Reviewerly/exHarmony huggingface dataset:
Analysis of PV Performances in Manchester by PVLIB Python

Renewable energy is a popular topic in our modern society, and one of the renewable energy types is solar energy. This project aims to use PVLIB Python to simulate and investigate the environmental and operating tasks related to a PV system installed in Manchester, the UK. For example, the variations of irradiance during a day and a year and how the environmental factors can affect PV systems performances. The project’s specific dates are Mar 21 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> , Jun 21 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> , Sep 22 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> , and Dec 21 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> in 2021.The simulation results indicate the peak irradiance during a day occurs at noon; and the summer solstice has the largest accumulative irradiance among the four days, that’s why its daily solar power generation is higher than the other three days. Currents and powers are positively related to irradiance, but overmuch irradiance can reduce voltages and efficiencies slightly; increasing PV temperature can also make PV efficiencies lower, that’s why the highest PV efficiency is at the winter solstice.
Detecting Malicious Domains Using Statistical Internationalized Domain Name Features in Top Level Domains

The Domain Name System (DNS) is a core Internet service that translates domain names into IP addresses. It is a distributed database and protocol with many known weaknesses that subject to countless attacks including spoofing attacks, botnets, and domain name registrations. Still, the debate between security and privacy is continuing, that is DNS over TLS or HTTP, and the lack of adoption of DNS security extensions, put users at risk. Consequently, the security of domain names and characterizing malicious websites is becoming a priority. This paper analyzes the difference between the malicious and the normal domain names and uses Python to extract various malicious DNS identifying characteristics. In addition, the paper contributes two categories of features that suppers Internationalized Domain Names and scans domain system using five tools to give it a rating. The overall accuracy of the Random Forest Classifier was 95.6%.
Research on the Design of Network Security Architecture of Power Grid Enterprises

In view of the current network security architecture of power grid enterprises does not adapt to new regulatory regulations, does not adapt to the development trend of digitalization, and the new technology of network security is not covered, this paper designs a set of network security architecture containing element views, capability views and measures views on the basis of the IPDRR network security architecture model, combined with the requirements of power grid enterprises for network security architecture, which covers the network security requirements of “collection, transmission, storage, application” and information system life cycle at the level of information system architecture. Meet new regulations and provide leadership with an understanding of the security posture of the enterprise, improving the organization’s ability to defend against attacks.
BeverageMaster: Smart Beverage Ordering Assistant

Common approaches to food and beverage ordering services in the UAE include mobile, web applications, and phone calls. These approaches are successful in practice but can be inaccessible or costly. For instance, many mobile and web applications can be inaccessible to visually impaired customers. Assigning employees to answer phone calls from customers can be costly, and ineffective at peak times. This paper contributes, BeverageMaster, a system that is designed with usability principles in mind. The system helps customers order beverages by interacting with a virtual assistant. The system uses DialogFlow, a natural language understanding platform, to build voice-based interactions with users. The interactions allow customers to customize their order in terms of type, size, quantity, and ingredients. We plan to usability test the system and collect feedback from potential users.
Evaluation of Supervised Machine Learning Approaches for Credit Card Fraud Detection

Credit card fraud is one of the most common attacks affecting millions annually. Detecting fraud transactions using traditional software is not suitable anymore. The attackers are becoming more intelligent in their way of attacking. The investment of machine learning techniques in detecting suspicious activities is the trend nowadays. It helps stop fraud transactions without preventing benign transactions from being completed. This paper applies different supervised machine learning algorithms to a given dataset, such as random forest, gradient boosted trees, logistic regression, k-nearest neighbors, artificial neural network, and others. Consequently, the artificial neural network and the logistic regression algorithms show the highest performance according to the ROC-AUC performance measure. However, the k-nearest neighbor’s algorithm performs better according to the F1-score performance measure.
Evaluating Web Search Engines’ Results for Personalization and User Tracking

