Reviewerly/exHarmony huggingface dataset:
Comparison of Face Embedding Approach Versus CNN-Based Image Classification Approach for Human Race Detection from Face

Human race detection using face with deep learning technique is an active research area.Wadibhasme, Rupesh It helps expand growing areas like human–computer interface, understanding user demographics.Nandi, Amit It provides great insight into a better understanding of demographics and diversity among the population.Wadibhasme, Bhavesh Understanding ethnic diversification among user base can help many commercial applications to improve and optimize their products and services better suited for the community needs.Sawarkar, Sandip Development around race detection is already an active area of research and improving its performance with speed is one of them. In this study, we compared FaceNet architecture-based features extraction technique to detect the race and compared with plain CNN-based classification techniques. Comparative results support the claim that race detection problem is better handled by embedding-based approach than a plain image classification approach. Embedding-based techniques also provide a competitive edge over other methods used in this comparative study.
Genetic Programming for Evolving Similarity Functions Tailored to Clustering Algorithms

Clustering is the process of grouping related instances of unlabelled data into distinct subsets called clusters. While there are many different clustering methods available, almost all of them use simple distance-based (dis)similarity functions such as Euclidean Distance. However, these and most other predefined dissimilarity functions can be rather inflexible by considering each feature equally and not properly capturing feature interactions in the data. Genetic Programming is an evolutionary computation approach that evolves programs in an iterative process that naturally lends itself to the evolution of functions. This paper introduces a novel framework to automatically evolve dissimilarity measures for a provided clustering dataset and algorithm. The results show that the evolved functions create clusters exhibiting high measures of cluster quality.
Queuing Parallel Computing CAD Tasks in the Design and Optimization of IC Topography

In recent years, the development of personal computers hardware has been aimed at increasing the number of processor cores. At the same time, the efficiency and reliability of computer interconnecting networks is increased. This enables the introduction and development of parallel and distributed processing methods also in CAD systems. The paper presents the problems related to the parallelization of the computational process and the methods of solving them, based on the example of typical computational experiments used in the design and optimization of integrated circuits topography.
Research on Electronic Forensics System of Digital Copier

The leakage cases of digital photocopiers caused by improper use and supervision bring huge challenge to our country information security. In order to solve the technical problem in the process of copier data forensics, a digital copier electronic forensics system is proposed based on the theory and application of data recovery and electronic data forensics technology. We study the key technologies of data format analysis and information recovery in the data storage of photocopier. The development of electronic forensics system of digital duplicator is realized by using data management technology and .NET development platform, the function of electronic data recovery and forensics of digital copier is realized. Through the experimental analysis of the digital photocopier electronic forensics system, the function feasibility of the system is verified. The digital copier electronic forensics system is applied to the actual inspection and forensics, which has achieved good results in various aspects such as operation convenience and recovery ability.
The Interference of the Bahasa Melayu Sarawak in the English Language Written Production

Malaysia is a unique country that comprises a variety of races, ethnicities, nativelanguages as well as dialects used in daily lives. However, due to the differences among thedialects used by different ethnicities and the target language, the interference of learners’ firstlanguage is one of the major factors that affect the English Language acquisition process. InSarawak, the rampant use of the Bahasa Melayu Sarawak dialect as a means of communicationamongst the locals posed an interference on the pupils’ English Language written skillsespecially. Therefore, the purpose of this study is to identify the interference of the BahasaMelayu Sarawak in pupils’ written production focusing on grammatical and lexical aspects. SixYear 5 pupils in an urban school were purposively selected for this study. A qualitative casestudy with the use of observation and document as the data collection tools were employed.The data that were collected from a checklist were analysed based on the linguistic taxonomyof errors while data collected from documents were analysed based on the ClassroomLanguage Interaction checklist. The findings show that there is a significant interference of theBahasa Melayu Sarawak based on the numerous errors made by the pupils grammatically andlexically. In conclusion, it is recommended that English teachers are made aware of theseinterferences so that appropriate measures and interventions could be taken to reduce theinterference of the Bahasa Melayu Sarawak in the English Language written work.
A Method for Recognizing Feature Regions of Object Model Based on Instance Segmentation

The instance segmentation technology for the feature region of the object model has the problem of slow network model convergence and general recognition accuracy. It is particularly important to enhance the accuracy of the local feature recognition of the object model by improving the instance segmentation network. Carry out all-round data enhancement operations for the data set model, and propose a new instance segmentation network model based on the maskrcnn architecture improvement, which includes deepening the feature extraction architecture, using the nonlinear activation function mish, and the loss function regression in the target detection The use of ciou in, and the use of the training optimizer ranger, improve the accuracy of the recognition of the local features of the two-dimensional object model, and perform the instance segmentation of the feature region. The data set verification confirms the convergence performance and model accuracy of the new instance segmentation architecture.
The Architecture of Autonomic Cloud Resource Management

