Reviewerly/exHarmony huggingface dataset:
Minimum Spanning Set Selection in Graph Kernels

Kernel-based learning models such as support vector machines (SVMs) can seamlessly deal with graph structures thanks to suitable kernel functions that compute a particular similarity between pairs of data samples. In the last two decades, a plethora of graph kernels have been proposed, motivated by theoretical properties or designed specifically for an application domain. Computing graph kernels however presents a significant cost for both training and inference, since predictions on unseen graphs require evaluating the kernel e.g. between the new sample and all support vectors, and solutions to an SVM optimization problem are not guaranteed to be sparse. In this paper, we present a method to select a minimum set of spanning vectors for the solutions of SVMs, based on the rank-revealing QR decomposition of the kernel Gram matrix. We apply it on 18 real-world classification tasks on chemical compounds, showing its effectiveness to reduce the computational burden in performing inference on unseen graphs by minimizing the number of support vectors without penalizing accuracy. This in turn gives us a tool to better analyze the trade-off between accuracy, expressiveness and inference cost among different graph kernels.
Matching-Graphs for Building Classification Ensembles

Ensemble learning is a well known paradigm, which combines multiple classification models to make a final prediction. Ensemble learning often demonstrates significant benefits, in particular a better classification performance than the individual ensemble members. However, in order to work properly, ensemble methods require a certain diversity of its members. One way to increase this diversity is to randomly select a subset of the available data for each classifier during the training process (known as bagging). In the present paper we propose a novel graph-based bagging ensemble that consists of graph neural networks. The novelty of our approach is that the ensemble operates on substantially augmented graph sets. The graph augmentation technique, in turn, is based on so-called matching-graphs, which can be computed on arbitrary pairs of graphs. In an experimental evaluation on five graph data sets, we show that this novel augmentation technique paired with a bagging ensemble is able to significantly improve the classification accuracy of several reference systems.
GNN-DES: A New End-to-End Dynamic Ensemble Selection Method Based on Multi-label Graph Neural Network

Most dynamic ensemble selection (DES) techniques rely solely on local information to single out the most competent classifiers. However, data sparsity and class overlap may hinder the region definition step, yielding an unreliable local context for performing the selection task. Thus, we propose in this work a DES technique that uses both the local information and classifiers' interactions to learn the ensemble combination rule. To that end, we encode the local information into a graph structure and the classifiers' information into multiple meta-labels, and learn the DES technique end-to-end using a multi-label graph neural network (GNN). Experimental results over 35 high-dimensional problems show the proposed method outperforms most evaluated DES techniques as well as the static baseline, suggesting its suitability for dealing with sparse overlapped data.
A Study of eXplainable Artificial Intelligence: A Systematic Literature Review of the Applications

eXplainable Artificial Intelligence (XAI) has attracted researchers in various domains over the last few years. Explainable AI includes the explainability in the AI systems which capable of explaining their decisions. This study performs a systematic literature review on XAI. In the first phase, we collected 78 high-quality web of science research journal papers from the Scopus data. It revealed that IEEE access and Expert systems with applications are the main targeted journals for researchers for XAI. Our study applies an Apriori algorithm and network analysis to get the dominant theme and check the connectivity among the methods/techniques respectively. The analysis showed that Robotics, Financial Services, Healthcare, Banking, Security, and business are the most dominant areas where XAI provides an explainability to the artificial intelligence (AI) systems. Based on our analysis, this literature review provides a future direction for researchers, academicians, and industrialists.
Robust Representation Learning

Abstract Representation learning models, especially pre-trained models, help NLP systems achieve superior performances on multiple standard benchmarks. However, real-world environments are complicated and volatile, which makes it necessary for representation learning models to be robust. This chapter identifies different robustness needs and characterizes important robustness problems in NLP representation learning, including backdoor robustness, adversarial robustness, out-of-distribution robustness, and interpretability. We also discuss current solutions and future directions for each problem.
OpenBMB: Big Model Systems for Large-Scale Representation Learning

Abstract Big pre-trained models (PTMs) have received increasing attention in recent years from academia and industry for their excellent performance on downstream tasks. However, huge computing power and sophisticated technical expertise are required to develop big models, discouraging many institutes and researchers. In order to facilitate the popularization of big models, we introduce OpenBMB, an open-source suite of big models, to break the barriers of computation and expertise of big model applications. In this chapter, we will introduce the core toolkits in OpenBMB, including BMTrain for efficient training, OpenPrompt and OpenDelta for efficient tuning, BMCook for efficient compression, and BMInf for efficient inference.
Evaluation of User’s Psychological Sense in Tele-Immersion Robot Avatar

