Reviewerly/exHarmony huggingface dataset:
Privacy-aware Human Activity Recognition with Smart Glasses for Digital Therapeutics

Human Activity Recognition (HAR) holds valuable potential as a digital biomarker, offering insights into individuals' physical activities and well-being. Leveraging wearable devices to monitor human activities can serve as an integral component of digital therapeutics, offering valuable insights into an individual's health and behavior. However, privacy concerns in HAR systems pose a significant challenge as users' data can be at risk of exposure and necessitates robust protection measures. To mitigate these concerns, the adoption of privacy-preserving techniques, such as federated learning, becomes necessary. This paper addresses the topic of Privacy-aware Human Activity Recognition (HAR) with Smart Glasses and explores a federated learning approach to achieve accurate recognition while preserving data privacy. The study highlights the trade-off between model complexity, recognition performance, and data privacy. Initially, a comparison was conducted between a complex deep learning network (STResNet) and a simpler deep learning network (DANA). The complex network exhibited an F1-Macro score of 0.87, while the simpler network achieved 0.82. Subsequently, the simpler network was further evaluated by comparing it to its federated learning implementation. The results revealed that the simpler network attained an F1-Macro score of 0.85, whereas federated learning achieved a slightly lower score of 0.80. These findings shed light on the performance disparities between the different approaches.
Feasibility of Universal Anomaly Detection Without Knowing the Abnormality in Medical Images

Many anomaly detection approaches, especially deep learning methods, have been recently developed to identify abnormal image morphology by only employing normal images during training. Unfortunately, many prior anomaly detection methods were optimized for a specific "known" abnormality (e.g., brain tumor, bone fraction, cell types). Moreover, even though only the normal images were used in the training process, the abnormal images were often employed during the validation process (e.g., epoch selection, hyper-parameter tuning), which might leak the supposed "unknown" abnormality unintentionally. In this study, we investigated these two essential aspects regarding universal anomaly detection in medical images by (1) comparing various anomaly detection methods across four medical datasets, (2) investigating the inevitable but often neglected issues on how to unbiasedly select the optimal anomaly detection model during the validation phase using only normal images, and (3) proposing a simple decision-level ensemble method to leverage the advantage of different kinds of anomaly detection without knowing the abnormality. The results of our experiments indicate that none of the evaluated methods consistently achieved the best performance across all datasets. Our proposed method enhanced the robustness of performance in general (average AUC 0.956).
New trend for LWE application in different computer science fields

Problems of large keys in the on-lattice ring signature link due to reduce to the lattice, based on the problem of fault-tolerant learning on the ring (RLWE), according to "Homomorphic Commitment→∑-Protocol→Fiat-Shamir Transformation" reconstructs a linkable ring signature scheme on a lattice. First construct a base “Homomorphic commitment scheme” on polynomial ring of RLWE difficult, and use the Fiat-Shamir transformation method. The method converts the ∑-protocol into a linkable ring signature scheme, and suggests linkable ring signature scheme model. It is resistant to quantum computer attacks. compared with the previous linkable ring signature schemes on lattices, proposed methods with high computational, simple and less time frame because the ring elements in the scheme are taken from small polynomials which represents as case of application in computer science.
A General Overview on the Categories of Image Features Extraction Techniques: A Survey

In the image processing’s field and computer vision it’s important to represent the image by its information. Image information comes from the image’s features that extracted from it using feature detection/extraction techniques and features description. Features in computer vision define informative data. For human eye its perfect to extract information from raw image, but computer cannot recognize image information. This is why various feature extraction techniques have been presented and progressed rapidly. This paper presents a general overview of the feature extraction categories for image.
A Model Ensemble Approach for Conversational Quadruple Extraction

Fine-grained sentiment analysis of dialogue text is crucial for the model to understand the conversational participants’ viewpoints and provide accurate responses in generating replies. Unfortunately, in the field of conversational opinion mining, coarse-grained dialogue emotion analysis remains the mainstream approach, despite being unable to meet the actual needs in some specific scenarios such as customer service question and answer system. This work focuses on conversational aspect-based sentiment quadruple analysis, which aims to detect the sentiment quadruple of target-aspect-opinion-sentiment in a dialogue. In this study, we mainly extract triplets and judge the unique sentiment, which is determined by the target and opinion terms together. For this purpose, we fine-tune the pre-trained language models using the DiaASQ dataset. We optimize the rotation positional information embedding by combining the actual length of the dialogue text and use adversarial training to enhance the model’s performance and robustness. Finally, We use beam search ensemble algorithm to improve the entire triplet extraction system’s performance. Our system achieved an average F1 score 40.50 that ranked second in the Chinese dataset and fifth in the general dataset for the Conversational Aspect-based Sentiment Quadruple Analysis shared task at NLPCC-2023.
Enhancing MOBA Game Commentary Generation with Fine-Grained Prototype Retrieval

