Reviewerly/exHarmony huggingface dataset:
Comparison and Analysis of Solar Irradiance Forecasting Techniques

The paper shows the study and analysis of two techniques for forecasting solar irradiance i.e., ARIMA (Autoregressive Integrated Moving Average) technique and FL (Fuzzy Logic) technique. ARIMA is a time-series-based statistical method while Fuzzy Logic is regarded as an intelligent method. The required solar irradiance dataset for forecasting techniques has been taken from NREL (National Renewable Energy Laboratory). The outcomes of these forecasting techniques have been evaluated in terms of error i.e., MAE and RMSE. Comparing both the techniques in terms of these errors reveals that the Fuzzy Logic time series-based forecasting method is better than the ARIMA forecasting technique.
Design of Multimedia English Online Teaching Platform under Wireless Network Communication Technology

The traditional online teaching platform has poor compatibility due to the high data delay in practical applications. Therefore, a Multimedia English online teaching platform under wireless network communication technology is designed. Aiming at the actual functional requirements of online teaching, the overall architecture of the Multimedia English online teaching platform is designed. In this architecture, the hardware of the data collector, memory, and main controller is deployed to build the platform. Based on the wireless network communication technology, the software modules are divided into two parts. It is made of a number of functional modules. The design of the platform functional modules is mainly based on the main functions of the administrator submodule, the teacher submodule, and the student submodule. At the same time, the weight of the indicators is determined by combining the analytic hierarchy process and the evaluation domain for the quality evaluation of Multimedia English online teaching is established. The level model completes the platform design. The experimental results show that the test results of the designed platform meet the expected goals and can effectively improve the quality and efficiency of Multimedia English online teaching. The teaching quality is always higher than 95%, and the average teaching efficiency is 96.27%.
Pedagogical assessment of augmentation

The education challenges in the information technology progress are considered. In particular, the problem of artificial intelligence is equal to humans and thus poses a challenge to the teaching system. The augmentation concept as the human and machine’s interaction that enhances the capabilities of each party is analyzed. We assessed these opportunities as the ability to create more value and get more personal benefit on the economic plane. But a worthy place for a human - to be above technology. Purpose of the study is to provide a pedagogical assessment of augmentation. The analysis of futurological research on human-machine synergy gave the opportunity to determine the social consequences and educational prospects of such development. It is questioned the prospect of singularity, the point at which artificial intelligence will match or exceed human intelligence. We will become dependent on «the society of the program», but it will not depend on us. It causes the spontaneous progress of techno-science. On the study basis (questionnaire), there were identified the predominant models of pedagogical interaction in traditional and online learning; described changes in teachers’ behavior under the influence of computer technology. We emphasized the threats of computer-mediated learning: «cool» [cold] environment according to Jean Baudrillard, «robot» as a pedagogical communication model, hyper-formalization of the educational process. But the concept of increase can be useful in the short term. The results of the study confirm the conclusions about the need to humanize education, balance technology and live communication. This is possible through the appeal to live communication as a «hot» system, inclusion of the classical literature, folklore in the digital learning tools narratives, the soft use of design thinking and principles of game design in teaching, leaving free space for human creativity, choice, decisions that contradict the logic of machines
FROST: Fallback Voice Apps Recommendation for Unhandled Voice Commands in Intelligent Personal Assistants

Intelligent personal assistants (IPAs) such as Amazon Alexa, Google Assistant and Apple Siri extend their built-in capabilities by supporting voice apps developed by third-party developers. Sometimes the smart assistant is not able to successfully respond to user voice commands (aka utterances). There are many reasons including automatic speech recognition (ASR) error, natural language understanding (NLU) error, routing utterances to an irrelevant voice app, or simply that the user is asking for a capability that is not supported yet. The failure to handle a voice command leads to customer frustration. In this article, we introduce a fallback skill recommendation system (FROST) to suggest a voice app to a customer for an unhandled voice command. There are several practical issues when developing a skill recommender system for IPAs, i.e., partial observation, hard and noisy utterances. To solve the partial observation problem, we propose collaborative data relabeling (CDR) method. To mitigate hard and noisy utterance issues, we propose a rephrase-based relabeling technique. We evaluate the proposed system in both offline and online settings. The offline evaluation results show that the FROST system outperforms the baseline rule-based system. The online A/B testing results show a significant gain of customer experience metrics.
Completeness and Diversity in Depth-First Proof-Number Search with Applications to Retrosynthesis

