Reviewerly/exHarmony huggingface dataset:
Building AgriRef: an AI embedded virtual reference app for farmers

Purpose This paper aims to discuss the process of building a 24×7 reference platform for facilitating the farmers with the easy access of information at any time from any location. It takes the text string as input and process it to respond with the desired result to the user. Design/methodology/approach An interactive Web-based chatbot named as AgriRef was developed using free version of Dialogflow. The intents were defined based on the conversation flow diagram. Furthermore, the application was integrated with website on local server and telegram application. Findings With this chatbot application, the farmers will able to get answers of their queries. It provides the human-like conversational interface to the farmers. It will also be useful for librarians of agricultural libraries to save time in answering common queries. Originality/value This paper describes the various steps involved in developing the chatbot application using Dialogflow.
Blockchain for Additive Manufacturing

Abstract This article first describes a typical additive manufacturing (AM) process chain, which involves the transaction of digital information to manufacture physical products. The digitized nature of AM exposes the technology to increased vulnerabilities, posing a hurdle to its mass adoption. The article presents motivation for using blockchain, which is a decentralized, immutable ledger that is shared on a peer-to-peer network. The article presents the advantages of blockchain integration to AM supply chains. These involve aspects of data security, supply chain and logistics, finance, value creation, and scope expansion. The article also presents the opportunities and challenges of blockchain technology.
Random Forest Based Adaptive DBSCAN for Reducing Noise in mmWave Radar Point Clouds

The application of millimeter-wave(mmWave) radar in human activity recognition has attracted significant attention because of its insensitivity to ambient lighting and privacy concerns. Millimeter-wave radar will be transformed into a point cloud as an input. This paper proposes a system that dynamically adjusts DBSCAN to handle point cloud noise. It consists of four main components: random forest to determine minPts, KNN to compute Eps, Cluster Merging, and Noise Re-judgment to optimize the point cloud clustering problem in human activity recognition. The proposed new method makes the dynamic selection of the two parameters minPts and Eps of DBSCAN, as well as the design of a new clustering method for human point cloud clustering based on the property of local sparsity of point cloud of the human body taken by radar. We build a dataset based on a millimeter-wave radar of 10 volunteers. Based on this dataset using the Rand Index and Purity assessment, our proposed method has higher accuracy than other methods, reaching average accuracy of 88.52% and 84.86%, respectively, almost 18% higher than other methods.
Designing In-Storage Computing for Low Latency and High Throughput Homomorphic Encrypted Execution

Data providers are concerned about data privacy when a third party handles their data. A typical example is cloud computing. In such cases, homomorphic encryption can be used to ensure data privacy. Although homomorphic encryption allows the execution of operations with encrypted data without decryption, its execution time is long. Previous studies have focused on the field-programmable gate array (FPGA)-based acceleration of homomorphic encryption. However, the overhead of data transfer between a host and an FPGA is a bottleneck owing to the limited bandwidth of a peripheral component interconnect express (PCIe). In-storage computing (ISC), i.e., using a computational storage device (CSD), can reduce the data-transfer overhead. In this study, a computational model coupled with a CSD is proposed to accelerate homomorphic operations. Simulations show that our proposed ISC model reduces latency by 71.2% and achieves 3. 5x throughput of a homomorphic encrypted convolutional neural network compared with 16 FPGA boards with 16 PCIe lanes and sufficient solid-state drives (SSDs), i.e., without CSDs.
Can Visual Linguistic Models become Knowledge Bases: A Prompt Learning Framework for Size Perception

Empowering machines to understand our physical world should go beyond models with only natural language and models with only vision. Vision and language comprise a growing field of study that attempts to bridge the gap between natural language processing and computer vision communities by enabling models to learn visually grounded language. However, as an increasing number of pre-trained visual linguistic models focus on the alignment between visual regions and natural language, it is difficult to claim that these models capture certain properties of objects in their latent space, such as size. Inspired by recent trends in prompt learning, this study designed a prompt learning framework for two visual linguistic models, ViLBERT and ViLT, and used manually crafted prompt templates to evaluate the consistency of performance of these models in comparing the size of objects.
Validating Streaming JSON Documents with Learned VPAs

We present a new learning algorithm for realtime one-counter automata. Our algorithm uses membership and equivalence queries as in Angluin's L* algorithm, as well as counter value queries and partial equivalence queries. In a partial equivalence query, we ask the teacher whether the language of a given finite-state automaton coincides with a counter-bounded subset of the target language. We evaluate an implementation of our algorithm on a number of random benchmarks and on a use case regarding efficient JSON-stream validation.
Highly Reliable Time-of-Flight L-Shaped Gesture Recognition System.

