Reviewerly/exHarmony huggingface dataset:
Great Dane Natural-Inspired Search Algorithm

ABSTRACTThe naturally inspired algorithm plays an important role in solving optimization problem. Naturally, inspired algorithm is used to search the optimized result from the large dataset. Many algorithms have been proposed already to solve the complex problem and result was proved. In this paper, we propose a new algorithm called Great Dane natural inspired search algorithm. This algorithm inspires the behaviour of pet animal Great Dane dog. The behaviour of Great Dane is very strong to protect the home. The behaviour of Great Dane is used to find the optimal solution from the large data base. The proposed algorithm is used to solve the single and multi-variable cost function problems through the generation of binary and integer structured Great Dane. The Great Dane algorithm is tested and implemented using De-Jong Type I function and the results provide the performance of the algorithm is better than the other evolutionary algorithms.
A Practical 2-step Approach to Assist Enterprise Question-Answering Live Chat

Live chat in customer service platforms is critical for serving clients online. For multi-turn question-answering live chat, typical Question Answering systems are single-turn and focus on factoid questions; alternatively, modeling as goal-oriented dialogue limits us to narrower domains. Motivated by these challenges, we develop a new approach based on a framework from a different discipline: Community Question Answering. Specifically, we opt to divide and conquer the task into two sub-tasks: (1) Question-Question Similarity, where we gain more than 9% absolute improvement in F1 over baseline; and (2) Answer Utterances Extraction, where we achieve a high F1 score of 87% for this new sub-task. Further, our user engagement metrics reveal how the enterprise support representatives benefit from the 2-step approach we deployed to production.
A survey of ship target feature extraction based on video surveillance

Abstract Ship target feature extraction plays an important role in ship recognition and tracking based on video surveillance. However, the current feature extraction of ship targets based on video surveillance does not have a complete summary, so this article reviews these methods. This article summarizes the main ship target feature extraction methods used in ship target recognition and tracking in different application environments, including traditional artificial ship feature extraction and automated learning ship feature extraction. It focuses on the core ideas, advantages and disadvantages of the representative methods in each type of problem, and discusses the current problems in the feature extraction of ship targets and the future development of feature extraction technology.
A Simple yet Effective Method for Sentence Ordering

Sentence ordering is the task of arranging a given bag of sentences so as to maximise the coherence of the overall text. In this work, we propose a simple yet effective training method that improves the capacity of models to capture overall text coherence based on training over pairs of sentences/segments. Experimental results show the superiority of our proposed method in in- and cross-domain settings. The utility of our method is also verified over a multi-document summarisation task.
Analisa Datamining dengan Metode Klasifikasi C4.5 Sebagai Faktor Penyebab Tanah Longsor

Landslides are geological events where soil movement occurs such as falling rocks or large lumps on the ground. Landslides often occur when it rains, although not always. In addition, landslides generally occur in areas with steep slopes. With the C4.5 method it can be used to classify data that has numeric and categorical attributes. The results of the classification process in the form of rules can be used to predict the value of the discrete type attribute from a new record. Data obtained from the National Disaster Management Agency regarding the factors causing landslides can produce an accuracy value of 77.78%, meaning that the resulting rules or rules are close to 100%, it can be concluded that to classify the factors causing landslides using C4.5 by looking at the node the highest is slope. To find out these factors can provide input to the Disaster Management Agency to be more concerned with the factors that caused the landslides
BYOL works even without batch statistics

Bootstrap Your Own Latent (BYOL) is a self-supervised learning approach for image representation. From an augmented view of an image, BYOL trains an online network to predict a target network representation of a different augmented view of the same image. Unlike contrastive methods, BYOL does not explicitly use a repulsion term built from negative pairs in its training objective. Yet, it avoids collapse to a trivial, constant representation. Thus, it has recently been hypothesized that Batch normalization (BN) is critical to prevent collapse in BYOL. Indeed, BN flows gradients across batch elements, and could leak information about negative views in the batch, which could act as an implicit negative (contrastive) term. However, we experimentally show that replacing BN with a batch-independent normalization scheme (namely, a combination of group normalization and weight standardization) achieves performance comparable to vanilla BYOL (73.9% vs. 74.3% top-1 accuracy under the linear evaluation protocol on ImageNet with ResNet-50). Our finding disproves the hypothesis that the use of batch statistics is a crucial ingredient for BYOL to learn useful representations. * Equal contribution; the order of first authors was randomly selected. Preprint. Under review.
Colour Features Extraction Techniques and Approaches for Content-Based Image Retrieval (CBIR) System

