Reviewerly/exHarmony huggingface dataset:
Path Planning Method With Constant Bending Angle Constraint for Soft Growing Robot Using Heat Welding Mechanism

Soft growing robots, a new soft mobile robot, have recently attracted considerable interest. There are many soft growing robots, some of which have irreversible growing and bending motion mechanisms. For such robots, path planning methods that provide information on bending timing improve operation efficiency. Although various path planning methods have already been developed, they cannot be applied to our growing robot that uses a heat welding mechanism because it has a constraint that all bending angles are the constant value. This letter proposes a novel path planning method with constant bending angle constraint. The proposed algorithm was developed based on the rapidly-exploring random tree star (RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> ) algorithm. The method incorporates an algorithm for reducing unnecessary nodes from obtained paths keeping bending angles constant and improving path optimality. We confirmed that the proposed method generates paths whose bending angles are constant. In addition, we experimented with moving our robot along the path in the field with some obstacles. The result showed that the proposed method enabled the robot to reach to target place, avoiding obstacles. The proposed method improves the operating efficiency of our soft growing robot.
Rethinking Parallel Memory Access Pattern in Number Theoretic Transform Design

Number theoretic transform (NTT) is widely applied as a fundamental component of next-generation cryptosystems. This brief introduces a novel high-low interactive memory access pattern for an out-of-place NTT design, which can be configurable in degree of parallelism. To achieve high area efficiency, the memory component of the proposed pattern is flexibly selected referring to the design parameters. Then for the first time, we present a quantitative analysis about the relevance between the degree of parallelism and the number of computing cycles. More importantly, we put forward a universal method of avoiding the memory conflict and obtaining the minimal number of computing cycles in a configurable NTT design. Based on the above optimization techniques, we develop the first parameterized out-of-place NTT architecture. Experimental results on FPGA show that our design can achieve higher efficiency in LUTs and BRAMs compared with previous works.
DupHunter: Detecting Duplicate Pull Requests in Fork-Based Development

The emergence of numerous fork-based development platforms facilitates the development of Open-Source Software (OSS) projects. Developers across the world can fork software projects and submit their Pull Requests (PRs) to the projects. However, as the number of forks increases, numerous duplicate PRs might be submitted. These duplicate PRs may cause extra code review workload and frustrate developers working on the projects. To detect duplicate PRs, many approaches have been proposed, which analyze the similarity of different elements in PRs. However, previous approaches still suffer from unsatisfied detection accuracy due to two challenges. That is, they ignore the syntactic structural information of text elements in PRs and lack the joint reasoning between different elements of two PRs. In this study, we propose an automated duplicate PRs detector named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DupHunter</i> ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dup</b> licate PRs <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Hunter</i> ), which includes a graph embedding component and a duplicate PRs detection component to address the above challenges. The graph embedding component uses a feature graph to represent a PR. It encodes the syntactic structure and semantics of text elements (e.g., the title and the description), as well as the knowledge of non-text elements (e.g., the submission time), to address the syntactic structural information challenge. The duplicate PRs detection component tackles the joint reasoning challenge using a graph matching network, which enables the information exchange and matching across different elements of two feature graphs with an attention coefficient mechanism. Experiments on 26 open-source projects show that DupHunter achieves an average <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F1-score@1</i> value of 0.650, significantly outperforming the state-of-the-art approaches by 3.2% to 48.1%. DupHunter can accurately detect duplicate PRs, with an average <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Precision@1</i> value of 0.922 and an average <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Recall@1</i> value of 0.502.
Deep Composite Face Image Attacks: Generation, Vulnerability and Detection

Face manipulation attacks have drawn the attention of biometric researchers because of their vulnerability to Face Recognition Systems (FRS). This paper proposes a novel scheme to generate Composite Face Image Attacks (CFIA) based on facial attributes using Generative Adversarial Networks (GANs). Given the face images corresponding to two unique data subjects, the proposed CFIA method will independently generate the segmented facial attributes, then blend them using transparent masks to generate the CFIA samples. We generate 526 unique CFIA combinations of facial attributes for each pair of contributory data subjects. Extensive experiments are carried out on our newly generated CFIA dataset consisting of 1000 unique identities with 2000 bona fide samples and 526000 CFIA samples, thus resulting in an overall 528000 face image samples. We present a sequence of experiments to benchmark the attack potential of CFIA samples using four different automatic FRS. We introduced a new metric named Generalized Morphing Attack Potential (G-MAP) to benchmark the vulnerability of generated attacks on FRS effectively. Additional experiments are performed on the representative subset of the CFIA dataset to benchmark both perceptual quality and human observer response. Finally, the CFIA detection performance is benchmarked using three different single image based face Morphing Attack Detection (MAD) algorithms. The source code of the proposed method together with CFIA dataset will be made publicly available: https://github.com/jagmohaniiit/LatentCompositionCode.
DRO: Deep Recurrent Optimizer for Video to Depth

