Reviewerly/exHarmony huggingface dataset:
Why Skip If You Can Combine: A Simple Knowledge Distillation Technique for Intermediate Layers

With the growth of computing power neural machine translation (NMT) models also grow accordingly and become better. However, they also become harder to deploy on edge devices due to memory constraints. To cope with this problem, a common practice is to distill knowledge from a large and accurately-trained teacher network (T) into a compact student network (S). Although knowledge distillation (KD) is useful in most cases, our study shows that existing KD techniques might not be suitable enough for deep NMT engines, so we propose a novel alternative. In our model, besides matching T and S predictions we have a combinatorial mechanism to inject layer-level supervision from T to S. In this paper, we target low-resource settings and evaluate our translation engines for Portuguese--English, Turkish--English, and English--German directions. Students trained using our technique have 50% fewer parameters and can still deliver comparable results to those of 12-layer teachers.
Research Replication Prediction Using Weakly Supervised Learning

Knowing whether a published research result can be replicated is important.Carrying out direct replication of published research incurs a high cost.There are efforts tried to use machine learning aided methods to predict scientific claims' replicability.However, existing machine learning aided approaches use only hand-extracted statistics features such as p-value, sample size, etc. without utilizing research papers' text information and train only on a very small size of annotated data without making the most use of a large number of unlabeled articles.Therefore, it is desirable to develop effective machine learning aided automatic methods which can automatically extract text information as features so that we can benefit from Natural Language Processing techniques.Besides, we aim for an approach that benefits from both labeled and the large number of unlabeled data.In this paper, we propose two weakly supervised learning approaches that use automatically extracted text information of research papers to improve the prediction accuracy of research replication using both labeled and unlabeled datasets.Our experiments over real-world datasets show that our approaches obtain much better prediction performance compared to the supervised models utilizing only statistic features and a small size of labeled dataset.Further, we are able to achieve an accuracy of 75.76% for predicting the replicability of research.
A Data Mining Method Using Deep Learning for Anomaly Detection in Cloud Computing Environment

Aiming at problems such as slow training speed, poor prediction effect, and unstable detection results of traditional anomaly detection algorithms, a data mining method for anomaly detection based on the deep variational dimensionality reduction model and MapReduce (DMAD-DVDMR) in cloud computing environment is proposed. First of all, the data are preprocessed by a dimensionality reduction model based on deep variational learning and based on ensuring complete data information as much as possible, the dimensionality of the data is reduced, and the computational pressure is reduced. Secondly, the data set stored on the Hadoop Distributed File System (HDFS) is logically divided into several data blocks, and the data blocks are processed in parallel through the principle of MapReduce, so the k-distance and LOF value of each data point can only be calculated in each block. Thirdly, based on stochastic gradient descent, the concept of k-neighboring distance is redefined, thus avoiding the situation where there are greater than or equal to k-repeated points and infinite local density in the data set. Finally, compared with CNN, DeepAnt, and SVM-IDS algorithms, the accuracy of the scheme is increased by 10.3%, 18.0%, and 17.2%, respectively. The experimental data set verifies the effectiveness and scalability of the proposed DMAD-DVDMR algorithm.
Hybrid Cloud Workflow Scheduling Method With Privacy Data

Privacy protection is an important problem in workflow scheduling, for which scheduling tasks with privacy constraints to reliable resources is essential. In this article, we consider the scheduling problem of Spark applications in a hybrid cloud with deadline and privacy constraints. A scheduling algorithm framework is proposed, which consists of four algorithm components. Candidate strategies are developed for each algorithm component. The components and parameter values are statistically calibrated over a comprehensive set of random instances. The proposed algorithm is compared to modified classical algorithms for similar problems. The experimental results indicate that the proposed algorithm outperforms the compared algorithms under different application scales, deadlines, and private VMs.
Toward Recognizing More Entity Types in NER: An Efficient Implementation using Only Entity Lexicons

In this work, we explore the way to quickly adjust an existing named entity recognition (NER) system to make it capable of recognizing entity types not defined in the system.As an illustrative example, consider the case that a NER system has been built to recognize person and organization names, and now it requires to additionally recognize job titles.Such a situation is common in the industrial areas, where the entity types required to recognize vary a lot in different products and keep changing.To avoid laborious data labeling and achieve fast adaptation, we propose to adjust the existing NER system using the previously labeled data and entity lexicons of the newly introduced entity types.We formulate such a task as a partially supervised learning problem and accordingly propose an effective algorithm to solve the problem.Comprehensive experimental studies on several public NER datasets validate the effectiveness of our method.
Human Activity Identification using CNN

