Reviewerly/exHarmony huggingface dataset:
Mathematically Based Assessment of the Accuracy of Protection of Cardiac Data Realized with the Help of Cryptography and Steganography

This paper describes the application of cryptography and steganography in the protection of cardiac databases. The original cardiac data studied were transformed using a Daubechies wavelet transform. The next step is to conduct Energy Packing Efficiency-based compression. A watermark is added to the received data to protect against unauthorized access, before hybrid cryptography is performed using a suitably selected encryption algorithm. The presented and analyzed algorithm includes protection using public and symmetric key cryptography. The proposed software algorithm is performed on real electrocardiographic, photoplethysmographic, and Holter cardio data. We have evaluated the effectiveness of the applied approach and concluded that a sufficient level of protection of the studied data has been achieved, with methods of authentication and accuracy applied to the users.
Raw material form recognition based on Tesseract-OCR

For bearing companies, there are still many handwritten forms in the inspection of raw materials at present, and raw material data is also related to the traceability of finished products, which will inevitably affect the progress of the entire production process and product quality. Based on OCR technology and Tesseract-OCR character recognition foundation, this paper designs a set of raw material inspection form intelligent recognition and matching system. After performing denoising and binarization preprocessing on the form uploaded after manual inspection, the structure of the form is extracted and stored after character recognition processing, which can effectively improve work efficiency and reduce the error rate. The experimental results show that this experiment has a good result in the realization of electronic operation of the raw material form.
DeepFakes Detection: the DeeperForensics Dataset and Challenge

Abstract Recent years have witnessed exciting progress in automatic face swapping and editing. Many techniques have been proposed, facilitating the rapid development of creative content creation. The emergence and easy accessibility of such techniques, however, also cause potential unprecedented ethical and moral issues. To this end, academia and industry proposed several effective forgery detection methods. Nonetheless, challenges could still exist. (1) Current face manipulation advances can produce high-fidelity fake videos, rendering forgery detection challenging. (2) The generalization capability of most existing detection models is poor, particularly in real-world scenarios where the media sources and distortions are unknown. The primary difficulty in overcoming these challenges is the lack of amenable datasets for real-world face forgery detection. Most existing datasets are either of a small number, of low quality, or overly artificial. Meanwhile, the large distribution gap between training data and actual test videos also leads to weak generalization ability. In this chapter, we present our on-going effort of constructing DeeperForensics-1.0, a large-scale forgery detection dataset, to address the challenges above. We discuss approaches to ensure the quality and diversity of the dataset. Besides, we describe the observations we obtained from organizing DeeperForensics Challenge 2020, a real-world face forgery detection competition based on DeeperForensics-1.0. Specifically, we summarize the winning solutions and provide some discussions on potential research directions.
Why Computer AI Will Never Do What We Imagine It Can

In response to questions about the author’s previous proof (in Narrative 29.1) that computers contain a hardware limit that renders them permanently incapable of reading or writing narrative, this article draws upon the author’s work with Deep Neural Networks, Judea Pearl’s do-calculus, GPT-3, and other current-generation AI to logically demonstrate that no computer AI (quantum or otherwise) has ever learned, or will ever learn, to produce or process novels or any other kind of narrative (including scripts, short fiction, political speeches, business plans, scientific hypotheses, technology proposals, military strategies, and plots to take over the world).
The Education Cloud Platform for Digital Resources with Block Chain under Intelligent Learning Environment

The education cloud platform has developed the education cloud standard specification system, which has laid the foundation for the establishment of the cloud public service system.It has developed the education cloud basic support, conquered the relevant key technologies of education cloud services, modeled the education cloud green security service guarantee, and built a safe and reliable education cloud public service.Infrastructure is the foundation of the environment; without infrastructure, there is no ecological soil for growth.The most difficult thing about the combination of education and technology is that the integration itself is an integration of value orientations.The overall structure is based on the structure of the blockchain, which is designed according to the security needs of digital educational resources.Under the premise of solving the problem, it can design and implement the operation of forming a chain of data blocks, so as to solve the problem of data asynchrony.
An Empirical Study of CNN-LSTM on Class Imbalance Datasets for Violence Video Detection

