Reviewerly/exHarmony huggingface dataset:
Improving the Efficient Neural Architecture Search via Rewarding Modifications

Nowadays, a challenge for the scientific community concerning deep learning is to design architectural models to obtain the best performance on specific data sets. Building effective models is not a trivial task and it can be very time-consuming if done manually. Neural Architecture Search (NAS) has achieved remarkable results in deep learning applications in the past few years. It involves training a recurrent neural network (RNN) controller using Reinforcement Learning (RL) to automatically generate architectures. Efficient Neural Architecture Search (ENAS) was created to address the prohibitively expensive computational complexity of NAS using weight sharing. In this paper we propose Improved-ENAS (I-ENAS), a further improvement of ENAS that augments the reinforcement learning training method by modifying the reward of each tested architecture according to the results obtained in previously tested architectures. We have conducted many experiments on different public domain datasets and demonstrated that I-ENAS, in the worst-case reaches the performance of ENAS, but in many other cases it overcomes ENAS in terms of convergence time needed to achieve better accuracies.
Traffic Type Recognition Method for Unknown Protocol—Applying Fuzzy Inference

In this paper, we propose a system that can recognize traffic types without prior knowledge of static features such as protocol header information by combining protocol analysis based on an ecological sequence alignment algorithm in a bioinformatics and fuzzy inference system. The algorithm proposed in this paper obtained up to a 91% level of performance at a similar level to several existing algorithms in experiments using datasets containing various types of traffic. In addition, it showed an excellent accuracy of 82.5% or more even under severe conditions that lowered the amount of data to a level of at least 40% or only included data in the middle of the traffic. This shows that the problem of dependence on initial data that frequently occurs in existing machine learning and deep learning-based traffic classification algorithms does not appear in the proposed algorithm. Furthermore, based on the ability to directly extract traffic characteristics without being dependent on static field values, it has secured the ability to respond with a small number of data by taking advantage of the flexibility of the membership function of the fuzzy inference engine. Through this, the applicability to low-power and low-performance environments such as IoT networks was confirmed. In this paper, we describe in detail the theoretical background for constructing such an algorithm and relevant experiments and considerations for actual verification.
A Group Recognition Method of Scientific and Technological Personnel based on Relational Graph

The key problem in the fine management of science and technology is to model the behavior characteristics of scientific and technical personnel and then find groups through various related cooperative relationships. Aiming at the analysis of team relationship of scientific and technical personnel data, this paper proposed a method to recognize the group of scientific and technological personnel based on relational graph. The relationship model of scientific and technological personnel was designed, and based on this, the relational graph was constructed with the relationship identification and extraction from source data. A frequent item mining algorithm based on Hadoop was proposed, which enabled getting the group of scientific and technological personnel by mining and analysis of data in relational graph. In this paper, the proposed method was experimented on both open and private data sets, and compared with several classical algorithms. The results showed that the method proposed in this paper has a significant improvement in execution efficiency.
Qadratic residue codes over 𝔽p + u𝔽p + v𝔽p∗

In this paper, we study quadratic residue (QR) codes of prime length [Formula: see text] over the ring [Formula: see text] with [Formula: see text] and [Formula: see text], where [Formula: see text] and [Formula: see text] are distinct odd prime numbers. We analyze some basic properties of cyclic codes of length [Formula: see text] over [Formula: see text], we define QR codes by their generating idempotents. Further, we discuss the extended QR codes. We present a considerable number of good [Formula: see text]-ary codes as Gray images of QR codes over [Formula: see text] by considering the case when [Formula: see text] is an odd prime.
An appraisal on the methods and techniques of recommender models for personalised marketing campaigns

Abstract Recommender models for personalized marketing empower businesses to provide personalized recommendations of goods or services to customers to fulfil their requirements, thus ultimately improves the customer buying experience. Various recommender models powered by robust machine learning algorithms were reviewed on the methods and techniques to appraise its performance concerning the personalized marketing campaigns. Recommender models can be broadly categorized into four types such as content-based, collaborative-based, knowledge-based and hybrid-based. The content-based recommendation is suitable when the system, user or product is new where classification and regression algorithms are mostly implemented. The collaborative-based recommendation is suitable when a more accurate prediction is required where Neighbour-based models, Bayesian methods, rule-based models, decision trees, and latent matrix factorization models may be implemented in this scenario. Knowledge-based recommenders are well suited for recommendations that address explicitly defined user requirements. Different types of recommenders use different sources of data and inherently have different strengths and weaknesses. Selecting the suitable recommender model with the consideration of the scenario and domain of application is very crucial. Therefore, an in-depth research is required and done on the emphasis of the application of recommender models in the personalized marketing especially on the hybrid models with a more efficient deployment for mass applications in this contemporary data-driven business world.
Joint Obfuscation for Privacy Protection in Location-Based Social Networks

