Reviewerly/exHarmony huggingface dataset:
Over-the-Air Computation for Distributed Machine Learning and Consensus in Large Wireless Networks

We give an overview of the problem of Over-the-Air (OTA) computation and the currently available research, and of two important applications: Distributed consensus algorithms and distributed Machine Learning. For the latter, we specifically consider an application of OTA computation in Horizontal and Vertical Federated Learning. We then survey some of our own contributions to these fields, which include a scheme for analog Over-the-Air computation that works with a large class of fast-fading wireless channels, as well as applications to distributed consensus and Vertical Federated Learning. Moreover, we give an overview of our initial research on how Physical Layer Security can be applied to secure Over-the-Air computation against eavesdroppers.
Gaze tracking technology for flight simulator

In order to equip the flight simulator with the corresponding gaze tracking system to further enhance the training quality for pilot cadets, a cross ratio-based gaze tracking system with an inexpensive monocular infrared camera and four infrared LEDs was proposed in this paper. Since it was difficult for traditional machine learning algorithms to extract eye features in the large field-of-view (FOV) infrared environment, a robust face detector based on deep neural network (DNN) was adopted to locate eyes with 68 facial landmarks more accurately. On this basis, the Gaussian Laplacian (LoG) blob detection algorithm was used to extract glints directly. Then the eye image was filtered by Gaussian to denoise and the pupil region was highlighted by global thresholding. Considering some areas of the pupil region were corroded by glints, the morphological operator closed operation was used to fill them. After that the pupil center was estimated by ellipse fitting. Finally, the gaze points were estimated by the constant cross ratio in the 2D projective space Through the experimental analysis after calibration, the system has high accuracy and practical value.
DeepThought: A Reputation and Voting-Based Blockchain Oracle

Thanks to built-in immutability and persistence, the blockchain is often seen as a promising technology to certify information. However, when the information does not originate from the blockchain itself, its correctness cannot be taken for granted. To address this limitation, blockchain oracles—services that validate external information before storing it in a blockchain—were introduced. In particular, when the validation cannot be automated, oracles rely on humans that collaboratively cross-check external information. In this paper, we present DeepThought, a distributed human-based oracle that combines voting and reputation schemes. An empirical evaluation compares DeepThought with a state-of-the-art solution and shows that our approach achieves greater resistance to voters corruptions in different configurations.
LGFFN-GHI: A Local-Global Feature Fuse Network for Gastric Histopathological Image Classification

Gastric cancer remains the third most common cause of cancer-related death.Histopathological examination of gastric cancer is the gold standard for the diagnosis of gastric cancer.However, manual pathology examination is timeconsuming and laborious.Computer-aided diagnosis (CAD) systems can assist pathologists in diagnosing pathological images, thus improving the efficiency of disease diagnosis.In this paper, we propose a two-branch network named LGFFN-GHI, which can classify histopathological images of gastric cancer into two categories: normal and abnormal.LGFFN-GHI consists of two parallel networks, ResNet18 and Pvt-Tiny, which extract local and global features of microscopic gastric tissue images, respectively.We propose a feature blending module (FFB) that fuses local and global features at the same resolution in a cross-attention manner.This enables ResNet18 to acquire the global features extracted by Pvt-Tiny, while enabling Pvt-Tiny to acquire the local features extracted by ResNet18.We conducted experiments on a novel publicly available sub-size image database of gastric histopathology (Ga-sHisSDB).The experimental results show that LGFFN-GHI achieves an accuracy of 96.814%, which is 2.388% and 3.918% better than the baseline methods ResNet18 and Pvt-Tiny, respectively.Our proposed network exhibits high classification performance, demonstrating its effectiveness and future potential for the gastric histopathology image classification (GHIC) task.
The Trend of Industrial Design from the Perspective of Metaverse

With the rapid development of the Internet, the concept of metaverse has been put forward, and the development of metaverse will affect all industries. At present, metaverse is still at the stage of infrastructure construction, but the development route is gradually clear. This paper summarizes the concept, formation factors and core of the metaverse, combs and analyzes the cutting-edge technologies and cases under the metaverse background, and comes to the conclusion that the core of the metaverse is the integration of virtual and reality, and "experience" is the core of the integration of virtual and reality. Under the metaverse background, there have been new changes in the field of industrial design, this paper analyzes the two realization paths and related technologies of metauniverse, as well as the development trend of industrial design in the direction of design tools, design performance and industrial chain. VR, AR, MR, game engine and digital Twin are important technologies affecting the field of industrial design. The value of "virtual" has been paid more and more attention. How to make good use of these technologies to combine virtual assets with reality is an important development trend in the field of industrial design.
Enabling Real-time AI Inference on Mobile Devices via GPU-CPU Collaborative Execution

