Reviewerly/exHarmony huggingface dataset:
Learning optics and electrodynamics as part of the bachelor of information technology security curriculum

The bachelor program in Information Technology Security of the Universidad Autonoma de Nuevo Leon used to teach the students the use of hardware and software needed for their professional development; moreover, the constant evolution of technology and the imminent arrival of new technologies such as Quantum computers, 5G, 6G, and more lead to needing to include a solid base on Physics at the first semesters of the curriculum. Considering this, the recent updates in the academic program of the Bachelor of Information Technology Security include a subject called Transmission Signals, where fundamental electrodynamics and optics are the main topics covered, including the explanation of its application in Computer, Telecommunications, Cryptography, and Security in IT. The main objective of the subject is to prepare the students to understand how the technology works and the science behind it, which will lead to the opportunity for the student to easily understand future technologies from a solid foundation of knowledge in Electrodynamics and Optics. The subject of Transmission Signals has been taught since 2019, and the present work shows a summary of its content, the projects developed by the students, the impact of this subject in the curriculum, and the future work proposed to improve the integral development of the IT Security students.
Provably Unforgeable Threshold EdDSA with an Offline Participant and Trustless Setup

Abstract We present an EdDSA-compatible multi-party digital signature scheme that supports an offline participant during the key-generation phase, without relying on a trusted third party. Under standard assumptions, we prove our scheme secure against adaptive malicious adversaries. Using a classical game-based argument, we prove that if there is an adversary capable of forging the scheme with non-negligible probability, then we can build a forger for the original EdDSA scheme with non-negligible probability. The scheme requires only two communication rounds in the signature generation phase and avoids expensive multi-party evaluation of cryptographic hash functions. We present our solution in a setting where two parties generate the keys and compute signatures, with a third party which can be brought online after the key generation when one of the other parties becomes unavailable. This setting is a perfect fit for custodial solutions where partially trusted services are employed by a user to increase resiliency. We provide also a possible solution to retain the resiliency of the recovery in the presence of a malicious party.
Financial Development and Economic Growth: Empirical Evidence from High-Income Countries

The purpose of this study will be to analyze financial development on economic growth high-income countries.This study uses the panel data with a fixed effect model approach.Data is secondary data starting from 1998-2019 sourced from the World Bank.The results showed that, domestic credit to private sector had a not significant effect on economic growth high-income countries, bank overhead costs had a not significant effect on economic growth high-income countries, net interest margin had a not significant effect on economic growth high-income countries, non-interest income has a significant effect on economic growth high-income countries, gross domestic savings has a significant effect on economic growth high-income countries.
Effectiveness Writing Drama Based Scripts Local Wisdom of High School Students Country 2 Majene

This study aims to (1) describe the ability of write drama script based wisdom local in SMA Negeri 2 Majene.Students (2) prove the effectiveness of -based drama learning Mandar local wisdom in class XI SMA Negeri 2 Majene.The design in this study used a Randomized Pretest-Postest Comparation Group Design.There were 2 93 students of class XI SMA Negeri 2 Majene as the research population.The sample was divided into two groups, namely the control and experimental classes, each amounting to 3 3 people.Sampling using random sampling.The technique of collecting data is through tests .This study compared the results of the treatment in the control group which write drama scripts using conventional media and the experimental group who wrote drama script using the local wisdom of Mandar using the t test.Research result shows that (1) ability in write drama script conventional in the group control Fulfill criteria minimum completeness is the average value reached 76.55 while ability group writing experiment drama script using local wisdom media Mandar in class XI SMA Negeri 2 Majene reach criteria minimum completeness is the average score reached 88.55, and (2) wrote drama script based wisdom effective Mandarin local applied to students class XI SMAN 2 Majene.This thing proven in the results of hypothesis testing use analysis statistics inferential Statistical Package for Social Science (SPSS) version 18 for windows type t-test independent sample test was obtained value of t count of 6.053 and > t -table ( t_((df = 64,0.05))) of 2,000 so hypothesis zero (H0) is rejected and the hypothesis alternative (H1) is accepted.
Data Center Efficiency Model: A New Approach and the Role of Artificial Intelligence