Robot avatars are attracting attention as a means of remote communication. In this study, a tele-immersion system was developed in which the user’s own body transitions into the body of a robot avatar, enabling communication with a remote person as if they were in the same place. Its use of a VR headset, 360-degree camera, and motion tracking is in contrast to systems where a robot avatar is operated using a PC. A prototype of this system was developed and tested through evaluation experiments. As a result, users of the remote avatar felt as if they were in the same room with and collaborating with the other person, confirming the system’s effectiveness as a tele-immersion system. In addition, users generally felt a sense of body ownership and a sense of agency towards the robot avatar, suggesting the generation of a sensation that their own body is transferred to the robot avatar’s body.
Faster Segmented Sort on GPUs

Efficient parallel implementations of various sorting algorithms on modern hardware platforms are essential to numerous application areas. In this paper, we first measure the performance of the leading segmented sort implementation on CUDA-enabled GPUs and determine optimal setups using the resulting runtimes. Subsequently, we propose a number of changes that improve efficiency for segments of specific lengths. Furthermore, an alternative key-only version is introduced, that is specifically optimized to just sort keys instead of key-value pairs, which allows for further optimization. Performance is evaluated by comparing runtimes of the original algorithm with our improved version for segments of different lengths resulting in average speedups between 1.26 and 1.35 on four GPUs of different generations (Pascal, Volta, Ampere, Ada Lovelace). Furthermore, comparison to alternative segmented sort implementations from CUB and ModernGPU results in average speedups of at least 2.2 and 2.5, respectively, across all tested architectures. To illustrate how our improved sorting algorithm can be beneficial in a practical application, we have integrated it into the MetaCache-GPU pipeline for metagenomic DNA classification resulting in speedups of up to 25.6% for the sorting step. Code is publicly available at https://gitlab.rlp.net/pararch/faster-segmented-sort-on-gpus .
llm-japanese-dataset v0: Construction of Japanese Chat Dataset for Large Language Models and Its Methodology

This study constructed a Japanese chat dataset for tuning large language models (LLMs), which consist of about 8.4 million records. Recently, LLMs have been developed and gaining popularity. However, high-performing LLMs are usually mainly for English. There are two ways to support languages other than English by those LLMs: constructing LLMs from scratch or tuning existing models. However, in both ways, datasets are necessary parts. In this study, we focused on supporting Japanese in those LLMs and making a dataset for training or tuning LLMs in Japanese. The dataset we constructed consisted of various tasks, such as translation and knowledge tasks. In our experiment, we tuned an existing LLM using our dataset and evaluated the performance qualitatively. The results suggest that our dataset is possibly beneficial for LLMs. However, we also revealed some difficulties in constructing LLMs in languages other than English.
Reducing Electric Energy Consumption of Servers in Multi-version Timestamp Ordering Algorithm

The Multi-Version Timestamp Ordering (MVTO) algorithm is so far proposed to implement the Multi-Version Concurrency Control (MVCC). The MVTO algorithm can enhance the concurrency of transactions but does not consider the electric energy consumption to perform transactions. The Energy-Saving Multi-Version Timestamp Ordering with Virtual Machines (ESMVTO-VM) algorithm is proposed to not only enhance the concurrency of transactions but also reduce the electric energy consumption of servers in our previous studies. In this paper, an Improved ESMVTO-VM (IESMVTO-VM) algorithm is newly proposed to more reduce the electric energy consumption of servers by improving the ESMVTO-VM algorithm. The electric energy consumption of servers is shown to be more reduced in the IESMVTO-VM algorithm than the ESMVTO-VM algorithm in evaluation.
Presence Communication Metaverse Conference System

Due to the emergence of the new coronavirus infectious disease, video call through teleconferencing systems is used for telework and telelearning in companies and educational institutions, respectively. Furthermore, communication using character avatars in the Metaverse space is gaining widespread popularity. On the other hand, it is difficult to accurately read nonverbal information such as line of sight and gestures in the aforementioned forms of tele- and virtual communication. Therefore, in this study, a Metaverse conference system using real-time video avatars is developed for formal situations.
The Effects of Scale and Distribution on a Deep Neural Network Iterative Classification System of Spatial Data Streams