Violence detection has become an important topic of video surveillance in the last decade. Some studies in violence video detection demonstrated that learned features from Convolution Neural Network (CNN) gives high accuracy compared to handcrafted features. For this reason, we evaluate several CNN architectures to detect violence action in video. This work compares five pretrained networks VGG16, VGG19, ResNet50, Inception V3, and Xception. Then, the extracted features from each frame are forwarded to a long short-term memory (LSTM) network. We evaluate the pretrained networks on class imbalance datasets since violence video detection might suffer from class imbalance. Two public datasets are being used to evaluate the model; hockey fight dataset and violent crowd dataset. Our experiment results show that InceptionV3 achieved better performance in most cases.
QXTools: A Julia framework for distributed quantum circuit simulation

QXTools is a framework for simulating quantum circuits using tensor network methods.Weak simulation is the primary use case where given a quantum circuit and input state QXTools will efficiently calculate the probability amplitude of a given output configuration or set of configurations.Given this ability one can sample from the output distribution using random sampling approaches.QXTools is intended to be used by researchers interested in simulating circuits larger than those possible with full wave-function simulators or those interested in research and development of tensor network circuit simulation methods.See Brennan et al. ( 2021) for more complete background and scaling results and Brayford et al. ( 2021) for details about deploying in containerised environments.
Automated data extraction of electronic medical records: Validity of data mining to construct research databases for eligibility in gastroenterological clinical trials

Electronic medical records (EMRs) are adopted for storing patient-related healthcare information. Using data mining techniques, it is possible to make use of and derive benefit from this massive amount of data effectively. We aimed to evaluate validity of data extracted by the Customized eXtraction Program (CXP).The CXP extracts and structures data in rapid standardised processes. The CXP was programmed to extract TNFα-native active ulcerative colitis (UC) patients from EMRs using defined International Classification of Disease-10 (ICD-10) codes. Extracted data were read in parallel with manual assessment of the EMR to compare with CXP-extracted data.From the complete EMR set, 2,802 patients with code K51 (UC) were extracted. Then, CXP extracted 332 patients according to inclusion and exclusion criteria. Of these, 97.5% were correctly identified, resulting in a final set of 320 cases eligible for the study. When comparing CXP-extracted data against manually assessed EMRs, the recovery rate was 95.6-101.1% over the years with 96.1% weighted average sensitivity.Utilisation of the CXP software can be considered as an effective way to extract relevant EMR data without significant errors. Hence, by extracting from EMRs, CXP accurately identifies patients and has the capacity to facilitate research studies and clinical trials by finding patients with the requested code as well as funnel down itemised individuals according to specified inclusion and exclusion criteria. Beyond this, medical procedures and laboratory data can rapidly be retrieved from the EMRs to create tailored databases of extracted material for immediate use in clinical trials.
Analysis of Machine Learning Algorithms for Facial Expression Recognition

People can identify emotion from facial expressions easily, but it is more difficult to do it with a computer. It is now feasible to identify feelings from images because of recent advances in computational intelligence. Emotional responses are those mental states of thoughts that develop without conscious effort and are naturally associated with facial muscles, resulting in different facial expressions such as happy, sad, angry, contempt, fear, surprise etc. Emotions play an important role in nonverbal cues that represent a person’s interior thoughts. Intimate robots are expanding in every domain, whether it is completing requirements of elderly people, addressing psychiatric patients, child rehabilitation, or even childcare, as the human–robot interface is grabbing on every day with the increased demand for automation in every industry. We evaluate and test machine learning algorithms on the FER 2013 data set to recognize human emotion from facial expressions, with some of them achieving the highest accuracy and the others failing to detect emotions. Many researchers have used various machine learning methods to identify human emotions during the last few years. In this research article, we analyze eight frequently used machine learning techniques on the FER 2013 dataset to determine which method performs best at categorizing human facial expression. After analyzing the results, it is found that the accuracy of some of the algorithms is quite satisfactory, with 37% for Logistic Regression, 33% for K-neighbors classifier, 100% for Decision Tree Classifier, 78% for Random Forests, 57% for Ada-Boost, 100% for Gaussian NB, 33% for LDA (Linear Discriminant Analysis), and 99% for QDA (Quadratic Discriminant Analysis). Furthermore, the experimental results show that the Decision Tree and Gaussian NB Classifier can correctly identify all of the emotions in the FER 2013 dataset with 100% classification accuracy, while Quadratic Discriminant Analysis can do so with 99% accuracy.
Designing and Evaluating the Impact of Using a Blended Art Course and Web 2.0 Tools in Saudi Arabia