With the development of the esports industry, more and more people are immersing themselves in watching various competitive matches, such as MOBA (Multiplayer Online Battle Arena) matches. Although MOBA games are attractive, the complexity of the games themselves also makes it difficult for many audiences to enjoy them easily without the assistance of professional commentators. This work studies using AI techniques to generate game commentaries automatically. Compared to human commentators, AI commentators can be more objective and work at any time and place at a low cost. Following the previous MOBA-E2C framework, we first use event handlers to extract various highlight events from the game metadata and organize them as event tables; then, this task can be regarded as a table-to-text task. Subsequently, this work proposes a BART-based MOBA-FPBART framework for further improving the generation quality of MOBA game commentaries by retrieving the human-written prototypes as guidance. On the one hand, in few-shot scenarios, we use a Fine-Grained Prototype Retrieval method to retrieve more relevant prototypes based on the characteristics of event tables. On the other hand, we also use a Corse-Grained Prototype Retrieval method in zero-shot scenarios. Experimental results on Dota2-Commentary have demonstrated our approach can notably outperform previous SOTA MOBA-FuseGPT in various metrics.
Non‐Volatile Hybrid Optical Phase Shifter Driven by a Ferroelectric Transistor

Abstract Optical phase shifters are essential elements in photonic integrated circuits (PICs) and function as a direct interface to program the PICs. Non‐volatile phase shifters, which can retain information without a power supply, are highly desirable for low‐power static operations. Here a non‐volatile optical phase shifter is demonstrated by driving a III‐V/Si hybrid metal‐oxide‐semiconductor (MOS) phase shifter with a ferroelectric field‐effect transistor (FeFET) operating in the source follower mode. Owing to the various polarization states in the FeFET, multistate non‐volatile phase shifts up to 1.25π are obtained with CMOS‐compatible operation voltages and low switching energy up to 3.3 nJ. Furthermore, a crossbar array architecture is proposed to simplify the control of non‐volatile phase shifters in large‐scale PICs and verify its feasibility by confirming the selective write‐in operation of a targeted FeFET with a negligible disturbance to the others. This work paves the way for realizing large‐scale non‐volatile programmable PICs for emerging computing applications such as deep learning and quantum computing.
SCA-CLS: A New Semantic-Context-Aware Framework for Community-Oriented Lexical Simplification

Community-oriented lexical simplification aims to transform complex words within a sentence into semantically consistent but simple substitute words from a community-specific vocabulary. Most state-of-the-art contextual word embedding models generate substitutes by extracting contextual information of complex words. Although these models take context into account, they fail to capture rich semantics of complex words with polysemy, resulting in many spurious and semantically non-equivalent candidates. Thus, this paper proposes a novel Semantic-Context-Aware framework for Community-oriented Lexical Simplification (SCA-CLS), which integrates gloss (sense definition) into BERT to identify the actual sense of the complex word (especially for polysemy) in current context and ranks substitutes by proposed gloss similarity. In addition, a new complexity feature is proposed to enhance substitute ranking. Experiment results on Wikipedia dataset show that SCA-CLS outperforms the state-of-the-art Merge-Sort model on both substitute generation and ranking tasks, indicating its effectiveness for community-oriented lexical simplification.
Addressing blockchain privacy and efficiency challenges in mobile environments: an optimization strategy for lightweight Application of data analysis in the field of investment

As advancing technology makes a viable option to value a stocks and also determine their economic value has become increasingly important.In the global securities market, changes in stock prices can have a significant impact on changes in the global economy. For enterprises, stock price fluctuations reflect their current financial situation, changes in industry supply and demand, and changes in the international macro environment. [1]When people interact the internet information system, large data which is based on this system will be generated by the people. To some extend, bid data can reflect the law and problems for some certain field, and make some predictions with a certain range. In order to analysis the law of stock price, we need to combine the stock itself and the changing market factors so that we can obtain accurate price forecast. When Solving nonlinear and the complex problem, the LSTM model has better performance to process the data[2]. In addition, the CNN model is also one of the most potential model which can process a large amount of data to improve the accuracy of prediction[3]. This paper will select six indicators to conduct establishment of arbitrage pricing model.Trying to optimize the model and find the factors which are more correlated with stock price in these six factors.We first set up a simple linear regression model and then use stepwise function.Secondly, we set up ridge regression and lasso regression and delete some of these factors. Then we randomly select 70percent of the from the compete data, and set up an evaluation index of the computational model. And finally we find in these data, step regression perform best comparing with the ridge regression and lasso regression, and degree of this model for the EBAY can reach over 90 percent.
Thermal Crosstalk Effects in a Silicon Photonics Neuromorphic Network