In this paper we explore the scalability and distribution of a spatial data stream processing system by expanding a previously-proposed deep neural network-based classification system. Three distributions are studied: a centrally controlled cluster, a peer-to-peer system, and a hybrid of both. We also study the effect of expanding the number of classes being classified, including the gradual addition of classes to be classified to the neural network. We find that the deep neural network classification system does expand to more classes though it is not amenable to the gradual addition of classes during the networks training and that this is not improved by pre-training the network on all classes. We also find that the network performs asymmetrically depending on the number of classes being classified with optimal accuracy occurring with about half to a quarter as many classes as output nodes in the network. We finish this paper offering some recommendations to potentially use this system in a distributed manner to increase accuracy for the total set of classes.
Barriers to Blockchain Adoption by Saudi Higher Education Institutions: A Structural Equation Analysis

Empirical investigations of the factors influencing blockchain adoption by higher education institutions in Saudi Arabia are rare. This study aimed to partially fill this knowledge gap by focusing on barriers to blockchain adoption. The results from a survey of 289 academic and IT professionals in the Saudi higher education system confirmed the negative effects of privacy and security concerns, the association of blockchain with finance only, and language concerns on blockchain adoption. At the same time, the impact of the lack of knowledge was not observed. The theoretical and practical implications of the results are discussed.
Certificate Service for Unboxing Video Images

We propose an online service system that enables the customer to record a video evidence of the whole process of unboxing a package of products bought by mail order under the assumption that the package is delivered and locked in an automatic package locker by the carrier service. The system is made up of (i) a client application for the android devices that starts filming the unboxing process before acquiring the one-time password to unlock the locker and keeps uploading all the video frames to the server and (ii) a server program that stores each video frame with its arrival time with their hash-based signature. Keep filming the package throughout the unboxing process until the customer makes sure that the purchased products are missing, damaged, defected, or, otherwise in proper conditions, the customer gains a video evidence for claiming flaws by the vendor or the carrier if any.
Auto-Divide GNN: Accelerating GNN Training with Subgraph Division

Graph Neural Networks (GNNs) have gained considerable attention in recent years for their exceptional performance on graph-structured data. Sampling-based GNN training is the most common method used for training GNNs on large-scale graphs, and it is often accelerated by caching feature data on the GPU. However, the emergence of more complex models and datasets with higher feature dimension requires more GPU memory for training, which limits the acceleration performance of GPU caching and can even result in out-of-memory errors. To release more GPU memory for the cache in a transparent way, we propose a subgraph division method, which trains several smaller micrographs instead of an entire subgraph at each training iteration. However, it is non-trivial to combine subgraph division with GPU caching due to the redundancy between micrographs. To tackle this challenge, we introduce an auto-profile method that searches for the best-performing subgraph division scheme based on training perception and probability analysis. Additionally, we develop an estimation-based caching strategy to lift the caching hitting rate against the diverse graph structures. These ideas are integrated to Auto-Divide GNN, a framework for accelerating sampling-based GNN training. The multi-GPU evaluations on three representative GNN models and five large graphs demonstrate that Auto-Divide GNN achieves significant speedups of up to 5.61 $$\times $$ and 3.13 $$\times $$ over two state-of-the-art GNN frameworks, DGL and PaGraph, respectively.
BitHist: A Precision-Scalable Sparse-Awareness DNN Accelerator Based on Bit Slices Products Histogram

Memory and power-sensitive edge devices benefit from quantized models based on precision-scalable CNN accelerators. These accelerators can process CNN models that with different data precisions, which rely on the precision scalable multiply-accumulate (MAC) unit. Among all types of MAC units, the spatial precision scalable MAC (SPM) unit is an attractive one as it is flexible and can convert the decrease in data width into an increase in throughput. However, it becomes energy-inefficient due to the need for more shifters and high-width adders as the bit width of the operand increases. Taking advantage of the limited number of unique products of 2-bit unsigned multiplication in the existing SPM, this paper proposes a new MAC method based on the unique product histogram, which is orthogonal to the existing methods. Based on the proposed MAC method, this paper also proposes the BitHist, an efficient DNN accelerator that exploits both bit-level and data-level sparsity. The evaluation results illustrate that BitHist saves 57% of the area compared to the BitFusion and provides up to 4.60 $$\times $$ throughput per area and 17.4 $$\times $$ energy efficiency. Additionally, BitHist can achieve a 2.28 $$\times $$ performance gain from sparsity exploitation.
Application of adaptive improved DE algorithm based on multi-angle search rotation crossover strategy in multi-circuit testing optimization

