Reviewerly/exHarmony huggingface dataset:
A Classification and Novel Class Detection Algorithm for Concept Drift Data Stream Based on the Cohesiveness and Separation Index of Mahalanobis Distance

Data stream mining has become a research hotspot in data mining and has attracted the attention of many scholars. However, the traditional data stream mining technology still has some problems to be solved in dealing with concept drift and concept evolution. In order to alleviate the influence of concept drift and concept evolution on novel class detection and classification, this paper proposes a classification and novel class detection algorithm based on the cohesiveness and separation index of Mahalanobis distance. Experimental results show that the algorithm can effectively mitigate the impact of concept drift on classification and novel class detection.
Double-Criteria Active Learning for Multiclass Brain-Computer Interfaces

Recent technological advances have enabled researchers to collect large amounts of electroencephalography (EEG) signals in labeled and unlabeled datasets. It is expensive and time consuming to collect labeled EEG data for use in brain-computer interface (BCI) systems, however. In this paper, a novel active learning method is proposed to minimize the amount of labeled, subject-specific EEG data required for effective classifier training, by combining measures of uncertainty and representativeness within an extreme learning machine (ELM). Following this approach, an ELM classifier was first used to select a relatively large batch of unlabeled examples, whose uncertainty was measured through the best-versus-second-best (BvSB) strategy. The diversity of each sample was then measured between the limited labeled training data and previously selected unlabeled samples, and similarity is measured among the previously selected samples. Finally, a tradeoff parameter is introduced to control the balance between informative and representative samples, and these samples are then used to construct a powerful ELM classifier. Extensive experiments were conducted using benchmark and multiclass motor imagery EEG datasets to evaluate the efficacy of the proposed method. Experimental results show that the performance of the new algorithm exceeds or matches those of several state-of-the-art active learning algorithms. It is thereby shown that the proposed method improves classifier performance and reduces the need for training samples in BCI applications.
Investigating students’ interest on reading journal articles: Materials, reasons and strategies

This article investigates students’ reading materials, reasons for reading journal articles, and strategies in handling its difficulties. The data was collected by the use of qualitative method with structured interview. A number of eight students were purposely selected as the participants of this study, each representing eight different units studying in the seventh semester at a university in Banda Aceh, Indonesia. In analyzing the data, this research employed the qualitative descriptive analysis of data organization, data examination and data explanation. The findings showed that the favorite reading materials for students are website articles and social media captions, followed by non-fiction readings and newspapers. It is also found that preparing assignment is the utmost popular reason for reading journal articles for the students. Students also said that looking up in dictionary, internet surfing, consulting friends and lecturers, more practices, predicting the meaning of the words, and partial reading were some strategies they used to tackle the problems of reading journal articles. The implication of this study can be of actual practice to the academic reading course and curriculum and material development, especially for future improvement on students’ reading performance and proficiency.
Ergativity and depth of analysis

In this paper, I argue that “depth of analysis” does not deserve the prestige that it is sometimes given in general linguistics. While language description should certainly be as detailed as possible, general linguistics must rely on worldwide comparison of languages, and this cannot be based on language-particular analyses. Rigorous quantitative comparison requires uniform measurement, and this implies abstracting away from many language-particular peculiarities. I will illustrate this on the basis of ergative patterns, starting out from I.A. Mel’čuk’s (1981) proposal for Lezgian. This proposal was not successful, but why not? And why is Baker’s (2015) theory of dependent case likewise unsuccessful? By contrast, quantitative worldwide research has found striking similarities of ergative coding patterns, which can be explained by the efficiency theory of asymmetric coding. I will argue that this success is due to a more cautious approach to understanding Human Language, which does not rely on the Mendeleyevian vision for grammar (that all grammars are made from the same innate building blocks).
Using DICOM Tags for Clustering Medical Radiology Images into Visually Similar Groups

The data stored in a Picture Archiving and Communication System (PACS) of a clinical centre normally consists of medical images recorded from patients using select imaging techniques, and stored metadata information concerning the details on the conducted diagnostic procedures - the latter being commonly stored using the Digital Imaging and Communications in Medicine (DICOM) standard. In this work, we explore the possibility of utilising DICOM tags for automatic annotation of PACS databases, using K-medoids clustering. We gather and analyse DICOM data of medical radiology images available as a part of the RadiologyNet database, which was built in 2017, and originates from the Clinical Hospital Centre Rijeka, Croatia. Following data preprocessing, we used K-medoids clustering for multiple values of K, and we chose the most appropriate number of clusters based on the silhouette score. Next, for evaluating the clustering performance with regard to the visual similarity of images, we trained an autoencoder from a non-overlapping set of images. That way, we estimated the visual similarity of pixel data clustered by DICOM tags. Paired t-test (p < 0.001) suggests a significant difference between the mean distance from cluster centres of images clustered by DICOM tags, and randomly-permuted cluster labels.
Continuous reinforcement learning to adapt multi-objective optimization online for robot motion

