Reviewerly/exHarmony huggingface dataset:
iCorrVision-3D: An integrated python-based open-source Digital Image Correlation Software for in-plane and out-of-plane measurements (Part 2)

<h2>Abstract</h2> This work presents an out-of-the-box python-based open-source 3D Digital Image Correlation (3D-DIC) software for both in-plane and out-of-plane full-field measurements, denoted by <i>iCorrVision-3D</i>. The software includes an integrated stereo grabber for image acquisition, stereo calibration, numerical stereo correlation and post-processing modules. The main objective is to provide a complete integrated 3D-DIC system for users. All important DIC setting parameters can be easily controlled by the user from an intuitive graphical interface. For instance, the interpolation strategy and correlation techniques that are usually not open for users are available for modifications in <i>iCorrVision-3D</i>. The proposed software can be used in a great number of applications in engineering. Results indicated that the <i>iCorrVision-3D</i> software is robust and accurate in reconstructing the 3D shape of objects and in evaluating the out-of-plane full-field displacement of specimens being tested.
Collaborative program comprehension via software visualization in extended reality

In software visualization research, various approaches strive to create immersive environments by employing extended reality devices. In that context, only few research has been conducted on the effect of collaborative, i.e., multi-user, extended reality environments. We present our journey toward a web-based approach to enable (location-independent) collaborative program comprehension using desktop, virtual reality, and mobile augmented reality devices. We designed and implemented three multi-user modes in our web-based live trace visualization tool ExplorViz. Users can employ desktop, mobile, and virtual reality devices to collaboratively explore software visualizations. We conducted two preliminary user studies in which subjects evaluated our VR and AR modes after solving common program comprehension tasks. The VR and AR environments can be suitable for collaborative work in the context of program comprehension. The analyzed feedback revealed problems regarding the usability, e.g., readability of visualized entities and performance issues. Nonetheless, our approach can be seen as a blueprint for other researchers to replicate or build upon these modes and results. ExplorViz’s multi-user modes are our approach to enable heterogeneous collaborative software visualizations. The preliminary results indicate the need for more research regarding effectiveness, usability, and acceptance. Unlike related work, we approach the latter by introducing a multi-user augmented reality environment for software visualizations based on off-the-shelf mobile devices.
Comparative Results of Spam Email Detection Using Machine Learning Algorithms

Among the problems caused by spam email are loss of productivity and increase in network resources consumption. Sometimes spam email contain malware as attachments or include links for phishing websites, leading to theft and loss of data. Many email servers are filtering spam but the process becomes increasingly difficult as spammers try to create messages that look similar to normal email. In this paper we implemented five Machine Learning Algorithms in the Python language using the scikit-learn library and we compared their performance against two publicly available spam email corpuses. The discussed algorithms are: Support Vector Machine, Random Forest, Logistic Regression, Multinomial Naive Bayes and Gaussian Naive Bayes.
Using Amazon Managed Blockchain for ePHI An Analysis of Hyperledger Fabric and Ethereum

Blockchains emerged in the past decade with applications across a myriad of domains, however this nascent field has so far been commonly associated with cryptocurrencies. The secure and decentralized nature of blockchains offers benefits across a wide range of industries, including healthcare which remains the largest focus of cyber crimes today. In this work, we demonstrate a Blockchain implementation as a proof of concept for the storage of electronic Protected Health Information (ePHI) related to the COVID-19 pandemic. We use two Amazon Managed Blockchain services, Hyperledger Fabric and Ethereum, to store medical data in Amazon Web Services (AWS). While the two frameworks provide a secure resource for medical data, depending on the chosen implementation, the cost can grow quickly based on the number of requests, which may make them prohibitive for applications such as COVID-19 vaccine passports.
Text Data Processing in Requirement Specifications as a Reference for Similarities Between Use Case Diagrams and Use Case Descriptions for Smart Sleeping Lamp Application Documents

