Reviewerly/exHarmony huggingface dataset:
Deep Active Transfer Learning for Image Recognition

In recent years, deep learning has revolutionized the field of computer vision and has achieved state-of-the-art performance in a variety of applications. However, training a robust deep neural network necessitates a large amount of hand-labeled training data, which is time-consuming and labor-intensive to acquire. Active learning and transfer learning are two popular methodologies to address the problem of learning with limited labeled data. Active learning attempts to select the salient and exemplar instances from large amounts of unlabeled data; transfer learning leverages knowledge from a labeled source domain to develop a model for a (related) target domain, where labeled data is scarce. In this paper, we propose a novel active transfer learning algorithm with the objective of learning informative feature representations from a given dataset using a deep convolutional neural network, under the constraint of weak supervision. We formulate a loss function relevant to the research task and exploit the gradient descent algorithm to optimize the loss and train the deep network. To the best of our knowledge, this is the first research effort to propose a task-specific loss function integrating active and transfer learning, with the goal of learning informative feature representations using a deep neural network, under weak human supervision. Our extensive empirical studies on a variety of challenging, real-world applications depict the merit of our framework over competing baselines.
Spatio-Temporal Short Term Photovoltaic Generation Forecasting with Uncertainty Estimates using Machine Learning Methods

The availability of accurate short-term forecasts of the infeed of volatile renewable power generation is of crucial importance for the safe and economic operation of future energy networks. Classical numerical weather prediction (NWP) methods offer a limited temporal and spatial resolution that is not suitable for the prediction of the infeed of individual photovoltaic (PV) systems. By combining weather data from a spatially distributed sensor network, improvements of short-term forecasts can be achieved. The suitability of a data set consisting of multiple PV sites is analyzed for this task. In this paper, spatiotemporal nowcasting methods are developed based on statistical and machine learning algorithms. The methods considered are Auto Regressive-Moving Average (ARMA) and Long Short-Term Memory (LSTM) neural networks. The increase of performance of using data from multiple sites is highlighted. Additional estimates of the forecasting uncertainty are developed using quantile regression (QR).
An efficient deadline constrained and data locality aware dynamic scheduling framework for multitenancy clouds

Summary Scheduling and resource allocation in clouds is used to harness the power of the underlying resource pool. Service providers can meet quality of service (QoS) requirements of tenants specified in Service Level Agreements. Improving resource allocation ensures that all tenants will receive fairer access to system resources, which improves overall utilization and throughput. Real‐time applications and services require critical deadlines in order to guarantee QoS. A growing number of data‐intensive applications drive the optimization of scheduling through utilizing data locality in which the scheduler locates a task and ensures the task's relevant data to be on the same server. Choosing suitable scheduling mechanisms for running applications that support multitenancy has consistently been a major challenge. This work proposes a new adaptive Deadline constrained and Data locality aware Dynamic Scheduling Framework “ 3DSF“ that orchestrates different schedulers based on varied requirements. This framework considers tenants' deadline‐based QoS requirements, cloud system's performance and a method of resource allocation to improve resource utilization, system throughput and reduce jobs' completion time. 3DSF contains: (a) a real‐time, preemptive, deadline constrained job scheduler, (b) an optimized data locality aware scheduler, (c) an improved Dominant Resource Fairness greedy resource allocation approach, and (d) an adaptive suite to integrate above‐mentioned schedulers together.
Role of Smart Contracts in Halal Supply Chain Management

Now, more than ever, with the existence of the new blockchain technology, smart contracts started its revolution. Although the industry has been practices smart contracts using blockchain technology, limited study available on how blockchain technology contributes to Halal supply chain management. Thus, the aim of this chapter is to review the roles, promises and benefits of smart contracts based on blockchain technology for Halal supply chain management applications. An extensive literature exploration is conducted to reveal the existing literature on smart contracts using blockchain technology. The authors have faith in that the findings in this chapter can be used as a solid base for the researchers and practitioners of smart contracts in Halal supply chain management.
Improving k-Means Clustering Performance with Disentangled Internal Representations

Deep clustering algorithms combine representation learning and clustering by jointly optimizing a clustering loss and a non-clustering loss. In such methods, a deep neural network is used for representation learning together with a clustering network. Instead of following this framework to improve clustering performance, we propose a simpler approach of optimizing the entanglement of the learned latent code representation of an autoencoder. We define entanglement as how close pairs of points from the same class or structure are, relative to pairs of points from different classes or structures. To measure the entanglement of data points, we use the soft nearest neighbor loss, and expand it by introducing an annealing temperature factor. Using our proposed approach, the test clustering accuracy was 96.2% on the MNIST dataset, 85.6% on the Fashion-MNIST dataset, and 79.2% on the EMNIST Balanced dataset, outperforming our baseline models.
Noise-Sampling Cross Entropy Loss: Improving Disparity Regression Via Cost Volume Aware Regularizer