We propose a methodology to include thermal crosstalk effects in the modeling of neuromorphic photonic circuits. Through component-level simulations of device building blocks and thermal analysis, we are able to successfully account for thermal effects, as shown by a comparison with experimental measurements of a 3×3 programmable optical circuit.
Trade-off relations of geometric coherence

Abstract Quantum coherence is an important quantum resource and it is intimately related to various research fields. The geometric coherence is a coherence measure both operationally and geometrically. We study the trade-off relation of geometric coherence in qubit systems. We first derive an upper bound for the geometric coherence by the purity of quantum states. Based on this, a complementarity relation between the quantum coherence and the mixedness is established. We then derive the quantum uncertainty relations of the geometric coherence on two and three general measurement bases in terms of the incompatibility respectively, which turn out to be state-independent for pure states. These trade-off relations provide the limit to the amount of quantum coherence. As a byproduct, the complementarity relation between the minimum error probability for discriminating a pure-states ensemble and the mixedness of quantum states is established.
Calibration of Video Cameras Frame Rate based on Onsets Tracking Technique

SummaryThis paper presents a calibration method developed at the Standards and Calibration Laboratory (SCL) for calibration of the frame rate of a video camera. The measurement setup includes a signal generator, a flashing light source and an in-house developed intensity analysis program. The calibration principle is based on analysing the intensity variation of a flashing light source recorded by a video camera under test. The onsets tracking technique is applied to track the glowing onsets instance of the flashing light source at particular frames, which offers traceable reference for frame rate setting of a video camera. Frame rates of a high speed camera calibrated by the proposed method are presented and discussed in this paper.
Towards a standardised cross-sectoral data access agreement template for research: a core set of principles for data access within trusted research environments

IntroductionTrusted Research Environments (TREs) are secure computing environments that provide access to data for approved researchers to use in studies that can save and improve lives. TREs rely on Data Access Agreements (DAAs) to bind researchers and their organisations to the terms and conditions of accessing the infrastructure and data use. However, DAAs can be overly lengthy, complex, and can contain outdated terms from historical data sharing agreements for physical exchange of data. This is often cited as a cause of significant delays to legal review and research projects starting. ObjectivesThe aim was to develop a standardised DAA optimised for data science in TREs across the UK and framed around the `Five Safes framework' for trustworthy data use. The DAA is underpinned by principles of data access in TREs, the development of which is described in this paper. MethodsThe Pan-UK Data Governance Steering Group of the UK Health Data Research Alliance led the development of a core set of data access principles. This was informed by a benchmarking exercise of DAAs used by established TREs and consultation with public members and stakeholders. ResultsWe have defined a core set of principles for TRE data access that can be mapped to a common set of DAA terms for UK-based TREs. Flexibility will be ensured by including terms specific to TREs or specific data/data owners in customisable annexes. Public views obtained through public involvement and engagement (PIE) activities are also reported. ConclusionsThese principles provide the foundation for a standardised UK TRE DAA template, designed to support the growing ecosystem of TREs. By providing a familiar structure and terms, this template aims to build trust among data owners and the UK public and to provide clarity to researchers on their obligations to protect the data. Widespread adoption is intended to accelerate health data research by enabling faster approval of projects, ultimately enabling more timely and effective research.
Blockchain Technology Research and Application: A Literature Review and Future Trends

Blockchain, as the foundation for cryptocurrencies, has recently garnered significant attention. It serves as an immutable distributed ledger technology, which allows transactions to be carried out reliably in a decentralized environment. Blockchain-based applications are emerging, covering numerous fields, including financial services, reputation systems, the Internet of Things (IoT), and more. However, blockchain technology still faces several challenges, such as scalability, security, and other unresolved issues that need to be overcome. This article provides a comprehensive overview of blockchain technology and its applications. We commence with a summary of the development of blockchain, followed by an examination of the blockchain architecture and a systematic review of the research and application of blockchain technology in different fields from the perspectives of academic research and industry technology. Additionally, we briefly outline the technical challenges and recent developments. We also explore possible future trends in blockchain.
A Privacy-Preserving Takeaway Delivery Service Scheme

