Reviewerly/exHarmony huggingface dataset:
Assessing the COVID-19 Vaccination Process via Functional Data Analysis

This manuscript aims to assess the evolution of the COVID-19 vaccination process in some American and European countries via Functional Data Analysis (FDA). Specifically, Functional Principal Components Analysis and Functional Clustering were implemented in a data set consisting of four COVID-19-related variables such as total cases per million, total deaths per million, total tests per thousand, and people fully vaccinated per hundred to explain heterogeneity in the vaccination process. We found that FDA methods are suitable to describe our study problem as, for example, the first two functional principal component corresponding to each variable explains above 96% of the variance. FDA techniques allow us to conclude that vaccines avoid people’s deaths from COVID-19, but they do not stop the propagation of the virus.
Evaluation Method of Enterprise Economic Management Model Effectiveness Based on Deep Data Mining

Traditional methods have some problems in evaluating the effectiveness of enterprise economic management model, such as low evaluation accuracy and long time-consuming. An evaluation method of enterprise economic management model effectiveness based on deep data mining is proposed. According to the screening principle of the effectiveness evaluation indicators of enterprise economic management mode, screen the effectiveness evaluation indicators of enterprise economic management mode, refer to the characteristic attributes of candidate indicators, analyze the dimension of candidate indicators, quantify the evaluation indicators, and calculate the index weight by using deep data mining technology to realize the effectiveness evaluation of enterprise economic management mode. The example analysis results show that the evaluation method effectively improves the accuracy of the effectiveness evaluation of enterprise economic management model.
Efficient Distributed DNNs in the Mobile-Edge-Cloud Continuum

In the mobile-edge-cloud continuum, a plethora of heterogeneous data sources and computation-capable nodes are available. Such nodes can cooperate to perform a distributed learning task, aided by a learning controller (often located at the network edge). The controller is required to make decisions concerning (i) data selection, i.e., which data sources to use; (ii) model selection, i.e., which machine learning model to adopt, and (iii) matching between the layers of the model and the available physical nodes. All these decisions influence each other, to a significant extent and often in counter-intuitive ways. In this paper, we formulate a problem addressing all of the above aspects and present a solution concept called RightTrain, aiming at making the aforementioned decisions in a joint manner, minimizing energy consumption subject to learning quality and latency constraints. RightTrain leverages an expanded-graph representation of the system and a delay-aware Steiner tree to obtain a provably near-optimal solution while keeping the time complexity low. Specifically, it runs in polynomial time and its decisions exhibit a competitive ratio of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2(1+\epsilon)$ </tex-math></inline-formula> , outperforming state-of-the-art solutions by over 50%. Our approach is also validated through a real-world implementation.
Forensic Identification of Foreign-Language Speakers by the Method of Structural-Melodic Analysis of Phonograms

The crime rate observed in any country in the world necessitates the study of the parameters of speech signal in terms of foreign speech in order to carry out phonoscopic examinations in an ethnic language (for example, gypsy). This study is of pragmalinguistic nature and is not intended to be a typological study and comparison of the languages under consideration. The need for an expert who is not a native speaker of the language under study to determine the key prosodic characteristics of foreign language speech, taking into account the perceptual-auditory and acoustic types of analysis of phonograms in order to identify a foreign-language speaker, poses a number of qualitatively new tasks for computer linguistics. Prosody plays a special role in personality identification as a fundamental paraverbal means of spoken speech communication. This paper describes a technique for identifying foreign-language speakers in Tajik and Gypsy languages by an expert who is a native speaker of the Russian language, by studying the prosodic organization of an utterance using perceptual-auditory and acoustic types of analysis based on the identification module as part of a specialized sound editor SIS II (developed by LLC Speech Technology Center, St. Petersburg), a universal acoustic-linguistic platform for the study of voice and speech, designed to automate the process of speaker identification.
Modular Architecture for Classical Simulation of Quantum Circuits

Duke ARTIQ Extensions (DAX) provides a framework for writing modular control software for ion-trap quantum systems. DAX allows users to interface with the system at the gate level using an intermediate representation called DAX.program. However, DAX does not have the tools needed to simulate these programs to see quantum state evolutions, as DAX only allows for simulations at the device level. We propose a modular simulation framework, DAX.program-sim (DPS), that can simulate quantum programs at the level of quantum operations. This addition to DAX for classical simulation of quantum systems is vital for testing, benchmarking, and verification of quantum hardware. The DPS pipeline is designed to have input identical to the one that runs on the hardware. Our architecture allows flexible backends for simulation, enabling both gate-level simulations and pulse-level simulations with and without noise. We demonstrate this unified workflow, executing the same DAX.program file in simulation as well as on hardware. As a specific example, we run benchmarking circuits using simulators targeting ion trap quantum computers and compare them to hardware results of the same circuits.
Advanced Modeling Techniques for Mission Planning of Marine Multi-Vehicles systems: What’s Next?