Aim/Purpose: This study designed and evaluated the impact of using a blended course and Web 2.0 tools into the “Design Fundamentals and Elements” course of a fine arts bachelor’s program at a Saudi Arabian university. The study also examined how students used Web 2.0 tools to improve their learning in the design of a blended (hybrid) course following the Quality Matters TM Higher Education Rubric (QMHER). Background: Web 2.0 tools such as Voki, YouTube, Pinterest, and Instagram feature educational technology that offers resources, helps instructors submit their lesson plans, create presentations, and conduct online student meetings. Methodology: The research adopted a mixed-method approach: a qualitative and quantitative pilot study. Data were collected through a pre-survey, pre-test, and post-test for one focus group, and in-depth individual interviews. Contribution: Blended courses should be integrated with the QMHER and Web 2.0 tools into art and design curricula. Art and design teaching and learning should be transformed through Web 2.0 tools, allowing students to explore their design capabilities in the fine arts, art education, interior design, fashion design, and graphic design fields. Findings: The findings revealed three themes in the data: 1) how to design blended learning best with the QMHER in an art education course, 2) Voki’s impact on learning 21st-century skills in a blended course, and 3) Voki’s impact on personal interactions from students’ perspectives. Recommendations for Practitioners: The study suggests that blended courses featuring Voki animation activities play an important role in art education during the Covid-19 pandemic; students’ favorite Web 2.0 tools can improve their learning, and Voki can offer a new method and animation tool that can be integrated into activities for art students. Recommendation for Researchers: Blended courses using Voki should be implemented as they can lead to improvements in students’ 21st-century skills (including technology literacy, creativity, decision-making, problem-solving, and collaboration and communication). Impact on Society: The QMHER was valuable for students’ understanding of the course requirements and to improve their grades. Moreover, many Web 2.0 tools were helpful in teaching and learning art and design concepts. The findings highlight how students’ 21st-century skills increased using Voki in a blended course. Future Research: The QMHER should be applied internationally to online and blended higher education courses.
Geospatial Perspective Reprojections for Ground-Based Sky Imaging Systems

Sky imaging systems use lenses to acquire images concentrating light beams in a sensor. The light beams received by the sky imager have an elevation angle with respect to the device normal. Thus, the pixels in the image contain information from different areas of the sky within the imaging system field of view. The area of the field of view contained in the pixels increases as the elevation angle of the incident light beams decreases. When the sky imager is mounted on a solar tracker, the light beam's angle of incidence in a pixel varies over time. This investigation formulates and compares two geospatial reprojections that transform the original euclidean frame of the imager plane to the geospatial atmosphere cross-section where the sky imager field of view intersects the cloud layer. One assumes that an object (i.e., cloud) moving in the troposphere is sufficiently far so the Earth's surface is approximated \emph{flat}. The other transformation takes into account the curvature of the Earth in the portion of the atmosphere (i.e., voxel) that is recorded. The results show that the differences between the dimensions calculated by both geospatial transformations are in the order of magnitude of kilometers when the Sun's elevation angle is below $30^\circ$.
Polariton-Based Quantum Memristors

Information processing and storage by the same physical system is emerging as a promising alternative to traditional computing platforms. In turn, this requires the realization of elementary units the memory content of which can be easily tuned and controlled. Here, we introduce a polariton-based quantum memristor where the memristive nature arises from the intercavity polariton exchange and is controlled by a time-varying atom-cavity detuning. A dynamical hysteresis is characterized by the fluctuations in the instantaneous polariton number, where the history information is encoded into a dynamical phase. Using a Lindblad master-equation approach, we find that features of the quantum memristor dynamics, such as the area and circulation of the hysteresis loop, showcase a kind of ``plasticity'' controlled by quantum state initialization. This makes this quantum memristor very versatile for a wide range of applications.
A Novel Text-to-Speech Synthesis System Using Syllable-Based HMM for Tamil Language

A text-to-speech (TTS) is an intellectual system that converts the given language text into speech output. TTS system synthesizer can be evaluated using different aspects such as naturalness, ability of expressing intelligence, preference of synthetic speech, and some of the human perception factors like comprehensibility. TTS system is used in many application areas where the human assistance is to be removed. For example, the TTS program allows people with visual impairments or reading disabilities to listen to written words on a home computer. TTS system for Tamil language has the complexity of adaptive pronunciations that are dependent on the language constructs. This paper introduces a hidden Markov model (HMM)-based speech synthesis for Tamil language.
Current status of research support services in university libraries of Pakistan

