Reviewerly/exHarmony huggingface dataset:
Learning-based Multivariate Real-Time Data Pruning for Smart PMU Communication

This paper proposes a novel machine learning-based multivariate real-time data pruning and prediction framework for smart PMU (phasor measurement unit) communication. In an Internet-of-Things (IoT) enabled smart grid monitoring application, the proposed data-driven pruning technique exploits cross- and auto-correlation in multiple attributes sensed by a PMU (IoT node). The attributes are classified into base and nonbase groups based on their ability to aid prediction of the remaining attributes. The idea of transmitting only base attributes reduces the data dimensionality significantly. A reconstruction algorithm is designed for the edge node (local Phasor Data Concentrator) for efficient data reconstruction. The performance of the proposed framework is evaluated on large-scale real-time data from the PMUs. Comparison of the proposed technique with the closest state-of-the-art multi-threaded uni-variate data pruning algorithm in literature demonstrates around 40% more bandwidth saving and ∼42% reduction in retraining count.
Quality-of-Decision-Driven Machine-Type Communication

Machine-type communication (MTC) has been considered as one of the key enablers for intelligent Internet of Things (IoT) applications. However, existing evaluating metrics, no matter the Quality of Service (QoS) or Quality of Experience (QoE), cannot truly and accurately reflect the quality of MTC when it serves machine decision making. To address this problem, this work proposes a new performance index, Quality of Decision (QoD), to elegantly capture the essence of MTC and precisely depict the corresponding functionality. First, the physical components and logical processes that affect the quality of MTC in analytics-oriented scenarios are carefully studied. Second, through jointly considering the factors of the commonness and individuality, we propose a layered QoD framework capable of independently evaluating and monitoring the quality of data to be acquired, delivered, and processed for enabling machine decision making. Third, we design a typical QoD-driven transmission scheme for video analytics by avoiding over provisioning of sensing and communication capabilities, which shows super efficiency compared to traditional human-perception-oriented approaches. We believe that the utilization of QoD will significantly promote the application of MTC in building various intelligent IoT systems.
Fitness First

With side effect free terminals and functions it is possible to evaluate the fitness of genetic programming trees from their parents without creating them. This allows selection before forming the next generation. Thus avoiding unfit runt Genetic Algorithm individuals, which will themselves have no children. In highly diverse GA populations with strong selection, more than 50% of children need not be created. Even with two parent crossover, in converged populations, $$e^{-2}$$ = 13.5% can be saved. Eliminating bachelors and spinsters and extracting the smaller genetic material of each mating before crossover, reduces storage in an N multi-threaded implementation for a population M to $$\le $$ 0.63M+N, compared to the usual M+2N. Memory efficient crossover achieves 692 billion GP operations per second, 692 giga GPops, at runtime on a 16 core 3.8 GHz desktop.
Similarity Normalized Euclidean Distance on KNN Method to Classify Image of Skin Cancer

Research on skin cancer patients in terms of optimal classification measurements is still very lacking, so it is necessary to do research that aims to get optimal values in distance measurements with normalized Euclidean distance on the KNN method to classify images of skin cancer patients. The method which is used to classify various types of data such as numbers, images, text is the K-Nearest Neighbor (KNN) method. Basically KNN, however, accepts numeric data so that data other than numeric extract them into numeric. As in this paper, the classifying images of Skin Cancer sufferers consisting of malignant and benign images is performed by extracting data with a Gray-Level Co-occurrence matrix (GLCM) to obtain numerical data from skin cancer images. The GLCM process in this paper makes the matrix be divided into contrast, dissimilarity, homogeneity, energy, correlation and ASM. Then the process is classified where the process with KNN performs the same which usually uses the Euclidean distance compared to the normalized Euclidean distance. The classification process also produces validation applying the accuracy technique calculated by MAPE. The results in this paper testing with Euclidean distance achieved MAPE of 0.71043036% by testing with Normalized Euclidean distance achieving MAPE of 0.3151053%. This showed the similarity in image classification using KNN is more optimal by using the normalized Euclidean distance approach.
The intraday dynamics and intraday price discovery of bitcoin

Bitcoin as an alternative investment asset has received phenomenal investor attention in recent years. This study examines the intraday dynamics and price discovery of Bitcoin traded on the leading cryptocurrency exchanges. Using tick-level cryptocurrency market data over the period of January 2017 – June 2021, we find that trading volume, volatility, and liquidity of Bitcoin against USD and EUR increase significantly during the LNY time (i.e., the overlapping trading hours of London and New York) and exhibit an inverted U-shape, while those of Bitcoin against JPY peak during both the Asian hours and the LNY hours. Finally, we show evidence of the dominance of the overlapping trading hours of London and New York in the intraday price discovery on Bitcoin markets. The empirical findings could have some implications on intraday traders to decide when and where to trade, as well as on policy makers who seek to regulate cryptocurrency market.
Named Entity Recognition for Vietnamese Real Estate Advertisements

