Reviewerly/exHarmony huggingface dataset:
TrendSpotter: Forecasting E-commerce Product Trends

Internet users actively search for trending products on various social media services like Instagram and YouTube which serve as popular hubs for discovering and exploring fashionable and popular items. It is imperative for e-commerce giants to have the capability to accurately identify, predict and subsequently showcase these trending products to the customers. E-commerce stores can effectively cater to the evolving demands of the customer base and enhance the overall shopping experience by offering recent and most sought-after products in a timely manner. In this work we propose a framework for predicting and surfacing trending products in e-commerce stores, the first of its kind to the best of our knowledge. We begin by defining what constitutes a trending product using sound statistical tests. We then introduce a machine learning-based early trend prediction system called TrendSpotter to help users identify upcoming product trends. TrendSpotter is a unique adaptation of the state-of-the-art InceptionTime model\citeInceptionTime that predicts the future popularity of a product based on its current customer engagement, such as clicks, purchases, and other relevant product attributes. The effectiveness of our approach is demonstrated through A/B tests, where we first showcase the effectiveness of our statistical test based labeling strategy, resulting in an incremental sales lift of 59 bps\footnotebps or basis points are a measure of percentages. 1 bps = 0.01% across two experiments on home page and search page. Subsequently, we conduct a comparison between our machine learning model and the statistical labeling baseline and observe an additional sales gain of 14 bps, reflecting the importance of early identification of trending products.
FedPSE: Personalized Sparsification with Element-wise Aggregation for Federated Learning

Federated learning (FL) is a popular distributed machine learning framework in which clients aggregate models' parameters instead of sharing individual data. In FL, clients frequently communicate with the server under limited network bandwidth, raising the communication challenge. Multiple compression methods have been proposed to reduce the transmitted parameters. However, these techniques show that the federated performance degrades significantly with Non-IID (non-identically independently distributed) datasets. To address this issue, we propose an effective method called FedPSE, which solves the efficiency challenge of FL with heterogeneous data. FedPSE compresses the local updates on clients using Top-K sparsification and aggregates these updates on the server by element-wise aggregation. Then clients download the personalized sparse updates from the server to update their individual local models. We then theoretically analyze the convergence of FedPSE under the non-convex setting. Moreover, extensive experiments on four benchmark tasks demonstrate that our FedPSE outperforms the state-of-the-art methods on Non-IID datasets in terms of efficiency and accuracy.
Non-Uniform Adversarial Perturbations for Discrete Tabular Datasets

We study the problem of adversarial attack and robustness on tabular datasets with discrete features. The discrete features of a tabular dataset represent high-level meaningful concepts, with different sets of vocabularies, leading to requiring non-uniform robustness. Further, the notion of distance between tabular input instances is not well defined, making the problem of producing adversarial examples with minor perturbations qualitatively more challenging compared to existing methods. Towards this, our paper defines the notion of distance through the lens of feature embeddings, learnt to represent the discrete features. We then formulate the task of generating adversarial examples as abinary set selection problem under non-uniform feature importance. Next, we propose an efficient approximate gradient-descent based algorithm, calledDiscrete Non-uniform Approximation (DNA) attack, by reformulating the problem into a continuous domain to solve the original optimization problem for generating adversarial examples. We demonstrate the effectiveness of our proposed DNA attack using two large real-world discrete tabular datasets from e-commerce domains for binary classification, where the datasets are heavily biased for one-class. We also analyze challenges for existing adversarial training frameworks for such datasets under our DNA attack.
FAF: A Risk Detection Framework on Industry-Scale Graphs

It is neither effective nor profitable for individuals to attempt to bypass Ant Group's comprehensive risk control system. However, there are still criminals (such as underground loan sharks and professional hackers,), who may teach borrowers how to use hacking techniques to circumvent Ant Group and its partners' risk control system. Despite the fact that our risk control system has greatly reduced fraud losses for merchants in practice, a significant number of intermediary-related frauds still occur. During our investigation into fraud events at Zhima Credit Renting (ZCR), we discovered that more than 30 percent of fraud cases were directly linked to malicious intermediaries. To address this issue, we propose an anti-abettor fraud detection framework, called FAF (Fraud-Abettor-Fraud), specifically designed to combat intermediary-related frauds on an industry-wide scale. We have developed a series of algorithms under the FAF framework, which outperform commonly used risk detection methods and meet real-world business requirements. In this paper, we use ZCR's risk management application as a real-world example-which has deployed FAF for over 1 year-to demonstrate the superiority of the FAF framework compared to existing methods.
If It’s Provably Secure, It Probably Isn’t: Why Learning from Proof Failure is Hard (Transcript of Discussion)