Requirement Specification is defined as a condition or capability that must be met or possessed by a system or system component to comply with contracts, standards, specifications, or in other officially enforced documents. In this study, the example of the SRS document used is the Smart Sleeping Lamp Application, a software application to help and provide solutions to everyone who experiences insomnia. There are differences in interpreting the activities that exist in the Use Case Diagram artifact, with the Use Case Description, which provides an overview of the functionality of a process, so that it can show the involvement of an activity related to the Use Case Diagram. The purpose of this study is to process text data between the Use Case Diagram and the Use Case Description contained in the Software Requirements Specification (SRS) to produce similarity between the artifacts contained in the SRS. In this study, the extraction process was implemented in several parts, namely: Use Case Description (on UCDES1, UCDES2, UCDES3, UCDES4, UCDES5), and Use Case Diagrams (on UCD01, UCD02, UCDE03, UCD04, UCDE05). There are important things produced in this study, namely: The highest similarity is found in the UCDES1 and UCD01 documents with a similarity value of 0.69713076, the highest similarity is obtained in documents between the words “sleeping” and “lights” with a similarity value of 0.7500, the highest value Kappa Score in the program python uses the Gwet's AC1 formula of 0.06097 which means “Less than Chance-agreement,” and the results obtained from the calculation of the questionnaire from the experts are 0.84787 which means “Almost Perfect.”
Poxture: Human Posture Imitation Using Neural Texture

Human pose imitation, which aims to generate an image with a source character’s appearance, the source character’s shape, and a target character’s posture, has many potential applications in virtual reality, augmented reality, games, movies, etc. It is incredibly challenging due to non-rigid human body motions, significant variations in clothing textures, and self-occluded human bodies in 2D images. In this paper, we propose Poxture, a novel human posture imitation method with neural texture, to address the challenges mentioned above. Concretely, first, we build a dense mapping between a source SMPL human body model (shape and posture) and its corresponding texture (appearance). Then, we apply a neural texture generator to recover the complete texture of the source character. At last, we wrap the source neural texture to the source SMLP model with a target pose to generate the desired image by a GAN model. Poxture does not require any annotations, and our framework can fully disentangle the source character’s appearance, shape, and pose, which enjoys several advantages: 1) It can synthesize high-resolution images with detailed textures, thanks to the learned neural textures containing both visible and invisible parts and high-frequency information; 2) It can imitate complex actions with various appearances and body figures since the complete texture of the source character is acquired. We compare our method with previous methods, showing state-of-the-art results on two challenging benchmarks. Extensive experiments demonstrate that, given any character, our method can manipulate this avatar imitating arbitrary posture.
Backdoor Attacks on Crowd Counting

Crowd counting is a regression task that estimates the number of people in a scene image, which plays a vital role in a range of safety-critical applications, such as video surveillance, traffic monitoring and flow control. In this paper, we investigate the vulnerability of deep learning based crowd counting models to backdoor attacks, a major security threat to deep learning. A backdoor attack implants a backdoor trigger into a target model via data poisoning so as to control the model's predictions at test time. Different from image classification models on which most of existing backdoor attacks have been developed and tested, crowd counting models are regression models that output multi-dimensional density maps, thus requiring different techniques to manipulate. In this paper, we propose two novel Density Manipulation Backdoor Attacks (DMBA- and DMBA+) to attack the model to produce arbitrarily large or small density estimations. Experimental results demonstrate the effectiveness of our DMBA attacks on five classic crowd counting models and four types of datasets. We also provide an in-depth analysis of the unique challenges of backdooring crowd counting models and reveal two key elements of effective attacks: 1) full and dense triggers and 2) manipulation of the ground truth counts or density maps. Our work could help evaluate the vulnerability of crowd counting models to potential backdoor attacks.
Program adverbs and Tlön embeddings

Free monads (and their variants) have become a popular general-purpose tool for representing the semantics of effectful programs in proof assistants. These data structures support the compositional definition of semantics parameterized by uninterpreted events, while admitting a rich equational theory of equivalence. But monads are not the only way to structure effectful computation, why should we limit ourselves? In this paper, inspired by applicative functors, selective functors, and other structures, we define a collection of data structures and theories, which we call program adverbs, that capture a variety of computational patterns. Program adverbs are themselves composable, allowing them to be used to specify the semantics of languages with multiple computation patterns. We use program adverbs as the basis for a new class of semantic embeddings called Tl\"on embeddings. Compared with embeddings based on free monads, Tl\"on embeddings allow more flexibility in computational modeling of effects, while retaining more information about the program's syntactic structure.
Convergence of the Value Function in Optimal Control Problems with Unknown Dynamics

We deal with the convergence of the value function of an approximate control problem with uncertain dynamics to the value function of a nonlinear optimal control problem. The assumptions on the dynamics and the costs are rather general and we assume to represent uncertainty in the dynamics by a probability distribution. The proposed framework aims to describe and motivate some model-based Reinforcement Learning algorithms where the model is probabilistic. We also show some numerical experiments which confirm the theoretical results.
Improved Initialization of State-Space Artificial Neural Networks

