Reviewerly/exHarmony huggingface dataset:
Analyzing the Reliability of Convolutional Neural Networks on GPUs: GoogLeNet as a Case Study

Convolutional Neural Networks (CNNs) are used for tasks such as object recognition. Once a CNN model is used in a radiative environment, reliability of the system against soft errors is a crucial issue, especially in safety-critical and high-performance applications that bound with real-time response. Selectively-hardening techniques do improve the reliability of these systems. However, the hard question in selective techniques is "how to exclusively select code portions to harden, to safe the performance from being degraded". In this paper, we propose a comprehensive analysis methodology for CNN-based classification models to confidently determine the only vulnerable parts of the source code. To achieve this, we propose a technique, Layer Vulnerability Factor (LVF) and adopt another technique, Kernel Vulnerability Factor (KVF). We apply these techniques to GoogLeNet, which is a famous image classification model, to validate our methodology. We precisely identify the parts of the GoogLeNet model that need to be hardened instead of using expensive duplication solutions.
CEDULE+: Resource Management for Burstable Cloud Instances Using Predictive Analytics

Nearly all principal cloud providers now provide burstable instances in their offerings. The main attraction of this type of instance is that it can boost its performance for a limited time to cope with workload variations. Although burstable instances are widely adopted, it is not clear how to efficiently manage them to avoid waste of resources. In this article, we use predictive data analytics to optimize the management of burstable instances. We design CEDULE+, a data-driven framework that enables efficient resource management for burstable cloud instances by analyzing the system workload and latency data. CEDULE+ selects the most profitable instance type to process incoming requests and controls CPU, I/O, and network usage to minimize the resource waste without violating Service Level Objectives (SLOs). CEDULE+ uses lightweight profiling and quantile regression to build a data-driven prediction model that estimates system performance for all combinations of instance type, resource type, and system workload. CEDULE+ is evaluated on Amazon EC2, and its efficiency and high accuracy are assessed through real-case scenarios. CEDULE+ predicts application latency with errors less than 10%, extends the maximum performance period of a burstable instance up to 2.4 times, and decreases deployment costs by more than 50%.
An Emulation Platform for Evaluating the Reliability of Deep Neural Networks

In recent years, Deep Neural Networks have been increasingly adopted by a wide range of applications characterized by high-reliability requirements, such as aerospace and automotive. In this paper, we propose an FPGA-based platform for emulating faults in the architecture of DNNs. The approach exploits the reconfigurability of FPGAs to mimic faults affecting the hardware implementing DNNs. The platform allows the emulation of various kinds of fault models enabling the possibility to adapt to different types, devices, and architectures. In this work, a fault injection campaign has been performed on a convolutional layer of AlexNet, demonstrating the feasibility of the platform. Furthermore, the errors induced in the layer are analyzed with respect to the impact on the whole network inference classification.
Attention Combination of Sequence Models for Handwritten Chinese Text Recognition

Handwritten Chinese text recognition (HCTR) methods can generally be divided into two categories: explicit segmentation based methods and implicit segmentation based methods. The explicit segmentation based approaches are superior in the accuracy rate of character recognition, while the implicit segmentation based approaches can provide more fluent recognition result due to the end-to-end recognition mode of it. In this paper, we propose to combine this two kinds of approaches using convolutional combination strategy. The proposed system includes multiple encoders and a decoder in the form of full convolution. It combines the recognition results of explicit segmentation based HCTR methods and implicit segmentation based HCTR methods, and generates the final recognition output. A novel attention combination mechanism is proposed to solve this one-to-many attention problem. Experiments on the ICDAR13 dataset show that the proposed method improves the accurate rate by 4.96%. The experimental results demonstrate the effectiveness of proposed method.
Interactive Machine Learning Tool for Clustering in Visual Analytics

Clustering is an important task in knowledge discovery with the goal of finding groups of similar data points in a dataset. Today there are many different approaches to clustering, including methods to incorporate user decisions into the clustering process. Some of these interactive approaches fall into the category of visual analytics and emphasize the power of visualizations to help find clusters manually in various types of datasets or to verify the results of clustering algorithms. The interactive projection-based clustering (IPBC) is an open-source and parameter-free method using user input on interactive visualizations to cluster high-dimensional data. This work introduces the IPBC approach and compares it to the results of accessible visual analytics approaches for clustering, showing that IPBC can outperform them.
MEmoFC: introducing the Multilingual Emotional Football Corpus

