Reviewerly/exHarmony huggingface dataset:
A hybrid model of implementing a smart production factory within the Industry 4.0 framework

Purpose This study aims to introduce the model of implementation to run the smart production factories. The study also aims to investigate the Industry 4.0 technologies as enablers to deal with challenges in the way of implementation. Design/methodology/approach This contribution benefits from two teams of experts to evaluate the challenges and technologies of Industry 4.0. The Hanlon method is applied to evaluate, rank and prioritise the challenges which are initially scored by experts’ Team 1. Then, the adjacency matrix among enablers and challenges is extracted through the opinions of experts’ Team 2. The study also uses fuzzy cognitive map (FCM) to evaluate the real weights of technologies and challenges, rank and prioritise subsequently. Findings A total of 8 challenging obstacles and 24 key technologies have been evaluated. The findings reveals that recruit and retention of experienced managers, undefined return on investment and recruit and retention of multi-skilled workers are the most serious challenges in the way of implementing smart production factories. Furthermore, big data, IT-based management and Internet of Things are the top-ranked key enablers to face the challenges. Originality/value To the best of the authors’ knowledge, this study is one of the pioneering studies that uses Hanlon method to evaluate industrial challenges. Integrating Hanlon method and FCM leads to a comprehensive model of evaluation and ranking which is another novelty of this contribution. Although many research studies have been released to implement the smart factories, practical model of implementation for production factories is identified as a literature gap.
Emission of an atom inside a one-dimensional atomic cavity

We study the emission of a single-photon by a two-level emitter inside an atomic cavity consisting of two atomic mirrors coupled to a one-dimensional waveguide. With proper atomic separations, we realize a frequency comb as well as spectrum narrowing in the waveguide with the symmetric setting, and also unidirectional spectrum narrowing with asymmetric parameters. Within a suitable range, we can control the central frequency of the narrowed spectra by modulating atomic separations. Due to the collective interaction among the emitters, entanglement sudden birth and revival are observed between the mirrors.
JGNN: Graph Neural Networks on native Java

We introduce JGNN, an open source Java library to define, train, and run Graph Neural Networks (GNNs) under limited resources. The library is cross-platform and implements memory-efficient machine learning components without external dependencies. Model definition is simplified by parsing Python-like expressions, including interoperable dense and sparse matrix operations and inline parameter definitions. GNN models can be deployed on smart devices and trained on local data.
Lossless LUT Compressions for Image Enhancement

Methodologies are proposed to reduce lookup table count losslessly by using an appropriate straight line as a reference. In addition, the straight line implementation is discussed, using a shifter and lookup table. The mirroring, shifting, and folding are presented for the tone curve that cannot be easily implemented using the straight line. A lookup table read-only memory architecture suitable for implementing a system on display panel and the verification using actual images is discussed through field programmable gate array and MATLAB simulations, respectively. The evaluations and comparisons of the performances of the proposed lookup table compression system are described.
FAKE CURRENCY DETECTION USING CNN

Right now, Fake Currency Acknowledgment System is intended to identify the fake paper cash to check whether it is phony or unique. The current fake issue due to demonetization impacts the financial framework and furthermore in other fields. Another methodology of Convolution Neural Network towards ID of phony money notes through their pictures is inspected right now is similarly better than past picture handling strategies. This technique is in light of Deep Learning, which has seen huge achievement in picture arrangement assignments lately. This method can support the two individuals and machine in recognizing a phony cash note continuously through a picture of the equivalent. The proposed framework can likewise be conveyed as an application in the cell phone which can push the general public to recognize the phony and unique cash notes. The Precision in the proposed framework can be expanded through the high number of unique and phony notes. Key Words: CNN, Currency, Deep Learning.
An Efficient Hardware Accelerator for Block Sparse Convolutional Neural Networks on FPGA

Field Programmable Gate Array (FPGA) has become an excellent hardware accelerator solution for convolutional neural networks (CNN). Meanwhile, optimizing methods such as model compression have been proposed. As most CNN accelerators focus on dense neural networks, to solve the problem of difficult hardware deployment due to irregular networks, we propose a method for sparse neural networks in our work. The storage and coding format of sparse data obtained by the block pruning method is designed to make it friendly to implement on FPGA. Besides, we also propose an efficient and simple data flow by the planarization of the whole convolution calculation process. The experimental result demonstrates that our implementation can achieve clock frequency of 190MHz, power consumption of 13.32W and inferencing speed of 16.37ms. Compared with some typical Mobilenet implementation schemes, our method has been proven to achieve a better balance between frequency, accuracy, power consumption and speed.
Can Evolutionary Clustering Have Theoretical Guarantees?

