Reviewerly/exHarmony huggingface dataset:
Spatial-aware data partition for distributed memory parallelization of ANN search in multimedia retrieval

Content-based multimedia retrieval (CBMR) applications are becoming very popular in several online services which handles large volumes of data and are submitted to high query rates. While these applications may be complex, finding the nearest neighboring objects (multimedia descriptors) is typically their most time consuming operation. In order to address this problem, several recent works have proposed distributed memory parallelization of approximate nearest neighbors (ANN) search. These solutions employ a variety of ANN algorithms and different parallelization strategies. In this paper, we have identified the currently used parallelization strategies (Data Equal Split (DES) and Bucket Equal Split (BES)) and systematically evaluated their performance. We have also developed a framework to simplify the deployment of ANN algorithms in distributed memory machines with customized parallelization or data partition strategies. We further proposed a novel class of data partition/parallelization strategies that takes into account the data spatial proximity. Our approaches (SABES and SABES++) improves data locality and the system efficiency as compared to DES and BES. For instance, SABES++ achieved speedups of 4.2× and 1.8× on top of DES and BES, respectively, in the baseline case (40 nodes). Further, SABES and SABES++ also attained higher multi-node scalability and the gains vs DES and BES increase a larger number of nodes. SABES++ is 14.5× faster than DES when 160 nodes are used.
Energy-Efficient and Load-Aware VM Placement in Cloud Data Centers

Abstract VM consolidation has been proposed as an effective solution to improve resource utilization and energy efficiency through VM migration. Improper VM placement during consolidation may cause frequent VM migrations and constant on–off switching of PMs, which can significantly hurt QoS and increase energy consumption. Most existing algorithms on efficient VM placement suffer the problem of easily falling into a sub-optimum prematurely since they are heuristic. Also, they do not achieve a good balance between multiple different goals, such as resource utilization, QoS, and energy efficiency. To address this problem, we propose an effective and efficient VM placement approach called MOEA/D-based VM placement, with the goal of optimizing energy efficiency and resource utilization. We develop an improved MOEA/D algorithm to search for a Pareto-compromise solution for VM placement. Our experiment results demonstrate that the proposed multi-objective optimization (MOO) model and VM placement solution have immense potential as it offers significant cost savings and a significant improvement in energy efficiency and resource utilization under dynamic workload scenarios.
Semantic Segmentation of Whole-Body Bone Scan Image Using Btrfly-Net

Cancer is still a problem in Indonesia for both the country and its sufferer. A way to reduce the impact of this problem is to detect the presence of cancer early on. For detecting the presence of cancer in the bones, we must perform a whole-body bone scan and segment it afterward. In many studies about whole-body bone scan images segmentation, the data usually comes from one source. Therefore, data scarcity problems may occur. The idea of using different sources may have an impact since there are size differences in bone geometry between different ethnicities. In this study, a system that can segment whole-body bone scan images automatically is proposed. The system is designed by using the Btrfly-Net method. Furthermore, the impact of using cross-domain dataset and augmentation methods is observed. The result shows that the system can segment bone scan with 0.856 and 0.780 dice coefficient score using the Btrfly-Net and U-Net methods, respectively. Moreover, augmentation method could increase the Btrfly-Net system metric over 1% while decreasing U-Net system metric over 2-6%. The decreasing metric occurrence in the U-Net system is not caused by the augmentation method directly. Furthermore, there are metric differences in using cross-domain dataset over 3-6%. However, further analysis shows that the cross-domain themselves do not cause the differences.
Security risks of global software development life cycle: Industry practitioner's perspective

Abstract Software security has become increasingly important because the malicious attack and other hacker risks of a computer system have grown popularity in the last few years. As a result, several researchers have examined security solutions as early as the requirement engineering phase. With the growth of the software business and the internet, there is a need to understand the security risks against each phase of the software development life cycle (SDLC). This study aims to empirically investigate and prioritize the risks that could negatively impact the software security aspects of SDLC in the context of global software development (GSD). To achieve the study objectives, we conducted an industrial empirical study to determine the impact of software security threats against each phase of SDLC. Furthermore, the fuzzy analytical hierarchy process (FAHP) was used to prioritize the list of software security risks against the SDLC. The results and analysis of this study provide a ranked‐based decision‐making framework, which assists the practitioners in considering the most critical security risks on priority. The results show “improper plan for secure requirement identification, inception, authentication, authorization, and privacy,” “lack of threat models updating,” “lack of output validation,” “lack of certification in the final release and archive,” and “spoofing” as the top‐ranked security risks of SDLC in GSD. In addition, the application of FAHP is novel in this domain as it is helpful to address multicriteria decision‐making problems.
A secure annuli CAPTCHA system