Bioinformatics technologies play a significant and growing role in life science research, and as these technologies develop, so does the complexity of data. The challenge of biological data growth has given rise to a number of bioinformatics data centers that offer services and solutions ranging from large-scale biosystems analyze that accounts for entire OMICs to nanoscale experiments where molecular modeling can provide insight o structure and dynamics of molecular complexes of biological components. Obviously, this kind of research requires a highly specialized level of computational and statistical expertise, as well as high-performance resources. The importance of information technology is growing, as is the use of computer information systems throughout the world. There are more and more specialized data centers and they consume more energy. The development of new strategies for energy efficiency of data centers is becoming relevant. These strategies aim to reduce the amount of energy consumed by data centers and their environmental impact without sacrificing performance. The article examines performance metrics, proposes a new method for data center energy efficiency, and discusses the role of artificial intelligence techniques in achieving these goals.
Assessing the value of ontologically unpacking a conceptual model for human genomics

Although the knowledge about human genomics is available to all scientists, information about this scientific breakthrough can often be difficult to fully comprehend and share. A Conceptual Schema of the Human Genome was previously developed to assist in describing human genome-related knowledge, by representing a holistic view of the relevant concepts regarding its biology and underlying mechanisms. This model should become helpful for any researcher who works with human genomics data. We, therefore, perform the process of ontological unpacking on a portion of the model, to facilitate domain understanding and data exchange among heterogeneous systems. The ontological unpacking is a transformation of an input conceptual model into an enriched model based on a foundational ontology. The preliminary analysis and enrichment process are supported by the ontological conceptual modeling language OntoUML, which has been applied previously to complex models to gain ontological clarity. The value of the used method is first assessed from a theoretical point of view: the transformation results in significant, diverse modeling implications regarding the characterization of biological entities, the representation of their changes over time, and, more specifically, the description of chemical compounds. Since the ontological unpacking process is costly, an empirical evaluation is conducted to study the practical implications of applying it in a real learning setting. A particularly complex domain such as metabolic pathways is either described by adopting a traditional conceptual model or explained through an ontologically unpacked model obtained from a traditional model. Our research is evidence that including a strong ontological foundation in traditional conceptual models is useful. It contributes to designing models that convey biological domains better than the original models.
Multiple space based cascaded center point network for object detection

Abstract For the purpose of object detection, numerous key points based methods have been suggested. To alleviate the imbalance problem that some objects may be missing when a single-center-point based network is used for object detection, we propose a brand-new multiple space based cascaded center point network (MSCCPNet) for object detection. Particularly, we first bulid a novel structure to alleviate the imbalance problem in detecting different scale objects by scanning more objects in different scale spaces. We then propose a cascaded center point structure to predict the category and confidence of the object by integrating the results of the two centers with the idea of choosing the high confidence and discarding the low confidence. Finally, we determine the object’s location by predicting the center point deviation as well as the width and height of the object. Our MSCCPNet shows competitive accuracy when compared with many sample classical object detection algorithms on GeForce RTX 2080Ti, according to the results of experiments on PASCAL VOC datasets and COCO datasets.
A Deep Learning-Based Pipeline for the Generation of Synthetic Tabular Data

The recent and rapid progresses in Machine Learning (ML) tools and methodologies paved the way for an accessible market of ML services. In principle, small and medium-sized enterprises, as well as big companies, could act as providers and consumers of services, resulting in an intense exchange of ML services where a consumer may ask many providers for a service preview based on its particular business case, that is, its data. In practice, however, many potential service consumers are reluctant to release their data, when seeking for ML services, because of privacy or intellectual property concerns. As a consequence, the market of ML services is not as fluid as it could be. An alternative to providing real data when looking for an ML service consists in generating and releasing synthetic data. The synthetic data should (a) allow the service provider to preview an ML service whose performance is predictive of the one the same service will achieve on the real data and (b) prevent the disclosure of the real data. In this paper, we propose a data synthesis technique tailored to a family of very relevant business cases: supervised and unsupervised learning on single-table datasets and relational datasets. Our technique is based on generative deep learning models and we instantiate it in three variants: standard Variational Autoencoders (VAEs), β-VAEs, and Introspective VAEs. We experimentally evaluate the two variants to measure the degree to which they meet the two requirements above, using several performance indexes that capture different aspects of the quality of the generated data. The results suggest that data synthesis is a practical answer to the need of decoupling ML service providers and consumers and, hence, can favor the arising of an active and accessible market of ML services.
Stability of Characteristics of Physical Random Number Generators