Clustering is a fundamental problem in many areas, which aims to partition a given data set into groups based on some distance measure, such that the data points in the same group are similar while that in different groups are dissimilar. Due to its importance and NP-hardness, a lot of methods have been proposed, among which evolutionary algorithms are a class of popular ones. Evolutionary clustering has found many successful applications, but all the results are empirical, lacking theoretical support. This paper fills this gap by proving that the approximation performance of the GSEMO (a simple multi-objective evolutionary algorithm) for solving four formulations of clustering, i.e., k-tMM, k-center, discrete k-median and k-means, can be theoretically guaranteed. Furthermore, we consider clustering under fairness, which tries to avoid algorithmic bias, and has recently been an important research topic in machine learning. We prove that for discrete k-median clustering under individual fairness, the approximation performance of the GSEMO can be theoretically guaranteed with respect to both the objective function and the fairness constraint.
Comparing the Orchestration of Quantum Applications on Hybrid Clouds

Early quantum computers are being offered by Cloud providers, with the ability to run Hybrid Quantum-Classical (HQC) applications that span Quantum and Classical (binary) computing resources. These HQC algorithms help benchmark and validate the performance of such near-term quantum hardware. Equally important is the software platform that allows a remote user to program the quantum computer on the cloud. The recent Qiskit Runtime platform from IBM allows a seamless and prioritized access to quantum backends and co-located classical hardware within their hybrid cloud for optimized execution of HQC workloads. In contrast, IBM’s Circuit API offers finer application definition but the classical resources are not co-located but placed farther away. Here, we study and contrast the execution flows of a Variational Quantum Eigensolver (VQE) application defined using Qiskit Runtime, and an equivalent implementation using the Circuit API. We quantify the latencies of the various components in these execution. This offers insights on how to better orchestrate the execution of quantum applications across such hybrid resources.
Creative NFT-Copyrighted AR Face Mask Authoring Using Unity3D Editor

In this paper, we extend well-designed 3D face masks into AR face masks and demonstrate the possibility of transforming this into an NFT-copyrighted AR face mask that helps authenticate the ownership of the AR mask user so as to improve creative control, brand identification, and ID protection. The output of this project will not only potentially validate the value of the NFT technology but also explore how to combine the NFT technology with the AR technology so as to be applied to the e-commerce and e-business aspects of the multimedia industry.
Model-Agnostic Decentralized Collaborative Learning for On-Device POI Recommendation

As an indispensable personalized service in Location-based Social Networks (LBSNs), the next Point-of-Interest (POI) recommendation aims to help people discover attractive and interesting places. Currently, most POI recommenders are based on the conventional centralized paradigm that heavily relies on the cloud to train the recommendation models with large volumes of collected users' sensitive check-in data. Although a few recent works have explored on-device frameworks for resilient and privacy-preserving POI recommendations, they invariably hold the assumption of model homogeneity for parameters/gradients aggregation and collaboration. However, users' mobile devices in the real world have various hardware configurations (e.g., compute resources), leading to heterogeneous on-device models with different architectures and sizes. In light of this, We propose a novel on-device POI recommendation framework, namely Model-Agnostic Collaborative learning for on-device POI recommendation (MAC), allowing users to customize their own model structures (e.g., dimension & number of hidden layers). To counteract the sparsity of on-device user data, we propose to pre-select neighbors for collaboration based on physical distances, category-level preferences, and social networks. To assimilate knowledge from the above-selected neighbors in an efficient and secure way, we adopt the knowledge distillation framework with mutual information maximization. Instead of sharing sensitive models/gradients, clients in MAC only share their soft decisions on a preloaded reference dataset. To filter out low-quality neighbors, we propose two sampling strategies, performance-triggered sampling and similarity-based sampling, to speed up the training process and obtain optimal recommenders. In addition, we design two novel approaches to generate more effective reference datasets while protecting users' privacy. Extensive experiments on two datasets have shown the superiority of MAC over advanced baselines.
Unsupervised Dialogue Topic Segmentation with Topic-aware Contrastive Learning

Dialogue Topic Segmentation (DTS) plays an essential role in a variety of dialogue modeling tasks. Previous DTS methods either focus on semantic similarity or dialogue coherence to assess topic similarity for unsupervised dialogue segmentation. However, the topic similarity cannot be fully identified via semantic similarity or dialogue coherence. In addition, the unlabeled dialogue data, which contains useful clues of utterance relationships, remains underexploited. In this paper, we propose a novel unsupervised DTS framework, which learns topic-aware utterance representations from unlabeled dialogue data through neighboring utterance matching and pseudo-segmentation. Extensive experiments on two benchmark datasets (i.e., DialSeg711 and Doc2Dial) demonstrate that our method significantly outperforms the strong baseline methods. For reproducibility, we provide our code and data at: https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/dial-start.
Strategy-aware Bundle Recommender System

