Reviewerly/exHarmony huggingface dataset:
Context-Dependent Anomaly Detection with Knowledge Graph Embedding Models

Increasing the semantic understanding and contextual awareness of machine learning models is important for improving robustness and reducing susceptibility to data shifts. In this work, we leverage contextual awareness for the anomaly detection problem. Although graphed-based anomaly detection has been widely studied, context-dependent anomaly detection is an open problem and without much current research. We develop a general framework for converting a context-dependent anomaly detection problem to a link prediction problem, allowing well-established techniques from this domain to be applied. We implement a system based on our framework that utilizes knowledge graph embedding models and demonstrates the ability to detect outliers using context provided by a semantic knowledge base. We show that our method can detect context-dependent anomalies with a high degree of accuracy and show that current object detectors can detect enough classes to provide the needed context to show good performance within our example domain.
Exploration Methods in Reinforcement Learning

Exploration and exploitation serve as cornerstones of reinforcement learning. The performance of agent is highly dependent on the exploration to obtain useful information in the unknown environment, which can be exploited to optimize the agent. Thus, the trade-off between exploration and exploitation is an essential problem in reinforcement learning. In this paper, we focus on the various exploration methods in reinforcement learning and discuss the strengths and weaknesses of these methods.
MIMO Detector Selection With Federated Learning

In this paper, we develop a dynamic detection network (DDNet) based detector for multiple-input multiple-output (MIMO) systems. By constructing an improved DetNet (IDetNet) detector and the OAMPNet detector as two independent network branches, the DDNet detector performs sample-wise dynamic routing to adaptively select a better one between the IDetNet and the OAMPNet detectors for every samples under different system conditions. To avoid the prohibitive transmission overhead of dataset collection in centralized learning (CL), we propose the federated averaging (FedAve)-DDNet detector, where all raw data are kept at local clients and only locally trained model parameters are transmitted to the central server for aggregation. To further reduce the transmission overhead, we develop the federated gradient sparsification (FedGS)-DDNet detector by randomly sampling gradients with elaborately calculated probability when uploading gradients to the central server. Based on simulation results, the proposed DDNet detector consistently outperforms other detectors under all system conditions thanks to the sample-wise dynamic routing. Moreover, the federated DDNet detectors, especially the FedGS-DDNet detector, can reduce the transmission overhead by at least 25.7% while maintaining satisfactory detection accuracy.
Optimal Boxes: Boosting End-to-End Scene Text Recognition by Adjusting Annotated Bounding Boxes via Reinforcement Learning

Text detection and recognition are essential components of a modern OCR system. Most OCR approaches attempt to obtain accurate bounding boxes of text at the detection stage, which is used as the input of the text recognition stage. We observe that when using tight text bounding boxes as input, a text recognizer frequently fails to achieve optimal performance due to the inconsistency between bounding boxes and deep representations of text recognition. In this paper, we propose Box Adjuster, a reinforcement learning-based method for adjusting the shape of each text bounding box to make it more compatible with text recognition models. Additionally, when dealing with cross-domain problems such as synthetic-to-real, the proposed method significantly reduces mismatches in domain distribution between the source and target domains. Experiments demonstrate that the performance of end-to-end text recognition systems can be improved when using the adjusted bounding boxes as the ground truths for training. Specifically, on several benchmark datasets for scene text understanding, the proposed method outperforms state-of-the-art text spotters by an average of 2.0% F-Score on end-to-end text recognition tasks and 4.6% F-Score on domain adaptation tasks.
Implementasi Data Mining dalam Memprediksi Stok Herbal menggunakan Algoritma Apriori

Nafii Store is one of the stores engaged in selling herbal products. With the large number of public requests for herbal products during the COVID-19 pandemic, stock availability is needed to increase sales transactions for these products. To avoid the limited supply of herbs, an information system is needed that can help store leaders to find out what kind of products should be increased in stock according to customer demand. The appropriate application to overcome these problems is the development of the concept of data mining. Based on the Nafii Store herbal sales data sample, which amounted to 24 transactions, this study will use a minimum support = 16% and a minimum confidence = 70%. The results of the determination of the Association Rules and testing with the Tanagra application that the pattern of herbal purchases are: If you buy Baarik Forest Honey 800 grams then buy Baarik Forest Honey 470 grams 80%, If you buy Black Seed with Ajwa Kurma stamps then buy Darusyifa Black Bitter Honey 83.3%, If buy Darusyifa Bitter Black Honey then buy Black Seed with Ajwa Dates stamped 83.3%. The results of this study can be used by the store management to make decisions on the availability of herbal products at the Nafii Store.
Graph Visualization of the Terminological Thesaurus by F.M. Dostoevsky's Gospel Text (using the Example of Bigram Models)