Human activity recognition [HAR] is a field of study that deals with identifying, interpreting, and analyzing the actions specific to the movement of human beings. Currently, the activity recognition system like (HAR) is becoming a huge field of innovative work with an emphasis on advanced machine learning algorithms, innovations that focus on increasing safety while decreasing the costs of monitoring, which helps in the field of healthcare, child care, surveillance, sports or keeping track of behavioral pattern of human beings. This model aims to develop a system that recognizes activities like sitting, standing, walking, sleeping, reading, and tilting using CNN. It is done by a supervised learning method, which is an ML task where a function is trained that provides output by mapping it to input, i.e., the activity will be recognized based on the activity defined/labeled in the data.
Modeling and Verification of the Nervos CKB Block Synchronization Protocol in UPPAAL

The Nervos CKB (Common Knowledge Base) is a public permission-less blockchain designed for a peer-to-peer crypto- economy network. The CKB block synchronization protocol is an important part of the Nervos CKB, which provides a set of rules that participating nodes must obey while synchronizing their blocks. The protocol contains three stages: Connecting Header, Downloading Block and Accepting Block. In this paper, we develop the formal model of the CKB block synchronization protocol and verify some important properties of the protocol using the UPPAAL model checker. Based on the formal model, the reliability of CKB can be guaranteed.
HexTouch: A Wearable Haptic Robot for Complementary Interactions to Companion Agents in Virtual Reality

We propose a forearm-mounted robot that performs complementary touches in relation to the behaviors of a companion agent in virtual reality (VR). The robot consists of a series of tactors driven by servo motors that render specific tactile patterns to communicate primary emotions (fear, happiness, disgust, anger, and sympathy) and other notification cues. We showcase this through a VR game with physical-virtual agent interactions that facilitate the player-companion relationship and increase user immersion in specific scenarios. The player collaborates with the agent to complete a mission while receiving affective haptic cues with the potential to enhance sociality in the virtual world.
Dynamic Cloud Workflow Scheduling with a Heuristic-Based Encoding Genetic Algorithm

Cloud computing is a powerful and scalable computing platform that enables the virtualization, share and on-demand use of computing resources. Scientific workflows on clouds are promising for handling computational-intensive and complex scientific computing tasks. The scientific workflow scheduling problem has been regarded as an intractable optimization problem that determines the performance of a scientific cloud workflow management system. The problem becomes even more challenging if the dynamic and heterogeneous characteristics of cloud workflows are taken into account. In order to adapt to the dynamic environment, this paper proposes a hybrid genetic algorithm (HGA) algorithm. Different from the traditional evolutionary algorithms for workflow scheduling that uses a direct encoding scheme, the proposed HGA uses an indirect encoding scheme, i.e., a schedule is encoded as a sequence of heuristic rules. Since there have been some widely-studied heuristic information for scheduling on a directed acyclic graph, this heuristic information is adopted by HGA to improve performance. In addition, under the dynamic batch-processing environment, it is found that the results returned by HGA in the form of heuristic-based can still adaptive to the changes. The experimental results validate that HGA is promising.
Sentiment Analysis of Student’s Opinion on Programming Assessment: Evaluation of Naïve Bayes over Support Vector Machines

This study investigates the performance of machine learning algorithms for sentiment analysis of students’ opinions on programming assessment. Previous researches show that Support Vector Machines (SVM) performs the best among all techniques, followed by Naïve Bayes (NB) in sentiment analysis. This study proposes a framework for classifying sentiments, as positive or negative using NB algorithm and Lexicon-based approach on small data set. The performance of NB algorithm was evaluated using SVM. NB and SVM conquer the Lexicon-based approach opinion lexicon technique in terms of accuracy in the specific area for which it is trained. The Lexicon-based technique, on the other hand, avoids difficult steps needed to train the classifier. Data was analyzed from 75 first year undergraduate students in School of Computing, Universiti Teknologi Malaysia taking programming subject. The student’s sentiments were gathered based on their opinions for the zero-score policy for unsuccessful compilation of program during skill-based test. The result of the study reveals that the students tend to have negative sentiments on programming assessment as it gives them scary emotions. The experimental result of applying NB algorithm yields a prediction accuracy of 85% which outperform both the SVM with 70% and Lexicon-based approach with 60% accuracy. The result shows that NB works better than SVM and Lexicon-based approach on small dataset.
The Experimental Study of Performance Impairment of Big Data Processing in Dynamic and Opportunistic Environments