A bundle is a group of items that provides improved services to users and increased profits for sellers. However, locating the desired bundles that match the users' tastes still challenges us, due to the sparsity issue. Despite the remarkable performance of existing approaches, we argue that they seldom consider the bundling strategy (i.e., how the items within a bundle are associated with each other) in the bundle recommendation, resulting in the suboptimal user and bundle representations for their interaction prediction. Therefore, we propose to model the strategy-aware user and bundle representations for the bundle recommendation.
Unsupervised Rate Distortion Function-Based Band Subset Selection for Hyperspectral Image Classification

Due to significant inter-band correlation resulting from use of hundreds of contiguous spectral bands, band selection (BS) is one of most widely used methods to reduce data dimensionality for band redundancy removal. A challenge for BS is how to design an effective criterion which can select bands with preserving crucial spectral information, while also avoiding selecting highly correlated bands. Information theory turns out to be one of best means to address such issue in terms of information redundancy, specifically, the rate distortion function (RDF) of Shannon’s 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> noisy source coding (or joint source and channel coding) theorem, which has been widely used in image compression/coding. This paper presents a novel unsupervised RDF-based band subset selection (RDFBSS) for hyperspectral image classification (HSIC). To accomplish this goal, a new concept of the area under an RDF curve, A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RDF</sub> similar to the area under a receiver operating characteristic (ROC), A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">z</sub> defined in hyperspectral target detection is coined and defined as a criterion for BSS. Since BSS generally requires an exhaustive search for an optimal band subset, two iterative algorithms similar to sequential (SQ) N-FINDR and successive (SC) N-FINDR for finding endmembers, called sequential (SQ) RDFBSS and successive (SC) RDFBSS, can be derived and coupled with Ardf as a criterion to find optimal band subsets. The experimental results demonstrate that RDFBSS is indeed a very effective BS method to find best possible band subsets and also performs better than most recent BS methods.
Improved ShuffleNetV2 for Garbage Image Classification

This paper proposes a novel lightweight classification network based on ShuffleNet V2 to promote the accuracy of garbage image classification. Compared with the conventional network, there is a package of optimizations to both ameliorate the classification accuracy and inference speed of the model. Firstly, the optimized network introduces the Leaky ReLU as a substitute for the activation function. It also adopts the squeeze and excitation network to enhance important features. In addition, the flattened layer is applied to downscale the data, and three fully connected layers are used for enhancing the learning capability. Furthermore, the model is pre-trained based on transfer learning, wherein the feature knowledge learned from ImageNet datasets is transferred to garbage classification task. Extensive experiments on the garbage image dataset verify the overall performance, which improves the classification accuracy by 6.3% with a 35.4% reduction in time consumption.
4Dジオラマを用いたまちづくりゲーム開発のPBL

This pragmatic research project aims to employ a PBL that utilizes a 4D diorama to develop a game for community development. A distinguishing characteristic of this educational undertaking is the identification of four visions and three stages of missions, with teams comprising an educator, students, and practitioners collaborating with the study area residents and civic tech participants. The area was surveyed and documented through fieldwork, maps, and photographs, and a digital diorama was created using QGIS to depict the past, present, and ideal future of the area. The diorama was then used in a combination of online and offline rogaining activities. The outcomes of the project included the formulation of a diorama creation strategy for local education, and the conceptual design of the community development game.
BFDAC: A Blockchain-based and Fog Computing-assisted Data Access Control Scheme in Vehicular Social Networks

The Vehicular Social Networks (VSNs) provide passengers, drivers and vehicles with multiple services, such as safe driving, data sharing and traffic management. However, transmitting data in VSNs can expose information such as the user’s identity and location. Malicious users who tamper with shared data can even cause serious traffic accidents. Considering the privacy protection and secure transmission of shared data in VSNs, we propose a blockchain-based and fog computing-assisted data access control scheme (BFDAC). We combine the multi-authority CP-ABE algorithm with the consortium blockchain to avoid the security and trust issues in the form of centralized key management, and outsource part of the decryption calculations to roadside units (RSUs) as fog nodes to realize lightweight calculation for users. Our BFDAC scheme also supports user tracking and revocation, while users can also revoke shared data saved in the cloud. Security analysis shows that the BFDAC scheme can effectively protect the shared data. Experiments show that our BFDAC scheme reduces 32.8% in storage cost, 9.5% in encryption cost, and 57.1% in outsourced decryption cost compared to previous ones.
Data for Digital Forensics: Why a Discussion on “How Realistic is Synthetic Data” is Dispensable