This article introduces a continuous reinforcement learning framework to enable online adaptation of multi-objective optimization functions for guiding a mobile robot to move in changing dynamic environments. The robot with this framework can continuously learn from multiple or changing environments where it encounters different numbers of obstacles moving in unknown ways at different times. Using both planned trajectories from a real-time motion planner and already executed trajectories as feedback observations, our reinforcement learning agent enables the robot to adapt motion behaviors to environmental changes. The agent contains a Q network connected to a long short-term memory network. The proposed framework is tested in both simulations and real robot experiments over various, dynamically varied task environments. The results show the efficacy of online continuous reinforcement learning for quick adaption to different, unknown, and dynamic environments.
Multi-Label Learning based Semi-Global Matching Forest

Semi-Global Matching (SGM) approximates a 2D Markov Random Field (MRF) via multiple 1D scanline optimizations, which serves as a good trade-off between accuracy and efficiency in dense matching. Nevertheless, the performance is limited due to the simple summation of the aggregated costs from all 1D scanline optimizations for the final disparity estimation. SGM-Forest improves the performance of SGM by training a random forest to predict the best scanline according to each scanline’s disparity proposal. The disparity estimated by the best scanline acts as reference to adaptively adopt close proposals for further post-processing. However, in many cases more than one scanline is capable of providing a good prediction. Training the random forest with only one scanline labeled may limit or even confuse the learning procedure when other scanlines can offer similar contributions. In this paper, we propose a multi-label classification strategy to further improve SGM-Forest. Each training sample is allowed to be described by multiple labels (or zero label) if more than one (or none) scanline gives a proper prediction. We test the proposed method on stereo matching datasets, from Middlebury, ETH3D, EuroSDR image matching benchmark, and the 2019 IEEE GRSS data fusion contest. The result indicates that under the framework of SGM-Forest, the multi-label strategy outperforms the single-label scheme consistently.
Particle Swarm Contour Search Algorithm

In this article, we present a new algorithm called Particle Swarm Contour Search (PSCS)—a Particle Swarm Optimisation inspired algorithm to find object contours in 2D environments. Currently, most contour-finding algorithms are based on image processing and require a complete overview of the search space in which the contour is to be found. However, for real-world applications this would require a complete knowledge about the search space, which may not be always feasible or possible. The proposed algorithm removes this requirement and is only based on the local information of the particles to accurately identify a contour. Particles search for the contour of an object and then traverse alongside using their known information about positions in- and out-side of the object. Our experiments show that the proposed PSCS algorithm can deliver comparable results as the state-of-the-art.
Long-term real time object tracking based on multi-scale local correlation filtering and global re-detection

Abstract This paper investigates long-term visual object tracking which is a complex problem in computer vision community and big data analysis, due to the variation of the target and the surrounding environment. A novel tracking algorithm based on local correlation filtering and global keypoint matching is proposed to solve problems occurred during long-term tracking such as occlusion, target-losing, etc. The algorithm consists of two major components: (1) local object tracking module, and (2) global losing re-detection module. The local tracking module optimizes the conventional correlation filtering algorithm. Firstly, the Color Name feature is applied to increase the color sensitivity. Secondly, a scale traversal is employed to accommodate target scale changes. In the global losing re-detection module, the target losing judgment and global re-detection is realized by keypoint feature models of foreground and background. The proposed tracker achieves the 1st place in the VTB50 test set with 81.3% precision and 61.3% success rate, which outperforms other existing state-of-the-art trackers by over 10%. And it achieves the 2nd place in our Chasing-Car test set with a higher real-time performance 43.2 fps. The experimental results show that the proposed tracker has higher accuracy and robustness when dealing with situations like object deformation, occlusion and target-losing, etc.
Quantum computers scale up: Constructing a universal quantum computer with a large number of qubits will be hard but not impossible

The classic Rubik's Cube has 43,252,003,274,489,856,000 different states. You might well wonder how people are able to take a scrambled cube and bring it back to its original configuration, with just one color showing on each side. Some people are even able to do this blindfolded after viewing the scrambled cube once. Such feats are possible because there's a basic set of rules that always allow someone to restore the cube to its original state in 20 moves or less.
Selection of Optimum Fractal Model for Detection of Stream Sediments Anomalies

