Reviewerly/exHarmony huggingface dataset:
Punctuation Prediction in Bangla Text

Punctuation prediction is critical as it can enhance the readability of machine-transcribed speeches or texts significantly by adding appropriate punctuation. Furthermore, systems like Automatic Speech Recognizer (ASR) produce texts that are unpunctuated, making the readability difficult for humans and also hampers the performance of various natural language processing (NLP) tasks. Such NLP related tasks have been investigated thoroughly for English; however, very limited work is done for punctuation prediction in the Bangla language. In this study, we train a bidirectional recurrent neural network (BRNN) along with Attention model with a plausibly large Bangla dataset. Afterwards, we apply extensive postprocessing techniques for predicting punctuation more accurately with the employed model. Initially, we perform experimentation with a relatively imbalanced dataset, and our model shows promising results F1=56.9 for Period) in punctuation prediction. Later, we also investigate the model’s performance using a balanced Bangla dataset to achieve higher performance scores ( F1=62.2 for Question). Thus, the goal of this study is to propose an efficient approach that can predict punctuation in Bangla texts effectively. Our study also includes investigation on how our postprocessing techniques affect the prediction performance. Being an early attempt for the punctuation prediction in Bangla text, our work is expected to significantly contribute in the NLP field for the Bangla language, and will pave the way for future work with the Bangla language in this direction.
Virtual Reality Training Simulation for Controlled Land Unmanned Vehicle: A Feasibility Model

Today, the use of technology is indispensable in daily life, being this an important part to facilitate to the greatest extent possible the activities carried out by human beings as an example of the scope that technology has and the impact it generates in our lives. Having its origins in the last century, virtual reality has become one of the most sophisticated systems with different applications in different environments. This chapter presents an overview of a virtual reality (VR)-based training tool for users to get qualified as pilots capable of handling real-life unmanned vehicles. This work shows some characteristics of VR systems, and the scope is to present the advantages of using virtual reality software as a training tool, promoting that virtual reality training for use of an unmanned vehicle could be as effective as real-life training.
Logic, Co-ordination and the Envelope of our Beliefs

Abstract Each of us has a story which we can think of as a set of beliefs, hopefully consistent. We make our decisions in view of our beliefs which may be probabilistic, in the general case, but simple yes or no as in this paper. Our beliefs are our envelope just as the shell of a tortoise is its envelope. Decision theory—or single agent game theory tells us when to make the best choice in a game of us against nature. But nature has no desire to further or frustrate our efforts. Nature is mysterious but not malign. Things change when there are other agents involved. Then the best thing for us to do will depend on what they do. And they will think the same. And we predict their actions in terms of what we think their beliefs are. But how do we coordinate with others whose beliefs are different? This paper addresses the issue of working together despite different beliefs.
Hybrid Hierarchical Clustering dalam Pengelompokan Daerah Rawan Bencana Tanah Longsor di Sulawesi Selatan

This study aims to describe and classify areas prone to landslides in South Sulawesi. The method used is Hybrid Hierarchical Clustering. The data used is landslide disaster data sourced from the National Disaster Management Agency (BNPB) for 2018-2020 in South Sulawesi. The variables used are the number of landslides, deaths, damaged houses, injured victims, and damaged public facilities. Grouping using the Hybrid Hierarchical Clustering method with mutual clusters using bottom-up and top-down methods. Grouping with bottom-up method produces 2 groups, top-down method produces 2 groups and 1 best mutual cluster. The ratio results in the bottom-up method is 0.84, the top-down method is 1.07 and the mutual cluster is 0.84. The grouping results obtained were 2 groups.
Log-related Coding Patterns to Conduct Postmortems of Attacks in Supervised Learning-based Projects

Adversarial attacks against supervised learning a algorithms, which necessitates the application of logging while using supervised learning algorithms in software projects. Logging enables practitioners to conduct postmortem analysis, which can be helpful to diagnose any conducted attacks. We conduct an empirical study to identify and characterize log-related coding patterns, i.e., recurring coding patterns that can be leveraged to conduct adversarial attacks and needs to be logged. A list of log-related coding patterns can guide practitioners on what to log while using supervised learning algorithms in software projects. We apply qualitative analysis on 3,004 Python files used to implement 103 supervised learning-based software projects. We identify a list of 54 log-related coding patterns that map to six attacks related to supervised learning algorithms. Using Lo g Assistant to conduct P ostmortems for Su pervised L earning ( LOPSUL ) , we quantify the frequency of the identified log-related coding patterns with 278 open-source software projects that use supervised learning. We observe log-related coding patterns to appear for 22% of the analyzed files, where training data forensics is the most frequently occurring category.
DRBR-HDR: Dual-Branch recursive band reconstruction network for HDR with large motions