Digital forensics depends on data sets for various purposes like concept evaluation, educational training, and tool validation. Researchers have gathered such data sets into repositories and created data simulation frameworks for producing large amounts of data. Synthetic data often face skepticism due to its perceived deviation from real-world data, raising doubts about its realism. This paper addresses this concern, arguing that there is no definitive answer. We focus on four common digital forensic use cases that rely on data. Through these, we elucidate the specifications and prerequisites of data sets within their respective contexts. Our discourse uncovers that both real-world and synthetic data are indispensable for advancing digital forensic science, software, tools, and the competence of practitioners. Additionally, we provide an overview of available data set repositories and data generation frameworks, contributing to the ongoing dialogue on digital forensic data sets’ utility.
Information Extraction of Medical Materials: An Overview of the Track of Medical Materials MedOCR

In the medical and insurance industry, electronic medical record materials contain a lot of information, which can be extracted and applied to various businesses through artificial intelligence technology, which will greatly reduce labor costs and improve efficiency. However, it is difficult to extract. At present, most of them rely on manual input. Using Optical Character Recognition (OCR) and Natural Language Processing (NLP) technology to electronize and structure the information on these paper materials has gradually become a hot spot in the current industry. Based on this, we constructed a medical material information extraction data set Medical OCR dataset (MedOCR) [1], and we also held the “Medical inventory invoice OCR element extraction Task” evaluation competition based on the eighth China Health Information processing Conference (CHIP2022), in order to promote the development of medical material information extraction technology. A total of 18 teams participated in the competition, most of which used an OCR-based extraction system. For the evaluation index Acc, the best performing teams reached 0.9330 and 0.9076. The task of the competition focuses on information extraction technology, and MedOCR will be open for researchers to carry out related technical research for a long time.
Technologies in Long-Term Care and Nursing Homes

Long-term care is defined as a variety of services designed to meet a person’s health and personal care needs which help people live as independently and safely as possible when they can no longer perform everyday activities on their own. Depending on the needs and social context of the patient, long-term care can be provided in different places by different kinds of formal or informal caregivers, either at home or in an institutionalised setting, which from here on we will refer to as the nursing home. In all these fields, technology has an increasing role to play.
Short Paper: Verifiable Decryption for BGV

In this work we present a direct construction for verifiable decryption for the BGV encryption scheme by combining existing zero-knowledge proofs for linear relations and bounded values. This is one of the first constructions of verifiable decryption protocols for lattice-based cryptography, and we give a protocol that is simpler and at least as efficient as the state of the art when amortizing over many ciphertexts. To prove its practicality we provide concrete parameters, resulting in proof size of less than $$44 \tau $$ KB for $$\tau $$ ciphertexts with message space 2048 bits. Furthermore, we provide an open source implementation showing that the amortized cost of the verifiable decryption protocol is only 76 ms per message when batching over $$\tau = 2048$$ ciphertexts.
Yoga Posture Recognition by Learning Spatial-Temporal Feature with Deep Learning Techniques

Yoga posture recognition remains a difficult issue because of crowded backgrounds, varied settings, occlusions, viewpoint alterations, and camera motions, despite recent promising advances in deep learning. In this paper, the method for accurately detecting various yoga poses using DL (Deep Learning) algorithms is provided. Using a standard RGB camera, six yoga poses — Sukhasana, Kakasana, Naukasana, Dhanurasana, Tadasana, and Vrikshasana — were captured on ten people, five men and five women. In this study, a brand-new DL model is presented for representing the spatio-temporal (ST) variation of skeleton-based yoga poses in movies. It is advised to use a variety of representation learners to pry video-level temporal recordings, which combine spatio-temporal sampling with long-range time mastering to produce a successful and effective training approach. A novel feature extraction method using Open Pose is described, together with a DenceBi-directional LSTM network to represent spatial-temporal links in both the forward and backward directions. This will increase the efficacy and consistency of modeling long-range action detection. To improve temporal pattern modeling capability, they are stacked and combined with dense skip connections. To improve performance, two modalities from look and motion are fused with a fusion module and compared to other deep learning models are LSTMs including LSTM, Bi-LSTM, Res-LSTM, and Res-BiLSTM. Studies on real-time datasets of yoga poses show that the suggested DenseBi-LSTM model performs better and yields better results than state-of-the-art techniques for yoga pose detection.