Abstract This paper introduces a new corpus of paired football match reports, the Multilingual Emotional Football Corpus, (MEmoFC), which has been manually collected from English, German, and Dutch websites of individual football clubs to investigate the way different emotional states (e.g. happiness for winning and disappointment for losing) are realized in written language. In addition to the reports, it also contains the statistics for the selected matches. MEmoFC is a corpus consisting of comparable subcorpora since the authors of the texts report on the same event from two different perspectives—the winner’s and the loser’s side, and from an arguably more neutral perspective in tied matches. We demonstrate how the corpus can be used to investigate the influence of affect on the reports through different approaches and illustrate how game outcome influences (1) references to the own team and the opponent, and (2) the use of positive and negative emotion terms in the different languages. The MEmoFC corpus, together with the analyzed aspects of emotional language will open up new approaches for targeted automatic generation of texts.
Renewable Energy Prediction through Machine Learning Algorithms

This paper aims to implement an efficient renewable energy selection (either solar or wind) based on the chosen geographic location of Aguascalientes, Mexico through a Machine Learning (ML) method. Likewise, the information listed below will provide both a critical analysis and review of the state-of-the-art applications for ML Algorithms such as Support Vector Machines (SVM), Linear Regression (LR) and Neural Network Models (NNM). Rigorous data measurements taken over a period of six months, including those of solar irradiance, temperature, wind speed and wind direction to name a few, were the inputs used in different algorithms in order to find t he o ne t hat could most accurately predict future weather conditions. Based on the obtained results, the best ML Algorithm ended up being Random Forest; an approach that is capable of building an accurate prediction through the calculation of two crucial parameters; Mean Square Error (MSE) and Mean Absolute Error (MAE).
Revisiting Refactoring Mechanics from Tool Developers’ Perspective

Refactoring is a widely used development practice, available in mainstream IDEs. A previous work conducted a survey with developers, and finds that most of them use their experience to define refactoring mechanics for each refactoring type. However, we do not know whether a similar scenario happens in the context of refactoring tool developers. In this work, we conduct a study to compare refactoring mechanics implemented by tool developers. We apply 10 types of refactorings to 157,339 programs using 27 refactoring implementations from three tools, and compare the outputs. The refactoring implementations of Eclipse, NetBeans, and JRRT have different mechanics in all refactoring types.
A Lightweight Mobile Outdoor Augmented Reality Method Using Deep Learning and Knowledge Modeling for Scene Perception to Improve Learning Experience

(2021). A Lightweight Mobile Outdoor Augmented Reality Method Using Deep Learning and Knowledge Modeling for Scene Perception to Improve Learning Experience. International Journal of Human–Computer Interaction: Vol. 37, No. 9, pp. 884-901.
Maggy

Running extensive experiments is essential for building Machine Learning (ML) models. Such experiments usually require iterative execution of many trials with varying run times. In recent years, Apache Spark has become the de-facto standard for parallel data processing in the industry, in which iterative processes are implemented within the bulk-synchronous parallel (BSP) execution model. The BSP approach is also being used to parallelize ML trials in Spark. However, the BSP task synchronization barriers prevent asynchronous execution of trials, which leads to a reduced number of trials that can be run on a given computational budget. In this paper, we introduce Maggy, an open-source framework based on Spark, to execute ML trials asynchronously in parallel, with the ability to early stop poorly performing trials. In the experiments, we compare Maggy with the BSP execution of parallel trials in Spark and show that on random hyperparameter search on a convolutional neural network for the Fashion-MNIST dataset Maggy reduces the required time to execute a fixed number of trials by 33% to 58%, without any loss in the final model accuracy.
WRFMR: A Multi-Agent Reinforcement Learning Method for Cooperative Tasks

Multi-agent reinforcement learning (MARL) for cooperative tasks has been extensively studied in recent years. The balance of exploration and exploitation is crucial to MARL algorithms' performance in terms of the learning speed and the quality of the obtained strategy. To this end, we propose an algorithm known as the weighted relative frequency of obtaining the maximal reward (WRFMR), which uses a weight parameter and the action probability to balance exploration and exploitation and accelerate convergence to the optimal joint action. For the WRFMR algorithm, each agent needs to share the state and the immediate reward and does not need to observe the actions of the other agents. Theoretical analysis on the model of WRFMR in cooperative repeated games shows that each optimal joint action is an asymptotically stable critical point if the component action of every optimal joint action is unique. The box-pushing task, the distributed sensor network (DSN) task, and a strategy game known as blood battlefield are used for empirical studies. Both the DSN task and the box-pushing task involve full cooperation, while blood battle comprises both cooperation and competition. The simulation results show that the WRFMR algorithm outperforms the other algorithms regarding the success rate and the learning speed.
Discovering Frequent Spatial Patterns in Very Large Spatiotemporal Databases