The development of technologies leads to the need of revising the methods used to obtain cryptographic keys. The randomness parameters of sequences generated by physical random sequence generators are affected by the physical parameters of the recording equipment and the environment. The requirements for the randomness of the sequence, when passing the binary test, are obtained. It is shown that as the sequence length increases, the requirements for possible deviations from the equiprobable distribution of 0 and 1 increase. The randomness stability of the sequences generated by the generator based on the study of photons is estimated. The oscillator under study consists of an LED and a compact silicon photomultiplier designed to detect low-power light radiation. Possible physical processes leading to the deterioration of the randomness of sequences are shown. The possibility of using a generator of random numerical sequences based on a small-sized silicon photomultiplier for cryptographic purposes is estimated.
Remote Sensing Image Super-Resolution With Residual Split Attention Mechanism

Recently, deep-learning-based methods have become the current mainstream of remote sensing image super-resolution (SR) due to their powerful fitting ability. However, they are still unsatisfactory in large-scale factor SR scenarios. The more complicated information distribution of images further increases the difficulty of reconstruction. In this paper, we propose a novel residual split attention group (RSAG) to maintain the overall structural and the local details simultaneously. Specifically, an upscale module that makes the network jointly consider hierarchical priors, which assists in the prediction of high-frequency information, and a residual split attention module to adaptively explore and exploit the global structure information in low-level feature space. In addition, an artifact removal strategy is proposed to reduce excessive artifacts and further boost the performance. By progressively connecting the above modules and incrementally fusing the multi-level intermediate feature maps, the fidelity of high-frequency detail information is improved. Finally, we propose a residual split attention network by stacking several RSAGs for reconstructing high-resolution remote sensing images. Extensive experiment results demonstrate that the proposed approach achieves better quantitative metrics and visual quality than the state-of-the-art approaches.
Stock verification via new media

Any library needs to be updated with reading collection as well as services to serve the user community. For this purpose, libraries are procuring books as well as non-book materials. Occasionally libraries may accept books on gratis from the donors. But it is essential to test whether this collection is integral or reduced, such as book theft or any other natural reasons like damage, etc. This is where stock verification is required. Stock verification is an important annual event in any library. This technique is used in any library for entails scanning the data with a barcode scanner, matching the scanned data with the library database, and creating a report using the MS Excel and any LMS, as described here. Generally during the summer break in May and June, the library undertakes a stock verification of its holdings. The limitations of traditional stock verification methods are also discussed in the study.
Encourage reading habits in print &amp; digital ages

The habit of reading is an essential trait to create a well-educated society. Reading helps to develop good thinking techniques and produce new information. This article attempts to consider reading habits in the print and digital age. The development of healthy reading should remain a goal in education for students to be successful in their future studies and work life. The purpose of this article is to analyze students' reading habits, see what benefits reading can bring to an individual, and investigate the effect of screen time has on reading.
Privacy-Aware Cloud Architecture for Collaborative Use of Patients’ Health Information

Cloud computing appears to be the dreamed-of vision of the healthcare industry; it refers to means of storing and accessing data and programs over the Internet instead of the computer’s hard drive. However, the adoption of cloud computing requires solving several issues, and information privacy is a major one. This work proposes a cloud architecture design for the healthcare information system. The proposed architecture enables storing and sharing information in a privacy-preserving manner. Patients’ information in the proposed architecture is divided into four categories identified in the case study data analysis. User identity management protocol (U-IDM) is employed for controlling access to patients’ information, and patients have means of control over who can access their information. A scenario-based instantiation validated the proposed architecture’s privacy-preserving patient data exchange. The instantiation proved that the proposed architecture allows sharing healthcare information without violating the privacy of patients.
Architecture of Hardware and Software for Unidirectional Data Transmission in Computer Networks

The results of a study of the architecture of hardware and software for unidirectional data transmission in computer networks are presented. The architecture is presented in the form of optical galvanic isolation, media converters and proxy servers, excluding bidirectional data transmission at the hardware level. Work on unidirectional data transmission is carried out on the basis of the UDP transport protocol, which provides operation without establishing bidirectional interaction. Unidirectional data transfer is carried out by a proxy server running a special sender’s software that performs unidirectional data file transfer. This software converts the source data file into a unidirectional stream of UDP datagrams. To receive a unidirectional stream of UDP datagrams, a proxy server is used, on which the recipient’s special software is running. The receiving proxy server receives, processes and generates the source data file. The reliability of the unidirectional transmission of data files is ensured by redundancy (multiple transmission) and checksum verification.
Secure Partitioning of Cloud Applications, with Cost Look-Ahead