Methods and means of scientific visualization through graphs are actively used today in many areas, including the humanities.The effectiveness of their use is also evident in philological research.The article describes the graph model of the terminological thesaurus of the Gospel text by F.M. Dostoevsky, based on the clustering of digrams, taking into account their thematic belonging to different discourses.The identification and analysis of digrams as conceptual points of a terminological thesaurus is important for its network visualization, thanks to which it is clear in which conceptual conjugation key terms "work", how they are distributed among certain clusters.In one cluster there can be terms from different discourses.This is an important indicator of how the terms are conjugated, how integral in this case is the metalanguage of the studies of the Gospel text by F.M. Dostoevsky.Thus, the terminological thesaurus performs in this case a diagnostic and prognostic role.
A Hierarchical Deliberative Architecture Framework based on Goal Decomposition

Performing a complex autonomous mission with a multi-robot system requires to integrate several deliberative approaches to perform task allocation, optimization, and execution control. Implementing such a deliberative architecture is a complex task: it requires the developer to master the decision algorithms themselves (e.g., automated planning models), to have a good knowledge of the involved robotic platforms, and to think about how these elements will be assembled as a system architecture. We propose a framework to help designing such deliberative architectures. The framework relies on the concept of a hierarchical structure of actors, each actor managing goals with specific planning or optimization approaches, and delegating sub-goals to other actors.
Accelerating NLP Tasks on FPGA with Compressed BERT and a Hardware-Oriented Early Exit Method

In recent years, Natural Language Processing (NLP) has gradually become a heated topic in research area, and Transformer-based pretrained models (the most widely used is BERT) have achieved state-of-the-art results in many NLP tasks. But since Transformer-based pretrained models always contain extensive parameters, and consume much time in computing, it is really difficult to employ them on resource-limited embedded platform or mobile devices. To resolve this issue, we utilize the ALBERT model with the improved early exit method (ELBERT) and propose an efficient VLSI architecture for it in an algorithm and hardware co-design way. First of all, by using the quantization and encoder-level parameter sharing techniques, the storage space for saving BERT is reduced from 1208.88 MB to 20.99MB with little accuracy loss, which makes it possible to store all the weights on-chip. Secondly, we present a hardware-friendly design for the improved early-exit method. In contrast to the original ALBERT implementation, the accelerator combining ALBERT model with improved early exit can obtain a speed-up of 4.59x. Thirdly, owing to our efficient hardware design, experiments demonstrate that our FPGA accelerator can achieve a performance-per-watt of 1.29 fps/W, which is 25.97x over NVIDIA 2080 Ti GPU. On the whole, we introduce an efficient accelerator on FPGA with compressed BERT and a hardware-oriented early exit method, which solves the challenge to deploy the BERT to resource-constrained edge platforms with strict latency and memory requirements.
Black-Box Few-Shot Knowledge Distillation

Knowledge distillation (KD) is an efficient approach to transfer the knowledge from a large “teacher” network to a smaller “student” network. Traditional KD methods require lots of labeled training samples and a white-box teacher (parameters are accessible) to train a good student. However, these resources are not always available in real-world applications. The distillation process often happens at an external party side where we do not have access to much data, and the teacher does not disclose its parameters due to security and privacy concerns. To overcome these challenges, we propose a black-box few-shot KD method to train the student with few unlabeled training samples and a black-box teacher. Our main idea is to expand the training set by generating a diverse set of out-of-distribution synthetic images using MixUp and a conditional variational auto-encoder. These synthetic images along with their labels obtained from the teacher are used to train the student. We conduct extensive experiments to show that our method significantly outperforms recent SOTA few/zero-shot KD methods on image classification tasks. The code and models are available at: https://github.com/nphdang/FS-BBT .
STEM-DRR: Potential strategy for increasing awareness of disaster risks

Indonesia is a country, which is prone to geological and hydrometeorological hazards, including volcanic eruptions, earthquakes, tsunamis, floods, and droughts. Systematic initial responses based on efforts for pre-disaster prevention and its corresponding countermeasures need to be prepared for the improvement of the capacity of local communities in managing disaster risks and associated recoveries. Education is, then, considered a way of improving basic knowledge necessary for disaster preparedness and promoting a recovery strategy. Learning about earth-related Disaster Risks Reduction (DRR) fits with Science, Technology, Engineering and Mathematics (STEM) education since a disaster could be best examined across multi-disciplines. While the implementation of a STEM-DRR approach in a classroom setting remains challenging, the current study examined a pattern to follow in STEM-DRR as a potential strategy for increasing awareness of disaster risks at schools. The method used in this study is an exploration with a semi-systematic literature review to reduce a possible bias when facing various terms and procedures in the early development of this approach. The results imply a better look for a future direction of integrated research combining disaster education and STEM. This study could lead to an effective strategy for DRR in school environments with two critical aspects as follows: (1) Strategy for STEM-DRR education depends on both the type and location of a disaster, and (2) A follow-up study to handle the shortcomings found from this study needs to be performed with a broader range of research area coverages, including multidisciplinary approaches.
An Investigation and Research on the Problem of English Learning Burnout and the Factors Affecting Middle School Students