Industry 4.0 means the fourth revolution in the field of industry or fourth revolution which is mainly used for automation M to M, H to M communication, digitization, and exchange of data in different technology; this is very beneficial for cloud computing automatic technologies and IOT. Industry 4.0 is not only handling the internal operation of IOT devices, but with the help of cloud computing technology environment, a huge amount of data is stored in a centralized manner. They allow other smaller enterprises to access their centralized data or technology who would not be able to build their own data set. In the context of automatic computing in cloud resource management, the allocation of on-demand resource is one of the major utilizations in the field computing to maintain the computing cost and Qos (Quality of service). To minimize the cost of hosting node cloud resource management, Industry 4.0 is allowing users to access many services on an on-demand basis, but the only restriction is that this service is based on pay-per-use means if the user wants to use these services, but they have to pay something as these resources are not free to use. Now, this chapter focuses on some important challenges like SLA Violation to find the faulty node with the help of fault management mechanism, etc. All things are clearly explained with the help of flow diagram.
A framework for investigating search engines' stemming mechanisms: A case study on Bing

Summary Big data attracts the attention of governments and a lot of companies today. The developments in technology and the Internet make it one of the important sources of big data. It is easy to get lost in the enormous amount of information contained on the Internet if there were no search engines. Knowing how the search engines work will be helpful to access the desired information. This work aims to be a guide for accessing the right information and also to help to understand search engine stemming and indexing algorithm for interested parties. In this article, we have developed a framework that could be used to investigate the stemming mechanisms of search engines. Our framework also uses Word2vec to analyze semantic relations. We have used our framework to investigate the stemming algorithm of the search engine Bing for English language. In order to achieve that we have used this framework to select words, create queries, send them to Bing, and finally analyze the millions of returned results. We have discussed the results in the context of our article. The results indicate that our framework is useful for analyzing the stemming mechanisms of search engines.
WITHDRAWN: ‘Writing across’ as a mode of research

The Publisher regrets that this article is an accidental duplication of an article that has already been published, https://doi.org/10.1016/j.lcsi.2019.02.007 The duplicate article has therefore been withdrawn. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/our-business/policies/article-withdrawal.
Workflow Scheduling in Clouds using Pareto Dominance for Makespan, Cost and Energy

Cloud computing is a suitable computing paradigm that delivers scalable resources for big data sciences to support ever-increasing scientific applications. However, it has become more challenging with a massive and heterogeneous pool of resources, which calls for an efficient and effective resource provisioning strategy to decide the number and type of resources in order to meet certain quality of service (QoS) requirements. Moreover, when to lease or release the resources in clouds is also crucial for maximizing overall resource utilization, reducing energy consumption as well as monetary cost. In this work, we propose a novel heuristic algorithm based on genetic operators and non-dominance sorting, named Pareto Dominance for Makespan, Cost and Energy (PDMCE), to efficiently assign scientific workflows with intricate inter-task dependencies to cloud resources to reduce total execution time, monetary cost, and energy consumption. In our experiments, PDMCE demonstrates the best performance with a smaller computational time among all three algorithms in comparison.
Evolutionary Inherited Neuromodulated Neurocontrollers with Objective Weighted Ranking

In the physical world, individuals compete against others within their own population or separately evolving populations. Robotic agents will soon face this coevolutionary and adversarial reality. Many challenges not encountered in the evolution of single populations are encountered in adversarial coevolution. These challenges can render single evolutionary approaches either less effective or ineffective. Most problems have a fundamental goal, but also feature secondary desirable objectives. Here, evader agents in the pursuit-evasion game that do not elude capture are effectively useless. Secondly, when applied to evolve real robots online in a coevolutionary context, non-optimal robots can be erratic and cause damage. Objective hierarchy can be used to define the importance of each objective, and promote quicker optimization of primary objectives such as evasion. The Evolutionary Inherited Neuromodulated Neurocontroller (EINN) method incorporates objective weighted ranking (OWR), a novel objective hierarchy method that promotes optimization of the primary objective in simultaneous multi-objective optimization. EINN is compared to the previously demonstrated Lamarckian-inherited Neuromodulated MultiObjective Evolutionary Neurocontroller (LNMOEN), and shown to be effective in a single evolutionary context.
Electronic Medical Community: What Rules, What Specifications?

