Reviewerly/exHarmony huggingface dataset:
Multi-objective four dimensional imprecise TSP solved with a hybrid multi-objective ant colony optimization-genetic algorithm with diversity

In real world, most of the combinatorial optimization problems are multi-objective and it is difficult to optimize them simultaneously. In the literature, some individual algorithms (ACO, GA, etc.) are available to solve such discrete multi-objective optimization problems (MOOPs), particularly trav elling salesman problems (TSPs). Here a hybrid algorithm combining ACO and GA with diversity is developed to solve discrete multi-objective TSPs and named MOACOGAD. Generally in TSP, routes for travel are not considered as lengths of routes remain unaltered. In real life, there may be several routes for travel from one destination to another and conditions of those routes may also be different such as good, rough, bad, etc. In practical, travel costs and travel times are not defined precisely and represented by fuzzy data. When fuzzy travel costs and fuzzy travel times per unit length are involved, the lengths and conditions of the routes along-with the types of conveyances for travel become important. In some cases, risk of travel is also involved. In this paper a four dimensional imprecise TSP including source, destination, conveyances and routes under some risk factors are formulated and solved by the developed MOACOGAD. The model is illustrated numerically. As particular cases three and two dimensional multi-objective imprecise TSPs are derived and solved.
Using improved firefly algorithm based on genetic algorithm crossover operator for solving optimization problems

Firefly algorithm (FA) is one of the most recently introduced stochastic, nature-inspired, meta-heuristic approaches that have seen countless applications in solving various types of optimization problems. The major source of inspiration leading to the development of FA is the phenomenon of light e mission by fireflies that attract other fireflies for their potential mates. All the fireflies are unisexual and attract each other according to the intensities of their flash lights. Higher the flash light intensity, higher is the power of attraction and vice versa. For solving optimization problem, the brightness of flash is associated with the fitness function to be optimized. The firefly algorithm is advantageous over other optimization algorithms due to its flexibility, simplicity, robustness and easy implementation but a major drawback associated with the standard FA applied for solving different optimization problems is poor exploitation capability when the randomization factor is taken large during firefly changing position. This poor exploitation may lead to skip the most optimal solution even present in the vicinities of the current solution which results in poor local convergence rate that ultimately degrades the solution quality. To overcome this problem, the crossover operator of genetic algorithm (GA) is incorporated into firefly position changing stage that results in better exploitation capability which improves the local convergence rate resulting in better solution quality. The performance of the proposed approach has been compared with standard FA, GA, artificial bee colony (ABC) and ant colony optimization (ACO) algorithms in terms of convergence rate for various types of minimization and maximization optimization functions.
Longitudinal Adversarial Attack on Electronic Health Records Data

Although deep learning models trained on electronic health records (EHR) data have shown state-of-the-art performance in many predictive clinical tasks, the discovery of adversarial examples (i.e., input data that are engineered to cause misclassification) has exposed vulnerabilities with lab and imaging data. We specifically consider adversarial examples with longitudinal EHR data, an area that has not been previously examined because of the challenges with temporal high-dimensional and sparse features. We propose Longitudinal AdVersarial Attack (, a saliency score based adversarial example using a method that requires a minimal number of perturbations and that automatically minimizes the likelihood of detection. Features are selected and modified by jointly modeling a saliency map and attention mechanism. Experimental results with longitudinal EHR data show that an substantially reduce model performance for attention-based target models (from AUPR = 0.5 to AUPR = 0.08).
Removing Reflection From a Single Image With Ghosting Effect

Removing the undesired reflections of images taken through glass is an important problem in digital photography and many other vision applications. The so-called ghosting effect, i.e., the pattern repetitiveness in reflection, is an effective cue used by existing techniques to remove reflection from images. Existing methods take a two-stage approach that first estimates the parameters of ghosting effect and then models reflection removal as a two-layer separation problem: reflection layer and latent image layer. This paper aims at addressing one main challenge in such an approach, i.e., how to distinguish the repetitive patterns on the later image layer and the ghosting patterns on the reflection layer. Based on the observation that the number of repeats of natural image patterns is often different from that of ghosting patterns, we propose a wavelet transform based regularization method. Together with a novel weighting scheme, the proposed method is capable of accurately separating two layers, and experimental results justified its advantages over the existing ones on both synthetic and real data set.
MARINE: Multi-relational Network Embeddings with Relational Proximity and Node Attributes

Network embedding aims at learning an effective vector transformation for entities in a network. We observe that there are two diverse branches of network embedding: for homogeneous graphs and for multi-relational graphs. This paper then proposes MARINE, a unified embedding framework for both homogeneous and multi-relational networks to preserve both the proximity and relation information. We also extend the framework to incorporate existing features of nodes in a graph, which can further be exploited for the ensemble of embedding. Our solution possesses complexity linear to the number of edges, which is suitable for large-scale network applications. Experiments conducted on several real-world network datasets, along with applications in link prediction and multi-label classification, exhibit the superiority of our proposed MARINE.
Should executives go to jail over cybersecurity breaches?

