Reviewerly/exHarmony huggingface dataset:
Chatbots in Education: The impact of Artificial Intelligence based ChatGPT on Teachers and Students

The assimilation of artificial intelligence into education is an inevitable phenomenon. Intelligent tools provide opportunities such as personalized instruction, automated assessment and, last but not least, teaching with virtual assistants or chatbots. Chatbots and other virtual assistants have been on the market for years and have been used for educational purposes on various Q&amp;A platforms. ChatGPT, however, can be considered as an intellectual revolution of chatbots that have been released so far. ChatGPT has taken the concept of artificial intelligence to a new level and it was inevitable that it would make its debut in education. However, in schools, this level and relative novelty can bring not only benefits but also dangers. In this article, we discuss the potential of ChatGPT in the educational process. We look at the impact it can have on students, pointing out both the positives and the negatives. We then look at the opportunities for teachers. Finally, conclusions are drawn and suggestions are made as to the aspects that teachers and students need to adhere to in order to effectively implement ChatGPT in their everyday education.
Quantum Computing: A Bubble Ready to Burst or a Looming Breakthrough?

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angularParameter: A Novel Optimization Technique for Deep Learning Models

Training of deep learning models requires an optimization algorithm that minimizes error and maximizes accuracy. The development of an efficient optimization algorithm is one of the most important areas of research. angularGrad is the latest optimization method that considers the only direction of the previous two successive gradients but obviates the angular coefficient of a parameter. To deal with this limitation, an optimization method, i.e. angularParameter has been proposed. The proposed angularParameter adapts the step size and updates parameter depending on the angular coefficient of the previous two consecutive parameters and offers a smoother trajectory. Here, two distinct angularParameter methods are launched depending on the Cosine and Tangent function for determining the angular information of the parameter. The empirical analysis of the angularParameter is done using the non-convex function for comparison with Adam, diffGrad, and angularGrad (cos and tan). The image classification performances are also evaluated on CIFAR10 and CIFAR100 data sets using VGG16, ResNet34, ResNet18, ResNet50, and DenseNet121 network for detecting the effects of angularParameter, and also compared with SGDM, diffGrad, Adam, and angularGrad (cos and tan) based on CNN architecture.
Building a Recommendation System for Travel Location Based on User Check-Ins on Social Network

A lot of information is being gathered on social networks; this information is used for various purposes, one of them is to build a recommendation system. Although it has evolved so much, capable of suggesting all sorts of content to users, such as news, posts, friends, etc., it often overlooks one important content, which is places. Most social networks currently don’t allow users to perform many activities related to location, so place recommendation doesn’t usually provide reliable results. But given a social network that let users rate places for various criteria, it is possible to build a more reliable recommendation system for places.
MSVT: Multiple Spatiotemporal Views Transformer for DeepFake Video Detection

Recently, DeepFake videos have developed rapidly, causing new security issues in society. Due to the rough spatiotemporal view, existing video-based detection methods struggle to capture fine-grained spatiotemporal information, resulting in limited generalization ability. In addition, although the transformer has achieved great success in the past few years, the application of transformer on deepfake video detection still needs to be studied. To solve this problem, in this paper, we propose a novel Multiple Spatiotemporal Views Transformer (MSVT) with Local Spatiotemporal View (LSV) and Global Spatiotemporal View (GSV), to mine more detailed spatiotemporal information. Firstly, for establishing the LSV, different from existing works that sparsely sample a single frame to build the input sequence, we employ the local-consecutive temporal view to capture vital dynamic inconsistency. Furthermore, the extracted frame features within each group are fed to the temporal transformer followed by the feature fusion module, to generate group-level spatiotemporal features. Then, we further establish Global Spatiotemporal View (GSV) by feeding all the frame features within the whole video to the temporal transformer followed by the feature fusion module. Finally, we propose a novel global-local transformer (GLT) to effectively integrate these multi-level features for mining more subtle and comprehensive features. Extensive experiments on six large datasets demonstrate that our MSVT outperforms state-of-the-art detection methods.
A Robust Watermarking Algorithm for Medical Images Based on DWT-Daisy and Discrete Cosine Transform

With the development of digital medical imaging technology, the transmission of medical data in the network is becoming more and more frequent. In order to protect patients’ privacy information during transmission, a robust medical watermarking algorithm based on multilevel discrete wavelet transform (M-DWT), Daisy descriptor and discrete cosine transform (DCT) are proposed. The algorithm first performs a three-level DWT transform on the medical image, takes the center point of its low-frequency part LL3 sub-band as the center pixel point of Daisy descriptor, calculates the Daisy descriptor matrix of this point, and performs DCT transformation on it to obtain a 32-bit binary feature vector of the medical image. Then, we associate the feature vector with the watermark to complete the embedding and extraction of the medical watermark. In addition, in order to enhance the security of the watermark information without destroying the medical data information, the watermark is encrypted using the logical mapping, and the zero watermark technology is also adopted. The simulation results show that the proposed algorithm has strong robustness against traditional attacks and geometric attacks. In particular, the algorithm performs better under Gaussian noise attacks and rotation attacks. It also better balances the conflict between the robustness and invisibility of the image watermarking algorithm.
Fully Autonomous Brick Pick and Place in Fields by Articulated Aerial Robot: Results in Various Outdoor Environments

