Reviewerly/exHarmony huggingface dataset:
Weighted entropic associative memory and phonetic learning

The Entropic Associative Memory (EAM) holds declarative but distributed representations of remembered objects. These are characterized as functions from features to discrete values in an abstract amodal space. Memory objects are registered or remembered through a declarative operation; memory recognition is defined as a logical test and cues of objects not contained in the memory are rejected directly without search; and memory retrieval is a constructive operation. In its original formulation, the content of basic memory units or cells was either on or off, hence all stored objects had the same weight or strength. In the present weighted version (W-EAM) we introduce a basic learning mechanism to the effect that the values of the cells used in the representation of an object are reinforced by the memory register operation. As memory cells are shared by different representations, the corresponding associations are reinforced too. The memory system supports a second form of learning: the distributed representation generalizes and renders a large set of potential or latent units that can used for recognizing novel inputs, which can in turn be used for improving the performance of both the deep neural networks used for modelling perception and action, and of the memory operations. This process can be performed recurrently in open-ended fashion and can be used in long term learning. An experiment in the phonetic domain using the Mexican Spanish DIMEx100 Corpus was carried out. This corpus was collected in a controlled noise-free environment, and was transcribed manually by human trained phoneticians, but consists of a relatively small number of utterances. DIMEx100 was used to produced the initial state of the perceptual and motor modules, and for testing the performance of the memory system at such state. Then the incremental learning cycle was modelled using the Spanish CIEMPIESS Corpus, consisting of a very large number of noisy untagged speech utterances collected from radio and TV. The results support the viability of the Weighted Entropic Associative Memory for modelling cognitive processes, such as phonetic representation and learning, for the construction of applications, such as speech recognition and synthesis, and as a computational model of natural memory.
Pitfalls and guidelines for using time-based Git data

Many software engineering research papers rely on time-based data (e.g., commit timestamps, issue report creation/update/close dates, release dates). Like most real-world data however, time-based data is often dirty. To date, there are no studies that quantify how frequently such data is used by the software engineering research community, or investigate sources of and quantify how often such data is dirty. Depending on the research task and method used, including such dirty data could affect the research results. This paper presents an extended survey of papers that utilize time-based data, published in the Mining Software Repositories (MSR) conference series. Out of the 754 technical track and data papers published in MSR 2004–2021, we saw at least 290 (38%) papers utilized time-based data. We also observed that most time-based data used in research papers comes in the form of Git commits, often from GitHub. Based on those results, we then used the Boa and Software Heritage infrastructures to help identify and quantify several sources of dirty Git timestamp data. Finally we provide guidelines/best practices for researchers utilizing time-based data from Git repositories.
Evaluation of two semi-supervised learning methods and their combination for automatic classification of bone marrow cells

Differential bone marrow (BM) cell counting is an important test for the diagnosis of various hematological diseases. However, it is difficult to accurately classify BM cells due to non-uniformity and the lack of reproducibility of differential counting. Therefore, automatic classification systems have been developed in which deep learning is used. These systems requires large and accurately labeled datasets for training. To overcome this, we used semi-supervised learning (SSL), in which learning proceeds while labeling. We used three methods: self-training (ST), active learning (AL), and a combination of these methods, and attempted to automatically classify 16 types of BM cell images. ST involves data verification, as in AL, before adding them to the training dataset (confirmed self-training: CST). After 25 rounds of CST, AL, and CST + AL, the initial number of training data increased from 425 to 40,518; 3682; and 47,843, respectively. Accuracies for the test data of 50 images for each cell type were 0.944, 0.941, and 0.976, respectively. Data added with CST or AL showed some imbalances between classes, while CST + AL exhibited fewer imbalances. We suggest that CST + AL, when combined with two SSL methods, is efficient in increasing training data for the development of automatic BM cells classification systems.
Gaussian Patch Mixture Model Guided Low-Rank Covariance Matrix Minimization for Image Denoising

Image denoising is one of the most important tasks in image processing. In this paper, we study image denoising methods by using similar patches which have low-rank covariance matrices to recover an underlying image which is corrupted by additive Gaussian noise. In order to enhance global patch-matching results, we make use of a Gaussian mixture model with an auxiliary image to determine different groups of patches. The auxiliary image is an output of BM3D. The noisy version of covariance matrix is formed by each group of patches from the given noisy image. Its low-rank version can be estimated by using covariance matrix nuclear norm minimization, and the resulting denoised image can be obtained. Experimental results are reported to show that the proposed method outperforms the state-of-the-art denoising methods, including testing deep learning methods, in the peak signal-to-noise ratio, structural similarity values, and visual quality.
A novel approach for power load forecast based on GAN data augmentation