Burnout" mainly refers to students' boredom and tiredness of learning due to the excessive pressure and burden of school work for a long time, and the gradual loss of interest in learning, which leads to negative emotions.As scholars at home and abroad have studied burnout in-depth, the concept of "English burnout" has emerged because English as a second language causes pressure and difficulties in students' learning process, which leads to students' fatigue in English learning.This study investigates the phenomenon of English learning burnout and its influencing factors among middle school students and proposes strategies to solve the problem to improve the quality and level of their English learning.This paper discusses both theoretical and experimental aspects.
Threat Landscape Across Multiple Cloud Service Providers Using Honeypots as an Attack Source

As the number of users of cloud services increases, so does the frequency of attacks against cloud infrastructure and systems. Cloud service providers do possess some control over the security of their systems. Nonetheless, these measures are only partially effective. End-users and clients should be informed of which cloud service provider delivers the best security for their services. This study aims to investigate the threat landscape of three cloud service providers: Google Cloud Services, Linode, and Amazon Web Services. Honeypots have been added to adequately monitor attacks and the attack sequence, allowing for a more thorough examination of the attack procedure. This research will clearly lay out the statistics regarding the Cloud service provider with the least threat landscape, as well as provide supporting information for all stakeholders in the cloud computing industry’s cyber threat awareness and provide recommendations for the base security configurations of all public-facing infrastructure.
Improving the Storage Utilization of 0-Complete Trees as Index Structures

A compact 0-complete tree is an indexing mechanism that can be effectively used for large databases with long and variable-size keys. Compared to B-trees, compact 0-complete trees eliminate search values from secondary indices altogether. They are replaced with small surrogates whose typical eight-bit length will be adequate for most practical key lengths. Hence the secondary indices are simply hierarchical collections of (surrogate, pointer)-pairs. However, the overall storage performance of a compact 0-complete tree structure suffers from its dependency on the distribution of keys. We present a structural modification of 0-complete trees coupled with retrieval and maintenance algorithms that improve their storage utilization by reducing page-underfilling. This is achieved by introducing the notion of a sequence set that is analogous to that of $$\text {B}^{+}$$ -trees.
Learning Enabled Fast Planning and Control in Dynamic Environments with Intermittent Information

This paper addresses a safe planning and control problem for mobile robots operating in communication- and sensor-limited dynamic environments. In this case the robots cannot sense the objects around them and must instead rely on intermittent, external information about the environment, as e.g., in underwater applications. The challenge in this case is that the robots must plan using only this stale data, while accounting for any noise in the data or uncertainty in the environment. To address this challenge we propose a compositional technique which leverages neural networks to quickly plan and control a robot through crowded and dynamic environments using only intermittent information. Specifically, our tool uses reachability analysis and potential fields to train a neural network that is capable of generating safe control actions. We demonstrate our technique both in simulation with an underwater vehicle crossing a crowded shipping channel and with real experiments with ground vehicles in communication-and sensor-limited environments.
ASSISTER: Assistive Navigation via Conditional Instruction Generation

We introduce a novel vision-and-language navigation (VLN) task of learning to provide real-time guidance to a blind follower situated in complex dynamic navigation scenarios. Towards exploring real-time information needs and fundamental challenges in our novel modeling task, we first collect a multi-modal real-world benchmark with in-situ Orientation and Mobility (O &M) instructional guidance. Subsequently, we leverage the real-world study to inform the design of a larger-scale simulation benchmark, thus enabling comprehensive analysis of limitations in current VLN models. Motivated by how sighted O &M guides seamlessly and safely support the awareness of individuals with visual impairments when collaborating on navigation tasks, we present ASSISTER, an imitation-learned agent that can embody such effective guidance. The proposed assistive VLN agent is conditioned on navigational goals and commands for generating instructional sentences that are coherent with the surrounding visual scene, while also carefully accounting for the immediate assistive navigation task. Altogether, our introduced evaluation and training framework takes a step towards scalable development of the next generation of seamless, human-like assistive agents.
Research Strategies of English Linguistics from the Perspective of English Corpus