Purpose Research support services (RSS) is an emerging and popular area in university libraries, whose increasing importance has been well documented since the early 2010s. This study aims to identify the status of RSS provided in the university libraries of Pakistan and to compare the results with relevant international studies. The research also reports on the perception of librarians regarding the application of RSS in Pakistani university libraries. Design/methodology/approach A quantitative survey was conducted of the chief librarians/head librarians working in the 175 university libraries of Pakistan. A structured questionnaire was designed and pre-tested with national and international research experts, faculty members and library professionals. SPSS was used to calculate descriptive statistics. Results of the study were compared with previous literature from an international perspective. Findings Results of the study indicated that most of the university libraries are providing basic RSS and that they have good collections of both general and subject-specific works to meet the needs of researchers. Most respondents not only were interested in providing RSS but also emphasized that libraries should upgrade their collection to meet researchers’ requirements. However, results of the study also indicated that there was noticeably less support for both the more advanced and newer research support services. Practical implications Because the delivery of RSS enables libraries to help meet a university’s strategic research goals, the findings will be of interest to university library and information science executives, policymakers and administration. The suggested recommendations highlight those service areas which are most in need of improvement. Originality/value This research provides an updated perspective on the delivery of research support services by university libraries in Pakistan.
Low-Communication Multiparty Triple Generation for SPDZ from Ring-LPN

The SPDZ protocol for multi-party computation relies on a correlated randomness setup consisting of authenticated, multiplication triples. A recent line of work by Boyle et al. (Crypto 2019, Crypto 2020) has investigated the possibility of producing this correlated randomness in a silent preprocessing phase, which involves a “small” setup protocol with less communication than the total size of the triples being produced. These works do this using a tool called a pseudorandom correlation generator (PCG), which allows a large batch of correlated randomness to be compressed into a set of smaller, correlated seeds. However, existing methods for compressing SPDZ triples only apply to the 2-party setting. In this work, we construct a PCG for producing SPDZ triples over large prime fields in the multi-party setting. The security of our PCG is based on the ring-LPN assumption over fields, similar to the work of Boyle et al. (Crypto 2020) in the 2-party setting. We also present a corresponding, actively secure setup protocol, which can be used to generate the PCG seeds and instantiate SPDZ with a silent preprocessing phase. As a building block, which may be of independent interest, we construct a new type of 3-party distributed point function supporting outputs over arbitrary groups (including large prime order), as well as an efficient protocol for setting up our DPF keys with active security.
Digital disruption: a managers’ eye view

Purpose The purpose of this study is to ascertain the drivers of digital disruption and its consequences from a managerial viewpoint. Understanding the drivers and consequences of digital disruption can help business managers modify and align their organizational structures and strategies with digital disruptors to promote survival in the marketplace. Design/methodology/approach This study used survey data from US managers. Of 1,000 managers, 272 provided responses eligible for use in the analysis. The study used EQS 6.2 software to analyze the data. Findings Eight hypothesized relationships were tested in this study. The results of this study indicate that convergence of intelligence, convergence of technology, support from C-level executives, organizational cultures of innovation and managerial skills act as drivers of digital disruption. The results also show that digital disruption improves both user experience and firms’ digital disruptive performance. Originality/value This study builds upon the disruptive innovation theory. This study demonstrates that both technology- and organization-induced drivers serve as predictors of digital disruption. Digital disruption affects user experience and firms’ digital disruptive performance. In addition, user experience influences firms’ digital disruptive performance. Overall, this study improves our understanding of the role of technology- and organization-induced drivers of businesses’ responses to digital disruption and provides contributions to theory and practice.
Adaptive Illumination Based Depth Sensing Using Deep Superpixel and Soft Sampling Approximation

Dense depth map capture is challenging in existing active sparse illumination based depth acquisition techniques, such as LiDAR. Various techniques have been proposed to estimate a dense depth map based on fusion of the sparse depth map measurement with the RGB image. Recent advances in hardware enable adaptive depth measurements resulting in further improvement of the dense depth map estimation. In this paper, we study the topic of estimating dense depth from depth sampling. The adaptive sparse depth sampling network is jointly trained with a fusion network of an RGB image and sparse depth, to generate optimal adaptive sampling masks. Deep learning based superpixel sampling and soft sampling approximation are applied. We show that such adaptive sampling masks can generalize well to many RGB and sparse depth fusion algorithms under a variety of sampling rates (as low as 0.0625&#x0025;). The proposed adaptive sampling method is fully differentiable and flexible to be trained end-to-end with upstream perception algorithms.