Abstract At present, the concept of Smart Grid is intimately related to huge volumes of data generation, known as Big Data. These play a key role as far as the improvement of the network operation is concerned and, above all, when it comes to energy consumption. Hence the load forecast based on Big Data processing represents a reliable alternative in carrying out an accurate load prediction. As such, a new approach for the power load forecast, based on GAN data augmentation, for a hospital, within a Smart Grid, will be presented in this work.
The ROC Diagonal is Not Layperson’s Chance: A New Baseline Shows the Useful Area

In many areas of our daily lives (e.g., healthcare), the performance of a binary diagnostic test or classification model is often represented as a curve in a Receiver Operating Characteristic (ROC) plot and a quantity known as the area under the ROC curve (AUC or AUROC). In ROC plots, the main diagonal is often referred to as "chance" or the "random line". In general, however, this does not correspond to the layperson's concept of chance or randomness for binary outcomes. Rather, this represents a special case of layperson's chance, or the ROC curve for a classifier that has the same distribution of scores for the positive class and negative class. Where the ROC curve of a model deviates from the main diagonal, there is information. However, not all information is "useful information" compared to chance, including some areas and points above the diagonal. We define the binary chance baseline to identify areas and points in a ROC plot that are more useful than chance. In this paper, we explain this novel contribution about the state-of-art and provide examples that classify benchmark data.
Data fusion information group (DFIG) model meets AI+ML

The data fusion information group (DFIG) model is widely popular, extending and replacing the joint director of the labs (JDL) model as a data fusion processing framework that considers data/information exchange, user/team involvement, and mission/task design. The DFIG/JDL provides an initial design from which enhancements in analytics, learning, and teaming result in opportunities to improve data fusion methodologies. This paper highlights recent artificial intelligence/machine learning (AI/ML), deep learning, reinforcement learning, and active learning capabilities with that of the DFIG model for analysis and systems engineering designs. The general DFIG construct is applicable to many AI/ML systems; however, the focus of the paper provides useful considerations for the data fusion community to consider based on prior implemented approaches. The main ideas are: level 0 DFIG data preprocessing through AI/ML methods for data reduction, level 1/2/3 DFIG object/situation/impact assessment using AI/ML/DL methods for awareness, level 4 DFIG process refinement with reinforcement learning for control, and level 5/6 DFIG user/mission refinement with active learning for human-machine teaming.
RETRACTED: Cloud data security mechanism using the lightweight cryptography

Cloud computing has become the most attracting day to use for the largest scope association at present or for the person who needs various organization administration with small expense. Cingular's data on a regular basis is kept on the open cloud that is accessible for all to access. This raises some of the fundamental issues in contrast to the adaptable administration offered by cloud suppliers, such as confidentiality, integrity, availability, authorization, and more. As of late, there are a lot of options available for securing information and the best way is to use encryption. Encryption will not be able to provide enough protection, considering the fragile data of the customers. This additionally burns away more opportunity to perform encryption and decoding measures for each query. Furthermore, it is not a good practice to consider client-operated in light of the fact that once client's information is transferred to the cloud premises, the client does not have direct power over this information. Thinking about this reality we should consider the security of the client's critical data on the cloud worker.
EPC

Edge-cloud video analytics systems capture video streams by edge cameras and send the video streams to the cloud for analytics to support applications like video surveillance, VR/AR, autonomous driving, etc. Video streams captured at the edge may contain sensitive objects, e.g., a human being. Existing studies propose adding noise to the intermediate video analytics results, encrypting video frames, etc. In this paper, we take an orthogonal approach where we remove, a.k.a. denaturing, the sensitive objects at the edge side before sending a video frame to the cloud.
Generation of twelve-partite entanglement from two symmetric four-wave mixing processes

Entanglement shared by multipartite is an important resource for quantum technologies. Recently, the hexapartite entanglement generated from the four-wave mixing (FWM) process with a spatially structured pump (SSP) has been experimentally demonstrated (Zhang et al., 2020). To further increase the number of entangled beams, we theoretically propose a scheme for generating twelve-partite entanglement based on two independent symmetric FWM processes with a SSP and one linear beam splitter (BS). The entanglement properties are analyzed by utilizing the positivity under partial transposition criterion, and we find that there is twelve-partite entanglement in the whole interaction region and the degree of entanglement increases as the interaction time increases. In addition, we investigate the dependence of entanglement characteristics of the system on transmissivity of BS and eigenmode structure embedded within the system as well to further study the underlying entanglement structure. Our results pave the way for the experimental generation of multipartite entanglement from the SSP-based FWM process.
A Stochastic Grammar Approach to Predict Flight Phases of a Hypersonic Glide Vehicle