There are increasing interests of studying the video-to-depth (V2D) problem with machine learning techniques. While earlier methods directly learn a mapping from images to depth maps and camera poses, more recent works enforce multi-view geometry constraints through optimization embedded in the learning framework. This paper presents a novel optimization method based on recurrent neural networks to further exploit the potential of neural networks in V2D. Specifically, our neural optimizer alternately updates the depth and camera poses through iterations to minimize a feature-metric cost, and two gated recurrent units iteratively improve the results by tracing historical information. Extensive experimental results demonstrate that our method outperforms previous methods and is more efficient in computation and memory consumption than cost-volume-based methods. In particular, our self-supervised method outperforms previous supervised methods on the KITTI and ScanNet datasets. Our source code will be made public.
Boundary-Aware Network With Two-Stage Partial Decoders for Salient Object Detection in Remote Sensing Images

Salient object detection is a binary pixel-wise classification to distinguish objects in an image, and also have attracted many research interests in the optical Remote Sensing Images (RSIs). The existing state-of-the-art method exploits the full encoder-decoder architecture to predict salient objects in the optical RSIs, suffering from the problem of unsmooth edges and incomplete structures. To address these problems, in this paper, we propose a Boundary-Aware Network (BANet) with two-stage partial decoders sharing the same encoders for salient object detection in RSIs. Specifically, a Boundary-Aware Partial Decoder (BAD) is introduced at the first stage to focus on learning clear edges of salient objects. To solve the pixel-imbalance problem between boundary and background, an edge-aware loss is proposed to guide learning the BAD network. The resulting features are then employed in turn to enhance high-level features. Afterwards, the Structure-Aware Partial Decoder (SAD) is further introduced at the second stage to improve the structure integrity of salient objects. To alleviate the problem of incomplete structures, the structural similarity loss is further proposed to supervise learning the SAD network. In a consequence, our proposed BANet can predict salient objects with clear edges and complete structure, while reducing model parameters due to the discardment of low-level features. Besides, training a deep neural network requires a large amount of images, and the current benchmark datasets for optical remote sensing images are not large enough. Therefore, we also create a large-scale challenging dataset for salient object detection in RSIs. Extensive experiments demonstrate that our proposed BANet outperforms previous RSI SOD models on all existing benchmark datasets and our new presented dataset available at https://github.com/QingpingZheng/RSISOD.
A Generic Cryptographic Deep-Learning Inference Platform for Remote Sensing Scenes

Deep learning plays an essential role in multidisciplinary research of Remote sensing. We will encounter security problems during the data acquisition, processing, and result generation stages. Therefore, secure deep-learning inference services are one of the most important links. Some theoretical progress has been made in cryptographic deep-learning inference, but it lacks a general platform that can be realized in reality. Constantly modifying the corresponding models to approximate the plaintext results reveal the model information to a certain extent. This paper proposes a generic post-quantum platform named the PyHENet, which perfectly combines cryptography with plaintext deep learning libraries. Secondly, we optimize the convolution, activation, and pooling functions and complete the ciphertext operation under floating point numbers for the first time. Moreover, the computation process is accelerated by SIMD and GPU parallel computing. The experimental results show that the PyHENet is closer to the plaintext inference platform than any other cryptographic model and has satisfactory robustness. The optimized PyHENet obtained a better accuracy of 95.05% in the high-resolution NaSC-TG2 database, which the Tiangong-2 space station received.
The role of quality 4.0 in supporting digital transformation: Evidence from telecommunication industry

