Reviewerly/exHarmony huggingface dataset:
Application of Blockchain Technology in Electric Vehicle Charging Piles Based on Electricity Internet of Things

With the continuous development of urban intelligence, as traffic, power grids, and electric vehicles are new ideas to solve energy shortages and air control problems, they have received widespread attention from the society and strong support from the government. The charging pile is a key hub for data exchange and has typical characteristics of IoT terminals. However, the guidance of the grid connection of electric vehicles is not standardized, and the security and stability of the power grid will inevitably be affected. The blockchain has the characteristics that data is difficult to tamper with and decentralized. Based on these two characteristics, the information recorded by the blockchain is more authentic and reliable. This paper is aimed at realizing the global unified identification and management of large terminal equipment based on ubiquitous power Internet of Things equipment with the help of blockchain technology and at providing safe, efficient, and stable online management services for a large number of devices in the ubiquitous power Internet of Things. In this paper, we analyze the application possibility of blockchain technology in the current electricity market and apply blockchain technology to the electricity market to solve the drawbacks of the electricity market, as well as adding blockchain analytics to the renewable energy electricity market. The experimental data analysis shows that with the increase of the length of the blockchain, the blocking time of the new area increases correspondingly, but the overall performance declines slightly, so this scheme can meet the practical needs of a large number of concurrent access terminals in the ubiquitous power Internet of Things. It can be seen that the successful application of blockchain technology based on the power Internet of Things in electric vehicle charging piles has greatly improved work efficiency.
Ni máquina, ni humano ni disponible: Do College Admissions Offices Use Chatbots and Can They Speak Spanish?

Colleges continue to use technology to connect students to information, but a research gap exists regarding how colleges use a ubiquitous technology in the business world: chatbots. Moreover, no work has addressed whether chatbots address Spanish-speaking students seeking higher education in the form of automated (AI) chatbot responses in Spanish or Spanish-programmed chatbots. This study randomly sampled 331 United States institutions of higher education to learn if these institutions embed chatbots on their undergraduate admissions websites and if these chatbots have been programmed to speak Spanish. Results suggest 21% of institutions (n=71) embed chatbots into their admissions websites and only 28% of those chatbots (n= 20) were programmed to provide Spanish-language admissions information. Implications for college access and equity for English learners and L1 Spanish speakers are addressed.
An Ensemble of Arabic Transformer-based Models for Arabic Sentiment Analysis

In recent years, sentiment analysis has gained momentum as a research area.This task aims at identifying the opinion that is expressed in a subjective statement.An opinion is a subjective expression describing personal thoughts and feelings.These thoughts and feelings can be assigned with a certain sentiment.The most studied sentiments are positive, negative, and neutral.Since the introduction of attention mechanism in machine learning, sentiment analysis techniques have evolved from recurrent neural networks to transformer models.Transformer-based models are encoder-decoder systems with attention.Attention mechanism has permitted models to consider only relevant parts of a given sequence.Making use of this feature in encoder-decoder architecture has impacted the performance of transformer models in several natural language processing tasks, including sentiment analysis.A significant number of Arabic transformer-based models have been pretrained recently to perform Arabic sentiment analysis tasks.Most of these models are implemented based on Bidirectional Encoder Representations from Transformers (BERT) such as AraBERT, CAMeLBERT, Arabic ALBERT and GigaBERT.Recent studies have confirmed the effectiveness of this type of models in Arabic sentiment analysis.Thus, in this work, two transformer-based models, namely AraBERT and CAMeLBERT have been experimented.Furthermore, an ensemble model has been implemented to achieve more reasonable performance.
Severely Degraded Underwater Image Enhancement with a Wavelet-based Network

Underwater images are important in marine science and ocean engineering fields owing to giving color information, low cost, and compact.Yet obtained underwater images are often degraded and restoring and enhancing wavelength selective signal attenuation of underwater images depending on complex underwater physical process is essential in practical application.While recently developed deep learning is a promising choice, constructing sufficiently large dataset covering whole real images is challenging, peculiar to underwater image processing.In order to supplement relatively small dataset, previous studies alternatively construct an artificial underwater image dataset based on a physical model or Generative Adversarial Network.Also, incorporating traditional signal processing methods into the network architecture has shown promising success, though enhancement of severely degraded underwater images remains to be a big issue.In this paper, we tackle underwater image enhancement based on an encoder-decoder based deep learning model incorporating discrete wavelet transform and whitening and coloring transform.We also construct a severely degraded real underwater image dataset.The presented model shows excellent results both qualitatively and quantitatively in the artificial and real image dataset.Constructed dataset is available at https://github.com/tkswalk/2022-IJACSA.
An Improved K-Nearest Neighbor Algorithm for Pattern Classification