With the booming development of the Internet and e-Commerce, advertising has appeared in almost all areas of life, especially in the real estate domain. Understanding these advertising posts is necessary to capture the status of real estate transactions and rent and sale prices in different areas with various properties. Motivated by that, we present the first manually annotated Vietnamese dataset in the real estate domain. Remarkably, our dataset is annotated for the named entity recognition task with lots of entity types. In comparison to other Vietnamese NER datasets, our dataset contains the largest number of entities. We empirically investigate a strong baseline on our dataset using the API supported by the spaCy library, which comprises four main components: tokenization, embedding, encoding, and parsing. For the encoding, we conduct experiments with various encoders, including Convolutions with Maxout activation (MaxoutWindowEncoder), Convolutions with Mish activation (MishWindowEncoder), and bidirectional Long short-term memory (BiLSTMEncoder). The experimental results show that the MishWindowEncoder gives the best performance in terms of micro F1-score (90.72 %). Finally, we aim to publish our dataset later to contribute to the current research community related to named entity recognition.
Clarity method of fog and dust image in fully mechanized mining face

At present, the abnormal state of equipment and surrounding rocks in the fully mechanized mining face is mainly detected by visual methods. However, the vision sensor works in a low-light environment and it is affected by factors such as water fog and dust, which lead to blurred images. The defogging algorithm of image based on boundary constraint and context regularization has a good effect on image restoration in the daily environment, but the recovery quality is poor in low illumination environment. Therefore, a method based on boundary constraint and nonlinear context regularization is proposed. The model of fog and dust image is established, and the transmittance function is roughly estimated by boundary constraint method. Then, the nonlinear context regularization method based on logarithmic transformation is used to estimate and optimize the scene transmission model to improve the brightness of the image, and the low illumination fog and dust image is restored by the optimized transmittance function. The logarithmic transformation multiple is selected according to the peak value of image brightness. In order to highlight the effectiveness of our method, the widely used and improved Dark Channel Prior or other methods are used for comparison. The experiment results indicate that our method can effectively remove fog and dust and improve the brightness of the image of the fully mechanized face. It is of great significance to ensure safe production and safety of workers and equipment in coal mine.
Fake Profile Detection from the Social Dataset for Movie Promotion

Product promotion for increasing the sale of the product is critical in today&#x2019;s competitive environment. Online medium of promotion is vital in this regard. Product promotion especially in the field of movies goes through fake promotion issues. Movies are promoted by the entities through fake rating. This work primary focuses on the detection of fake profiles. To accomplish this collaborative filtering with the pre-processing mechanism is used. Demonstration of work will be done through movie lense dataset. The nature of proposed approach is modular; this means entire work will be divided into phase. Data acquisition is performed in the first phase. After collecting the dataset, pre-processing mechanism is applied by using nominal conversion. Collaborative filtering is applied along with clustering to determine the fake promotion of within movie lense dataset. Nominal conversion is also required since recommender system may not able to handle string values. By the classification accuracy we can show the result of the proposed work.
Carbon-Aware Load Balance Control of Data Centers With Renewable Generations

With the increasing environmental issues, the energy consumption and carbon emissions of data centers have become a major concern. However, in previous works, the cost and carbon reduction potential of geographically dispersed data centers with renewable generations is not fully explored due to the difficulty in matching renewable generations with stochastic incoming jobs. In this paper, the temporal and spatial variability of the carbon footprint and electricity price is revealed. Integrated with the distributed characteristics of renewable generations and geographically dispersed feature of the data centers, the carbon emission reduction potential of data centers with renewable generations is explored, leading to triple uncertainties in electricity price, fuel mix and renewable generation. To navigate such a potential, a virtual queue algorithm is designed, which makes online strategies for job scheduling of the data centers. By introducing historical correction terms, high-precision matching of the workload and renewable generation can be achieved with triple uncertainties. This leads to the economic and environmental friendliness of the proposed mechanism, which can achieve O(T) expected regret and constraint violation. Simulations based on real-world data from several states of Australia demonstrate the effectiveness of the proposed framework in cost and carbon emission reduction with triple uncertainties.
Streaming Graph Embeddings via Incremental Neighborhood Sketching