Hypersonic glide vehicles (HGVs) fly at very high velocities and demonstrate high agility, which creates challenges in forecasting their flight behavior (e.g., aerodynamics, flight paths, maneuvers). In this paper, we describe a method for predicting future flight phases of a HGV. Flight phases could be modeled as categorical labels that correspond to different types of flight behavior. For our purposes, we define flight phases based on magnitude of rate of change of total energy (i. e., summation of kinetic and potential energy) of a vehicle. We use methods from natural language processing to model the flight phases as &#x201C;words&#x201D; and the HGV trajectories as &#x201C;sentences.&#x201D; We learn a &#x201C;grammar&#x201D; from the HGV trajectories, which we use for our prediction task. Given &#x201C;words&#x201D; from the initial part of a HGV trajectory and the &#x201C;grammar&#x201D;, we can predict future &#x201C;words&#x201D; in the &#x201C;sentence&#x201D; (i.e., future HGV flight phases in the trajectory). We demonstrate that this approach successfully predicts future flight phases for HGV trajectories, especially in scenarios with limited training data.
Dynamic Graduation Project Allocation Based on Student-Teacher Profile Compatibility

Selecting an appropriate graduation project and supervisor is an important step for BSc and MSc students; a good compatibility between the student and the teacher, in terms of personality and supervisory style, contributes to enhanced achievement, engagement and well-being. Nevertheless, few approaches have been proposed in the literature to use these compatibility factors for providing student-teacher matching recommendations. In this context, we propose a system called Smart Project Allocation (SPA) which facilitates the supervisor selection process by providing personalized suggestions based on comprehensive student and teacher models (including personality, supervisory style and topics of interest). In addition, the platform provides a mechanism for automatic project allocation, which dynamically assigns projects to students in a more efficient, fair and transparent manner than the traditional manual approach.
Identity and Access Management Framework for Multi-tenant Resources in Hybrid Cloud Computing

While more organizations have been trying to move their infrastructure to the cloud in recent years, there have been significant challenges in the identity management in the hybrid cloud. This paper showcases a novel identity and access management framework for shared resources in a multi-tenant hybrid cloud environment that implements "mirror" identities of on-premise identities in the cloud. Following the best security practices, the framework ensures that only rightful users can use their mirror identities in the cloud. Furthermore, the paper also proposes a technique in scaling the framework to accommodate large-scale enterprises that provides a comprehensive and scalable solution for enterprises to implement identity and access control in their hybrid cloud infrastructure. Although the paper focuses on implementing the framework in Google Cloud Platform, it can be easily applied to any major public cloud platform.
Reinforcing Penetration Testing Using AI

In a space mission operations context, assets of very high value are operated by several data systems. The security and resilience of these data systems has become a primary concern, due to the increasing ubiquity of the internet and mutability of the threat landscape. Penetration testing is a well established method to identify system security weaknesses, however, is typically high-cost and effort-intensive. The European Space Agency has developed a prototype automated penetration testing framework, called PenBox. Given the volume of parameters and possibilities for the PenBox automated test execution sequence, applied Machine Learning theory presents an interesting opportunity for enhancement. This paper presents the approach and results obtained towards research on the integration of Artificial Intelligence (AI) techniques, in particular Reinforcement Learning (RL), to PenBox. The adopted approach to frame this case study involves the Q-learning paradigm that seeks to learn a policy that maximizes the total reward, according to the quality associated with the certain action to take; the reward itself is the pivotal element that shapes the intelligent agent&#x0027;s behaviour through the trial-and-error theory, without any human interaction. To build this representative space, an existing network simulator was used and further adopted to improve representativeness and alignment with possible PenBox actions. In this paper, we highlight how Deep Q-Learning ensures a certain level of randomization, thus providing the basis for future evolution of the developed AI model to suit the real-world autonomous penetration testing software, with the aim of optimizing the process in terms of performance and cost effectiveness, and enabling an innovative autonomous capability for optimal attack path definitions without human in the loop.
A Real-Time IoT System and ML algorithms: A Comparative Study

Wearable sensors are frequently used for monitoring physical activities and medical conditions. A variety of sensors are used, such as the accelerometer (ACC), gyroscope (GYR), and magnetometer (MAG), which are often embedded in Inertial Measurement Units (IMU). Data collected from these sensors can be used to identify context or physical activities through context-aware learning methods that apply a variety of learning algorithms. Implementing a real-time system (RTS) that serves a specific application like fall detection or heart condition, for example, is challenging as response time must be within a certain interval. This response time depends on the speed of data collection and transmission as well as the prediction time. Even though a lot of research was done in this area, to the best of our knowledge there is no comparative study based on the criteria we are using here. In this paper, we propose an Edge-based RTS for health-related applications and conduct a comparative study of several Machine Learning Algorithms (MLA) according to four criteria: source of data, sampling period, prediction time (PT), and success rates (SR). Even though MLA demonstrate different behavior toward these criteria, our simulation results showed that it is possible to implement a RTS that can identify or predict accurately physical activities within acceptable time constraints which are application dependant. Simulations were performed on wearable sensors’ data that we collected from 24 participants practicing five different physical activities. Our simulation results showed that the Decision Tree algorithm, with SR of 97.93% and PT of 0.17 seconds, outperformed all other algorithms.
Same person, different platform

