Reviewerly/exHarmony huggingface dataset:
AccelTran: A Sparsity-Aware Accelerator for Dynamic Inference With Transformers

Self-attention-based transformer models have achieved tremendous success in the domain of natural language processing. Despite their efficacy, accelerating the transformer is challenging due to its quadratic computational complexity and large activation sizes. Existing transformer accelerators attempt to prune its tokens to reduce memory access, albeit with high compute overheads. Moreover, previous works directly operate on large matrices involved in the attention operation, which limits hardware utilization. In order to address these challenges, this work proposes a novel dynamic inference scheme, DynaTran, which prunes activations at runtime with low overhead, substantially reducing the number of ineffectual operations. This improves the throughput of transformer inference. We further propose tiling the matrices in transformer operations along with diverse dataflows to improve data reuse, thus, enabling higher energy efficiency. To effectively implement these methods, we propose AccelTran, a novel accelerator architecture for transformers. Extensive experiments with different models and benchmarks demonstrate that DynaTran achieves higher accuracy than the state-of-the-art top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> hardware-aware pruning strategy while attaining up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.2\times $ </tex-math></inline-formula> higher sparsity. One of our proposed accelerators, AccelTran-Edge, achieves 330K <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> higher throughput with 93K <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> lower energy requirement when compared to a Raspberry Pi device. On the other hand, AccelTran-Server achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5.73\times $ </tex-math></inline-formula> higher throughput and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.69\times $ </tex-math></inline-formula> lower energy consumption compared to the state-of-the-art transformer co-processor, Energon. The simulation source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/jha-lab/acceltran</uri> .
PT-SSIM: A Proactive, Trustworthy Self-Sovereign Identity Management System

Digital identity management (DIM) systems have become challenging, especially, given the current progression in ubiquitous environments enclosing cooperative Internet of Things (IoT) devices, individuals, and organizations. Self-sovereign identity (SSI) has recently surfaced to manifest the notion of decentralized DIMs enlivened by users’ autonomy. This article presents a proactive, trustworthy solution for managing digital users’ data and identity information without risking its integrity, security, privacy, and confidentiality. Accordingly, we propose a decentralized SSI-enabled cloud storage model that enables secure access control and frequent data integrity checking (IC) mechanism. The presented model ensures the resiliency of SSI-enabled operations, including users’ registration, data identification, external information distribution, IC operations, identity information authentication and verification, and decentralized external and shareable operations.
A Blockchain-Based Tourism Industry

Blockchain technology is highly regarded for its potential to revolutionize various economic sectors and meet consumer needs. In the tourism industry, blockchain has attracted investor attention due to its characteristics, such as decentralisation, disintermediation, security, transparency, and immutability. Blockchain applications like smart contracts, tokens, cryptocurrencies, decentralised applications (Dapps), and decentralised autonomous organisations (DAOs) have the potential to transform the relationship between tourism providers and tourists by improving trust, automating processes, and reducing costs. However, widespread adoption of blockchain in tourism is limited due to several challenges, including a lack of awareness and expertise, high energy consumption, and a lack of regulatory frameworks for data management and income taxation. Despite these challenges, blockchain technology's potential benefits make it an essential area to watch in the future.
Meaningful Shares based Single Secret Sharing Scheme using Chinese Remainder Theorem and XOR Operation

Secret-sharing techniques have become increasingly sophisticated over the years and now focus on generating random shares. These shares do not provide an attacker with any confidential information, however, the randomness of the shares can be suspicious. To further protect the meaningful shares and prevent any suspicious activity, meaningful shares can be generated rather than random ones. Generating meaningful shares can make it difficult for the attacker to identify them as such, and can even fool the attacker into thinking that the meaningful shares are simply ordinary images. This can be an effective way to keep confidential information secure by making meaningful shares appear simple image to attackers. Recent advancements in secret sharing schemes have made it possible to produce meaningful shares, lowering the likelihood that suspicion will be raised when shared secret images are used. These shares are unfortunately unsuitable for secret sharing schemes due to their poor visual quality. The proposed approach provides a meaningful shares based on (t, t) single secret sharing scheme utilizing the Chinese Remainder Theorem (CRT) and XOR operation, which improves the visual quality of meaningful sharing. The secret image is divided into t meaningful shares that are practically identical to the meaningful cover image. To reconstruct the secret, t meaningful shares are required. This scheme is designed to safeguard information from intruders, even if they possess some hints. This approach does this by not releasing any details, whether full or partial if the shares are insufficient. This is distinct from other systems which may release either full or partial data, potentially offering attackers clues to obtain access. The proposed method was tested in various experiments to assess its effectiveness. The results of these tests indicated that the proposed method was highly effective, attack resistive, and gives the lossless recovery.
Mapping of geological complexity and analyzing its relationship with the distribution of gold deposits in the Guangxi Gold Ore Province, Southern China

