Reviewerly/exHarmony huggingface dataset:
Foldscope as an Innovative Teaching Tool

This study deals with the descriptive analysis of the opinion of a pilot group of students at the University of Salamanca about the use of an innovative origami microscope: the Foldscope. Data were collected from an online satisfaction survey of 92 students of Plant Histology (Degree in Biology) during their laboratory practices. These surveys collected their opinions about the educational effectiveness of an innovative and economic foldable microscope made of paper. The foldscope is easy to use and can be used individually or collectively, thanks to its projection capability. The results show that the participants have a positive impression regarding the use of this tool to study plant structures, and they recommend its use in the years thereafter. In this regard, it is demonstrated that the price of microscopes is no longer a problem for the teaching of some subjects, such as Plant Histology.
On the Classification of ℤ<sub>p</sub>ℤ<sub>p</sub><sup>2</sup>-Linear Generalized Hadamard Codes

The ${\mathbb{Z}_p}{\mathbb{Z}_{{p^2}}}{\text{ - additive}}$ codes are subgroups of $\mathbb{Z}_p^{{\alpha _1}} \times \mathbb{Z}_{{p^2}}^{{\alpha _2}}$. A${\mathbb{Z}_p}{\mathbb{Z}_{{p^2}}}{\text{ - linear}}$ generalized Hadamard (GH) code is a GH code over ${\mathbb{Z}_p}$ which is the Gray map image of a ${\mathbb{Z}_p}{\mathbb{Z}_{{p^2}}}$-additive code. A recursive construction of ${\mathbb{Z}_p}{\mathbb{Z}_{{p^2}}}{\text{ - additive}}$ GH codes of type (α <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> , α <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> ; t <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> , t <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> ) with t <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> , t <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> ≥ 1 is known, and for which types the corresponding ${\mathbb{Z}_p}{\mathbb{Z}_{{p^2}}}$-linear GH codes are nonlinear over ${\mathbb{Z}_p}$ is also known. In this paper, we generalize some known results for ${\mathbb{Z}_p}{\mathbb{Z}_{{p^2}}}$-linear GH codes with p = 2 to any p≥3 prime when ${\alpha _1} \ne 0$. First, we present new recursive constructions of ${\mathbb{Z}_p}{\mathbb{Z}_{{p^2}}}{\text{ - linear}}$ GH codes having the same type, and show that we obtained equivalent codes. Then, we compute the rank of some families of ${\mathbb{Z}_p}{\mathbb{Z}_{{p^2}}}{\text{ - Linear }}$ GH codes. Finally, we show that, unlike ${\mathbb{Z}_4}{\text{ - linear}}$ Hadamard codes, the ${\mathbb{Z}_{{p^2}}}{\text{ - Linear }}$ GH codes are not included in the family of ${\mathbb{Z}_p}{\mathbb{Z}_{{p^2}}}{\text{ - Linear }}$ GH codes with ${\alpha _1} \ne 0$ when p ≥ 3 prime.
Combining Deep Learning with Good Old-Fashioned Machine Learning

We present a comprehensive, stacking-based framework for combining deep learning with good old-fashioned machine learning, called Deep GOld. Our framework involves ensemble selection from 51 retrained pretrained deep networks as first-level models, and 10 machine-learning algorithms as second-level models. Enabled by today’s state-of-the-art software tools and hardware platforms, Deep GOld delivers consistent improvement when tested on four image-classification datasets: Fashion MNIST, CIFAR10, CIFAR100, and Tiny ImageNet. Of 120 experiments, in all but 10 Deep GOld improved the original networks’ performance.
A telemedical interface for at‐home cognitive testing

The COVID-19 pandemic has limited in-lab cognitive testing. While at-home alternatives exist (testing over the phone), differences in test design and delivery complicate direct comparison of most in-lab and at-home tests. Here we describe the design, infrastructure, and implementation of the California Cognitive Assessment Battery (CCAB), a cognitive test battery and administration system. Using automated remote administration over cellular networks, identical computerized cognitive tests can be administered at-home or in the lab.The CCAB comprises 30+ computerized cognitive tests. Test delivery is automated through text, text-to-speech, and graphical instructions; test scoring is also automated, using automatic speech recognition for verbal responses. The test delivery infrastructure consists of two components: the test kit (MS Surface Pro tablet computer with mouse, headset, and cellular dongle) , and a web-browser application "CCAB Examiner," used by the test administrator to control test delivery, view real-time progress and scores, and monitor/chat with the participant via AV feeds. The system is coordinated by a cloud-based server which also delivers the CCAB Examiner application to any browser. Once connected, the "CCAB Test Station" application and CCAB Examiner communicate over a commercial Communication as a Service (CAAS) provider, which transmits AV streams and a text messaging protocol. CCAB Test Station is designed to isolate control, communications, and testing functions for maximal fault tolerance. For example, test administration continues automatically even if Examiner and Test Station lose connection. Test data is uploaded to the server during sessions and re-synced when the test kit returns.442 participants underwent three 90-minute CCAB test sessions on successive days (72 total tests per participant). More than 98% of participants finished all three sessions, and >99% of tests were completed. Cellular connection failures occurred occasionally, but >98% resulted only in the loss of A/V feeds for the examiner, while tests continued automatically and examiners were able to monitor real-time performance data.The CCAB permits identical tests to be administered at home or in the laboratory, while cellular-network based remote administration allows for nationwide access for all socioeconomic groups, even for people in remote areas without Wi-Fi, or those with limited technical expertise.
Almost Qudits in the Prepare-and-Measure Scenario

