Reviewerly/exHarmony huggingface dataset:
Gesture Recognition in Millimeter-Wave Radar Based on Spatio-Temporal Feature Sequences

Gesture recognition is a pivotal technology in the realm of intelligent education, and millimeter-wave (mmWave) signals possess advantages such as high resolution and strong penetration capability. This paper introduces a highly accurate and robust gesture recognition method using mmWave radar. The method involves capturing the raw signals of hand movements with the mmWave radar module and preprocessing the received radar signals, including Fourier transformation, distance compression, Doppler processing, and noise reduction through moving target indication (MTI). The preprocessed signals are then fed into the Convolutional Neural Network-Time Domain Convolutional Network (CNN-TCN) model to extract spatio-temporal features, with recognition performance evaluated through classification. Experimental results demonstrate that this method achieves an accuracy rate of 98.2% in domain-specific recognition and maintains a consistently high recognition rate across different neural networks, showcasing exceptional recognition performance and robustness.
Promoting the Transparency of AI-Generated Inferences

Many businesses today use artificial intelligence to generate inferences from the personal data they collect. This practice allows companies to better understand individual consumer preferences. However, it often occurs secretly, without consumers being aware of it. Consumers' privacy may then be compromised as they lack control over the accuracy, flow, and use of the inferential data. Although current data protection regulations, like the General Data Protection Regulation (GDPR), provide data subject rights, access to these inferences is not guaranteed. Businesses can deny inferences access requests by exploiting the broad scope of trade secrecy law, citing their interest in protecting such data as trade secrets. This Essay deems it essential to reexamine the scope and application of trade secrets law in this context. After providing a descriptive analysis of the underlying legal frameworks in the U.S. and EU that empower businesses to categorize consumer inferences as trade secrets, the Essay suggests that data protection or consumer protection authorities should carefully examine the scope of trade secrets law in their respective jurisdictions and issue guidelines to limit potential abuse of the law.
Blockchain Mining Power and Market Quality in Cryptocurrency Trading

This paper investigates how the aggregate mining power of blockchain affects the market quality of cryptocurrency trading platforms. Using historical intraday data of three cryptocurrencies traded on three platforms from January to June 2021, we observe that a decrease in the blockchain’s aggregate mining power results in wider price discrepancies across platforms and reduced liquidity. We also analyze abnormal hash-rate and find consistent results that support our previous findings. The contribution of this study lies in empirically exploring the connection between blockchain mining and the trading environment of the crypto market, highlighting the importance of considering the impact of blockchain validation process when designing fair and efficient markets for crypto assets.
Searching Together: A Theory of Human-AI Co-Creativity

The recent developments have enabled generative AI (GAI) to produce content across various modalities that is often indistinguishable from human-generated content, challenging the long-held belief that creativity is a human prerogative. In the light of this transformative technological evolution, we propose a novel theory of human-AI co-creativity (HACC), conceptualizing creativity as a joint search process. This creative search consists of three main tasks—construction of a search space, movement through this space, and evaluation of points in it. In developing a framework of multi-actor joint search, we systematically analyze the space of possible configurations of human-AI collaboration for creative tasks, along the dimensions of specialization of actors and sequencing of tasks. By delving into the intersections of human creativity and advancements in GAI, we explore the implications of the human-AI collaborative creative process within organizational contexts and spur future discussions around developing human and AI co-creativity while sustaining human skills and agency.
Discriminatory Discretion: Theory and Evidence From Use of Pretrial Algorithms

This article examines the biased usage of an algorithm, an understudied topic relative to the massive body of research that examines how algorithms may be biased. Using highly detailed administrative data, I study a large sample of high-stakes decision makers--New Jersey police and judicial officers--who are armed with a freely available algorithm. When officers consider requesting a warrant for a defendant's detention, they have complete discretion over whether to consult an algorithmic risk score that predicts a defendant’s likelihood of failing to appear in court as well as the defendant’s likelihood of being rearrested if released. I find that officers frequently choose not to look at information that is free, simple, and non-binding. Moreover, the choice of whether to view the algorithm is far from random. Controlling for underlying risk, officers are less likely to consult the risk score for black defendants (relative to white defendants) accused of lesser crimes, but the relationship is reversed for severe crimes. Then, once the risk scores are seen, officers are more likely to issue warrants for black defendants, again controlling for risk. The black-white warrant gap is smallest for the most and least risky defendants, and grows for more moderate-risk defendants. I organize these empirical facts in a novel taste-based discrimination framework in which agents are averse to certain groups, but also averse to appearing prejudiced. The key prediction of this avoidant animus is that agents will discriminate more in situations that are more ambiguous in an effort to curate their preferred image. I conclude by discussing policy implications for prejudice reduction, automation, and the discretionary use of decision aids.
Machine Learning Techniques in Computer-Aided Diagnosis for Effective Detection of Malignant Tissues