This paper aims to explore a selection of algorithms useful for mission planning of systems of many Autonomous Underwater Vehicles (AUVs) in the underwater environment. Specific attention is put on vehicle routing and path planning problems. Two main classes of approaches are explored: centralized and decentralized control, with possible intermediate solutions. We highlight pros and cons of both approaches and we open new scenarios, where decentralized control will be increasingly adopted, benefiting from its properties of adaptability to changing and/or unknown environments.
Conformed Thoughts, Representational Systems, and Creative Procedures

In our daily lives, we exercise and practice extensively logical–symbolic reasoning, which we call “conformed thoughts”. These are self-controlled and deliberate and can be the result of concepts, models of knowledge of a culture or group. They are adaptable and can be changed by our habits, behaviors, beliefs, and actions. This, in turn, will cause changes in our way of thinking. Our main interest in this chapter is to present a representational model, promoted by information processing, that will be able to point out questions about computational algorithms—in particular, artificial intelligence—and present the contribution of art and creative procedures in this process.
Soft Contrastive Learning With Q-Irrelevance Abstraction for Reinforcement Learning

The difference between training and testing environments is a huge challenge to generalizing reinforcement learning (RL) algorithms. We propose a soft contrastive learning with a coarser approximate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -irrelevance abstraction for RL (SCQRL) to increase RL generalization. Specifically, we specify the coarser approximate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -irrelevance abstraction as the feature of the state with a theoretical analysis for better generalization ability. We construct a positive and negative sample selection mechanism based on the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> value for contrastive learning to achieve efficient representation learning. Considering the selection error of positive and negative samples, we design soft contrastive learning and combine it with RL in the form of an auxiliary task to propose SCQRL. The generalization experiments on several Procgen environments demonstrate that SCQRL outperforms the excellent generalized RL algorithm.
Deep Learning-Based Detection for Transmission Towers Using UAV Images

With two network frameworks, Inception V2 and ResNet50, as feature extraction networks, Faster region-based convolutional neural network (R-CNN) and single-shot multibox detector (SSD) models were used to detect transmission tower targets based on VOC format datasets of forest areas. The results showed that the mean average precision (mAP) of transmission tower detection achieved using the Faster R-CNN model reached 90% and 94% based on the Inception V2 framework and the ResNet50 framework, respectively, while the mAP of the SSD model reached 84% and 92% with the Inception V2 framework and the ResNet50 framework, respectively. The Faster R-CNN had a longer detection time than that of the SSD, but its detection accuracy was higher than that of the SSD. Compared with that of ResNet50, the detection accuracy of Inception V2 was lower, while the detection time was shorter. Deep learning technology can effectively and rapidly detect the positions of transmission tower targets.
Blockchain-Based Patient-to-Patient Health Data Sharing

The Covid-19 pandemic catalyzed many exciting forms of health data sharing. Aside from the institution-to-institution health data sharing among cooperating institutions for research and discovery of insights in healthcare, individual-to-many and individual-to-individual health data sharing also came to the fore. However, the security risks involved here are substantial since health data disclosures can lead to privacy and security breaches or complications. In this research, we present a scheme to enable individuals to share details of medical experiences with other individuals or interested groups. Our system provides the sharing entities with anonymity and thus, facilitates rapid dissemination of empirical insights during public health emergencies like Covid-19.
Benchmarking Data Augmentation Techniques for Tabular Data

Imbalanced learning and small-sized datasets are usual in machine learning problems, even with the increased data availability provided by recent developments. The performance of learning algorithms in the presence of unbalanced data and significant class distribution skews is known as the "imbalanced learning problem". The models' performance on such problems can drastically decrease for certain classes with an uneven distribution because the models do not learn the distributive features of the data and present accuracy too favorable for a specific set of classes of data. As an example, this can have negative consequences when talking about cancer detection since the model may poorly identify unhealthy patients. Hence, data augmentation techniques are usually conceived to evaluate how models would behave in non-data-scarce environments, generating synthetic data that mimics the characteristics of real data. By applying those techniques, the amount of available data can be increased, balancing the class distributions. However, there are no standardized data augmentation processes that can be applied to every domain of tabular data. Therefore, this study aims to identify which characteristics of a dataset provide a better performance when synthesizing samples by a data augmentation technique in a tabular data environment.
Inheriting and Fusing Beliefs of Logically Heterogeneous Objects

