Reviewerly/exHarmony huggingface dataset:
Deep multi-modal fusion network with gated unit for breast cancer survival prediction

Accurate survival prediction is a critical goal in the prognosis of breast cancer patients because it can help physicians make more patient-friendly decisions and further guide appropriate treatment. Breast cancer is often caused by genetic abnormalities, which prompts researchers to consider information such as gene expression and copy number variation in addition to clinical data in their studies. The integration of these multi-modal data can improve the predictive power of models. However, with the highly unbalanced information of breast cancer patient data, it becomes a new challenge for breast cancer patient survival prediction to fully extract the characteristic information of these multi-modal data and to consider the complementarity of this information. To this end, we propose a deep multi-modal fusion network (DMMFN) to predict the five-year survival of breast cancer patients by integrating clinical data, copy number variation data, and gene expression data. The imbalanced dataset is first processed using the oversampling method SMOTE-NC. Then the abstract modal features of the multi-modal data are extracted by the two-layer one-dimensional convolutional neural network and the bi-directional long short-term memory network. Next, the weight coefficients of each modal data are dynamically adjusted using gated multimodal units to obtain fusion features. Finally, the fusion features are fed into the MaxoutMLP classifier to obtain the final prediction results. We conducted experiments on the METABRIC dataset to verify the validity of the multi-modal data and compared it with other methods. The comprehensive performance evaluation shows that DMMFN has better prediction performance.
Dynamic obstacle detection method based on U–V disparity and residual optical flow for autonomous driving

Abstract As a core step of obstacle avoidance and path planning, dynamic obstacle detection is critical for autonomous driving. This study aimed to propose a dynamic obstacle detection method based on U–V disparity and residual optical flow for autonomous driving. First, a drivable area of an unmanned vehicle was detected using U–V disparity images. Then, obstacles in the drivable area were detected using U–V disparity images and the geometric relationship between obstacle size and its disparity. Finally, the motion likelihood of each obstacle was estimated by compensating the camera ego-motion. The innovation of the proposed method was that the searching range of the moving obstacles was greatly narrowed by detecting the obstacles in the drivable area, which greatly improved not only the moving obstacle detection efficiency but also the detection accuracy. Datasets from the KITTI benchmark and our self-acquired campus scene data were chosen as testing samples. The experimental results showed that our method could achieve high detection precision, low missed detection rate and less time consumption.
Quantum image processing on real superconducting and trapped-ion based quantum computers

Abstract The size and number of images and the amount of data we process every day have grown rapidly over the last years. Quantum computers promise to process this data more efficiently since classical images can be stored in quantum states. Experiments on quantum computer simulators prove the paradigms this promise is built on to be correct. However, currently, running the very same algorithms on a real quantum computer is often too error-prone to be of any practical use. We explore the current possibilities for image processing on real quantum computers. We redesign a commonly used quantum image encoding technique to reduce its susceptibility to errors. We show experimentally that the current size limit for images to be encoded on a quantum computer and subsequently retrieved with an error of at most 5 % is 2 × 2 pixels. A way to circumvent this limitation is to combine ideas of classical filtering with a quantum algorithm operating locally, only. We show the practicability of this strategy using the application example of edge detection. Our hybrid filtering scheme’s quantum part is an artificial neuron, working well on real quantum computers, too.
Context-Aware 3D Points of Interest Detection via Spatial Attention Mechanism

Detecting points of interest is a fundamental problem in 3D shape analysis and can be beneficial to various tasks in multimedia processing. Traditional learning-based detection methods usually rely on each vertex’s geometric features to discriminate points of interest from other vertices. Observing that points of interest are related to not only geometric features on themselves but also the geometric features of surrounding vertices, we propose a novel context-aware 3D points of interest detection algorithm by adopting the spatial attention mechanism in this article. By designing a context attention module, our approach presents a novel deep neural network to simultaneously pay attention to the geometric features of vertices and their local contexts during extracting points of interest. To obtain satisfactory extraction results, our method adaptively assigns different weights to those features in a data-driven way. Extensive experimental results on SHREC 2007, SHREC 2011, and SHREC 2014 datasets show that our algorithm achieves superior performance over existing methods.
Lecturer Eye Contact on the Students’ English Learning Motivation: Motivated or Demotivated