An image retrieval system was developed purposely to provide an efficient tool for a set of images from a collection of images in the large database that matches the user's requirements in similarity evaluations such as image content similarity, edge, and colour similarity.Retrieving images based on the contents which are colour, texture, and shape is called content-based image retrieval (CBIR).This paper discusses and describes about the colour features technique for image retrieval systems.Several colour features technique and algorithms produced by the previous researcher are used to calculate the similarity between extracted features.This paper also describes about the specific technique about the colour basis features and combined features (hybrid techniques) between colour and shape features.
Spherical panorama compositing through depth estimation

Abstract In this paper, we propose to work in the 2.5D space of the scene to facilitate composition of new spherical panoramas. For adding depths to spherical panoramas, we extend an existing method that was designed to estimate relative depths from a single perspective image through user interaction. We analyze the difficulties to interactively provide such depth information for spherical panoramas, through three different types of presentation. Then, we propose a set of basic tools to interactively manage the relative depths of the panoramas in order to obtain a composition in a very simple way. We conclude that the relative depths obtained by the extended depth estimation method are enough for the purpose of compositing new photorealistic panoramas through a few elementary editing tools.
STOCHASTIC PSEUDOSPIN NEURAL NETWORK WITH TRIDIAGONAL SYNAPTIC CONNECTIONS

Context. To reduce the computational resource time in the problems of diagnosing and recognizing distorted images based on a fully connected stochastic pseudospin neural network, it becomes necessary to thin out synaptic connections between neurons, which is solved using the method of diagonalizing the matrix of synaptic connections without losing interaction between all neurons in the network. Objective. To create an architecture of a stochastic pseudo-spin neural network with diagonal synaptic connections without loosing the interaction between all the neurons in the layer to reduce its learning time. Method. The paper uses the Hausholder method, the method of compressing input images based on the diagonalization of the matrix of synaptic connections and the computer mathematics system MATLAB for converting a fully connected neural network into a tridiagonal form with hidden synaptic connections between all neurons. Results. We developed a model of a stochastic neural network architecture with sparse renormalized synaptic connections that take into account deleted synaptic connections. Based on the transformation of the synaptic connection matrix of a fully connected neural network into a Hessenberg matrix with tridiagonal synaptic connections, we proposed a renormalized local Hebb rule. Using the computer mathematics system “WolframMathematica 11.3”, we calculated, as a function of the number of neurons N, the relative tuning time of synaptic connections (per iteration) in a stochastic pseudospin neural network with a tridiagonal connection Matrix, relative to the tuning time of synaptic connections (per iteration) in a fully connected synaptic neural network. Conclusions. We found that with an increase in the number of neurons, the tuning time of synaptic connections (per iteration) in a stochastic pseudospin neural network with a tridiagonal connection Matrix, relative to the tuning time of synaptic connections (per iteration) in a fully connected synaptic neural network, decreases according to a hyperbolic law. Depending on the direction of pseudospin neurons, we proposed a classification of a renormalized neural network with a ferromagnetic structure, an antiferromagnetic structure, and a dipole glass.
RETRACTED ARTICLE: Stress Sensitivity of Loose Sandstone and Sports Training Management Based on Image Recognition

This paper studies the stress sensitivity and training management of loose sandstone based on image recognition in detail. Loose sandstone is one of the most common storage rocks, which accounts for a large proportion at home and abroad. In the process of developing loose sandstone reservoir, it is basic to obtain the mechanical parameters correctly. Because of the high porosity, high permeability and weak separation strength of loose sandstone, the paper discusses the influence of gap water pressure, and temperature on mechanical parameters of loose sandstone. The compressive strength of loose sandstone decreases with the increase of the gap water pressure. The elastic modulus of loose sandstone will decrease greatly under low clearance water pressure, but it is slow to decrease under high clearance water pressure, and Poisson ratio will decrease first and then increase. But the accuracy of quadratic fitting of compression strength, elastic modulus, and clearance water pressure is high, but the accuracy of Poisson ratio is low and the correlation is weak, which shows that there is no accurate correlation between Poisson ratio and clearance water pressure. With the increase of temperature, the compressive strength, and elastic modulus of loose sandstone begin to decrease, then rise, and then decrease rapidly after reaching the critical temperature. This is not good for the stability of the rock. Based on the above research, the general university physical education outside the curriculum is finally fully understood, which provides the basis for the decision-making and proposal of relevant departments. This paper uses literature, questionnaire, interview, mathematics statistics, logical analysis, and other methods to take 93 universities as the subjects of investigation. This paper investigates the status of university administration, coaches, athletes, and stadiums, and then uses SPSS12.0 system and Excel software to make statistics and processing of data, so as to provide strategies for sports training management.
Mobile Dictionary For Hitu Ethnic Language