The aim of the review is to summarize our knowledge in digital medicine today and present a structural scheme with different perspectives for its development. The electronic medical community is forming spontaneously due to technological progress, the present epidemiological status of the community and tendency for electronic cooperation.
Personal Data, Non-personal Data, Anonymised Data, Pseudonymised Data, De-identified Data

The primary scope of application of the General Data Protection Regulation—Regulation (UE) 2016/679 (GDPR)—is ‘personal data’; ‘data’ that is not personal data can be freely processed within the legal framework of the Regulation (UE) 2018/1807. Although the European data protection framework recognises these two categories of data—‘personal data’ and ‘non-personal data’—reality reveals ‘a lot in between’ the opposite endpoints. There are accordingly considerable complications in drawing the boundaries between personal and non-personal data. In this article, we will review some of the main issues related to the usual classification of data as personal, anonymous, pseudonymous, de-identified data, and suggest that the most realistic way to approach the different problems is to recognise the dynamic nature of the data.
Identify, analyze and report networking issues in streaming services

End-user streaming services have grown exponentially in the past years. The mechanisms to serve the enormous amounts of content available have seen a lot of improvements in reliability, flexibility, and overall quality. However, a plaguing problem is related to the last part of the delivery infrastructure: home networks. These networks are managed in a myriad of ways. Poor configuration and misuse reflect directly in the quality of the video streaming experience. Detecting home network issues and appropriately reporting them is key in providing the right solution when end-users report problems with the streaming services. — We propose a microservices led system that includes a client-side service that collects data and key performance indicators about the home network. The cloud-enabled microservices collect, store, analyze and correlate such data for enabling preemptive or on-demand actions. We propose that the client-side library is available on a variety of platforms for wider acceptance. The cloud-side of the system is proposed to store the telemetry data securely. The analytics systems perform real-time analysis and correlation between various events to create predictions to identify specific issues, or on a per-user basis, or based on a specific network configuration type. — As an outcome of the analytics and predictions, various dashboards and alarms are generated to allow visual representation of the system or tenant or user status and also enable setting up alarms and notifications that can be integrated with other systems that can address them and take actions.
A Method of Polyphone Disambiguation Based on Semantic Extension

In text-to-speech (TTS) system, polyphone disambiguation is the significant and difficult point in the process of grapheme-to-phoneme(G2P) conversion. In the existing methods, polyphone disambiguation mainly uses statistics or context information. When the data to be predicted is short texts or spoken language, it is difficult for the previous models to distinguish the correct pronunciation due to insufficient information. Therefore, this paper intends to report an improved design for polyphone disambiguation. Semantic extension has been used through the method of feature fusion, we creatively encode the context feature, word meaning feature, part of speech feature and pronunciation feature of polyphone, and construct three kinds of polyphone disambiguation models using bidirectional LSTM neural network. It is shown in the experiment that the accuracy of the polyphone disambiguation model based on word meaning can be improved by about 7% compared with the traditional methods.
ECNet: Edge-aware Context-aggregation Network for Transparent and Reflective Object Detection

Highly transparent and reflective objects are prevalent in life, yet their existence severely degrades the performance of existing object detectors. In this paper, we propose a novel Edge-aware Context-aggregation Network (ECNet) for transparent and reflective object detection, with three well-designed modules introduced to enhance the ability of extracting distinctive features. Specially, we develop the Edge Detection Module (EDM) to make the network pay more attention to the boundary areas; present the Depth Feature Fusion Module (DFFM) to extract content discontinuity in depth maps; and introduce the Multi-respective-field Feature Extraction Module (MFEM) which fusing multi-receptive-field features to reveal the texture discontinuity. Extensive experiments show the proposed ECNet surpasses the state-of-the-art detectors by at least 7.4% mAP in transparent and reflective object detection, which well demonstrates the effectiveness of the proposed method.
Phygital Customer Experience Mixed Approach of Augmented Reality and Customer Experience (DCX) in the Context of Heritage Tourism

What if customer experience was the primary driver of digital transformation? Indeed, new hybrid experiences have emerged thanks to daily-life technologies that fused both digital and physical worlds. This kind of experience uses applications and connected objects (IoT) to adapt and respond to consumer needs. Augmented reality and virtual reality are one of the most functionally and emotionally memorable phygital experiences that create value for customers. In this chapter, the authors chose heritage tourism as the study field for different reasons. While the adoption of augmented and virtual reality is gaining grownd, cultural heritage sites have started to consider the possibilities offered by these new and innovative technologies. This industry is starting to integrate AR and VR in several ways to attract more people. However, it is necessary to study how modern technology can be developed and implemented in a meaningful way to improve the tourist's experience. An AR- and VR-based mobile application has been developed and tested at Antonine's Baths in Carthage, Tunisia.
AABC:ALBERT-BiLSTM-CRF Combining with Adapters

