Reviewerly/exHarmony huggingface dataset:
Investigating the Chinese postman problem on a quantum annealer

The recent availability of quantum annealers has fueled a new area of information technology where such devices are applied to address practically motivated and computationally difficult problems with hardware that exploits quantum mechanical phenomena. D-Wave annealers are promising platforms to solve these problems in the form of quadratic unconstrained binary optimization. Here we provide a formulation of the Chinese postman problem that can be used as a tool for probing the local connectivity of graphs and networks. We treat the problem classically with a tabu algorithm and simulated annealing, and using a D-Wave device. The efficiency of quantum annealing with respect to the simulated annealing has been demonstrated using the optimal time to solution metric. We systematically analyze computational parameters associated with the specific hardware. Our results clarify how the interplay between the embedding due to limited connectivity of the Chimera graph, the definition of logical qubits, and the role of spin-reversal controls the probability of reaching the expected solution.
Line detection via a lightweight CNN with a Hough layer

Line detection is an important computer vision task traditionally solved by Hough Transform. With the advance of deep learning, however, trainable approaches to line detection became popular. In this paper we propose a lightweight CNN for line detection with an embedded parameter-free Hough layer, which allows the network neurons to have global strip-like receptive fields. We argue that traditional convolutional networks have two inherent problems when applied to the task of line detection and show how insertion of a Hough layer into the network solves them. Additionally, we point out some major inconsistencies in the current datasets used for line detection.
An approach to enhance age invariant face recognition performance based on gender classification

There is a need for facial biometric based authentication system that responds quickly despite large number of registered users. The main challenge in these authentication systems is accuracy along with quick response time. The response time can be lowered by having lesser number of comparisons of the probe image with gallery images. To enhance the response time we propose gender classification as a pre-processing stage so that the validation of the user continues by taking only those facial images which are in accordance with the gender of the user. We have used Principal Component Analysis to create male and female Eigen spaces during the training phase of the classifier.To validate our classifier we have used ORL, Indian and FG-NET databases. Our proposed method gives improved recognition accuracy for the images having ageing variations.The proposed Gender classification model gives an improved accuracy of 57.44%, 59.13% and 61.13 % for the probe images taken from the FG-NET database with training images from ORL, Indian and FG-NET databases respectively as compared to PCA.Also the number of comparisons are reduced by an average of 38% by incorporating the gender classification as the pre-processing stage in the age invariant face recognition system.
A Comparative Evaluation of Population-based Optimization Algorithms for Workflow Scheduling in Cloud-Fog Environments

This work presents a comparative evaluation of four population-based optimization algorithms for workflow scheduling in cloud-fog environments. These algorithms are as follows: Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE) and GA-PSO. This work also provides the motivational groundwork for the weighted sum objective function for the workflow scheduling problem and develops this function based on three objectives: makespan, cost and energy. The recently proposed FogWorkflowSim is used as the simulation environment with the aforementioned objectives serving performance metrics. Results show that hybrid combination of the GA-PSO algorithm exhibits slightly better than the standard algorithms. Future work will include expansion of the workflows used by increasing the number of tasks as well as adding some more workflows. The addition of some more objectives to the weighted objective function will also be pursued.
Learning to Evaluate Translation Beyond English: BLEURT Submissions to the WMT Metrics 2020 Shared Task

The quality of machine translation systems has dramatically improved over the last decade, and as a result, evaluation has become an increasingly challenging problem. This paper describes our contribution to the WMT 2020 Metrics Shared Task, the main benchmark for automatic evaluation of translation. We make several submissions based on BLEURT, a previously published which uses transfer learning. We extend the metric beyond English and evaluate it on 14 language pairs for which fine-tuning data is available, as well as 4 “zero-shot” language pairs, for which we have no labelled examples. Additionally, we focus on English to German and demonstrate how to combine BLEURT’s predictions with those of YiSi and use alternative reference translations to enhance the performance. Empirical results show that the models achieve competitive results on the WMT Metrics 2019 Shared Task, indicating their promise for the 2020 edition.
Finance, governance and inclusive education in Sub-Saharan Africa

Purpose The purpose of this paper is to assess the importance of credit access in modulating governance for gender-inclusive education in 42 countries in Sub-Saharan Africa with data spanning the period 2004–2014. Design/methodology/approach The generalized method of moments is used as empirical strategy. Findings The following findings are established: First, credit access modulates government effectiveness and the rule of law to induce positive net effects on inclusive “primary and secondary education.” Second, credit access also moderates political stability and the rule of law for overall net positive effects on inclusive secondary education. Third, credit access complements government effectiveness to engender an overall positive impact on inclusive tertiary education. Originality/value Policy implications are discussed with emphasis on sustainable development goals.
Motion Consistency-Based Correspondence Growing for Remote Sensing Image Matching

