Reviewerly/exHarmony huggingface dataset:
The Image Super-Resolution Algorithm Based on the Dense Space Attention Network

The deep learning technique has been recently used in the image super-resolution. When the deep learning network is too deep, it is difficult to train this network to make it converge. Furthermore, the problem of gradient loss occurs for a very deep network, which makes the gradient at front layers go to zero, and thus it is impossible to train these layers. Current deep learning in image super-resolution has the following two shortcomings. One is that the features of each convolutional layer are not fully utilized. The other is that the low-resolution input features, containing rich low-frequency information, are treated equally for each channel, and not well utilized. Therefore, a DSAB (dense spatial attention block) is proposed as a basic structure for the network. The features extracted from a DSAB can be treated as a feature matrix. All the feature matrices extracted through the DSABs are concated in the row direction, and are then the inputs for a convolution layer with the kernel size of 3 by 3. The whole network of the proposed algorithm is then given and described in details. The implementation details of the algorithm is then described. The data set of DIV2K, which contains 900 high-resolution images, is used for training of the network. The test images are from Set5 and Set14, as in other papers. Experimental results show that the proposed network model is better than several other representative methods, and the edges of the reconstructed image are clearer and sharper.
Measuring the usability of machine translation in the classroom context

Abstract Usability is a key factor for increasing adoption of machine translation. This study aims to measure the usability of machine translation in the classroom context by comparing translation students’ machine translation post-editing output with their manual translation in two comparable translation tasks. Three dimensions of usability were empirically measured: efficiency, effectiveness, and satisfaction . The findings suggest that machine translation post-editing is more efficient than human translation and post-editing produces fewer errors than human translation. While the types of errors vary, errors in terms of accuracy outnumber those related to fluency. In addition, participants perceive the amount of time and work that is saved when post-editing to be greater benefit than the overall utility of post-editing. Likewise, students report a strong desire to learn post-editing skills in training programs.
High-dimensional multi-level maximum variance threshold selection for image segmentation

Maximum variance threshold selection (MVTS) has been extensively used in image thresholding. However, conventional MVTS algorithms are time-consuming, in particular when the number of threshold values increases. One approach to moderate this problem is to use population-based metaheuristic algorithms, but, due to the curse of dimensionality, the efficacy of such algorithms may drop sharply in higher-dimensional search spaces. Since various population-based algorithms have been presented in the literature, in this paper, we benchmark the performance of 10 such algorithms for MVTS, focussing in particular on more recent metaheuristics that have received significant attention. As such, the algorithms we evaluate include, apart from the more established differential evolution (DE), and particle swarm optimisation (PSO), artificial bee colony (ABC), the imperialist competitive algorithm (ICA), the grey wolf optimiser (GWO), moth fame optimisation (MFO), the dragonfly algorithm (DA), the sine cosine algorithm (SCA), the multi-verse optimiser (MVO), and the salp swarm algorithm (SSA). We assess these algorithms on a set of benchmark images with regards to objective function value and structural similarity index (SSIM), and also perform a non-parametric statistical test, the Wilcoxon signed-rank test, to compare the algorithms statistically.
Privacy-Preserving Subgraph Matching Scheme With Authentication in Social Networks

With the popularity of social networks, a great variety of new social applications have been generated for impromptu group formation and communications. Among those applications, the subgraph matching has become a hot research area in social networks. Due to the huge cost of managing and computing graph data, it may have to outsource the computations to the cloud server. However, the most critical problem is that the cloud server leaks the graph information during the processing of the graph data, and the external attackers modify the graph information during the transmission on the public channel. Thus, confidentiality and authentication have been critical attributes in the subgraph matching query service. In this article, we present an efficient and privacy-preserving subgraph matching scheme with authentication in social networks. Using the proposed scheme, the cloud can accomplish the subgraph matching query process without obtaining any sensitive information about the users. Additionally, we achieve data integrity verification and user authentication. Each receiver can verify if the received messages come from the legal sender and have not been tampered. The detailed security and efficiency analysis show that the proposed scheme not only satisfies security requirements but also achieves high-efficiency in local users, and it is suitable for many practical applications.
Guiding Symbolic Natural Language Grammar Induction via Transformer-Based Sequence Probabilities