In this paper we document the transcript of the discussion of a presentation in which we explored the ways in which security proofs can fail, and their broader lessons for security engineering.
Comprehensive evaluation of innovation and entrepreneurship ability in the context of sustainable development

In order to address issues related to poor reliability and accuracy in traditional evaluation methods, this paper proposes a comprehensive method for evaluating innovation and entrepreneurship capabilities within the context of sustainable development. This proposed method entails establishing a system for evaluating the influencing factors impacting sustainable development, followed by employing the Page Rank algorithm to compute the values of each node in the indicator. The indicator's influencing factors' weights are achieved through normalisation processing. Subsequently, a comprehensive evaluation function is constructed, and modifications are made to obtain the evaluation results, ultimately leading to a comprehensive evaluation of innovation and entrepreneurship capabilities. Results indicate that the evaluation method's reliability is 0.976, the evaluation accuracy reaches 99.3%, and the evaluation time takes only 7.9 seconds.
Metaverse, Education, and Inclusion: Integrative Review Using the PRISMA Method

The metaverse, or immersive environments, is a reality and, at the same time, a promise for the future. It can be seen, therefore, as a process under construction. Thus, more than finding ready-made uses or applications, it is necessary to identify spaces for this new concept to be applied properly in education and in an inclusive way. In this way, the objective of this article is, through an integrative review using the Prisma method, to identify elements applicable to inclusive education in immersive environments. The main result is the identification of several elements that can be applied in inclusive education, such as the opportunity to create a welcoming environment that can improve social skills, the possibilities of carrying out simulations, providing access to knowledge to different profiles, but also the identification of technical limitations as well as teacher preparation.
A Study On Kindergarten Teachers’ Experience of The Digital Media Utilization Class

The purpose of this study is to find out the experience of kindergarten teachers’ digital media utilization classes by conducting focus group interviews. Focus group interviews were conducted from October 30, 2022 to November 29, 2022 with 12 teachers who are in charge of each of three private kindergartens in Seoul. As a result of the study, first, the necessity of classes using digital media was found to reflect the trend of the times, the role of an interesting assistant teacher. Second, growing from a simple recipient to a creator to a provider, integrated education through digital media were discovered as teaching and learning methods for classes using digital media. Third, using digital media as various teaching materials, classes using mobile, classes using multimedia. Fourth, the limitations of digital media classes resulted in a lack of knowledge of digital device utilization, teacher questioning for digital media utilization classes. Fifth, the teacher's demand for classes using digital media showed training to strengthen digital technology capabilities, and foster teachers' correct awareness of digital media utilization.
Developing Management and Leadership Course: A Case Study

ABSTRACT This case study describes the development of a management and leadership (M/L) iSchool course. To design a course that balanced traditional topics with current professional needs, we identified M/L topics currently covered in iSchools’ curricula, then assessed the importance of these topics for information professions through a survey of academic and professional communities. Survey participants selected topics in the areas of information economy, organizational management, and project management that they thought were most important for iSchool students to learn. They also suggested additional topics related to communication, diversity, equity and inclusion (DEI), financial literacy and strategic planning skills. Thirteen survey participants were interviewed by the researchers; they suggested further topics related to emotional intelligence and personal skills, such as time‐management, risk‐taking and confidence. This paper aims to support a culture of transparency in curricula design and, by sharing methods, findings, and lessons learned, ease the faculty tasks of course development.
The use of restricted Boltzmann machines for clustering collaborative filtering

&lt;div&gt;&lt;p class="Abstract"&gt;Collaborative filtering-based recommender systems have been successfully serviced through commercial online systems to assist people with searching the information useful to them. However, several problems inherent in such systems still exist, although a lot of research work has been devoted to finding solutions. This work focuses on clustering collaborative filtering to address the scalability problem. It proposes a novel method to determine the clustering criteria for enhancing the prediction and recommendation accuracy of the systems, which is typically degraded when the clustering algorithm is integrated into collaborative filtering. We use a restricted Boltzmann machine to find the genre preference of users, which is then inputted into the clustering algorithm to cluster users. Various experiments are conducted to evaluate the performance of the proposed method. As a result, our method showed superior performance in terms of various performance criteria compared to previous clustering collaborative filtering methods and some of the major traditional systems.&lt;/p&gt;&lt;/div&gt;
Behavior and self-efficacy modulate learning in virtual reality simulations for training: a structural equation modeling approach

