Reviewerly/exHarmony huggingface dataset:
Strict Timed Causal Consistency as a Hybrid Consistency Model in the Cloud Environment

Cloud computing is a model of distributed systems. This system allows users to access virtual resources including the processing power, storage, applications, etc. Storage as a Service (SaaS) is one of the cloud computing services. Cloud storage systems provide this service for the end-users, and deliver data availability and durability as well as global accessibility throughout the Internet. High data availability and scalability are very crucial criteria for the end-users in cloud storage systems. To achieve them, we need replication in these systems. However, the replication brings about asynchronization of data among replicas in different cloud data-centers. It reduces the performance of the cloud storage systems as well. Therefore, replication is one of the crucial challenges in cloud storage systems. These systems need to ensure that the data are synchronized among different replicas by implementing consistency policies. In this paper, we present the Strict Timed Causal Consistency (STCC) as a hybrid consistency model which can be considered as an extension to the cloud computing. This consistency model has two components: client-side, and server-side. At the client-side, this model supports monotonic read, monotonic write, read your write, and write follow read consistencies. At the server-side, it supports the Timed Causal Consistency (TCC) as well. Additionally, it is stronger than the client-centric and is more flexible than the data-centric approaches. In spite of partition tolerance, our proposed method guarantees the consistency and satisfies data availability. Cassandra is a NoSQL database with high scalability and availability. Cassandra comes with multiple consistency levels as a service such as ONE, ALL, QUORUM, etc. We have examined the proposed approach with respect to different consistency levels of Cassandra and Causal Consistency (CC). Yahoo Cloud Serving Benchmark (YCSB) consists of a number of workloads which are used to evaluate our proposed method. We have executed different workloads on the Cassandra clusters and with respect to which we have made a comparison between the performance of our proposed method and the four other different consistency levels in Cassandra. The experimental results based on the comparison between the proposed method and ONE, ALL, QUORUM, as well as the CC consistencies, on a Cassandra cluster with 24 nodes, testify that on average our approach has reduced the stale read rate by 24% on workload-A, and on workload-B by 25%. Also, the system throughput with respect to workload-A has increased by more than 20%. Besides, when we applied our proposed STCC on workload-B the system throughput increased by almost 35%.
Convolutional Neural Network Architecture for Offline Handwritten Characters Recognition

Automatic handwriting recognition systems are a very wide-ranging research topic for many years. This type of intelligent systems is applied in various fields: Checks processing, forms processing, automatic processing of handwritten answers in an examination, etc. The purpose of this work is to propose a new Convolutional Neural Network (CNN) model and compare its performance with those of K-Nearest Neighbors (KNN) technique. Both used for the classification of complex multiclass problems. In this paper, we implement and evaluate the performance of this two-machine learning algorithms used to predict handwritten characters. The training and testing data have been extracted from the MNIST digits and letters database, which contains pre-processed images. The results obtained using different similarity measures such as accuracy, sensitivity and specificity confirm that the classification obtained by our proposed CNN architecture is the most accurate compared to the KNN studied in this work.
Calibration-Free Image-Based Trajectory Tracking Control of Mobile Robots With an Overhead Camera

To make the controller implementation easier and to enhance the system robustness and control performance in the presence of the camera parameter uncertainties, it is very desired to develop vision-based control approaches without any offline or online camera calibration. In this article, we propose a new calibration-free image-based trajectory tracking control scheme for nonholonomic mobile robots with a truly uncalibrated fixed camera. By developing a novel camera-parameter-independent kinematic model, both offline and online camera calibration can be avoided in the proposed scheme, and any knowledge of the camera is not needed in the controller design. The proposed trajectory tracking control scheme can guarantee exponential convergence of the image position and velocity tracking errors. To illustrate the performance of the proposed scheme, experimental results are provided in this article. Note to Practitioners-This article was motivated by the vision-based motion control problem of mobile robots in uncalibrated environments. Existing vision-based motion control approaches for nonholonomic mobile robots generally depend on offline precise/coarse or online numerical/adaptive calibration of the camera intrinsic and extrinsic parameters and require precise or coarse knowledge of the camera in their implementation. This article presents a novel calibration-free image-based trajectory tracking control scheme, which can be implemented easily in real environments without any offline or online calibration of the camera parameters and can be used to efficiently control the motion of nonholonomic mobile robots with an arbitrarily placed and truly unknown overhead camera. Experimental results show that the proposed scheme can achieve satisfactory trajectory tracking control performance despite the lack of any knowledge about the camera intrinsic and extrinsic parameters and the presence of unknown camera lens distortions, and hence, can provide a simple but efficient solution to the vision-based motion control problem of nonholonomic mobile robots.
Non-reference Quality Assessment Model using Deep learning for Omnidirectional Images