Cancer is the most general term collectively used for a variety of diseases that can affect any part of the body. Malignant tumors and neoplasms are other words that are used for cancer. Cancer is the quick development of aberrant cells that proliferate outside of their normal borders and have the potential to move to other organs and infiltrate nearby bodily parts and is the leading cause of death worldwide. Medical experts may use computer-aided Diagnosis (CAD) as a second opinion and to save their valuable time from manual analysis. Practitioners use various modalities such as X-ray, Computed Tomography (CT), and Magnetic Resonance Imaging (MRI). X-ray images are helpful in early diagnosis, but for effective diagnosis and to pinpoint the malignant issues, MRI and CT images are preferred. Even though MR images are fine-grained and detailed images of organs, during the acquisition process some rician and Gaussian noise may creep up. It is necessary for a diagnostic system to denoise the images before segmentation and classification to attain better accuracy for the models. This chapter aims to provide insights into the machine learning algorithms used by various CAD systems which aim to detect and segment various types of cancers such as prostate, cervix, pancreas, colon, and kidney.
TNSAR: Temporal Evolution Network Embedding Based on Structural and Attribute Retention

Network embedding (NE) focuses on mapping each node within a network into a condensed vector representation of lower dimensional, while simultaneously retaining the original network structure and attribute features as faithfully as possible. It applies to a wide range of tasks of network mining, including link prediction, node classification, user recommendation, etc. Most existing network embedding models primarily aim at static networks, which require node knowledge in the full-time span. However, various real-world network is dynamic, and the nodes in the network change frequently. Although dynamic network embedding has garnered attention from a limited number of researchers, their approach has been restricted to learning node representations in isolation, without considering the integration of higher-order topological structure and attribute features of the nodes. To better address the above problems, a Temporal Evolution Network Embedding Model based on Structural and Attributes Retention (TNSAR) is proposed. In summary, our contributions include Introducing the graph decomposition technique for capturing global higher-order topological features, including higher-order structure, a fusion of attribute and topological information in the Graph Convolutional Network (GCN) network for node representation, and the temporal preservation component. The experimental results validate the effectiveness of TNSAR in network embedding tasks, showcasing its superiority over baseline methods.
RefactorScore: Evaluating Refactor Prone Code

We propose <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RefactorScore</small> , an automatic evaluation metric for code. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RefactorScore</small> computes the number of refactor prone locations on each token in a candidate file and maps the occurrences into a quantile to produce a score. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RefactorScore</small> is evaluated across 61,735 commits and uses a model called <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RefactorBERT</small> trained to predict refactors on 1,111,246 commits. Finally, we validate <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RefactorScore</small> on a set of industry leading projects providing each with a <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RefactorScore</small> . We calibrate <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RefactorScore</small> 's detection of low quality code with human developers through a human subject study. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RefactorBERT</small> , the model driving the scoring mechanism, is capable of predicting defects and refactors predicted by <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RefDiff 2.0</small> . To our knowledge, our approach, coupled with the use of large scale data for training and validated with human developers, is the first code quality scoring metric of its kind.
Classical Communication Coset State (t,n) Threshold Quantum Digital Signature