Quantum communication is often investigated in scenarios where only the dimension of Hilbert space is known. However, assigning a precise dimension is often an approximation of what is actually a higher-dimensional process. Here, we introduce and investigate quantum information encoded in carriers that nearly, but not entirely, correspond to standard qudits. We demonstrate the relevance of this concept for semi-device-independent quantum information by showing how small higher-dimensional components can significantly compromise the conclusions of established protocols. Then we provide a general method, based on semidefinite relaxations, for bounding the set of almost qudit correlations, and apply it to remedy the demonstrated issues. This method also offers a novel systematic approach to the well-known task of device-independent tests of classical and quantum dimensions with unentangled devices. Finally, we also consider viewing almost qubit systems as a physical resource available to the experimenter and determine the optimal quantum protocol for the well-known random access code.
Design of a Virtual Reality-Based Learning System for Spoken English

Virtual reality is an important technology that is fast gaining global attention in different spheres of life particularly in the education sector. In view of this, this study designs a distance learning system for spoken English based on virtual reality, firstly, the overall design of the teaching system and the hardware and software of the system are designed, then a double-supervised signal convolutional neural network algorithm is proposed for the speech data recognition function of the system, and finally the testing of the system performance and the simulation analysis of the algorithm are carried out. The results show that the step response curve of the system designed in this study is gradually stabilized after 11s of operation, although there are certain fluctuations in the initial stage; the speaking scoring function of the system is more influenced by the sampling period T. When T is at 3 and 4, the speaking scoring speed of the teaching system is 33s~42s, which is significantly better than other intervals. The number of information submission and feedback was approximately the same and the interaction activity was very high after students used the system designed in this study, reflecting that student were more motivated to learn spoken English after using the system. The final loss rate using Goog Le Net is smaller and more convergent compared to the loss rate of the other three CNN models trained. The convolutional neural network algorithm constructed in this study has a very high accuracy rate in the recognition of English speech data, which is significantly better than other recognition models. To a certain extent, this study can provide guidance for the construction of English-speaking distance learning system, and more needs of users can be considered in future research.
Dataset of open-source software developers labeled by their experience level in the project and their associated software metrics

Developers are extracted from 17 open-source projects from GitHub. Projects are chosen that use the java programming language, the Spring framework and Maven/Gradle build tools. Along with these developers, 24 software engineering metrics are extracted for each of them. These metrics are either calculated by analyzing the source code or relative to project management metadata. Each of these developers then are manually searched for in professional social media such as LinkedIn or Twitter to be labeled with their experience level in their project. Outliers are statistically detected and manually re-assigned when needed. The resulting dataset contains 703 anonymized developers qualified by their 24 project-related software engineering metrics and labeled for their experience. It is suitable for empirical software engineering studies that need to connect developers' level of experience to tangible software engineering metrics.
Cloud-Based Centralizing system for academic history, plagiarism prevention management in Higher Education Institution IN DRC: Benefit, Challenges

Cloud computing is becoming increasingly popular in the twenty-first century, with demand coming from all sectors of industry, including higher education institutions. In this paper, we address the benefits and limitations of cloud computing in the context of higher education institutions in DRC. Adopting modern cloud computing technology is projected to minimize the cost of IT-based solutions and services, as well as ICT infrastructure. The purpose of this work is to establish a system on cloud based that will enable the centralization and exchange of data on students' academic careers, to prevent plagiarism, to avoid fraud and falsification of transcripts, Share the researches and academic works between the universities to avoid plagiarism. Many challenges must be resolved before cloud computing can be successfully deployed in higher education.
High-performance repetition cat code using fast noisy operations