This paper proposed a "Locally Adaptive K-Nearest Neighbor (LAKNN) algorithm" for pattern exploration problem to enhance the obscenity of dimensionality.To compute neighborhood local linear discriminant analysis is an effective metric which determines the local decision boundaries from centroid information.KNN is a novel approach which uses in many classifications problem of data mining and machine learning.KNN uses class conditional probabilities for unfamiliar pattern.For limited training data in high dimensional feature space this hypothesis is unacceptable due to disfigurement of high dimensionality.To normalize the feature value of dissimilar metrics, Standard Euclidean Distance is used in KNN which s misguide to find a proper subset of nearest points of the pattern to be predicted.To overcome the effect of high dimensionality LANN uses a new variant of Standard Euclidian Distance Metric.A flexible metric is estimated for computing neighborhoods based on Chi-squared distance analysis.Chi-squared metric is used to ascertains most significant features in finding k-closet points of the training patterns.This paper also shows that LANN outperformed other four different models of KNN and other machine-learning algorithm in both training and accuracy.
STEAMTEACH Austria: Towards a STEAM Professional Development Program

The STEAMTEACH (STEAM Education for Teaching Professionalism) project works with teacher trainers to develop a program of Transcultural STEAM Professional Development for in- and pre-service teachers. Following a literature review, a semi-structured questionnaire then interviews approach was used to elicit current practice, challenges for teachers and recommendations from Austrian teacher trainers. These recommendations were then compared with and supported by STEAM academic literature. It was found that none of the institutions offered a STEAM approach and it does not appear in the curriculum. Indeed, this fact is the primary challenge to overcome. Recommendations were that teachers should work in multidisciplinary, supportive, collaborative groups and networks. Problem and project-based learning were identified as key learning methodologies. A mix of physical and on-line working synchronously and asynchronously was recommended for online professional development because teachers can participate irrespective of time and place. Educational technology can support collaboration, and also ‘plug and play’ technologies including game-based learning can be inserted into an overall STEAM approach. The affective factor was judged essential for students and teachers.
"A Study on Korean Role Play Activity using Metaverse –Focusing on Platform ZEPETO–"

This study aims to present a role play plan for Korean language learners to improve their communication skills in a practical context by using ZEPETO. ZEPETO is a platform that combines life-logging and virtual worlds with augmented reality operated by NAVERZ. After examining the value of role play and ZEPETO in speaking education as a previous study, the possibility of using ZEPETO in role play was discussed. The activity plan was based on < Cyber Korean >, a textbook developed with content oriented toward blended learning that can harmonize the characteristics of online learning with the advantages of offline learning. As a result of the study, first, ZEPETO’s “strong avatar service” is provided so that the movement of the avatar can be controlled as in real life, so learners can create their own avatars, move in virtual space, and actively participate in activities. When the characteristics are applied to the role play, various role play activities will be made and the learning effect will be increased. Using a variety of worlds, the context of communication outside the classroom is brought into the classroom so that learners are exposed to it in advance. In addition, since the course of the role play activity can be recorded with the provided lifelogging function, it can be used as a material for customized feedback by the teacher or peers after the activity, and learners will be able to reflect and modify their own learning activities. Second, the list of worlds that can be used in all units of the selected intermediate textbook < Cyber Korean > and that can be used in each unit is presented. Finally, a specific role play method using ZEPETO as a study material was proposed. The role play activity design using ZEPETO consists of five steps: ‘Introduction’, ‘Role Preparation’, ‘Role Play Composition’, ‘Role Play Demonstration’ and ‘Finishing’. Rather than a role play composed of fragments in the classroom, this episodic role play connected in ZEPETO has the advantage of improving actual communication skills by allowing conversations in scenes that cannot be experienced in the classroom. The proposed role play plan will not only be an activity plan that can be used online, but can also be used as a complementary learning activity even after switching to offline face-to-face classes. (Dongguk University)
The adoption of cloud accounting information system in Jordanian financial firms: Influencing factors

The advent of cloud accounting is a relatively recent development that has had a significant impact on the operation of financial companies. Within this framework, there is a paucity of information regarding the elements that may influence the adoption of cloud accounting information systems. This study builds an effect model based on the information system success theory, and then experimentally evaluates it with data from 391 employees working in Jordanian financial organizations using structural equation modeling (SEM). The results of these tests are presented. It was discovered that concepts such as perceived utility, information quality, system quality, and service quality had an effect on the adoption of cloud accounting information systems.
WasmFuzzer: A Fuzzer for WasAssembly Virtual Machines

