Reviewerly/exHarmony huggingface dataset:
Deep neural network analysis models for complex random telegraph signals

Time-fluctuating signals are ubiquitous and diverse in many physical, chemical, and biological systems, among which random telegraph signals (RTSs) refer to a series of instantaneous switching events between two discrete levels from single-particle movements. A reliable RTS analysis is a crucial prerequisite to identify underlying mechanisms related to device performance and sensitivity. When numerous levels are involved, complex patterns of multilevel RTSs occur and make their quantitative analysis exponentially difficult, hereby systematic approaches are often elusive. In this work, we present a three-step analysis protocol via progressive knowledge-transfer, where the outputs of the early step are passed onto a subsequent step. Especially, to quantify complex RTSs, we resort to three deep neural network architectures whose trained models can process raw temporal data directly. We furthermore demonstrate the model accuracy extensively with a large dataset of different RTS types in terms of additional background noise types and amplitude size. Our protocol offers structured schemes to extract the parameter values of complex RTSs as imperative information with which researchers can draw meaningful and relevant interpretations and inferences of given devices and systems.
The use of weather nowcasting convolutional neural network extrapolators in cardiac <scp>PET</scp> imaging

Algorithms to predict short-term changes in local weather modalities have been used in meteorology for many years. These algorithms predict the temporospatial change in the movement of weather patterns such as cloud cover or precipitation. This paper extends convolutional neural network models for weather prediction/nowcasting to predict evolution in the extrapolation of sequentially acquired count data seen with cardiac positron tomography (PET) data to expected value temporally rather than spatially.Six different algorithms used for nowcasting were modified and applied to confirm the approach. These algorithms were trained on an image data set of both simulated ellipsoids and simulated cardiac PET data. The peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) were calculated for each of these trained models. They were compared to the BM3D denoising algorithm as a baseline comparison to a standard method of image denoising.Most of the implemented algorithms showed a significant improvement in both PSNR and SSIM when compared with the baseline standard, especially when the algorithms were implemented in combination. The best results were obtained with a combination of the ConvLSTM and TrajGRU algorithms with a PSNR improvement over the standard of 5 and more than double the SSIM metric.This approach of using serially acquired count data to extrapolate a future expected representation through convolutional neural networks has been shown to produce accurate representations of the expected value when compared with a baseline analytic methodology. This paper confirms that algorithms such as these can be used to substantially improve image estimation and shows significant improvement over a baseline standard.
Human behavior analysis on political retweets using machine learning algorithms

The exponential rise in the use of social media has resulted in a massive increase in the volume of unstructured text created. This content is presented through messages, conversations, postings, and blogs. Microblogging has become a popular way for people to share what they are thinking. Many people express their thoughts on various issues relating to their hobbies. As a result, microblogging websites have become a valuable resource for opinion mining and sentiment research. Twitter is a well-known microblogging network, with over 500 million new tweets posted daily. The goal of this study was to mine tweets for political sentiments. The extraction of tweets relating to India's well-known political leaders of different states & parties in India and applying the polarity detection analysis of human behavior on the retweeted messages As a result, the sentiment classification algorithm is designed to determine whether tweets are more likely to predict the popularity of certain politicians among the general public. The subjectivity and polarity present in the tweets of political leaders are compared. The engagements of these leaders are then taken into account to determine their popularity. All these comparisons are then portrayed using data visualizations.
Reinforcement learning with smart contracts on blockchains