Picking and placing of objects by aerial robots in fields is an important and challenging task for a range of outdoor activities, including industry and rescue operations. General strategies depend on the magnetic force to the pick object; however, this approach lacks both generality and robustness. Therefore, we focus on an articulated structure to grasp bricking. Another issue in performing pick-and-place tasks in fields is autonomous recognition using onboard sensors. In this article, we present a fully autonomous pick-and-place scheme in outdoor environments using articulated aerial robots. First, an articulated robot model with an actively tiltable sensor is developed to guarantee robustness in both state estimation and object detection. Second, object detection methods are designed according to the distance between the robot and target object. Third, a comprehensive motion strategy is developed to perform an autonomous object searching, picking, and placing sequence. In particular, a visual servoing method for robot position control is also proposed in this motion strategy to improve the robustness while approaching the target. Finally, we present the experimental results of autonomous brick picking and placing by our proposed methods in various outdoor environments, including a real robotics challenge, the 2020 Mohamed Bin Zayed International Robotics Challenge (MBZIRC 2020). To the best of our knowledge, our study represents the first successful attempt at fully autonomous object grasping by an articulated aerial robot in a field.
Application of Combined SWOT and AHP Analysis to Assess the Virtual Reality and Select the Priority Factors for Education

Over the past decade, virtual reality (VR) technology has been increasingly used in educational settings. Its benefits and opportunities are opening up new avenues for learning. This research attempts to capture the strategic core factors involved in the evaluation of VR in education by implementing the A’WOT (AHP-SWOT) method, a combination of SWOT (Strengths, Weaknesses, Opportunities and Threats) analysis and the Analytic Hierarchy Process (AHP). SWOT analysis presents a comprehensive summary of important forces and challenges that are necessary for the development of VR education. However, The SWOT analysis does not have a way of measuring weights analytically and determining the importance of the components. Therefore, AHP analysis was applied to quantify and rank the factors that affect the functioning of the system. The proposed framework analysis allows the priorities of the factors contained in the SWOT analysis to be accurately and analytically determined and measurable.
ReLAC: Revocable and Lightweight Access Control with Blockchain for Smart Consumer Electronics

The increasing popularity of consumer smart devices has led to concerns about the security of electronic data. Blockchains, which are tamper-proof, can be used to store electronic data securely, preserving the privacy of individuals and devices. However, most of the consumer smart devices are resource-constrained, so the computational overhead of blockchain based schemes should not be ignored. Furthermore, the dynamic change of consumer smart devices in real-world scenarios is also an open issue. In this paper, a revocable and lightweight access control scheme (ReLAC) is proposed to realize deduplication and dynamic user revocation for smart consumer electronics/devices using blockchain. To protect the privacy of consumers and devices in the access matrix, the partially hidden policy is adopted in the proposed scheme. Note that, no detailed attribute values of the consumers or the devices are included in the ciphertext transmission. Finally, outsourced decryption is applied to solve the resource-constrained problem of smart consumer electronics/devices. The proposed scheme is proved to be s-IND-CPA secure in the standard model if there exists no one can crackle the decisional q-BDHE assumption. The performance analysis and experiments demonstrate that the proposed scheme is secure and computational efficiency for the application in consumer electronic systems.
Consensus Knowledge Exploitation for Partial Query Based Image Retrieval

Partial Query based Image Retrieval (PQIR) enables a search engine to perform an interactive retrieval given by only an initial query and actively provide alternative feedbacks for a user to refine a set of retrieval results. It alleviates the deficiency in practice interactive image retrieval that requires the user to laboriously provide detailed feedbacks, and enables the retrieval on-the-fly with the incomplete initial query. Although significant progress has been made, existing works remain have challenge in actively providing more discriminative feedbacks. To address this challenge, we propose a novel Attributes&Objects-based Consensus Extraction and Representation (AoCer) framework. Specifically, we formulate a simple but effective Attribute&Object Feedback (AOF) paradigm, which employs both attributes and objects as intermediate feedbacks to carry out multiple rounds of interaction. To mine the intrinsic associations among concepts and enhance their feature representations, we further propose an Interventional Consensus Representation Learning (ICRL) module, which mainly constructs an interventional concept graph to yield the Interventional Consensus Representation (ICR). In addition, a Dual-Head Feedback Sampler (DHFS) is developed to sample objects and attributes for conducting the next round retrieval. Extensive experiments demonstrate the superiority of the proposed framework. Our source code will be released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/zhangy0822/AoCer</uri> .
Multi-Granularity Detector for Vulnerability Fixes