In recent years, location-based social networks (LBSNs) such as Foursquare have emerged that enable users to share with each other, their (geographical) locations together with the semantic information associated with their locations. The semantic information captures the type of a location and is usually represented by a semantic tag. Semantic tag sharing increases the threat to users’ location privacy which is already at risk because of location sharing. The existing solution to protect the location privacy of users in such LBSNs is to obfuscate the location and the semantic tag independently of each other in a so called disjoint obfuscation approach. More precisely, in this approach, the semantic tag is obfuscated i.e., replaced by a more general tag. Also, the location is obfuscated i.e., replaced by a generalized area (called the cloaking area) made of the actual location and some of its nearby locations. However, since in this approach the location obfuscation is performed in a semantic-oblivious manner, an adversary can still increase his chance to infer the actual location by detecting semantic incompatibility between the locations in the cloaking area and the obfuscated semantic tag. In this work, we address this issue by proposing a joint obfuscation approach in which the location obfuscation is performed based on the result of the semantic tag obfuscation. We also provide a formal framework for evaluation and comparison of our joint approach with the disjoint approach. By running an experimental evaluation on a dataset of real-world user mobility traces, we show that in almost all cases (i.e., for different values of the obfuscation parameters), the joint approach outperforms the disjoint approach in terms of location privacy protection. We also study how different obfuscation parameters can affect the performance of the obfuscation approaches. In particular, we show how changing these parameters can improve the performance of the joint approach.
CycleGAN-Based High-Quality Non-Parallel Voice Conversion with Spectrogram and WaveRNN

This paper proposes Scyclone, a high-quality voice conversion (VC) technique without parallel data training. Scyclone improves speech naturalness and speaker similarity of the converted speech by introducing CycleGAN-based spectrogram conversion with a simplified WaveRNN-based vocoder. In Scyclone, a linear spectrogram is used as the conversion feature, which avoids quality degradation due to extraction errors. The subjective experiments show that Scyclone is significantly better than CycleGAN-VC2, one of the existing state-of-the-art parallel-data-free VC techniques.
Evaluating the Effect of Compression Settings in the Classification of Image File Formats

The classification of file fragments of various file formats is an important task in many applications such as intrusion detection systems, web content filtering, and digital forensics. To date, many research works have presented various feature sets and methods for the task of file fragments classification. Despite this variety, no research work has mainly focused on image file formats in particular. In this paper, the classification of the image file formats is studied. Moreover, we examine the effect of different compression settings on the accuracy of a trained model. It is shown that when during the training phase only specific compression settings are considered, the trained machine performs poorly for unseen compression settings. Considering this fact, we propose our method, in which, fragments with different compression settings but the same file format are merged to form a more general class label. We compare our approach with three other methods proposed in the literature. Results indicate that the proposed feature set leads to a more accurate classifier.
Development status, problems and solutions of speech recognition technology

Abstract The purpose of automatic speech recognition technology is to enable the machine to “understand” human voice, and convert human voice into text information, which is the key technology to realize human-computer interaction. This paper briefly introduces the development of speech recognition technology and the acoustic model involved, analyzes the problems of noise interference in the environment, the low recognition rate of dialect speech, and the need to improve the speech recognition effect in the far-field environment, and puts forward solutions such as optimizing the acoustic model, building dialect corpus, accurately modeling the sound transmission environment, the future development is prospected.
Learning Local Feature Descriptions in 3D Ultrasound