Pre-training models (PTMs) are language models pre-trained on a large corpus, which can learn general language representations through training tasks within the model. PTMs complete various NLP tasks by connecting with downstream models. PTMs can avoid building new models from the beginning. Therefore, they are widely used in the NLP field. In order to achieve the goal of completing multiple tasks with merely one model, the scale of PTM has been rising. However, the larger scale brings a larger amount of parameters, which also means more parameters to be adjusted in the future. Therefore, this article proposes a new AABC (Adapters-ALBERT-BiLSTM-CRF) model which is introduced Adapters on ALBERT. The Adapters remain unchanged during the pre-training phase and only adjusting the Adapters module can achieve the best effect during the fine-tuning. In order to verify the reduction of the model’s adjustment parameters, AABC experimented on three tasks: named entity recognition (NER), sentiment analysis (SA), and natural language inference (NLI). Tests results show that the AABC model performs better than the BERT model on classification tasks with types less than five. Also, AABC is better than the rival model in terms of tuning parameters. On the 7 datasets of SA and NLI tasks, the average parameter counts of AABC is only 2.8% of BERT-BiLSTM-CRF. Experimental results demonstrate that the proposed method a potential classification model.
E-Library Implementation Using Android Studio

In this digital era, anything can be obtained over the internet, so people don't have to deal with any inconveniences or waste most of our time. E-Library implementation using android studio is an application where all the e-textbooks will be available to the students handy. It is an android application; it makes the students easy to view the textbooks they need. Students need not go in search for books to the library or other websites. Sometimes there may be lack of books too in the library. Using this application, the student or the user should have to first login then choose their branch, and then choose the year of their course, then choose the semester and then all the subjects related to that semester will be displayed and the subject can be then selected. Once you choose the subject the e-textbook will be displayed either in pdf format or you will be redirected to the website. If the students need any other textbooks they can mention it in the feedback block. The admin can then view the reviews/feedback given by the user and upload the e-textbooks accordingly.
A variational approach for estimation of monocular depth and camera motion in autonomous driving

In this paper, a new direct computational approach to dense 3D reconstruction in autonomous driving is proposed to simultaneously estimate the depth and the camera motion for the motion stereo problem. A traditional Structure from Motion framework is utilized to establish geometric constrains for our variational model. The architecture is mainly composed of the texture constancy constraint, one-order motion smoothness constraint, a second-order depth regularize constraint and a soft constraint. The texture constancy constraint can improve the robustness against illumination changes. One-order motion smoothness constraint can reduce the noise in estimation of dense correspondence. The depth regularize constraint is used to handle inherent ambiguities and guarantee a smooth or piecewise smooth surface, and the soft constraint can provide a dense correspondence as initial estimation of the camera matrix to improve the robustness future. Compared to the traditional dense Structure from Motion approaches and popular stereo approaches, our monocular depth estimation results are more accurate and more robust. Even in contrast to the popular depth from single image networks, our variational approach still has good performance in estimation of monocular depth and camera motion.
Performing arithmetic using a neural network trained on images of digit permutation pairs

Abstract In this paper, a neural network is trained to perform simple arithmetic using images of concatenated handwritten digit pairs. A convolutional neural network was trained with images consisting of two side-by-side handwritten digits, where the image’s label is the summation of the two digits contained in the combined image. Crucially, the network was tested on permutation pairs that were not present during training in an effort to see if the network could learn the task of addition, as opposed to simply mapping images to labels. A dataset was generated for all possible permutation pairs of length 2 for the digits 0–9 using MNIST as a basis for the images, with one thousand samples generated for each permutation pair. For testing the network, samples generated from previously unseen permutation pairs were fed into the trained network, and its predictions measured. Results were encouraging, with the network achieving an accuracy of over 90% on some permutation train/test splits. This suggests that the network learned at first digit recognition, and subsequently the further task of addition based on the two recognised digits. As far as the authors are aware, no previous work has concentrated on learning a mathematical operation in this way. This paper is an attempt to demonstrate that a network can learn more than a direct mapping from image to label, but is learning to analyse two separate regions of an image and combining what was recognised to produce the final output label.