Many websites and applications rely on CAPTCHA for protection from bot attacks. Otherwise, users and businesses will be exposed to risks. Although several different CAPTCHA systems have been proposed, the development of deep learning algorithms allows attackers to create more efficient and accurate attack methods. Many studies have shown that existing CAPTCHA systems are no longer safe, especially text-based CAPTCHA. To resolve this issue, a simple, secure, and effective annuli CAPTCHA system is proposed in this paper. In the proposed system, the annuli CAPTCHA image containing the overlapping of circles and ovals is randomly generated. The user wishing to gain access to the system is required to answer correctly the total number of circles and ovals in the image to prove that he/she is not a bot. The security of our proposed CAPTCHA system is verified by three attack methods. Additionally, the usability survey of our CAPTCHA system conducted by anonymous questionnaires shows that our system is user friendly. In other words, the proposed system maintains a high level of usability under the premise of high security. Compared with the existing CAPTCHA system, our CAPTCHA system is significantly better in terms of security, usability and ease of implementation.
Dynamics of quantum correlations in two 2-level atoms coupled to thermal reservoirs

Abstract In this paper, we examine the dynamics of quantum correlations in two noninteractive two-level atoms coupled to two separate identical thermal reservoirs. The two atoms are initially produced in a Gisin state, which is a blend of a maximally entangled two-qubit state and a separable mixed state. Quantum entanglement is measured by logarithmic negativity, while the nonclassical correlations are characterized by trace distance discord and local quantum uncertainty. Using the mean photon number of reservoirs and spontaneous emission rates of atoms as inputs, we explore how these quantum resources behave. Consequently, we demonstrate that the dynamics of quantum entanglement and quantum correlations depend upon the parameters driving the system. Significantly, we further demonstrate that specific parameters may be tweaked to preserve the quantum resources in the system. The results give a full grasp of the quantum features of such a two-level atomic system, showing capabilities to construct quantum technology.
Identification of geochemical anomalies related to mineralization: A case study from porphyry copper deposits in the Qulong-Jiama mining district of Tibet, China

Geochemical exploration data plays a vital role in mineral prospectivity mapping (MPM) for discovering unknown mineral deposits. In this study, compositional balance analysis (CoBA), unsupervised and supervised learning are collaboratively used to improve the practice of identification of geochemical anomalies related to mineralization in the Qulong-Jiama mining district of Tibet. For CoBA, five balances of geochemical elements were constructed by the sequential binary partition (SBP) technique, facilitating the interpretation of geochemical/geological processes and contrasting with geochemical anomalies generated by unsupervised and supervised learning. The iterative self-organizing data analysis techniques algorithm (ISODATA) and isolation forest (iForest) are used for preprocessing the geochemical data and therefore optimizing the appropriate training sets (e.g., positive and negative training samples) for supervised learning. Different training datasets including locations of known mineralization, randomly selected positive samples from resulting clusters by ISODATA and outliers by iForest are fed to the support vector machine (SVM) and random forests (RF) algorithm. The results of high AUC values indicate that supervised learning can effectively delineate prospective areas both with SVM and RF, however, the excessive large area of high-prospectivity and a bias toward known mineralization makes the outcomes of MPM unpractical. Nevertheless, a hybrid of the CoBA, unsupervised learning and supervised learning could alleviate such situations and provide insights into MPM using the geochemical data.
Learning modality-fused representation based on transformer for emotion analysis

Modality-fused representation is an essential and challenging task in multimodal emotion analysis. Previous studies have already yielded remarkable achievements. However, there are two problems: insufficient feature interaction and rough data fusion. To investigate these two challenges more deeply, first, a hybrid architecture, which consists of convolution and a transformer, is proposed to extract local and global features. Second, for extracting more sufficient mutual features from multimodal datasets, our model is comprised of three parts: (1) the interior transformer encoder (TE) aims to extract the intramodality characteristics from the current monomodality; (2) the between TE aims to extract the intermodality feature between two different modalities; and (3) the enhance TE aims to extract the target modality enhance feature from multimodality. Finally, instead of directly fusing features by a linear function, we employ a popular and widely used multimodal factorized high-order pooling mechanism to obtain a more distinguishable feature representation. Extensive experiments on three multimodal sentiment datasets (CMU-MOSEI, CMU-MOSI, and IEMOCAP) demonstrate that our approach reaches the state-of-the-art in an unaligned version setting. Compared with the mainstream methods, our proposed method shows superiority in both word-aligned and unaligned settings.
Selection of human evaluators for design smell detection using dragonfly optimization algorithm: An empirical study