The main purpose of this research is a comparative study among four different fractal models including Concentration-Perimeter/Area (C-P/A), Concentration-Number (C-N), Concentration-Area (C-A) and Concentration-Perimeter (C-P) for delineation of stream sediments Au anomalies based on catchment basins in Aghkand region, NW Iran. In this study, a total of 920 stream sediment samples were utilized to determine the geochemical anomalies of Au using the fractal models for selection of optimum model. As a result, the Au anomalies were correlated with geological units located in the western and SW parts of the region that mainly consist of andesite rocks and tuffs. To certify this, 78 litho-geochemical sets of data were utilized to validate the C–P/A, C–N, C–A and C–P fractal models for Au by logratio matrix. The overall accuracy rates are 0.97, 0.96, 0.95 and 0.95 for the C– P/A, C–N, C–A, C–P fractal models, respectively. It showed that the C–P/A model was the optimum fractal model in the study region.
AzterTest: Open source linguistic and stylistic analysis tool

Text Analysis is a useful process to assist teachers in the selection of the most suitable texts for their students. This task demands the analysis of several text features, which is done mostly manually (e.g. syntactic complexity, words variety, etc.). In this paper, we present an open source tool useful for linguistic and stylistic analysis, called AzterTest. AzterTest calculates 153 features and obtains 90.09 % in accuracy when classifying into three reading levels (elementary, intermediate, and advanced). AzterTest is available also as web tool.
Trust in Recommender Systems: A Deep Learning Perspective.

A significant remaining challenge for existing recommender systems is that users may not trust the recommender systems for either lack of explanation or inaccurate recommendation results. Thus, it becomes critical to embrace a trustworthy recommender system. This survey provides a systemic summary of three categories of trust-aware recommender systems: social-aware recommender systems that leverage users' social relationships; robust recommender systems that filter untruthful noises (e.g., spammers and fake information) or enhance attack resistance; explainable recommender systems that provide explanations of recommended items. We focus on the work based on deep learning techniques, an emerging area in the recommendation research.
Increasing Geographic Literacy through the Development of Computer Supported Collaborative Learning

This paper aims to develop computer supported collaborative learning to improve geographic literacy. This study used research and development with the ASSURE model. The results of this development are in the form of basic geography concept modules with 3 videos integrated into e-learning. The media and material validation show that the product developed was very good and worth testing. In addition, the results of media validation by taking into account indicators of attractiveness, efficiency and effectiveness, were declared very good and students were enthusiastic about learning to use this media. This product can be an alternative medium for the 21st century learning environment that applies e-learning in the learning process.
On the design of hardware architectures for parallel frequent itemsets mining

Algorithms for Frequent Itemsets Mining have proved their effectiveness for extracting frequent sets of patterns in datasets. However, in some specific cases, they do not obtain the expected results in an acceptable time. For this reason, Field Programmable Gates Array-based architectures for Frequent Itemsets Mining have been proposed to accelerate this task. The current paper proposes a search strategy for Frequent Itemsets Mining based on equivalence classes partitioning. The partitioning on equivalence classes allows dividing the search space into disjoint sets that can be processed in parallel. Consequently, this paper presents the design and implementation of two hardware architectures that exploit the nested parallelism in the proposed search strategy. These hardware architectures are capable of obtaining frequent itemsets regardless of the number of distinct items and the number of transactions in the dataset, which are the main issues reported in the reviewed literature. Furthermore, the proposed architectures explore the trade-off between acceleration and hardware resource utilization. The experimental results obtained demonstrate that the proposed search strategy can be scaled to achieve a speedup in the processing time of 40 times faster than software-based implementations.
Health monitoring of a computer network based on sequential analysis of serial pattern

Health monitoring of a computer network based on sequential analysis of serial pattern
A New Model for Measuring Customer Utility Trust in Online Auctions

Reports from the US Federal Bureau of investigation have indicated a lot of unethical activities taking place on the internet environments.Despite some technical challenges, ethical related complaints have elicited studies on trust in online commercial transactions like auctioneering.By looking closely on issues that are related to online trust, this study has developed a model that can be used to ascertain trust levels in various websites.The trust issues taken into consideration are Industry Reputation (Ri), Website Reputation (Wi), E-loyalty (Le), Price Premiums (Pp), Social Network (Sn), Value of Good (Vg), Ease of Use (Eu) and Company Satisfactory Transactions (St).In this respect, the level of trust in an auctioneering website has been equated to one's utility.Presumably, if one derives satisfaction from more goods at the company, it is evident that his trust level will be higher.The model has been found to be of good reliability and validity in measuring trust levels in a survey in this study.
A Study of Critical Flicker Fusion Threshold among Smartphone Users