Frequent pattern mining is an important model in data mining. It involves finding all patterns in a transactional database that satisfy the user-specified minimum support (minSup) constraint. The minSup controls the minimum number of transactions that a pattern must cover in a transactional database. Since only minSup is used to evaluate a pattern's interestingness, the frequent pattern model implicitly assumes that spatial information of the items will not impact the interestingness of a pattern in the database. This assumption limits the applicability of the frequent pattern model in many real-world applications. It is because patterns whose items are close to each other are typically more attractive to the user than the patterns whose items are far from each other in a coordinate system. With this motivation, this paper proposes a novel model of frequent spatial pattern that may exist in a spatiotemporal database. An efficient pattern-growth algorithm, called Frequent Spatial Pattern-growth (FSP-growth), has also been presented to mine all desired patterns in a database. Experimental results demonstrate that our algorithm is efficient. The usefulness of the proposed patterns has also been shown with a real-world application.
The Performance of Active-Contour and Region Growing Methods Against Noises in the Segmentation of Computed-Tomography Scans

Lung cancer is a prevalent cancer that needs early diagnosis because of its deadly repercussion. Computer systems with certain image processing techniques should be used to increase the speed and accuracy of lung cancer detection. Because of high resolution and low noise, CT images are normally used to process medical images. However, the inevitable noise is introduced in CT images, because of uncertain statistics in physical measurements in CT images. In general, speckle noise, Gaussian noise, and salt and pepper noise occurred primarily in MRI, CT scan, and ultrasound images. In this article, we compared the performance of two segmentation algorithms, the region growing algorithm with the combination of Watershed and Active Contour algorithms, which are more applicable and efficient against the speckle noise and Gaussian noise in medical CT images. Results show that the proposed approach is more effective in segmentation of lung nodules and can be a valuable aid for physicians working in the daily routine of oncology.
Compositionality of Safe Communication in Systems of Team Automata

We study guarantees for safe communication in systems of systems composed of reactive components that communicate through synchronised execution of common actions. Systems are modelled as (extended) team automata, in which, in principle, any number of component automata can participate in the execution of a communicating action, either as a sender or as a receiver. We extend team automata with synchronisation type specifications, which determine specific synchronisation policies fine-tuned for particular application domains. On the other hand, synchronisation type specifications generate communication requirements for receptiveness and responsiveness. We propose a new, liberal version of requirement satisfaction which allows teams to execute arbitrary intermediate actions before being ready for the required communication, which is important in practice. Then we turn to the composition of systems and show that composition behaves well with respect to synchronisation type specifications. As a central result, we investigate criteria that ensure the preservation of local communication properties when (extended) team automata are composed. This is particularly challenging in the context of weak requirement satisfaction.
Privacy attacks against deep learning models and their countermeasures

Recently, deep learning is considered an important concept that is used in a lot of important applications, which require accurate models, such as image classification, identification of audio, intrusion detection, and face recognition. However, building a good deep learning model needs a huge amount of data that is not easily provided, especially in the applications that need sensitive information. Accordingly, researchers propose multiple methodologies for sharing the model rather than sharing the dataset itself. Nevertheless, it has been proven that the shared models still could leak a lot of sensitive information about the private data. In this work, we introduce a survey about the attacks that could be launched against the shared models and the countermeasures that could be taken to preserve the privacy of the sensitive data that is used for the training process.
Traffic video‐based intelligent traffic control system for smart cities using modified ant colony optimizer

Abstract Road traffic congestion is a serious problem in today's world and it happens because of urbanization and population growth. The traffic reduces the transport efficiency in the city, increases the waiting time and travel time, and also increases the usage of fuel and air pollution. To overcome these issues this papers propose an intelligent traffic control system using the Internet of Vehicles (IoV). The vehicles or nodes present in the IoV can communicate between themselves. This technique helps in determining the traffic intensity and the best route to reach the destination. The area of study used in this paper is Vellore city in Tamilnadu, India. The city map is separated into many segments of equal size and Ant Colony Algorithm (AOC) is applied to the separated maps to find the optimal route to reach the destination. Further, Support Vector Machine (SVM) is used to calculate the traffic density and to model the heavy traffic. The proposed algorithm performs better in finding the optimal route when compared to that of the existing path selection algorithms. From the results, it is evident that the proposed IoV‐based route selection method provides better performance.
Behavioral modeling based on cloud computing and target user recommendation for English cloud classroom