Tools for automatic image analysis are gaining importance in the clinical workflow, ranging from time-saving tools in diagnostics to real-time methods in image-guided interventions. Over the last years, ultrasound (US) imaging has become a promising modality for image guidance due to its ability to provide volumetric images of soft tissue in real-time without using ionizing radiation. One key challenge in automatic US image analysis is the identification of suitable features to describe the image or regions within, e.g. for recognition, alignment or tracking tasks. In recent years, features that were learned data-drivenly provided promising results. Even though these approaches outperformed hand-crafted feature extractors in many applications, there is still a lack of feature learning for local description of three-dimensional US (3DUS) images. In this work, we present a completely data-driven feature learning approach for 3DUS images for usage in target tracking. To this end, we use a 3D convolutional autoencoder (AE) with a custom loss function to encode 3DUS image patches into a compact latent space that serves as a general feature description. For evaluation, we trained and tested the proposed architecture on 3DUS images of the liver and prostate of five different subjects and assessed the similarity between the decoded patches and the original ones. Subject-and organ-specific as well as general AEs are trained and evaluated. Specific AEs could reconstruct patches with a mean Normalized Cross Correlation of 0.85 and 0.81 at maximum in liver and prostate, respectively. It can also be shown that the AEs are transferable across subjects and organs, with a small accuracy decrease to 0.83 and 0.81 (liver, prostate) for general AEs. In addition, a first tracking study was performed to show feasibility of tracking in latent space. In this work, we could show that it is possible to train an AE that is transferable across two target regions and several subjects. Hence, convolutional AEs present a promising approach for creating a general feature extractor for 3DUS.
Power Quality Problem Classification Methods Based on Active Transfer Learning

In this paper, we propose approach for classification of power quality problem using active transfer learning. Active learning is suitable for solving problems with real industrial data because even a small amount of labeled data can derive good performance. Active learning can derive performance by querying human data labelers for data the model does not learn or trained. Active transfer learning utilizes transfer learning for data sampling of the trained model. However, querying too much data can put pressure on human data labelers. Thus, we suggest the solution of this problem by limiting query in two ways. One is to limit the number of queries to 100 and the other is dynamically controlled the number of queries according to the accuracy. As a result, we derive better predictive performance with suggest method and compare the results with the traditional method.
Denoising Auto-Encoder based State Estimation of Distribution Systems Under Natural Disaster

Under natural disasters, confirming the failure location of distribution systems is the basis for post-disaster repair work. We can estimate the state of distribution systems by collecting and analyzing real-time data from sensing devices installed in the distribution systems. However, due to the destructive nature of the natural disaster, the collected data often in a missing mode. The reason for the missing data is not only the damage of monitored object but also the damage of the monitoring devices and the failure of communication systems. As a result, the collected data are in the form of limited credibility, leading to the difficulty of failure location for distribution systems under nature disaster. In this paper, a denoising auto-encoder based state estimation method is proposed to solve the problem. By adding a process of missing data randomly in the training phase, the scenario of the monitored object damage can be identified, and then the state of distribution systems can be estimated effectively. The superior performance of the proposed method is verified through simulation and experiments.
Representing Unordered Data Using Complex-Weighted Multiset Automata

Unordered, variable-sized inputs arise in many settings across multiple fields. The ability for set- and multiset-oriented neural networks to handle this type of input has been the focus of much work in recent years. We propose to represent multisets using complex-weighted multiset automata and show how the multiset representations of certain existing neural architectures can be viewed as special cases of ours. Namely, (1) we provide a new theoretical and intuitive justification for the Transformer model's representation of positions using sinusoidal functions, and (2) we extend the DeepSets model to use complex numbers, enabling it to outperform the existing model on an extension of one of their tasks.
“Private” JPEG Images for Earth Science Purposes

Abstract The 5G era that is about to come will exponentially increase the amount of digital data produced. Already, the giant amount of digital data that is produced every day by sensors, users and digital devices has called for scalable and efficient decentralized approaches to data storage and elaboration and to a Fog/Edge computing paradigm that requires efficient networking and storage for better services related to the compression and security of digital data. In this paper, we will explore a unified approach that merges compression and security in one step, by reviewing the current research in this field and by presenting new experimental evidence and new ideas for secure compression of two-dimensional data (for instance digital images). The purpose of our study is also the masking and unmasking of the sensitive biometric data, i.e. face and eyes, that are contained within a JPEG image that contains human faces.
Смена образовательных парадигм в высшем образовании в условиях четвертой промышленной революции

Within the framework of the fourth Industrial Revolution (4IR), the inevitable restructuring of the usual processes in education is underway, the transition from trivial forms to more progressive, from conservative, encyclopedic-oriented knowledge, to business-oriented, giving skills and competencies. It is necessary to track global trends for a general understanding of the direction in which technological development will take place and how it may affect the field of education, in particular, higher education. Much more interdisciplinary education, research and innovation are required. This article explores the impact of 4IR on higher education and helps determine the development vector in the near future. In the era of the industrial revolution, it is possible to flexibly combine various business models, in the center of which a higher education institution can become a well-structured strategy. The Industrial Revolution allows you to digitize processes and vertically integrates processes throughout an organization. It also fully integrates all internal processes of all stakeholders, this personifies the paradigm shift from “centralized” to “decentralized” production, with the result that universities are no longer just learning, but they are easily integrated into the information network, where there are business partners and students, graduates. In other words, the idea of integrating processes and linking all objects in the economy is emphasized in the 4th industrial revolutionary century.
A Quantization Matrix Design using Total Variation for Sparse Representation-based Image Coding