Introduction: There is a rising interest in using virtual reality (VR) applications in learning, yet different studies have reported different findings for their impact and effectiveness. The current paper addresses this heterogeneity in the results. Moreover, contrary to most studies, we use a VR application actually used in industry thereby addressing ecological validity of the findings. Methods and Results of Study1: In two studies, we explored the effects of an industrial VR safety training application on learning. In our first study, we examined both interactive VR and passive monitor viewing. Using univariate, comparative, and correlational analytical approaches, the study demonstrated a significant increase in self-efficacy and knowledge scores in interactive VR but showed no significant differences when compared to passive monitor viewing. Unlike passive monitor viewing, however, the VR condition showed a positive relation between learning gains and self-efficacy. Methods and Results of Study2: In our subsequent study, a Structural Equation Model (SEM) demonstrated that self-efficacy and users’ simulation performance predicted the learning gains in VR. We furthermore found that the VR hardware experience indirectly predicted learning gains through self-efficacy and user simulation performance factors. Conclusion/Discussion of both studies: Conclusively, the findings of these studies suggest the central role of self-efficacy to explain learning gains generalizes from academic VR tasks to those in use in industry training. In addition, these results point to VR behavioral markers that are indicative of learning.
Integrating Gaze, image analysis, and body tracking: Foothold selection during locomotion

Relatively little is known about the way vision is use to guide locomo-tion in the natural world. What visual features are used to choose paths in natural complex terrain? How do walkers trade off different costs such as getting to the goal, minimizing energy, and satisfying stability constraints? To answer these questions, it is necessary to monitor not only the eyes and the body, but also to represent the three dimensional structure of the terrain. We used photogrammetry techniques to do this, and found substantial regularities in the choice of paths. Walkers avoid paths that involve changes in height and choose more circuitous and flatter paths. This stable tradeoff is related to the walker’s leg length and reflects both energetic and stability constraints. Gaze data and path choices suggest that subjects take into account the terrain approximately 5 steps ahead, and so are planning routes as well as particular footplants. Such planning ahead allows the minimization of energetic costs. Thus locomotor behavior in natural environments is controlled by decision mechanisms that attempt to optimize for multiple factors in the context of well-calibrated sensory and motor internal models.
Information Channels of Deep Neural Networks

Deep Neural Networks (DNN) which employ multiple convolutional layers have shown remarkable accuracy in image recognition, image reconstruction, audio classification and other machine learning applications. However, a theoretical framework to explain the internal mechanism of these DNNs still remains elusive. There have been attempts to use Information Theory to crack open the DNN “black box” by showing that information is squeezed through an Information Bottleneck (IB) formed by the layers of the DNN. IB analysis in the literature is mostly based on fully connected networks and the analysis of convolutional neural networks remains extremely sparse. In analyzing the IB behavior of DNNs, the inputs and outputs of each layer are vectorized and the spatial and temporal properties of the images are ignored while computing the mutual information. In this work, we analyze DNNs which consist of convolutional layers. Each convolutional kernel along with the corresponding activation function is viewed as an Information Channel (IC). We use the spatiotemporal properties of the images to compute the Mutual Information (MI) between the channel input and output and demonstrate that the DNNs generalize and learn by reducing the Shannon capacity of these ICs while maximizing the accuracy of prediction.
Efficient and Privacy-Preserving Neural Network Prediction Scheme with TEE

With the rapid development of machine learning, MLaaS has infiltrated into many fields such as image recognition, natural language processing, medical diagnosis, and so on. However, in MLaaS, data interaction between users and service providers is inevitable, and both users' private data and servers' model parameters are at risk of privacy disclosure. In order to solve this problem, homomorphic encryption is an extensively used technique to process private information over ciphertexts. However, since homomorphic encryption only supports linear operations, approximation techniques are required to calculate nonlinear functions, which leads to the loss of prediction accuracy and heavy computation overhead. Therefore, in this paper, we propose a secure neural network prediction scheme combining the trusted execution environment and homomorphic encryption with different security assumptions. Specifically, we first define the security model of TEE-assisted neural network prediction. Then, by combining a lightweight homomorphic encryption technique with TEE, we design secure neural network prediction protocols under different security levels, with which neural network prediction can be securely processed with high performance and accuracy. Finally, we evaluate the performance of our scheme on the MNIST, Fashion-MNIST, and KMNIST datasets, and the results demonstrate that our scheme indeed improves the prediction efficiency and accuracy compared to traditional homomorphic encryption-based schemes with polynomial approximation.
On the Interpretable Adversarial Sensitivity of Iterative Optimizers