A novel approach to automated learning of syntactic rules governing natural languages is proposed, based on using probabilities assigned to sentences (and potentially longer word sequences) by transformer neural network language models to guide symbolic learning processes like clustering and rule induction. This method exploits the learned linguistic knowledge in transformers, without any reference to their inner representations; hence, the technique is readily adaptable to the continuous appearance of more powerful language models. We show a proof-of-concept example of our proposed technique, using it to guide unsupervised symbolic link-grammar induction methods drawn from our prior research.
Efficient Training of Semantic Image Segmentation on Summit using Horovod and MVAPICH2-GDR

Deep Learning (DL) models for semantic image segmentation are an emerging trend in response to the explosion of multi-class, high resolution image and video data. However, segmentation models are highly compute-intensive, and even the fastest Volta GPUs cannot train them in a reasonable time frame. In our experiments, we observed just 6.7 images/second on a single Volta GPU for training DeepLab-v3+ (DLv3+), a state-of-the-art Encoder-Decoder model for semantic image segmentation. For comparison, a Volta GPU can process 300 images/second for training ResNet-50, a state-of-the-art model for image classification. In this context, we see a clear opportunity to utilize supercomputers to speed up training of segmentation models. However, most published studies on the performance of novel DL models such as DLv3+ require the user to significantly change Horovod, MPI, and the DL model to improve performance. Our work proposes an alternative tuning method that achieves near-linear scaling without significant changes to Horovod, MPI, or the DL model. In this paper, we select DLv3+ as the candidate TensorFlow model and implement Horovod-based distributed training for DLv3+. We observed poor default scaling performance of DLv3+ on the Summit system at Oak Ridge National Laboratory. To address this, we conducted an in-depth performance tuning of various Horovod/MPI knobs to achieve better performance over the default parameters. We present a comprehensive scaling comparison for Horovod with MVAPICH2-GDR up to 132 GPUs on Summit. Our optimization approach achieves near-linear (92%) scaling with MVAPICH2-GDR. We achieved a “mIOU” accuracy of 80.8% for distributed training, which is on par with published accuracy for this model. Further, we demonstrate an improvement in scaling efficiency by 23.9% over default Horovod training, which translates to a 1.3× speedup in training performance.
An Optimized Approach for Virtual Machine Live Migration in Cloud Computing Environment

For efficient management of the cloud, there is a requirement of the virtualization in the emergent stage of cloud computing where resources such as memory, servers, virtual machines are shared across the World Wide Web. For getting or availing other facilities such as load balancing, auto-scaling, and fault tolerance, etc., in cloud computing live migration of virtual machine is required. Migrating virtual machines from one node to another without suspending the VMs is an important feature of cloud computing so that users do not have to deal with any kind of service downtime. In this research paper, a comparative study has been done for various methods of migrating the data from one node server to another node server. After that it also recognizes the optimization of the live virtual machine in migration techniques. Therefore, an optimized approach has been identified that will be beneficial in a live migration of virtual machines without affecting the node servers in a cloud computing environment.
RCA-NET: Image Recovery Network with Channel Attention Group for Image Dehazing

In this paper, we propose an image dehazing network with a channel attention model. Most existing methods try to resolve the dehazing problem through an atmospheric transmission model, but always fail to get promising results since the real-world physical imaging system is of high complexity. Therefore, we propose recovering a fog-free image from its foggy image using an end-to-end pipeline which can produce more realistic results. We apply a channel-wise attention model into our network and also employ the perceptual loss for supervision. Experimental results indicate that our method performs better than several state-of-the-art algorithms.
Validation of Abstract Side-Channel Models for Computer Architectures

Observational models make tractable the analysis of information flow properties by providing an abstraction of side channels. We introduce a methodology and a tool, Scam-V, to validate observational models for modern computer architectures. We combine symbolic execution, relational analysis, and different program generation techniques to generate experiments and validate the models. An experiment consists of a randomly generated program together with two inputs that are observationally equivalent according to the model under the test. Validation is done by checking indistinguishability of the two inputs on real hardware by executing the program and analyzing the side channel. We have evaluated our framework by validating models that abstract the data-cache side channel of a Raspberry Pi 3 board with a processor implementing the ARMv8-A architecture. Our results show that Scam-V can identify bugs in the implementation of the models and generate test programs which invalidate the models due to hidden microarchitectural behavior.
Improving security in industry 4.0 by extending OPC-UA with usage control

