Reviewerly/exHarmony huggingface dataset:
Enhanced Sentiment Analysis Technique using Machine Learning (B.R.A.G.E technique)

Sentiment Analysis have been the most growing topic in the recent years. It is the use of text analysis to examine the opinion or attitude towards a topic. In the past years, there have been a significant growth in the volume of research on Sentiment Analysis, on different detection level such as document level, sentence level and feature level. One of the famous existing sentiment analysis models is Naïve Bayes, a supervised machine learning model. In this study, we identified that the existing Naïve Bayes model trained and tested with incident/accident-related dataset gave an accuracy level of 71%. Additionally, this study describes how the proposed B.R.A.GE. technique has slightly enhanced the sentiment analysis prediction accuracy using incident/accident-related dataset. In conclusion, the proposed B.R.A.G.E technique has not significantly improved the accuracy but hence could be further improvised.
Funnelling Ensemble Method for Writer Identification (Fem-Wi)

This paper presents an efficient text independent funnelling ensemble method for writer identification (FEM-WI). The proposed method is a 2-level system of classifier ensembles for efficient writer identification. In the proposed work, 1 st level consists of feature dependent multiple base classifiers trained with different feature descriptors obtained from word images. 2 nd level consists of a single meta-classifier trained with meta-features obtained as outputs of base classifiers. Meta-classifier gives the final author-class output for input word images. In this work, 4 new feature descriptors are also proposed. They are based on contour fraglets and white runs of binary handwritten word images. They are (i) Histogram of orientation (HOO), (ii) Fraglet histogram of oriented displacement (FHOD), (iii) Relative run length (RRL), and (iv) Zoned Relative run length (ZRRL). The features are efficient and discriminative for usage in writer identification task at hand. Performance of the proposed features and FEM-WI method have been evaluated with IAM and Firemaker datasets, posting very high performance.
Quantum Machine Learning: Strategies Based on Quantum Annealing and Gated Quantum Computing

Quantum Computers (QCs) are machines based on quantum bits (qubits) that take full advantage of the laws of quantum mechanics (QM). They hold the promise to outperform current classical computing capabilities. There is an expectation that machine learning (ML) will be among the most influential killer apps for the first generation of commercially-available QCs, and specifically for the hardware that will dominate the QC landscape during the so called Noisy Intermediate-Scale Quantum (NISQ) era. It is predicted that ML will be impacted in the near-term by both Adiabatic Quantum Annealers (AQAs) and gated QCs. Commercial AQAs with more than 100 qubits have been available for more than a decade while no gated QC with more than 100 qubits is currently commercially available. As a result, there is already a rich history of applying AQAs to ML problems. On the other hand, gated QC hardware is attracting great interest today since it is the foundation upon which a future general-purpose (rather than an application-specific) QC will be based. The recent progress in gated QC hardware, algorithms, and early proof-of-concept applications point to these devices also making substantial inroads into ML, and they currently enjoy the lion's share of the scientific publications related to quantum ML (QML). In this talk, we will review the most basic QM concepts behind qubits, the use of the so-called bra-ket notation to describe the state vector of a system of qubits, and the density matrix formalism. Those are often the things that intimidate and confuse researchers who are new to QM and discourage them from trying to understand published QC algorithms. We will build intuition about the elusive concept of quantum interference, its connection to quantum probability amplitudes, and how interference (usually in a hidden way) may influence probabilistic outcomes compared to those in classical probabilistic models. Finally, we will try to address typical misunderstandings about quantum parallelism. We will proceed by introducing AQAs and their connection with un-directed probabilistic graphical models (Markov Random Fields), which are an important part of the modern ML toolkit. Two main tasks - combinatorial optimization and sampling from complex probability distributions - will be discussed. We will also discuss the reasons for why the introduction of the AQA hardware reignited interest in generative models based on Restricted Boltzmann Machine (RBMs). We will then move to the gated QC concept, the circuit model of computation, and the field of the quantum feedforward neural networks. We will introduce a family of variational quantum algorithms and explain why they are expected to become the most promising solution given the practical limitations of near-term QCs, specifically the NISQ hardware.
Adaptively Secure Laconic Function Evaluation for $$\mathsf {NC}^1$$

