Reviewerly/exHarmony huggingface dataset:
Intelligent Entertainment: Shaping Policies on the Algorithmic Generation and Regulation of Creative Works

The virtual spaces where intellectual property (IP) meets artificial intelligence (AI) are shaping the future of work. Creative industry firms employ AI to plan, edit, and remix artistic and literary works, in use cases ranging from computer games and graphics to short stories, news articles, songs, screenplays, films, and beyond. Policy in this area is driven in significant part by lobbyists' and lawyers' moves to implement AI to control the content of creative works and online communications and intergovernmental efforts to enhance Internet copyright enforcement amidst floods of digital speech. In the far future, intellectual property law may become less relevant as advances in computing and AI contribute to an abundance of creative works. In the near term, the copyrightability of AI-generated works and the governance of human expression by AI systems are coming to the fore. The significance of human creators could fade as either the authors or the users of generative adversarial networks and other AI implementations create and own expressive output on a previously unimagined scale. Copyright law may inhibit this process by classifying works such as those generated by OpenAI's GPT-3 model as potentially unlawful to create or publish without the permission of the writers, recording artists, and camera operators with whose works the models are trained. Alternatively, copyright and intellectual property in general might excuse and empower AI programming that helps people create using "data" without substantially replicating a specific "work" or "works."The use of AI to enforce legal or social norms represents a growing market and will raise public policy concerns. Courts and legislators know that the ease and speed of digital communications create gaps in legal enforcement. As a consequence, governments and industry conjure up AI solutions for detecting and limiting the spread of illegal digital content. While some may welcome reductions in the difference between legal obligations and legal compliance, others warn that unreasonable and overbroad application of legal norms will harm innocent parties and public discourse. This essay therefore considers the case for limiting AI's role in enforcing intellectual property rights to protect Internet users from the erroneous flagging and removal of their original commentary and creative works. It concludes with some observations concerning the importance of human lawyers, judges, and jurors in governing fruits of the imagination. The essay introduces a symposium on AI and the future of intellectual property with a focus on big data and algorithms. The contributors consider AI as the defendant in a lawsuit, AI as a means to control the proliferation of IP rights and to promote the fair use of entertaining or informative works, AI as an engine that mines texts and images for fuel, and AI as a threat to the traditional freedoms that humans have crafted.
Efficient and Enhanced High Throughput Image Denoising Using Chronical Fuzzy Set

A basic and fundamental step in image processing for any type of applications is removing noise from a query image. The significant ideal property of a best and perfect image de-noising model is that to preserve edges and noise has to be removed entirely. An efficient fuzzy based filter integrated with modified rules set is implemented in this research work for noise reduction of images corrupted with at-most noise. Proposed design consists of two variant stages. The first stage produces a fuzzy derivative for whole eight different directions. Final stage uses these fuzzy derivatives to implement fuzzy smoothing by weighting the participation of neighbouring pixel values. Type 2 fuzzy structure distinguishes the noisy pixels in the satellite picture and transforms the picture into a binary picture, which is gone through the Adaptive Nonlocal Mean Filter (ANLMF) for the noise rectification. Ultimately, for the picture improvement, the kernel-based addition plan has to be carried out, which is done through the proposed filtering of fuzzy. The above whole process christened as chronic fuzzy system. In Proposed architecture, we present an energy efficient algorithm for making the system more robust and it is developed on a Cadence 90-nm technology. The energy per sample for 8-bit test pattern has been reduced by 64% and power consumption is reduced by 44% when compared to existing architectures.
Features of engineering thinking development by means of 3D technologies

Abstract The problem that the study is aimed at is due to the need to create a special engineering style of thinking within the digital learning space, which implies a willingness to research, creativity, responsibility, intellectual skills, and supported by modern high-tech tools to provide resources for solving the problems of formation of Industry 4.0. The purpose of the study is to identify the features of the use of 3D technology for the formation of engineering thinking, as an important competence of the in-demand specialist in the digital society. The methodology is based on theoretical and methodological analysis and generalization of fundamental scientific works, the significance of which in the field of digitalization of education, training of engineering and technical personnel, three-dimensional modeling, application of software for the development of thinking, is recognized by the scientific community. The paper specifies the essence of the concept of “engineering thinking” precisely in the context of training specialists for Industry 4.0 and substantiates the potential of 3D-technologies for the formation of engineering thinking as an essential skill of in-demand professionals of the future. The authors formulate the principles and directions of support by the mentor of the research activities of students in 3D modeling. The system of work on an interdisciplinary project with the allocation of skills significant for the formation of engineering thinking is presented. In conclusion, the features of the formation of engineering thinking by means of 3D technologies are summarized in the process of the entire learning path from preschool education to professional retraining.
Towards Smart e-Infrastructures, A Community Driven Approach Based on Real Datasets