We revisit Depth-First Proof-Number Search (DFPN), a well-known algorithm for solving two-player games. First, we consider the completeness property of the algorithm and its variants, i.e., whether they always find a winning strategy when there exists one. While it is known that the standard version is not complete, we show that the combination with the simple Threshold Controlling Algorithm is complete, solving an open problem from the area. Second, we modify DFPN to compute a diverse set of solutions rather than just a single one. Finally, we apply this new variant in Chemistry to the synthesis planning of new target molecules (Retrosynthesis). In this domain a diverse set of many solutions is desirable. We apply additional modifications from the literature to the algorithm and show that it outperforms Monte-Carlo Tree-Search, another well-known algorithm for the same problem, according to a natural diversity measure.
An Efficient Convolutional Neural Network to Detect and Count Blood Cells

Blood cell analysis is an important part of the health and immunity assessment. There are three major components of the blood: red blood cells, white blood cells, and platelets. The count and density of these blood cells are used to find multiple disorders like blood infections (anemia, leukemia, among others). Traditional methods are time-consuming, and the test cost is high. Thus, it arises the need for automated methods that can detect different kinds of blood cells and count the number of cells. A convolutional neural network-based framework is proposed for detecting and counting the cells. The neural network is trained for the multiple iterations, and a model having lower validation loss is saved. The experiments are done to analyze the performance of the detection system and results with high accuracy in the counting of the cells. The mean average precision is achieved when compared to ground truth provided to respective labels. The value of the average precision is found to be ranging from 70% to 99.1%, with a mean average precision value of 85.35%. The proposed framework had much less time complexity: it took only 0.111 seconds to process an image frame with dimensions of 640×480 pixels. The system can also be implemented in low-cost, single-board computers for rapid prototyping. The efficiency of the proposed framework to identify and count different blood cells can be utilized to assist medical professionals in finding disorders and making decisions based on the obtained report.
Toxicity at Work Place: Coping Strategies to Reduce Toxins at Work Place

Employee or leader negativity affects the performance of the workforce, thus leading to loss and poor growth of the organization. The purpose of this research is to understand toxins at work place and their impact on performance of employees. This research reviews the literature associated with factors leading to toxicity at work places. The study also provides an overview of the issues faced by the organisations due to toxic behaviour of the employees especially working in IT firms. Data was collected from IT employees and was analysed by using Structural Equation Modelling(SEM). There results indicated that these toxins had negative relationship with employee engagement. Toxins present in organisations lead to a feeling of dissatisfaction, and unwillingness to take initiative. Dynamic changes in the IT sector with the behavioural issues were not addressed adequately in the reported literature. With newer generations, Gen Z and centennials, taking up key positions in the organizations it was imperative to conduct such study. Keywords: Employee toxicity, employee engagement, IT employees
Using resource allocation for seamless service provisioning in cloud computing

A Resources allocation is one of the most important things in cloud computing because the environment in which work depends is heterogeneous environment and there are many who request the same service at the same time. Two criteria are used to be considered. First, the time for completing all the required tasks on the available resources and secondly the energy consumed and is extracted from the number of processes that used to complete those tasks. While uploading an application to the Cloud, load balancer allocates the appealed instances for physical computers to balance the computational load of several applications through physical computers. this paper presents some resource allocation problems and issues that can solve with help of load balancing techniques and algorithms. In addition, the performance of the algorithm was measured on seven mathematical functions, and the best results were for the algorithm pyogenes.
Discovering regression-detection bi-knowledge transfer for unsupervised cross-domain crowd counting

Despite impressive progress in crowd counting over the last years, it is still an open challenge to reliably count crowds across visual domains. This paper addresses this setting, presenting an unsupervised cross-domain crowd counting framework able to perform unsupervised adaptation across domains with available unlabeled target data. We achieve this by learning to discover bi-knowledge transfer between regression- and detection-based models from a labeled source domain. The dual source knowledge of the two models is heterogeneous and complementary as they capture different modalities of crowd distribution. Specifically, we start by formulating the mutual transformations between the outputs of regression- and detection-based models as two scene-agnostic transformers which enable knowledge transfer between the two models. Given the regression- and detection-based models and their mutual transformers learnt on the source, we then introduce a self-supervised co-training scheme to encourage the knowledge transfer between the two models on the target. We further enhance the model adaptation with our modified mixup augmentation strategy. A thorough benchmark analysis against the most recent cross-domain crowd counting methods and detailed ablation studies show the advantage of our method.
A Robust Method to Protect Text Classification Models against Adversarial Attacks