The current study aimed to shed light on the influence of quality 4.0 including Data, Analytics, Connectivity, Collaboration, App Development, Scalability, Management Systems, Compliance, Culture, Leadership, and Competency in supporting organizational efforts of digital transformation among Jordanian telecommunication organizations. A quantitative approach was adopted to achieve the previously mentioned aim. An online questionnaire was filled out by a convenient sample of 141 quality managers and employees within Jordanian telecommunication organizations: namely Zain, Umniah, and Orange. SPSS was used to screen and analyze primary data and AMOS version 23 was used to test the study hypotheses. The results of the study accepted the main hypothesis which indicated that quality 4.0 increases organizations seeking to accomplish digital transformation due to its ability to present better chances for the organization to develop and adhere to quality standards. The study recommended supporting organizational IT infrastructure to make a good environment for quality 4.0 adoption.
RoboCup2022 KidSize League Winner CIT Brains: Open Platform Hardware SUSTAINA-OP and Software

We describe the technologies of our autonomous soccer humanoid robot system that won the RoboCup2022 Humanoid KidSize League. For RoboCup2022, we developed both hardware and software. We developed a new hardware SUSTAINA-OP. We aimed to make it easier to build, harder to break, and easier to maintain than our previous robot. SUSTAINA-OP is an open hardware platform. As the control circuit, we selected a computer with higher processing power for deep learning. We also developed its software. In terms of image processing, the new system uses deep learning for all object detection. In addition, for the development of action decision-making, we built a system to visualize the robot's states and solved many problems. Furthermore, kicking forward at an angle action is added as a new tactical action. In RoboCup2022, even when the robots were facing each other with the ball between them, by this action the robot succeeded in getting the ball out in the direction of the opponent's goal.
Using artificial intelligence as business opportunities on the market: An overview

The usage of artificial intelligence as business opportunities on the markets has a wide potential for many fields. Due to the rapid development of technology, artificial intelligence is quite expensive but useful. The research is done by making basic SWOT analysis (analysis Strenghts-Weaknesses-Opportunities-Threats) of the R&amp;D (Research and Development) stage in the Czech Republic and by document analysis from articles on Web of Science and Scopus. The results shows, that the AI is beneficial for large number of fields and that the AI (Artificial Intelligence) has a great potential for implicating this in business, healthcare, economics and many more. The AI has potential to improve people’s life and work.
Fi-Eclat: An enhancement of incremental Eclat algorithm

Nowadays, there is a large amount of data growth at all organizational scales. To find useful information, the right data mining technique needs to be used. One of the popular techniques is called the equivalence class transformation (Eclat) algorithm which is gaining the benefits of the vertical data format used in the association rule mining technique. However, like horizontal data representation, vertical data still suffering from huge memory consumption. The technique then has been extended by using the Incremental Eclat (i-Eclat) algorithm which is embedded with the incremental approach. Inspired by the dynamic data transactions in a database, a new algorithm has been adopted to optimize the performance of the current algorithm called the Fast Incremental (Fi-Eclat) algorithm. The research proposes an optimization of the performance of the i-Eclat algorithm. It relies on the incremental approach implemented on the database that consists of five (5) benchmark datasets from Frequent Itemset Mining (FIMI). The outcome of the experiment indicates an improvement of 32% in time execution for the proposed algorithm.
A New Approach for Visual Analytics Applying to Multivariate Data of Student Intakes in the University

Human always has the ability to view things and phenomena, but to understand about it that depends on the ability to perceive as well as combine with existing experiences and knowledge which is available in every human to understand it. This research studies the human vision organs, infographics capture characteristics of the human eye, human vision awareness progress, and human visual viewing - thinking mechanisms to design visual analytics system of multivariate data (mdVAS), model of viewing - thinking visually analyzing, and visual graphs for discovering knowledge from multivariate data (mD). This mdVAS will support to upgrade human vision awareness progress that includes stages such as viewing - perceiving - cognizing - analyzing - understanding - remembering (vpcaurHs). This progress, especially the analysis stage plays a key that opens the door for human to understand the profound significance of things. Humans who want to understand the profound insights of things must bring out questions, at that time the mdVAS will enhance knowledge for humans to answer questions that humans themselves brought out. This approach will help humans, especially leaders, who have a profound insight into mD. This research illustrates experiments on educational data to manage the student intakes to help leadership on policy and decision making.
Extended Property-level k-vertex Cardinality Constraints Model for Graph Databases

