Reviewerly/exHarmony huggingface dataset:
A Multi-Mode 8K-MAC HW-Utilization-Aware Neural Processing Unit with a Unified Multi-Precision Datapath in 4nm Flagship Mobile SoC

Recent work on neural-network accelerators has focused on obtaining high performance in order to meet the needs of real-time applications with vastly different performance requirements, including high precision computation, efficiency for various Deep Learning (DL) layer types, and extremely low power to run always-on applications. Applying a single mode or datatype uniformly across these different scenarios would be less efficient than using different operating modes according to different operating scenarios. For example, super-resolution typically requires FP16 precision for higher image quality, while NNs for face-detection need only INT4 or INT8 precision. Using higher precision than INT8 for face detection would result in higher power consumption. A highly programmable NPU capable of covering the diverse workloads observed in the real world is therefore desired. In this paper, we present a neural processing unit (NPU) optimized with the following features: i) reconfigurable data prefetching and operational flow for high compute utilization, ii) multi-precision MACs supporting INT4,8,16, and float16, iii) a dynamic operation mode to cover extremely low-power or low-latency requirements. These features provide the flexibility needed by real world applications within the power constraints of various product domains.
Real-time instruction-level verification of remote IoT/CPS devices via side channels

Abstract In recent years, with the rise of IoT technology, wireless Cyber-Physical Systems (CPS) have become widely deployed in critical infrastructure, including power generation, military systems, and autonomous and unmanned vehicles. The introduction of network connectivity for data transfer, cloud support, etc., into CPS, can lead to malware injection. Meanwhile, outsourcing of advanced technology node fabrication overseas makes it difficult to protect these devices from malicious modification and hardware Trojans. For solving these issues, traditional anomaly detection methods insert monitoring circuits or software into the target device but come with high overhead and power consumption. Alternative anomaly detection methods occur offline and use large equipment like oscilloscopes and PCs to collect and process side-channel traces. While they can achieve high accuracy in detecting various anomalies, they are difficult to use in practice due to their large, expensive setups. In this paper, we introduce a new instruction-level verification methodology that uses a low-cost, external add-on to monitor the power traces of a target device. This methodology possesses fine-grained granularity and could protect the target device from any malware or hardware Trojans that alter even a single instruction inside the target device. The hardware used is a tiny (20 $$\times $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>×</mml:mo> </mml:math> 20 mm), custom-designed PCB called RASC that collects power traces, performs real-time malware detection, and transmits outcomes to security administrators via Bluetooth. The proposed methodology is demonstrated on 6 benchmarks with two types of malware on an Atmel AVR device, and the accuracy between offline and real-time malware detection is compared.
Predictions of cervical cancer identification by photonic method combined with machine learning

Cervical cancer is one of the most commonly appearing cancers, which early diagnosis is of greatest importance. Unfortunately, many diagnoses are based on subjective opinions of doctors-to date, there is no general measurement method with a calibrated standard. The problem can be solved with the measurement system being a fusion of an optoelectronic sensor and machine learning algorithm to provide reliable assistance for doctors in the early diagnosis stage of cervical cancer. We demonstrate the preliminary research on cervical cancer assessment utilizing an optical sensor and a prediction algorithm. Since each matter is characterized by refractive index, measuring its value and detecting changes give information about the state of the tissue. The optical measurements provided datasets for training and validating the analyzing software. We present data preprocessing, machine learning results utilizing four algorithms (Random Forest, eXtreme Gradient Boosting, Naïve Bayes, Convolutional Neural Networks) and assessment of their performance for classification of tissue as healthy or sick. Our solution allows for rapid sample measurement and automatic classification of the results constituting a potential support tool for doctors.
Image Encryption Schemes Based on a Class of Uniformly Distributed Chaotic Systems

This paper proposes a method to construct a one-dimensional discrete chaotic system. First, we define a generalized distance function to control the boundedness of the one-dimensional discrete system. Based on Marotto’s theorem, one-dimensional discrete systems are proven to be chaotic in the sense of Li–Yorke, and the corresponding chaos criterion theorem is proposed. The system can be distributed uniformly by adjusting the parameters. In this paper, we propose an image encryption scheme based on a uniformly distributed discrete chaotic system and DNA encoding. DNA encoding and decoding rules are determined by plain text. The experimental results demonstrate that our encryption algorithm has a large key space, high key sensitivity, and fast encryption speed and can resist differential and statistical attacks.
Review of Automated Vulnerability Analysis of Smart Contracts on Ethereum