The Guangxi gold ore province contains >370 gold deposits with total resources of >260 t Au. Factor analysis (FA) of geochemical data, and calculation of lineament density within 10 km, 20 km, and 30 km circular search grids across a geological map, were applied in Guangxi to highlight geological complexity and to analyze its relationships with the distribution of gold deposits. The FA of stream sediment geochemical data for 42 elements, subjected first to center logarithmic transformation (clr), yield several element combinations that allowed distinction of metallogenic signatures from geological backgrounds. The element combinations of the first and second factors were Cd–Co–Cr–Cu–Li–Mn–Ni–P–Sb–Y–Zn–Mg and Be–Bi–La–Nb–Pb–Sn–Th–U–W–Zr–Al, and the spatial distributions of their factor scores overlap the Devonian to Permian strata and granite, respectively. The distribution of factor scores of mineralization-related element combination, Ag–As–Au–Mo, coincides with 70 % of gold deposits. The lineament density mapping can highlight different-scale faults and lithological contacts by using circular search grids with different diameters. The C–A fractal modeling for lineament density has indicated four populations, namely low, moderate-low, moderate-high, and high lineament densities. About ∼80 to ∼90 % of the known gold deposits exist in areas with moderate-low to moderate-high lineament densities. This highlights the importance of distinguishing geological complexity based on faults and lithological contacts, which are commonly the pathways for the focused fluid flow and gold ore formation. The deposit-scale geological complexity mapping has predicted several exploration targets, where high-grade gold orebodies have been prospected successfully by drilling in 2018. The geological complexity mapping is a kind of geochemical and geological complementation for resource potential assessment.
A Robust Object Recognition Using Modified YOLOv5 Neural Network

Human beings can recognize and detect the object in any image or video with ease, but when it comes to machines dealing with the same task, it’s quite challenging. In the past years, there has been significant progress in the area of computer vision. However, researchers are still working on enhancing the performance of object recognition algorithms in terms of accuracy and speed. This paper presents one of the greatest CNN representatives, You Only Look Once (YOLO), which tackles object detection in the simplest and most efficient way. This study primarily focuses on object recognition with modified YOLOv5. Here, we have implemented the model on a dataset comprising five objects. We have analyzed various preprocessing and feature extraction techniques to enhance the performance of the proposed model. The highest mean average precision (mAP) achieved by the modified YOLOv5 is 90.7%. The recognition performance of the modified YOLOv5 is compared with the YOLOv4 algorithm. It is also compared with various conventional object detection algorithms outside the YOLO family, such as Support vector machine (SVM), deep Convolutional neural network (CNN), and EfficientNet. The results proved to be promising as compared to the other state-of-the-art techniques.
A Fourier Domain Feature Approach for Human Activity Recognition &amp; Fall Detection

Commonly, the senses of vision and hearing decrease as the age increase of a human. The most affected organs are hearing and vision due to aging. Elder people consequence a variety of problems while living Activities of Daily Living (ADL) for the reason of age, sense, loneliness and cognitive changes. These cause the risk to ADL which leads to several falls. Getting real life fall data is a difficult process and are not available whereas simulated falls become ubiquitous to evaluate the proposed methodologies. From the literature review, it is investigated that most of the researchers used raw and energy features (time domain features) of the signal data as those are most discriminating. However, in real life situations fall signal may be noisy than the current simulated data. Hence the result using raw feature may dramatically changes when using in a real life scenario. This research is using frequency domain Fourier coefficient features to differentiate various human activities of daily life. The feature vector constructed in this article using that the Fast Fourier Transform are robust to noise, level of detail representation and rotation invariant. In this research, two different supervised classifiers kNN and SVM are used for evaluating the method. Two standard publicly available datasets are used for benchmark analysis. This research shows more discriminating results are obtained applying kNN classifier than the SVM classifier. Various standard measure including Standard Accuracy (SA), Macro Average Accuracy (MAA), Sensitivity (SE) and Specificity (SP) has been accounted. In all cases, the proposed method outperforms energy features whereas competitive results are shown with raw features. It is also noticed that the proposed method performs better than the recently risen deep learning approach in which data augmentation method were not used.
Uranium as reference element to estimate the background of “Anthropocene” sensitive trace elements in sediments of the land-ocean continuum (Ulla-Arousa, NW Iberian Atlantic Margin)