This study aims to describe the effect of lecturer eye contact on student learning motivation and the students’ perception on it during their interaction in EFL classroom. It employs a descriptive method in cluster random sampling taking 30 students of English Education class. The data sources of this study were observation, questionnaire, and interview and analyzed descriptively. The observation results that students are more likely to pay attention to the material, more enthusiastic in learning and more respectful to the lecturers having and keeping an eye contact in the teaching and learning process. The result of the questionnaire shows that the lecturer eye contact significantly affects the students’ learning motivation indicated by the mean score of the questionnaire, that is 3.71 falling into high category. These two findings are supported by the data gained from the interview in which most students argued that lecturer eye contact has many positive impacts on their learning. An excessive eye contact of the lecturer to the students, however, can become a hindrance to draw students’ focus on the materials presented. Therefore, the lecturer eye contact to nurture students’ learning motivation should be considerably sufficient and not excessive.
Eye Controlled Mouse

Abstract: Numerous individuals who have neuro-locomotor deficits or are disabled by injury are unable to use PCs for abecedarian tasks, for example, sending or receiving dispatches, scouring the web, watching their TV programme, or stirring filmland. In a previous study, eyes were set up to be a great candidate for ubiquitous computing since they always move when they engage with calculating gear. Using this underlying knowledge from eye movements, these instances might be renewed using computers. For this aim, we offer a mouse gesture control mechanism that can only be used by mortal eyes. The goal of this study is to provide an open-source generic eye- gesture control system that can efficiently capture eye movements and allow the stoner to do conduct counterplotted to specified eye movements gestures via computer camera. After identifying it on the stoner's face, it tracks the pupil's motions. It must be accurate in real time for the stoner to use it comfortably, just like any other everyday prejudice.
Impacts of COVID-19 on nursing care in the head and neck oncology outpatient clinic

This study aims to analyze the impacts of COVID-19 on the care of cancer patients in the head and neck surgery outpatient clinic, through the procedures performed in the years 2019, 2020, and 2021. Method: Qualitative, descriptive study about the procedures performed by the nursing team in a head and neck surgery outpatient clinic. Data were collected from January 2019 to December 2021 and computed in an Excel spreadsheet. Results: They showed a negative disparity in the quantity of patient care in the head and neck surgery outpatient clinic in 2019 compared to 2020, in which there was a decrease of 33%. Comparing the results of the year 2019 with 2021 we noticed a drop of 25%. However, when compared to the year 2020 to 2021, there was an increase of 12%. In the number of procedures performed in 2020, there was a decrease of 31% compared to 2019, and, compared to 2021, there was a decrease of 26%, but in the year 2020 to 2021, there was an increase of 13%. Conclusion: The COVID-19 pandemic generated a strong impact on the care provided to patients of the head and neck surgery outpatient clinic, leading to a reduction in the care and provision of nursing care to this clientele, but at the beginning of the pandemic scenario the institutions had to redesign the care protocols to cope with the current situation of the country and the world, thus promoting safer and more efficient assistance.
GPS-BASED LOCATION TRACKING FOR PATIENTS WITH ALZHEIMER’S

A lot of people with Alzheimer's disease and other cognitive disorders are reported missing every year.As a consequence, individual with dementia frequently needs to have their current location monitored.To improve these people's and families lies the quality of life, a reliable and cost-effective tracking system is needed.Continuous monitoring, assistance, intervention, and support place a significant financial and human strain on both the individuals and their caregivers.A potential option for objective, dependable, and remote ambient assisted living monitoring, evaluation, and assistance is available in the form of interconnected sensing technology, such as IoT wearables and gadgets.To ensure the safety of Alzheimer's patients when they leave the house, this proposed framework combines GPS systems with a microcontroller.The patient's position's global coordinates will be given to the caregiver.An admonition is shipped off to the overseer in the event that the patient goes past a preset sweep.The medication's name and dosage are sent to the device and displayed on the app.When a patient collapses, the fall is detected, the caregiver is informed, and emergency care can be provided.
BIG DATA OVER A DETECTED COMMUNITY

Network analysis relies largely on discovering similarity across communities, whilst a community can be strengthened with the help of content information.However, it is largely inhibited by noise that is present in most networks, especially in the link structure.This paper presents a basic approach to combine content with link information in graph-based structures to assist community discoveries.It also tries to reduce the impact of noises commonly found in social networking sites as well as Web-based information networks.We propose to calculate strength of a signal between nodes across the network by combining the link strength, which denotes the probability of that link lying inside a community, with similar content that can be estimated using cosine similarity, or the Jaccard coefficient.Furthermore, we discuss an edge-sampling process to retain locally-relevant edges for every node of the graph.The graph that results could be then clustered by using standard algorithms for community discoveries, such as Markov-clustering and METIS.We experimented on real-world datasets (Wikipedia, CiteSeer and Flickr) by changing sizes and parameters in order to understand the efficacy of our approach versus existing ones.We tried to find a beneficial method to combine approaches for a content and link analysis and a faster biased, graph-sampling approach.
Advanced Audio Signal Processing for Speaker Recognition and Sentiment Analysis