Design smell detection is considered an efficient activity that decreases maintainability expenses and improves software quality. Human context plays an essential role in this domain. In this paper, we propose a search-based approach to optimize the selection of human evaluators for design smell detection. For this purpose, Dragonfly Algorithm (DA) is employed to identify the optimal or near-optimal human evaluator’s profiles. An online survey is designed and asks the evaluators to evaluate a sample of classes for the presence of god class design smell. The Kappa-Fleiss test has been used to validate the proposed approach. The results show that the dragonfly optimization algorithm can be utilized effectively to decrease the efforts (time, cost ) of design smell detection concerning the identification of the number and the optimal or near-optimal profile of human experts required for the evaluation process. A Search-based approach can be effectively used for improving a god-class design smell detection. Consequently, this leads to minimizing the maintenance cost.
Inter-domain fusion and intra-domain style normalization network for unsupervised domain adaptive person re-identification

Unsupervised domain adaptive person re-identification (UDA re-ID) improves the discriminative ability and robustness of models on target domains by transferring supervised information from source domains to target domains. However, since the data distributions of source and target domains may be extremely distinct, it is difficult to directly transfer supervised information from source domains to target domains. In addition, most UDA re-ID methods mainly devote to weakening the differences between source and target domains, while ignoring the style differences between intra-domain samples. To solve the above issues, we propose an inter-domain fusion and intra-domain style normalization network (DFDSN-Net) for UDA re-ID. Specifically, DFDSN-Net consists of three components: the backbone network, the inter-domain fusion module (DFM) and the intra-domain style normalization module (DSNM). DFM generates the more discriminative and comprehensive representations by deeply fusing the features of source and target domains, thus alleviating the differences between source and target domains. DSNM improves the feature extraction capability of the network by eliminating the style differences between intra-domain samples while focusing on the useful information eliminated by instance normalization. Using both DFM and DSNM, DFDSN-Net can simultaneously weaken the impacts caused by inter-domain differences and style differences between intra-domain samples. We conducted several comparison experiments and ablation experiments on several UDA re-ID tasks, and the experimental results indicate that DFDSN-Net is effective and outperforms most state-of-the-art UDA re-ID methods.
“Creativia” Metaverse Platform for Exhibition Experience

Web 3.0 has occasioned an increase in research interest in the metaverse. The research interest in metaverse gained attention during the pandemic with the increased use of online (virtual) platforms for day-to-day activities and work. Museums and exhibition centers were affected during the pandemic. Using online (virtual) platforms for museums and exhibitions became popular. The metaverse is a 3D immersive experience platform where users can leverage their custom avatar to explore. This work is an overview of a metaverse platform for exhibition experience named ‘creativia’, The work also demonstrated the performance of the creativia platform prototype for the exhibition. The case study involved the 5G Idea Factory Center of the Kumoh National Institute of Technology, Gumi, Korea.
Lightweight Blockchain Assisted Unauthorized UAV Access Prevention in the Internet of Military Things

Unmanned aerial vehicles (UAVs) offer important strategic advantages in the Internet of Military Things (IoMT). On the other hand, the use of the UAV by an untrusted party may threaten security and perhaps endanger the vital operation of the IoMT network. The IoMT network additionally faces significant challenges from data falsification and manipulation through unauthorised access. For the purpose of addressing these challenges, this research presents a blockchain-based architecture and user authentication mechanism for the purpose of identifying and keeping track of unauthorized UAVs in the IoMT network. Blockchain technology not only stores data on the central control server but also forbids unauthorized access, data manipulation, and illegal intrusions. Additionally, a unique consensus model for UAV communication has been created. By streamlining the resource-constrained mining process, the created consensus can improve the efficiency of blockchain transactions between UAVs.
Generating and Modifying High Resolution Fashion Model Image using StyleGAN

In this paper, a research of synthesizing fashion model images by utilizing a state-of-the-art generative adversarial network (i.e., GAN) is introduced. After training GAN with fashion model images, the network was able to generate realistic fashion model images having various characteristics such as pose and clothes. Moreover, two image modifications named Fashion Model Morphing and Fashion Transfer are also proposed by merging attributes of two generated fashion model images. The research investigates the effectiveness of using GAN for fashion to create a large number of images for exploring new design and styles. The generated images are even more beneficial for fashion industries because the generated images have no legal issues such as portrait right and copyright.
A Study on Corporate Information Assets Management System Using NFT

