Reviewerly/exHarmony huggingface dataset:
Ecological media perspective on WhatsApp status features

The presence of the internet today makes it easy for users to carry out communication activities without limitation of distance and time. Even instant messaging such as WhatsApp, which has the primary function as a medium for communication, can be a mediator for self-existence and cannot be separated from its users to share daily activities through the WhatsApp Status feature. The purpose of this study was to analyze the Status feature on Instant Messaging WhatsApp as a mediator to show self-existence without distance and time limits based on media ecology theory. The method in this study is a qualitative method with purposive data collection techniques. Researchers also conducted interviews with seven informants who were students and workers. This study's results indicate that the presence of this feature affects various actions, perceptions, and communication experiences in society. Ecological media has the perspective that electronic media has radically changed society. In essence, society is very dependent on technology that uses media, and that the social order of a society is based on its ability to deal with these technologies. Media, in general, act directly to form and organize culture. Apart from that, ecological media also has assumptions about how technology affects society, include the distinction between hot and cold media.
Advancing NeuroIS from a Dialectical Perspective

In spite of decades of research, the essence of central IS phenomena such as information, the IT artifact, and the Information System, remain unsettled. This aggravates the identification and definition of their neural correlates, which in turn may stymie the future potential of NeuroIS. To this end, the purpose of this paper is to define an intellectual core for the NeuroIS field, which is based on the dialectics between the individual and her social environment. Such a core enables a reconceptualization of information, the IT artifact, and the Information System as distinct ontological phenomena, however dialectically related. Accordingly, their neural correlates can be more distinctly defined, which opens up for alternative research questions. An example of a NeuroIS study addressing such questions is provided. Thus, the paper contributes to the formulation of a foundation for the future advancement of NeuroIS.
Interhost Orchestration Platform Architecture for Ultrascale Cloud Applications

Cloud data centers exploit many memory page management techniques that reduce the total memory utilization and access time. Mainly these techniques are applied to a hypervisor in a single host (intra-hypervisor) without the possibility to exploit the knowledge obtained by a group of hosts (clusters). We introduce a novel interhypervisor orchestration platform to provide intelligent memory page management for horizontal scaling. It will use the performance behavior of faster virtual machines to activate prefetching mechanisms that reduce the number of page faults. The overall platform consists of five modules-profiler, collector, classifier, predictor, and prefetcher. We developed and deployed a prototype of the platform, which comprises the first three modules. The evaluation shows that data collection is feasible in real-time, which means that if our approach is used on top of the existing memory page management techniques, it can significantly lower the miss rate that initiates page faults.
Improving Real-time Recognition of Morphologically Rich Speech with Transformer Language Model

Transformer models have become to state-of-the-art in natural language understanding, their use for language modeling in Automatic Speech Recognition (ASR) is also promising. Albeit Transformer based language models were shown to improve ASR performance, their computational complexity makes their application in real-time systems quite challenging. It has been also shown that the knowledge of such language models can be transferred to traditional n-gram models, suitable for real-time decoding. This paper investigates the adaptation of this transfer approach to morphologically rich languages, and in a real time scenario. We propose a new method for subword-based neural text augmentation with a Transformer language model, which consists in retokenizing the training corpus into subwords, based on a statistical data-driven approach. We demonstrate that ASR performance can be augmented by yet reducing the vocabulary size and alleviating memory consumption.
A random process approach to study the performance of engineering students in professional institutions

In this paper, an analysis of the performance of Engineering students of Professional College in Coimbatore District has been done using the concept of Absorbing Markov Chain. The study has been carried out to assess various performance attributes of the learners of engineering stream. The outcomes of four programmes offered by an Engineering College for eight semesters in four years have been studied and it has been attempted to arrive out the mean value of the time used-up by the students during a semester, total semesters taken to get the graduation on completion and the probability of a student getting graduated from the institution or withdrawn from the course.
Speeding up the data extraction of machine learning approaches: a distributed framework

In the last decade, mining software repositories (MSR) has become one of the most important sources to feed machine learning models. Especially open-source projects on platforms like GitHub are providing a tremendous amount of data and make them easily accessible. Nevertheless, there is still a lack of standardized pipelines to extract data in an automated and fast way. Even though several frameworks and tools exist which can fulfill specific tasks or parts of the data extraction process, none of them allow neither building an automated mining pipeline nor the possibility for full parallelization. As a consequence, researchers interested in using mining software repositories to feed machine learning models are often forced to re-implement commonly used tasks leading to additional development time and libraries may not be integrated optimally.
The Task and Workslip Scheme for Personal Agent Systems