Ghosting artifacts due to misaligned imaging and missing content of the moving regions are major challenges of synthesizing high dynamic range (HDR) images from multiple low-dynamic range (LDR) with different exposures in dynamic scenes. Therefore, it hopes the HDR reconstruction model can align the LDRs’ features and restore the missing content without artifacts. In the paper, a new dual-branch recursive band reconstruction network for high dynamic range (DRBR-HDR) is proposed to generate credible result in missing content regions, which not only uses global features as supplementary information to help local features from different receptive fields for efficient feature alignment but also designs a series of coarse-to-fine band representation to better repair missing areas in the process of recursion. In addition, we introduce an interactive attention mechanism for local branches to alleviate ghosting artifacts. The experimental results demonstrate that DRBR-HDR achieves state-of-the-art performance compared with that of the prevailing HDR reconstruction methods in various challenging scenes. Index Terms—inverse tone mapping, band reconstruction, global features, high dynamic range images.
Virtual Job Expo: A Practical Approach to Virtual Reality in Different Development Engines

Virtual reality (VR) is a technology that allows emulating a synthetic environment giving users the perception of being elsewhere or augmented reality that mixes the real environment with additional elements creating the illusion that the environment is a mixture of both worlds, the virtual and the real. These technologies allow people through devices to recreate sensations from visual to tactile becoming increasingly realistic, detailed, interactive, and exciting. Virtual reality has not only served for recreational purposes but has been a fundamental part in education, in research, and in the replacement of activities that at the time entailed physical effort and threaten safety and even involve high costs or simply the provided environments it was impossible in a real environment to be present in each of them. One of the targets that this work promotes is the use of virtual reality into the workforce research. Actually, employers search for candidates who have evolved the use of new technologies to select and hire human capital. At this work, a review of two platforms that uses VR (virtual reality) in job expos is exposed, with the aim of showing its usefulness as a means of virtual rapprochement between employers and job seekers
Improving multispectral pedestrian detection with scale‐aware permutation attention and adjacent feature aggregation

Abstract High quality feature fusion module is one of the key components for multispectral pedestrian detection system in challenging situations, such as large‐scale variance and occlusion. Although attention mechanism is one of the most effective ways for feature refining, the correlation between attention and scales in feature pyramid still remains unknown. Therefore, a scale‐aware permutated attention module is proposed to enhance features of objects with different scales adaptively in the feature pyramid. Specifically, four different local and global attention sub‐modules are investigated to refine feature maps with different permutations in the Feature Pyramid Networks, improving the quality of the feature fusion. Besides, to address the high miss‐rate issue for small‐sized pedestrians, an adjacent‐branch feature aggregation module is proposed to aggregate features across different scales, taking both semantic context and spatial resolution into consideration. Both modules can benefit from each other with significant performance improvement in terms of efficiency and accuracy, when equipped with the dual‐branch CenterNet detection framework. Experiments on the KAIST and FLIR datasets demonstrate its superior performance compared with other state‐of‐the‐arts.
Multi‐armed bandit heterogeneous ensemble learning for imbalanced data

Abstract One of the most widely used approaches to the class‐imbalanced issue is ensemble learning. The base classifier is trained using an unbalanced training set in the conventional ensemble learning approach. We are unable to select the best suitable resampling method or base classifier for the training set, despite the fact that researchers have examined employing resampling strategies to balance the training set. A multi‐armed bandit heterogeneous ensemble framework was developed as a solution to these issues. This framework employs the multi‐armed bandit technique to pick the best base classifier and resampling techniques to build a heterogeneous ensemble model. To obtain training sets, we first employ the bagging technique. Then, we use the instances from the out‐of‐bag set as the validation set. In general, we consider the basic classifier combination with the highest validation set score to be the best model on the bagging subset and add it to the pool of model. The classification performance of the multi‐armed bandit heterogeneous ensemble model is then assessed using 30 real‐world imbalanced data sets that were gathered from UCI, KEEL, and HDDT. The experimental results demonstrate that, under the two assessment metrics of AUC and Kappa, the proposed heterogeneous ensemble model performs competitively with other nine state‐of‐the‐art ensemble learning methods. At the same time, the findings of the experiment are confirmed by the statistical findings of the Friedman test and Holm's post‐hoc test.
Towards explainable community finding

