Reviewerly/exHarmony huggingface dataset:
FTKHUIM: A Fast and Efficient Method for Mining Top-K High-Utility Itemsets

High-utility itemset mining (HUIM) is an important task in the field of knowledge data discovery. The large search space and huge number of HUIs are the consequences of applying HUIM algorithms with an inappropriate user-defined minimum utility threshold value. Determining a suitable threshold value to obtain the expected results is not a simple task and requires spending a lot of time. For common users, it is difficult to define a minimum threshold utility for exploring the right number of HUIs. On the one hand, if the threshold is set too high then the number of HUIs would not be enough. On the other hand, if the threshold is set too low, too many HUIs will be mined, thus wasting both time and memory. The top-k HUIs mining problem was proposed to solve this issue, and many effective algorithms have since been introduced by researchers. In this research, a novel approach, namely FTKHUIM (Fast top-k HUI Mining), is introduced to explore the top-k HUIs. One new threshold-raising strategy called RTU, a transaction utility (TU)-based threshold-raising strategy, has also been shown to rapidly increase the speed of top-k HUIM. The study also proposes a global structure to store utility values in the process of applying raising-threshold strategies to optimize these strategies. The results of experiments on various datasets prove that the FTKHUIM algorithm achieves better results with regard to both the time and search space needed.
Micro Expression Recognition Using Convolution Patch in Vision Transformer

Humans possess an intrinsic ability to hide their true emotions. Micro-expressions are subtle changes in facial muscles that are involuntary by nature and easy to hide. To address these issues, several machine and deep learning models have been proposed in the past few years. Convolution neural network (CNN) is a deep learning method that has widely been adopted in vision-related tasks due to its remarkable performance. However, CNN suffers from overfitting due to a large number of trainable parameters. Additionally, CNN cannot capture global information with respect to an input image. Furthermore, the identification of important regions for the classification of micro-expressions is a challenging task. Self-attention mechanism addresses these issues by focusing on key areas. Furthermore, specific transformers, known as vision transformers are widely explored in vision-related applications. However, existing vision transformers divide an input image into a fixed number of patches due to which local correlation of image pixels is lost. Further, a vision transformer relies on self-attention mechanism which effectively captures global dependencies but does not exploit the local spatial relationships in an image. In this work, we propose a vision transformer based on convolution patches to overcome this problem. The proposed algorithm generates c number of feature maps from input images using c filters through convolution operation. These feature maps are then applied to a transformer model as fixed-size image patches to perform classification. Thus, the proposed architecture leverage advantages of both convolutional layers and transformer, and captures both spatial information and global dependencies respectively, leading to improved performance. The performance of the proposed model is evaluated on three benchmark datasets: CASME-I, CASME-II, and SAMM and compared with state-of-the-art machine and deep learning models, which generated classification accuracy of 95.97%, 98.59%, and 100%, respectively.
The Future of Drug Development with Quantum Computing

Novel medication development is a time-consuming and expensive multistage procedure. Recent technology developments have lowered timeframes, complexity, and cost dramatically. Current research projects are driven by AI and machine learning computational models. This chapter will introduce quantum computing (QC) to drug development issues and provide an in-depth discussion of how quantum computing may be used to solve various drug discovery problems. We will first discuss the fundamentals of QC, a review of known Hamiltonians, how to apply Hamiltonians to drug discovery challenges, and what the noisy intermediate-scale quantum (NISQ) era methods and their limitations are. We will further discuss how these NISQ era techniques can aid with specific drug discovery challenges, including protein folding, molecular docking, AI−/ML-based optimization, and novel modalities for small molecules and RNA secondary structures. Consequently, we will discuss the latest QC landscape’s opportunities and challenges.
Quantifying the benefits of PV module shading for building heat gain reduction: A machine learning approach

This study investigates the indirect benefit due to the shading effect from PV rooftop systems installed on building envelopes by proposing a new method based on a machine-learning algorithm. The study gathered a year of data from a 25 kWp PV rooftop system installed on a metal sheet roofing. It classifies the dataset into four categories based on solar irradiance and ambient temperature to represent Thailand's climate. There evaluated the RTTV and case study from implemented projects with metal sheet roofing in Thailand to estimate the potential electricity bill savings from the PV module shade effect. The results reveal an inverse relationship between the percentage of heat reduction and heat gain reduction, with the most significant reduction occurring under high solar irradiance and ambient temperature conditions. The shading effect caused by the PV modules provides an indirect benefit of approximately 38 kWh/kWp/year from electricity bill savings. In contrast, the PV system production yields around 1401 kWh/kWp/year based on radiation of about 1839 kWh/m2/year. The study estimates that energy-saving possibilities range from 0.038 to 0.052 kWh/m2/day, depending on the weather conditions, equivalent to an electricity saving of approximately 6.33 kWh/m2/year from air conditioner use.
A systematic review of federated learning incentive mechanisms and associated security challenges