e-Infrastructures have powered the successful penetration of e-services across domains, and form the backbone of the modern computing landscape. e-Infrastructure is a broad term used for large, medium and small scale computing environments. The increasing sophistication and complexity of applications have led to even small-scale data centers consisting of thousands of interconnects. However, efficient utilization of resources in data centers remains a challenging task, mainly due to the complexity of managing physical nodes, network equipment, cooling systems, electricity, etc. This results in a very strong carbon footprint of this industry. In recent years, efforts based on machine learning approaches have shown promising results towards reducing energy consumption of data centers. Yet, practical solutions that can help data center operators in offering energy efficient services are lacking. This problem is more visible in the context of medium and small scale data center operators (the long tail of e-infrastructure providers). Additionally, a disconnect between solution providers (machine learning experts) and data center operators has been observed. This article presents a community-driven open source software framework that allows community members to develop better understanding of various aspects of resource utilization. The framework leverages machine learning models for forecasting and optimizing various parameters of data center operations, enabling improved efficiency, quality of service and lower energy consumption. Also, the proposed framework does not require datasets to be shared, which alleviates the extra effort of organizing, describing and anonymizing data in an appropriate format.
Accident Analysis and reconstruction

In any crime scene investigation situation, whether simple or mysterious ones, forensic expert’s crime scene visit play a very vital role in providing clues and helps in drawing up accurate and logical conclusions. Through different types of case exhibits and their pattern of examination in physics point-of-view, how the technology changes the hierarchy of old pattern. From the cases study, importance of new technology is exemplified, using the latest technique, computer based software to examine the exhibit. Importance in cases related to footwear impression, tyre-tread mark and tool marks analysis are reported. Road Accident Analysis require lots of efforts and investigation Physics and Maths based analysis. Through this presentation the importance of forensic investigation in such type of cases discussed
Research on Automatic Generation of Stimulator Based on CAN-FD Verification Platform

In this paper, we use Python script to read the function detailed description documents of CAN-FD IP, extract the required information according to the main keywords and other information, and automatically generate the qualified System Verilog constrained randomized register configuration stimulator. The simulation results show that the stimulator can meet the design requirements for the configuration of excitation, and can effectively improve the design efficiency of the verification platform.
Scale adaptive region covariance descriptor for visual tracking

Abstract This paper presents an adaptive approach for scale estimation in a tracking-by-detection framework. The proposed method works by learning covariance descriptor based on multi-layer instance search region. Our results show that the proposed approach significantly improves the performance in term of detection rate compared to region covariance descriptor with using a fixed bounding box (single scale). From this work, it is believed that we have constructed a greater solution in choosing best layer for this descriptor, permitting to move forward to the next issues such as fast motion or motion blur for achieving a robust tracking system.
Interactive Methods for Investigating Frequency Modulation Applied in the Educational Process

The development of the modern information and communication technologies leads to changes in students and their understanding of the world. Teachers need to take into account the interests of the new “digital” generations and adapt their methods of teaching to them, using more interactive techniques, offering different approaches to explore a problem or a task at the same time. This paper introduces different approaches for studying the topic “Investigating Frequency Modulation” in two disciplines “Communication Circuits” and “Radio Communication Technologies” for the bachelors of the specialty “Internet and Mobile Communications”.
Modeling of Photovoltaic Installation Performance Taking into Account Seasonal Phenomena of Different Climate Zones

The task of mathematical model building for the dynamics of the generated power by a photovoltaic installation is considered. Models of such installations are actual both for the industrial electricity generation and for the creation of special intelligent modules with autonomous power supply. To build this mathematical model, it is proposed to use the method of structural identification based on a combination of swarm intelligence elements and interval data analysis methods. This is due to the fact that the requirements to mathematical model accuracy are contained into the obtained experimental data. And, as a result, the guaranteed prognostic properties of the model are ensured. At this, it is proposed to build the model of dynamics of the generated power by photovoltaic installation taking into account the peculiarities of seasonal phenomena of a certain geographical area in the paper. The results of analysis of known models of such type have shown that they may be unsuitable for certain climate zonez. In particular, this applies to such climate zones which are characterized by seasonal phenomena inherent in different seasons. During synthesis of the mentioned mathematical model, it is proposed to take into account the seasonal peculiarities inherent in the photovoltaic installation location by modifying the structural elements formation phase in the task of model structural identification. This allowed to adapt the method of structural identification for building models of dynamics of the generated power by photovoltaic installation (within the inductive approach) which can be used for any place in the world.
Application of Adaptive Contrast Stretching Algorithm in Improving Face Recognition Under Varying Illumination Conditions