Being contemporary with this technology-driven era, search engines have appeared on the scene to play an irrefutably profound role in our everyday life. Latterly, light has been shed on the trend of personalization, which comes into play whenever different search results are being tailored for a group of users who have issued the same search query. The unpalatable fact that myriads of search results are being manipulated has perturbed a horde of people. With regards to that, personalization can be instrumental in spurring the Filter Bubble effects, which revolves around the inability of certain users to gain access to the typified contents that are allegedly irrelevant per the search engine’s algorithm. In harmony with that, there is a wealth of research on this area. Each of these has relied on using techniques revolving around creating Google accounts that differ in one feature and issuing identical search queries from each account. The search results are often compared to determine whether those results are going to vary per account. Thereupon, we have conducted six experiments that aim to closely inspect and spot the patterns of personalization in search results. In a like manner, we are going to examine how the search results are going to vary accordingly. In all of the tasks, three different metrics are going to be measured, namely, the number of total hits, the first hit, and the correlation between hits. Those experiments are centered around fulfilling the following tasks. Firstly, setting up four VPNs that are located at different geographic locations and comparing the search results with those obtained in the UAE. Secondly, performing the search while logging in and out of a Google account. Thirdly, searching while connecting to different networks: home, phone, and university networks. Fourthly, using different search engines to issue the search queries. Fifthly, using different web browsers to carry out the search process. Finally, creating and training six Google accounts.
Intelligent Recognition Method of Substation Pointer Instrument Based on Deep Learning

Instrument patrol inspection of substation is an important part of intelligent operation and maintenance of substation. To replace manual patrol inspection to realize automatic patrol inspection of substation instruments, this paper designs a strategy combined with deep learning and computer vision technology. Firstly, the instrument area is detected and located by using object detection algorithm YOLOv4, and then the dashboard area is segmented by using U-Net network based on the detection results; After that, ellipse fitting and perspective transformation are performed on the segmentation results; Then, the introduction of attention mechanism in U-Net is analyzed and the U-Net network is used to segment the scale area and pointer area. Finally, the computer vision technology is used for fitting processing and combined with the prior information get by YOLOv4, the instrument reading is obtained. The results shows that recognition is basically similar from this strategy to manual reading, and the “machine generation” can be realized.
Research on Energy Internet Demonstration Project Management Based on Pre-Event, during Event, and Post-Event Control

The Energy Internet demonstration system is divided into two levels–top-level design and project management. Top-level design mainly aims to resolve the overall operating logic of the demonstration system, clarify the overall ideas, goals, demonstration types, and implementation methods, and establish a work organization process through design. At the project management level, the three-stage control method–post-event control, during event control, and post-event control is adopted for the management control logic. Also, related project management theories and tools are applied. We then propose targeted management control methods for the project screening layout, project execution management, and project effect evaluation design to achieve full-process control and method design of the demonstration system.
Становлення та теоретичне осмислення поетики монтажу і кадру в кінематографі

Fiction and cinema are in the mode of constant interactive exchanges. At the initial stage of its formation as an art form, cinema not only actively borrowed, used and processed popular literary plots, but also transformed into its expressive system (poetics) purely literary means of expression. In its turn, fiction even before the advent of cinema used techniques that from a modern point of view can be classified as “cinematic”. In the process of developing its language (film poetics), the art of cinema not only “discovered”, transformed these means, but also expressed and improved them. As a result, the reverse effect arose and the poetics of cinema began to be integrated into the expressive and pictorial system of the art of speech, and especially actively from the 20s of last century. This process should be considered in the context of the synthesis of arts, the intensification of which took place at the turn of the nineteenth and twentieth centuries, when it was claimed that the art of the modern era acquired new forms of expression. In order to study the elements of film poetics in literary texts, it is necessary to thoroughly review the main components of film language, they are categories “editing” and “frame”, the first definitions of which were given by film directors (L. Kulieshov, S. Eizenshtein, V. Pudovkyn, M. Romm, A. Tarkovskyi). and literary critics (V. Shklovskyi, Yu. Tynianov, Yu. Lotman). Accompanying them should be considered cinematic means “plan”, “angle” and “rhythm”, the consideration of which includes the analysis of their manifestations in works of fiction. The functionality of the installation is easier and more accurate to explore the data of the history of its formation and attitude to it. Therefore, this exploration is devoted to the initial stages of development of cinema, in particular its specific means, as well as the achievements of the classics of cinema.
Efficient Hardware-Aware Neural Architecture Search for Image Super-Resolution on Mobile Devices