Gesture recognition is a field of study that involves recognizing human movements and gestures through sensors. In this paper, a basic gesture recognition system is proposed that uses three Time-of-Flight (TOF) VL53L0X distance sensors positioned in an L-shape able to recognize gesture through cover glass and up to 40 cm of distance. The system is capable of recognizing four basic gestures: swipe right, swipe left, swipe up and swipe down. This system can be applied in various fields such as Human-Computer Interaction (HCI), Gaming, Virtual Reality (VR) and Robotics, this paper will focus on the implementation and evaluation of the proposed system. The inspiration for the system is to simplify interaction with medical panel PCs and monitors while improving the hygienic aspect of the same, while taking into consideration data privacy.
Research on Lightweight High-resolution Network Human Pose Estimation Based on Self-attention

At present, when deep learning networks in the field of human pose estimation improve prediction accuracy, often accompany the improvement of network structure complexity, which brings about the improvement of network model parameters and computational complexity, making it difficult to deploy on devices with small computing power for practical application. On the basis of HRNet, this paper devises a lightweight human pose estimation network SAGNet that integrates the self-attention mechanism and Ghost Module, which introduces the self-attention module to get higher prediction accuracy in the transition process from the third to the fourth stage of HRNet, and replaces the standard convolution in HRNet with Ghost Module for network lightweight. The experimental results show that in the same experimental environment, on the coco 2017 validation set, SAGNet greatly reduces the amount of parameters and the complexity of computation compared with HRNet while maintaining the prediction accuracy at a similar level.
Behavior and Sentiment Analysis of Smart Digital Societies Using Deep Machine Learning Technologies

Prediction of behavior and sentiment cues plays a pervasive role in smart digital society including the domains of healthcare and social behavior and sentiment research. The social behavior and sentiment analysis can be performed from the individuals’ offline or online mutual communications and interactions. The corpus of these communications and interactions is considered important for visualizing the behavior and sentiment of a smart digital society. This chapter delineates how these features could be implemented or subsequently fused with other pertinent behavioral, sentimental, and physiological traits to enable different machine learning (ML) techniques to perform the classification problem. This chapter also entails the various challenges and application domains associated with the task of behavior and sentiment analysis. Perhaps, the accuracy of prediction along with the efficacy to imbibe the role of individuals’ behavior and sentiment determinants and to render the explicit explanations to the behavior and sentiment predicted are also indispensable. This is because these aspects can enhance the trust, liability, and reliability of the system. However, the majority of behavior and sentiment analysis approaches do not reflect the logical inferences and explanations for the pursuit of behavior and sentiment prediction. Finally, this chapter describes the deep learning method that has been emerged forth as a pragmatic ML method. Deep learning (DL) possesses the characteristic of enhancing learning related to the data and outputted the superior quality of results. Different DL-based methods have been implemented in different problem domains successfully and researchers also implanted deep learning techniques within the purview of behavior and sentiment prediction. This chapter also incorporates the implementation details of deep learning-based approaches for this purpose.
Active Learning for SAT Solver Benchmarking

Abstract Benchmarking is a crucial phase when developing algorithms. This also applies to solvers for the SAT (propositional satisfiability) problem. Benchmark selection is about choosing representative problem instances that reliably discriminate solvers based on their runtime. In this paper, we present a dynamic benchmark selection approach based on active learning. Our approach predicts the rank of a new solver among its competitors with minimum runtime and maximum rank prediction accuracy. We evaluated this approach on the Anniversary Track dataset from the 2022 SAT Competition. Our selection approach can predict the rank of a new solver after about 10 % of the time it would take to run the solver on all instances of this dataset, with a prediction accuracy of about 92 %. We also discuss the importance of instance families in the selection process. Overall, our tool provides a reliable way for solver engineers to determine a new solver’s performance efficiently.
A Truss-Based Framework for Graph Similarity Computation

The study of graph kernels has been an important area of graph analysis, which is widely used to solve the similarity problems between graphs. Most of the existing graph kernels consider either local or global properties of the graph, and there are few studies on multiscale graph kernels. In this article, the authors propose a framework for graph kernels based on truss decomposition, which allows multiple graph kernels and even any graph comparison algorithms to compare graphs at different scales. The authors utilize this framework to derive variants of five graph kernels and compare them with the corresponding basic graph kernels on graph classification tasks. Experiments on a large number of benchmark datasets demonstrate the effectiveness and efficiency of the proposed framework.
Style Semantic Disentangle Network for Multi-Source Domain Generalization