AI-powered mobile applications are becoming increasingly popular due to recent advances in machine intelligence. They include, but are not limited to mobile sensing, virtual assistants, and augmented reality. Mobile AI models, especially Deep Neural Networks (DNN), are usually executed locally, as sensory data are collected and generated by end devices. This imposes a heavy computational burden on the resource-constrained mobile phones. There are usually a set of DNN jobs with deadline constraints waiting for execution. Existing AI inference frameworks process incoming DNN jobs in sequential order, which does not optimally support mobile users’ real-time interactions with AI services. In this paper, we propose a framework to achieve real-time inference by exploring the heterogeneous mobile SoCs, which contain a CPU and a GPU. Considering characteristics of DNN models, we optimally partition the execution between the mobile GPU and CPU. We present a dynamic programming-based approach to solve the formulated real-time DNN partitioning and scheduling problem. The proposed framework has several desirable properties: 1) computational resources on mobile devices are better utilized; 2) it optimizes inference performance in terms of deadline miss rate; 3) no sacrifices in inference accuracy are made. Evaluation results on an off-the-shelf mobile phone show that our proposed framework can provide better real-time support for AI inference tasks on mobile platforms, compared to several baselines.
Assessing Similarity-Based Grammar-Guided Genetic Programming Approaches for Program Synthesis

Grammar-Guided Genetic Programming is widely recognised as one of the most successful approaches for program synthesis, i.e., the task of automatically discovering an executable piece of code given user intent. Grammar-Guided Genetic Programming has been shown capable of successfully evolving programs in arbitrary languages that solve several program synthesis problems based only on a set of input-output examples. Despite its success, the restriction on the evolutionary system to only leverage input/output error rate during its assessment of the programs it derives limits its scalability to larger and more complex program synthesis problems. With the growing number and size of open software repositories and generative artificial intelligence approaches, there is a sizeable and growing number of approaches for retrieving/generating source code based on textual problem descriptions. Therefore, it is now, more than ever, time to introduce G3P to other means of user intent (particularly textual problem descriptions). In this paper, we would like to assess the potential for G3P to evolve programs based on their similarity to particular target codes of interest (obtained using some code retrieval/generative approach). We particularly assess 4 similarity measures from various fields: text processing (i.e., FuzzyWuzzy), natural language processing (i.e., Cosine Similarity based on term frequency), software clone detection (i.e., CCFinder), plagiarism detector(i.e., SIM). Through our experimental evaluation on a well-known program synthesis benchmark, we have shown that G3P successfully manages to evolve some of the desired programs with three of the used similarity measures. However, in its default configuration, G3P is not as successful with similarity measures as with the classical input/output error rate at evolving solving program synthesis problems.
Repository Landscape in Turkiye and GCRIS: The first National Research Information System

This paper describes the history and developments of research infrastructures and open science policies in Turkiye. Moreover, it focuses on the GCRIS (Grand Current Research Information Systems), Turkiye's first Research Information System by inter-national standards, emphasizing the need for internationally interoperable research infrastructures in Turkiye. GCRIS Research Information System, implemented on the open-source software DSpace-CRIS 6.3, was developed with data analytics in mind and continues to be improved by Research Ecosystems Inc. As a strategic partner, Izmir Institute of Technology (IZTECH) is the first university to use GCRIS. Other Universities have used GCRIS since then. With the increase in the number of universities using GCRIS, Turkiye's Research Ecosystem will be trackable and measurable much better thanks to GCRIS intelligent reporting sys- tem. Most importantly, not only the research outputs of Turkiye will be more visible, but also research infrastructures’ integration will facilitate with the European Open Science Cloud (EOSC) and other initiatives worldwide.
Work In Progress: Towards Adaptive RF Fingerprint-based Authentication of IIoT devices

As IoT technologies mature, they are increasingly finding their way into more sensitive domains, such as Medical and Industrial IoT, in which safety and cyber-security are of great importance. While the number of deployed IoT devices continues to increase exponentially, they still present severe cyber-security vulnerabilities. Effective authentication is paramount to support trustworthy IIoT communications, however, current solutions focus on upper-layer identity verification or key-based cryptography which are often inadequate to the heterogeneous IIoT environment. In this work, we present a first step towards achieving powerful and flexible IIoT device authentication, by leveraging AI adaptive Radio Frequency Fingerprinting technique selection and tuning, at the PHY layer for highly accurate device authentication over challenging RF environments.
Multi-Stage Generation of Tile Images Based on Generative Adversarial Network

