Reviewerly/exHarmony huggingface dataset:
Improving Cost Estimation in Internet Advertising Using Machine Learning: Preliminary Results

In the internet advertising industry, web and mobile applications that display ads need to choose high-paying ads to increase their revenue. Ad mediators create various decision mechanisms to select ads that will generate higher revenues in order to increase the revenue of advertising applications. One type of these decision mechanisms is to select and deliver the ad with the highest eCPM (Effective Cost Per Mille) value from ads that can be placed in an ad slot. The eCPM value varies depending on different external factors for different applications. It is not possible for domain experts to make successful predictions by analyzing different sets of external factors for many applications and to keep these predictions constantly updated. Therefore, eCPM values were automatically predicted separately for each application on different ad slots and different countries using time series analysis and machine learning algorithms. SARIMA, MLP, CNN and LSTM algorithms are used to make predictions. The LSTM algorithm has generally yielded better results in eCPM estimation. As a result of the trials conducted with a limited number of users of the two applications on production environment, an increase in daily income per user was observed.
Public Fora and Freedom of Expression

This paper focuses on freedom of expression in the age of digitalization specifically on social media public fora. As the current political climate worldwide is polarising and both democracies and dictatorships are tightening their grip on ‘hate speech’ and other ‘unwanted’ discourse, we as people should focus on what is being done with our freedom of expression. As it is obvious that not all expression can be free, e.g. child pornography, we should focus on both what expression we should limit, but also how: who should have the power to limit the expression and how should they do it? The argument of the paper is that the current corporation-lead black-box censorship in social media creates a danger for the freedom of expression as the corporations, often foreign for most people, seem to use the platform to affect the functioning of the democracies’ legislation processes. Thus, if we value our democracies, the censorship should be conducted in a justified and as open as possible manner and never without a sound reason.
Hand Movement Pattern Recognition Based on Convolutional Neural Network And AlexNet Architecture

This paper presents a method for classifying hand movements for use in wheelchairs, robots, and artificial hand prostheses. Deep learning neural networks are a new method that has been welcomed by researchers in the last decade and has been used more in image and speech processing. In this study, using hand convolution neural network, 6 images of hand movements were taken from 93 healthy individuals and examined. The results show that the proposed method has been able to accurately detect hand movement with 97.36% accuracy.
FM-based Supervised Learning for Categorical Data Classification in Manufacturing Process

For complex manufacturing process, the raw features are always in a heterogeneous manner that contains lots of high dimensional process parameters and categorical targets (e.g. product quality "Pass" or "Fail") without numerical measurements. It makes the classification problems quite challenging. To improve the classification accuracy, additional data structure is necessarily considered, e.g. feature interactions. Factorization Machines (FM) is a general classification method which considers the effect of feature interactions towards target. This paper discusses the classification performance of FM methods and its extensions to manufacturing processes and other industry fields. Real-world datasets are employed for the validation of classification performance. Experimental results show that FM-based approaches efficiently improve the classification accuracy about 2.49%-5.94% compared with conventional classification methods.
A Decentralized Data Processing Framework Based on PoUW Blockchain

Conventional master/slave-based data processing frameworks are vulnerable to single point of failure and performance bottlenecks of the master node. In contrast, blockchain systems adopt a decentralized framework and are capable of aggregating enormous computing resources. In this paper, we propose a blockchain-based data processing framework that utilizes the advantages of the blockchain for solving the drawbacks of the centralized framework. In our framework, the blockchain stores the task information and the adopted proof of useful work consensus enables nodes to process tasks using their computing resources, while competing for the leader (who dispatches pending tasks to the blockchain). Extensive simulations show that the proposed framework is better than the centralized framework in terms of the throughput and the task response time.
Modeling of Multilayer Multicontent Latent Tree and Its Applications

Latent tree model (LTM) is a probabilistic tree-structured graphical model, which can reveal the hidden hierarchical causal relations among data contents and play a key role in explainable artificial intelligence. However, because current LTM modeling techniques are only suitable for single-content variable, the applications of LTMs are somewhat limited. Toward this end, a multilayer LTM (ML-LTM) is first presented to deal with the hierarchical clustering issues of multicontent variables. Second, we further develop an ML-LTM-based multicontent recommendation system. Our experiment results show that the proposed ML-LTM can achieve 90% recommendation accuracy, but the current LTM can only has 20%. Third, we propose an incremental update approach for ML-LTM that can save five-sixth updating time comparing with the whole-model retraining approach for achieving the same recommendation accuracy.
A Post Dynamic Clustering Approach for Classification-based Image Retrieval