In contrast to HPC clusters, when big data is processing in a distributed, particularly dynamic and opportunistic environment, the overall performance must be impaired and even bottlenecked by the dynamics of overlay and the opportunism of computing nodes. The dynamics and opportunism are caused by churn and unreliability of a generic distributed environment, and they cannot be ignored or avoided. Understanding impact factors, their impact strength and the relevance between these impacts is the foundation of potential optimization. This paper derives the research background, methodology and results by reasoning the necessity of distributed environments for big data processing, scrutinizing the dynamics and opportunism of distributed environments, classifying impact factors, proposing evaluation metrics and carrying out a series of intensive experiments. The result analysis of this paper provides important insights to the impact strength of the factors and the relevance of impact across the factors. The production of the results aims at paving a way to future optimization or avoidance of potential bottlenecks for big data processing in distributed environments.
Provably Safe Real-Time Receding Horizon Trajectory Planning for Linear Time-Invariant Systems

Safe operation in spaces under uncertainty is crucial for robotic systems, especially for mobile robots. Assorted unknown quantities such as external force or estimation error can be considered disturbances. However, many robust trajectory planning algorithms that take effects of disturbances into account often accompany heavy computational load or are excessive conservatism. In this paper, we present a provably safe real-time receding horizon trajectory planning algorithm for linear time-invariant (LTI) systems. The proposed method ensures the same level of safety that other reachability-based robust planning algorithms provide, while not overestimating the reachable set. We verify the proposed framework through simulation with a six-degree-of-freedom system, in which the proposed method generates safe trajectories faster than 100 Hz.
Feature selection based on fuzzy joint mutual information maximization

Nowadays, real-world applications handle a huge amount of data, especially with high-dimension features space. These datasets are a significant challenge for classification systems. Unfortunately, most of the features present are irrelevant or redundant, thus making these systems inefficient and inaccurate. For this reason, many feature selection (FS) methods based on information theory have been introduced to improve the classification performance. However, the current methods have some limitations such as dealing with continuous features, estimating the redundancy relations, and considering the outer-class information. To overcome these limitations, this paper presents a new FS method, called Fuzzy Joint Mutual Information Maximization (FJMIM). The effectiveness of our proposed method is verified by conducting an experimental comparison with nine of conventional and state-of-the-art feature selection methods. Based on 13 benchmark datasets, experimental results confirm that our proposed method leads to promising improvement in classification performance and feature selection stability.
SPAA: Stealthy Projector-based Adversarial Attacks on Deep Image Classifiers

Light-based adversarial attacks use spatial augmented reality (SAR) techniques to fool image classifiers by altering the physical light condition with a controllable light source, e.g., a projector. Compared with physical attacks that place hand-crafted adversarial objects, projector-based ones obviate modifying the physical entities, and can be performed transiently and dynamically by altering the projection pattern. However, subtle light perturbations are insufficient to fool image classifiers, due to the complex environment and project-and-capture process. Thus, existing approaches focus on projecting clearly perceptible adversarial patterns, while the more interesting yet challenging goal, stealthy projector-based attack, remains open. In this paper, for the first time, we formulate this problem as an end-to-end differentiable process and propose a Stealthy Projector-based Adversarial Attack (SPAA) solution. In SPAA, we approximate the real Project-and-Capture process using a deep neural network named PCNet, then we include PCNet in the optimization of projector-based attacks such that the generated adversarial projection is physically plausible. Finally, to generate both robust and stealthy adversarial projections, we propose an algorithm that uses minimum perturbation and adversarial confidence thresholds to alternate between the adversarial loss and stealthiness loss optimization. Our experimental evaluations show that SPAA clearly outperforms other methods by achieving higher attack success rates and meanwhile being stealthier, for both targeted and untargeted attacks.
Overlap Training to Mitigate Inconsistencies Caused by Image Tiling in CNNs