Bosonic cat qubits stabilized by two-photon driven dissipation benefit from exponential suppression of bit-flip errors and an extensive set of gates preserving this protection. These properties make them promising building blocks of a hardware-efficient and fault-tolerant quantum processor. In this paper, we propose a performance optimization of the repetition cat code architecture using fast but noisy CNOT gates for stabilizer measurements. This optimization leads to high thresholds for the physical figure of merit, given as the ratio between intrinsic single-photon loss rate of the bosonic mode and the engineered two-photon loss rate, as well as a very interesting scaling below threshold of the required overhead, to reach an expected level of logical error rate. Relying on the specific error models for cat qubit operations, this optimization exploits fast parity measurements, using accelerated low-fidelity CNOT gates, combined with fast ancilla parity-check qubits. The significant enhancement in the performance is explained by: 1- the highly asymmetric error model of cat qubit CNOT gates with a major component on control (ancilla) qubits, and 2- the robustness of the error correction performance in presence of the leakage induced by fast operations. In order to demonstrate these performances, we develop a method to sample the repetition code under circuit-level noise that also takes into account cat qubit state leakage.
Discrete matrix factorization cross-modal hashing with multi-similarity consistency

Abstract Recently, matrix factorization-based hashing has gained wide attention because of its strong subspace learning ability and high search efficiency. However, some problems need to be further addressed. First, uniform hash codes can be generated by collective matrix factorization, but they often cause serious loss, degrading the quality of hash codes. Second, most of them preserve the absolute similarity simply in hash codes, failing to capture the inherent semantic affinity among training data. To overcome these obstacles, we propose a Discrete Multi-similarity Consistent Matrix Factorization Hashing (DMCMFH). Specifically, an individual subspace is first learned by matrix factorization and multi-similarity consistency for each modality. Then, the subspaces are aligned by a shared semantic space to generate homogenous hash codes. Finally, an iterative-based discrete optimization scheme is presented to reduce the quantization loss. We conduct quantitative experiments on three datasets, MSCOCO, Mirflickr25K and NUS-WIDE. Compared with supervised baseline methods, DMCMFH achieves increases of $$0.22\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>0.22</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> , $$3.00\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>3.00</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> and $$0.79\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>0.79</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> on the image-query-text tasks for three datasets respectively, and achieves increases of $$0.21\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>0.21</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> , $$1.62\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>1.62</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> and $$0.50\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>0.50</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> on the text-query-image tasks for three datasets respectively.
Hybrid Hyper Chaotic Map with LSB for Image Encryption and Decryption

There are number of images that transmitted through the web for various usages like medical imaging, satellite images, military database, broadcasting, confidential enterprise, banking, etc. Thus, it is important to protect the images confidentially by securing sensitive information from an intruder. The present research work proposes a Hybrid Hyper Chaotic Mapping that considers a3D face Mesh model for hiding the secret image. The model has a larger range of chaotic parameters which are helpful in the chaotification approaches. The proposed system provides excellent security for the secret image through the process of encryption and decryption. The encryption of the secret image is performed by using chaos encryption with hyper hybrid mapping. The hyper hybrid mapping includes enhanced logistic and henon mapping to improve the computation efficiency for security to enhance embedding capacity. In the experiment Fingerprint and satellite image is used as secret image. The secret image is encrypted using a Least Significant Bit (LSB) for embedding an image. The results obtained by the proposed method showed better enhancements in terms of SNR for the 3D Mesh model dataset as 77.85 dB better compared to the existing models that achieved Reversible data hiding in the encrypted domain (RDH-ED) of 33.89 dB and Multiple Most Significant Bit (Multi-MSB) 40 dB. Also, the results obtained by the proposed Hybrid Hyper chaotic mapping showed PSNR of 65.73 dB better when compared to the existing Permutation Substitution and Boolean Operation that obtained 21.19 dB and 21.27 dB for the Deoxyribonucleic Acid (DNA) level permutation-based logistic map.
Presence of Open Science Skills in Learning Outcomes at the Lis Study Programs in Croatia