This paper proposes a quantization matrix design method for sparse representation-based image coding. In the proposed method, we define the complexity of the atoms in the dictionary designed by K-SVD as a simple total variation, and design a quantization matrix to quantize the sparse coefficients based on the similarity with the complexity in the DCT atoms of HEVC. This method makes it possible to perform coefficient quantization adaptively for each atom's feature. Experimental results show that the proposed method provides higher coding efficiency in comparison with conventional uniform quantization matrix from a point of perceptual picture quality.
Greedy-Based PSO with Clustering Technique for Cloud Task Scheduling

Effective scheduling of cloud tasks is very much essential for a cloud computing environment. The cloud tasks are the user requests to be processed in a cloud environment. A number of cloud resources are consumed to process cloud tasks. Task scheduling optimizes the consumption of cloud resources and reduces the makespan time. The paper is aimed at reducing the makespan time in a cloud environment by introducing the new method Modified Greedy Particle Swarm Optimization with Clustered Approach (MGPSOC). The MGPSOC algorithm makes use of clustering with bio-inspired techniques. The proposed method showed good results when compared with the existing algorithm Greedy Particle Swarm Optimization Algorithm (G&PSO).
MU-MIMO User Grouping for WLANs supporting Indoor VR Applications

Virtual Reality (VR) applications require low latency transmission of high-definition video frames to support immersive user experience without incurring side-effects like motion sickness. In this paper, we claim that state-of-the-art MU-MIMO user selection schemes fail to achieve the aforementioned goal due to their focus on building the best MU-MIMO group with maximal throughput. To fill the gap, we propose two new scheduling schemes to effectively reduce the user-experienced latency in WLAN-based VR systems. Via extensive simulations, we compare the performance of our proposed schedulers with that of Guidepost, a well-known MU-MIMO scheduler, to show the need of considering inter-group dependency in user scheduling to best support VR service provisioning. The latency enhancement by the proposed algorithms is revealed as up to 23.2% in the average sum airtime and up to 36.9% in the maximum sum airtime.
SemOIR: An ontology-based semantic information retrieval system

This paper describes a semantic vector space model (SeVSM) and an information retrieval system based on the model. The SeVsmaims to improve information retrieval performance for domain-specific systems. In this model, we use an ontology to build the relations between any two keywords to solve the performance deficiency caused by the basic hypothesis of a vector space model (VSM) where keywords are mutually independent. Then we designed and developed a semantic ontology-based information retrieval (SemOIR) system based on the SeVSM model. An experimental study using 15 queries from different domains confirms the effectiveness of the SeVSM and the usability of the SemOIR system. The proposed model and the system contribute significantly to the application of semantic retrieval for digital libraries and e-commerce systems.
Performance Improvement of Video Classification using Generated Labeled Data

As the development of deep learning techniques is growing, applications using deep learning have been spreading. Among various applications, images and videos related applications are the most common example of the practical deep learning application. The performances in those applications have been boosted by adopting deep learning techniques. To achieve performance, securing a large amount of data-oriented to target tasks is crucial. In this paper, we have designed the experiments to examine the effect of generated data on both where the dataset can be easily collected and hard to secure. We use state-of-the-art generative model, MCnet, to enlarge the Sexually Harmful Contents dataset and UCF-101. By training C3D with augmented data, we measure the classification performance. The generated labeled data have increased the performance by 7% on harmful content detection.
Applying Social Network Extraction With Named Entity Recognition to the Examination of Political Bias Within Online News Articles

We aim to expand the application of social network extraction with NER tools, which to date is largely limited to fiction. With the premise that news articles resemble mini-stories, this study explores the extraction of social networks from online United States news articles to examine relationships between political bias and network features. We find statistical significance with most trends, and find no substantial differences between Liberal and Conservative bias, but bias and neutrality. Furthermore, this study identifies several issues with social network analysis, proposing a more rigorous examination of textual characteristics that affect network features.