This work presents a framework that provides ongoing control on actions execution in the industrial environment exploiting the OPC Unified Architecture (OPC-UA) framework and the Usage Control (UCON) paradigm. We present a fine-grained usage control model, referred as OPC-UCON, satisfying security and privacy needs of the OPC-UA framework. Our proposed framework exploits the OPC-UA connectivity between simulated industrial components and uses the UCON paradigm for dynamically controlling actions execution according to fine-grained policies reported in the standardized format. The UCON paradigm, in a form of the system, is in charge of controlling the process of dynamic policy reevaluation and the possibility of revoking already granted authorization by stopping previously authorized actions if conditions do not satisfy policy anymore. We presented the implementation and deployment of the proposed framework in a simulated industrial environment with relevant security policies to reflect the advantages of the OPC-UCON model.
Swarm Intelligence Algorithms: A Tutorial

The chapter presents one of the well-known nature-inspired optimization method named as ant colony optimization (ACO). The method is inspired from the foraging behavior of some ant species to seek the shorted path between their nest and food source, without using visual cues. This skill of ants is due to the release and deposition of organic compound on their root, known as pheromone trails. Generally, the most of this compound is produced when ants are traveling with food. The concentration of pheromone increases as more ants travel through this path which further helps the following ant colony to take shortest part. Most importantly, the pheromone evaporates after sometime therefore adjacent ants sense the intensity level of compound and follow the highly concentrated pheromone path. This behavior of ant colony effectively modeled in some set of mathematical equations to optimize complex real-life engineering optimization problems. This chapter illustrates the basics of ACO method by following a step-by-step example. Matlab and C++ source codes of ACO algorithm are also provided for hands-on experience.
Why and when are loops useful in genetic programming?

Executing a code segment repeatedly using loops is crucial for human programmers to write complex software. Similarly, in genetic programming, mechanisms that enable the multiple executions of a group of instructions seem to be useful in increasing the success rates for a number of software synthesis problems. In this paper, we investigate the effects of loops on the success rates for different types of problems.
Towards a Pittsburgh-style LCS for learning manufacturing machinery parametrizations

We present a first evaluation of a new accuracy-based Pittsburgh-style learning classifier system (LCS) for supervised learning of multi-dimensional continuous decision problems: The SupRB-1 (Supervised Rule-Based) learning system. Designed primarily for finding parametrizations for industrial machinery, SupRB-1 learns an approximation of a continuous quality function from examples (consisting of situations, choices and associated qualities---all continous, the first two possibly multi-dimensional) and is then able to make an optimal choice as well as predict the quality of a choice in a given situation. This paper shows and discusses preliminary results of SupRB-1's performance on an additive manufacturing problem.
Subverting Linux' integrity measurement architecture

Integrity is a key protection objective in the context of system security. This holds for both hardware and software. Since hardware cannot be changed after its manufacturing process, the manufacturer must be trusted to build it properly. However, it is completely different with software. Users of a computer system are free to run arbitrary software on it and even modify BIOS/UEFI, bootloader, or Operating System (OS).
Wireless Home Assistive System for Severely Disabled People

A lot of people with severe disabilities such as amyotrophic lateral sclerosis, motor neuron diseases, cerebral palsy, stroke, and spinal cord injury with intubation always have different degrees of communication problems. Therefore, it is very important to develop an effective and easy use assistive communication system for the severely disabled. In this study, a wireless home assistive system (WHAS) with different types of assistive input accessories sensors, Morse code translator, and human machine interface is developed and tested to help the severely disabled communicate with people and machines. A Morse code translator is implemented as an assistive communication core device to facilitate the input of the severely disabled. For the proposed human–machine interfaces, personal computer-based alternative augmentative communication is developed for patients to communicate with other people easily. To promote the quality of life, the home appliance control interface is developed for the severely disabled to directly control the functions of home appliances by themselves. The experimental results showed that the proposed WHAS is practical and feasible. Therefore, the proposed approach can help severely disabled individuals effectively interact with their surroundings.
Critical evaluation of sine cosine algorithm and a few recommendations

Sine Cosine Algorithm (SCA) is one of the highly-referred optimization algorithms in the literature. The present study contributes by discovering three shortcomings of SCA and making a few recommendations. We show that the mathematical model of SCA does not work as explained in the original paper and its performance can be improved by modifying the position-updating equation of SCA as stated in the original paper. Moreover, we empirically and statistically show that sine and cosine functions, which make this algorithm different from the others, can be replaced with a simple random variable having a value in the range [`-1, 1] without degrading the overall performance of the algorithm. Furthermore, we demonstrate that the behavior of SCA is biased for the functions having the global optimum at the origin. Finally, on the basis of the analysis of SCA, we make two recommendations for the meta-heuristics designers regarding the selection of the benchmarks and mapping of the inspiration when designing a new algorithm.
Refining the evaluation of the degree of security of a system built using security patterns

