Reviewerly/exHarmony huggingface dataset:
The intellectual potential of the individual: a thesaurus approach to solving the problem

A methodology for studying modern problems of the development of the intellectual potential of an individual in the context of the digital transformation of society is proposed. A distinctive feature of this methodology is that it is based on the information approach and the results of the theory of thesauri developed by professor V.A. Lukov. The signs of degradation of the intellectual potential of a person in modern society, caused by the formation of a new informational human environment, are shown. The ways and features of the development of the intellectual potential of an individual in the context of the digital transformation of Russia are considered. The recommendations for government bodies and the scientific and educational community of the country are given.
Scene Text Localization Using Lightweight Convolutional Networks

Various research initiatives have been reported regarding highly effective results for the text detection problem, which consists of detecting textual elements, such as words and phrases, in digital images. Text localization is an important step on very widely used mobile applications, for instance, on-the-go translations and recognition of text for the visually impaired. At the same time, edge computing is revolutionizing the way embedded systems are architected by moving complex processing and analysis to end devices (e.g., mobile and wearable devices). In this context, the development of lightweight networks that can be run in devices with restricted computing power and with a minimum latency as possible is essential to make plenty of mobile-oriented solutions feasible in practice. In this work, we investigate the use of efficient object detection networks to address this task, proposing the fusion of two lightweight neural network architectures, MobileNetV2 and Single Shot Detector (SSD), into our approach named MobText. As experimental results in the ICDAR’11 and ICDAR’13 datasets demonstrates that our solution yields the best trade-off between effectiveness and efficiency in terms of processing time, achieving the state-of-the-art results on the ICDAR’11 dataset with an F-measure of $$96.09\%$$ and an average processing time of 464 ms on a smartphone device, over experiments executed on both dataset images and with images captured in real time from the portable device.
Comparative Analysis of light weight practices for SPI in small and medium software organizations

Software process improvement (SPI) is typically one of the most significant areas considered by Software industry when boosting the whole efficiency of its business processes and practices. Implementing SPI helps achieve objectives such as speed growth, product quality or cost reduction and to identify shortfalls in the software development cycle and fix them in a better way. In the industry, different models for improving software processes are used including CMMI, PSP, SPICE, MSF, RUP, ISO etc. All these models consist of a very comprehensive set of practices and to follow all these practices is quite expensive for the companies. These models have significant advantages, but it is hard for small and medium businesses to use them because of their expense. However, in literature there are some lightweight practices for those software organizations that cannot afford the expensive ones. In this research, we performed a comparative study on customized software engineering process (CSEP) and lightweight practices (LWP) proposed in literature on industrial software projects. The objective is to study which of these practices are effective if performed by an organization. We also measure which of these practices give better defect rate, delivery, schedule deviation, and performance. The comparison on a software project shows that the lightweight practices have improved defect rate, delivery time, and productivity.
Classification of Personality Traits by Using Pretrained Deep Learning Models

Nowadays, personality traits analysis has become one of the important things since international companies need to hire employees and be used in education and forensic verification. In this paper, three pre-trained models of deep learning were evaluated to classify an individual's personality traits from his signature after analyzing, processing, and labeling the data and dividing it into five categories according to the Big Five factor. The analysis is based on 6600 images divided into three groups (training, testing, and validation). Data Augmentation was used to overcome the lack of data and its imbalance. Also, transfer learning was used that represented by the three models (VGG16, Inception, and ResNet50), which work on the principle of freezing the first layers and updating the last layers to take advantage of the pre-trained weights and obtain the lowest error rate. Results showed that the ResNet-50 achieved the best classification accuracy with up to 99% and the lowest error rate with 0.0304. While the InceptionV3 model outperformed VGG16 in the training phase of 99%, but in the validation phase, the VGG16 provided the Highest accuracy of 98% and the least error of 0.1090.
iNL: Implicit non-local network

The attention mechanism of computer vision represented by a non-local network improves the performance of numerous vision tasks while bringing computational burden for deployment Wang et al. (2018). In this work, we explore to release the inference computation for non-local network by decoupling the training/inference procedure. Specifically, we propose the implicit non-local network (iNL). During training, iNL models the dependency between features across long-range affinities like original non-local blocks; during inference, iNL could be reformulated as only two convolution layers but can rival non-local network. In this way, the computation complexity and the memory costs are reduced. In addition, we take a further step and extend our iNL into a more generalized form, which covers the attentions of different orders in computer vision tasks. iNL brings steady improvements on multiple benchmarks of different vision tasks including classification, detection, and instance segmentation. In the meantime, it provides a brand–new perspective to understand the attention mechanism in deep neural networks.
Degree-aware In-network Aggregation for Federated Learning with Fog Computing