Laconic Function Evaluation (LFE) protocols, introduced by Quach et al. in FOCS'18, allow two parties to evaluate functions laconically, in the following manner: first, Alice sends a compressed "digest" of some function – say $$\mathscr {C}$$ – to Bob. Second, Bob constructs a ciphertext for his input $$ M $$ given the digest. Third, Alice, after getting the ciphertext from Bob and in full knowledge of her circuit, can recover $$\mathscr {C}( M )$$ and (ideally) nothing more about Bob's message. The protocol is said to be laconic if the sizes of the digest, common reference string ( $$\mathsf {crs}$$ ) and ciphertext are much smaller than the circuit size $$|\mathscr {C}|$$ . Quach et al. put forward a construction of laconic function evaluation for general circuits under the learning with errors (LWE) assumption (with sub-exponential approximation factors), where all parameters grow polynomially with the depth but not the size of the circuit. Under LWE, their construction achieves the restricted notion of selective security where Bob's input $$ M $$ must be chosen non-adaptively before even the $$\mathsf {crs}$$ is known. In this work, we provide the first construction of LFE for $$\mathsf {NC}^1$$ , which satisfies adaptive security from the ring learning with errors assumption (with polynomial approximation factors). The construction is based on the functional encryption scheme by Agrawal and Rosen (TCC 2017).
Identifying and Describing Sentence Level Constructions in Korean

This paper aims to propose and test several strategies for identifying and describing sentence-level constructions in Korean from the perspective of construction grammar. Although Goldberg (2006: 5) has proposed a modified definition of constructions that covers even common and predictable patterns, many construction grammarians have tended to favour a butterfly collecting approach, focusing primarily on patterns with unusual semantic and grammatical properties. However, the application of Hilpert
Abstract

An abstract class provides a partial implementation that other classes can build on. When a class is declared as abstract, it means that the class can contain incomplete members that must be implemented in derived classes, in addition to normal class members.
Fusion of Signal-Based Multichannel CNN-LSTM Models for Human Activity Recognition using Wearable Sensors

Human activity recognition (HAR) is one of the emerging fields under machine learning that has received multiple technological advancements recently. HAR monitors and recognizes human motions or activities and classifies them. Multichannel deep learning (DL) models have proven to extract outstanding levels of performance out of HAR. In this paper, we investigate fusion approaches to combine the outputs from multiple channels whose inputs are the signals from wearable sensors attached to different parts of the body. Each channel is based on a hybrid of one-dimensional convolutional neural networks and long short-term memory. According to where the fusion takes place and which channel outputs are fused together, we study and investigate six fusion approaches. Their classification performances are evaluated and compared by using well-known public HAR datasets. The best fusion approach achieves 98.77% and 96.51% in terms of accuracy when using the PAMAP2 dataset and the DaLiAc dataset, respectively.
Sparse Multiple Instance Learning for Elderly People Balance Ability

Falling occurs when a person’s body (excluding their feet) accidentally touches the ground, resulting in severe injury for the elderly. As a response, determining old people’s balance characteristics and assisting them in preventing falls is important. We proposed the sparse SyMIL with SCAD to find the important factors that may cause an elderly person to fall, which can yield realistic and accurate results. This work can also be used in future research about falling prevention and balance supervision.
Human-Object Interaction Detection: 1D Convolutional Neural Network Approach Using Skeleton Data

Human-object interaction detection is a somewhat recently emerged scientific topic, which is mainly due to the advent of deep learning algorithms. Most current methods are performed on single images, detecting separately humans and objects, using state-of-the-art pose detection and object detection networks. The networks ease the overall task by allowing for learning of the readily inferred features. When adding the time dimension into the equation, this task becomes more complex, as temporal features between frames have to be taken into account. The paper aims to show an approach for detecting human interactions in videos, which utilizes several different methods - YOLOv5 for object detection, CSR-DCF and Kalman Filter for object tracking, and ID Convolutional Neural Network (1D-CNN) for real-time interaction detection. The overall algorithm is purposed for salient and rigid (modern-solid) objects in mind, positioned in closed-door scenes. The dataset and task are privately defined, that is they are relevant to this work only and cannot be compared to other works. The overall algorithm is tested on a subset of the PKU Multi-Modality Dataset (PKUMMD).
Type-1 and type-2 radial basis function neural networks Mandami system to evaluate quality features

This paper presents a methodology that uses the central composite design and the radial basis function neural networks in type-1 or in interval type-2 model to generate a network that evaluates quality features in an industrial image processing. The methodology includes a couple of radial basis functions as Huygen’s tractrix and triangular membership functions as complementary contributions that have not been reported in literature as radial basis functions. The advantage of using this proposal is that the training is not required to get an accurate result, also the generation of the IT2 RBFNN fuzzy rule base for evaluating quality characteristics is simplified by using the central composite design method and statistical indicators extracted from the product specification data. Experimental results show an error reduction of 90% when the interval type-2 Mandami Radial basis function neural network was compared against its type-1 counterpart using the Gaussian membership functions onto a radial basis function network. On the other hand, the implementation of the Huygen’s tractrix, found a reduction error of 50% in comparison to the Gaussian function.
Research and Implementation of Small Object Detection Algorithm for Power Embedded Devices