The Consumer Data Protection Act, a new bill introduced by Senator Ron Wyden, is proposing “jail time of up to 20 years for executives who knowingly sign off on incorrect or inaccurate annual certifications of their companies’ data-security practices.” The bill also recommends that companies be fined “up to 4 percent of their annual revenue.” While the critics consider the penalties too harsh and severe, the proposed legislation reflects two key realities – a) active involvement and commitment of senior management is essential to achieving a high level of cybersecurity preparedness; and b) legislation and fear of severe penalties (such as Sarbanes-Oxley Act of 2002 and European Union’s General Data Protection Regulation) is often necessary to motivate desired organizational behavior. In an increasingly digital ecosystem characterized by high levels of electronic connectivity, vulnerability to cyberattacks is growing. Organizations are in a perpetual state of breach with rapidly expanding attack surfaces and evolving threat vectors. Protecting confidential data and related digital assets is becoming critical to survival and success. Senior management must come to terms with this new business reality and give strategic priority to cybersecurity preparedness and investments. Research finds active involvement of top management in cyber risk mitigation initiatives to be a critical success factor and best practice. The onus is also on senior management to create a high-performance security culture founded on three key cornerstones – commitment, preparedness, and discipline. They also must lead the charge in establishing a cybersecurity governance structure characterized by joint ownership, responsibility, and accountability.
Design of a pavement scanning system based on structured light of interference fringe

In recent years, pavement scanning technology based on three-dimensional structured light has been widely used due to its multiple advantages. However, structured light of interference fringe was rarely applied in pavement scanning because of the low image quality when scanning surfaces with large sizes. The purpose of this study is to design a pavement scanning system based on structured light of interference fringe. The system aims to acquire pavement fringe images with high quality, which contain the three-dimensional information of the objects to measure. The basic principle of the scanning system is described in this paper. A fringe generator, which adopts a new fiber fixing method, is manufactured. It can emit fringe with higher spatial frequency. Then, the system parameters are determined. The influences of fringe spatial frequency f0 and incident angle ɸ on the quality of fringe images are analyzed, which verifies the rationality of the design. The scanning system can be used to scan the pavement distress and obtain the relevant fringe images.
Assessing Incremental Testing Practices and Their Impact on Project Outcomes

Software testing is an important aspect of the development process, one that has proven to be a challenge to formally introduce into the typical undergraduate CS curriculum. Unfortunately, existing assessment of testing in student software projects tends to focus on evaluation of metrics like code coverage over the finished software product, thus eliminating the possibility of giving students early feedback as they work on the project. Furthermore, assessing and teaching the process of writing and executing software tests is also important, as shown by the multiple variants proposed and disseminated by the software engineering community, e.g., test-driven development (TDD) or incremental test-last (ITL). We present a family of novel metrics for assessment of testing practices for increments of software development work, thus allowing early feedback before the software project is finished. Our metrics measure the balance and sequence of effort spent writing software tests in a work increment. We performed an empirical study using our metrics to evaluate the test-writing practices of 157 advanced undergraduate students, and their relationships with project outcomes over multiple projects for a whole semester. We found that projects where more testing effort was spent per work session tended to be more semantically correct and have higher code coverage. The percentage of method-specific testing effort spent before production code did not contribute to semantic correctness, and had a negative relationship with code coverage. These novel metrics will enable educators to give students early, incremental feedback about their testing practices as they work on their software projects.
Improving the realism of synthetic images through a combination of adversarial and perceptual losses