Inheritance has intensively been studied in both object-oriented programming (Oop) and knowledge representation and reasoning (KRR). On the other hand, the approaches to multiple inheritance and related method resolution, developed in both domains, remain separated. The primary goal of this paper is to demonstrate how these approaches may be integrated using inheritance expressions. In particular, we examine inheritance as a belief bases management machinery designed to operate in dynamically changing environments where objects are embedded and act. We focus on objects that are belief bases containers, potentially participating in complex distributed reasoning scenarios. We show that inheritance expressions, inspired both by Oop and KRR, provide a simple yet flexible and powerful means for expressing inheritance and related belief/knowledge fusion.
Development of Mobile Learning Applications with Augmented Reality to Build VHS Students' Critical Thinking

Cameras and photography aids are one of the learning materials in multimedia vocational schools which are still mostly taught by the lecture method and the learning media used are less interactive such as printed books, so the process of understanding the material becomes less in-depth. This learning requires objects in the learning process, but because of the high price of photographic equipment, it is difficult for students to learn it either at school or independently. This study develops a mobile learning media for android about cameras and photography aids based on augmented reality which provides camera object observation features and photography aids in 3D and augmented reality so that it can trigger students' critical thinking patterns better. Assessment with UEQ on this learning media get an average value of Excellent. After using this learning media, students were tested for critical thinking and got the results that 66.6% of the total respondents were classified as high-level critical thinking, 25% classified as moderate level critical thinking, and the remaining 8.33% classified as low-level critical thinking. At the time of developing this learning media, he received advice from material and media experts in the form of navigation improvements, material revisions, and user interface revisions. The future development of this application requires a database system that can make it easier for teachers to manage student data in the application.
Comparison Between SVM and DistilBERT for Multi-label Text Classification of Scientific Papers Aligned with Sustainable Development Goals

The scientific articles identification with the 17 sustainable development goals of the UN 2030 Agenda is a valuable task for research and educational institutions. Finding an efficient and practical multi-label classification model using machine or deep learning remains relevant. This work refers to the performance comparison of a text classification model that combines Label Powerset (LP) and Support Vector Machine (SVM) against a transfer learning language model such as DistilBERT in 5 different imbalanced and balanced dataset scenarios of scientific papers. A proposed classification process was implemented with performance metrics, which have confirmed that the combination LP-SVM continues to be an option with remarkable results in multi-label text classification.
Forecasting the Principal of Code Technical Debt in JavaScript Applications

JavaScript (JS) is one of the most popular programming languages for developing client-side applications mainly due to allowing the adoption of different programming styles, not having strict syntax rules, and supporting a plethora of frameworks. The flexibility that the language provides may accelerate the development of application, but also pose threats to the quality of the final software product, e.g., introducing Technical Debt (TD). TD reflects the additional cost of software maintenance activities to implement new features, occurring due to poorly developed solutions. Being able to forecast the levels of TD in the future can be extremely valuable in managing TD, since it can contribute to informed decision making when designating future repayments and refactoring budget among a company's projects. Despite the popularity of JS and the undoubtful benefits of accurate TD forecasting, in the literature, there is available only a limited number of tools and methodologies that are able to: (a) forecast TD during software evolution, (b) provide a ground-truth TD quantifications to train forecasting, since TD tools that are available are based on different rulesets and none is recognized as a state-of-the-art solution, (c) take into consideration the language-specific characteristics of JS. As a main contribution for this study, we propose a methodology (along with a supporting tool) that supports the aforementioned goals based on the Backward Stepwise Regression and Auto-Regressive Integrated Moving Average (ARIMA). We evaluate the proposed approach through a case study on 19,636 releases of 105 open-source applications. The results point out that: (a) the proposed model can lead to an accurate prediction of TD, and (b) the Number of appearances of the “new” and “eval” keyword along with the number of “anonymous” and “arrow” functions are among the features of JavaScript language that are related to high levels of TD.
COBREX: A Tool for Extracting Business Rules from COBOL