Abstract This study based on the standard differential evolution (DE) algorithm was carried out to address the issues of control parameter imprinting, mutation process, and crossover process in the standard DE algorithm as well as the issue of multidimensional circuit testing optimization. A rotation control vector was introduced to expand the search range in the poor strategy to the circumference range of the individual and the parent target individual, and a rotation crossover operator and a binomial poor operator were combined. Finally, an improved adaptive DE algorithm based on a multi-angle search rotation crossover strategy was obtained. The research will improve the DE algorithm to optimize the testing of multidimensional circuits. It can be noted that the improved average precision value is 0.9919 when comparing the precision recall curves of the DE algorithm before and after the change, demonstrating a significant improvement in accuracy and stability. The fitness difference of the 30-dimensional problem is discovered to be between 0.25 × 10 3 and 0.5 × 10 3 by comparing the box graphs of the 30-dimensional problem with that of the 50-dimensional problem. On the 50-dimensional problem, when calculating the F4–F10 function, the fitness difference of the improved DE algorithm is 0.2 × 10 4 –0.4 × 10 4 . In summary, the improved DE algorithm proposed in this study compensates for the shortcomings of traditional algorithms in complex problem calculations and has also achieved significant optimization results in multidimensional circuit testing.
Toward Extracting Orientation of Personal Interest for Human Activity Recognition

In this paper, we propose a system that detects an orientation of personal interest for realizing human activity recognition. It is expected that human activity is closely associated with the orientation of personal interest. Therefore, the proposed system acquires head pose and face orientation from video streaming using OpenFace 2.0, a facial feature estimate tool, and extracts the detection of orientation of personal interest. To verify the effectiveness of the proposed system, we investigated the estimation accuracy of the orientation of interest by four participants. As a result, the best estimation in the experiment was obtained with an absolute coordinate error of approximately 100 mm and an absolute angular error of approximately $$6.5^\circ $$ . In addition, we evaluated the useful accuracy of the system through a qualitative evaluation.
Method for Promoting the Introduction of DevSecOps to Improve Security Quality

With the advancement of IT, the cycle from development to operations in software development is getting faster, and mainly many large companies have been adopting DevOps to keep up. Meanwhile, DevSecOps, a development methodology designed to improve the security quality of DevOps, is gaining attention due to the increasing number of security incidents. However, DevSecOps adoption is still not well understood enough to maintain an adequate level of security. We therefore developed a method to promote the introduction of DevSecOps for improving security quality in software development. Specifically, we first analyzed the results of several software development exercises by students at the Chiba Institute of Technology to investigate how security features are implemented. Next, we conducted a detailed analysis of these implementations at each stage of the development process to clarify which ones should be implemented at each step and to identify guidelines for promoting the adoption of DevSecOps.
Context-Aware Approaches in Cognitive Cloud and Cybersecurity

This paper will present context-aware approaches to the implementation of secure Cognitive Cloud services as well as the creation of IT security systems. In particular, the use of external factors and environmental features in defining user-oriented and distributed security protocols will be presented. The use of external information from the surrounding world allows the definition of security protocols that are based on cognitive systems and are resistant to typical cyberattacks.
Blockchain implementation in smart cities: Discussion on performance indicators

Background: Blockchain project implementation in smart cities represents a novel challenge in information technologies. Lack of functional framework and guidelines impact these implementations and add additional uncertainty. Authors in their research on this subject seek to discover a functional framework to improve the implementation process. The research is divided into 3 parts: identification of best application areas, critical success factors, and implementation performance indicators. Purpose: The goal of this paper is to present findings on the performance indicators and offer additional insight into their nature as well as point to lesser-known performance indicators of blockchain projects. Study design/methodology/approach: Authors used the Delphi technique and according to the methodology, a panel of 33 experts was presented with a list of performance indicators and asked to add additional performance indicators as well as to assess their importance. The research foundation was based on the literature review which resulted in 9 performance indicators for the blockchain project implementation in smart cities. Findings/conclusions: The experts proposed 8 additional performance indicators; however, only 1 of them had required consensus to be accepted by the panel. This paper will disclose both groups of performance indicators and preserve them for further scientific discussion since the performance indicators that were proposed by the individual experts but did not reach panel consensus will not be contained in the further research results. After all rounds of the Delphi method were finished the experts concluded the list of the 5 most important performance indicators for the implementation of blockchain projects in the smart city. Limitations/future research: Due to limited resources and lack of experts, the size of the panel is limited to only 33 experts. In addition, smart cities are usually managed by the public sector, so access to all data is limited. The findings presented in the paper can be further used to improve the efficiency of blockchain projects.