Currently in the power industry, there has been a demand for intelligent computing and real-time feedback at the edge using embedded devices. However, in some scenarios, the excessive computation still seriously affects the practicality of intelligent edge devices. Take transmission inspection drone aerial images as an example, the requirement to identify some small objects at large resolution makes the inference model can only be deployed on servers or desktops with strong computing power support. In order to realize the practicality of edge intelligence devices for small object detection, this paper adopts the strategy of hierarchical object detection. This paper first analyzes the problems faced by small object detection under constrained computation and proposes the strategy of using hierarchical object detection, then refines the design and validation of the sliding window selection for hierarchical object detection, and finally selects the base model to be used considering the accuracy, inference speed, and other factors. This enables fast detection of small objects such as missing pins on resource-limited embedded devices with good detection results.
The Practices and Challenges of Generating Nonidentifiable Data

This article summarizes the key findings of a Canadian Anonymization Network study of several large data custodians who utilize deidentification and similar privacy-enhancing processes prior to engaging in analytics, secondary uses, and disclosure of personal information.
Pivot-V: An Optimized Algorithm for Password Generation in PCFGs Model

Password authentication is popular in identity authentication. However, it is vulnerable to guessing attack. Recently, attacks based on PCFGs (Probability Context Free Grammars) shows its effectiveness. In PCFGs, when the number of generated passwords is very large (such as more than 10^10), the generation time of passwords is too long. This paper improves the Pivot algorithm of traditional PCFGs model and adopts the pre-terminal structure based on Pivot-V algorithm to optimize the password generation. The experimental results show that the password generation speed of the optimized model is 4.9 times faster than that of the traditional PCFGs model.
Neural Networks in Recommender Systems with an Optimization to the Neural Attentive Recommender Model

The impact of recommender systems on e– commerce, marketing, and user entertainment has long been established. To combat the problem of information overload on the internet, they seek to improve customer-company interactions and provide customers with individualized online product or service recommendations. There are several types of recommender systems, and two of the most common are– Content based & Collaborative filtering-based recommender systems and they are examined in this paper. With the rapid rise in processing efficiency and as the capacity to process deeper neural networks became increasingly feasible, the application of deep learning to recommender systems was inevitable. In this study, we hope to guide the reader through the implementation of Neural networks (a fast deep learning algorithm) to create highly reliable recommender systems using the two aforementioned methodologies. The importance of a "Hybridized" system of recommendation is also illustrated. Finally, we propose a statistical optimization strategy on the Neural Attentive Recommender Model and examine the assumptions, advantages & results in the form of an experimental methodology.
Application of Feature based Face Detection in Adaptive Skin Pixel Identification Using Signal Processing Techniques

Changes in illumination can substantially impact the apparent color of the skin, jeopardizing the effectiveness of any color-based segmentation method. Our solution to this problem is to use adaptive technology to generate skin color models in real-time. We employ a Viola-Jones feature-based face detector built-in MATLAB to sample faces inside a picture in a moderate-recall, high-precision configuration. We extract a set of pixels that are likely to be from skin areas from these samples. Then, filter them based on their relative luma values to remove non-skin face characteristics, producing a set of pixels. We train a unimodal Gaussian function to model the skin color in the provided image in the normalized rg color space using this representative set–a combination of the modeling strategy and color space that aids us in various ways. Subsequently, a developed function is employed for each pixel in the picture, allowing the likelihood that each pixel represents skin to be calculated. Application of a binary threshold to the computed probabilities may used to segment the skin. We discuss various current techniques in this work, detail the methodology behind our new proposed model. Moreover, provide the outcomes of its application to random photos of individuals with recognizable faces, which we found to be quite encouraging, and explores its possibilities for usage in real-time systems.
Unsupervised Learning for Stereo Depth Estimation using Efficient Correspondence Matching

To avoid using costly ground-truth, several recent methods formulate the unsupervised depth estimation problem as the task of disparity estimation. However, most of them for monocular depth estimation cannot perform explicit depth maps, while even some methods for stereo depth estimation cannot efficiently use stereo-view information. In this work, we provide a novel cost volume construction approach (matching cost layer) to extract easy-to-learn cost volume by matching feature correspondences between stereo images, which can satisfy the unsupervised learning strategy better. We also propose a new network architecture that learns more reliable spatial location information of objects from fully fused stereo features. Extensive experiments of our method on the KITTI datasets demonstrate its superiority over the state-of-the-art methods.
Text Categorization of Job Vacancy Using Recurrent Neural Network Method