In recent years Machine Learning and Blockchain technologies have been at the spearhead of innovation, both in the research and application fields. Machine Learning is predominantly used to enable data knowledge extraction while Blockchain excels in providing a 'public ledger' upon which data are securely, consistently and irreversibly recorded. Machine Learning may use data stored on Blockchains and pursue to exploit distributed computing resources. On the other hand, Blockchain may exploit Machine Learning and capitalize user data and establish marketplaces for Machine Learning models. In this work we propose a combination of Machine Learning and in particular Reinforcement Learning (RL) and Imitation Learning (IL) with Blockchain. RL allows a software agent to interact with its environment and learn – via 'trial and error' techniques – based exclusively on its own activity, experiences and observations. The software agent will learn via an interactions' reward/ penalize set of measures, immediately received from its own environment. Designing an interactions' reward/penalize mechanism is challenging as designers need to draw focused techniques securing that agents' immediate environment will consistently recognize and reward desirable agent behaviour and that the rewarding mechanism cannot be tapped, corrupted or circumvented. In this work, we have approached this via a coordinated collaboration of RL and IL. A Trainer Agent takes on the task of training Trainee agents using RL/IL via recording its own environmental behaviour in demonstration files. In this respect trainees may imitate trainers' good practices and get effectively trained. This work proposes the concept of an expert trainer software agent (the Trainer Agent) who records its own behaviour in demonstration files and distributes these files via Blockchain to other (receiving) software agents (Trainee agents). Trainees' training is applied using RL techniques (i.e. reward/ penalize) in conjunction with IL (based on demo files). Demo files are 'stored' on smart contract Blockchains, who in the end get to reward Trainer Agents; pro-rated according to the level with which the Trainer has assisted to the improvement of the Trainee agent models. The invariant Blockchain structure with its unmodifiable smart contracts' nature secure the demo files and nurture credible all interactions among stakeholders involved. The developed application (dApp) fully automates the workflow of trading demonstration files and of training the Trainee agents.
VoicePM: A Robust Privacy Measurement on Voice Anonymity

Voice-based human-computer interaction has become pervasive in laptops, smartphones, home voice assistants, and Internet of Thing (IoT) devices. However, voice interaction comes with security and privacy risks. Numerous privacy-preserving measures have been proposed for hiding the speaker's identity while maintaining speech intelligibility. However, existing works do not consider the overall tradeoff between speech utility, speaker verification, and inference of voice attributes, including emotional state, age, accent, and gender. In this study, we first develop a tradeoff metric to capture voice biometrics as well as different voice attributes. We then propose VoicePM, a robust Voice Privacy Measurement framework, to study the feasibility of applying different state-of-the-art voice anonymization solutions to achieve the optimum tradeoff between privacy and utility. We conduct extensive experiments using anonymization approaches covering signal processing, voice synthesis, voice conversion, and adversarial techniques on three speech datasets that include both English and Chinese speakers to showcase the effectiveness and feasibility of VoicePM.
Simulation Research on the Application of Visual Image Capture Technology in Sports Injury Rehabilitation

In order to improve the effect of sports strategy formulation, this paper combines artificial intelligence assisted agile Internet of Things to conduct research on sports simulation scheme, and builds an intelligent sports simulation system.The sports simulation model designed in this paper can change the mechanism parameters according to the actual situation of the moving object, establish the fixed coordinate system and local coordinate system of the parallel platform model, and establish the mathematical model of the parallel platform.Moreover, according to the principle of vector closure, the inverse kinematics position solution is analyzed, and the inverse kinematic velocity solution is derived by using the equation of the inverse position solution.The experimental research results show that the sports simulation system based on artificial intelligence-assisted agile Internet of Things proposed in this paper has a good performance in sports simulation.
PHENOMENON OF PROGNOSTIC COMPETENCE OF FUTURE PRIMARY SCHOOL TEACHERS IN SCIENTIFIC LITERATURE

Prognostic competence of future primary school teachers is an important component of their professional competence. It includes the teacher's ability to predict possible learning outcomes of students and adjust their work in time. Prognostic competence of a teacher of primary grades of a general secondary education institution is considered as the ability to predict and plan educational process, safe educational environment, as well as the results of children's learning, based on a set of prognostic knowledge, skills and abilities. The prognostic knowledge of future teachers is characterized by their understanding of the specifics of forecasting, the content of forecasting activity, mastering the theoretical foundations of forecasting, determining the stages and actions during forecasting, understanding and concentrating on the correctness of selecting the final and expected result, understanding actions during forecasting, a clear vision of actions and events, etc. We consider the prognostic skills of the future teacher of junior grades as a system of predicting the future result, making corrections and changes on the way to its achievement, the ability to see future circumstances, analyze the received data and information in order to obtain the expected result. The prognostic activity of a future primary school teacher, which is systematically carried out according to a clear algorithm, contributes to the gradual formation of automated prognostic skills. The algorithm for the formation of prognostic competence should include six stages, each of which qualitatively affects the development of the components of the competence of future primary school teachers, such as: (1) familiarization with the theoretical foundations of forecasting, (2) mastering practical methods of forecasting, (3) using forecasting in the educational process, (4) evaluation and analysis of forecasting effectiveness, (5) practical implementation of forecasts in professional activities, (6) constant improvement of the level of competence in the field of forecasting. The process of formation of prognostic competence of future primary school teachers is influenced by various factors: their knowledge of psychology, pedagogy, teaching methods, practical experience of working with elementary school students, as well as the internal motives of the individual, the desire for self-realization, and self-motivation.
A novel image recommendation model based on user preferences and social relationships

