of disconnection between behavior characteristics and behavior description. In this paper, we use the designed convolutional neural network to extract the spatiotemporal features of the crowd, combine the motion descriptors and emotional descriptors corresponding to the features, that is to say, to describe the features in multiple directions; with the help of unsupervised deep learning models, we train normal behaviors, and use crowd psychology knowledge to conduct research on crowd situation analysis; multi-class SVM training and using it to distinguish different types of features will help to achieve the description and prediction of crowd behavior. Furthermore, through the design of modular schemes to reduce the complexity of the calculation and improve the efficiency of the algorithm. The purpose of this article is to design a new method to achieve the effective extraction of various features of the crowd and the precise identification of abnormal behaviors in complex crowds. Experiments verify that the algorithm in this paper can accurately describe the behavior of complex people.
Text Summarization and Its Types

Text summarization is a compressing technique of the original text to form a summary which will provide the same meaning and information as provided by the original version. Summarizer helps in saving time and increasing efficiency. This chapter gives the full insight of text summarizers, which can be categorized based on methodology, function and target reader, dimension, and language. Various researches have been conducted in the field of text summarization using different approaches. Consequently, the chapter aims to provide an overview of how text summarizers work with different methods and state their domain-oriented applications. Additionally, the authors discuss multi-lingual text summarization in detail. This chapter focuses on showing the effectiveness and shortcomings of text summarization approaches by comparing them.
Hybrid Method for Biomedical Image Poisson Denoising

Convolutional neural networks (CNN) have recently become the main tool for the problem of biomedical image denoising. Nevertheless, CNN-based methods strongly depend on the used training set, and even small differences in the input data can cause output disturbance. One of the possible ways to tackle this problem is to use hybrid denoising methods that include combinations of CNN and "classical" image denoising algorithms. In this paper we present a hybrid iterative DeepRed algorithm for Poisson and mixed Poisson-Gaussian denoising with an automatic choice of the number of iterations. The choice is based on the control of biomedical image structures. Here we analyze the presence of regular structures in the ridge areas at the difference between noisy and filtered images by a multiscale ridge based approach. It was also discovered that the application of the Anscombe transform in the hybrid method improves denoising results. Test results for retinal image dataset DRIVE and Set12 natural images show practical applicability of the method.
Applying the T5 language model and duration units normalization to address temporal common sense understanding on the MCTACO dataset

In this paper, we present the work we did on the MCTACO dataset, which is concerned with temporal common sense understanding in natural language processing. We begin by describing our approach that we called T5NCSU (T5 Normalization Common Sense Understanding), which relies on preprocessing techniques like duration units normalization and the use of the recently released T5 text-to-text pre-trained language model, we then present and discuss our results. Using our approach we were able to obtain the state-of-the-art on the MCTACO dataset leaderboard.