Deep learning techniques have been recently widely used in the field of texture image generation.There are still two major problems when applying them to tile image design work.On the one hand, there is still lack of enough diverse ceramic tile images for the training process.On the other hand, the output image is difficult to control and adjust, and cannot meet the designer's requirements of interactivity.Therefore, we propose a multi-stage generation algorithm of tile images based on generative adversarial network(GAN).First, the multi-scale attention GAN is applied to generate controllable texture image.Then, the SWAG texture synthesis GAN is also applied to obtain controllable and diverse image style.And finally, through the style iteration mechanism and the multiple step magnification method based on image superresolution reconstruction network, the final tile images can be automatically generated with larger-size and higher-precision.The relevant experiments demonstrate that our method can not only generate high-quality tile images in a relatively short period of time, but also consider human interaction to a certain extent, and maintain a certain degree of control over the main texture and style of the final generated tile images.It has good and wide application value.
AIACC-Training: Optimizing Distributed Deep Learning Training through Multi-streamed and Concurrent Gradient Communications

There is a growing interest in training deep neural networks (DNNs) in a GPU cloud environment. This is typically achieved by running parallel training workers on multiple GPUs across computing nodes. Under such a setup, the communication overhead is often responsible for long training time and poor scalability. This paper presents AIACC-Training, a unified communication framework designed for the distributed training of DNNs in a GPU cloud environment. AIACC-Training permits a training worker to participate in multiple gradient communication operations simultaneously to improve network bandwidth utilization and reduce communication latency. It employs auto-tuning techniques to dynamically determine the right communication parameters based on the input DNN workloads and the underlying network infrastructure. AIACC-Training has been deployed to production at Alibaba GPU Cloud with 3000+ GPUs executing AIACC-Training optimized code at any time. Experiments performed on representative DNN workloads show that AIACC-Training outperforms existing solutions, improving the training throughput and scalability by a large margin.
Measuring Early Detection of Anomalies

Early detection is a matter of growing importance in multiple domains as network security, health conditions over social network services or weather forecasts related disasters. It is not enough to make a good decision but it also needs to be made on time. In this paper, we define a method to evaluate detection of anomalies in time-aware systems. To do so, we present the early detection problem from a generic perspective, examine the evaluation metrics available and propose a new metric, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TaP (Time aware Precision)</i> . A set of experiments using three different datasets from different fields are performed in order to compare the behaviour of the different metrics. Two different approaches were followed, first a batch evaluation is performed, followed by a streaming evaluation which allows to present a more realistic behaviour of the systems. For both steps, we propose two sets of experiments. The first one using baseline models, followed by the evaluation of a set of Machine Learning algorithms results. The presented metric allows the amount of items needed to take a decision to be taken into account, not depending on the specific dataset but on the nature of the problem to solve.
An Efficient Person Clustering Algorithm for Open Checkout-free Groceries

Open checkout-free grocery is the grocery store where the customers never have to wait in line to check out. Developing a system like this is not trivial since it faces challenges of recognizing the dynamic and massive flow of people. In particular, a clustering method that can efficiently assign each snapshot to the corresponding customer is essential for the system. In order to address the unique challenges in the open checkout-free grocery, we propose an efficient and effective person clustering method. Specifically, we first propose a Crowded Sub-Graph (CSG) to localize the relationship among massive and continuous data streams. CSG is constructed by the proposed Pick-Link-Weight (PLW) strategy, which picks the nodes based on time-space information, links the nodes via trajectory information, and weighs the links by the proposed von Mises-Fisher (vMF) similarity metric. Then, to ensure that the method adapts to the dynamic and unseen person flow, we propose Graph Convolutional Network (GCN) with a simple Nearest Neighbor (NN) strategy to accurately cluster the instances of CSG. GCN is adopted to project the features into low-dimensional separable space, and NN is able to quickly produce a result in this space upon dynamic person flow. The experimental results show that the proposed method outperforms other alternative algorithms in this scenario. In practice, the whole system has been implemented and deployed in several real-world open checkout-free groceries.
Effects of User Construction Behavior on User Experience in a Virtual Indoor Environment