In the field of quantum communication, classical communication processes have become a central open problem, especially in quantum threshold signature schemes. However, the existing quantum threshold signature schemes do not solve this problem well, and the signature verification process is relatively complicated. Therefore, the aim of this study is to propose a novel quantum threshold digital signature scheme that aims to realize classical communication and simplify the quantum threshold digital signature communication model while ensuring the security of the signature. Our approach constructs a binary linear secret-sharing matrix based on the properties of the coset states in the quantum state to share the master secret value and distribute the quantum tokens of the members. Unlike traditional schemes, our verification operation does not require the intervention of an arbitrator, thus simplifying the signature-verification process. In addition, we propose a novel reuse quantum-token protocol based on the length of the hash message to-be-signed to enable single-member signatures over multiple bits. This scheme has other potential applications, including eavesdropper identification, quantum threshold proxy signatures, and more flexible quantum threshold schemes. In addition, complex measurement operations are not required to verify the signatures, thus fulfilling the requirements of practical applications. In this paper, we present an innovative approach for solving the classical communication problem in quantum threshold signature schemes, which successfully solves the central open problem in this field by transforming the measurements of quantum states into classical communication. We believe that this approach will provide a more feasible solution for secure communication.
A Method of Self-Supervised Denoising and Classification for Sensor-Based Human Activity Recognition

Human activity recognition (HAR) is an important subfield of pervasive computing and pattern recognition. While researchers have achieved remarkable results in feature extraction and classification for sensor-based HAR, they have encountered performance bottlenecks. Sensor signal denoising has emerged as an excellent approach to enhance the performance of sensor-based HAR architectures. In this study, we propose a novel self-supervised blind denoising method for sensor signals, which serves as a new module in the HAR task and significantly improves the overall system performance. Our method applies the idea of masking from image inpainting to temporal signal processing, and utilizes the neighboring signal to predict the signal at the center, leveraging independent noisy measurements and temporal relationships between real neighboring signals, without the need to understand the noise distribution. The denoising function is learned from samples, and the denoised signals are then fed into the classification model. Experimental results conducted on benchmark datasets WISDM, UCI-HAR, and PAMAP2 demonstrate the effectiveness of our denoising method. After denoising, the inner-class divergence of actions decreases, while the outer-class divergence increases. As a result, the recognition accuracy of the system significantly improves to 98.6%, 97.64%, and 97.12% on the respective datasets.
Catch me if you can: A Secure Bilateral Access Control System With Anonymous Credentials

Bilateral access control model, emerging as a novel paradigm in access control, has garnered extensive deployment within the domain of fog computing. This model offers on-demand data services, enabling the efficient identification of sensitive data without resorting to resource-intensive decryption procedures. Nonetheless, prevailing solutions exhibit impracticalities. Specifically, they fall short in supporting adaptive security, while presuming unwavering trustworthiness of the central authority. In this paper, we introduce a pioneering fine-grained and adaptively secure bilateral access control system through enhancements to the matchmaking attribute-based encryption (MABE) framework. We give a formalized definition of MABE, incorporating desirable security features such as blindness and unlinkability, aimed at capturing potential misconduct by the central authority. We propose a generic construction of MABE, drawing upon attribute-based encryption (ABE) and anonymous credential schemes (ACS), with provable security via formal security reduction in the adaptive model. We present an efficient instantiation of the MABE framework by introducing a practical ACS solution, wherein a cryptographic accumulator is employed to enhance performance. Experimental simulations substantiate that our solution not only has superior functionalities but also demonstrates performance on par with state-of-the-art solutions.
Long-Term Trajectory Prediction of Hypersonic Glide Vehicle Based on Physics-Informed Transformer

The long-term prediction of hypersonic glide vehicle (HGV) trajectory based on radar tracking data still faces challenges. Existing approaches are hard to provide satisfactory performances in long-term HGV trajectory prediction tasks due to insufficient long-range dependency extraction ability and cumulative errors. To address the above issues, a trajectory prediction model physics-informed Transformer (PIT) is proposed in this work. The top$\tau$-mean attention mechanism and the generative decoder are designed in this model to extract the trajectories' long-range dependency and reduce the cumulative errors. Furthermore, the physical knowledge is applied at both the input and output ends of the model, which further improves the model's prediction accuracy even in the case of missing a small amount of radar tracking data. A simulated HGV trajectory dataset is established based on the dynamic equations for the PIT trajectory prediction model. The experimental results show that the PIT performs better than other state-of-the-art deep neural network models. The additional experiments on a small-scale dataset and datasets with missing tracking data further verify the excellent performance and robustness of the PIT in these cases.
A Decentralized Resource Allocation in Edge Computing for Secure IoT Environments