COBOL (Common Business-Oriented Language) has had a strong presence for the last five decades and is still prevalent in the finance and banking sectors and other organizations. The systems which were earlier written in COBOL have now become legacy systems. Therefore, it has become essential to maintain and migrate these legacy COBOL systems. These legacy systems on which companies rely consist of embedded logic to run their business rules and day-to-day operations. Due to ever-changing requirements, these business rules need to be revisited and updated regularly. Understanding the code that enforces the business rules is critical for system evolution. However, this is time-consuming, laborious, and error-prone. Also, the documentation of these systems is sometimes inadequate and may be inconsistent with current organizational policies. Furthermore, the number of current-age developers working on COBOL has been drastically reduced, and they are mainly unfamiliar with legacy systems. To aid this, we propose a tool called COBREX to extract COBOL business rules using a CFG-based approach. The tool’s main aim is to help the researchers and practitioners to understand COBOL source code by extracting and comprehending the business rules. The demo of the tool can be found here - https://youtu.be/3QODmOkISL0 and the details of the tool can be found here -https://rishalab.github.io/COBREXdoc/.
Robust Watermarking for Video Forgery Detection with Improved Imperceptibility and Robustness

Videos are prone to tampering attacks that alter the meaning and deceive the audience. Previous video forgery detection schemes find tiny clues to locate the tampered areas. However, attackers can successfully evade supervision by destroying such clues using video compression or blurring. This paper proposes a video watermarking network for tampering localization. We jointly train a 3D-UNet-based watermark embedding network and a decoder that predicts the tampering mask under simulated attacks. The perturbation made by watermark embedding is close to imperceptible. Considering that there is no off-the-shelf differentiable video codec simulator, we propose to mimic video compression by ensembling simulation results of other typical attacks, e.g., JPEG compression and blurring, as an approximation. Experimental results demonstrate that our method generates watermarked videos with good imperceptibility and robustly and accurately locates tampered areas within the attacked version.
Video Restoration Framework and Its Meta-adaptations to Data-Poor Conditions

Restoration of weather degraded videos is a challenging problem due to diverse weather conditions e.g., rain, haze, snow, etc.. Existing works handle video restoration for each weather using a different custom-designed architecture. This approach has many limitations. First, a custom-designed architecture for each weather condition requires domain-specific knowledge. Second, disparate network architectures across weather conditions prevent easy knowledge transfer to novel weather conditions where we do not have a lot of data to train a model from scratch. For example, while there is a lot of common knowledge to exploit between the models of different weather conditions at day or night time, it is difficult to do such adaptation. To this end, we propose a generic architecture that is effective for any weather condition due to the ability to extract robust feature maps without any domain-specific knowledge. This is achieved by novel components: spatio-temporal feature modulation, multi-level feature aggregation, and recurrent guidance decoder. Next, we propose a meta-learning based adaptation of our deep architecture to the restoration of videos in data-poor conditions (night-time videos). We show comprehensive results on video de-hazing and de-raining datasets in addition to the meta-learning based adaptation results on night-time video restoration tasks. Our results clearly outperform the state-of-the-art weather degraded video restoration methods. The source code is available at: https://github.com/pwp1208/Meta_Video_Restoration .
Making the Cloud Monitor Real-Time Adaptive

The recent Cloud Monitor invention [1] aims to open actual insights, and give greater agency, to stakeholders in the Cloud ecosystem. But the varied nature and diverse user and provider needs, challenge the practicality of creating custom continuous cloud monitoring solutions using it. To accommodate customized needs at scale for dynamic systems and operating environments, we take a first step towards a flexible adaptation mechanism that automates individual monitoring requests in a custom way. We propose a real-time adaptive continuous monitoring extension to the Cloud Monitor and demonstrate potential value from the Cloud User's perspective. We present a reasonable scenario using a real cloud environment. Our result suggests that, despite the need for more research into robustness, this mechanism enables improved agency of Cloud stakeholders. It enables the manageability of continuous monitoring with the Cloud Monitor under dynamic conditions. Ultimately, stakehold-ers can monitor the actual Quality of Service delivered by their cloud in use better and at their own discretion.
Pinpointing the System Reliability Degradation in NISQ Machines

The growth of the need for quantum computers in many domains such as machine learning, numerical scientific simulation, and finance has urged quantum computers to produce more stable and less error-prone results. However, mitigating the impact of the noise inside each quantum device remains a present challenge. This paper utilizes the system calibration data collected from the existing IBMQ machines, applying reliability degradation analysis to generate the reliability degradation matrix (RDM). We define multiple new evaluation metrics based on the reliability degradation matrix to compare the reliability between qubits, qubit topologies, and quantum machines. New evaluation metrics can be used for exploring the most error-robust quantum machine. This contribution increases the users’ expectation of result accuracy. It opens the opportunities for studying the insight of correlation between qubits that may further motivate the quantum compiler design for the qubit mapping.