Adversarial examples are an emerging threat of machine learning (ML) models, allowing adversaries to substantially deteriorate performance by introducing seemingly unnoticeable perturbations. These attacks are typically considered to be an ML risk, often associated with the black-box operation and sensitivity to features learned from data of deep neural networkss (DNNs), and are rarely viewed as a threat to classic non-learned decision rules, such as iterative optimizers. In this work we explore the sensitivity to adversarial examples of iterative optimizers, building upon recent advances in treating these methods as ML models. We identify that many iterative optimizers share the properties of end-to-end differentiability and existence of impactful small perturbations, that make them amenable to adversarial attacks. The interpretablity of iterative optimizers allows to associate adversarial examples with modifications to the traversed loss surface that notably affect the location of the sought minima. We visualize this effect and demonstrate the vulnerability of iterative optimizers for compressed sensing and hybrid beamforming tasks, showing that different optimizers tackling the same optimization formulation vary in their adversarial sensitivity.
Incentivizing Edge AI with Accuracy Preserving via Online Randomized Auctions

Provisioning machine learning inference near the users at the network’s edge is emerging as a promising area for Edge AI. Due to the excessive energy consumption of edge devices, their owners often lack the motivation to actively contribute to their edge resources. To tackle this issue, we propose an incentive mechanism based on auctions, which enables edge device owners to submit their bids and compensates such bids via reward payments, ensuring the minimization of inference accuracy loss. We formulate a nonlinear mixed-integer program problem with the objective of minimizing the social cost, including accuracy loss cost, edge device cost, and service provider cost in the edge inference system. Then an Online Learning algorithm is devised to find the solutions, based on the primal-dual. To calculate the remuneration, we design a payment allocation algorithm based on the bid-winning probabilities. Our rigorous theoretical analysis shows that our algorithm designed achieves sub-linear growth on dynamic regret and dynamic fit over time while preserving the economic properties of truthfulness and individual rationality. Finally, multiple experiments validate the efficacy of the proposed auction mechanism algorithm from various perspectives compared with three other existing algorithms.
Collaborative Redundancy Reduction for Communication-based Low-Latency Video Analytics in Autonomous Driving

Autonomous driving vehicles, leveraging on-board cameras for real-time surrounding environment sensing, have boosted the development of safe and efficient transportation. However, limited on-board computation resources pose a challenge in obtaining low-latency video analytics, as performing multiple analytics tasks simultaneously with processing massive redundancy in spatial, temporal, and semantic dimensions related to video analytics is of daunting computational complexity, resulting in long analytics latency. In this paper, we propose CoVA, a collaborative architecture based on multi-hop V2V communications, to achieve low-latency and accurate video analytics. Specifically, CoVA first builds the same lane-based vehicle fleets and formulates a multi-objective optimization problem, aiming to minimize spatial redundancy and latency with reliable communications. Then, a variable-threshold video frame filtering algorithm is designed to dynamically handle unpredictable surrounding environment changes by adjusting the similarity threshold for video frames captured by vehicles, which minimizes temporal redundancy while guaranteeing analytics accuracy. Furthermore, considering the semantic redundancy, CoVA reconstructs the original formulated problem using collaborative vehicle sensing and processing across lanes. Finally, joint redundancy minimization and efficient resource allocation optimization is proposed for properly allocating computation and bandwidth resources. Experimental results demonstrate the superiority of CoVA against other video analytics approaches, significantly reducing up to 70% in latency while only sacrificing less tolerated accuracy.
Enhancing Object Audio Control within an Immersive Sound System

Real time control over sound source positioning and movement is a primary advantage of object-based sound systems over channel-based systems. This paper describes a controller, processing engine, and associated library that enhances this object control. Apart from graphic spatialization control, the controller allows, for each object, the selection of its rendering algorithm, distance-based level, high frequency attenuation, reverb parameters and speaker isolation or locking. OSC control within the controller enables further control options.
Quantum Constant Propagation

Abstract A quantum circuit is often executed on the initial state where each qubit is in the zero state. Therefore, we propose to perform a symbolic execution of the circuit. Our approach simulates groups of entangled qubits exactly up to a given complexity. Here, the complexity corresponds to the number of basis states expressing the quantum state of one entanglement group. By doing that, the groups need neither be determined upfront nor be bound by the number of involved qubits. Still, we ensure that the simulation runs in polynomial time - opposed to exponential time as required for the simulation of the entire circuit. The information made available at gates is exploited to remove superfluous controls and gates. We implemented our approach in the tool quantum constant propagation (QCP) and evaluated it on the circuits in the benchmark suite MQTBench. By applying our tool, only the work that cannot be carried out efficiently on a classical computer is left for the quantum computer, hence exploiting the strengths of both worlds.