Abstract: Automatic Speech Recognition (ASR) technology has revolutionized human-computer interaction by allowing users to communicate with computer interfaces using their voice in a natural way. Speaker recognition is a biometric recognition method that identifies individuals based on their unique speech signal, with potential applications in security, communication, and personalization. Sentiment analysis is a statistical method that analyzes unique acoustic properties of the speaker's voice to identify emotions or sentiments in speech. This allows for automated speech recognition systems to accurately categorize speech as Positive, Neutral, or Negative. While sentiment analysis has been developed for various languages, further research is required for regional languages. This project aims to improve the accuracy of automatic speech recognition systems by implementing advanced audio signal processing and sentiment analysis detection. The proposed system will identify the speaker's voice and analyze the audio signal to detect the context of speech, including the identification of foul language and aggressive speech. The system will be developed for the Marathi Language dataset, with potential for further development in other languages.
Blockchain-Based Know Your Customer (KYC) System using Advanced Encryption Standard Algorithm

Abstract: Know Your Customer (KYC) is a crucial regulatory obligation for banks and financial institutions to record customer information before providing financial services. However, traditional KYC processes are slow, costly, and prone to errors. To address these challenges, a proposed solution aims to use blockchain technology, smart contracts, Ganache, and Advanced Encryption Standard (AES) encryption in the KYC process. This solution leverages the immutability and transparency of the blockchain to create a secure and efficient KYC process. Smart contracts approve and secure the KYC process, eliminating intermediaries and reducing time and cost. Ganache tests and deploys smart contracts, while AES encryption ensures the confidentiality and integrity of customer data. The proposed architecture involves a proof-of-concept system that can be used as a basis for further development and implementation in the financial industry and other regulated sectors. By leveraging these technologies, the proposed solution can revolutionize KYC processes, providing a secure, decentralized, and efficient process while ensuring the privacy of customers
Evaluating the Impact of Test-Driven Development (TDD) on Software

Test-driven development (TDD), is a methodology for developing software that places an emphasis on the creation of automated tests before the creation of the real code.Proponents of test-driven development (TDD) contend that this methodology can boost overall software quality, cut down on the amount of time needed for development, and increase developer productivity.However, within the community of people who work on software development, there is still some debate about whether or not TDD is effective and to what extent it can be applied in practice.Through a review of the pertinent literature and a meta-analysis of empirical studies, this article performs an analysis of the effects that TDD has on software.The findings imply that TDD can lead to higher software quality, as assessed by metrics such as defect density and code coverage and can also result in faster development times.These benefits can be attributed to the fact that TDD can result in faster development times.On the other hand, the effect of TDD on the productivity of software developers is not as well understood, with some studies indicating benefits while others finding no meaningful difference.In its conclusion, the report addresses some of the shortcomings of previously conducted research and makes some recommendations for possible topics for further investigation.
Resist the type of BWH attack: through introducing discount factor and withdrawal threshold into Bitcoin

Aim: Since the emergence of the Block Withholding (BWH) attack, the prevailing approach in the environment has been to avoid attacking each other as a means of eliminating the threat posed by this attack. However, there is currently no effective and simple scheme to entirely prevent such attacks. To address this issue, this study proposes a novel blockchain model that combines the discount factor and withdrawal threshold to resist the risks associated with these attacks. Methods: This paper provides an overview of blockchain models and the BWH attack. We constructed a network simulation model based on the physical logic of Bitcoin and introduced the discount factor and withdrawal threshold as new parameters to enhance the model's resistance to BWH attacks. We evaluated the effect of these parameters on the model's ability to defend against BWH attacks from the perspective of rewards. Results: This paper presents a novel blockchain model that integrates discount factors and withdrawal thresholds and examines attacker behavior in this model from a rewards-based perspective by comparing different computational environments. Our experimental data indicate that this model has significant advantages in resisting BWH attacks. Conclusion: The experimental results demonstrate that the proposed model is highly effective in resisting profit-driven BWH attacks, with a 98% chance of successfully resisting such attacks at a fixed computational power. This indicates that the proposed model can effectively eliminate the potential threat posed by BWH attacks.
Deep Learning Based Differential Classifier of PRIDE and RC5

Deep learning-based cryptanalysis is one of the emerging trends in recent times. Differential cryptanalysis is one of the most potent approaches to classical cryptanalysis. Researchers are now modeling classical differential cryptanalysis by applying deep learning-based techniques. In this paper, we report deep learning-based differential distinguishers for block cipher PRIDE and RC5, utilizing deep learning models: CNN, LGBM and LSTM. We found distinguishers up to 23 rounds for PRIDE and nine rounds for RC5. To the best of our knowledge this is the first deep learning based differential classifier for cipher PRIDE and RC5.
Bit Security Estimation for Leakage-Prone Key Establishment Schemes