The metaverse elicits an extensive range of user curiosity by attractive virtual indoor environments, in which users can solve different tasks and conduct enormous collaborations. However, limited interaction methods are slowing down this development process, and the understanding of how such methods impact user experience is limited. To address this problem, we conducted a lab experiment with 150 valid participants to analyze the effect of user construction behavior on user satisfaction, perceived performance, and recommendation intention. By using the PROCESS 3.3 model, the results show that a higher level of participation facilitates satisfaction, which is a complete mediator that has a significant positive effect on perceived performance. Additionally, recommendation intention is positively affected by both satisfaction and perceived performance. Demographic factors could also generate significant impacts on user experience. We discuss the theoretical explanations, as well as design implications. Taken together, this study represents the first inquiry into the relationship between user construction behavior and user experience, thereby laying the groundwork for future interaction methods.
Towards a Methodology to Evaluate User Experience with Personalized Questionnaires for the Developments of Custom Systems

Systems are considered essential in organizations. These systems can be standard implementations, such as Enterprise Resource Planning (ERP) or custom developments to address specific problems. Considering custom developments, how do we evaluate usability and User eXperience (UX) to obtain indicators of improvements related to one domine? One methodology widely used is the user test, which is accomplished through surveys to collect information. Several questionnaires collect general information about usability and UX. Although, the information provided is broad and partially covers the feedback needed to improve specific areas of the systems. What hinders the decision-making process for improving the system in particular aspects of usability and UX. For this reason, it is necessary to adapt the general questionnaires to the needs of the system developed. In this work, we present a preliminary methodology that allows considering different available questionnaires to evaluate the usability and/or UX and relate them to a specific context, emphasizing the particularities of custom development. The proposed methodology considers eight stages, and this methodology was used in a practical case to evaluate a custom system developed for a company that produces and markets products. The results showed specific information that supported decision-making for improving usability and user experience about navigation, search, and visualization of the information displayed in the interface. Improve the minimalist design associated with the portable system version, improving efficiency to achieve specific activities, among other things. Future work intends to use the methodology in different case studies, refine it and establish an implementation guide.
Optimal Black-Box Traceability in Decentralized Attribute-Based Encryption

With the advent of Cloud-storages, secure sharing of encrypted data with fine-grained access control has become an important challenge. To enforce fine-grained access rights on encrypted data to different users, Attribute-Base Encryption (ABE) is a promising cryptographic tool. But in many situations, some pirate users sell their access privileges for monetary gain. Hence, an efficient Black-Box Traitor Tracing (BB-TT) system is vital to address the corresponding Pirate Decoders (PDs). But, in the existing BB-TTs, the tracing complexity is directly related to the maximum number of users in the system. This paper proposes an ABE-based access control system that provides a new Relaxed BB-TT. Our Relaxed BB-TT addresses the complexity problem by exploring the pirate user with an optimal number of decryption queries on the PD. The optimal number of queries leads to the exciting capability of traceability of stateful decoders (i.e., the decoder that can save the states among the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Tracer</i> 's queries). Finally, our system provides White-Box traceability and reduces the amount of trust in the authorities. We give formal security proofs and extensive experimental results on different classes of mobile devices, including a laptop and a smartphone.
A Multi-Resolution Channel Structure Learning Estimation Method of Geometry-Based Stochastic Model With Multi-Scene

Vehicle-to-vehicle (V2V) communication is the most typical application of the Internet of Vehicles, and it has many envisaged applications, such as driverless driving, collision warning and addressing traffic congestion. The realization of future intelligent transportation systems (ITSs) will require V2V communication technology, which in turn relies on accurate V2V channel estimation. As the possibilities that 5G technologies can be used for V2V communications are increasingly being explored, corresponding channel models used to describe the characteristics of V2V channels are also being updated, so the new channel estimation algorithms needed to be developed. Although the channel estimation method based on Convolutional Neural Network (CNN) has achieved remarkable success in communication problems in recent years, it cannot be well adapted to V2V channels under different scenarios and different modeling methods. In this article, a method is proposed for learning and estimating the channel structure in the 5G NR downlink that can adapt to the V2V channel under multi-scenes. The inherent block sparsity characteristics of the channel structure is adopted to combine the group lasso Alternating Direction Method of Multipliers (ADMM) algorithm with CNN, and residual dense network is employed to estimate and refine the V2V channel structure. Furthermore, the classifier and interpolation with pooling method are used to estimate the V2V channel structure under multi-scene and multi-resolution. Simulation results show that the performance of this method is better than other deep learning based estimation algorithms.