Determining the background is a crucial step in differentiating between natural and anthropogenic components in environmental samples. Geochemical normalization is a standard procedure for determining the background contents of trace elements in sediment samples, which involves the careful selection of a reference element. Aluminum (Al), iron (Fe), and other major, minor, and even trace constituents of sediments are commonly used as reference elements. A suitable reference element should behave conservatively, with contents that are not significantly affected by anthropogenic factors or post-depositional processes. In this study, the performance of uranium as a reference element in sediment samples taken from the Ulla River was tested. The Ulla River drains a mainly granitic basin in the Iberian Atlantic Margin, and samples were collected from headwaters to the coastal area. Iterative least squares simple regression was used to estimate the background contents (as background functions) of six trace elements of environmental concern, including arsenic (As), chromium (Cr), copper (Cu), nickel (Ni), lead (Pb), and zinc (Zn). Uranium demonstrated satisfactory performance with correlation coefficients ranging from approximately 0.7 for Cu to 0.9 for As. The background-estimated contents of the six target elements were in good agreement with several references at different geographical scales, with differences possibly attributed to peculiarities in the local lithology. Thus, uranium can be considered a suitable normalizing element. However, it was tested in a relatively homogenous granitic basin and, like any other reference element, should not be used automatically without empirical corroboration.
The Gödel-McKinsey-Tarski embedding for infinitary intuitionistic logic and its extensions

The Gödel-McKinsey-Tarski embedding allows to view intuitionistic logic through the lenses of modal logic. In this work, an extension of the modal embedding to infinitary intuitionistic logic is introduced. First, a neighborhood semantics for a family of axiomatically presented infinitary modal logics is given and soundness and completeness are proved via the method of canonical models. The semantics is then exploited to obtain a labelled sequent calculus with good structural properties. Next, soundness and faithfulness of the embedding are established by transfinite induction on the height of derivations: the proof is obtained directly without resorting to non-constructive principles. Finally, the modal embedding is employed in order to relate classical, intuitionistic and modal derivability in infinitary logic extended with axioms.
Decomposing shared networks for separate cooperation with multi-agent reinforcement learning

Sharing network parameters between agents is an essential and typical operation to improve the scalability of multi-agent reinforcement learning algorithms. However, agents with different tasks sharing the same network parameters are not conducive to distinguishing the agents' skills. In addition, the importance of communication between agents undertaking the same task is much higher than that with external agents. Therefore, we propose Dual Cooperation Networks (DCN). In order to distinguish whether agents undertake the same task, all agents are grouped according to their status through the graph neural network instead of the traditional proximity. The agent communicates within the group to achieve strong cooperation. After that, the global value function is decomposed by groups to facilitate cooperation between groups. Finally, we have verified it in simulation and physical hardware, and the algorithm has achieved excellent performance.
TAssembly: Data-driven fractured object assembly using a linear template model

Fractured object assembly is a fundamental problem across many disciplines such as digital archaeology, accident recovery, paleontology, cranio-facial and orthopaedic surgical repair, and many more. With advances in 3D acquisition devices and improved fidelity models of fractured pieces, a flurry of semi-automatic and automatic computational assembly solutions have been developed. These methods have the potential to reduce human labor in assembling fractured objects. The challenge is to balance the time efficiency with the fidelity of the reconstructed assembly. This paper introduces TAssembly, a data-driven approach to fractured object assembly. TAssembly leverages learned feature descriptors of the underlying objects. Its assembly pipeline seamlessly integrates multiple objective terms, including feature matching, rigid matching, and regularization for matching and stitching adjacent pieces. Many experimental results show that TAssembly significantly outperforms previous fractured object assembly methods.
Exploring knowledge management enablers for blockchain-enabled food supply chain implementations

Purpose With the advent of new technologies, the integration of blockchain-enabled food supply chain (FSC) implementations is on the rise. Considering the multilateral, comprehensive and complex nature of the whole blockchain-enabled FSC implementation process, understanding knowledge management (KM) practices will add value. Prior literature shows that only a few studies have focused on this topic. As such, this study aims to identify and establish key KM enablers for blockchain-enabled FSC implementations. Design/methodology/approach This study conducted a qualitative research approach. By conducting 22 in-depth interviews with experienced blockchain implementation experts in FSC contexts, this study provides interesting insights for academics and practitioners. Findings The results of the analysis highlighted eight critical KM enablers that directly influence blockchain-enabled FSC implementations. They include external enablers (i.e. regulation and market competition) as well as internal enablers (i.e. people – organizational learning, strategy and leadership, culture, information technology, organizational infrastructure, processes and activities). Originality/value This study is one of the few studies that identify KM enablers for blockchain-enabled FSC implementations. Considering the novelty of decentralized blockchain implementations in FSC and its importance in transforming silo-based knowledge exchange to a decentralized operational structure, this study attempts to identify factors that increase the efficiency of blockchain implementations in FSC contexts.
Scalable incomplete multi-view clustering via tensor Schatten p-norm and tensorized bipartite graph