Content-based Image Retrieval (CBIR) is the process of retrieving images similar to an input query image from a large image dataset. One of the currently trending techniques in this field is classification-based CBIR, which aims to reduce the search space and speed up the final image retrieval. However, owing to the thousands of images in the reduced search space, it takes considerable time to retrieve relevant images. This paper proposes a novel post dynamic clustering-based approach for classification-based CBIR to enhance retrieval accuracy and speed. Initially, a pre-trained CNN architecture is used to predict the class of the input query image and reduce the image search space. Here, clusters of the produced feature space. Next, a semantic cluster sorting technique is suggested to sort all these clusters based on their semantic order. Finally, an optimal subset of these sorted clusters is selected for final image retrieval, which comprises more semantically similar images. The performance of the proposed approach has been tested on five different image datasets. The experimental outcomes confirm that the proposed method is more efficient and faster than competing state-of-the-art schemes.
Energy Efficient and Performance Aware Multi-Objective Allocation Strategy in Cloud Environment

In Cloud Computing, high Energy Consumption and Service Level Agreement violations are the challenging issues that need to be addressed. Efficient resource management strategies are required to utilize resources in a better way. There are number of approaches focusing either on issue of high Energy Consumption or Service Level Agreement violations. But, the trade-off between the two is highly ignored. In this work, Energy and Performance Aware Best Fit Allocation policy is suggested that attempts to accommodate workload on already active energy efficient servers, keeps the idle servers to low power mode and, ensures minimum free leftover MIPS on the server to utilize it properly. Moreover, it attempts to keep some unallocated MIPS on the server to make the server capable to adjust to some sudden increase in resource requirements by the applications thus minimizing the need of frequent VM reallocation that in turn contributes to lower Service Level Agreement violations that are caused due to migrations. The experimental results have shown an average reduction of 24.62% and 63.37% in energy consumption and migration count respectively.
Monitoring and Analytics at INFN Tier-1: the next step

In modern data centres an effective and efficient monitoring system is a critical asset, yet a continuous concern for administrators. Since its birth, INFN Tier-1 data centre, hosted at CNAF, has used various monitoring tools all replaced, a few years ago, by a system common to all CNAF departments (based on Sensu, Influxdb, Grafana). Given the complexity of the inter-dependencies of the several services running at the data centre and the foreseen large increase of resources in the near future, a more powerful and versatile monitoring system is needed. This new monitoring system should be able to automatically correlate log files and metrics coming from heterogeneous sources and devices (including services, hardware and infrastructure) thus providing us with a suitable framework to implement a solution for the predictive analysis of the status of the whole environment. In particular, the possibility to correlate IT infrastructure monitoring information with the logs of running applications is of great relevance in order to be able to quickly find application failure root cause. At the same time, a modern, flexible and user-friendly analytics solution is needed in order to enable users, IT engineers and IT managers to extract valuable information from the different sources of collected data in a timely fashion. In this paper, a prototype of such a system, installed at the INFN Tier-1, is described with an assessment of the state and an evaluation of the resources needed for a fully production system. Technologies adopted, amount of foreseen data, target KPIs and production design are illustrated.
Almost Difference Set Pairs and Ideal Three-Level Correlation Binary Sequence Pairs

Almost difference set pairs are used in cryptography and coding theory, and a majority of the acknowledged almost difference set pairs are constructed by Chinese remainder theorem, cyclotomy or interleaving. In this paper, through the addition or deletion of elements from difference sets and difference set pairs, we ourselves construct almost difference set pairs and sequences thereof. Further, we obtain the ideal three-level correlation binary sequence pairs with high energy efficiency by some renown difference sets or difference set pairs, e.g. Palay-Hardmard difference sets.
Multi-view Story Characterization from Movie Plot Synopses and Reviews

This paper considers the problem of characterizing stories by inferring properties such as theme and style using written synopses and reviews of movies. We experiment with a multi-label dataset of movie synopses and a tagset representing various attributes of stories (e.g., genre, type of events). Our proposed multi-view model encodes the synopses and reviews using hierarchical attention and shows improvement over methods that only use synopses. Finally, we demonstrate how we can take advantage of such a model to extract a complementary set of story-attributes from reviews without direct supervision. We have made our dataset and source code publicly available at https://ritual.uh.edu/multiview-tag-2020.
Synchronizing the simplest classical system and then quantizing it