The expansion of Internet of Things (IoT) devices and their integration into a variety of vital sectors has created serious concerns regarding data protection, privacy, and resource management. As a promising model, edge computing has the ability to overcome these difficulties by putting the computing power closer to IoT devices. This article presents a novel approach for decentralized resource allocation in edge computing settings, with the goal of improving the security and efficiency of IoT systems. Edge nodes are critical in our proposed framework for managing and assigning computing resources to IoT devices, minimizing latency, and optimizing network traffic. The decentralization of resource distribution promotes resilience in the event of network outages or cyberattacks and provides robustness against single points of failure. We created a proof-of-importance (PoI) consensus mechanism for creating new blocks in the blockchain integrated edge-computing IoT devices. Therefore, the consensus mechanism will ensure the trust and security of IoT devices by authentication of each user in the network. We performed a series of experiments in a simulated edge-computing setting to assess the feasibility of our proposed method. We analyze the proposed system model based on the operation of three different file delivery and transactions. The simulation outcomes show that the blockchain system efficiently delivers the files and increases the transmission rate. We also compared our file delivery and transmission rate with existing techniques, and our proposed model provides a better result. Finally, we compared the power consumption of creating IoT nodes based on proof-of-work (PoW), proof-of-stake (PoS), and PoI. The proposed PoI consensus mechanism consumes less power than the other two methods.
CERT-DF: A Computing-Efficient and Robust Distributed Deep Forest Framework With Low Communication Overhead

As an alternative to the deep learning model, deep forest outperforms deep neural networks in many aspects with fewer hyperparameters and better robustness. To improve the computing performance of deep forest, ForestLayer proposes an efficient task-parallel algorithm S-FTA at a fine sub-forest granularity, but the granularity of the sub-forest cannot be adaptively adjusted. BLB-gcForest further proposes an adaptive sub-forest splitting algorithm to dynamically adjust the sub-forest granularity. However, with distributed storage, its BLB method needs to scan the whole dataset when sampling, which generates considerable communication overhead. Moreover, BLB-gcForest's tree-based vector aggregation produces extensive redundant transfers and significantly degrades the system's performance in vector aggregation stage. To deal with these existing issues and further improve the computing efficiency and scalability of the distributed deep forest, in this paper, we propose a novel Computing-Efficient and RobusT distributed Deep Forest framework, named CERT-DF. CERT-DF integrates three customized schemes, namely, block-level pre-sampling, two-stage pre-aggregation, and system-level backup. Specifically, CERT-DF adopts the block-level pre-sampling method to implement data blocks' local sampling eliminating frequent data remote access and maximizing parallel efficiency, applies the two-stage pre-aggregation method to adjust the class vector aggregation granularity to greatly decrease the communication overhead, and leverages the system-level backup method to enhance the system's disaster tolerance and immensely accelerate task recovery with minimal system resource overhead. Comprehensive experimental evaluations on multiple datasets show that our CERT-DF significantly outperforms the state-of-the-art approaches with higher computing efficiency, lower system resource overhead, and better system robustness while ensuring good accuracy.
Quantifying Actionability: Evaluating Human-Recipient Models

With the increasing use of machine learning and artificial intelligence(ML/AI) to inform decisions, there is a need to evaluate models beyond the traditional metrics, and not just from the perspective of the issuer-user (I-user) commissioning them but also for the recipient-user (R-user) impacted by their decisions. We propose evaluating R-user-focused actionability - the degree to which the R-user can influence future model predictions through feasible, responsible actions aligning with the I-user’s goals. We present an algorithm to categorize features as actionable, non-actionable, or conditionally non-actionable based on mutability and cost to the R-user. Experiments were carried out using tree models paired with SHAP and permutation feature importance on tabular datasets. Our key findings indicate noteworthy differences in global actionability across the different datasets, even in datasets that are purposed towards similar goals, and observable but less significant differences among the different model-interpreter combinations applied to the same datasets. Results suggest actionability depends on the entire pipeline, from problem definition and data selection to model choice and explanation method, that it provides a meaningful signal for model selection in valid use cases and merits further research across diverse real-world datasets. The research extends ideas of local and global model explainability to model actionability from the R-user perspective. Actionability evaluations can empower accountable, trustworthy A.I. and provide incentives for serving R-users, not just issuers.