With the ubiquitous use of mobile devices in our daily life, how to design a lightweight network for high-performance image super-resolution (SR) has become increasingly important. However, it is difficult and laborious to manually design and deploy different SR models on different mobile devices, while the existing network architecture search (NAS) techniques are expensive and unfriendly to find the desired SR networks for various hardware platforms. To mitigate these issues, we propose an efficient hardware-aware neural architecture search (EHANAS) method for SR on mobile devices. First, EHANAS supports searching in a large network architecture space, including the macro topology (e.g., number of blocks) and microstructure (e.g., kernel type, channel dimension, and activation type) of the network. By introducing a spatial and channel masking strategy and a re-parameterization technique, we are able to finish the whole searching procedure using one single GPU card within one day. Second, the hardware latency is taken as a direct constraint on the searching process, enabling hardware-adaptive optimization of the searched SR model. Experiments on two typical mobile devices demonstrate the effectiveness of the proposed EHANAS method, where the searched SR models obtain better performance than previously manually designed and automatically searched models. The source codes of EHANAS can be found at https://github.com/xindongzhang/EHANAS .
Motivation as a necessary condition for learning in the age of digitalization

The article discusses the features of the digitalization of the educational process, which includes the use of interactive teaching methods, and the transition to individualization, which contributes to the effectiveness of this process. The authors analyze the features of motivated learning and its dependence on modern digital technologies, highlight the characteristic features and types of motivation in the context of digital education, and show patterns of changes in motivation during the development of modern technologies. The article gives a characteristic of integrative, instrumental, as well as global, situational and problematic motivation, defines the basis of external and internal motivation. Particular attention is paid to the development of students' motivation in distance learning of foreign languages, including translation disciplines. The article defines the features of a motivated student, presents the factors for the formation of external motivation, and also highlights ways to maintain the attractiveness of educational work. Results showed that the introduction of digital technologies makes it possible to increase the interest and motivation of students in learning and contributes to the expansion of communication opportunities. At the same time, motivation is an important component of training specialists in any field.
Bayesian semiparametric joint modeling of a count outcome and inconveniently timed longitudinal predictors

The Women's Health Initiative (WHI) Life and Longevity After Cancer (LILAC) study is an excellent resource for studying the quality of life following breast cancer treatment. At study entry, women were asked about new symptoms that appeared following their initial cancer treatment. In this article, we were interested in using regression modeling to estimate associations of clinical and lifestyle factors at cancer diagnosis (independent variables) with the number of new symptoms (dependent variable). Although clinical and lifestyle data were collected longitudinally, few measurements were obtained at diagnosis or at a consistent timepoint prior to diagnosis, which complicates the analysis. Furthermore, parametric count models, such as the Poisson and negative binomial, do not fit the symptom data well. Thus, motivated by the issues encountered in LILAC, we propose two Bayesian joint models for longitudinal data and a count outcome. Our two models differ according to the assumption on the outcome distribution: one uses a negative binomial (NB) distribution and the other a nonparametric rounded mixture of Gaussians (RMG). The mean of each count distribution is dependent on imputed values of continuous, binary, and ordinal variables at a time point of interest (e.g. diagnosis). To facilitate imputation, longitudinal variables are modeled jointly using a linear mixed model for a latent underlying normal random variable, and a Dirichlet process prior is assigned to the random subject-specific effects to relax distribution assumptions. In simulation studies, the RMG joint model exhibited superior power and predictive accuracy over the NB model when the data were not NB. The RMG joint model also outperformed an RMG model containing predictors imputed using the last value carried forward, which generated estimates that were biased toward the null. We used our models to examine the relationship between sleep health at diagnosis and the number of new symptoms following breast cancer treatment in LILAC.
Machine learning methods for results merging in patent retrieval