The English cloud classroom learning system provides a simple and effective way to access cloud-based training and learning materials. Student records based on behavioral modeling and interactive volume are assumed that the degree of student participation in the course measure. Previous studies, typically the frequency of student activities while ignoring temporary information. Analysis of Students' Activities in the course over time. According to the online classroom activity data, similar behavior change with time students are clustered. These results are three different groups. A large majority of inactive students. Another group to active throughout the students. However, a group of students to the event, but their activity will be reduced throughout the course. These student groups exhibit different performance. Overall students more active, the better the results. In addition to these general trends, also identified a trend of alternative courses that can be found, for example, becomes an active group of students in the course. It suggests that students' behavior is identified from a single course of conduct than the more complex, multi-mode learning activities course. More research is essential. In this study, investigated the Portuguese teachers 'views on classroom management, the relationship between self-off and the efficacy of teachers' and teachers' perception of classroom behavioral teachers' perception of time. Use mediator and moderator variables on the cloud classroom management, and teachers of English teachers perceive the relationship between classroom conditions were cheating process analysis.
Hardware Implementation of Secure Lightweight Cryptographic Designs for IoT Applications

The recent expansion of the Internet of Things is creating a new world of smart devices in which security implications are very significant. Besides the claimed security level, the IoT devices are usually featured with constrained resources, such as low computation capability, low memory, and limited battery. Lightweight cryptographic primitives are proposed in the context of IoT while considering the trade-off between security guarantee and good performance. In this paper, we present optimized hardware, lightweight cryptographic designs, of 32-bit datapath, LED 64/128, SIMON 64/128, and SIMECK 64/128 algorithms, for constrained devices. Our proposed designs are investigated on Spartan-3, Spartan-6, and Zynq-7000 FPGA platforms in terms of area, speed, efficiency, and power consumption. The proposed designs achieved a high throughput up to 891.99 Mbps, 838.95 Mbps, and 210.13 Mbps for SIMECK 64/128, SIMON 64/128, and LED 64/128 on Zynq-7000, respectively. A deep comparison between our three proposed designs is elaborated on different FPGA families for adequate FPGAs-based application deployment. Test results and security analysis show that not only can our proposed designs achieve good encryption results with high performance and a low reduced cost but also they are secure enough to resist statistical attacks.
Texture Analysis of Breast Cancer via LBP, HOG, and GLCM techniques

Abstract Breast cancer is a prevailing reason for death, and it is a particular kind of tumor that is popular among ladies across the world. Till presently, there is no efficient method to stop the appearance of the breast tumor. Accordingly, early detection is the first stage in the diagnosis of breast tumors and reduces mortality. Screening Mammography is the most effective technique for early detection of breast tumors. Great experience and large practices of specialists are wanted when examining breast tissue in a mammogram. In this work, feature extraction techniques are offered as methods to decrease false-positive that occur in breast diagnosis. Mini-MIAS database used to evaluate these approaches. LBP, HOG, and GLCM are feature extraction techniques used for analyzing mass tissue and extract features from the ROI. Contrast, energy, correlation, and homogeneity are used as features properties. These features utilized as the input to the different classifiers which achieved the best results. To improve the diagnosis ability, “contrast limited adaptive histogram equalization” utilized as a preprocessing system. The best results gained in this work by LBP method and logistic regression classifier at ROI (30×30) where the accuracy 92.5%. The HOG method achieved the best results with the SVM classifier where accuracy 90% at ROI (30×30). GLCM provides the best results with the KNN classifier where the accuracy 89.3% at ROI (30×30). The greatest accuracy reached in the case of ROI (30×30) in all feature extraction methods that used in this work.
Many-Shot from Low-Shot: Learning to Annotate Using Mixed Supervision for Object Detection

Object detection has witnessed significant progress by relying on large, manually annotated datasets. Annotating such datasets is highly time consuming and expensive, which motivates the development of weakly supervised and few-shot object detection methods. However, these methods largely underperform with respect to their strongly supervised counterpart, as weak training signals often result in partial or oversized detections. Towards solving this problem we introduce, for the first time, an online annotation module (OAM) that learns to generate a many-shot set of reliable annotations from a larger volume of weakly labelled images. Our OAM can be jointly trained with any fully supervised two-stage object detection method, providing additional training annotations on the fly. This results in a fully end-to-end strategy that only requires a low-shot set of fully annotated images. The integration of the OAM with Fast(er) R-CNN improves their performance by $$17\%$$ mAP, $$9\%$$ AP50 on PASCAL VOC 2007 and MS-COCO benchmarks, and significantly outperforms competing methods using mixed supervision.