This work proposed a novel algorithm, adaptive contrast stretching algorithm (ACS), in improving face recognition under varying illumination conditions. The ACS algorithm, whose building blocks are tuned logarithm filter and anisotropic diffusion filter (ADF), was used to preprocess samples of face images obtained from the extended Yale face database B. The resulting preprocessed data was split into training and testing datasets. While the training dataset was used to train a deep convolutional neural network (DCNN), the testing dataset was subdivided into four subsets based on the azimuthal angle of illumination. In order to compare the recognition accuracy obtained from using the ACS algorithm, the face images in the training dataset were successively processed using discrete cosine transform, difference of Gaussian, weber faces, multi-scale retinex and single-scale retinex. The respective output images obtained from each technique were used to train the DCNN. The result obtained from each technique showed that the developed ACS algorithm significantly outperformed other algorithms used in this study with an accuracy of 95%. This value is 2.5% greater than the unimproved version of the ADF, which is currently one of the acclaimed techniques used by most computer vision researchers in the surveyed literature.
Recognition of Radar Modulation Type Using Inter-Pulse Data in Complex Electromagnetic Environment

Recognition of radar emitter signals is important in radar reconnaissance systems of modern warfare. Recognizing radar-emitted signal is challenged due to the complexity of electromagnetic signals. Thus, this paper presents a new approach to recognize the radar modulation type of constant pulse repetition interval (PRI) in dense electromagnetic environments. In the presence of missing pulses, a constant PRI type recognition scheme (CPTRS) is proposed, too. The selected features exploit the difference and the variation information of pulse trains to describe inter-pulse intervals. The proposed CPTRS identifies a pulse train with randomly distributed missing pulses.
Research on reliability data mining technology of electronic components

Abstract This paper deeply analyzed the types of information that can be mined in component reliability data. According to specific needs, the clustering measurement method of reliability data sample similarity is studied, the distance measurement strategy is obtained, the algorithm steps and analysis methods of Apriori association rule data mining are improved, the reliability data prediction and analysis methods based on statistical methods are compared. AR prediction model, MA model and other component reliability information time prediction methods have formed data mining software such as clustering, correlation analysis and prediction, discovered a large amount of hidden reliability information, and realized the comprehensive mining and utilization of electronic component reliability data.
Research on SURF Feature Matching Algorithm Based on Lab Color Space

The alignment of images is an important task in the processing of images, which is also known as registration. Among the feature based registration methods, Speeded Up Robust Features (SURF) has been proposed as a computationally efficient approach. The SURF algorithm not only does not consider color information, but the matching accuracy is also rather low. In this paper, an improved SURF algorithm for feature extraction is proposed. An approach is proposed to use the Lab color space to extract the color information of the color image when extracting the feature points based on SURF, and adds the feature descriptors. In this sense, the feature descriptors of different feature points by proposing method are unique. And this method makes up for the shortcoming that SURF cannot use the color information of color images. This improved method promotes the precision of matching. The results show that the matching accuracy of this algorithm is higher than that of the original SURF when physical interference, rotation, brightness change, and scale change occur.
Context Analysis and Estimation of Mobile Users Considering the Time Series of Data

Recently, the demand for understanding mobile user's activities has been increasing in the fields such as customer behavior analysis. Our previous study shows that mobile user's context can be estimated to some extent using various sensor data of mobile phone and user's bio-signals by applying machine learning methods. In this paper, we propose to analyze and estimate the context by considering the time series of data to improve the accuracy. For the analysis and the estimation, using the data collected from two subjects, convolutional neural network (CNN) and various machine learning methods to classify the data into pre-defined eight and seven contexts are applied and compared. The results show that CNN with 256 window width achieve the highest macro F1-score of 97.6% and 97.1% for each subject, respectively. It suggests that the context analysis and estimation using sensor data and bio-signal can be done much more accurately by considering the time series of data.
An Analysis of DevOps Architecture for EMIS based on jBPM