Text classification is one of the main tasks in natural language processing. Recently, adversarial attacks have shown a substantial negative impact on neural network-based text classification models. There are few defenses to strengthen model predictions against adversarial attacks; popular among them are adversarial training and spelling correction. While adversarial training adds different synonyms to the training data, spelling correction methods defend against character variations at the word level. The diversity and sparseness of adversarial perturbations of different attack methods challenge these approaches. This paper proposes an approach to correct adversarial samples for text classification tasks. Our proposed approach combines grammar correction and spelling correction methods. In this, we use Gramformer for grammar correction and Textblob for spelling correction. These approaches are generic and can be applied to any text classification model without any retraining. We evaluated our approach with two state-of-the-art attacks, DeepWordBug and TextBugger, on three open-source datasets IMDB, CoLA, and AGNews. The experimental results show that our approach can effectively counter adversarial attacks on text classification models while maintaining classification performance on original clean data.
Sonification of Computer Processes: The Cases of Computer Shutdown and Idle Mode

Software is intangible, invisible, and at the same time pervasive in everyday devices, activities, and services accompanying our life. Therefore, citizens hardly realize its complexity, power, and impact in many aspects of their daily life. In this study, we report on one experiment that aims at letting citizens make sense of software presence and activity in their everyday lives, through sound: the invisible complexity of the processes involved in the shutdown of a personal computer. We used sonification to map information embedded in software events into the sound domain. The software events involved in a shutdown have names related to the physical world and its actions: write events (information is saved into digital memories), kill events (running processes are terminated), and exit events (running programs are exited). The research study presented in this article has a “double character.” It is an artistic realization that develops specific aesthetic choices, and it has also pedagogical purposes informing the causal listener about the complexity of software behavior. Two different sound design strategies have been applied: one strategy is influenced by the sonic characteristics of the Glitch music scene, which makes deliberate use of glitch-based sound materials, distortions, aliasing, quantization noise, and all the “failures” of digital technologies; and a second strategy based on the sound samples of a subcontrabass Paetzold recorder, an unusual and special acoustic instrument which unique sound has been investigated in the contemporary art music scene. Analysis of quantitative ratings and qualitative comments of 37 participants revealed that the sound design strategies succeeded in communicating the nature of the computer processes. Participants also showed in general an appreciation of the aesthetics of the peculiar sound models used in this study.
Captions: The Unsung Heroes of Data Communication

Data visualization has been recognized as an important tool in communicating data-related findings. Figures often contain a dense amount of information and readers rely on accompanying text to help guide them to the important takeaways. Captions provide the reader with the context needed to interpret visualizations and help tie together the written and visual narratives. Alt-text also plays a role in making the visualization accessible to a broader audience, translating visual cues for those with visual impairments. However, the art of writing captions and other accompanying text for visualizations has rarely been explicitly taught as a companion to the pedagogy of effective data visualization. We describe a variety of classroom activities and assignments that explicitly focus on what makes an effective caption, interpreted broadly, and that build awareness for a variety of audiences for data-related findings.
Development and Regulation of Connected Combined Products: Reflections From the Medtech &amp; Pharma Platform Association