In recent years, deep learning methods are becoming more widely used; however, large quantities of labeled training data are required for most models. Labeling large datasets is tedious, expensive, and time consuming. Generating large labeled synthetic datasets, on the other hand, is easier and less expensive since annotations are available. But there is usually a large gap between the distribution of the synthetic and real data. In this paper, we propose a novel method based on Generative Adversarial Networks (GANs) to improve the realism of the synthetic images while preserving the annotation information. In our work the inputs of the GANs are synthetic images instead of random vectors. Furthermore, we describe how a perceptual loss can be utilized while introducing the basic features and techniques from adversarial networks for obtaining better results. We evaluate our approach for appearance-based gaze direction classification on the MPIIGaze dataset. The results show that our generated refined images are more realistic and better preserve the annotation information than the refined images generated by the state-of-the-art methods.
Performative technologies and teacher subjectivities: A conceptual framework

Critical educational literature suggests that an increased reliance upon performative technologies is currently transforming the very foundations from which teacher subjectivities are constructed. Arguably though, the number of studies pointing to this risk or tendency is considerably larger than the ones theorising why this should be the case. Further, in those cases where the relationship between performative technologies and teacher subjectivities is theorised, the psychological mechanisms that the technologies appeal to are seldom brought to the fore. Based on this, the purpose of this article is to theorise the psychological mechanisms that performative technologies appeal to and work through, by means of identifying, systematising and elaborating extant understandings of such mechanisms in the critical educational literature. The results are presented in the form of a conceptual framework (referred to as the CMIS ‐framework) which suggests that one and the same performative technology may play many different roles, where each such role appeals to and works through a particular psychological mechanism. Importantly, depending on the type of psychological mechanism that is appealed to, the CMIS ‐framework suggests that this will lead to teachers (un)consciously conducting particular forms of subjectivising work upon themselves, here referred to as compliance, mirroring, identification and self‐realisation ( CMIS ).
MgCrab

Recent deterministic database systems have achieved high scalability and high availability in distributed environments given OLTP workloads. However, modern OLTP applications usually have changing workloads or access patterns, so how to make the resource provisioning elastic to the changing workloads becomes an important design goal for a deterministic database system. Live migration, which moves the specified data from a source machine to a destination node while continuously serving the incoming transactions, is a key technique required for the elasticity. In this paper, we present MgCrab, a live migration technique for a deterministic database system, that leverages the determinism to maintain the consistency of data on the source and destination nodes at very low cost during a migration period. We implement MgCrab on an open-source database system. Extensive experiments were conducted and the results demonstrate the effectiveness of MgCrab.
Statistical Express Control of the Peak Values of the Differential-Thermal Analysis of Solid Materials

The method of non-destructive control of the molecular structure of solid materials is improved, the distinguishing feature of which is a new sign of suitability for the peak values of the thermodynamic process in solid materials, which made it possible to increase the reliability of the non-destructive control. A statistical norm for deciding on the suitability of solid materials was introduced, which made it possible to organize statistical express control of solid materials under conditions of industrial production of electronic devices. In the practical plan, a structural scheme and an algorithm for measuring control of the peak values of the differential-thermal analysis of solid materials are proposed, on the basis of which a microprocessor based device for statistical express control is developed.
Learning multi-layer coarse-to-fine representations for large-scale image classification

Recent studies on large-scale image classification mainly focus on categorizing images into 1000 object classes, and all these 1000 object classes are atomic and mutually exclusive in the semantic space. However, for a much larger set of image categories (such as the ImageNet 10k dataset), some of them may come from the high-level (non-leaf) nodes of the concept ontology and could contain some other lower-level categories semantically. The research that classifies images into large numbers of image categories with such inter-category subsumption correlations has received rare attention. In this paper, a Visual-Semantic Tree is learned to organize 10k image categories hierarchically in a coarse-to-fine fashion, where both the inter-category visual similarities and inter-category semantic correlations are seamlessly integrated for tree construction. Additionally, a deep learning method is developed by integrating the Visual-Semantic Tree with deep CNNs to learn more discriminative tree classifiers for large-scale image classification. Our experimental results have demonstrated that the proposed Visual-Semantic Tree can effectively organize large-scale structural image categories and significantly boost the classification accuracy rates for both atomic image categories and high-level image categories.
Decision Controller for Object Tracking With Deep Reinforcement Learning