In recent years, DevOps(Combination of Development and Operations) is an emerging software engineering practice. More and more organizations have begun to adopt DevOps method. This paper develops an educational management information system(EMIS) based on jBPM initiated by the DevOps concept, and proposes a quantitative analysis method based on the analytic hierarchy process(AHP) for its system evaluation. Preliminary evaluation results show that this method can help us understand the maturity of the system's DevOps architecture.
Guided Super-Resolution Restoration of Single Image Based on Image Quality Evaluation Network

SISR (Single image super-resolution) has always been a key problem in image processing field. In recent years, deep learning has been successfully used to SISR reconstruction. However, most of the previous deep learning methods use L2 norm based on pixel pairs as loss function, which results in a high peak signal-to-noise ratio (PSNR) value, but the perception quality has not been improved. When using Generative Adversarial Network (GAN), although it has good perception quality, PSNR is lower. So we’ll generate realistic results when both of them are used well. The image quality evaluation (IQA) network is to evaluate the image quality, so as to obtain good PSNR value and perception quality. In this paper, we use image quality assessment network to guide the SISR reconstruction network. Besides that, our proposed Super-resolution reconstruction of single image method is composed of several our given cross-attention units (CA) and is trained iteratively. Experimental results demonstrate that our method in qualitative and quantitative is better than others.
DPCA: A Novel Strategy to Refine the Traffic Classification Results in 5G Context

Traffic classification has been a problem that the academic community has been studying for decades. Under the background of the 5th generation mobile networks (5G), the huge amount and wide variety of traffic data have increased the difficulty of this issue. They put forward a higher demand for both the speed and accuracy of the classifier. In this paper, instead of improving the classification method, the authors propose to refine the classification result by optimizing the feature extraction process. A new extraction method named Discriminant Principal Component Analysis (DPCA) has been proposed to illustrate the effectiveness of this strategy. In the DPCA method, the authors first project the data on the direction of maximum entropy. Then, they look for the most discriminative dimensions and re-project the data onto them to extract a feature model. The extracted model has been applied to multiple classifiers for verification. Experimental results indicate that the feature model extracted by the proposed method can improve the result of traffic classification in both accuracy and running time. Proposing a feasible scheme to solve the traffic classification problem under the background of 5G network.
β-Cores: Robust Large-Scale Bayesian Data Summarization in the Presence of Outliers

Modern machine learning applications should be able to address the intrinsic challenges arising over inference on massive real-world datasets, including scalability and robustness to outliers. Despite the multiple benefits of Bayesian methods (such as uncertainty-aware predictions, incorporation of experts knowledge, and hierarchical modeling), the quality of classic Bayesian inference depends critically on whether observations conform with the assumed data generating model, which is impossible to guarantee in practice. In this work, we propose a variational inference method that, in a principled way, can simultaneously scale to large datasets, and robustify the inferred posterior with respect to the existence of outliers in the observed data. Reformulating Bayes theorem via the β-divergence, we posit a robustified generalized Bayesian posterior as the target of inference. Moreover, relying on the recent formulations of Riemannian coresets for scalable Bayesian inference, we propose a sparse variational approximation of the robustified posterior and an efficient stochastic black-box algorithm to construct it. Overall our method allows releasing cleansed data summaries that can be applied broadly in scenarios involving structured and unstructured data contamination. We illustrate the applicability of our approach in diverse simulated and real datasets, and various statistical models, including Gaussian mean inference, logistic and neural linear regression, demonstrating its superiority to existing Bayesian summarization methods in the presence of outliers.
Coherence-Based Complex Network Analysis of Absence Seizure EEG Signals.

This paper addressed the issue of epileptic absence seizures developing a complex brain network model based on the estimation of the coherence between electroencephalographic (EEG) signals. The EEG signals indeed reflect the abnormalities in the cortical electrical activity caused by epilepsy. A dataset of 10 absence patients was analyzed, including 63 seizures. The model was analyzed over the time to assess if changes in the network parameters matched the brain state (ictal: seizure), (non-ictal: seizure free). During the ictal states, the characteristic path length (\(\lambda \)) decreased and the global efficiency (GE), the average clustering coefficient (CC) and the small worldness (SW) increased, as expected, because of the abnormal synchronization associated with absence seizure onset. The connection matrices preceding the ictal states (8 s before) were thresholded and the corresponding connectivity scalp maps, showing the active links between EEG channels, were displayed. Such connectivity maps showed the interaction between channels and provided information about the abnormal recruitment mechanism associated with seizure development: the involvement of the cortical areas appears progressive and that every subject exhibited peculiar recurrent patterns of area activation.