The study of English linguistics is a relatively difficult problem of the times, and corpus plays a decisive role in its research.In the process of building a sound corpus, it is usually done by means of topic.This paper is based on the research method of literature.Looking at the current construction of English corpus, there are still a series of problems such as incomplete corpus, poor construction system, and isolation of disciplines.Therefore, the study of English linguistics should promote the intersection between different disciplines, effectively integrate different types of corpora with unified specifications, and scientifically analyze relevant theories and specific data information of linguistics.In the position of building a multilingual corpus, we should strengthen the effective research of English linguistics, expand the scope of translation and oral application of the English corpus, and enhance the use effect of the corpus.This paper discusses the characteristics of English corpus, the development trend of linguistics and research strategies, hoping to promote the development of English linguistics for a long time.
Hyperspectral Super-Resolution by Unsupervised Convolutional Neural Network and Sure

Recent advances in deep learning (DL) reveal that the structure of a convolutional neural network (CNN) is a good image prior (called deep image prior (DIP)), bridging the model-based and DL-based methods in image restoration. However, optimizing a DIP-based CNN is prone to over-fitting leading to a poorly reconstructed image. This paper derives a loss function based on Stein's unbiased risk estimate (SURE) for unsupervised training of a DIP-based CNN applied to the hyperspectral image (HSI) super-resolution. The SURE loss function is an unbiased estimate of the mean-square-error (MSE) between the clean low-resolution image and the low-resolution estimated image, which relies only on the observed low-resolution image. Experimental results on HSI show that the proposed method not only improves the performance, but also avoids overfitting. Codes are available at https://github.com/hvn2/SURE-MS-HS
Performance Comparison of Improved Common Sequence to Sequence Paraphrasing Models

Paraphrase generation is a widely known and complex task in the field of Natural Language Processing (NLP). With a variety of paraphrasing models, many researchers have developed their own methodology to overcome this task with better efficiency and quality paraphrases. In this paper, Text-to-Text Transfer Transformer (T5) and Back-Translation guided multi-round Paraphrase Generation (BTmPG), two popular algorithms in paraphrasing models, are compared. Paired paraphrased sentences are used to train both models. The goal of paraphrasing models is to generate paraphrases with a high degree of semantic similarity and the fewest possible grammatical errors and sentence structure copying. For the purpose of paraphrase generation, both the T5 and BTmPG models have been optimized. This study aims to compare the performance of the T5 and BTmPG models and identify factors that have a significant influence on output quality. The accuracy and precision of the T5 and BTmPG models' paraphrase quality are calculated using BLEU and ROUGE1. Despite requiring more time for training, the T5 model outperformed the BTmPG model slightly, according to the results. Despite having a smaller dataset size, the MRPC dataset was found to be more efficient than the QQP dataset. With proper grammar and lexicality, both models produced results that were satisfactory.
SA-Unet: A Phase Unwrapping method based on DCNN

Phase unwrapping is critical in many applications. Its purpose is to retrieve continuous phase information from discrete unwrapped phases, eliminate periodic aliasing of phases, and generate absolute phases with uniform spatial distribution. The phase unwrapping has been widely used in various cutting-edge fields, such as interferometry, fringe projection, and MRI. Phase unwrapping is an ill-posed problem, and its key point is to obtain accurate periodic ordinal information. However, when there is a lot of noise and aliasing, effective phase unwrapping still faces huge challenges. This paper converts phase unwrapping into a semantic segmentation problem. We propose a new network architecture SA-Unet, which adopts an encoder-decoder structure and adds a spatial attention module. To train SA-Unet, a simulated dataset with noise is produced. In this paper, we introduce focal loss and L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> norm to avoid dataset class imbalance problem. Experiments show that the SA-Unet proposed in this paper has higher accuracy and better robustness than other phase unwrapping methods.
Vehicle-Road Cooperative Task Offloading with Task Migration in MEC-Enabled IoV

Mobile edge computing (MEC) is considered as a key technology for addressing computation-intensive and delay-critical applications in the Internet of vehicles (IoV). In MEC-enabled IoV, vehicles lighten their computing load by offloading tasks to edge servers. However, the high speed mobility of vehicles and time-varying network environment brings tough challenges to task offloading. In addition, considering only roadside units (RSUs) or vehicles as offloading objects lead to the waste of computing resources and increase the process delay of task. To this end, we formulate the reduction of task processing delay and improvement of service reliability as an utility maximization problem and propose a distributed vehicle-road cooperative task offloading scheme with task migration. Then we use RSUs and surrounding vehicles as offloading objects and divide offloading tasks into multiple subtasks for offloading objects and local parallel processing, which improves the utilization rate of computing resources. Meanwhile, we reduce the task processing failure by migrating the computing results of offloading subtasks. The offloading scheme is formulated as a mixed-integer nonlinear optimization problem, and a multi-agent deep Q-learning network (MADQN) algorithm is proposed to find the near-optimal offloading objects and number of offloading subtasks. Simulation results show that the proposed approach significantly improves the total task processing speed and service reliability.