PurposePatients taking a medicinal product in a homecare setting typically use a medical device to facilitate the injection process. Reductions in wireless connectivity costs, combined with the rapid adoption of smartphones with connectivity to cloud-based services, are enabling these drug delivery devices to now be connected to a digital ecosystem as connected combined products (CCPs). The purposes of this article are to identify the challenges in developing and releasing these products when they straddle different regulatory frameworks and standards and to highlight gaps in the European Union regulations.MethodsIndustry subject matter experts from pharmaceutical, medical device, and consultancy companies, who are members of the Medtech & Pharma Platform Association, formed 4 working groups to address current best practice for developing and releasing CCPs and the different relevant regulatory frameworks. The 4 areas studied were clinical and regulatory, usability and human factors engineering, development and life cycle management, and cybersecurity.FindingsDevelopment teams require new skills to create innovative products that have a good safety profile and are simple to use, such as design thinking to understand user needs and systems engineering to manage complexity and ensure interoperability. Risk management process should integrate cybersecurity, data privacy, and data integrity, whereas design control processes should enable asynchronous development cycles for hardware and software components. Regulatory frameworks exist for individual components within the CCP. However, for a complex product, regulatory guidance is needed when combining components with different risk and safety profiles and to ensure that the responsibilities and liabilities of companies contributing components are clear. The efficient management of software changes and product updates, as well as dealing with end-of-life hardware and backward compatibility to older software versions, needs agile approaches when it comes to regulatory updates.ImplicationsThe regulatory uncertainties and development processes outlined in this article need to be addressed. We call for joint discussions among the various stakeholders in the fields of medicinal products, medical devices, and in vitro diagnostics, as well as standalone software, data protection, and cybersecurity experts, together with regulators and lawmakers in the European Union to meet in focused discussion groups with the aim of devising pragmatic solutions and regulations for the benefit of the sector and hence the patients it serves.
SAD: Website Fingerprinting Defense Based on Adversarial Examples

Website fingerprinting (WF) attacks can infer website names from encrypted network traffic when the victim is browsing the website. Inherent defenses of anonymous communication systems such as The Onion Router(Tor) cannot compete with current WF attacks. The state-of-the-art attack based on deep learning can gain over 98% accuracy in Tor. Most of the defenses have excellent defensive capabilities, but it will bring a relatively high bandwidth overhead, which will seriously affect the user’s network experience. And some defense methods have less impact on the latest website fingerprinting attacks. Defense-based adversarial examples have excellent defense capabilities and low bandwidth overhead, but they need to get the complete website traffic to generate defense data, which is obviously impractical. In this article, based on adversarial examples, we propose segmented adversary defense (SAD) for deep learning-based WF attacks. In SAD, sequence data are divided into multiple segments to ensure that SAD is feasible in real scenarios. Then, the adversarial examples for each segment of data can be generated by SAD. Finally, dummy packets are inserted after each segment original data. We also found that setting different head rates, that is, end points for the segments, will get better results. Experimentally, our results show that SAD can effectively reduce the accuracy of WF attacks. The technique drops the accuracy of the state-of-the-art attack hardened from 96% to 3% while incurring only 40% bandwidth overhead. Compared with the existing proposed defense named Deep Fingerprinting Defender (DFD), the defense effect of SAD is better under the same bandwidth overhead.
LABOR MARKET REQUIREMENTS AND KEY TRENDS IN THE FORMATION OF “QUALITY” FUTURE SPECIALIST OF THE SECOND AND THIRD LEVELS OF PREPARATION FOR PREPARATION SPECIALTIES 011 “EDUCATIONAL, PEDAGOGICAL SCIENCES”

the article is dedicated to the topical problem of forming a “quality” future teacher-leader in the conditions of Master’s and postgraduate studies under the specialty 011 “Educational, pedagogical sciences”. The essence of the term “quality seeker for higher education” in the dimension of the theory and practice of vocational education is defined. Based on the defined labor market requirements to the skills of modern specialists in higher education and experimental data on the experience of undergraduate and graduate students training under the specialty 011 “Educational, pedagogical sciences” in NTU “KhPI”, key trends in future lecturers training are identified and substantiated. It is proposed to consider and implement in the university the prospect of increasing the level of professional competence of lecturers of non-pedagogical profiles.
Identification of Damage to Ancient Manuscripts at Bandar Cimanuk Indramayu Museum as an Effort to Formulate Manuscript Preservation Strategies

One of the relics that are considered as tangible results of the written evidence that still exists is the manuscript.The text contains a document of thoughts, feelings, and knowledge of a nation that produces the text itself.Therefore, the text is also a cultural product that can relatively provide information about the perspectives, attitudes, and behavior of a nation that lived in its day.Thus, Herman Soemantri (1979: 1) says that ancient manuscripts are a source of knowledge that can support our efforts in studying, knowing, understanding, and then presenting the development of the nation's culture.Most ancient manuscripts are kept in palaces, royal families, communities, libraries and museums.This study uses a qualitative descriptive methodology by describing various facts found in the field.The results of this study describe the various conditions of the ancient manuscripts in Bandar Cimanuk Museum.