The security of Cloud applications is a major concern for application developers and operators. Protecting users’ data confidentiality requires methods to avoid leakage from vulnerable software and unreliable Cloud providers. Recently, trusted execution environments (TEEs) emerged in Cloud settings to isolate applications from the privileged access of Cloud providers. Such hardware-based technologies exploit separation kernels, which aim at safely isolating the software components of applications. In this article, we propose a methodology to determine safe partitionings of Cloud applications to be deployed on TEEs. Through a probabilistic cost model, we enable application operators to select the best trade-off partitioning in terms of future re-partitioning costs and the number of domains. To the best of our knowledge, no previous proposal exists addressing such a problem. We exploit information-flow security techniques to protect the data confidentiality of applications by relying on declarative methods to model applications and their data flow. The proposed solution is assessed by executing a proof-of-concept implementation that shows the relationship among the future partitioning costs, number of domains and execution times.
Factors influencing consumer behavior towards the purchase decision of instant megono of Anglur Selur production Pekalongan City

Abstract Pekalongan City is an area that has a variety of traditional culinary, one of which is megono. Instant megono is a megono product that is preserved and produced by CV Anglur Selur Pekalongan City. This study aims to find out the factors that influence consumer behavior towards instant megono purchasing decisions. This study uses data analysis methods in the form of qualitative descriptive and quantitative analysis. Determination of the location of the study using the purposive sampling method while the determination of the sample of respondents used the accidental sampling method with 100 respondents. Data analysis uses multiple linear regression analysis. The results of the t-test show consumer habits (X1), family / friends / relatives as a reference (X2), income level (X3), lifestyle (X4), product price (X5), product availability (X6), self-confidence (X7), and self-encouragement (X8) significantly influenced the purchase decision of instant megono.
Quantization-Aware NN Layers with High-throughput FPGA Implementation for Edge AI

Over the past few years, several applications have been extensively exploiting the advantages of deep learning, in particular when using convolutional neural networks (CNNs). The intrinsic flexibility of such models makes them widely adopted in a variety of practical applications, from medical to industrial. In this latter scenario, however, using consumer Personal Computer (PC) hardware is not always suitable for the potential harsh conditions of the working environment and the strict timing that industrial applications typically have. Therefore, the design of custom FPGA (Field Programmable Gate Array) solutions for network inference is gaining massive attention from researchers and companies as well. In this paper, we propose a family of network architectures composed of three kinds of custom layers working with integer arithmetic with a customizable precision (down to just two bits). Such layers are designed to be effectively trained on classical GPUs (Graphics Processing Units) and then synthesized to FPGA hardware for real-time inference. The idea is to provide a trainable quantization layer, called Requantizer, acting both as a non-linear activation for neurons and a value rescaler to match the desired bit precision. This way, the training is not only quantization-aware, but also capable of estimating the optimal scaling coefficients to accommodate both the non-linear nature of the activations and the constraints imposed by the limited precision. In the experimental section, we test the performance of this kind of model while working both on classical PC hardware and a case-study implementation of a signal peak detection device running on a real FPGA. We employ TensorFlow Lite for training and comparison, and use Xilinx FPGAs and Vivado for synthesis and implementation. The results show an accuracy of the quantized networks close to the floating point version, without the need for representative data for calibration as in other approaches, and performance that is better than dedicated peak detection algorithms. The FPGA implementation is able to run in real time at a rate of four gigapixels per second with moderate hardware resources, while achieving a sustained efficiency of 0.5 TOPS/W (tera operations per second per watt), in line with custom integrated hardware accelerators.
Spatial Hierarchical Attention Network Based Video-guided Machine Translation

Video-guided machine translation, as one type of multimodal machine translation, aims to engage video contents as auxiliary information to address the word sense ambiguity problem in machine translation. Previous studies only use features from pre-trained action detection models as motion representations of the video to solve the verb sense ambiguity and neglect the noun sense ambiguity problem. To address this, we propose a video-guided machine translation system using both spatial and motion representations. For the spatial part, we propose a hierarchical attention network to model the spatial information from object-level to video-level. We investigate and discuss spatial features extracted from objects with pre-trained convolutional neural network models and spatial concept features extracted from object labels and attributes with pre-trained language models. We further investigate spatial feature filtering by referring to corresponding source sentences. Experiments on the VATEX dataset show that our system achieves a 35.86 BLEU-4 score, which is 0.51 score higher than the single model of the SOTA method. Experiments on the How2 dataset further verify the generalization ability of our proposed system.