Recent end-to-end deep neural networks for disparity regression have achieved the state-of-the-art performance. However, many well-acknowledged specific properties of disparity estimation are omitted in these deep learning algorithms. Especially, matching cost volume, one of the most important procedure, is treated as a normal intermediate feature for the following softargmin regression, lacking explicit constraints compared with those traditional algorithms. In this paper, inspired by previous canonical definition of cost volume, we propose the noise-sampling cross entropy loss function to regularize the cost volume produced by deep neural networks to be unimodal and coherent. Extensive experiments validate that the proposed noise-sampling cross entropy loss can not only help neural networks learn more informative cost volume, but also lead to better stereo matching performance compared with several representative algorithms.
O (m log m) instance selection algorithms—RR-DROPs

This paper is focused on an instance selection algorithm for classification purposes. We propose a new fast version of DROP algorithms with complexity reduced to O(m log m), while the original complexity was O(m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ). The new RR-DROP algorithms use random region hashing forests and jungle, and several other data structures to keep the computational complexity as low as possible. The proposed algorithms can be used for huge datasets, with classification remaining unchanged, as proven by a statistical analysis on several datasets.
MAKEDONKA: Applied Deep Learning Model for Text-to-Speech Synthesis in Macedonian Language

This paper presents MAKEDONKA, the first open-source Macedonian language synthesizer that is based on the Deep Learning approach. The paper provides an overview of the numerous attempts to achieve a human-like reproducible speech, which has unfortunately shown to be unsuccessful due to the work invisibility and lack of integration examples with real software tools. The recent advances in Machine Learning, the Deep Learning-based methodologies, provide novel methods for feature engineering that allow for smooth transitions in the synthesized speech, making it sound natural and human-like. This paper presents a methodology for end-to-end speech synthesis that is based on a fully-convolutional sequence-to-sequence acoustic model with a position-augmented attention mechanism—Deep Voice 3. Our model directly synthesizes Macedonian speech from characters. We created a dataset that contains approximately 20 h of speech from a native Macedonian female speaker, and we use it to train the text-to-speech (TTS) model. The achieved MOS score of 3.93 makes our model appropriate for application in any kind of software that needs text-to-speech service in the Macedonian language. Our TTS platform is publicly available for use and ready for integration.
Generating Chinese Poetry from Images via Concrete and Abstract Information

In recent years, the automatic generation of classical Chinese poetry has made great progress. Besides focusing on improving the quality of the generated poetry, there is a new topic about generating poetry from an image. However, the existing methods for this topic still have the problem of topic drift and semantic inconsistency, and the image-poem pairs dataset is hard to be built when training these models. In this paper, we extract and integrate the Concrete and Abstract information from images to address those issues. We proposed an infilling-based Chinese poetry generation model which can infill the Concrete keywords into each line of poems in an explicit way, and an abstract information embedding to integrate the Abstract information into generated poems. In addition, we use non-parallel data during training and construct separate image datasets and poem datasets to train the different components in our framework. Both automatic and human evaluation results show that our approach can generate poems which have better consistency with images without losing the quality.
Proposal of Feedback System based on Skeletal Analysis in Physical Education Classes

In this study, we report on a visualization and evaluation system for human movement trajectories during physical education classes. In physical education classes, it is difficult for students to grasp their own movements objectively, and it is often the case that they are unable to recognize the problem. Instructors need to observe students' detailed behaviors, but it is difficult to find the cause because they move so fast. Therefore, we visualized the skeletal coordinates during exercise in physical education classes using the Open Pose algorithm and evaluated the behavior during exercise. To verify the evaluation method of the developed system, we compare the result of the developed system and the result of an expert physical education teacher. As a result, we confirmed some movements that can be evaluated by the system and others that are difficult to evaluate.
Neuromodulated Patience for Robot and Self-Driving Vehicle Navigation

Robots and self-driving vehicles face a number of challenges when navigating through real environments. Successful navigation in dynamic environments requires prioritizing subtasks and monitoring resources. Animals are under similar constraints. It has been shown that the neuromodulator serotonin (5-HT) regulates impulsiveness and patience in animals. In the present paper, we take inspiration from the serotonergic system and apply it to the task of robot navigation. In a set of outdoor experiments, we show how changing the level of patience can affect the amount of time the robot will spend searching for a desired location. To navigate GPS compromised environments, we introduce a deep reinforcement learning paradigm in which the robot learns to follow sidewalks. This may further regulate a tradeoff between a smooth long route and a rough shorter route. Using patience as a parameter may be beneficial for autonomous systems under time pressure.
A Random Number Generator Based on Irregular Sampling and Transient Effect Ring Oscillators