Image quality assessment (IQA) has been a popular research topic in image processing. However, most studies until now have been focusing on traditional images and only a few focused on omnidirectional images. Unlike in the case of traditional images, the users can only view a part of 360-degree images at a time, and thus tend to focus more on specific regions of the image. This makes predicting quality scores for omnidirectional images a challenging task since most existing models for traditional images usually treat all regions of the image equally. In this paper, we propose an omnidirectional image quality assessment model based on deep learning. This model focuses on learning the features of the middle region of input images. The model first automatically predicts the quality scores for patches sampled from the input image. The quality score of the image will then be calculated by weighted averaging of the patch quality scores based on their positions. Experimental results show that the proposed model provides very promising accuracy for predicting quality scores of omnidirectional images.
Data-Driven Elicitation and Optimization of Dependencies between Requirements

Requirement dependencies affect many activities in the software development life cycle such as design, implementation, testing, release planning and change management. They are the basis for various software development decisions. However, requirements dependencies extraction is not only error-prone but also a cognitively and computationally complex problem that consumes substantial efforts, since most of the requirements are documented in natural language. This paper proposes a novel approach to extracts requirements dependencies utilizing natural-language processing (NLP) and weakly supervised learning (WSL) in two stages. In the first stage, binary dependencies (basic dependencies:dependent/independent) are identified, which are further analyzed to detect the type of the dependency in the second stage. An initial evaluation of this approach on the PURE data set - European Rail Traffic Management System - was carried out using three machine learners (Random Forest, Support Vector Machine and Naive Bayes), which were then compared and tested. Results showed that all the three learners exhibited similar accuracy measures, while SVM needed additional parameter tuning. The machine learners' accuracy was further improved by applying weakly supervised learning to generate pseudo annotations for unlabelled data. Based on these results, agenda is to provide decision support under a dynamic use case scenario that includes (i) continuous updates and analysis of dependencies, (ii) identification of the general types of dependencies, and (iii) dependencies as a key driver of the decision support for the product releases.
Major–Minor Long Short-Term Memory for Word-Level Language Model

Language model (LM) plays an important role in natural language processing (NLP) systems, such as machine translation, speech recognition, learning token embeddings, natural language generation, and text classification. Recently, the multilayer long short-term memory (LSTM) models have been demonstrated to achieve promising performance on word-level language modeling. For each LSTM layer, larger hidden size usually means more diverse semantic features, which enables the LM to perform better. However, we have observed that when a certain LSTM layer reaches a sufficiently large scale, the promotion of overall effect will slow down, as its hidden size increases. In this article, we analyze that an important factor leading to this phenomenon is the high correlation between the newly extended hidden states and the original hidden states, which hinders diverse feature expression of the LSTM. As a result, when the scale is large enough, simply lengthening the LSTM hidden states will cost tremendous extra parameters but has little effect. We propose a simple yet effective improvement on each LSTM layer consisting of a large-scale Major LSTM and a small-scale Minor LSTM to break the high correlation between the two parts of hidden states, which we call Major-Minor LSTMs (MMLSTMs). In experiments, we demonstrate the LM with MMLSTMs surpasses the existing state-of-the-art model on Penn Treebank (PTB) and WikiText-2 (WT2) data sets and outperforms the baseline by 3.3 points in perplexity on WikiText-103 data set without increasing model parameter counts.
Characterization of microseepage-induced diagenetic changes in the Upper-Red Formation, Qom region, Iran. Part I: Outcrop, geochemical, and remote sensing studies