There are many decisions which are usually made heuristically both in single object tracking (SOT) and multiple object tracking (MOT). Existing methods focus on tackling decision-making problems on special tasks in tracking without a unified framework. In this paper, we propose a decision controller (DC) which is generally applicable to both SOT and MOT tasks. The controller learns an optimal decision-making policy with a deep reinforcement learning algorithm that maximizes long term tracking performance without supervision. To prove the generalization ability of DC, we apply it to the challenging ensemble problem in SOT and tracker-detector switching problem in MOT. In the tracker ensemble experiment, our ensemble-based tracker can achieve leading performance in VOT2016 challenge and the light version can also get a state-of-the-art result at 50 FPS. In the MOT experiment, we utilize the tracker-detector switching controller to enable real-time online tracking with competitive performance and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${10\times }$ </tex-math></inline-formula> speed up.
Image Deblocking Detection Based on a Convolutional Neural Network

With the development of multimedia processing technology, it is becoming much easier to manipulate and tamper with digital video without leaving any visual clues.Because video compression is very common in digital videos, the tamper might employ powerful multimedia deblocking methods to cover up the video tampering traces.Motion JPEG (MJPEG) is one of the most popular video formats, in which each video frame or interlaced field of a digital video sequence is compressed separately as a JPEG image.By splitting the MJPEG video into JPEG image frames, the tamper might employ powerful multimedia deblocking methods to cover up the video tampering traces.To the best our knowledge, there is no existing method for the forensics of deblocking.In this paper, we propose a novel method to detect deblocking, which can automatically learn feature representations based on a deep learning framework.We first train a supervised convolutional neural network (CNN) to learn the hierarchical features of deblocking operations with labeled patches from the training datasets.The first convolutional layer of the CNN serves as the preprocessing module to efficiently obtain the tampering artifacts.Then, we extract the features for an image with the CNN on the basis of a patch by applying a patch-sized sliding-window to scan the whole image.The generated image representation is then condensed by a simple feature fusion technique, i.e., regional pooling, to obtain the final discriminative feature.The experimental results on several public datasets demonstrate the superiority of the proposed scheme.
Coverless Information Hiding Method Based on Web Text

Coverless information hiding has become a hot topic because it can hide secret information (SI) into carriers without any modification. Aiming at the problems of the low hiding capacity (HC) and mismatch in text big data, a novel method of coverless information hiding by retrieving the massive amount of web text on the Internet. First, the proposed method uses a web spider technology to capture web texts associated with SI to construct a web-text library. Second, some texts containing SI are searched and the optimal web text is selected from them. Then, the location of the SI in the selected web text is described by using a 2-D coordinate system. Finally, the URL of the web text is combined with the obtained location information and then sent to the recipient. The experimental results and analysis show that the performances are improved in terms of HC, hiding success rate, and security.
Can financial development influence economic growth: The sub-Saharan analysis?

Orientation: Financial sector development in a vast majority of sub-Saharan African countries has the potential to reduce the volatility of growth.Research purpose: This article is aimed at determining the influence of financial development on economic growth in selected sub-Saharan African countries.Motivation for the study: In most of the sub-Saharan countries, financial sectors are among the world’s least developed, and the absence of deep, efficient financial markets puts major constraints on economic growth.Research approach/design and method: This article employed panel autoregressive and distributive lag model to determine the relationship between financial development and economic growth.Main findings: The results indicated that there exists a short- and a long-run relationship between financial development and economic growth in the selected countries. In the long run, bank credit to the private sector and liquid liabilities have a positive influence on economic growth, with gross domestic savings exhibiting a negative influence.Practical/managerial implications: This article makes recommendations that as financial stability, both globally and within countries, generates jobs and improves productivity, more effort should be made in ensuring an effective and sound developed financial sector system.Contribution/value-add: The financial-economic growth nexus indicate that a well-functioning financial market development can promote economic growth. However, some controversies exist as some evidence indicated that a negative or positive financial development–growth nexus exists, so there was a need to find out what is the sub-Saharan case. Furthermore, there was a need to find development regulatory and macroeconomic policies that enhance growth.
Compute-Efficient Neural-Network Acceleration

To enhance the performance of FPGA-based neural-network accelerators, maximizing both operating clock rates and compute efficiency is paramount. Streamlining data movement between memory and compute holds the key to boosting these metrics. To unleash latent performance in FPGA-based inference processors, we outline a convolutional neural network accelerator that operates at 92.9% of the peak FPGA clock rate. First, we map neural-network operators to a minimalist hardware architecture to simplify data movement between memory and compute. Doing so enables the design to close timing at high clock rates. Second, we describe a schedule that keeps compute utilization high. We apply this architecture to classify MNIST, CIFAR-10, and ImageNet datasets. This design achieves 95.5% compute efficiency with GoogLeNet, whose nested topology makes creating an efficient design especially challenging.