Abstract The importance of digital data in forensic contexts has been increasing continuously (e.g., Grant 2013 ; Layton, Watters &amp; Dazeley 2010 ; Wright 2013 ), with individuals holding an average of 8.5 different social media accounts in 2018 ( Statista 2021a ). Even though numerous studies have investigated registers on social media platforms (e.g., Seargeant &amp; Tagg 2014 ; Zappavigna 2013 ), it has rarely been attempted to describe individual styles of one and the same person on different platforms – a research gap this paper attempts to address with the help of an exploratory hypothesis-generating study. The data is drawn from Instagram and Twitter, and comprises 1,800 posts from three media representatives and/or writers that hold accounts with both platforms. The results of the analysis suggest that the use of some features (e.g., emoji, hashtags) is strongly influenced by the respective platforms, while other features (e.g., patterns of punctuation, use of types of speech acts) remain stable and thus offer promising avenues for authorship analysis.
Factors Influencing the Willingness to Download COVID-19 Contact Tracing Apps: The Moderating Effect of Persuasive Design and Smartphone Usage Experience

The COVID-19 pandemic culminated in the global roll-out of contact tracing apps, also known as exposure notification apps (ENAs), to contain the spread of the virus. ENAs notify users about a possible infection and advises them to take certain health measures such as self-isolation and COVID-19 test. While some work has been done on the factors that influence ENA adoption, little attention has been paid to the moderating effect of persuasive design and smartphone usage experience. This paper sets out to bridge this gap using the Government of Canada’s official app “COVID Alert” and Canadian non-adopter population (n = 133) as a case study. Of the seven factors we investigated, perceived trust (β = 0.29, p &lt; 0.01) and perceived enjoyment (β = 0.21, p &lt; 0.05) turned out to be the only significant predictors of non-adopters’ willingness to download the app in the overall model. Both persuasive design and smartphone usage experience moderated some of the relationships in the model. For example, while perceived enjoyment is significant in the persuasive design submodel (β = 0.34, p &lt; 0.01) and low-experience submodel (β = 0.36, p &lt; 0.001), it is not in the control design and high-experience submodels. Moreover, perceived enjoyment (β = 0.36, p &lt; 0.001) and privacy concern (β = -0.46, p &lt; 0.05) turned out to be the strongest predictors in the low-experience and high-experience submodels, respectively. These results indicate that, to improve the download of ENAs, sponsors should implement and highlight app features that foster trust in marketing campaigns and app-store descriptions. Particularly, they should highlight their privacy-protection feature and hedonic attribute among the high-experience and low-experience groups, respectively.
Methods Modernizing Statistical Reporting in Medical Journals: Challenges and Future Directions

Despite the advent of enormous computing power and the switch to the web as the primary means of reporting empirical research results, papers available online present data on a screen exactly as if it was a printed page. It is now straightforward to program analyses to create interactive figures that allow readers to hover the cursor over a graph and see numerical estimates and tables that allow presentation of data according to user-defined inputs. We recommend that interactive apps for statistical results be routinely included in the HTML versions of medical research papers or as supplementary material.
One-shot many-to-many facial reenactment using Bi-Layer Graph Convolutional Networks

Facial reenactment is aimed at animating a source face image into a new place using a driving facial picture. In a few shot scenarios, the present strategies are designed with one or more identities or identity-sustained suffering protection challenges. These current solutions are either developed with one or more identities in mind, or face identity protection issues in one or more shot situations. Multiple pictures from the same entity have been used in previous research to model facial reenactment. In contrast, this paper presents a novel model of one-shot many-to-many facial reenactments that uses only one facial image of a face. The proposed model produces a face that represents the objective representation of the same source identity. The proposed technique can simulate motion from a single image by decomposing an object into two layers. Using bi-layer with Convolutional Neural Network (CNN), we named our model Bi-Layer Graph Convolutional Layers (BGCLN) which utilized to create the latent vector's optical flow representation. This yields the precise structure and shape of the optical stream. Comprehensive studies suggest that our technique can produce high-quality results and outperform most recent techniques in both qualitative and quantitative data comparisons. Our proposed system can perform facial reenactment at 15 fps, which is approximately real time. Our code is publicly available at https://github.com/usaeed786/BGCLN.