WebAssembly is a fast, safe, and portable low-level language suitable for diverse application scenarios.And The WebAssembly virtual machines are widely used by Web browsers or Blockchain platforms as execution engine.When there is a bug in the implementation of the Wasm virtual machine, the execution of WebAssembly may lead to errors or vulnerability in the application.Due to the grammar checks by WASM VMs, fuzzing at the binary level is ineffective to expose the bugs because most inputs cannot reach the deep logic within the WASM VM.In this work, we propose WasmFuzzer, a bytecode level fuzzing tool for WASM VMs.WasmFuzzer proposes to generate initial seeds for Fuzzing at the Wasm bytecode level and it also designs a systematic set of mutation operators for Wasm bytecode.Furthermore, WasmFuzzer proposes an adaptive mutation strategy to search for the best mutation operators for different fuzzing targets.Our evaluation on 3 real-life Wasm VMs shows that WasmFuzzer can significantly outperform AFL in terms of both code coverage and unique crash.
Verifying BDI Agents in Dynamic Environments

The Belief-Desire-Intention (BDI) architecture is a popular framework for rational agents, yet most verification approaches are limited to analysing the behaviours of an agent in a subset of all possible environments.However, in practice, BDI agents operate in dynamic environments where the exact occurrence of external changes is difficult to predict.For safety/security we need to assess whether the agent behaves as required in all circumstances.To address this, we define environments, accounting for both sensor information about physical changes and new tasks to be completed, as a non-deterministic finitestate automata.We give an environment-enabled extension to the Conceptual Agent Notation (CAN) language including an executable semantics via an encoding to Milner's bigraphs and the BigraphER tool.We illustrate the framework through a simple Unmanned Aerial Vehicle (UAV) example that is verified using mainstream tools including PRISM model checker.Results show our approach can automatically identify agent design flaws to aid agent programmers in design, debugging, and analysis.
Towards Lightweight Detection of Design Patterns in Source Code

Identifying which design patterns exist in source code helps maintenance engineers better understand source code and determine if new requirements can be satisfied.Automated techniques for finding design patterns generally require much time to label training datasets or to specify rules/queries for each pattern, and is difficult to extend support to secure design patterns (SDPs) and combination patterns.To address these challenges, we introduce PatternScout, a technique for automatic generation of SPARQL queries from UML Class diagrams and Sequence diagrams.These queries are used to detect patterns in the source code.Our results indicate that PatternScout can detect object-oriented design patterns (OODP) with accuracy that is comparable or better than existing techniques.It can also generate queries for SDPs that can be represented as UML Class diagrams.
VeriBlock: A Blockchain-Based Verifiable Trust Management Architecture with Provable Interactions

There has been considerable advancement in the use of blockchain for trust management in large-scale dynamic systems. In such systems, blockchain is mainly used to store the trust score or trust-related information of interactions among the various entities. However, present trust management archi-tectures using blockchain lack verifiable interactions among the entities on which the trust score is calculated. In this paper, we propose a blockchain-based trust management framework that allows independent trust providers to implement different trust metrics on a common set of trust evidence and provide individual trust value. We employ geo-location as proof of interaction. Some of the existing proposals rely upon geo-location data, but they do not support trust calculation by multiple trust providers. Instead, they can only support a centralised system. Our proposed architecture does not depend upon a single centralised third-party entity to ensure trusted interactions. Our architecture is supported by provable interactions that can easily be verified using blockchain. Therefore, it allows a high degree of confidence in trust management by ensuring the actual interactions between the entities. We provide a detailed design and development of the architecture using real-world use case examples. The proof of prototype was implemented on the Ethereum blockchain platform. Experimental results demonstrate that the employment of independent trust providers adequately provides a high degree of trust scores and that the proposed architecture can be used in a real-world environment.
Modelling of IoT-WSN Enabled ECG Monitoring System for Patient Queue Updation

The advancement of communication technologies has led to the interconnection of different sensors using the Internet of Things (IoT) and Wireless Sensor Network (WSN).WSN for healthcare applications has expanded exponentially due to evolving advantages such as low power requirement of sensors, transmission accuracy, and cost-efficiency.For heart attack patients, the future lies in ECG monitoring in which wearable sensors can be used to acquire patient information.In this paper, an attempt has been made to develop a novel IoT-enabled WSN to record patient information for detection of heart attack and to update queue of patients to ensure prioritized medical attention to critical patients.In the WSN, the Rayleigh Fading channel has been used to transmit data that can be accessed using the cloud repository by the medical staff remotely.The distance from the patient to the medical staff is calculated using Euclidean distance.Further, SNR in comparison to throughput and BER has been computed.The higher SNR indicates the maximum information transfer from patient to hospital staff.The proposed system uses the Grasshopper Optimization and CBNN based disease classification system and bubble sort algorithm has been used for updating patient queue.The proposed GHOA and CBNN has shown improved accuracy of 2.14% over existing techniques like CNN which has accuracy around 82% for R-R feature selection of ECG signals as compared to 82.72% shown by GHOA-CBNN.
Attention-based CNN-ConvLSTM for Handwritten Arabic Word Extraction