The detection of communities of nodes is an important task in understanding the structure of networks. Multiple approaches have been developed to tackle this problem, many of which are in common usage in real-world applications, such as in public health networks. However, clear insight into the reasoning behind the community labels produced by these algorithms is rarely provided. Drawing inspiration from the machine learning literature, we aim to provide post-hoc explanations for the outputs of these algorithms using interpretable features of the network. In this paper, we propose a model-agnostic methodology that identifies a set of informative features to help explain the output of a community finding algorithm. We apply it to three well-known algorithms, though the methodology is designed to generalise to new approaches. As well as identifying important features for a post-hoc explanation system, we report on the common features found made by the different algorithms and the differences between the approaches.The online version contains supplementary material available at 10.1007/s41109-022-00515-6.
Compositional Techniques and Sound Spatialisation in Shanghai-Based Electronic Music since 2000

To create a bridge between young students and senior scholars, the Shanghai Conservatory invited musicians who studied abroad to teach at the Conservatory beginning in the early 2000s. The result was the return to Shanghai of prominent composers. This article examines the impact of this project on electronic music composition and teaching and suggests how these developments relate to the broader historical context of Chinese electronic music. Through an analysis of representative works, it surveys compositional techniques and approaches to sound diffusion and spatialisation among composers associated with the Shanghai Conservatory. It demonstrates how these techniques, which reflect the international training of these composers, are used to express a range of subjects from abstract and philosophical concepts to concrete aspects of nature and human action.
Feature Selection Using Games with Imperfect Information (FSGIN)

Game Theory (GT) is the study of strategic decision making. By virtue of its importance, several GT based methodologies for Feature Selection (FS) are proposed in recent times. FS problem can be abstracted as a game by considering each feature as a player and their values as their strategies. Additionally, overall goal of the game is set to classify a data instance appropriately. Most of the existing GT based FS techniques are restricted to Zero Sum Games, Non-Zero Sum Games and Cooperative Games. The classical setting of assuming that all the details of players are known to all players cannot hold in many real-world problems. When the given features are independent, they cannot be treated alike and a characteristics based uncertainty persists among the features. This uncertainty is handled by none of the game forms used in the existing methods. Unlike the mentioned game techniques, Bayesian Games (BG) address the games with imperfect information. This paper investigate the FS problem in terms of BG and proposes a novel method to select the best features. The proposed BG based FS method is a filter type FS method and it starts with identifying Principle Features (PF) and proceeds to play global pairwise Bayesian games between those PF to obtain feature scores. Later, the features are ranked using these scores. In the final stage, a forward selection method with Support Vector Machine (SVM) is used to evaluate the classification performance of the ranked features and helps in the selection of the optimal set of features. Besides these, to improve the scalability of the proposed method MapReduce paradigm is exploited. In order to show the efficacy of the proposed method, experiments are carried out with seven real-world datasets from UCI and Statlog repositories. The promising results showed a significant improvement in the classification performance with fewer selected features than which is achieved using the existing methods.
HS-Gen: a hypersphere-constrained generation mechanism to improve synthetic minority oversampling for imbalanced classification

Abstract Mitigating the impact of class-imbalance data on classifiers is a challenging task in machine learning. SMOTE is a well-known method to tackle this task by modifying class distribution and generating synthetic instances. However, most of the SMOTE-based methods focus on the phase of data selection, while few consider the phase of data generation. This paper proposes a hypersphere-constrained generation mechanism (HS-Gen) to improve synthetic minority oversampling. Unlike linear interpolation commonly used in SMOTE-based methods, HS-Gen generates a minority instance in a hypersphere rather than on a straight line. This mechanism expands the distribution range of minority instances with significant randomness and diversity. Furthermore, HS-Gen is attached with a noise prevention strategy that adaptively shrinks the hypersphere by determining whether new instances fall into the majority class region. HS-Gen can be regarded as an oversampling optimization mechanism and flexibly embedded into the SMOTE-based methods. We conduct comparative experiments by embedding HS-Gen into the original SMOTE, Borderline-SMOTE, ADASYN, k -means SMOTE, and RSMOTE. Experimental results show that the embedded versions can generate higher quality synthetic instances than the original ones. Moreover, on these oversampled datasets, the conventional classifiers (C4.5 and Adaboost) obtain significant performance improvement in terms of F 1 measure and G -mean.
NATURAL KIND SEMANTICS FOR A CLASSICAL ESSENTIALIST THEORY OF KINDS