Domain generalization (DG) aims to generalize a model trained on multiple source domains to an unseen target domain with different distributions. An effective approach in DG is to generate samples of novel domain properties by data augmentation, which extends the representation space of source domains and enables the model to learn semantic invariant representations across domains. However, existing methods usually underappreciate the inherent intra-domain style invariance within each domain when synthesizing new domains, resulting in limited diversity of augmented data. We introduce an extremely intuitive perspective of multi-domain data augmentation, which can generate style-diversified features with other domain styles via the Multi-Domain Feature Stylization (MDFS) module, extending the style representations of the source domains. By incorporating stylized features in the training, the model is encouraged to learn automatically robust representations against domain shift. Nevertheless, the out-of-domain styles in the stylized features more susceptible lead to the entanglement of style and semantic representations. In this paper, we propose a novel style-semantic contrastive loss to further disentangle correlations between the above two types of representations in the latent feature space. Moreover, to robust the semantic consistency of original and stylized features, we utilize the semantic consistency regularization to maintain the consistency of the model in terms of predicted probabilities. Extensive experiments and analysis on three benchmark datasets show that our proposed method outperforms the state-of-the-art methods.
Fuzzy logic inference system based quality prediction model for HD HEVC video streaming over wireless networks

Checking the quality of video transmitted over a wireless network is critical to improving network performance. In this paper, we evaluate the HEVC/H.265 video coding standard in terms of quantization parameters (QP), video content, and the degradation impact of transmission channels on quality. Additionally, studying Quality of Service (QoS) and Quality of Experience (QoE) will allow us to study multimedia applications in wireless ad hoc networks. This paper presents (1) the performance evaluation of QP value variation for different video contents on HEVC/H265, (2) the investigation of the impact of packet loss and jitter on the QoS of transmission sequences, and (3) the fuzzy logic model proposed to evaluate the performance of transmission sequences. The results show that using different QP values can counteract the effects of packet loss and jitter and improve the received video quality.
Expanding the Edge: Enabling Efficient Winograd CNN Inference With Deep Reuse on Edge Device

Deep learning on edge devices is becoming increasingly important, especially with the explosion of IoT devices. For example, the total number of devices connected to IoT reaches 29 billion in 2022. Convolutional neural networks (CNNs), as common deep learning representatives, are among the most popular neural networks in knowledge and data engineering. However, CNN employs a high degree of computing. In comparison to the training phase, the inference process is more frequently done on low-power computing equipments, such as edge devices. The limited computing resource and high computation pressure limit the effective use of CNN algorithms at the edge. Fortunately, a minimal filtering algorithm called Winograd can reduce convolution calculations by minimizing multiplication operations. We find that Winograd convolution can be accelerated further by <i>deep reuse</i> technique, which reuses the similar data and computation processes. In this paper, we propose a new inference method, called DREW, which combines deep reuse with Winograd for further accelerating CNNs. DREW handles three difficulties. First, it can detect the similarities from the complex minimal filtering patterns by clustering. Second, it reduces the online clustering cost in a reasonable range. Third, it provides an adjustable method in clustering granularity balancing the performance and accuracy. We perform evaluation on Raspberry PI and NVIDIA Jetson AGX Xavier edge devices, and experiments show that on five popular networks, 1) DREW further accelerates the Winograd convolution by an average of 8.27× speedup. Even for the highly parallel Winograd implementation, DREW still can provide 2.21× speedup. 2) When DREW is applied to end-to-end Winograd CNN inferences, DREW achieves 5.94× the average performance speedup with no ( <inline-formula><tex-math notation="LaTeX">$&lt; $</tex-math></inline-formula> 0.4%) accuracy loss. 3) Energy consumption is an important factor for inference in practice. DREW reduces the number of convolution operations to 10% of the original operations, thus achieving up to 60% energy-efficiency benefits than the original Winograd inference.
Efficient (Masked) Hardware Implementation of Grain-128AEADv2