Programs on public blockchains often handle valuable assets, making them attractive targets for attack. At the same time, it is challenging to design correct blockchain applications. Checking code for potential vulnerabilities is a viable option to increase trust. Therefore, numerous methods and tools have been proposed with the intention to support developers and analysts in detecting code vulnerabilities. Moreover, publications keep emerging with different focus, scope, and quality, making it difficult to keep up with the field and to identify relevant trends. Thus, regular reviews are essential to keep pace with the varied developments in a structured manner. Regarding blockchain programs, Ethereum is the platform most widely used and best documented. Moreover, applications based on Ethereum are entrusted with billions of USD. Like on similar blockchains, they are subject to numerous attacks and losses due to vulnerabilities that exist at all levels of the ecosystem. Countermeasures are in great demand. In this work, we perform a systematic literature review (SLR) to assess the state of the art regarding automated vulnerability analysis of smart contracts on Ethereum with a focus on classifications of vulnerabilities, detection methods, security analysis tools, and benchmarks for the assessment of tools. Our initial search of the major on-line libraries yields more than 1,300 publications. For the review, we apply a clear strategy and protocol to assure consequent, comprehensive, and reproducible documentation and results. After collecting the initial results, cleaning up references, removing duplicates and applying the inclusion and exclusion criteria, we retain 303 publications that include 214 primary studies, 70 surveys and 19 SLRs. For quality appraisal, we assess their intrinsic quality (derived from the reputation of the publication venue) as well as their contextual quality (determined by rating predefined criteria). For about 200 publications with at least a medium score, we extract the vulnerabilities, methods, and tools addressed, among other data. In a second step, we synthesize and structure the data into a classification of both the smart contract weaknesses and the analysis methods. Furthermore, we give an overview of tools and benchmarks used to evaluate tools. Finally, we provide a detailed discussion.
Experiencing a Food Production Site Using Wearable Devices: The Indirect Impact of Immersion and Presence in VR Tours

Virtual reality (VR) is among the main technologies revolutionizing numerous sectors, including tourism. In the latter context, virtual tours (VTs) are finding increasing application. Providing an immersive and realistic human–machine interaction, VR tours can bring visitors to virtually experience destination areas. The proposed research presents a theoretical and empirical investigation of the role played by some technical VR features (i.e., presence, immersion, ease-of-use) on VR visitors’ enjoyment, satisfaction, and, accordingly, on the physical visit intention of the production site and neighboring areas. After having experienced a 360-degree VR tour of a food production site, created specifically for this study, 140 visitors were surveyed online. Results—emerging from a PLS structural equation model—show that immersion and presence both directly impact the enjoyment and indirectly the user’s VR tour satisfaction and visit intention. Further, if the VR tour is perceived as easy to use, it influences visitors’ satisfaction and physical visit intention. This study contributes to the novel VR literature, applied in the tourism sector, evidencing how immersive and enjoyable scenarios, experienced via widespread devices such as smartphones, may impact tourists’ choices. In food tourism, VR technologies can be fundamental in attracting new visitors to the production sites and neighboring areas.
Robust Hand Gesture Recognition Using HOG-9ULBP Features and SVM Model

Hand gesture recognition is an area of study that attempts to identify human gestures through mathematical algorithms, and can be used in several fields, such as communication between deaf-mute people, human–computer interaction, intelligent driving, and virtual reality. However, changes in scale and angle, as well as complex skin-like backgrounds, make gesture recognition quite challenging. In this paper, we propose a robust recognition approach for multi-scale as well as multi-angle hand gestures against complex backgrounds. First, hand gestures are segmented from complex backgrounds using the single Gaussian model and K-means algorithm. Then, the HOG feature and an improved 9ULBP feature are fused into the HOG-9ULBP feature, which is invariant in scale and rotation and enables accurate feature extraction. Finally, SVM is adopted to complete the hand gesture classification. Experimental results show that the proposed method achieves the highest accuracy of 99.01%, 97.50%, and 98.72% on the self-collected dataset, the NUS dataset, and the MU HandImages ASL dataset, respectively.
Telling Stories from Computational Notebooks: AI-Assisted Presentation Slides Creation for Presenting Data Science Work

Creating presentation slides is a critical but time-consuming task for data scientists. While researchers have proposed many AI techniques to lift data scientists' burden on data preparation and model selection, few have targeted the presentation creation task. Based on the needs identified from a formative study, this paper presents NB2Slides, an AI system that facilitates users to compose presentations of their data science work. NB2Slides uses deep learning methods as well as example-based prompts to generate slides from computational notebooks, and take users' input (e.g., audience background) to structure the slides. NB2Slides also provides an interactive visualization that links the slides with the notebook to help users further edit the slides. A follow-up user evaluation with 12 data scientists shows that participants believed NB2Slides can improve efficiency and reduces the complexity of creating slides. Yet, participants questioned the future of full automation and suggested a human-AI collaboration paradigm.
TransEffiDet: Aircraft Detection and Classification in Aerial Images Based on EfficientDet and Transformer