Word extraction is one of the most critical steps in handwritten recognition systems. It is challenging for many reasons, such as the variability of handwritten writing styles, touching and overlapping characters, skewness problems, diacritics, ascenders, and descenders' presence. In this work, we propose a deep-learning-based approach for handwritten Arabic word extraction. We used an Attention-based CNN-ConvLSTM (Convolutional Long Short-term Memory) followed by a CTC (Connectionist Temporal Classification) function. Firstly, the text-line input image's essential features are extracted using Attention-based Convolutional Neural Networks (CNN). The extracted features and the text line's transcription are then passed to a ConvLSTM to learn a mapping between them. Finally, we used a CTC to learn the alignment between text-line images and their transcription automatically. We tested the proposed model on a complex dataset known as KFUPM Handwritten Arabic Text (KHATT \cite{khatt}). It consists of complex patterns of handwritten Arabic text-lines. The experimental results show an apparent efficiency of the used combination, where we ended up with an extraction success rate of 91.7\%.
Hierarchical Relational Learning for Few-Shot Knowledge Graph Completion

Knowledge graphs (KGs) are known for their large scale and knowledge inference ability, but are also notorious for the incompleteness associated with them. Due to the long-tail distribution of the relations in KGs, few-shot KG completion has been proposed as a solution to alleviate incompleteness and expand the coverage of KGs. It aims to make predictions for triplets involving novel relations when only a few training triplets are provided as reference. Previous methods have mostly focused on designing local neighbor aggregators to learn entity-level information and/or imposing sequential dependency assumption at the triplet level to learn meta relation information. However, valuable pairwise triplet-level interactions and context-level relational information have been largely overlooked for learning meta representations of few-shot relations. In this paper, we propose a hierarchical relational learning method (HiRe) for few-shot KG completion. By jointly capturing three levels of relational information (entity-level, triplet-level and context-level), HiRe can effectively learn and refine the meta representation of few-shot relations, and consequently generalize very well to new unseen relations. Extensive experiments on two benchmark datasets validate the superiority of HiRe against other state-of-the-art methods.
PCAE: A framework of plug-in conditional auto-encoder for controllable text generation

Controllable text generation has taken a gigantic step forward these days. Yet existing methods are either constrained in a one-off pattern or not efficient enough for receiving multiple conditions at every generation stage. We propose a model-agnostic framework Plug-in Conditional Auto-Encoder for Controllable Text Generation (PCAE) towards flexible and semi-supervised text generation. Our framework is "plug-and-play" with partial parameters to be fine-tuned in the pre-trained model (less than a half). Crucial to the success of PCAE is the proposed broadcasting label fusion network for navigating the global latent code to a specified local and confined space. Visualization of the local latent prior well confirms the primary devotion in hidden space of the proposed model. Moreover, extensive experiments across five related generation tasks (from 2 conditions up to 10 conditions) on both RNN- based and pre-trained BART [26] based auto-encoders reveal the high capability of PCAE, which enables generation that is highly manipulable, syntactically diverse and time-saving with minimum labeled samples. We will release our code at https://github.com/ImKeTT/pcae.
Faster Light Detection Algorithm of Traffic Signs Based on YOLOv5s-A2

Traffic sign recognition systems have been applied to advanced driving assistance and automatic driving systems to help drivers obtain important road information accurately. The current mainstream detection methods have high accuracy in this task, but the number of model parameters is large, and the detection speed is slow. Based on YOLOv5s as the basic framework, this paper proposes YOLOv5S-A2, which can improve the detection speed and reduce the model size at the cost of reducing the detection accuracy. Firstly, a data augmentation strategy is proposed by combining various operations to alleviate the problem of unbalanced class instances. Secondly, we proposed a path aggregation module for Feature Pyramid Network (FPN) to make new horizontal connections. It can enhance multi-scale feature representation capability and compensate for the loss of feature information. Thirdly, an attention detection head module is proposed to solve the aliasing effect in cross-scale fusion and enhance the representation of predictive features. Experiments on Tsinghua-Tencent 100K dataset (TT100K) show that our method can achieve more remarkable performance improvement and faster inference speed than other advanced technologies. Our method achieves 87.3% mean average precision (mAP), surpassing the original model’s 7.9%, and the frames per second (FPS) value is maintained at 87.7. To show generality, we tested it on the German Traffic Sign Detection Benchmark (GTSDB) without tuning and obtained an average precision of 94.1%, and the FPS value is maintained at about 105.3. In addition, the number of YOLOv5s-A2 parameters is about 7.9 M.