The critical flicker fusion frequency (CFF) is the frequency at which a flickering stimulus is perceived to be constant; the quick recognition of variation in stimuli indicates the active coordination between eyes and brain.When human eyes get exposed to the light radiating from Smartphone screen for longer period that driven us into recognizable mental and visual fatigue results in sleep disturbances, mood fluctuations, insomnia, dry eyes, computer vision syndrome, misconceptions and tactile hallucinations.In the present study the CFF frequency test has been used to evaluate the mental and visual fatigue due to continuous Smartphone overuse.Total 160 respondents (80 girls and 80 boys) of 18 to 24 years age group were randomly selected for baseline data out of which 10 percent i.e. 16 (8 girls and 8 boys) respondents were selected to assess the change in critical flicker fusion (CFF) frequency among the Smartphone users before and after usage.Critical fusion frequency was recorded by Flicker Fusion Apparatus.The findings of the study showed that there was slight decrease in fusion and flicker frequency in both eyes of the respondents that indicated the visual fatigue in the respondents due to Smartphone use for longer period.There was no significant difference between before and after case of CFFF rate in participants.Decreased CFF frequency after using Smartphone indicated visual and mental fatigue in participants.The slight variation in CFFF rate may not giving instant effect but indicated the point of concern for heavy Smartphone users as its impact will be emerge in later phase of life.
Communication Profiling and Characterization of Deep Learning Workloads on Clusters with High-Performance Interconnects

Heterogeneous HPC systems with GPUs are increasingly getting equipped with on-node interconnects like PCIe and NVLink and inter-node interconnects like InfiniBand and Omni-Path. However, the efficient exploitation of these interconnects brings forth many challenges for MPI+CUDA applications. Little exists in the literature that captures the impact of these interconnects on emerging application areas like distributed Deep Learning (DL). In this paper, we choose Horovod; a distributed training middleware, to analyze and profile high-level application workloads (e.g., Training ResNet-50) instead of MPI microbenchmarks. It is challenging to use existing profilers like mpiP and nvprof as they only offer a black box approach and cannot profile emerging communication libraries like NCCL. To address this, we developed a profiler for Horovod that enables profiling of various communication primitives including MPI_Allreduce and ncclAllreduce for gradient exchange as well for Horovod's communication threads and response caches. We analyze the following metrics to gain insights into network-level performance on different interconnects: 1) Message size with tensor fusion, 2) Message size without tensor fusion, 3) Number of MPI and NCCL calls made for each message size, and 4) Time taken by each NCCL and/or MPI call. We also correlate these low-level statistics to higher level end-to-end training metrics like images per second. Three keys insights we gained are: 1) Horovod tensor fusion offers slight performance gains (up to 5%) for CPU-based training on InfiniBand systems, 2) For GPU-based training, disabling tensor fusion improved performance (up to 17%) for GPUs connected with PCIe, and 3) The allreduce latency profiles show some extreme performance variations for non-power-of-two message sizes for both CPUs and GPU on all interconnects when tensor fusion is enabled. To provide a comprehensive view of performance, we use a wide variety of systems with CPUs like Intel Skylake, AMD EPYC, and IBM POWER9, GPUs like Volta V100, and interconnects like PCIe, NVLink, InfiniBand, and Omni-Path.
Pixel-wise conditioned generative adversarial networks for image synthesis and completion

Generative Adversarial Networks (GANs) have proven successful for unsupervised image generation. Several works have extended GANs to image inpainting by conditioning the generation with parts of the image to be reconstructed. Despite their success, these methods have limitations in settings where only a small subset of the image pixels is known beforehand. In this paper we investigate the effectiveness of conditioning GANs when very few pixel values are provided. We propose a modelling framework which results in adding an explicit cost term to the GAN objective function to enforce pixel-wise conditioning. We investigate the influence of this regularization term on the quality of the generated images and the fulfillment of the given pixel constraints. Using the recent PacGAN technique, we ensure that we keep diversity in the generated samples. Conducted experiments on FashionMNIST show that the regularization term effectively controls the trade-off between quality of the generated images and the conditioning. Experimental evaluation on the CIFAR-10 and CelebA datasets evidences that our method achieves accurate results both visually and quantitatively in term of Fréchet Inception Distance, while still enforcing the pixel conditioning. We also evaluate our method on a texture image generation task using fully-convolutional networks. As a final contribution, we apply the method to a classical geological simulation application.