The identification of black-box nonlinear statespace models requires a flexible representation of the state and output equation. Artificial neural networks have proven to provide such a representation. However, as in many identification problems, a nonlinear optimization problem needs to be solved to obtain the model parameters (layer weights and biases). A well-thought initialization of these model parameters can often avoid that the nonlinear optimization algorithm converges to a poorly performing local minimum of the considered cost function. This paper introduces an improved initialization approach for nonlinear state-space models represented as a recurrent artificial neural network and emphasizes the importance of including an explicit linear term in the model structure. Some of the neural network weights are initialized starting from a linear approximation of the nonlinear system, while others are initialized using random values or zeros. The effectiveness of the proposed initialization approach over previously proposed methods is illustrated on two benchmark examples.
Pandemic Versus Financial Shocks: Comparison of Two Episodes on the Bitcoin Market

With its rising popularity, the Bitcoin has also become increasingly independent from global financial markets. Recently, it has joined the class of alternative assets. We use the newly developed wavelet methodology to analyze daily data to compare the COVID-19 pandemic at the beginning of 2020 with the bear market episode at the end of 2018. In both cases, attention signals and a general panic are the main drivers of the Bitcoin fluctuations. We show that the Bitcoin’s dynamic is more complex than the dynamics of standard financial assets. The Bitcoin is, on the one hand, subject to pandemic shocks but also represents an important source of attention signals. On the other hand, because the Bitcoin additionally reacts on an emotional basis, it might react faster than other assets and thus creates a market signal itself. Moreover, we identify short cycles (of several days), which may possibly be related to demand factors, while long cycles (of several weeks) seem to mirror supply factors and might be related to Bitcoin mining in China. Finally, the analysis underlines the importance of continuous financial education and communication by the supervisory authorities about new, alternative financial assets.
Hardware and software tools for teaching the basics of quantum informatics to lyceums students

The article defines the criteria for choosing a cloud-oriented platformfor mastering the basics of quantum informatics by students of a specialized(high) school: cross-browser; intuitive interface; the possibility of free (free) access; access without registration and simplified registration; the presence of a systematized reference system with examples; support for the development of the environment by the developer; support for working in a personal educational environment; support for working with quantum algorithms in graphical mode; automatic conversion of quantum algorithms from graphic format to program code text; support for the Ukrainian-language locale; availability of a mobile application; responsive design. The possibilities of platforms for implementing quantum algorithms from the following companies are analyzed: Microsoft, QuTech, Amazon Braket, IBM. The choice of the IBM Quantum Cloud-based platform is justified. Work at IBM Quantum Composer and IBM Quantum Lab is described. Information about quantum operations and Gates is presented: their designation in IBM Quantum Composer and IBM Quantum Lab; the gate Matrix; and the purpose of the gate. An example of implementing quantum teleportation in the form of a scheme and program is given.
Using pre-trained language models to resolve textual and semantic merge conflicts (experience paper)

Program merging is standard practice when developers integrate their individual changes to a common code base. When the merge algorithm fails, this is called a merge conflict. The conflict either manifests as a textual merge conflict where the merge fails to produce code, or as a semantic merge conflict where the merged code results in compiler errors or broken tests. Resolving these conflicts for large code projects is expensive because it requires developers to manually identify the sources of conflicts and correct them. In this paper, we explore the feasibility of automatically repairing merge conflicts (both textual and semantic) using k-shot learning with pre-trained large neural language models (LM) such as GPT-3. One of the challenges in leveraging such language models is fitting the examples and the queries within a small prompt (2048 tokens). We evaluate LMs and k-shot learning for both textual and semantic merge conflicts for Microsoft Edge. Our results are mixed: on one-hand, LMs provide the state-of-the-art (SOTA) performance on semantic merge conflict resolution for Edge compared to earlier symbolic approaches; on the other hand, LMs do not yet obviate the benefits of special purpose domain-specific languages (DSL) for restricted patterns for program synthesis.
The role of organisational commitment and leader-member exchange in knowledge application during the COVID-19 pandemic