The security guarantees of cryptographic primitives are subject to the assumption that established keys are known only by the legitimate users and no information about the key bits is known by illegitimate users.Unfortunately, this assumption may not be applicable in leakage-prone key establishment schemes.Namely, information leakage about an established key (defined as a bit inference rate of an adversary that is strictly greater than 50%) reduces its computational effort required in an exhaustive key search.In this paper, we present a methodology and a polynomial-time algorithm that determines the exact impact of information leakage on a generated bit sequence and expressed these findings in terms of the achieved level of bit security.Additional simulation results enable us to determine the achieved level of bit security of a leakage-prone bit sequence or, conversely, enable us to determine the length of a bit sequence necessary to achieve a selected level of bit security.
Wearable Real-time Air-writing System Employing KNN and Constrained Dynamic Time Warping

In the digital world, gesture recognition plays a crucial role in human-computer interaction (HCI). In this paper, we propose an innovative wearable air-writing system that allows users to write the English alphabet and Arabic numerals in free space without using any predefined gestures or rules. Based on an Inertial Measurement Unit (IMU), the proposed air-writing wearable device uses the constrained dynamic time warping (cDTW) algorithm for the distance measure and K-nearest neighbors (KNN) as the classifier. In addition, to increase the recognition accuracy and meet HCI requirements, we develop a novel method that allows users to rapidly switch to correct recognition results when the initial results are erroneous. In the experiment, the accuracy rate is 88.9% for the alphabet and 10 decimal digits in the user-dependent condition, and the recognition is in real-time, consuming only 0.427s for each character, which is superior to many other approaches that employ classic DTW or FastDTW. With the proposed HCI design, character input accuracy of over 95% can be obtained in about 1 second. We also simulated the application scenarios of Parkinson’s disease patients and obtained a high accuracy rate of 85.4%. Besides, we explored the variety of K values in KNN and w values in cDTW, and propose a multi-template system that gives new optimization directions for the KNN-cDTW algorithm.
Distributed Subspace Projection Graph Signal Estimation with Anomaly Interference

The estimation of graph signal is a vital problem in many distributed networks, such as vehicular networks, smart grids, unmanned aerial vehicles (UAVs), and the Internet of Things. In those networks, anomaly interference widely exists, such as network attack, noise, device fault, which will hazard the healthy of the entire system. In the paper, the estimation of graph signals with anomaly interference is investigated. We show that the graph signal estimation problem can be treated as a bandlimited subspace optimization problem, and propose a distributed subspace projection graph signal estimation algorithm based on the graph filter (DispGF), which can achieve better performance with less communication burden. In addition, a graph filter matrix that produces subspace projection is proposed to replace the nonsparse projection matrix, which guarantees distributed implementation and projection accuracy. Different from previous work, here, graph signal estimation is studied with no prior anomaly information. To this end, for FDI attack, random attack, noise interference, we propose anomaly detection and node localization scheme based on smoothness, that can achieve similar performance compared with the case of prior anomaly information known. Numerical experiments verify the effectiveness of the proposed DispGF algorithm. The convergence of the algorithm is theoretically analyzed.
EAIBench: An Energy Efficiency Benchmark for AI Training

The increase in computing power has prompted more considerable artificial intelligence (AI) model scales. From 341K multiply-accumulate operations (MACs) of LeNet-5 to 4.11G MACs of ResNet-50, the computational cost of image classification has increased by 10,000 times over two decades. On the other hand, it has inevitably brought about an increase in energy consumption, and benchmarking the energy efficiency of the modern AI workloads is also essential. Existing benchmarks, such as MLPerf and AIBench, focus on performance evaluation of AI computing, the time to the target accuracy (TTA) is the primary metric. Corresponding to the TTA metric, using the energy consumption, where the AI workload achieves the specific accuracy, is a straightforward energy measurement method. However, it is too time-consuming and power-hungry, which is unacceptable for energy efficiency benchmarking. This work introduces a new metric to quickly and accurately benchmark AI training workloads’ energy efficiency, called the Energy-Delay Product of one Epoch (EEDP). The EEDP is calculated based on the product of the energy and time consumption within one training epoch, where one epoch refers to one training cycle through the entire training dataset. It can reflect not only the energy consumption but also the time efficiency and suit the energy efficiency of the AI training workloads. Then, we introduce an AI training energy efficiency benchmark named EAIBench, which covers different energy efficiency dimensions, including dominant layers, computation intensities, and memory accesses. Our evaluation results demonstrate that EAIBench can provide reproducible and meaningful results in only dozens of minutes, which is hundreds of times faster than the existing AI training benchmark method.