Indonesians have many diverse ethnic groups. Each tribe has different traditions and cultures. As with language, each tribe has a different local language to communicate and interact with their community and environment. Hitu is one of the villages (Negeri) on Ambon Island, Central Maluku Regency. People in Tanah Hitu communicate every day using the Hitu language. Hituese is one of the local languages in Indonesia. The Hitu Country dictionary application is a mobile-based application that can make it easier for the people of Hitu Village to find translations from Hitu - Indonesian - English or vice versa. This is because many foreign and domestic tourists visit Hitu Village. People in Hitu can certainly speak Indonesian, but not all are able to speak it fluently. Therefore, this dictionary was created to facilitate the community in Hitu. This mobile application can display vocabulary translations that can be searched from Hitu to Indonesian and Indonesian to Hitu or vice versa. This dictionary will use Android-based mobile devices and will also take advantage of current developments that are very sophisticated. The Hitu dictionary application will be easy to carry anywhere and can be accessed every day.
Dynamic Enumeration of Similarity Joins

This paper considers enumerating answers to similarity-join queries under dynamic updates: Given two sets of n points A,B in ℝ^d, a metric ϕ(⋅), and a distance threshold r > 0, report all pairs of points (a, b) ∈ A × B with ϕ(a,b) ≤ r. Our goal is to store A,B into a dynamic data structure that, whenever asked, can enumerate all result pairs with worst-case delay guarantee, i.e., the time between enumerating two consecutive pairs is bounded. Furthermore, the data structure can be efficiently updated when a point is inserted into or deleted from A or B. We propose several efficient data structures for answering similarity-join queries in low dimension. For exact enumeration of similarity join, we present near-linear-size data structures for 𝓁₁, 𝓁_∞ metrics with log^{O(1)} n update time and delay. We show that such a data structure is not feasible for the 𝓁₂ metric for d ≥ 4. For approximate enumeration of similarity join, where the distance threshold is a soft constraint, we obtain a unified linear-size data structure for 𝓁_p metric, with log^{O(1)} n delay and update time. In high dimensions, we present an efficient data structure with worst-case delay-guarantee using locality sensitive hashing (LSH).
Visualizing the Human Capital Management Research Domain: A Bibliometric Analysis Approach

The research on human capital management has extensively emerged in recent years. Organizations need to know how to manage human capital effectively to boost performance. Many literature reviews have been published on the specific aspects of human capital management. However, less attention has been given to bibliometric and network analysis aspects. Hence, the purpose of this study is to visualize the research domain of human capital management by conducting a bibliometric analysis. The analysis was performed using Scopus Analyzer available in the Scopus database and VOSviewer software. A total of 1098 publications concerning human capital management were extracted from the Scopus database ranging from 2008 to 2018. Co-citation and co-word analysis were conducted to graphically illustrate the publication trend and to identify areas of current research interests. The outcome of this study may assist researchers in understanding the nature of the human capital management research domain from across the world and suggest future research directions.
Modeling Sense Structure in Word Usage Graphs with the Weighted Stochastic Block Model

We suggest to model human-annotated Word Usage Graphs capturing fine-grained semantic proximity distinctions between word uses with a Bayesian formulation of the Weighted Stochastic Block Model, a generative model for random graphs popular in biology, physics and social sciences.By providing a probabilistic model of graded word meaning we aim to approach the slippery and yet widely used notion of word sense in a novel way.The proposed framework enables us to rigorously compare models of word senses with respect to their fit to the data.We perform extensive experiments and select the empirically most adequate model.
Adaptive Estimation of the Processes Having Disorders in Navigation Applications Using Machine Learning

The statement of the problem of adaptive estimation of the processes having disorders is presented in the paper. The traditional approach and the neural network approach are used to solve the task. In the first of them, a multi-alternative method based on the Kalman filter bank is used to solve the estimation problems. The alternative approach uses a bank of neural network algorithms. The example of adaptive estimation of processes with disorders is considered in relation to trajectory tracking of a maneuvering object.
Criminological and forensic characteristics of forms of embezzlement committed through the use of information technology