Purpose This study aims to examine the role of organisational commitment (affective, normative, continuance) in influencing employees’ knowledge application behaviour during the COVID-19 pandemic. This study also probes the moderating role of leader–member exchange (LMX) in the association between organisational commitment and knowledge application. Design/methodology/approach This study used a sample of 206 employees working in various private sector organisations in Brunei Darussalam. Structural equation modelling using Smart-PLS was used to test the hypothesised relationships. Findings The findings show that affective and normative organisational commitment spurred employees’ knowledge application behaviour significantly during the COVID-19 crisis. However, the moderating effect of LMX could not be established in this study. Practical implications The findings provide managers with insights into the crucial role organisational commitment can play in encouraging knowledge application in an organisation. Originality/value Studies exploring the enabling factors of knowledge application are scarce, especially in the context of a global crisis such as the COVID-19 pandemic. This study develops a model and empirically validates the importance of organisational commitment for knowledge application amidst the COVID-19 pandemic. This study also provides insights for managers into how LMX can affect knowledge application outcomes, particularly during uncertain times.
Microexpressions in digital humans: perceived affect, sincerity, and trustworthiness

Abstract Digital agents with human-like characteristics have become ubiquitous in our society and are increasingly relevant in commercial applications. While some of them closely resemble humans in appearance (e.g., digital humans), they still lack many subtle social cues that are important for interacting with humans. Among them are the so-called microexpressions— facial expressions that are short, subtle, and involuntary. We investigate to what extent microexpressions in digital humans influence people's perceptions and decision-making in order to inform the practices of digital human's design. Our two experiments applied four types of microexpressions based on emotion type (happiness and anger) and intensity (normal and extreme). This paper is among the first to design and evaluate microexpressions with different intensity levels in digital humans. In particular, we leverage the possibilities of digitally (re)designing humans and human perception. These possibilities are feasible only in a digital environment, where it is possible to explore various microexpressions beyond real human beings' physical capabilities.
Single-Pixel Compressive Digital Holographic Encryption System Based on Circular Harmonic Key and Parallel Phase Shifting Digital Holography

An encryption system that combines compressive sensing (CS) and two-step parallel phase shifting digital holography (PPSDH) using double random phase encoding (DRPE) is presented in this paper. The two-step PPSDH is a linear inline holographic scheme and is much suitable for encrypting the 2D/3D information in a single exposure. The distribution of random phase mask (RPM) in the DRPE is implemented using circular harmonic key which increases the security of the encryption process. In this system, the keys used to encrypt are spatial positions of the planes, wavelength, and rotation of the circular harmonics in RPMs, and CS acts as an additional key that makes the system more secure than the conventional optical encryption methods. At the transmission end, two-step PPSDH is applied to encrypt the object information in single hologram. The digital mirror device (DMD) is placed between the object and a single-pixel detector for acquiring fewer hologram measurements. At the receiver end, the single digital hologram is numerically recovered by using a CS optimization problem. The original complex object field is decrypted from the CS recovered holograms by the inversion of two-step PPSDH process with the help of the correct keys. The numerical simulations are presented for complex 2D and 3D objects to test the feasibility of the proposed encryption and decryption system. The proposed method carried out intensity and phase reconstruction of the original object field using single-pixel compressive imaging. The computer simulation results demonstrated that the encrypted information is highly secured with the rotation of the circular harmonic key. The sensitivity of the decrypted intensity and phase images is also studied with variations of the encrypted keys. The obtained results show that the proposed encryption scheme is feasible and has better security performance and robustness.
Predicting Bitcoin prices : The effect of interest rate, search on the internet, and energy prices

The current study's goal is to explain the price of bitcoins. We examined the effect of Web search statistics, energy prices, and alternative investment (or cost of opportunity) on bitcoin prices in particular. The second goal is to find the algorithm with the best predictive power. Data were obtained from public and open data. We use a variety of machine learning algorithms to accomplish this. Statistical results were coherent according to the expectation.
Melanoma Skin Cancer Disease Detection Using Convolutional Neural Network

Melanoma, a deadly skin cancer, is caused due to the development of a malignant tumor of melanocytes. Melanoma is the most harmful form of carcinoma, caused by pigment-making cells, called melanocytes. Melanoma is responsible for 75 percent of skin cancer deaths. The survival rate of patients over the past five years' analysis shows that only 15% of patients' survival has been chronically treated. The problem lies in the manual system. There are about six maximum colors in the mole, so the problematic thing is the image, there are several shades and it is difficult to recognize by human beings. So we have developed this problem by using flask and the Pre-Trained Deep-learning model which is trained and tested with a dataset from HAM10000. This paper aims to detect and treat skin cancer in patients at an early stage in order to reduce the mortality rate.