We consider the efficient hardware implementation of Grain-128AEADv2, which is the second version of Grain-128AEAD (one of the lightweight cryptography finalist candidates). In order to counteract side-channel attacks, the efficient masked hardware implementation of Grain-128AEADv2 is also considered under the idea of domain-oriented masking. In detail, the so-called pipeline-like pre-computation technique is applied to increase the throughput-area ratio of the (masked) hardware implementation of Grain-128AEADv2. The performance of the (masked) hardware implementation of Grain-128AEADv2 is evaluated on ASIC and FPGA. For the unmasked version, the highest throughput-area ratio can be <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mn>2.14</mn> <mo> </mo> <mrow> <mi mathvariant="normal">M</mi> <mi mathvariant="normal">b</mi> <mi mathvariant="normal">p</mi> <mi mathvariant="normal">s</mi> </mrow> <mo>/</mo> <mrow> <mi>G</mi> <mi>E</mi> </mrow> </math> on ASIC and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mn>9.34</mn> <mo> </mo> <mrow> <mi mathvariant="normal">M</mi> <mi mathvariant="normal">b</mi> <mi mathvariant="normal">p</mi> <mi mathvariant="normal">s</mi> </mrow> <mo>/</mo> <mrow> <mi mathvariant="normal">S</mi> <mi mathvariant="normal">l</mi> <mi mathvariant="normal">i</mi> <mi mathvariant="normal">c</mi> <mi mathvariant="normal">e</mi> </mrow> </math> on FPGA. For the masked version, the highest throughput-area ratio can be <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mn>0.37</mn> <mo> </mo> <mrow> <mi mathvariant="normal">M</mi> <mi mathvariant="normal">b</mi> <mi mathvariant="normal">p</mi> <mi mathvariant="normal">s</mi> </mrow> <mo>/</mo> <mrow> <mi>G</mi> <mi>E</mi> </mrow> </math> on ASIC and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mn>1.72</mn> <mo> </mo> <mrow> <mi mathvariant="normal">M</mi> <mi mathvariant="normal">b</mi> <mi mathvariant="normal">p</mi> <mi mathvariant="normal">s</mi> </mrow> <mo>/</mo> <mrow> <mi mathvariant="normal">S</mi> <mi mathvariant="normal">l</mi> <mi mathvariant="normal">i</mi> <mi mathvariant="normal">c</mi> <mi mathvariant="normal">e</mi> </mrow> </math> on FPGA. Then, the security of the masked hardware implementation of Grain-128AEADv2 is verified with the simulated T-Test. To the best of our knowledge, this is the first published work about the (masked) hardware implementation of Grain-128AEADv2. In light of this, this contribution may help researchers and practitioners to accurately compare the efficiency and the security of the hardware implementation of Grain-128AEADv2 with those of other lightweight cryptography algorithms.
Accurate Generated Text Detection Based on Deep Layer-wise Relevance Propagation

Text generation models (TGMs) excel in producing text that matches the style of human language reasonably well. Such TGMs (e.g., GAN-based text generation model) can be misused for fake news and fake product reviews that can look authentic and fool humans. To distinguish the generated texts by TGM from the real texts, the latest detection models leverage learning algorithms with a variety of features to improve the detection performance against generated texts. However, the features included in these models are usually not relevant to the deep semantic correlation and fail to achieve accurate detection of the generated texts. To handle this problem, we propose a deep interpretable model to achieve accurate detection of the generated texts. In detail, a feature extraction method based on Layer-wise Relevance Propagation is proposed to improve the feature mining performance for detection of generated texts. Experiments based on real-world datasets demonstrate that the proposed model achieves more accurate generated text detection and significantly outperforms state-of-the-art detection schemes.
PETROCHEMICAL AND STRUCTURAL CHARACTERISTICS OF PORPHYRY COPPER MINERALIZATION IN THE ASTANEH ORE DEPOSIT, MIDDLE PART OF THE URUMIEH-DOKHTAR MAGMATIC ARC (IRAN)

Within the Urumieh-Dokhtar Magmatic Arc in the central part of Iran, the formation of which is associated with the Neotethys closure, there are many porphyry copper deposits and ore occurrences. One of them is the Astaneh porphyry copper ore deposit, located in the central part of the Saveh-Ardestan ore region southeast of Ardestan city. The purpose of this study is to investigate the petrochemical characteristics of rocks and to determine the relationship between the distribution of porphyry copper mineralization and tectonic position of faults within the study area. To achieve the goal, there were used the structural and geological data obtained in the fieldwork, as well as the results of mineralogical and geochemical analyses. The obtained results show that rocks of different composition of the Astaneh ore deposit (andesite, andesite-basalt, basalt, trachybasalt) were formed in the suprasubduction zone, and probably in the environment prior to the collision of the of continental plates. Paragenetic relationships and mineralogical analysis show that the evolution of mineralization of the Astaneh ore deposit can be divided into three stages: pre-ore, hypogene and supergene mineralization. Geochemical research based on the study of the content of the major chemical elements in the rocks of the region shows that igneous rocks belong to calc-alkaline basalts and geodynamically can be attributed to the products of magmatism of the ensial island arc. The results concluded that the main stages of the formation of a porphyry copper ore deposit in the study area attain maximum spatio-temporal similarity with the tectonomagmatic phases of the development of the Neotethys Ocean. In addition, the Southern Ardestan fault, running through the study area and intersecting the basement structures, forms wide permeable zones favorable for the formation of porphyry copper deposits therein.