Recent work on personalized recommendation advocates that exploring user visual preferences from their interactions with images has become a future endeavor for image recommendation. However, there are inevitable discrepancies between the images favored and the visual preference representations reflected by users, therefore we strive to reveal user visual preferences from their behaviors and social relationships. In this work, we propose a collaborative graph model of user preferences for image recommendation, which establishes a higher-order collaborative relationship between user visual preferences and image semantic information. In user visual preferences, the underlying visual preferences hidden in social relationship is explored by combining the idea of contrast learning. In the higher-order collaboration module, a recursive interest propagation strategy is used to aggregate user preferences and image visual information. The experimental results clearly show that the model has an improvement of about 8.82% and 7.25% on [email protected] and [email protected] compared to the latest methods.
Focalizing regions of biomarker relevance facilitates biomarker prediction on histopathological images

Image-based AI has thrived as a potentially revolutionary tool for predicting molecular biomarker statuses, which aids in categorizing patients for appropriate medical treatments. However, many methods using hematoxylin and eosin-stained (H&E) whole-slide images (WSIs) have been found to be inefficient because of the presence of numerous uninformative or irrelevant image patches. In this study, we introduced the region of biomarker relevance (ROB) concept to identify the morphological areas most closely associated with biomarkers for accurate status prediction. We actualized this concept within a framework called saliency ROB search (SRS) to enable efficient and effective predictions. By evaluating various lung adenocarcinoma (LUAD) biomarkers, we showcased the superior performance of SRS compared to current state-of-the-art AI approaches. These findings suggest that AI tools, built on the ROB concept, can achieve enhanced molecular biomarker prediction accuracy from pathological images.
Photonic Neural Networks with Kramers–Kronig Activation

Photonic neural networks (PNNs) are promising to replace conventional deep learning hardware due to their potentially higher energy efficiency and computational speed. Other than the fast progress on optical linear transformer, the nonlinear activators are much less mature. Usually, the optical activators employ the nonlinear mapping from input amplitude to output amplitude, which is limited by high threshold and loss, or rely on additional bias voltage and heterogeneous integration of external circuits. Herein, the activation induced by the Kramers–Kronig relationship is proposed, i.e., the connection between amplitude and phase of the light field, but not purely the light amplitude itself, namely, Kramers–Kronig activation (KKA). PNN with KKA exhibits learning capability apparently better than the activation‐free linear network and comparable to the network with popular activation functions like ReLU, Softplus, and so on. Moreover, PNN with KKA is highly programmable and cascadable supporting ultra‐deep networks. The essence of KKA is attributed to the learning of nonlinear features in relatively low‐dimensional R N × N space by linear features in high‐dimensional C N × N space. Considering besides the amplitude–phase coupling, several other parameters like wavelength, frequency, polarization, etc., can be also mutually linked. This approach may expand to new avenues for optical activations.
IS ARTIFICIAL INTELLIGENCE A THREAT TO THE QUALITY OF HIGHER EDUCATIONAL SERVICES?

The article is devoted to a very important modern problem – the growth of the quality of the higher educational services. The article highlights the situation in the market of the higher educational services with the participation of three players: the consumer of the educational services (students), the provider of the educational services (university teachers) and Artificial Intelligence. The definition of "Artificial Intelligence" is given in the article. The aim of the article is to find the answer to the research question: is Artificial Intelligence a threat to the quality of the higher educational services? The author used reliable and economically sound research methods. These were methods such as research of scientific sources; pedagogical observation and description of examples; modelling and forecasting, analysis and synthesis; designing a questionnaire for interviewing students. The author also used the capabilities of Artificial Intelligence in the process of designing the questionnaire to study students' opinions on the use of Artificial Intelligence for learning. A survey of students with verification of statistical hypotheses will give a practical answer to the research question. The author analyzed the scientific views of scientists on the use of Artificial Intelligence in education and, in particular, in higher education. Three examples of the use of Artificial Intelligence in the higher education services were analyzed for threats to the quality of the higher educational services. Four hypothetical variants of the interaction of three players in the market of higher educational services were analyzed: the consumer of educational services (students), the provider of educational services (university teachers) and Artificial Intelligence. This was the theoretical answer to the research question: in two variants out of four (50%), there is a threat to the quality of the higher educational services.
A statistical approach for assessing cyber risk via ordered response models