In recent years, analysis and optimization algorithm based on image data is a research hotspot. Aircraft detection based on aerial images can provide data support for accurately attacking military targets. Although many efforts have been devoted, it is still challenging due to the poor environment, the vastness of the sky background, and so on. This paper proposes an aircraft detection method named TransEffiDet in aerial images based on the EfficientDet method and Transformer module. We improved the EfficientDet algorithm by combining it with the Transformer which models the long-range dependency for the feature maps. Specifically, we first employ EfficientDet as the backbone network, which can efficiently fuse the different scale feature maps. Then, deformable Transformer is used to analyze the long-range correlation for global feature extraction. Furthermore, we designed a fusion module to fuse the long-range and short-range features extracted by EfficientDet and deformable Transformer, respectively. Finally, object class is produced by feeding the feature map to the class prediction net and the bounding box predictions are generated by feeding these fused features to the box prediction net. The mean Average Precision (mAP) is 86.6%, which outperforms the EfficientDet by 5.8%. The experiment shows that TransEffiDet is more robust than other methods. Additionally, we have established a public aerial dataset for aircraft detection, which will be released along with this paper.
A Novel Image Encryption Scheme Based on Elliptic Curves over Finite Rings

Image encryption based on elliptic curves (ECs) is emerging as a new trend in cryptography because it provides high security with a relatively smaller key size when compared with well-known cryptosystems. Recently, it has been shown that the cryptosystems based on ECs over finite rings may provide better security because they require the computational cost for solving the factorization problem and the discrete logarithm problem. Motivated by this fact, we proposed a novel image encryption scheme based on ECs over finite rings. There are three main steps in our scheme, where, in the first step, we mask the plain image using points of an EC over a finite ring. In step two, we create diffusion in the masked image with a mapping from the EC over the finite ring to the EC over the finite field. To create high confusion in the plain text, we generated a substitution box (S-box) based on the ordered EC, which is then used to permute the pixels of the diffused image to obtain a cipher image. With computational experiments, we showed that the proposed cryptosystem has higher security against linear, differential, and statistical attacks than the existing cryptosystems. Furthermore, the average encryption time for color images is lower than other existing schemes.
Two-Stage Deep Learning Method for Breast Cancer Detection Using High-Resolution Mammogram Images

Breast cancer screening and detection using high-resolution mammographic images have always been a difficult task in computer vision due to the presence of very small yet clinically significant abnormal growths in breast masses. The size difference between such masses and the overall mammogram image as well as difficulty in distinguishing intra-class features of the Breast Imaging Reporting and Database System (BI-RADS) categories creates challenges for accurate diagnosis. To obtain near-optimal results, object detection models should be improved by directly focusing on breast cancer detection. In this work, we propose a new two-stage deep learning method. In the first stage, the breast area is extracted from the mammogram and small square patches are generated to narrow down the region of interest (RoI). In the second stage, breast masses are detected and classified into BI-RADS categories. To improve the classification accuracy for intra-classes, we design an effective tumor classification model and combine its results with the detection model’s classification scores. Experiments conducted on the newly collected high-resolution mammography dataset demonstrate our two-stage method outperforms the original Faster R-CNN model by improving mean average precision (mAP) from 0.85 to 0.94. In addition, comparisons with existing works on a popular INbreast dataset validate the performance of our two-stage model.
Beating the 3 dB Limit for Intracavity Squeezing and Its Application to Nondemolition Qubit Readout

While the squeezing of a propagating field can, in principle, be made arbitrarily strong, the cavity-field squeezing is subject to the well-known 3 dB limit, and thus has limited applications. Here, we propose the use of a fully quantum degenerate parametric amplifier (DPA) to beat this squeezing limit. Specifically, we show that by {\it simply} applying a two-tone driving to the signal mode, the pump mode can, {\it counterintuitively}, be driven by the photon loss of the signal mode into a squeezed steady state with, in principle, an {\it arbitrarily high} degree of squeezing. Furthermore, we demonstrate that this intracavity squeezing can increase the signal-to-noise ratio of longitudinal qubit readout {\it exponentially} with the degree of squeezing. Correspondingly, an improvement of the measurement error by {\it many orders of magnitude} can be achieved even for modest parameters. In stark contrast, using intracavity squeezing of the semiclassical DPA {\it cannot} practically increase the signal-to-noise ratio and thus improve the measurement error. Our results extend the range of applications of DPAs and open up new opportunities for modern quantum technologies.
CONSEQUÊNCIAS DA PANDEMIA DA COVID-19 NA SAÚDE MENTAL, QUALIDADE DE VIDA E ATIVIDADE FÍSICA DE ADOLESCENTES: REVISÃO INTEGRATIVA