More and more applications based on location services continue to make our lives rich and convenient. However, location information may also quietly expose our privacy, such as work location, eating habits, etc. In this article, we have designed a solution to the problem of invisibly leaking our takeaway order information. In our scheme, users only need to submit a service request. The edge server is responsible for the service response and completes most calculations. The service platform generates orders based on the calculation results, sends them to the merchants and returns them, and selects suitable delivery men for the users. The proposed scheme uses non-interactive key exchange and secure Manhattan distance calculation to protect the location privacy of mobile users. Security analysis shows the proposed scheme is privacy-protected under our defined threat model. In addition, our program experiment proved to be feasible.
Deep Anomaly Detection for Automotive Components by Oversampling

Training of deep neural networks (DNNs) requires large amounts of data. However, the automotive components that are the subject of this research have an extreme lack of defective product data due to rapid model changes and a low defective product rate during the manufacturing process. Additionally, the anomaly areas are negligible. Data augmentation (DA), which increases data by image transformations, is a method for solving data deficiency. Particularly, a deep convolutional generative adversarial network (DCGAN) is frequently employed in the medical industry. DA is shown to have an effect on not small anomalies but on images that are accounted by the classification target for a large percentage of the total image.
Spectral continuous tuning technique based on liquid crystal

In order to solve the mechanical error caused by manual rotation of LCVR in polarization imaging system and to obtain high spatial resolution of images, a liquid crystal based continuous spectral tuning technique is proposed in this paper. By studying the electronically controlled birefringence effect of liquid crystal, adjusting the thickness of delay slide and applying voltage field to the liquid crystal box, the electronically controlled birefringence phenomenon can be generated to change the refractive index difference of o light and e light. The phase difference between the two is modulated by the control voltage to realize the output of different wavelengths. The continuous tuning technique of liquid crystal is applied to the tuning method of spectral band. The improved tuning technique can improve the quality of the obtained images from the experimental data.
Differential Privacy and $k$-Anonymity-Based Privacy Preserving Data Publishing Scheme With Minimal Loss of Statistical Information

Though anonymization mechanisms have made huge progress in fostering the secondary use of data, it is still very challenging to obtain adequate knowledge from anonymized data while preserving privacy. Most existing mechanisms anonymize entire sections of data and fail to maximally preserve the structure/values of real data. Consequently, the performance of those mechanisms and the output (i.e., the anonymized data) remain problematic in real-life scenarios due to the extensive and unneeded anonymization applied. To address these issues, we propose and implement a hybrid (differential privacy (DP) and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula> -anonymity) anonymization scheme that produces supreme-quality anonymized data that offers knowledge similar to real data without compromising privacy. Specifically, we implement a pair of algorithms that divide the dataset into privacy-violating and nonprivacy-violating partitions. Afterward, in a nonprivacy-violating partition, a relaxed privacy budget <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\epsilon$</tex-math> </inline-formula> is applied to numerical attributes, but most of the categorical attributes are retained (as is) for informative analysis. In privacy-violating partitions, fewer changes are applied to the data by using a reasonable value for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\epsilon$</tex-math> </inline-formula> and by exploiting the diversity in sensitive information. Experiments are conducted on three real-life datasets to prove the feasibility of our scheme for futuristic AI applications. Compared with state-of-the-art (SOTA) methods, our scheme preserves 60.81% of the originality in the anonymized data. The privacy risks are reduced by 20.05%, and utility is enhanced by 54.01% and 15.33% based on information loss (IL) and accuracy metrics. Furthermore, the time overhead is 3.13 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times$</tex-math> </inline-formula> lower than the SOTA methods.
The Impact of the Distance Weighting Function on the Performance of KNearest Neighbor Algorithm in Medical Data sets

The K nearest neighbor (KNN) technique is well recognized and extensively used in the field ofmachine learning classification algorithms. In this study, the performance of distance weighting functions,one of the most important factors affecting the performance of KNN, was compared. These functions areequal, inverse, and squared-inverse. The performance of the functions was examined in five differentmedical data sets. To evaluate the performances, a confusion matrix and five different metrics commonlyused to evaluate classification problems were used. Out of the five data sets used in the research, the inverseKNN method yielded effective outcomes in four of them, while both the equal and squared-diverse methodsachieved success in three data sets.