The Task and Workslip Scheme provides ways for people to automate tasks in their daily lives with personal systems. These systems should provide people with the ability to automate tasks in their lives, but most current approaches require technical expertise or reliance on outside organizations that may not have people's best interests at heart. The scheme provides philosophical guidelines describing how these systems should be constructed to ensure the autonomy and privacy of the people using them. The scheme also provides definitions for tasks, work-slips, agents and personal agent systems. A Situationally Aware Alarm Clock, Responsive Shopping List Manager and Proactive System Administrator Assistant are presented to demonstrate a reference implementation of the scheme and illustrate the benefits of building personal agent systems this way.
Email User Classification and Topic Modeling

Nowadays, the primary form of communication in most organizations is via email. Thousands of users communicate daily via email. And with an increasing amount of emails comes the fraudulent activities. It is necessary to identify the righteous user who wrote the email in any organization to avoid misuse of the mail server. Also, from a pool of emails, it can be helpful to filter out emails that belong to keywords of interest to focus on a certain topic and find out relevant users during any investigation or research. This paper proposes a method to analyze the content of the email and converts the textual data into vectorized features to predict the author of the email based on writing patterns of different users using Deep Learning methods. Along with that, this paper also leverages existing methods such as DBSCAN and LDA along with their performance to cluster similar emails with each other for the extraction of topics and keywords from the contents of emails. These techniques can serve as a beneficiary tool for any investigating party to learn important features and extract maximum information from the email corpus.
Broad Learning System Based on Nonlinear Transformation and Feedback Adjustment

Broad Learning System has been used in many applications. For example, face recognition, image classification and segmentation, time series prediction. A broad learning system algorithm based on nonlinear transformation and feedback adjustment proposed to improve the accuracy of the traditional broad learning system model. This paper analyzes the impact of data on the model from the perspective of probability statistics and feature mapping, and finds the best nonlinear mapping function from the angle of data tilt to accurate data sets. In terms of the accuracy of model training, fine-tuning the broad learning system in the form of a feedback model, set the appropriate number of fine-tuning and fine-tuning rates to improve the accuracy of the model training; In addition, combined with nonlinear transformation and feedback adjustment model, new algorithms and corresponding diagrams are given. In this paper, weather data sets are used to prove the rationality and effectiveness of the algorithm framework.
Formation of artistic and aesthetic needs in the modernization of Russian education: analytical review

The article provides an analytical review of scientific literature on the problem of the formation of artistic and aesthetic needs of the individual, presents various approaches, theses of scientists to solve this problem. The author formulates his vision of the formation of artistic and aesthetic needs as one of the priority directions in the modernization of Russian education.
Stabilization of Dataset Matrix Form for Classification Dataset Generation and Algorithm Selection

Datasets for the classification task are usually encoded by a matrix of numbers, the order of rows and columns does not matter. Swapping any two objects or features in it does not change the hidden target function and performance of the machine learning algorithms train of the dataset. However, in the dataset generation problem solution such symmetry is an obstacle. In this paper, we study several methods of the inverse transformation of classification dataset aiming to break the symmetry. We experimented with it in the meta-learning problems of datasets generation and algorithm selection which were solved by conditional generative adversarial nets with convolutional networks.
Facilitating the Identification of the Nannofossil Species in Cretaceous of Abu Dhabi Using Artificial Intelligence

Abstract Reliable chrono-stratigraphic correlation is crucial in the reservoir evaluation. Lithostratigraphic correlation is often inconsistent with depositional age due to high heterogeneity in lithology. Geological age determination based on nannofossils in Middle East gives a better control for the stratigraphic correlation, however it requires skilled experts and time for appraisal. This contribution explores if today's emerging AI technology provides a solution, by facilitating the identification of the nannofossil species on smear slide images. The result of our exercises shows that a deep-learning-based object detector reasonably and quickly identifies nannofossil species on micrographs and can deliver information useful for the geological age assessment. We find that separating detection and classification procedures improves the performance of the object detector. Further data collection and model tuning of the object detector are needed for the improvement of the object detection accuracy, especially for nannofossil species rarely observable.
Overview of Mac system security and its impact on digital forensics process