Purpose In federated search, a query is sent simultaneously to multiple resources and each one of them returns a list of results. These lists are merged into a single list using the results merging process. In this work, the authors apply machine learning methods for results merging in federated patent search. Even though several methods for results merging have been developed, none of them were tested on patent data nor considered several machine learning models. Thus, the authors experiment with state-of-the-art methods using patent data and they propose two new methods for results merging that use machine learning models. Design/methodology/approach The methods are based on a centralized index containing samples of documents from all the remote resources, and they implement machine learning models to estimate comparable scores for the documents retrieved by different resources. The authors examine the new methods in cooperative and uncooperative settings where document scores from the remote search engines are available and not, respectively. In uncooperative environments, they propose two methods for assigning document scores. Findings The effectiveness of the new results merging methods was measured against state-of-the-art models and found to be superior to them in many cases with significant improvements. The random forest model achieves the best results in comparison to all other models and presents new insights for the results merging problem. Originality/value In this article the authors prove that machine learning models can substitute other standard methods and models that used for results merging for many years. Our methods outperformed state-of-the-art estimation methods for results merging, and they proved that they are more effective for federated patent search.
From Within to Between: Knowledge Distillation for Cross Modality Retrieval

We propose a novel loss function for training text-to-video and video-to-text retrieval networks based on knowledge distillation. This loss function addresses an important drawback of the max-margin loss function often used in existing cross-modality retrieval methods, in which a fixed margin is used in training to separate matching video-and-caption pairs from non-matching pairs, treating all non-matching pairs the same and failing to account for the different degrees of non-matching. We address this drawback by introducing a novel loss for the non-matching pairs; this loss leverages the knowledge within one domain to train a better network for matching between two domains. This proposed loss does not require extra annotation. It is complementary to the existing max-margin loss, and it can be integrated into the training pipeline of any cross-modality retrieval method. Experimental results on four cross-modal retrieval datasets namely MSRVTT, ActivityNet, DiDeMo, and MSVD show the effectiveness of the proposed method. Code is available at: https://github.com/tqvinhcs/CrossKD .
Implications of false alarms in dynamic games on cyber-security

Cyber-threats including computer viruses, data breaches, and denial-of-service attacks have become increasingly prevalent and need to be handled effectively. Imperfect prevention of cyber-attacks inevitably results in false alarms from the defense system. We consider a dynamic game in which a defender operating an intrusion detection system is penalized for false alarms. By characterizing the equilibrium of the game, we search for the implications of false alarms under various circumstances. Specifically, we consider the cyber-insurance market, operational risk, and government intervention. Our model shows that both demand and supply of cyber-insurance can be low, which explains the underdevelopment of the cyber-insurance market. The analysis of an extended model with operational risk shows that reducing the operational risk decreases the cyber-risks but increases the false alarm rate. Regarding government intervention, we suggest a simple policy that achieves a socially optimal outcome.
Local officials' political capital and poverty alleviation

Abstract This study examined the effect of local officials' political capital on poverty alleviation by using a manually compiled database of county party secretaries (CPSs) in impoverished counties in China. The empirical results reveal that CPSs with political capital positively affect poverty alleviation. Mechanism tests indicated that CPSs with political capital contribute to greater agricultural and financial development, attract more enterprise investment, and increase fiscal expenditure. The promotional effect of local officials' political capital on poverty alleviation was influenced by CPSs' characteristics (e.g., tenure, education level, and age) and was less pronounced during central government inspection and when the provincial government was less concerned about local officials' poverty alleviation. Our results suggest that local officials' political capital can alleviate poverty in emerging economies.
In search of synchresis: an examination of compositional approaches in new audiovisual works for solo electric guitar and visual projections

This paper examines the interaction of sound and vision in audiovisual works for solo electric guitar, providing an overview of existing compositions before exploring in depth the approaches utilised in two new works commissioned by the author. Multimedia works which combine sound and vision represent a minority of the contemporary western art music repertoire, and are rarer still in works involving the electric guitar. The use of visuals in the small literature of audiovisual works for solo electric guitar varies widely, ranging from pre-recorded videos which simply play back over the course of a performance with no temporal alignment between sound and vision, to complex lighting mechanisms which respond to the music in real-time with clear parameter mapping. The bulk of the repertoire, however, demonstrates limited gestural synchronisation between the two media, with composers creating audiovisual cohesion through extra-temporal means. The works commissioned by the author – Svart-Hvít Ský á Himni by Icelandic composer Gulli Björnsson, and especially Akrasia by Australian composer Victor Arul – run counter to this trend, situating them as outliers which push the boundaries of parametric mapping in the repertoire for solo electric guitar and visuals.