The article's purpose is the criminological and forensic characteristics of the forms of embezzlement of funds by the use of information technology and international and foreign experience in combating this destructive phenomenon. The object of this article is the relationship that arises in connection with the implementation and counteraction to theft in the field of information technology. The authors used various methods of scientific cognition to write this work. In particular, historical, observation, generalization, comparison and analogy, statistical, analytical, and others. The article emphasizes that the emergence and rapid development of new information technologies do not always positively affect criminals because criminals can transform positive qualities into crime. In particular, crimes of embezzlement through the use of information technology are now widespread. This article has tried to provide a criminological description of the three main forms of cybercrime against money: carding, phishing, and embezzlement committed using NFC technology. In addition, emphasis was placed on the importance, role, tasks of computer and technical expertise in the investigation of embezzlement dedicated through the use of information technology. The importance of implementing international conventions and the positive experience of foreign countries in combating the embezzlement of funds committed through the use of information technology.
Projection of Turn Completion in Incremental Spoken Dialogue Systems

The ability to take turns in a fluent way (i.e., without long response delays or frequent interruptions) is a fundamental aspect of any spoken dialog system. However, practical speech recognition services typically induce a long response delay, as it takes time before the processing of the user’s utterance is complete. There is a considerable amount of research indicating that humans achieve fast response times by projecting what the interlocutor will say and estimating upcoming turn completions. In this work, we implement this mechanism in an incremental spoken dialog system, by using a language model that generates possible futures to project upcoming completion points. In theory, this could make the system more responsive, while still having access to semantic information not yet processed by the speech recognizer. We conduct a small study which indicates that this is a viable approach for practical dialog systems, and that this is a promising direction for future research.
PERANCANGAN SISTEM INFORMASI PENGREKRUTAN ANGGOTA BEM BERBASIS WEB DI INSTITUT BISNIS MUHAMMADIYAH BEKASI

The development of information technology today requires us to continue to adaptand develop. This has become an encouragement in an administration, namely theBekasi Muhammadiyah Business Institute to improve and develop and update anyvarious information to support a better service. Recruitment of BEM Members at theIBM Bekasi campus still uses the manual method, where the recruiting committee still uses leaflets. documents in recruiting Anggota BEM. The purpose of this research is to produce an information system application based on the recruitment website at IBM Bekasi which was previously still done manually into a system in the form of a website. The method that researchers use in this study is in the form of observation, interviews, analysis, as well as system design and development and system testing, system implementation
Random Probabilistic Circuits

Density estimation could be viewed as a core component in machine learning, since a good estimator could be used to solve many tasks such as classification, regression, and imputing missing values. The main challenge of density estimation is balancing the model expressiveness and its learning and inference complexity. Probabilistic circuits (PCs) model a probability distribution as a computational graph. By imposing specific structural properties on such models many inference tasks become tractable. However, learning PCs usually relies on greedy and time consuming procedures. In this paper we propose a new unified approach to efficiently learn PCs having several structural properties. We introduce extremely randomized PCs (XPCs), PCs with a random structure. We show their advantage on standard density estimation benchmarks when compared to other density estimators.
DPlis: Boosting Utility of Differentially Private Deep Learning via Randomized Smoothing

Abstract Deep learning techniques have achieved remarkable performance in wide-ranging tasks. However, when trained on privacy-sensitive datasets, the model parameters may expose private information in training data. Prior attempts for differentially private training, although offering rigorous privacy guarantees, lead to much lower model performance than the non-private ones. Besides, different runs of the same training algorithm produce models with large performance variance. To address these issues, we propose DPlis– Differentially Private Learning wIth Smoothing. The core idea of DPlis is to construct a smooth loss function that favors noise-resilient models lying in large flat regions of the loss landscape. We provide theoretical justification for the utility improvements of DPlis. Extensive experiments also demonstrate that DPlis can effectively boost model quality and training stability under a given privacy budget.
RoMa at SemEval-2021 Task 7: A Transformer-based Approach for Detecting and Rating Humor and Offense

In this paper we describe the systems used by the RoMa team in the shared task on Detecting and Rating Humor and Offense (HaHackathon) at SemEval 2021.Our systems rely on data representations learned through fine-tuned neural language models.Particularly, we explore two distinct architectures.The first one is based on a Siamese Neural Network (SNN) combined with a graph-based clustering method.The SNN model is used for learning a latent space where instances of humor and non-humor can be distinguished.The clustering method is applied to build prototypes of both classes which are used for training and classifying new messages.The second one combines neural language model representations with a linear regression model which makes the final ratings.Our systems achieved the best results for humor classification using model one, whereas for offensive and humor rating the second model obtained better performance.In the case of the controversial humor prediction, the most significant improvement was achieved by a finetuning of the neural language model.In general, the results achieved are encouraging and give us a starting point for further improvements.