Nowadays there are 3 main operating systems used, and Mac OS is one of them. Until now Apple published many iterations of their operating system and with that introduced many new features that are related to system security. Even though security-related changes go unnoticed, in the world of digital forensics this presents a challenge. Today encryption can be implemented on both, hardware and software level, which can make imaging Mac OS difficult. Besides, security which is meant to protect, user data is also used by criminals to restrict access to their computers. This paper will focus on the differences and problems that occur while creating a forensic image and extracting data from Mac OS. On top of that this paper will depict the impact devices equipped with “T1” or “T2” security chip have on digital forensic process and remediation methods.
Convolutional Neural Network Based Symbol Detector for Two-Dimensional Magnetic Recording

Conventional detection systems in hard disk drives (HDD) typically include a 2D partial response (PR) equalizer that pre-processes the readback signals and shapes the output to a controlled target response, followed by a maximum likelihood (ML) or maximum a posteriori (MAP) detector which outputs log-likelihood ratios (LLRs) to be passed to a channel decoder. Pattern dependent noise prediction (PDNP) algorithm [1] is usually incorporated into the metric computation of the trellis in the ML/MAP detector to combat media noise intrinsic to the magnetic recording (MR) channel. For next generation two-dimensional magnetic recording (TDMR) HDDs, such conventional systems would suffer from impractically large trellis state cardinality when performing multi-track detection, and they may no longer be capable of handling the increased nonlinearities in high density recording channels. This work investigates applying advanced machine learning techniques to TDMR. Convolutional neural networks (ConvNets) are employed in place of the PR equalizer and ML/MAP detector with PDNP to directly process the un-equalized readback signals and output soft estimates. ConvNets are special deep neural networks (DNNs) that assume the inputs are images and perform convolution instead of affine function in the network forward pass [2]. This enables far fewer parameters in ConvNets than regular DNNs of the same depth and therefore allows for deeper networks. The motivation to use ConvNets is the resemblance between data detection problem in MR and typical image processing problems. In MR channels, the write process converts temporal data into spatial patterns recorded on a magnetic medium, which transforms sequential correlation into spatial ISI/ITI. Data detection can be viewed as an image processing problem, proceeding from the 2D image of the shingled bits (see Fig. 1), to higher level abstractions of features by means of convolutional layers that finally allow classification of individual bits. Several variations of ConvNets are compared in terms of network complexity and performance. The best performing ConvNet detector can provide data storage density of up to 3.7489 Terabits/in <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> on low track pitch TDMR channel simulated with a grain flipping probabilistic (GFP) model.
Empowering the psychosocially vulnerable in a co-creation process with user need cards

This industry case study presents how a customized set of design techniques was used for empowering people with psychological disorders and intellectual disabilities in a co-creation process. The project focused on re-designing a service system and included mapping the requirements for digital services to support the new service model. Including psychosocially vulnerable people into a co-creation process to work with other stakeholders is a challenge for many design teams. We present how a set of user need cards in combination with personas and user journey mapping enabled us to amplify the voices of the vulnerable target group wo could not actively participate in the workshops. Furthermore, the cards enabled to foster empathy between the different stakeholders, helped to communicate more nuances from the field study and mitigated the group dynamics in a setting where policy makers, service providers and psychosocially vulnerable service users needed to co-create a new model for a service system.
Domain Adaptation of Transformers for English Word Segmentation

Word segmentation can contribute to improve the results of natural language processing tasks on several problem domains, including social media sentiment analysis, source code summarization and neural machine translation. Taking the English language as a case study, we fine-tune a Transformer architecture which has been trained through the Pre-trained Distillation (PD) algorithm, while comparing it to previous experiments with recurrent neural networks. We organize datasets and resources from multiple application domains under a unified format, and demonstrate that our proposed architecture has competitive performance and superior cross-domain generalization in comparison with previous approaches for word segmentation in Western languages.
Towards More Robust Detection for Small and Densely Arranged Ships in SAR Image

Independent of sunlight and weather conditions, synthetic aperture radar (SAR) imagery is widely applied to detect ships in marine surveillance. This paper proposes a ship target detection algorithm, aiming at the missing and false detection in the situation of dense ship targets in SAR images. Firstly, we used the spatial pyramid pooling (SPP) to enhance the feature extraction capability in different scales. Then, we modified the regression loss function with three factors of center distance, overlap area and length-width ratio to reduce the error of location. Finally, we proposed double threshold soft non maximum suppression (DTSOFT-NMS) to reduce the missing detections for dense ships. The experimental results reveal that our model exhibits excellent performance on the open SAR-ship-dataset and improves average precision (AP) by 6.5% compared with the baseline YOLOv3 model.
A Survey on Clustering Algorithms Based on Bioinspired Optimization Techniques