Data privacy preservation has drawn much attention in emerging machine learning applications, and thus collaborative training is getting much higher such as Federated Learning (FL). However, FL requires a central server to aggregate local models trained by different users. Thus, the central server may become a crucial network bottleneck and limit scalability. To remedy this issue, a novel Fog Computing (FC)-based FL is presented to locally train the model and cooperate to accomplish in-network aggregation to prevent overwhelm the central server. Then, the paper formulates a new optimization problem termed DAT to minimize the total communication cost and maximum latency jointly. We first prove the hardness and propose two efficient algorithms, ADAT-C and ADAT, for the special and general cases, respectively. Simulation and experiment results manifest that our algorithms at least outperform 30% of communication cost compared with other heuristics without sacrificing the convergence rate.
The Blockchain-Based Edge Computing Framework for Privacy-Preserving Federated Learning

Nowadays, privacy-preserving artificial intelligence is gaining traction, with the goal of learning multiple models based on private data without leaking any personal information. Since the existing multi-party computation methods and other encryption-based methods have their flaws, we developed our own blockchain-based edge computing framework to achieve the decentralization and enhance the efficiency. Our framework enables a trustful, simplified and asynchronous federated learning in IoT and provides a convenient and secret classification service. Extensive evaluations on efficiency are provided, confirming the performance of our solutions.
Temporal Early Exits for Efficient Video Object Detection

Transferring image-based object detectors to the domain of video remains challenging under resource constraints. Previous efforts utilised feature propagation to allow unchanged features to be propagated, however, the overhead is considerable when working with very slowly changing scenes from applications such as surveillance. In this paper, we propose temporal early exits to reduce the computational complexity of video object detection. Multiple temporal early exit modules with low computational overhead are inserted at early layers of the backbone network to identify the semantic differences between consecutive frames. Full computation is only required if the frame is identified as having a semantic change to previous frames; otherwise, detection results from previous frames are reused. Experiments on ImangeNet VID and TVnet show that our method speed up SOTA video object detection pipeline by 1.7× with an acceptable reduction of > 1% in mAP.
Towards modeling a Service-Oriented application managing a Smart City using UML 2.x

Context: The evolution of citizens' life in cities in term of services and the evolution of mobile technologies lead to the rise of the concept of “Smart City”. As an effect, the need for applications and software in Smart Cities takes more and more importance. This fact leads developers to look for models and techniques to automate software production, improve quality and reduce costs and time. Objective: In this work, we aim to model a Service-Oriented application managing a “Smart City”. The idea is to provide services that ensure inhabitants comfort and streamline energy consumption. For this aim, we need to model the structural and behavioral aspects of this Service-Oriented application. Method: In the literature, different studies modeled the structural aspect of Service-Oriented applications using the UML component diagrams or Service Component Architecture diagrams (SCA). To model the behavioral aspect, some studies used Business Process Model and Notation diagrams (BPMN). In this work, we use the Unified Modeling Language (UML 2.x) which allows us to design both structural and behavioral aspects. Results: In this work, we put the focus on some services of the “Smart City”. While using UML 2.x in modeling the structural and behavioral aspects of our Service-Oriented application, we obtain four class diagrams and four activity diagrams. These diagrams illustrate the services: energy management, public lighting, air quality and waste collection. Conclusion: It is concluded, on the one hand, that the class diagrams show the structure of the “Smart City”. On the other hand, the activity diagrams show the dynamicity between the different services. Thus, UML 2.x allows us to visualise both structural and behavioral aspects of our Service-Oriented application using the same modeling semantic instead of using different modeling languages.
Investigating Cross-Lingual Transfer Learning Techniques for Urdu Text Using Word Embeddings

The plethora of online content has paved the way for the development of sophisticated and advanced Natural Language Processing (NLP) and Information Retrieval (IR) tools. However, such tools are only available for English and other high-resource languages while being unavailable for low-resource languages such as Urdu. In this regard, generally, cross-lingual transfer learning techniques are adopted to utilize tools developed for the English language for low resource languages. In this paper, we evaluate the performance of three word-level transfer learning methods: OrthoMap, VecMap-supervised, and VecMap unsupervised for Urdu text. We further test these transfer learning methods for three tasks: propaganda identification, topic classification, and sentiment analysis. For this purpose, we augment an English-Urdu word dictionary and three datasets of Ur-En Propaganda, Ur-En News Dataset, and Ur-En Sentiment Corpus. Our analysis shows that the transfer learning methods optimize better for the short-text of Ur-En Sentiment Corpus with a precision of 40.1%. While for propaganda detection, the classifier attained an accuracy of 83% after transfer learning which is competitive with the 87% accuracy achieved after training the model on Urdu text data. We believe that this work will be beneficial for NLP, IR, and computational linguistic researchers working on Urdu language content.
A Research-Teaching Guide for Visual Data Analysis in Digital Humanities