The technology outflows of corporates are mainly targeted at information assets containing information such as core secrets or technical information, which can cause companies to lose technological competitiveness or serious economic damage, so security risk management is essential. For risk management of information assets, the corporates identify the asset, classify the security level according to its intrinsic value, and manage and control it by applying differential protection measures. As the development of ICT technology accelerates the change to the digital business environment, the protection area and assets of companies increase, making it difficult to identify and classify information assets and increasing the complexity of security risks. NFT is a technology that uses blockchain network to give the value of uniqueness and scarcity of digital assets such as images, trademarks, and game items, or real assets such as art, art, and clothing in a digital environment. In this paper, we propose a blockchain system that can support the integrated protection of corporate information assets by utilizing NFT in a rapidly changing security environment.
A Communication Efficient Approach of Global Training in Federated Learning

Federated learning is a privacy preserving method of training the model on server by utilizing the end users' private data without accessing it. The central server shares the global model with all end users, called clients of the network. The clients are required to train the shared global model using their local datasets. The updated local trained models are forwarded back to the server to further update the global model. This process of training the global model is carried out for several rounds. The procedure of updating the local model and transmitting back to the server rises the communication cost. Since several clients are involved in training the global model, the aggregated communication cost of the network is escalated. This article proposes a communication effective aggregation method for federated learning, which considers the volume and variety of local clients' data before aggregation. The proposed approach is compared with the conventional methods and it achieves highest accuracy and minimum loss with respect to aggregated communication cost.
Perceptual Encryption-based Privacy-Preserving Deep Learning in Internet of Things Applications

In the Internet of Things (IoT) ecosystem, cloud computations are widely utilized to train a deep learning model on public datasets. The trained model can then be deployed on edge devices as an inference engine to facilitate various IoT applications in real-time. For consistent accurate predictions, the model needs to be retrained on the most recent data. This necessitates the data share between cloud servers and edge devices. However, the data acquired by IoT end devices usually consists of sensitive information and sharing them with cloud services provider results in users' privacy issues. In addition, the exchange of large volume of data requires high bandwidth. This study proposes an extension of block-based perceptual encryption (PE) algorithm to enable DL computations in encryption domain in order to protect users' privacy. For this purpose, four different extensions of baseline PE methods were analyzed in terms of compression savings, encryption efficiency and privacy-preserving DL performance. The analyses have shown that when global contents are preserved in each color channel, the extended PE method offers better security and preserves compression savings with only 3% drop in accuracy as compared to baseline method.
Load balancing algorithm running on Open RAN RIC

The O-RAN alliance has received attention by presenting an O-RAN architecture. They standardizes wireless interfaces to solve the compatibility problem between multi- vendor of existing radio access network (RAN) architecture. They also disclose an open source framework, which is applicable to programmable base station equipment. In this paper, we analyze the xApp and the near-real time RAN intelligent controller (RIC) serviced by the O-RAN-compatible SD-RAN platform, developed by the open networking foundation (ONF), and address simple simulation results.
Human Action Recognition Method using YOLO and OpenPose

Human action recognition is a technology that recognizes human behavior through images. This can be used in video surveillance to detect abnormal situations without human intervention. To increase accuracy of behavior recognition, it is considerable method to use consecutive images. However, it increases time complexity due to the large number of videos to be processed in real system. Thus, the lightweight model which can recognize human behavior with just one image is necessary. To this end, we propose a lightweight behavior classification model based-on neural network which utilize human joint information.
Blockchain-based Federated Approach for Privacy-Preserved IoT-enabled Smart Vehicular Networks

Over the last few years, smart vehicles have continuously grown and connected to the Internet of Things (IoT), sensors, and advanced communication technologies. Then, it creates a cluster of distributed networks known as IoT-enabled Smart Vehicular Networks. Integrating smart vehicular networks, IoT, and the Internet of Vehicles (IoV) provide interactive solutions such as traffic efficiency, driving safety, autonomous driving, and robust information exchange in the smart city infrastructure. Still, Smart vehicular networks have challenges, such as privacy preservation, security, data authentication, communication bandwidth, and centralization due to vehicles and networks-related data directly stored in the traditional cloud. Motivated by advanced technologies, including Blockchain and Federated Learning, we propose an approach for Privacy-Preserved IoT-enabled Smart Vehicular Networks to address these challenges. The concept of Blockchain and Federated Learning is leveraged in the middle layer of the proposed work for privacy preservation and smart vehicle data authentication, stored at the cloud layer. Furthermore, we show the technological flow of the proposed approach for the IoT-enabled smart vehicular networks in the smart city.