In this growing informative world, in every domain, we can get a large amount of raw data, so it is a huge task to find proper and valid information from it. For this task, it is required to categorize data into different groups of similar behaviors. Over the years many authors have provided different techniques for clustering. Again, environment, domain, and applications are changing rapidly in different organizations, keeping this in mind many researchers are still modifying or developing new clustering algorithms. Now it is important to select or develop a proper clustering algorithm suitable for us. In this paper, we have tried to present some recent clustering algorithms. We have mainly focused on bioinspired optimization algorithms for the clustering problem. Later in the paper, we have also given a comparative study. This can help in selecting the proper clustering algorithm for the required domain.
An Analysis of K-Means, Particle Swarm Optimization and Genetic Algorithm with Data Clustering Technique

Data clustering is a popular data analysis approach for organizing similar objects without needing to be monitored - a collection of data, into meaningful groups (or clusters). Many approaches from different disciplines have been proposed as solutions for clustering problems and each algorithm has its advantages and drawbacks. This research paper offered a brief overview and performance comparison analysis between different existing data mining clustering strategies based on Particle Swarm Optimization, Genetic Algorithm, K-means and Hybrid PSO Solutions. The reasoning behind choosing these algorithms is that PSO has, due to the applicability of its advanced hybrid variants, reached a remarkable position in this field, and the Genetic Algorithm based unsupervised clustering technique provides a stable clustering performance with less computational time required. However, we found that Hybrid PSO solutions have been shown to produce excellent results in a wide variety of real-world data. In this paper, a brief review of these algorithms applied to data clustering has been described.
Applicational Status Analysis of Artificial Intelligence Technology in Middle School Education and Teaching

With the development of the times, the progress of society, the continuous improvement of human science and technology and cultural level, the traditional middle school education and teaching has been unable to meet the learning needs of middle school students in the new era. In order to better make secondary education and teaching conform to the current trend of development, this paper puts forward the application of artificial intelligence technology in middle school education and teaching, in order to improve the teaching level of middle school education. Intelligent teaching in the field of artificial intelligence technology is a new product under the development of the current era, and has a high status in middle school education and teaching. Therefore, this paper analyzes the necessity of carrying out intelligent teaching in the field of artificial intelligence technology in middle school education and teaching, and systematically expounds the shortcomings in current middle school education and teaching, and makes a deep analysis on the reform needed in the future middle school education and teaching. Through the research and analysis, it is found that the artificial intelligence technology proposed in this paper has a great effect on the innovation and reform of middle school education and teaching.
An Evidential Reasoning Framework for User Profiling Using Short Texts

Short texts-based user profiling is a challenging task despite its benefits to many applications such as job offering, item recommendation, and targeted advertisement. This work proposes an evidential reasoning framework for user profiling problem, which is capable of not only working with short texts but also dealing with uncertainty inherently in user corpora. The framework consists of three phases: representation learning, inference and combination, and user profile extraction. Particularly, in the first phase a word embedding technique is used to convert words into semantic vectors, then k-means clustering is utilized for learning abstract concepts that hide inside user plain texts, and then organizing the learned concepts into a hierarchical structure. In the second phase, by considering each document in user corpus as an evidential source that partially contributing to user profiles, and taking the concept hierarchy into account, we infer a mass function associated with each user document by maximum a posterior estimation, then apply Dempster’s rule of combination for fusing all documents’ mass functions into an overall one for the user corpus. In the third phase, we apply the so-called pignistic probability principle to extract top-n keywords from user’s overall mass function to define the user profile. Thanks to the ability of combining pieces of information from many documents, the proposed framework is flexible enough to be scaled when input data coming from not only multiple modes but different sources on web environments. Empirical results on datasets crawled from Twitter and Facebook validate the effectiveness of our proposed framework.
Motion Time Study with Convolutional Neural Network

Manufacturing and service industries use motion time study to determine work element time and standard time for production planning and process improvement. Traditional time study is performed by human analysts with stopwatches, and therefore, is subject to uncertainties from human errors. This study proposes an automated time study model featuring a convolutional neural network. The trained model can analyze a video footage of an operation to time the work elements. The timing data are compared with reference values using a statistical analysis. The result shows effectiveness of the model in accurately and consistently estimating the work element times and standard time of the operation.