The use of visualization to underpin distant reading arguments on cultural heritage data has established in the digital humanities domain. Novel strategies to represent data visually typically arise from interdisciplinary projects involving humanities and visualization scholars. However, the quality of outcomes might be inhibited as typical challenges of interdisciplinary research arise, and, at the same time, problem solving strategies are missing. I taught a course on visual data analysis in the digital humanities to let students with diverse study backgrounds experience those challenges in their early academic careers. This paper illustrates the research-teaching components of my course. This includes the contents of the theoretical training with active learning tasks, aspects of the practical training and considerations for teachers aiming to compose a related course.
Automated Sign to Speech Conversion Model using Deep Learning

Unable to communicate verbally is a disability. In order to exchange thoughts and interact, there exist severalways. The most predominant method involves use of hand-gestures. The prime motive of the proposed researchwork is to bridge the research gap in Sign Language Recognition with maximum efficiency. The goal is to replacethe human mediator with a machine to minimize human interference. This paper focuses on the recognition of ASLin real-time. In automatic sign language translator design the challenging part lies in selecting a good classifierto classify the static input gestures with high accuracy. CNN architecture is used to design a classifier for signlanguage recognition in the proposed system. The model and the pipeline architecture is developed by keras basedconvolutional neural network to classify 27 alphabets that is 26 English language alphabets and a unique character,space. With different parameter configurations, the system has trained the classifier with different parameters andtabulated the results. The proposed study achieved an efficiency of 99.88% on the test set. The result shows thatthe model accuracy improves as more data is fetched from various subjects for training.
Virtual Tour Design In Egypt Building Darussalam Gontor Putri University

University of Darussalam Gontor already have media brochures, video profil and social media as publicity and advertising tools. But now in the area of technology 4.0, the media are rated not enough, where people need a media where they can interact directly in it. The research is aimed to build a media interaction of the campus interactive based model of three dimensions by using the technology of virtual reality. Virtual Reality is considered able to deliver information in a more effective due to its ability to reach the sense of human beings, good eyesight, hearing, and touch trough interaction in real time. Data retrieved trough the stages of observation of the building as directly as well as through taking sample of photos and videos. An android based mobile application by implementing the first-person shooter (FPS) method to its user and designed to follow the ADDIE development model. In the future, it is hoped that the VR campus tour application can be used in multiplayer to get a fun group campus tour experience.
Traceability method between design documents and source codes based on SQL dependency

Information Retrieval (IR) technology was widely used in traceability between design documents and source codes. However, the vocabulary mismatch between the design documents and the source codes affects the performance of IR. Aiming at the above situation, a dynamic tracing method from design documents to source codes combining IR technology and SQL statement is proposed in management information system. Firstly, the similarity of the two is calculated by IR and the candidate links are generated; Then, the SQL statement required by the codes is automatically estimated according to the design documents, and the SQL statement is compared with the actual SQL statement in the codes to correct the design documents-codes similarity score; Finally, set a threshold to determine the trace links of the design documents to the source codes. The experimental results show that this method can improve the similarity score of code classes with relevant SQL statements in the design documents, so as to improve the ranking of code classes in the candidate links, extract the trace links that may be missing in IR method under the action of threshold, and finally improve the precision of trace results.
Efficient Detection of Counterfeit Banknotes Using Adaptive Network Based Fuzzy Inference Systems

Adaptive network based fuzzy inference system (ANFIS) is utilized for efficient detection of counterfeit banknotes. ANFIS is a hybrid system that employs a network architecture to fine-tune the membership functions of a fuzzy inference system. Three input space partitioning methods are investigated for banknote authentication. The average classification performances for 30 runs of 5-fold and 10-fold cross validation experiments are reported. The experimental results and statistical analyses confirm that ANFIS models based on subtractive clustering and grid partitioning offer better classification performance than the ANFIS model based on fuzzy c-means clustering for banknote authentication.
Noise Boosted Neural Receptive Fields