With the increasing reliance on Open Source Software, users are exposed to third-party library vulnerabilities. Software Composition Analysis (SCA) tools have been created to alert users of such vulnerabilities. SCA requires the identification of vulnerability-fixing commits. Prior works have proposed methods that can automatically identify such vulnerability-fixing commits. However, identifying such commits is highly challenging, as only a very small minority of commits are vulnerability fixing. Moreover, code changes can be noisy and difficult to analyze. We observe that noise can occur at different levels of detail, making it challenging to detect vulnerability fixes accurately. To address these challenges and boost the effectiveness of prior works, we propose MiDas (Multi-Granularity Detector for Vulnerability Fixes). Unique from prior works, MiDas constructs different neural networks for each level of code change granularity, corresponding to commit-level, file-level, hunk-level, and line-level, following their natural organization. It then utilizes an ensemble model that combines all base models to generate the final prediction. This design allows MiDas to better handle the noisy and highly imbalanced nature of vulnerability-fixing commit data. Additionally, to reduce the human effort required to inspect code changes, we have designed an effort-aware adjustment for MiDas's outputs based on commit length. The evaluation results demonstrate that MiDas outperforms the current state-of-the-art baseline in terms of AUC by 4.9% and 13.7% on Java and Python-based datasets, respectively. Furthermore, in terms of two effort-aware metrics, EffortCost@L and Popt@L, MiDas also outperforms the state-of-the-art baseline, achieving improvements of up to 28.2% and 15.9% on Java, and 60% and 51.4% on Python, respectively.
A Solution to Blockchain Smart Contract Based Parametric Transport and Logistics Insurance

Traditional insurance policies pay the insurer according to the actual loss or damage to a tangible object that is insured. Due to the numerous parties involved, the settlement procedure is usually highly drawn out. Comparatively, parametric insurance protects a policyholder from the occurrence of a particular event by disbursing a predetermined sum dependent on the severity of the event. In the transportation sector, supply chain disruptions caused by delays are precisely the kind of event that parametric insurance may be able to cover. Transportation delays can happen for several reasons. Events like adverse weather, heavy traffic, and poor road conditions can all slow down the logistics supply chain. An occurrence triggers parametric insurance claims. Unfavorable scenarios can be insured, and claims can be automatically processed depending on policy criteria and conditions. The standard centralized database technique can be utilized to build parametric insurance schemes; however, the data is open to manipulation. This article examines how to create parametric transport insurance using blockchain. It discusses user journey, technology solutions, design and implementation problems, and parametric service insurance. The blueprint for transportation parametric insurance can be used in other fields including Agriculture (Crop Insurance) (Jha et al, 2021), Disaster Insurance (Parametric disaster insurance, Feb. 2023), Aviation, Travel, and manufacturing industry.
Approximative and structural properties of sets in asymmetric spaces

Structural and approximative properties of sets implying their solarity are studied. It is shown that, in any finite-dimensional polyhedral space, each strict sun admits a continuous $\varepsilon$-selection for all $\varepsilon&gt;0$ and the metric projection onto it has cell-like values. In general asymmetric spaces, sufficient conditions for solarity of Chebyshev sets are put forward.
Few-shot Text-to-SQL Translation using Structure and Content Prompt Learning

A common problem with adopting Text-to-SQL translation in database systems is poor generalization. Specifically, when there is limited training data on new datasets, existing few-shot Text-to-SQL techniques, even with carefully designed textual prompts on pre-trained language models (PLMs), tend to be ineffective. In this paper, we present a divide-and-conquer framework to better support few-shot Text-to-SQL translation, which divides Text-to-SQL translation into two stages (or sub-tasks), such that each sub-task is simpler to be tackled. The first stage, called the structure stage, steers a PLM to generate an SQL structure (including SQL commands such as SELECT, FROM, WHERE and SQL operators such as &lt;", ?&gt;") with placeholders for missing identifiers. The second stage, called the content stage, guides a PLM to populate the placeholders in the generated SQL structure with concrete values (including SQL identifies such as table names, column names, and constant values). We propose a hybrid prompt strategy that combines learnable vectors and fixed vectors (i.e., word embeddings of textual prompts), such that the hybrid prompt can learn contextual information to better guide PLMs for prediction in both stages. In addition, we design keyword constrained decoding to ensure the validity of generated SQL structures, and structure guided decoding to guarantee the model to fill correct content. Extensive experiments, by comparing with ten state-of-the-art Text-to-SQL solutions at the time of writing, show that SC-Prompt significantly outperforms them in the few-shot scenario. In particular, on the widely-adopted Spider dataset, given less than 500 labeled training examples (5% of the official training set), SC-Prompt outperforms the previous SOTA methods by around 5% on accuracy.
FMI: Fast and Cheap Message Passing for Serverless Functions