In response to various privacy risks, researchers and practitioners have been exploring different paradigms that can leverage the increased computational capabilities of consumer devices to train machine learning (ML) models in a distributed fashion without requiring the uploading of the training data from individual devices to central facilities. For this purpose, federated learning (FL) was proposed as a technique that can learn a global machine model at a central master node by the aggregation of models trained locally using private data. However, organizations may be reluctant to train models locally and to share these local ML models due to the required computational resources for model training at their end and due to privacy risks that may result from adversaries inverting these models to infer information about the private training data. Incentive mechanisms have been proposed to motivate end users to participate in collaborative training of ML models (using their local data) in return for certain rewards. However, the design of an optimal incentive mechanism for FL is challenging due to its distributed nature and the fact that the central server has no access to clients’ hyperparameters information and the amount/quality data used for training, which makes the task of determining the reward based on the contribution of individual clients in FL environment difficult. Even though several incentive mechanisms have been proposed for FL, a thorough up-to-date systematic review is missing and this paper fills this gap. To the best of our knowledge, this paper is the first systematic review that comprehensively enlists the design principles required for implementing these incentive mechanisms and then categorizes various incentive mechanisms according to their design principles. In addition, we also provide a comprehensive overview of security challenges associated with incentive-driven FL. Finally, we highlight the limitations and pitfalls of these incentive schemes and elaborate upon open-research issues that require further research attention.
Human worker activity recognition in a production floor environment through deep learning

One of the aims of the 4th industrial revolution is to seamlessly connect equipment and personnel to enable a greater level of collaboration, which in turn will result in higher operational efficiency and improved decision making strategies. In this context, this work presents a deep learning based approach for automated activity recognition of two human workers who are carrying out typical manual assembly and part handling tasks in a production floor environment. A typical smartphone is employed as a wearable sensor recording acceleration in 3 directions, total acceleration, brightness, geomagnetic field strength, orientation in space and sound wave intensity and is attached to the wrist of the two workers, who subsequently perform a large number of experiments examining 5 activities of interest. For the classification of these activities, two types of neural networks are employed. Initially, the experimental signals are fed to the variational autoencoder neural network so as to extract the appropriate features and subsequently these features are added to the last layer of a long short-term memory (LSTM) time series network, which is trained to classify the 5 activities of interest. An additional goal of the present paper is the study of long-term signals (LTS), that is time series of much longer duration than those of the 5 aforementioned activities, including enough noise due to the existence of time periods corresponding to unrelated activities. Ultimately, a new methodology is proposed combining both trained neural networks mentioned above, in order to identify the desired activities within the LTS in a real time application.
Targeted adversarial attacks on wind power forecasts

Abstract In recent years, researchers proposed a variety of deep learning models for wind power forecasting. These models predict the wind power generation of wind farms or entire regions more accurately than traditional machine learning algorithms or physical models. However, latest research has shown that deep learning models can often be manipulated by adversarial attacks. Since wind power forecasts are essential for the stability of modern power systems, it is important to protect them from this threat. In this work, we investigate the vulnerability of two different forecasting models to targeted, semi-targeted, and untargeted adversarial attacks. We consider a long short-term memory (LSTM) network for predicting the power generation of individual wind farms and a convolutional neural network (CNN) for forecasting the wind power generation throughout Germany. Moreover, we propose the Total Adversarial Robustness Score (TARS), an evaluation metric for quantifying the robustness of regression models to targeted and semi-targeted adversarial attacks. It assesses the impact of attacks on the model’s performance, as well as the extent to which the attacker’s goal was achieved, by assigning a score between 0 (very vulnerable) and 1 (very robust). In our experiments, the LSTM forecasting model was fairly robust and achieved a TARS value of over 0.78 for all adversarial attacks investigated. The CNN forecasting model only achieved TARS values below 0.10 when trained ordinarily, and was thus very vulnerable. Yet, its robustness could be significantly improved by adversarial training, which always resulted in a TARS above 0.46.
DrivingVibe: Enhancing VR Driving Experience using Inertia-based Vibrotactile Feedback around the Head