This article explores the footprints of hydrocarbon microseepage in the Upper-Red Formation covering the Alborz petroleum reservoir in the Saveh-Qom basin, Iran. The adopted multidisciplinary approach combines field observation and sampling, detailed petrographical and geochemical analysis, and remote sensing mapping using Sentinel-2 and ASTER satellite data. The study revealed significant changes in the microseepage-affected zones when contrasted to unaffected lithofacies and led to the establishment of a new array of mineralogical and geochemical indicators for the phenomenon. These comprise partial bleaching of the red-beds, secondary goethite coatings, diagenetic clay minerals (e.g. illite-smectite, chlorite), and pervasive calcite, silica, and gypsum cementations. In terms of geochemistry, the major oxides, particularly the ratio of CaO + K2O/Na2O + MgO + CaO + K2O proved to be a reliable index for distinguishing microseepage-prone areas. Based on isotopic studies, a combination of three sources of carbon, comprising oxidized hydrocarbons, was recognized in the carbonate cements; yet the strata were shown to be moderately flushed by percolating fluids. Satellite remote sensing was successful in mapping reservoir-scale footprints of the microseepage systems. It indicated that the extent of the induced alterations, and thereby the outline of the underlying reservoir, is much larger than previously known. Overall, the secondary changes were shown to be a function of the geochemistry of the host-rock, the local environmental variables (i.e. climate and hydrogeology), and the 3D architecture of the trap. The substantiated indicators carry direct implications for onshore microseepage targeting and hold high potential to help reduce the exploration and development risks in frontier and mature basins.
pSConv: A Pre-defined Sparse Kernel Based Convolution for Deep CNNs

The high demand for computational and storage resources severely impedes the deployment of deep convolutional neural networks (CNNs) in limited resource devices. Recent CNN architectures have proposed reduced complexity versions (e.g,. SuffleNet and MobileNet) but at the cost of modest decreases in accuracy. This paper proposes pSConv, a pre-defined sparse 2D kernel based convolution, which promises significant improvements in the trade-off between complexity and accuracy for both CNN training and inference. To explore the potential of this approach, we have experimented with two widely accepted datasets, CIFAR-10 and Tiny ImageNet, in sparse variants of both the ResNet18 and VGG16 architectures. Our approach shows a parameter count reduction of up to 4.24× with modest degradation in classification accuracy relative to that of standard CNNs. Our approach outperforms a popular variant of ShuffleNet using a variant of ResNet18 with pSConv having 3 × 3 kernels with only four of nine elements not fixed at zero. In particular, the parameter count is reduced by 1.7× for CIFAR-10 and 2.29× for Tiny ImageNet with an increased accuracy of ~ 4%.
Inspecting adversarial examples using the fisher information

Adversarial examples are constructed by slightly perturbing a correctly processed input to a trained neural network such that the network produces an incorrect result. This work proposes the usage of the Fisher information for the detection of such adversarial attacks. We discuss various quantities whose computation scales well with the network size, study their behavior on adversarial examples and show how they can highlight the importance of single input neurons, thereby providing a visual tool for further analyzing the behavior of a neural network. The potential of our methods is demonstrated by applications to the MNIST, CIFAR10 and Fruits-360 datasets and through comparison to concurring methods.
Motion-Guided Spatial Time Attention for Video Object Segmentation

In this paper, we propose a novel motion-guided attention module to implant the spatial and time consistency in the correlation map of the current frame with the historical frames. Unlike other mask propagation based methods, our method regards the previous mask as a strong prior instead of concatenating it to the current frame or feature for propagation. Additionally, to reduce the gap between training and testing phase, we propose an improved optimization strategy, named sequence learning, which feeds a video in chronological order into the end-to-end network instead of several random-sampling frames when training. Sequence learning helps our model be better aware of the concept of tracking and recognition of object. We evaluated the proposed algorithm on the second YouTube-VOS test-challenge set and achieved a J&F mean score of 81.7%, ranked the second place on the VOS track. In the challenge, our method only uses ResNet-50 as the backbone and our score is very slightly worse than the first place score, i.e., 0.1%, which implies that our VOS framework is the state-of-the-art one.
A Channel Pruning Algorithm Based on Depth-Wise Separable Convolution Unit

Deep learning has made significant progress in many fields such as image identification, speech recognition and natural language processing, especially in the field of computer vision. The better performance of the neural network often built on deeper, wider network structure, more network parameters and more storage and often computational expensive. As a result, it is hard to deploy neural network to mobile and embedded devices. Therefore, compressing of convolutional neural networks is very necessary and practical. In this paper, we propose a channel pruning algorithm for depth-wise separable convolution units and introduce a new channel selection algorithm based on information gain and a method for quickly recovering network performance after pruning. The proposed method is implemented on MobileNet and validated on several popular datasets. The experimental results show that our method can achieve better experimental results on several image classification datasets, and also achieve good detection results on the PASCAL VOC image detection dataset.
Syntax parsing model of Kazakh simple sentences

This paper proposes a syntactic analysis of Kazakh simple sentences taking into account their semantics. To do this, first, the syntactic rules of sentences are described using formal grammar, then parsing trees and ontological models are built to determine the semantics of their components and the relationships between them. As a formal grammar used Chomsky's context-free grammar, and ontological models were built in the environment of Protege.
A universal cross language software similarity detector for open source software categorization