Graph embeddings have become a key paradigm to learn node representations and facilitate downstream graph analysis tasks.Many real-world scenarios such as online social networks and communication networks involve streaming graphs, where edges connecting nodes are continuously received in a streaming manner, making the underlying graph structures evolve over time.Such a streaming graph raises great challenges for graph embedding techniques not only in capturing the structural dynamics of the graph, but also in efficiently accommodating high-speed edge streams.Against this background, we propose SGSketch, a highly-efficient streaming graph embedding technique via incremental neighborhood sketching.SGSketch cannot only generate high-quality node embeddings from a streaming graph by gradually forgetting outdated streaming edges, but also efficiently update the generated node embeddings via an incremental embedding updating mechanism.Our extensive evaluation compares SGSketch against a sizable collection of state-of-theart techniques using both synthetic and real-world streaming graphs.The results show that SGSketch achieves superior performance on different graph analysis tasks, showing 31.9% and 21.9% improvement on average over the best-performing static and dynamic graph embedding baselines, respectively.Moreover, SGSketch is significantly more efficient in both embedding learning and incremental embedding updating processes, showing 54x-1813x and 118x-1955x speedup over the baseline techniques, respectively.
A Theoretical Approach to Ordinal Classification: Feature Space-Based Definition and Classifier-Independent Detection of Ordinal Class Structures

Ordinal classification (OC) is a sub-discipline of multi-class classification (i.e., including at least three classes), in which the classes constitute an ordinal structure. Applications of ordinal classification can be found, for instance, in the medical field, e.g., with the class labels order, early stage-intermediate stage-final stage, corresponding to the task of classifying different stages of a certain disease. While the field of OC was continuously enhanced, e.g., by designing and adapting appropriate classification models as well as performance metrics, there is still a lack of a common mathematical definition for OC tasks. More precisely, in general, a classification task is defined as an OC task, solely based on the corresponding class label names. However, an ordinal class structure that is identified based on the class labels is not necessarily reflected in the corresponding feature space. In contrast, naturally any kind of multi-class classification task can consist of a set of arbitrary class labels that form an ordinal structure which can be observed in the current feature space. Based on this simple observation, in this work, we present our generalised approach towards an intuitive working definition for OC tasks, which is based on the corresponding feature space and allows a classifier-independent detection of ordinal class structures. To this end, we introduce and discuss novel, OC-specific theoretical concepts. Moreover, we validate our proposed working definition in combination with a set of traditionally ordinal and traditionally non-ordinal data sets, and provide the results of the corresponding detection algorithm. Additionally, we motivate our theoretical concepts, based on an illustrative evaluation of one of the oldest and most popular machine learning data sets, i.e., on the traditionally non-ordinal Fisher’s Iris data set.
Extracting Sentiments from YouTube Comments

YouTube is the most used social media platform, and it has been the most popular website where users can post the video. The public generally does comment, like or dislike, video-sharing on a YouTube video. Comment plays a vital role in expressing opinions and mindset, and it is used as an expression of public opinion. The massive amount of comment comes mainly on famous channels where challenges arise to analyze public opinion or behavior regarding that particular video. This article proposes sentiment analysis on YouTube video by Natural Language Processing (NLP) technique. Sentiment Analysis is when comprehension, citation, and processing of text-based data is done, and it directly converts it into sentiment information. This analysis help users to get the report of their YouTube Video. The output of this analysis gives the classification of sentiment analysis, i.e., positive, negative, or neutral.
To miss-attend is to misalign! Residual Self-Attentive Feature Alignment for Adapting Object Detectors

Advancements in adaptive object detection can lead to tremendous improvements in applications like autonomous navigation, as they alleviate the distributional shifts along the detection pipeline. Prior works adopt adversarial learning to align image features at global and local levels, yet the instance-specific misalignment persists. Also, adaptive object detection remains challenging due to visual diversity in background scenes and intricate combinations of objects. Motivated by structural importance, we aim to attend prominent instance-specific regions, overcoming the feature misalignment issue. We propose a novel resIduaL seLf-attentive featUre alignMEnt (ILLUME) method for adaptive object detection. ILLUME comprises Self-Attention Feature Map (SAFM) module that enhances structural attention to object-related regions and thereby generates domain invariant features. Our approach significantly reduces the domain distance with the improved feature alignment of the instances. Qualitative results demonstrate the ability of ILLUME to attend important object instances required for alignment. Experimental results on several benchmark datasets show that our method outperforms the existing state-of-the-art approaches.
HeteroQA