We present DrivingVibe, which explores vibrotactile feedback designs around the head to enhance VR driving motion experiences. We propose two approaches that use a 360-degree vibrotactile headband: 1) mirroring and 2) 3D inertia-based. The mirroring approach extends the vibrotactile patterns of handheld controllers to actuate the entire headband uniformly. The 3D inertia-based approach uses the acceleration telemetry data that driving games/simulators export to motion platforms to generate directional vibration patterns, including: i) centrifugal forces, ii) horizontal acceleration/deceleration, and iii) vertical motion due to rough terrain. The two approaches are complementary as the mirroring approach supports all driving games because it does not require telemetry data, while the 3D inertia-based approach provides higher feedback fidelity for games that provide such data. We conducted a 24-person user experience evaluation in both passive passenger mode and active driving mode. Study results showed that both DrivingVibe designs significantly improved realism, immersion, and enjoyment (p&lt;.01) with large effect sizes for the VR driving experiences. For overall preference, 88% (21/24) of participants preferred DrivingVibe, with a 2:1 preference for 3D inertia-based vs. mirroring designs (14 vs. 7 participants). For immersion and enjoyment, 96% (23/24) of participants preferred DrivingVibe, with nearly a 3:1 preference (17 vs. 6 participants) for the 3D inertia-based design.
The quantification of surface abrasion on flint stone tools

Abstract Lithic artifacts are some of the most common and numerous remains recovered from paleolithic archaeological sites. However, these materials can undergo multiple post‐depositional alterations after their introduction into the archaeological record. Due to the high quantity of lithic remains recovered, a quick, flexible, and effective method for identifying degrees of alteration on the surface of lithic implements is highly desirable. The present study examines the use of gray level images to obtain quantitative data from the surface of flint artifacts and determine whether these images can detect the presence of post‐depositional alterations. An experimental collection of flints was subjected to sequential episodes of rounding in a tumbling machine. After each episode, photographs were taken with a microscope, resulting in quantitative surface values using gray level values. The quantitative surface values were used as variables in machine learning models to determine time of exposure and the most salient variables for discrimination. Our results indicate that the extraction of metrics from gray level images successfully capture changes in the surface of flint artifacts caused by post‐depositional processes. Additional results provide insight into which areas to sample when seeking post‐depositional alterations and underscore the importance of particle size in the generation of alterations.
Engineering Activity in the Context of Humanitarian Thinking

With the release of a package of state-level regulatory documents during the perestroika years, which set a new vector for the development of domestic education and, among other things, set the task of enriching the ideological, theoretical, humanitarian content of higher education, the reform of the country’s higher education began, which continues in waves to this day. The powerful flow of articles, reports, dissertations devoted to the humanization and humanitarization of engineering education, which has gushed since that time, does not weaken. The experiment has shown that artificial intelligence ChatGPT-4 generally gives an adequate idea of the current issues in this area of engineering pedagogy. In the article, for the first time, this task is projected on the formation of students’ engineering thinking of a humanitarian orientation, which is necessary to ensure their effectiveness in the labor market in especially cardinal changes in technology and the organization of engineering activities, used with an exponentially growing scale of the use of artificial intelligence and robotics in it. The idea is being developed that engineering thinking is inherently inseparable from the humanitarian one, which sets its direction, and the idea put forward by P.G. Shchedrovitsky’s hypothesis about programming as a dominant way of thinking of an engineer in an emerging new technological formation, which is based on goal setting with the subsequent design of an “activity tree” as a projection of a tree of goals. The purpose of the article is to substantiate the regularity of the organic strengthening of the humanitarian focus of engineering thinking in the course of the ongoing industrial revolution and the need to focus engineering pedagogy on this trend, based on the author’s proposed refinements of ontologically established ideas about engineering activities.
CHIP-KNNv2: A <u>C</u> onfigurable and <u>Hi</u> gh- <u>P</u> erformance <u>K</u> - <u>N</u> earest <u>N</u> eighbors Accelerator on HBM-based FPGAs