While there are novel approaches for detecting and categorizing similar software applications, previous research focused on detecting similarity in applications written in the same programming language and not on detecting similarity in applications written in different programming languages. Cross-language software similarity detection is inherently more challenging due to variations in language, application structures, support libraries used, and naming conventions. In this paper we propose a novel model, CroLSim, to detect similar software applications across different programming languages. We define a semantic relationship among cross-language libraries and API methods (both local and third party) using functional descriptions and a word-vector learning model. Our experiments show that CroLSim can successfully detect cross-language similar software applications, which outperforms all existing approaches (mean average precision rate of 0.65, confidence rate of 3.6, and 75% highly rated successful queries). Furthermore, we applied CroLSim to a source code repository to see whether our model can recommend cross-language source code fragments if queried directly with source code. From our experiments we found that CroLSim can recommend cross-language functional similar source code when source code is directly used as a query (average precision=0.28, recall=0.85, and F-Measure=0.40).
Locality Constraint Dictionary Learning With Support Vector for Pattern Classification

Discriminative dictionary learning (DDL) has recently gained significant attention due to its impressive performance in various pattern classification tasks. However, the locality of atoms is not fully explored in conventional DDL approaches which hampers their classification performance. In this paper, we propose a locality constraint dictionary learning with support vector discriminative term (LCDL-SV), in which the locality information is preserved by employing the graph Laplacian matrix of the learned dictionary. To jointly learn a classifier during the training phase, a support vector discriminative term is incorporated into the proposed objective function. Moreover, in the classification stage, the identity of test data is jointly determined by the regularized residual and the learned multi-class support vector machine. Finally, the resulting optimization problem is solved by utilizing the alternative strategy. Experimental results on benchmark databases demonstrate the superiority of our proposed method over previous dictionary learning approaches on both hand-crafted and deep features. The source code of our proposed LCDL-SV is accessible at https://github.com/yinhefeng/LCDL-SV.
Trajectory Planning With Shortest Path for Modified Uncalibrated Visual Servoing Based on Projective Homography

In order to improve the robustness and optimize trajectory of projective homography-based uncalibrated visual servoing (PHUVS) proposed in our previous work, an analytical expression of optimal trajectory for camera in projective homography space is proposed in this article, which is totally free of camera parameters and is corresponding to camera's shortest path in the 3-D space with straight path in translation and minimal geodesic in rotation. The projective homography is computed without scale ambiguity in both planning and tracking stages. The PHUVS controller is modified correspondingly to track the planned trajectory in projective homography space while maintaining superior characteristic of PHUVS under uncalibrated scenario. The simulations and experiments' results reveal the effectiveness and necessity of the proposed trajectory optimization method in the existence of large initial errors. Note to Practitioners-The state-of-the-art visual-guided robotic technology in industry usually requires system calibration, which is often costly, vulnerable, and challenging for ordinary workers. In our previous work, we offered an uncalibrated visual servo method based on projective homography named projective homography-based uncalibrated visual servoing (PHUVS), which is suitable for plug and play application for eye-in-hand robot visual servo tasks. However, PHUVS suffers from some defects, including undesirable 3-D space motion and local convergence. In this article, we proposed the trajectory planning method along with a modified PHUVS controller to improve the original one from the disadvantages mentioned earlier. This planning method is also calibration-free. With pure image information, a straight-line path in translational motion along with minimal geodesic in rotary motion can be achieved. This approach is capable of extending the range of applications for uncalibrated visual servo technology in robotic tasks, such as assembling, painting, and robotic machining.
Effect on Social Connectedness and Stress Levels by Using a Huggable Interface in Remote Communication

Affective communication technologies are designed to enhance awareness, social connectedness, and affectivity. Design strategies involve alternative methods to convey affection in computer-mediated scenarios, emphasizing on the importance of mediated physical contact. Therefore, we proposed a huggable interface to mediate social touch by sensing the user's hug gestures, transferring them to a paired device, and delivering them as simple cues. We investigated the effect of the huggable interface as a mediator with a physical embodiment and compared it with a similar communication interface represented by an agent with a virtual embodiment on a touch screen. During the experiments, we set up a scenario in which individuals with a close relationship watched movies and communicated with each other. Results showed the effect of both interfaces in terms of perceived social connectedness and stress levels. The discussion pointed out the potential and limitations of the proposed evaluation method, as well as of each type of interface as affective communication technology.