Open science is an ever-evolving phenomenon. Open science deals with the availability of data and publications, which includes an open approach with which publicly funded research is sought to be made available to all members of society and the public. It is an umbrella term that includes a multitude of assumptions about the future of knowledge creation and dissemination (Fecher &amp; Friesike, 2014). The Organization for Economic Co-operation and Development (OECD) emphasizes libraries as a key player and fundamental drivers of open science because they have adapted their role to today’s age and are now active in preserving, publishing, and disseminating digital scientific material in the form of publications, data, and other research-relate content. During their studies, LIS (Library and information science(s)) students acquire knowledge and develop a set of skills that will prepare them for work in today’s information environment. In 2020. LIBER’s (Ligue des Bibliothèques Européennes de Recherche – Association of European Research Libraries) Digital Skills for Library Staff and Researchers Working Group published a visual presentation of the necessary competencies for librarians and researchers. Information science studies, including those in Croatia, are exposed to constant changes in the field, including the development of open science. For this reason, it is necessary to constantly think about the adaptation of study programs through which students will acquire basic knowledge related to the promotion and advocacy of open science. The term “open science”, and everything it encompasses, should be an integral part of study programs in the field of information sciences. The aim of this paper is to explore the representation of skills and competencies for open science in study programs at the LIS studies in Croatia: Department of Information and Communication Sciences (Zagreb), Department of information sciences (Osijek) and Department of information science (Zadar). This research indicated the need for greater inclusion of the concepts of scientific communication and open science in the learning outcomes at the level of programs in the study of information sciences in Zadar, Zagreb, and Osijek.
VizXP: A Visualization Framework for Conveying Explanations to Users in Model Reconciliation Problems

Advancements in explanation generation for automated planning algorithms have moved us a step closer towards realizing the full potential of human-AI collaboration in real-world planning applications. Within this context, a framework called model reconciliation has gained a lot of traction, mostly due to its deep connection with a popular theory in human psychology, known as the theory of mind. Existing literature in this setting, however, has mostly been constrained to algorithmic contributions for generating explanations. To the best of our knowledge, there has been very little work on how to effectively convey such explanations to human users, a critical component in human-AI collaboration systems. In this paper, we set out to explore to what extent visualizations are an effective candidate for conveying explanations in a way that can be easily understood. Particularly, by drawing inspiration from work done in visualization systems for classical planning, we propose a visualization framework for visualizing explanations generated from model reconciliation algorithms. We demonstrate the efficacy of our proposed system in a comprehensive user study, where we compare our framework against a text-based baseline for two types of explanations – domain-based and problem-based explanations. Results from the user study show that users, on average, understood explanations better when they are conveyed via our visualization system compared to when they are conveyed via a text-based baseline.
Discriminative Mutual Learning for Multi-target Domain Adaptation

Unsupervised domain adaptation (UDA) has attracted much attention among those seeking to transfer a model from a labeled source domain to an unlabeled target domain. Many effective algorithms for single target domain adaptation (STDA) have been designed, however, STDA cannot satisfy the scenarios of transferring simultaneously to multiple target domains or transferring to a blending target domain. This paper proposes a novel discriminative mutual learning method for multi-target domain adaptation covering both blending target domain adaptation (BTDA) and multiple target domain adaptation (MTDA). Two key points are considered in the proposed method: one is to learn discriminative features for better prediction, and the other is to self-train the model with pseudo-labeled target data based on distance information. These two aspects are integrated through a mutual learning strategy via two different classifiers. According to extensive experiments on three domain adaptation benchmarks, the proposed method demonstrates the state-of-the-art performance in both BTDA and MTDA settings.
Fake Review Detection Using Machine Learning

E-commerce and online shopping is a booming industry, especially during the COVID-19 pandemic. Online shopping allows customers to browse and purchase products from home using just a phone or laptop. Customers often never end up seeing their product in real life before the purchase, and are instead dependent on photos and descriptions uploaded by the seller. Thus comes the need for customer reviews: an evaluation of a product by other buyers which can influence other shoppers’ decisions to make the purchase. However, reviews can be tainted to provide a fake or unrealistic depiction of a product. Sellers can pay people or robots to leave fake reviews under competitors or their own stores to increase/decrease sales turnout. Such reviews can be harmful to the buyer or other sellers, often ending up with an unhappy customer. Using supervised datasets consisting of real and fake reviews, we can train a variety of machine learning and deep learning models to recognize attributes differentiating between the two types of reviews. Big e-commerce platforms such as Amazon, Yelp, and Tripadvisor are all common targets of fake reviews, and the implementation of fake review detection could create a more assuring shopping experience for customers. In this paper, we analyze and break down customer review data and attempt to build models that form conclusions from it.
Interactive Drawing Interface for Editing Scene Graph

Scene graphs have been widely used in various visual applications, such as image retrieval and generation. However, it is time consuming and challenging to draw scene graphs, especially for images with complex scenes. To resolve this issue, we propose an interactive editing interface for scene graph representation in which users can draw scene graphs simply and conveniently. We provide alternatives for frequently used objects, attributes, and relationships in graph drawing. The proposed function design can greatly reduce the drawing time cost and improve drawing quality. We conducted a user study and confirmed that the proposed interface can help users draw the desired scene graph, as well as providing a good user experience.