The k-nearest neighbors (KNN) algorithm is an essential algorithm in many applications, such as similarity search, image classification, and database query. With the rapid growth in the dataset size and the feature dimension of each data point, processing KNN becomes more compute and memory hungry. Most prior studies focus on accelerating the computation of KNN using the abundant parallel resource on FPGAs. However, they often overlook the memory access optimizations on FPGA platforms and only achieve a marginal speedup over a multi-thread CPU implementation for large datasets. In this article, we design and implement CHIP-KNN: an HLS-based, configurable, and high-performance KNN accelerator. CHIP-KNN optimizes the off-chip memory access on modern HBM-based FPGAs such as the AMD/Xilinx Alveo U280 FPGA board. CHIP-KNN is configurable for all essential parameters used in the algorithm, including the size of the search dataset, the feature dimension and data type representation of each data point, the distance metric, and the number of nearest neighbors - K. In terms of design architecture, we explore and discuss the tradeoffs between two design versions: CHIP-KNNv1 (Ping-Pong buffer based) and CHIP-KNNv2 (streaming-based). Moreover, we investigate the routing congestion issue in our accelerator design, implement hierarchical structures to shorten critical paths, and integrate an open-source floorplanning optimization tool called TAPA/AutoBridge to eliminate the place-and-route issues. To explore the design space and balance the computation and memory access performance, we also build an analytical performance model. Given a user configuration of the KNN parameters, our tool can automatically generate TAPA HLS C code for the optimal accelerator design and the corresponding host code, on the HBM-based FPGA platform. Our experimental results on the Alveo U280 show that, compared to a 48-thread CPU implementation, CHIP-KNNv2 achieves a geomean performance speedup of 15×, with a maximum speedup of 45×. Additionally, we show that CHIP-KNNv2 achieves up to 2.1× performance speedup over CHIP-KNNv1 while increasing configurability. Compared with the state-of-the-art Facebook AI Similarity Search (FAISS) [ 23 ] GPU implementation running on a Nvidia Tesla V100 GPU, CHIP-KNNv2 achieves an average latency reduction of 30.6× while requiring 34.3% of GPU power consumption.
<scp>MapIntel</scp>: A visual analytics platform for competitive intelligence

Abstract Competitive Intelligence allows an organization to keep up with market trends and foresee business opportunities. This practice is mainly performed by analysts scanning for any piece of valuable information in a myriad of dispersed and unstructured sources. Here we present MapIntel, a system for acquiring intelligence from vast collections of text data by representing each document as a multidimensional vector that captures its own semantics. The system is designed to handle complex Natural Language queries and visual exploration of the corpus, potentially aiding overburdened analysts in finding meaningful insights to help decision‐making. The system searching module uses a retriever and re‐ranker engine that first finds the closest neighbours to the query embedding and then sifts the results through a cross‐encoder model that identifies the most relevant documents. The browsing or visualization module also leverages the embeddings by projecting them onto two dimensions while preserving the multidimensional landscape, resulting in a map where semantically related documents form topical clusters which we capture using topic modelling. This map aims at promoting a fast overview of the corpus while allowing a more detailed exploration and interactive information encountering process. We evaluate the system and its components on the 20 newsgroups data set, using the semantic document labels provided, and demonstrate the superiority of Transformer‐based components. Finally, we present a prototype of the system in Python and show how some of its features can be used to acquire intelligence from a news article corpus we collected during a period of 8 months.
Scaling Smart Contracts via Layer-2 Technologies: Some Empirical Evidence

Blockchain-based smart contracts can potentially replace certain traditional contracts through decentralized enforcement and reduced transaction costs. However, scalability is a key bottleneck hindering their broader application and adoption, often leading to concentrated or exclusive networks. To avoid falling short of the original promise of the technology, firms actively explore “layer-2” methods for scaling. We provide some initial evidence on the economic implications of a layer-2 scaling solution, which moves information aggregation from on-chain to off-chain peer-to-peer networks. A parallel-system experiment allows clean identification because we observe the same unit in the treatment and control systems at the same time. We find that this scaling solution reduces operating costs by 76%, and importantly, leads to decentralization with lower market concentration and more participation, which in turn improves data accuracy. The findings provide insights on how blockchain and smart contracting technologies evolve toward achieving decentralized and scalable trust. This paper was accepted by David Simchi-Levi, information systems. Funding: W. Cong received funding from Ripple’s university blockchain research initiative (UBRI). Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.00281 .
Contextualised Out-of-Distribution Detection Using Pattern Identification

In this work, we propose CODE, an extension of existing work from the field of explainable AI that identifies class-specific recurring patterns to build a robust Out-of-Distribution (OoD) detection method for visual classifiers. CODE does not require any classifier retraining and is OoD-agnostic, i.e., tuned directly to the training dataset. Crucially, pattern identification allows us to provide images from the In-Distribution (ID) dataset as reference data to provide additional context to the confidence scores. In addition, we introduce a new benchmark based on perturbations of the ID dataset that provides a known and quantifiable measure of the discrepancy between the ID and OoD datasets serving as a reference value for the comparison between OoD detection methods.