Evaluating the degree of security of a specific software system is a difficult problem and many metrics have been proposed. However, if the system has been built with a methodology that uses patterns as artifacts, a systematic and rather simple evaluation is possible and a metric has been proposed for this evaluation: perform threat enumeration, check if the patterns in the system can stop the identified threats, and calculate the coverage of these threats by the patterns. We refine here that approach by considering the additional effect of the policies (requirements) defined for the system and by using weights for threats and policies.
Toward Learning at Scale in Developing Countries

Advances in education technology are enabling tremendous advances in learning at scale. However, they typically assume resources taken for granted in developed countries, including reliable electricity, high-bandwidth Internet access, fast WiFi, powerful computers, sophisticated sensors, and expert technical support to keep it all working. This paper examines these assumptions in the context of a massive test of learning at scale in a developing country. We examine each assumption, how it was broken, and some workarounds used in a 15-month-long independent controlled evaluation of pre- to posttest learning and social-emotional gains by over 2,000 children in 168 villages in Tanzania. We analyze those gains to characterize who gained how much, using test score data, social-emotional measures, and detailed logs from RoboTutor. We quantify the relative impact of pretest scores, literate aspirations, treatment, and usage on learning gains.
ChiSeL

Subgraph querying is one of the most important primitives in many applications. Although the field is well studied for deterministic graphs, in many situations, the graphs are probabilistic in nature. In this paper, we address the problem of subgraph querying in large probabilistic labeled graphs. We employ a novel algorithmic framework, called ChiSeL, that uses the idea of statistical significance for approximate subgraph matching on uncertain graphs that have uncertainty in edges. For each candidate matching vertex in the target graph that matches a query vertex, we compute its statistical significance using the chi-squared statistic. The search algorithm then proceeds in a greedy manner by exploring the vertex neighbors having the largest chi-square score. In addition to edge uncertainty, we also show how ChiSeL can handle uncertainty in labels and/or vertices. Experiments on large real-life graphs show the efficiency and effectiveness of our algorithm.
Prediction Models in Healthcare Using Deep Learning

Predictive models are used to predict the unknown future events using a set of relevant predictors or variables by studying both present and historical data. Predictive modeling is also known as predictive analytics that uses the techniques of statistics, data mining, and artificial intelligence that can be applied to a wide set of applications. A predictive model in healthcare learns the historical data of patients to predict their future conditions and determine the treatment. In this review, the use of deep learning models such as LSTM/Bi-LSTM (Long Short-Term Memory/Bi-directional LSTM), RNN (Recurrent Neural Network), CNN (Convolutional Neural Network), RBM (Restricted Boltzmann Machine) and GRU (Gated Recurrent Unit) on different healthcare applications are highlighted. The results indicate that the LSTM/Bi-LSTM model is widely used in time-series medical data and CNN for medical image data. A deep learning model can assist healthcare professionals to make decisions regarding medications, hospitalizations quickly and thus save time and also serve the healthcare industry better. This paper analyzes the various predictive models used in healthcare applications using deep learning.
Fast Face-CPU: A Real-time Fast Face Detector on CPU Using Deep Learning

Face detection is a vision method that can detect the human faces in an image. This method plays a fundamental role in the success of human face classifications. Besides, the practical applications require to work in real-time on low-cost devices. Several traditional methods have implemented it, but there are constraints on accuracy. On the other hand, the success of deep learning method for extracting object features that can also be applied to distinguish faces and backgrounds. However, Deep Convolutional Neural Network (DCNN) requires heavy computation and tends to be slow when implemented on the CPU. The work in this paper builds the Fast Face-CPU (FFCPU) is a real-time face detector that can work on the CPU. The proposed architecture using the light CNN, which consists of two crucial modules, including the backbone to quickly extract facial features and the detection module as a predictor of multiple faces. As a result, the FFCPU obtains a superior speed by 209 frames per second on CPU and achieves state-of-the-art performances on several benchmark datasets when compared with other CPU-based models.