Abstract Proper evaluation of the risk associated to a cyber attack is a crucial aspect for many companies. There is an increasing need to plan for and implement effective ways to address cyber security, data security, and privacy protection. Estimating the risk of a successful cyber attack is an important issue, since this type of threat is proliferating and thus poses increasing danger to companies and the customers who use their services. While quantitative loss data are rarely available, it is possible to obtain a qualitative evaluation on an ordinal scale of severity of cyber attacks from experts of the sector. Hence, it is natural to apply order response models for the analysis of cyber risk. In particular, we rely on cumulative link models. We explain the experts' assessment of the severity of a cyber attack as a function of a set of explanatory variables describing the characteristics of the attack under consideration. A measure of diffusion of the effects of the attacks obtained via the use of a network structure is also incorporated into the set of explanatory variables of the model. Along with the description of the methodology, we present a detailed analysis of a real data set that includes information on serious cyber attacks occurred worldwide in the period 2017–2018.
Virtual Machine Replica Placement Using a Multiobjective Genetic Algorithm

Virtual machine (VM) replication is a critical task in any cloud computing platform to ensure the availability of the cloud service for the end user. In this task, one primary VM resides on a physical machine (PM) and one or more replicas reside on separate PMs. In cloud computing, VM placement (VMP) is a well-studied problem in terms of different goals, such as power consumption reduction. The VMP problem can be solved by using heuristics, namely, first-fit and meta-heuristics such as the genetic algorithm. Despite extensive research into the VMP problem, there are few works that consider VM replication when choosing a VMP. In this context, we proposed studying the problem of optimal VMP considering VM replication requirements. The proposed work frames the problem at hand as a multiobjective problem and adapts a nondominated sorting genetic algorithm (NSGA-III) to address the problem. VM replicas’ placement should consider several dimensions such as the geographical distance between the PM hosting the primary VM and the other PMs hosting the replicas. In addition, to this end, the proposed model aims to minimize (1) power consumption, (2) performance degradation, and (3) the distance between the PMs hosting the primary VM and its replica(s). The proposed method is thoroughly tested on a variety of computing environments with various heterogeneous VMs and PMs, including compute-intensive and memory-intensive environments. The obtained results illustrate the performance disparity between the adapted NSGA-III and MOEA/D methods and other methods of comparison, including heuristic and meta-heuristic approaches, with NSGA-III outperforming other comparison methods. For instance, in memory-intensive and in heterogeneous environments, the NSGA-III method’s performance was superior to the first-fit, next-fit, best-fit, PSO, and MOEA/D methods by 58%, 62%, 64%, 55%, and 31%, respectively.
Bootstrap-LOCI data mining methodology for anomaly detection in buildings energy efficiency

An automated methodology is proposed to identify anomalies in buildings' HVAC systems, through Local Correlation Integral (LOCI) algorithm, improved by Bootstrap to obtain a rule from its score distribution. It has been performed to solve the case study of anomaly detection for HVAC facilities maintenance in a clothing store in Panama. It is defined by a dataset composed of 24 daily readings recorded during 434 days. In each reading, 15 critical quality variables for thermal comfort and energy efficiency were monitored. For algorithm training, anomalous events recorded by HVAC system operators are considered. In this stage, the LOCI parameters that best fit the data are estimated, to obtain a score for each of the observations and then study their distribution by applying Bootstrap techniques to improve the classification performance. For the algorithm performance evaluation, cross-validation is used and from these results, it is compared with two benchmark supervised classification methods such as Logistic Regression and Support Vector Machines with polynomial kernel. The LOCI method has been improved by the application of bootstrap resampling, providing estimates of the LOCI score distribution and a critical value from which we define an anomalous observation. It provides the best balance between real anomaly detection and prevention of false alarm identification than current proposals for LOCI.