Serverless functions provide elastic scaling and a fine-grained billing model, making Function-as-a-Service (FaaS) an attractive programming model. However, for distributed jobs that benefit from large-scale and dynamic parallelism, the lack of fast and cheap communication is a major limitation. Individual functions cannot communicate directly, group operations do not exist, and users resort to manual implementations of storage-based communication. This results in communication times multiple orders of magnitude slower than those found in HPC systems. We overcome this limitation and present the FaaS Message Interface (FMI). FMI is an easy-to-use, high-performance framework for general-purpose point-to-point and collective communication in FaaS applications. We support different communication channels and offer a model-driven channel selection according to performance and cost expectations. We model the interface after MPI and show that message passing can be integrated into serverless applications with minor changes, providing portable communication closer to that offered by high-performance systems. In our experiments, FMI can speed up communication for a distributed machine learning FaaS application by up to 162x, while simultaneously reducing cost by up to 397 times.
Fast All-Pairs Shortest Paths Algorithm in Large Sparse Graph

Finding the All-Pairs Shortest Paths (APSP) in a graph is the key for various domains. Motivated by the graphs are sparse in most real-world applications, we store the whole graph as a compressed storage format in each process of the distributed computing clusters and combine the Floyd algorithm with the Dijkstra algorithm to solve the APSP problem in this work, which leads to the novel Fast APSP algorithm. In contrast to the state-of-the-art Part APSP algorithm, our algorithm adds some memory overhead to store the original sparse graph and uses local Floyd and global Dijkstra algorithms simultaneously. The payoff is the circumvention of expensive global communication, reducing one local FW operation, simplifying the Minplus function, and making its data access continuous. Furthermore, we propose a parallel framework to solve the problem of mismatch between the number of GPUs and the number of divisible blocks of a graph. The Fast APSP algorithm exhibits an average speedup of 16.97x compared to the CPU Dijkstra algorithm, 7.09x compared to the GPU Dijkstra algorithm, 7.09x compared to the Part APSP algorithm, and 4.6x compared to the decentralized Part APSP algorithm. It also shows good scalability in our experiments. It takes about 12.45 minutes to solve the APSP problem for the graph with 11,548,845 vertices by engaging 2048 GPUs.
GraphAGILE: An FPGA-Based Overlay Accelerator for Low-Latency GNN Inference

This article presents GraphAGILE, a domain-specific FPGA-based overlay accelerator for graph neural network (GNN) inference. GraphAGILE consists of (1) <i>a novel unified architecture design</i> with an <i>instruction set</i> , and (2) <i>a compiler</i> built upon the instruction set that can quickly generate optimized code. Due to the proposed instruction set architecture (ISA) and the compiler, GraphAGILE does not require any FPGA reconfiguration when performing inference on various GNN models and input graphs. For the architecture design, we propose a novel hardware module named Adaptive Computation Kernel (ACK), that can execute various computation kernels of GNNs, including general matrix multiplication (GEMM), sparse-dense matrix multiplication (SpDMM), and sampled dense-dense matrix multiplication (SDDMM). The compiler takes the specifications of a GNN model and the graph meta data (e.g., the number of vertices and edges) as input, and generates a sequence of instructions for inference execution. We develop the following compiler optimizations to reduce inference latency: 1) computation order optimization that automatically reorders the computation graph to reduce the total computation complexity, 2) layer fusion that merges adjacent layers to reduce data communication volume, 3) data partitioning with a partition-centric execution scheme that partitions the input graph to fit the available on-chip memory of FPGA, 4) kernel mapping that automatically selects execution mode for ACK, and performs task scheduling to overlap computation with data communication and achieves dynamic load balance. We implement GraphAGILE on a state-of-the-art FPGA platform, Xilinx Alveo U250. GraphAGILE can execute widely used GNN models, including GCN, GAT, GIN, GraphSAGE, SGC and other GNN models supported by GraphGym. Experimental results show that GraphAGILE achieves up to <inline-formula><tex-math notation="LaTeX">$47.1\times$</tex-math></inline-formula> ( <inline-formula><tex-math notation="LaTeX">$3.9\times$</tex-math></inline-formula> ) reduction in end-to-end latency, including the latency of compilation and hardware execution, compared with the state-of-the-art implementations on CPU (GPU), and achieves up to <inline-formula><tex-math notation="LaTeX">$2.9\times$</tex-math></inline-formula> reduction in hardware execution latency compared with the state-of-the-art FPGA accelerators.