Abstract The aim of this paper is to provide a complete Natural Kind Semantics for an Essentialist Theory of Kinds. The theory is formulated in two-sorted first order monadic modal logic with identity. The natural kind semantics is based on Rudolf Willes Theory of Concept Lattices. The semantics is then used to explain several consequences of the theory, including results about the specificity (species–genus) relations between kinds, the definitions of kinds in terms of genera and specific differences and the existence of negative kinds. First, I show under which conditions the Hierarchy principle, which has been subjected to counterexamples in the literature, holds. I also show that a different principle about the species–genus relations between kinds, namely Kant’s Law , follows from the essentialist theory. Second, I introduce two new operations for kinds and show that they can be used to provide traditional definitions of kinds in terms of genera and specific differences. Finally, I show that these operations of specific difference induce, for each kind, a uniquely specified contrary kind and a uniquely specified subcontrary kind, which can be used as semantic values for non-classical predicate negations of kind terms.
Training of IT-Specialists in Russian Universities: Statistical Analysis

The article presents the results of a statistical analysis of trends in the change in the number of students studying in IT specialties in Russian higher educational institutions, and also, based on the econometric methods of data analysis, characterizes universities with a high number and a significant proportion of students studying in information technology programs. The novelty of this study, according to the author, lies in the fact that the calculations are made on the basis of a large sample taking into account changes in its dynamic characteristics (information on the activities of 611 universities and 467 university branches for 2015/2016, 2017/2018 and 2019/2020 academic years was studied).Statistical analysis showed that in Russia only a few universities specialize in training students for IT field. So, in the 2019/2020 academic year, more than 60% of students studied in IT specialties in nine higher educational institutions alone. At the same time, IT-specialists are trained in half of the universities and in a third of the country's university branches. Over the period from 2015/2016 to 2019/2020 academic year, the number of students enrolled in IT specialties has increased in Russia by 31%.The regression analysis of the data revealed a positive relationship between the indicators of the number of students in IT specialties in universities (total number, absolute increase in the number and their share in total student body) and the following indicators: share of young professionals in the total number of faculty members; number of partner enterprises; number of technology parks and small businesses in the university; income from research and development and number of publications in Scopus. A negative relationship is observed between the number of students in IT specialties and the share of persons with academic degrees in the total number of faculty members, as well as between the share of students in IT specialties in the total number of students and the indicators of internationalization of the composition of students and teaching staff.The proposed approach made it possible, on the basis of a representative sample of Russian higher education institutions, to assess the relationship between the indicators of the number of IT students at the university and the results of its activities in the areas considered, as well as to determine the characteristics of higher education institutions that made a significant contribution to the training of IT specialists.The results of the study presented in the article may be of interest to the universities that plan to significantly increase the number of students in the coming years and improve the quality of admission of applicants for training in IT specialties.
Grammatical Errors in English Job Advertisements

There are many official job advertisement websites provided by companies that can be reached by job seekers. Many of them are available in English. The researcher aimed to identify the types of grammatical errors and the frequencies and percentages of each type of grammatical errors. The research method of this research was qualitative descriptive. The data of the research were taken from fifteen JS document publication job advertisement texts. Seven steps of Creswell data analysis were used in analyzing errors which included: organizing and preparing the data analysis, reading all the data, starting coding all of the data, using the coding process to generate a description of the setting as well as categories for analysis, quantifying the errors, interpreting the description, and drawing a conclusion. It was found that there were 250 errors made by advertisement text writers. Four types oferrors were found in the data.Such errors were 91 items (36.4%) of omission errors, 20 items (8.0%) of addition errors, 138 items (55.2%) of misformation errors, and 1 item (0.4%) of misordering errors.
A heated stack based type-2 fuzzy multi-objective optimisation system for telecommunications capacity planning

In this paper, we present the Heated Stack Algorithm (HS) which is a population based multi-objective evolutionary algorithm with temperature based on type-2 fuzzy logic meta-heuristic. Temperature plays a vital role in HS being used for two distinct procedures; Sorting and Crossover. In sorting, temperature is combined with the niche distance to determine the rank order of a population front. In crossover, the temperature of two population members are compared to determine the quantity of information to take from each parent. HS is a new optimisation algorithm capable of solving constrained real-world problems. This paper will present the HS application to a real-world capacity planning problem involving networking infrastructure. To proof the algorithm applicability to wider set of problems, we will report the HS results over a subset of the constrained multi objective problems used for optimisation competitions by the IEEE Congress on Evolutionary Computation (CEEC). In these problems we have compared to the popular NSGA-II and its successor NSGA-III. By use of the hyper-volume indicator, we find that the HS outperforms NSGA-II in 84% of cases, and outperforms NSGA-III in 69% of the cases.