I propose a discrete synchronization model of finite d-level systems and discuss what happens once superposition of states is allowed. The model exhibits various asymptotic behaviors that depend on the initial state. In particular, two antagonistic phenomena can occur: a quantum-to-classical transition and entanglement generation. Next, I generalize this model and show that it is possible to phase-lock a periodic dynamics of a single qubit to a periodic dynamics of a qudit stimulus.
Design of implementation method for blended Agile Project Management methodology in a manufacturing company

Since its appearance at the beginning of the century, Agile Project Management methodologies have been spreading from the software development environment where they were originated to almost all type of business and working activities, including manufacturing companies. One of the challenges of implementing this methodology is the design of the implementation process. Agile implicate a way of performing project management that breaks the traditional waterfall approach. This is why it is relevant to have a proper exposure to the concepts and an organised design process. Based on a case study of a metalworking manufacturing company of operating machines, a survey to students from master’s degree, and academic literature, the design of implementation has been divided in three steps: 1.??Exposure and comprehension of Agile methodologies. 2.??Analysis of current Project Management process and best fit for implementation. 3.??Design of blended Agile Project Management method. The outcomes of this work support the view that first exposure to Agile methodologies needs to be interactive including “on the job” activities, and the design of the implementation should be tailor made to the company and its processes, performed by an internal team, which is suggested to be cross-functional.
Research on Data Fusion of Pilot's LOS and Target Information

Aiming at the problem that the precision of gaze information data extraction is not high in the process of pilot's; flight, this paper puts forward the research on the fusion of the data of the landing point of sight and the target information, and feeds back the information through human-computer interaction to obtain the accurate target information. First, the non wearable eye tracker is used to track the line of sight, estimate the fixation point, obtain the fixation point coordinates, and match with the position coordinates of the instrument panel to get the dial where the fixation point is. Then, data fusion is carried out between the instrument panel where the fixation point is located and the flight parameters to obtain the information displayed on the instrument panel. Finally, the fixation information of the instrument panel is used as the input data to support the identification of the pilot's intention. Secondly, data fusion is carried out between the pilot's attention information and reaction time information, and a large amount of data is used as the input data. Machine learning is adopted Methods after information fusion, the decision- making of pilots was predicted.
Mercury’s Boots: Extending Visual Information and Enabling to Move Around a Remote Place for VR Avatar

In this research, we develop a mobile telepresence robot displaying a light field image of the VR avatar synchronizing its posture to that of the VR performer and VR controlling application rendering a point cloud of the scene before the robot to the VR system with an RGB-D camera. Both our robot and application communicate with each other via WebRTC. Our remote communication system compresses depth data approximately 100 kB, sends every about 200 ms and decodes it for 20 ms in average as the result of our performance test. In our online survey, 105 subjects generally expected our expecting effects, but some of them worried or suspected the limitation concerned to VR technology, network, or emotional expression of avatars.
Gesture identification in testing mode in the integrated sign language training system

Abstract The structure of an integral sign language teaching system based on a 3D computer character, which allows demonstrating fingerprints and words of a sign language using an avatar, is considered. The system provides the test mode when the user must demonstrate the word or fingerprint indicated by the system. The user feedback with the system is carried out using a standard webcam. The problems of gesture localization and identification in user testing mode are considered. The features of this mode are determined during the system operation. The algorithm for quick identification of human hand gestures is proposed. The method based on the signs, which are two-dimensional projections, is used for gesture identification. The projection recognition features are considered, the similarity metrics are analyzed, and three stages for match decision are identified. The experimental results show the stable operation of this algorithm in various lighting conditions and at the place where the gesture was shown.
Cyber Threat Extenuation for Substation Data Communication: Evaluation of Encrypt-Authenticator Prototype for IEC 60870–5 Data

Data communicated at many existing secondary electrical substations is currently not encrypted. In order to improve their data security level, a low-cost cryptography prototype is proposed. Both lightweight and conventional crypto-algorithm types are chosen to encrypt, authenticate and encrypt-authenticate data packets of IEC 60870-5, the data protocol being used at most substations. Implementations of selected algorithms are on a programmable logic arrays (FPGA) to meet communication time sensitivity while employing an eight-bit processor. The FPGA implementations are optimized for speed rather than footprint. The processor is mainly to handle processing of data packets either coming from or going into substation devices. The performance in term of latency for both the conventional and lightweight cryptography is evaluated to meet different data transmission speeds. The lightweight encryption has lower latency compared to the conventional one, while both types of authentication algorithms yield similar latency as expected.
On the Conservation of Laws in Complex Open Systems

The work develops a language environment for studying the behavior of complex systems. We consider a class of systems represented by a species of structure in the sense of N. Bourbaki. We learn morphisms of the base sets of these structures. Arbitrary morphisms can lead the system to undesirable behavior. Invariants can constraint morphisms and prevent unwanted behavior changes.
Research on Intelligent Planning Based on Self-awareness