This paper presents the FPGA (Field Programmable Gate Array) implementation and the coupling analysis of a metastability-based random number generator (RNG). The metastability is acquired through exploiting transient effect ring oscillators (TEROs) and the random bit generation is accomplished through irregular sampling of regular waveform method. As the proposed RNG is made up of only digital logic gates, it is prototyped on a Xilinx ZedBoard Zynq-7000 evaluation platform. It is demonstrated that the RNG output bitstream satisfies NIST 800-22 statistical tests of randomness without any need for post-processing and the proposed RNG can provide high data throughput. Furthermore, the proposed RNG uses less number of components compared to previously reported TERO based RNGs, thus enabling lower power consumption and smaller footprint. Additionally, the proposed RNG is subjected to correlation based cryptanalysis study. In classical ring oscillator (RO) based RNGs, the ring oscillators are required to be placed at a distance from each other to avoid locking phenomenon that may occur due to coupling between adjacent ROs. This study demonstrates that unlike classical RO based RNGs, the proposed RNG is insensitive to locking phenomenon and TERO structures are not required to be placed way from each other. Therefore, the proposed method lead to a more compact RNG design compared to classical RO based RNGs.
Distributed Machine Learning for Predictive Analytics in Mobile Edge Computing Based IoT Environments

Predictive analytics in Mobile Edge Computing (MEC) based Internet of Things (IoT) is becoming a high demand in many real-world applications. A prediction problem in an MEC-based IoT environment typically corresponds to a collection of tasks with each task solved in a specific MEC environment based on the data accumulated locally, which can be regarded as a Multi-task Learning (MTL) problem. However, the heterogeneity of the data (non-IIDness) accumulated across different MEC environments challenges the application of general MTL techniques in such a setting. Federated MTL (FMTL) has recently emerged as an attempt to address this issue. Besides FMTL, there exists another powerful but under-exploited distributed machine learning technique, called Network Lasso (NL), which is inherently related to FMTL but has its own unique features. In this paper, we made an in-depth evaluation and comparison of these two techniques on three distinct IoT datasets representing real-world application scenarios. Experimental results revealed that NL outperformed FMTL in MEC-based IoT environments in terms of both accuracy and computational efficiency.
Pseudo-gradient algorithm for identification of doubly stochastic cylindrical image model

Nowadays, image processing problems are becoming increasingly important due to development of the aerospace Earth monitoring systems, radio and sonar systems, medical devices for early diagnosis etc. But the most of the image processing works deal with images defined on rectangular two-dimensional grids or grids of higher dimension. In some practical situations, images are set on a cylinder. For example, images of a section of pipelines, worms, blood vessel, parts during turning, etc. The peculiarity of the domain for specifying such images requires its consideration in their models and processing algorithms. In the present paper, autoregressive models of cylindrical images are considered, expressions of the correlation function depending on the autoregression parameters are given. To represent heterogeneous images with random heterogeneities, «doubly stochastic» models are used in which one or more images control the parameters of the resulting image. The spiral scan of a cylindrical image can be considered as a quasiperiodic process due to the correlation of image rows. Pseudo-gradient algorithms for the modal identification are proposed. The statistical modeling showed that these algorithms give good model identification.
On the Structures of Representation for the Robustness of Semantic Segmentation to Input Corruption

Semantic segmentation is a scene understanding task at the heart of safety-critical applications where robustness to corrupted inputs is essential. Implicit Background Estimation (IBE) has demonstrated to be a promising technique to improve the robustness to out-of-distribution inputs for semantic segmentation models for little to no cost. In this paper, we provide analysis comparing the structures learned as a result of optimization objectives that use Softmax, IBE, and Sigmoid in order to improve understanding their relationship to robustness. As a result of this analysis, we propose combining Sigmoid with IBE (SCrIBE) to improve robustness. Finally, we demonstrate that SCrIBE exhibits superior segmentation performance aggregated across all corruptions and severity levels with a mIOU of 42.1 compared to both IBE 40.3 and the Softmax Baseline 37.5.
A Hybrid Paper Recommendation Method by Using Heterogeneous Graph and Metadata