This paper focuses on the problem of inconsistent predictions of modern convolutional neural networks (CNN) at patch (i.e. sub-image) boundaries. Limited by the graphics processing unit (GPU) resources, image tiling and stitching countermeasure have been applied for most megapixel images, that is, cutting images into overlapping tiles as CNN input, and then stitching CNN outputs together. However, we found that stitched (i.e. recovered) predictions have discontinuous grid-like noise. We propose a simple yet efficient overlap training framework to mitigate the inconsistent prediction at patch boundaries without changing the model architecture while improving the stability, robustness of the model. We have applied our solution to various CNNs (such as U-Net, DeepLab, RCF) and tested them on two real-world datasets. Extensive experiments suggest that the new framework is sufficient in reducing inconsistency and outperform these countermeasures. The source code and coloured figures are made publicly available online at: https://github.com/anyuzoey/Overlap-Training.git .
How detection ranges and usage stops impact digital contact tracing effectiveness for COVID-19

Abstract To combat the COVID-19 pandemic, many countries have adopted digital contact tracing apps. Various technologies exist to trace contacts that are potentially prone to different types of tracing errors. Here, we study the impact of different proximity detection ranges on the effectiveness and efficiency of digital contact tracing apps. Furthermore, we study a usage stop effect induced by a false positive quarantine. Our results reveal that policy makers should adjust digital contact tracing apps to the behavioral characteristics of a society. Based on this, the proximity detection range should at least cover the range of a disease spread, and be much wider in certain cases. The widely used Bluetooth Low Energy protocol may not necessarily be the most effective option in its current setting.
Fuzzy Logic Based Controller Design for a Typical Industrial Application Problem

In this paper, the fuzzy logic-based controller design for a typical industrial application problem is presented in brief with the hardwired model of the designed unit. This paper includes the plan and execution of mechanized clothing in a firm. The clothing has a washtub having a limit of around 60 kgs. The plan consolidates exceptional equipment to peruse the information from sensors in the tub like-the cleanser ought to be added relying upon the soil and the heaviness of the garments detected from the tub; relying upon the heaviness of the material to be washed, the water must come in through the gulf consequently; the number of wash-cycles will likewise rely upon the earth and the weight; The earth level should be checked toward the finish of each cycle and if the soil is under an ideal worth the cycle should be ended naturally. A PC is to be utilized to play out the different figurings and to control and screen all the phases of the venture. The product is composed to computerize the activity utilizing the idea of fluffy rationale.
The analysis of image acquisition method for Anti-UAV surveillance using cameras image

As the number of cases where human life and property are threatened by illegal use of UAV is rapidly increasing, a technology for detecting illegal UAV using deep learning algorithm is being researched. In order for the deep learning algorithm to work, an appropriate UAV object image size and quality are required. Unfortunately, in detecting a small UAV using a camera sensor, when the object image of the camera becomes small, the detection distance of the UAV is reduced due to the difficulty of detecting the UAV. Therefore, a large number of cameras are required to expand the detection distance. This paper suggests the methodology for configuring UAV surveillance area for image acquisition in terms of reducing the required number of cameras. Four methods for constructing the UAV surveillance area considering the optical limitations of the camera are proposed, and compared the proposed methods using a simple thin lens camera model.
Group Softmax Loss with Discriminative Feature Grouping

In the supervised learning framework, a softmax cross-entropy loss is commonly applied to train deep neural networks for high-performance classification. It, however, demands large amount of annotated data and fails to learn the discriminative networks on a smaller amount of data. In this paper, we propose a novel loss measure to train the networks such that discriminative feature representation can be learned even on the smaller-scale dataset. By means of feature grouping, we effectively expose non-discriminative feature components to representation learning and formulate two types of group softmax losses to cope with the grouped features. The proposed method encourages discriminative representation across all feature components, and from a theoretical viewpoint it renders adversarial training which works for alleviating over-fitting especially on scarce training data. The experimental results on image classification tasks demonstrate that the proposed loss favorably improves performance of CNNs on various-scale data.
Improved Training of Generative Adversarial Networks Using Decision Forests

Whilst Generative Adversarial Networks (GANs) have gained a reputation as powerful generative models, they are notoriously difficult to train and suffer from instability in optimisation. Recent methods for tackling this drawback have typically approached it by inducing better behaviour on the discriminator component of the GAN; these include loss function modification, gradient regularisation and weight normalisation to create a discriminator that is well-behaved from a Lipschitz perspective. In this paper, we propose a novel and orthogonal contribution which modifies the architecture of a GAN. Our method embeds the powerful discriminating capabilities inherent in decision forests within the discriminator of a GAN. Empirically, we test the effectiveness of our approach on the CIFAR-10, Oxford Flowers and CUB Birds datasets. We show that our technique is easy to incorporate into existing GAN baselines and offers improvements on Fréchet-Inception Distance (FID) scores by as high as 56.1% over several GAN baselines.