Graph-based incomplete multi-view clustering (IMVC) methods have drawn considerable attention due to their good performance in exploring the nonlinear structure of data. However, they still have the following shortcomings. First, graph construction and eigen decomposition of the Laplacian matrix included in the IMVC methods generally have high computational complexity. Second, most methods do not consider the impact of missing views and neglect the potential relationships between different views. Third, few algorithms consider both intra-view and inter-view information for clustering. Therefore, we innovatively propose a scalable incomplete multi-view clustering via the tensor Schatten p-norm and tensorized bipartite graph (SIMVC/TSTBG) method, which combines tensorized bipartite graph, graph completion, and tensor low-rank constraint into a joint framework. Concretely, we first construct bipartite graphs based on the selected m anchor points and the n data points, reducing the size of the graph from n×n to n×m(m<<n), which considerably reduces the computational complexity. Second, we adaptively complete the missing bipartite graph, which reduces the effect of missing view information on the clustering results. Third, to explore connections between missing views and mine high-order information between views, we splice the bipartite graphs into a tensor and impose a tensor low-rank constraint, i.e., the tensor Schatten p-norm, on it. At the same time, we also design an efficient algorithm to solve SIMVC/TSTBG. To our knowledge, we are the first successful practice to integrate the tensor technique with the scalable IMVC method. Compared with other IMVC methods, the results on seven datasets fully show the high efficiency and effectiveness of SIMVC/TSTBG.
Integration of Blockchain Technologies into Healthcare Delivery

Blockchain technology has the potential to transform healthcare delivery by enabling secure and efficient sharing of patient health data among healthcare providers, researchers, and patients themselves.The significant inventions and innovative developments in blockchain technology of today playa role in the business sector.The adoption of blockchain technology holds a series of blocks that contains data.It upholds trust among people no matter how far apart they are.Way past the years, the growth of blockchain technology has forced academics and healthcare professionals to examine novel approaches to integrate blockchain technology into a variety of industries (healthcare).The study offers a thorough analysis of the advantages of integration for blockchainbased healthcare technology.A keyword analysis using the Scopus database and a sentimental analysis with Twitter was conducted.The paper discussesand concludes that healthcare and blockchain integration is to take forward with the growing integration in the field of biochemistry, medicine, and health.
IDENTIFYING POTENTIAL PORPHYRY CU MINERALIZATION AT THE KIRAZLI DISTRICT IN BIGA PENINSULA (NW TURKEY): INSIGHTS FROM THE MAPPING HYDROTHERMAL ALTERATION BY USING SHORTWAVE INFRARED (SWIR) SPECTROMETRY

The Kirazlı mineral district is located at the center of the Biga peninsula metallogenic province, in a geological setting characterized by an extensional tectonic environment. The district hosts a high-sulfidation (HS) ore body with a total reserve of 33.86 Mt @ 0.69 g/t Au and 9.42 g/t Ag within a large-scale hydrothermal alteration. Although the ideal magmatic-hydrothermal models present the spatial and temporal association of HS-epithermal and porphyry Cu deposits, the porphyry Cu potential at the Kirazlı has not been evaluated, yet. Therefore, the mineral-based alteration mapping with the help of SWIR reflectance spectroscopy was carried out. Our study demonstrates that nine different mineral zone have been distinguished and mapped: (1) Silicification, (2) Alunite, (3) Kaolinite, (4) Dickite, (5) Illite, (6) Pyrophyllite, (7) Chlorite, (8) Sericite, and (9) Montmorillonite zones. Collectively, these alterations exhibit a zoned pattern, from central massive silicification to residual silica, NW-SE oriented alunite, and marginal montmorillonite-illite zones within widespread kaolinite – dickite dominated alteration in Kirazlı main zone. The occurrence of pyrophyllite and sericite dominates at the SE of the Kirazlı district, which refers to relative temperature increase. Subsequent drilling confirms the potential for deep prospecting of porphyry Cu mineralization in the region.