Objective: To assess the consequences of the covid-19 pandemic on the mental health, quality of life and physical activity of adolescents. Method: This is an integrative literature review of the last three years (from 2019 to 2021). The search for scientific publications took place from August to October 2021 in the PUBMED, LILACS, SCOPUS, EMBASE, WEB OF SCIENCE and SCIELO databases, with the following descriptors: “adolescent”, “coronavirus”, “quality of life”, “physical activity ” and “mental health”. Results: 3,028 publications were found, of which 1,514 were excluded and 756 abstracts were analyzed, with a final sample of 16 articles. The closing of schools and universities, despite being one of the widely used and effective measures to reduce the spread of the virus, affected the daily lives and lives of adolescents with possible repercussions on mental health, quality of life and physical activity. Conclusion: The COVID-19 pandemic has had a negative and significant impact on the mental health, quality of life and physical activity of adolescents. The fear of disease and risk of contamination added to the negative impacts of social isolation, although necessary, may be harming the general health of adolescents.
Neural Schrödinger Equation: Physical Law as Deep Neural Network

We show a new family of neural networks based on the Schrödinger equation (SE-NET). In this analogy, the trainable weights of the neural networks correspond to the physical quantities of the Schrödinger equation. These physical quantities can be trained using the complex-valued adjoint method. Since the propagation of the SE-NET can be described by the evolution of physical systems, its outputs can be computed by using a physical solver. The trained network is transferable to actual optical systems. As a demonstration, we implemented the SE-NET with the Crank-Nicolson finite difference method on Pytorch. From the results of numerical simulations, we found that the performance of the SE-NET becomes better when the SE-NET becomes wider and deeper. However, the training of the SE-NET was unstable due to gradient explosions when SE-NET becomes deeper. Therefore, we also introduced phase-only training, which only updates the phase of the potential field (refractive index) in the Schrödinger equation. This enables stable training even for the deep SE-NET model because the unitarity of the system is kept under the training. In addition, the SE-NET enables a joint optimization of physical structures and digital neural networks. As a demonstration, we performed a numerical demonstration of end-to-end machine learning (ML) with an optical frontend toward a compact spectrometer. Our results extend the application field of ML to hybrid physical-digital optimizations.
Evaluation of Machine Learning Methods Application in Temperature Prediction

Machin learning, Temperature prediction, Ensemble Models
Factors influencing behavior intentions to use virtual reality in education

Virtual reality (VR) is a new technology that has applications in a variety of sectors, including medical, education, gaming, psychology, and sociology. The application of VR in education is intriguing and warrants further examination, but research on the subject is currently restricted. VR can benefit education by allowing students to participate in memorable and engaging experiences that they would not otherwise be able to have. Traditional approaches are still used to teach students, which is an essential element of the curriculum for those who want to conceive problem-solving. As a result, there is a scarcity of study on VR deployment. In this paper, we investigated the factors affecting the adoption of VR in higher educational institutes. To this end, we extended the technology Acceptance Model (TAM) with four additional factors and formulated a set of hypotheses. The hypotheses are then evaluated using a dataset collected from 503 Jordanian students. The result shows that the factors perceived facilitating condition, perceived effort expectancy, and perceived compatibility significantly affected the intention to use VR systems and tools for educational purposes. We believe that this study will help decision makers to build sustainable learning and educational systems in Jordan universities.
Resource-Efficient Dissipative Entanglement of Two Trapped-Ion Qubits

We demonstrate a simplified method for dissipative generation of an entangled state of two trapped-ion qubits. Our implementation produces its target state faster and with higher fidelity than previous demonstrations of dissipative entanglement generation and eliminates the need for auxiliary ions. The entangled singlet state is generated in ∼7 ms with a fidelity of 0.949(4). The dominant source of infidelity is photon scattering. We discuss this error source and strategies for its mitigation.
Word Embedding Methods for Word Representation in Deep Learning for Natural Language Processing

Natural Language Processing (NLP) deals with analysing, understanding and generating languages likes human. One of the challenges of NLP is training computers to understand the way of learning and using a language as human. Every training session consists of several types of sentences with different context and linguistic structures. Meaning of a sentence depends on actual meaning of main words with their correct positions. Same word can be used as a noun or adjective or others based on their position. In NLP, Word Embedding is a powerful method which is trained on large collection of texts and encoded general semantic and syntactic information of words. Choosing a right word embedding generates more efficient result than others. Most of the papers used pretrained word embedding vector in deep learning for NLP processing. But, the major issue of pretrained word embedding vector is that it can’t use for all types of NLP processing. In this paper, a local word embedding vector formation process have been proposed and shown a comparison between pretrained and local word embedding vectors for Bengali language. The Keras framework is used in Python for local word embedding implementation and analysis section of this paper shows proposed model produced 87.84% accuracy result which is better than fastText pretrained word embedding vectors accuracy 86.75%. Using this proposed method NLP researchers of Bengali language can easily build the specific word embedding vectors for word representation in Natural Language Processing.