Community Question Answering (CQA) is a well-defined task that can be used in many scenarios, such as E-Commerce and online user community for special interests. In these communities, users can post articles, give comment, raise a question and answer it. These data form the heterogeneous information sources where each information source have their own special structure and context (comments attached to an article or related question with answers). Most of the CQA methods only incorporate articles or Wikipedia to extract knowledge and answer the user's question. However, various types of information sources in the community are not fully explored by these CQA methods and these multiple information sources (MIS) can provide more related knowledge to user's questions. Thus, we propose a question-aware heterogeneous graph transformer to incorporate the MIS in the user community to automatically generate the answer. To evaluate our proposed method, we conduct the experiments on two datasets: $\textMSM ^\textplus $ the modified version of benchmark dataset MS-MARCO and the AntQA dataset which is the first large-scale CQA dataset with four types of MIS. Extensive experiments on two datasets show that our model outperforms all the baselines in terms of all the metrics.
Local and Global Path Planning for Autonomous Mobile Robots Using Hierarchized Maps

We are currently facing a “labor crisis,” particularly in the field of logistics, because of reductions in the labor force. Therefore, industries must make their logistics more efficient by utilizing autonomous mobile robotics technologies. This paper proposes a hierarchized map concept that makes unmanned delivery tasks which use multiple autonomous robots more efficiently. Using our proposed concept, an autonomous mobile robot can move automatically on a more efficient path than using current methods. In addition, the management platform for autonomous robots can be used to prevent accidents such as collisions or deadlocks between autonomous robots.
A novel Frei‐Chen based fragile watermarking method for authentication of an image

Abstract In recent years, the protection of the intellectual property rights of digital media has become a considerable issue. Especially using digital image editing tools has made it very easy and fast to manipulate images. Therefore, manipulation detection of digital images has become a necessity. This article presents a novel Frei‐Chen based self‐embedding fragile watermarking technique implemented to blocks for image manipulate detection. The block key and the Frei‐Chen based block feature are calculated from the non‐overlapping image blocks of size . Exclusive‐OR operations are applied to the Frei‐Chen based block feature and the block key. After, a nine bit block authentication key is generated from the Frei‐Chen based block feature and the block key. Then the generated authentication key is embedded in the least significant bits of each block. Experimental results prove the effectiveness of the proposed method through the detection of the various attacks and obtain the excellent visual quality of watermarked images. Also, the results demonstrate that the proposed method detects all of the manipulated areas plainly.
FaultLine: Software-Based Fault Injection on Memory Transfers

Today's integrated memory controllers use complex hardware such as delay-lines to monitor and control signal timings during external memory transfers. Because memory chips with different timing specifications may be used, delay-line tuning registers often remain accessible and programmable from the application processor. In this paper, we introduce FaultLine and the concept of delay-line-based fault injection. First, we demonstrate that by modifying the delay-line calibration value through a simple register access, a malware may induce faults in memory transfers and jeopardize the security of concurrently running assets. Then, we experimentally evaluate the fault injection on an OS-capable system-on-chip by exposing cryptographic applications to corrupted data and retrieving their secret keys. We finally discuss why delay-line-based fault injection should be systematically considered as a potential threat in modern systems where entities with different privileges share external memories.
Tensor-Based Non-Rigid Structure from Motion

In this work we present a method that combines tensor-based face modelling and analysis and non-rigid structure-from-motion (NRSFM). The core idea is to see that the conventional tensor formulation for the face structure and expression analysis can be utilised while the structure component can be directly analysed as the non-rigid structure-from-motion problem. To the NRSFM problem part we further present a novel prior-free approach that factorises the 2D input shapes into affine projection matrices, rank-one 3D affine basis shapes, and the basis shape coefficients. The linear combination of the basis shapes thus yields the recovered 3D shapes upto an affine transformation. In contrast to most works in literature, no calibration information of the cameras or structure prior is required. Experiments on challenging face datasets show that our method, with and without the metric upgrade, is accurate and fast when compared to the state-of-the-art and is well suitable for dense reconstruction and face editing.
A Comprehensive Review of Blockchain Technology and Its Related Aspects in Higher Education

Blockchain enables the production of a distributed shared document of a digital transaction in which no third-party controls any data or related activities. Previously, blockchain was only used to send money, but it now has a wide range of applications in fields such as finance, health care, the Internet of Things (IoT), and many more. Within the educational sphere, blockchain presents numerous opportunities for dispersed, interoperable record management in higher education institutions. The study's main goal is to address current difficulties affecting educational institutions and to find blockchain characteristics that can help solve these problems. A systematic literature review approach was used to recognise and extract significant knowledge from the selected research studies. The current difficulties are represented in three ways in our research study: financial, digital, and physical. The study's findings reveal that the primary issues facing educational institutions are the possibility of manipulation, the difficulty of authenticating, and the complexity of exchanging records between institutions. This study examines blockchain characteristics such as fragmentation, traceability, and consensus system, with an emphasis on a few issues that are relevant in this field.