The amount of academic articles in digital libraries is increasing exponentially. This growth of scientific papers' growth made it difficult for researchers to obtain related papers from their queries. Recommendation systems can help them resolve the problem of information overload. However, existing paper recommender methods generally rely on the simple citation network, which ignores the semantic of papers and has the problem of cold start. In this paper, a hybrid paper recommendation approach AMHG is proposed which is based on a multi-level citation heterogeneous graph. Unlike existing works which only use the reference relationship, we consider the same or similar authors' papers to alleviate the cold start problem of zero-citation and newly published papers. Besides, the metadata information of papers is also incorporated into a representation model to generate better recommender results to alleviate the cold start problem. We use the authors' influence factors to reorder the candidate list outputting by MLP to obtain high-quality articles. Through experiments, we compare our model with several methods on the DBLP-REC dataset to demonstrate that AMHG outperforms state-of-the-art performance and the effectiveness of recommender.
PredicTor: Predictive Congestion Control for the Tor Network

In the Tor network, anonymity is achieved through a multi-layered architecture, which comes at the cost of a complex network. Scheduling data in this network is a challenging task and the current approach shows to be incapable of avoiding network congestion and allocating fair data rates. We propose PredicTor, a distributed model predictive control approach, to tackle these challenges. PredicTor is designed to schedule incoming and outgoing data rates on individual nodes of the Tor architecture, leading to a scalable approach. We successfully avoid congestion through exchanging information of predicted behavior with adjacent nodes. Furthermore, we formulate PredicTor with a focus on fair allocation of resources, for which we present and prove a novel optimization-based fairness approach. Our proposed controller is evaluated with the popular network simulator ns-3, where we compare it with the current Tor scheduler as well as with another recently proposed enhancement. PredicTor shows significant improvements over previous approaches, especially with respect to latency.
Study of football social responsibility in Iran with Fuzzy cognitive mapping approach

Considering the increasing importance of social activities in sports, the aim of this study was to design a model of football social responsibility in Iran with a fuzzy cognitive mapping approach. For this purpose the data was collected by 13 in-depth interviews with experts in the field of football social studies in Iran selected through purposive and snowball sampling. By substantive and theoretical coding of interviews, the concepts of football social responsibility were identified and defined. The results showed football social responsibility in Iran includes 8 dimensions of moral, cultural, legal, socio-economic, transparency, benevolently, knowledge and socio-environmental responsibility. Examining the causal relationships between the blocks indicates that moral responsibility has the highest number of effective relationships. Also, the effect of moral responsibility on legal responsibility and transparency responsibility was more important than other relationships. Thus the concept of social responsibility of football can be considered from a broader perspective.
Holographic Case-Based Reasoning

In this paper, we present a novel extension of CBR that allows cases to be more proactive at problem solving, by enriching case representations and facilitating richer interconnectedness between cases. We empirically study the improvements resulting from a holographic realization on experimental datasets. In addition to making CBR more cognitively appealing, the idea has the potential to lend itself as an elegant general CBR formalism of which diverse realizations of CBR can be viewed as instances.
A Comprehensive Survey on Data Center Network Architectures

In the current era, when storage and computation solutions are shifting from workstations to cloud, Data Center Networks (DCNs) are attaining great crest for big data transaction among integrated servers. Due to this exponential growth in cloud services, contemporary DCNs are suffering from various challenges such as scalability, energy efficiency, congestion and cost, directly influenced by the architectural design of DCNs. Hence, this letter aim to present the descriptive analysis of various data center network architectures in an organized manner along with the comparison of these architectures against DCN performance matrices. Finally, the letter summarize with the possible future enhancements in DCN architectures and operations.
Automatic hyperparameter tuning in on-line learning: Classic Momentum and ADAM

We propose a method that adapts hyperparameters, namely step-sizes and momentum decay factors, in on-line learning with classic momentum and ADAM. The approach is based on the estimation of the short- and long-term influence of these hyperparameters on the loss value. In the experimental study, our approach is applied to on-line learning in small neural networks and deep autoencoders. Automatically tuned coefficients surpass or roughly match the best ones selected manually in terms of learning speed. As a result, on-line learning can be a fully automatic process, producing results from the first run, without preliminary experiments aimed at manual hyperparameter tuning.
End-to-End Analysis for Text Detection and Recognition in Natural Scene Images

Right from the very beginning, the text has vital importance in human life. As compared to the vision-based applications, preference is always given to the precise and productive information embodied in the text. Considering the importance of text, recognition, and detection of text is also equally important in human life. This paper presents a deep analysis of recent development on scene text and compare their performance and bring into light the real modern applications. Future potential directions of scene text detection and recognition are also discussed.