Self-conscious intelligent planning is the process of Ego's cognition of his own state and environment and then planning his own ability to achieve new desires. Ego's planning is the embodiment of Ego's self-consciousness. Ego's self-consciousness is mainly composed of six major components: belief, ability, planning, desire, execution and behavior control mechanism. On this basis, combined with relevant research results such as knowledge representation, intelligent planning, and program semantics, comparative analysis The difference and connection between the intelligent planning description language and the self-conscious description language, designed a belief model based on self-consciousness, verified the planning results of acquaintances based on the belief model and trust degree, and based on Hoare axioms in axiom semantics to Ego Capability semantics are expressed, and the composition of Ego capabilities and Ego's planning are analyzed through specific examples.
Monitoring Smart Devices for Personal Health Care Using Deep Analytics

As indicated by the Health foundations, illnesses brought about by an undesirable way of life speak to the main source of death everywhere throughout the world. Along these lines, it is pivotal to monitor and evade clients' unfortunate practices. Existing wellbeing observing methodologies despite everything face numerous challenges of restricted knowledge because of deficient medicinal services information. Hence, this article proposes a shrewd individual wellbeing counsel for comprehensive and clever wellbeing checking and direction. The monitoring of personal healthcare screens both physiological and mental conditions of the client. The Score obtained from this layout is expected to assess the general wellbeing status of the client. At last, a testbed for check of possibility and relevance of the proposed framework was created. The experimental and reproduction results have indicated that the ace presented approach is effective for appropriate client state checking.
An ROI-Based Watermarking Technique for Image Content Recovery Robust Against JPEG

The paper proposes a method for image content recovery based on digital watermarking. Existing image watermarking systems detect the tampering and can identify the exact positions of tampered regions, but only a few systems can recover the original image content. In this paper, we suggest a method for recovering the regions of interest (ROIs). It embeds the semi-fragile watermark resistant to JPEG compression (for the quality parameter values greater than or equal to the predefined threshold) and such local tamperings as splicing, copy-move, and retouching, whereas is destroyed by any other image modifications. In the experimental part, the performance of the method is shown on the road traffic JPEG images where the ROIs correspond to car license plates. The method is proven to be an efficient tool for recovering the original ROIs and can be integrated into any JPEG semi-fragile watermarking system.
CongoFlux: Measuring greenhouse gas exchange to better understand the contribution of the Congo Basin's forests to fight climate change

Yangambi, in the Democratic Republic of Congo, is now home to the Congo Basin’s first eddy covariance station: the CongoFlux tower. Reaching high above the canopy, this structure delivers continuous and accurate data on greenhouse gas exchange between the atmosphere and the forest, which is critical to better understand the role that tropical forests play sequestrating carbon and mitigating climate change.
A novel infrared image enhancement algorithm based on atmospheric transmission model

In this paper, an infrared image enhancement algorithm based on atmospheric transmission is proposed. First, the algorithm estimates atmospheric light based on a priori principles, that is, the pixel area representing infrared atmospheric light is generally in the upper part of the image and its local contrast is low and its brightness is high. Then, the algorithm estimates the transmittance of the image based on the principle of maximum local contrast of the image, and it uses a guided filtering algorithm to optimize the transmittance. According to the physical model of infrared attenuation in the atmosphere, the infrared image before attenuation is restored. At the same time, this paper uses guided filtering to decompose the image into a basic sub-figure and a detail sub-figure. Among them, the SVD decomposition method is used to improve the brightness of the basic sub-figure. Then, the detail sub-figure and the basic sub-figure are processed by the wavelet transform method, and finally the enhanced infrared image is obtained. Experimental results show that the algorithm in this paper can effectively enhance the infrared image, and the enhancement effect is better than that of ordinary HE, CLAHE, Retinex and other algorithms.
Automatic Generation Method of Ancient Poetry Based on LSTM

This paper mainly focuses on the literary genre of ancient poetry with a certain rhythm and cadence, and proposes a novel automatic generation model of ancient poetry. The model uses about 300,000 Tang poems and Song poems as training data, uses One-hot encoding to process data, and uses long short-term memory networks (LSTMs) to learn the semantics of ancient poetry texts and conduct research across a single RNN structure. According to the user's requirements, the model can automatically calculate the most relevant coherent words in the context of the selected context, and generate common ancient poetry and specified words. The method of BLEU automatic evaluation and manual evaluation finally demonstrates the effectiveness of the experiment.