The need for information on job vacancies will increase over time. This results in information on job vacancies being updated very quickly and the data collected will be more numerous and varied. One way to group information is to tag each job vacancy. It can be possible that a job vacancy contains the characteristics of more than one category or is commonly referred to as multilabel This research aims to categorize job vacancies so that job vacancies can be grouped into appropriate categories automatically. The categories chosen to be categorized are Administration, Finance, IT, and Marketing. This research uses the Recurrent Neural Network method. In obtaining the optimal model, this research tested the hyperparameters. Testing on the Word2vec hyperparameter produces a dimension of 150 and a window size of 15. In the Recurrent Neural Network hyperparameter, the optimal model is obtained when the number of units in the Recurrent Neural Network is 200, the batch size is 32, and the threshold is 0.2. This research resulted in a Hamming score of 83.07% and an F1 score of 86.15%.
Adaptive Stopping Criteria-based A-RANSAC algorithm in Copy Move Image Forgery detection

Copy move forgery is one of the most common types of image forgery, and it is very important to detect this type of forgery. Feature-based forgery detection methods perform better than block-based methods. In this article, a new feature-based approach is suggested in the copy-move forgery detection process. In the suggested approach, first, the features extraction process is done based on SIFT. Second, matching process is based on the g2NN criteria. Finally, removal mismatches are done based on the suggested improved A-RANSAC that stopping criteria is presented based on the number of final matches. The stop time in the basic A-RANSAC method is based on the number of repetitions, which increases the execution time and decreases its speed. This suggested approach, in addition to proper accuracy, increases speed. The simulation results on MICC-F220 datasets affirm the suggested approach advantage in comparison with some other basic methods in terms of precision matching and execution time.
When Microservices Architecture and Blockchain Technology Meet: Challenges and Design Concepts

Microservices Architecture Style (MSA) and Blockchain technology are rapidly gaining ground as popular research topics in computer science, capturing the interest of researchers to explore new horizons for new applications. Integrating MSA and blockchain technology is not straightforward as the MSA faces challenges of confidentiality and functional analysis in order to bring about the emergence of autonomous and strongly cohesive functional units. Due to its embedded encryption, decentralizing feature and digital signature, the latest advances in blockchain possess the potential ability to tackle MSA design challenges. There are two primary advantages of integrating blockchain with MSA: firstly, the blockchain has the ability to handle the major challenges of MSA design; and secondly, MSA assist the blockchain thrive. This paper discusses the design concepts for creating blockchain applications adopting an MSA that may assist in mitigating these challenges, and addresses pertinent topics in a setting such as Blockchain as a Service (BaaS) architecture. Further, it also covers the open issues of using blockchain in conjunction with MSA.
Hybridizing Sentence Transformer Model with Multi-KNN for Biomedical Documents

In this digital era, multi-label text classification has been one of the real-time challenging tasks. Many methodologies have been proposed to solve it, but the low accuracy of the state-of-the-art technologies still makes this problem interesting and worth exploring. This research work explores a problem in which we must assign a set of labels to a given text from the MeSH Hierarchy (Medical Subject Headings), which is a regulated vocabulary. These documents are taken from the PubMed repository, which is generally considered as a big collection of medical domain scientific papers. There are approximately 30,000 labels as a MeSH category. Out of 30,000 labels, 10–15 labels should be assigned for a particular PubMed research article and thus it is an extreme multi-label classification problem (XMTC). These labels lie in a hierarchical structure and this complicates the problem of extreme multi-label classification. However, this research work considers a smaller version of the problem by taking advantage of the hierarchical nature of mesh root labels, wherein it means the problem is now not XMTC but still a multi-label classification problem. As multi-KNN has been widely used for multi-label classification, taking the advantage of a lot of embedding now available for representing the textual data, this research work suggests the hybridization of embeddings based on sentence transformer model and it helps to improve the performance of the multi-KNN algorithm. So, this paper has presented some of the practical approaches such as label sparsity removal to deal with the problem. Results Exploiting the Multi-KNN Algorithm on the biomedical articles, the experiments have shown the usefulness of the embeddings generated by sentence transformer models when compared with the embeddings generated by classic TF-IDF and Doc2Vec.
Toward an Integrative Approach for Making Sense Distinctions

Word senses are the fundamental unit of description in lexicography, yet it is rarely the case that different dictionaries reach any agreement on the number and definition of senses in a language. With the recent rise in natural language processing and other computational approaches there is an increasing demand for quantitatively validated sense catalogues of words, yet no consensus methodology exists. In this paper, we look at four main approaches to making sense distinctions: formal, cognitive, distributional, and intercultural and examine the strengths and weaknesses of each approach. We then consider how these may be combined into a single sound methodology. We illustrate this by examining two English words, "wing" and "fish," using existing resources for each of these four approaches and illustrate the weaknesses of each. We then look at the impact of such an integrated method and provide some future perspectives on the research that is necessary to reach a principled method for making sense distinctions.