Conventional neural networks (NNs) for image classification make use of a convolutional layer and a feedforward (FF) classification layer. This paper presents a novel classification layer architecture and a training paradigm, in which the FF layer is split into small and specialized FF nets called Noise Boosted Receptive Fields (NBRFs), one per class. Each i-th NBRF provides three membership degrees: to the i-th class, to the super class made by its complementary classes, and to an extra class representing out-of-classes images. The training process artificially generates extra-class samples, via image transformation and noise addition. Experimental results, carried out on MNIST, KMNIST and FMNIST datasets show that, with respect to an FF layer, the NBRF layer improves robustness and accuracy of classification. The repository with the source code and experimental data has been publicly released to facilitate reproducibility and widespread adoption.
Trajectory tracking of unmanned vehicle based on improved model predictive control

Driverless technology has become a hot spot of current research, in order to improve the speed of unmanned vehicle path planning. Firstly, the artificial potential field (APF) model is added to the objective function of model predictive control (MPC) as a path planning term to make path planning and trajectory tracking simultaneously, so as to optimize the model controller. Secondly, by introducing the adjustment factor of objective function to solve the problem that MPC objective function may have no optimal solution, and then linearize the objective function to accelerate the speed of solving the objective function. Finally, Matlab / Simulink and CarSim are used for joint simulation experiments to compare the path planning and tracking effects before and after the improvement and optimization of the controller. The experimental results show that the improved model predictive controller has faster solution speed.
Distributed Algorithms for Connectivity and MST in Large Graphs with Efficient Local Computation

We study distributed algorithms for large-scale graphs, focusing on the fundamental problems of connectivity and minimum spanning tree (MST). We consider the k-machine model, a well-studied model for distributed computing for large-scale graph computations, where k ≥ 2 machines jointly perform computations on graphs with n nodes (typically, n ≫ k). The input graph is assumed to be initially randomly partitioned among the k machines, a common implementation in many real-world systems. Communication is point-to-point, and the goal is to minimize the number of communication rounds (denoted Tc) of the computation.
Introduction

Consider waking up one winter morning in Finland and looking outside the window. It seems to become a nice sunny day which is ideal for a ski trip. To choose the right gear (clothing, wax) it is vital to have some idea for the maximum daytime temperature which is typically reached around early afternoon. If we expect a maximum daytime temperature of around plus 5 degrees, we might not put on the extra warm jacket but rather take only some extra shirt for change.
Performance of the Convolutional Neural Network Model in Identification of the Wireless Frequency Bands

The appearance of many security vulnerabilities is also increasing today with the widespread use of the wireless frequency band. This situation brings with it the research and development of various security measures as a necessity. Identification of wireless signals and transmitters properly in the environment becomes crucial situation. The efficient achievement of this process directly on the end devices also becomes necessary for the process to be fast. The purpose of this paper is to show the performance of deep learning methods for classification of wireless signals by making use of the software-defined radio devices. In particular, it is aimed to implement real-world applications and evaluate the results by utilizing software-based radio platforms. We investigated the performance of the classification made by deep learning algorithms on the I/Q data samples as a wireless signal representation that obtained on the receiver device by collected from transmitter devices. As an experimental result of the study, it has been seen that successful results are seen up to %80 in the detection of radio signals of the CNN model, which is especially efficient in image processing and identification applications. It has also been seen as a practical result that the increase in the number of layers in the CNN network model is not directly related to the performance of the network.
Parallel Population-Based Simulated Annealing for High-Dimensional Black-Box Optimization

In this paper, we present a simple yet efficient parallel version of simulated annealing (SA) for large-scale black-box optimization within the popular population-based framework. To achieve scalability, we adopt the island model, commonly used in parallel evolutionary algorithms, to update and communicate multiple independent SA instances. For maximizing efficiency, the copy-on-write operator is used to avoid performance-expensive lock when different instances exchange solutions. For better local search ability, individual step sizes are dynamically adjusted and learned during decomposition. Furthermore, we utilize the shared memory to reduce data redundancy and support concurrent fitness evaluations for challenging problems with costly memory consumption. Experiments based on the powerful Ray distributed computing library empirically demonstrate the effectiveness and efficiency of our parallel version on a set of 2000-dimensional benchmark functions (especially each is rotated with a 2000*2000 orthogonal matrix). To the best of our knowledge, these rotated functions with a memory-expensive data matrix were not tested in all previous works which considered only much lower dimensions. For reproducibility and benchmarking, the source code is made available at https://github.com/Evolutionary-Intelligence/PPSA.