Graph databases are nowadays considered the most appropriate solution for highly connected domains. Nevertheless, the lack of a fixed schema perplexes the implementation of business rules and inhibits the usage of graph database technology in practical use cases. To tackle this challenge, we study cardinality constraints in graph databases, as their focus is on the essential component of the property graph data model - relationships between entities. This paper presents an abstract cardinality constraints model for enforcing cardinality constraints in graph databases, which represent complex graph patterns. We extend our initial k-vertex cardinality constraints model to allow the representation of cardinality constraints between a node and a subgraph with given node and/or edge properties. Second, we implement the proposed model as procedures deployed in the Neo4j Graph Database Management System (GDBMS) to prevent adding new edges that violate k-vertex cardinality constraints to the graph database. Finally, we study the performance of the implemented approach on synthetic and real datasets and analyze its performance compared to the initial model. Overall, the query execution time (QET) of the procedure increases exponentially on larger datasets. Still, the added node/edge property-level evaluation does not show a significant performance effect on the edge insertion process.
Evaluation of the effects of blockchain technology characteristics on SCOR model supply chain performance measurement attributes using an integrated fuzzy MCDM methodology

ABSTRACTABSTRACTThis research aims to investigate and identify the effects of blockchain technology (BT) characteristics on SCOR supply chain (SC) performance measurement attributes. To achieve this, initially, the importance weights of blockchain characteristics (BC) in the context of the SC are determined by using the Interval Type-2 Fuzzy AHP. Furthermore, in order to rank Supply Chain Operation Reference (SCOR) model SC performance measurement attributes, the Interval Type-2 Fuzzy TOPSIS method is used. As for the conclusion, this research signifies that decentralised, security, and immutability are among the top three in terms of importance weights for SC while the consensus is ranked last and as for BC, SCOR model SC performance measurement attributes are ranked as reliability, asset management efficiency, responsiveness, cost, and agility. This study and its outcomes may serve organisations that are striving to boost their SC performance by applying BT.KEYWORDS: Blockchain technologySCOR modelsupply chain performance measurement attributesMulti-Criteria Decision Making (MCDM)interval type-2 fuzzy AHPinterval type-2 fuzzy TOPSIS Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data that support the findings of this study are available from the corresponding author, upon reasonable request.
VAQA: Visual Arabic Question Answering

Abstract Visual Question Answering (VQA) is the problem of automatically answering a natural language question about a given image or video. Standard Arabic is the sixth most spoken language around the world. However, to the best of our knowledge, there are neither research attempts nor datasets for VQA in Arabic. In this paper, we generate the first Visual Arabic Question Answering (VAQA) dataset, which is fully automatically generated. The dataset consists of almost 138k Image-Question-Answer (IQA) triplets and is specialized in yes/no questions about real-world images. A novel database schema and an IQA ground-truth generation algorithm are specially designed to facilitate automatic VAQA dataset creation. We propose the first Arabic-VQA system, where the VQA task is formulated as a binary classification problem. The proposed system consists of five modules, namely visual features extraction, question pre-processing, textual features extraction, feature fusion, and answer prediction. Since it is the first research for VQA in Arabic, we investigate several approaches in the question channel, to identify the most effective approaches for Arabic question pre-processing and representation. For this purpose, 24 Arabic-VQA models are developed, where two question-tokenization approaches, three word-embedding algorithms, and four LSTM networks with different architectures are investigated. A comprehensive performance comparison is conducted between all these Arabic-VQA models on the VAQA dataset. Experiments indicate that the performance of all Arabic-VQA models ranges from 80.8 to 84.9%, while utilizing Arabic-specified question pre-processing approaches of considering the special case of separating the question tool "Image missing"<!-- image only, no MathML or LaTex --> and embedding the question words using fine-tuned Word2Vec models from AraVec2.0 have significantly improved the performance. The best-performing model is which treats the question tool "Image missing"<!-- image only, no MathML or LaTex --> as a separate token, embeds the question words using AraVec2.0 Skip-Gram model, and extracts the textual feature using one-layer unidirectional LSTM. Further, our best Arabic-VQA model is compared with related VQA models developed on other popular VQA datasets in a different natural language, considering their performance only on yes/no questions according to the scope of this paper, showing a very comparable performance.