In this letter, we propose a remote sensing image matching method that is simple yet efficient to deal with different deformations. Inspired by the region growing strategy used in image segmentation, we integrate the motion consistency into the general region growing pipeline from a novel perspective. Specifically, we first obtain a subset with a high ratio inlier as the seed correspondence set. Then, to find more reliable correspondences, we formulate the motion consistency into the correspondence growing criterion, which is general to be suitable to many remote sensing applications. Extensive experimental results on the public available remote sensing data set show that our method achieves the best performance compared with state-of-the-art methods.
Automated Filtering of Eye Movements Using Dynamic AOI in Multiple Granularity Levels

Eye-tracking experiments involve areas of interest (AOIs) for the analysis of eye gaze data. While there are tools to delineate AOIs to extract eye movement data, they may require users to manually draw boundaries of AOIs on eye tracking stimuli or use markers to define AOIs. This paper introduces two novel techniques to dynamically filter eye movement data from AOIs for the analysis of eye metrics from multiple levels of granularity. The authors incorporate pre-trained object detectors and object instance segmentation models for offline detection of dynamic AOIs in video streams. This research presents the implementation and evaluation of object detectors and object instance segmentation models to find the best model to be integrated in a real-time eye movement analysis pipeline. The authors filter gaze data that falls within the polygonal boundaries of detected dynamic AOIs and apply object detector to find bounding-boxes in a public dataset. The results indicate that the dynamic AOIs generated by object detectors capture 60% of eye movements &amp; object instance segmentation models capture 30% of eye movements.
Mind the Pad -- CNNs Can Develop Blind Spots

We show how feature maps in convolutional networks are susceptible to spatial bias. Due to a combination of architectural choices, the activation at certain locations is systematically elevated or weakened. The major source of this bias is the padding mechanism. Depending on several aspects of convolution arithmetic, this mechanism can apply the padding unevenly, leading to asymmetries in the learned weights. We demonstrate how such bias can be detrimental to certain tasks such as small object detection: the activation is suppressed if the stimulus lies in the impacted area, leading to blind spots and misdetection. We explore alternative padding methods and propose solutions for analyzing and mitigating spatial bias.
Copycat CNN: Are random non-Labeled data enough to steal knowledge from black-box models?

Convolutional neural networks have been successful lately enabling companies to develop neural-based products, which demand an expensive process, involving data acquisition and annotation; and model generation, usually requiring experts. With all these costs, companies are concerned about the security of their models against copies and deliver them as black-boxes accessed by APIs. Nonetheless, we argue that even black-box models still have some vulnerabilities. In a preliminary work, we presented a simple, yet powerful, method to copy black-box models by querying them with natural random images. In this work, we consolidate and extend the copycat method: (i) some constraints are waived; (ii) an extensive evaluation with several problems is performed; (iii) models are copied between different architectures; and, (iv) a deeper analysis is performed by looking at the copycat behavior. Results show that natural random images are effective to generate copycats for several problems.
Real Time Density Based Traffic Surveillance System Integrated with Acoustic Based Emergency Vehicle Detection

As the problem of traffic congestion is prevalent in many metropolitan cities there is an urgent need for a traffic surveillance system which regulates the traffic in an efficient way. The current conventional methods such as timer based traffic control system is proved to be inferior to solve this issue and also the current systems does not give priority in the cases where emergency vehicles are detected. In this paper a smart traffic surveillance system has implemented where the access will be given to the lane having the highest density of traffic in the absence of emergency vehicle. When an emergency vehicle is detected in any lane, the priority shifts to the lane in which the emergency vehicle is present. Digital image processing techniques are used to compute the density of traffic and signal processing techniques are used to detect the presence of an emergency vehicle. The complete proposed system, from image and sound acquisition to canny edge detection and noise removal by Least Mean Square(LMS) filter and finally the green signal allotment to the lanes depending on the output results are illustrated with proper schematics and the final results are validated by hardware implementation.
Detection of Sociolinguistic Features in Digital Social Networks for the Detection of Communities

The emergence of digital social networks has transformed society, social groups, and institutions in terms of the communication and expression of their opinions. Determining how language variations allow the detection of communities, together with the relevance of specific vocabulary (proposed by the National Council of Accreditation of Colombia (Consejo Nacional de Acreditación - CNA) to determine the quality evaluation parameters for universities in Colombia) in digital assemblages could lead to a better understanding of their dynamics and social foundations, thus resulting in better communication policies and intervention where necessary. The approach presented in this paper intends to determine what are the semantic spaces (sociolinguistic features) shared by social groups in digital social networks. It includes five layers based on Design Science Research, which are integrated with Natural Language Processing techniques (NLP), Computational Linguistics (CL), and Artificial Intelligence (AI). The approach is validated through a case study wherein the semantic values of a series of “Twitter” institutional accounts belonging to Colombian Universities are analyzed in terms of the 12 quality factors established by CNA. In addition, the topics and the sociolect used by different actors in the university communities are also analyzed. The current approach allows determining the sociolinguistic features of social groups in digital social networks. Its application allows detecting the words or concepts to which each actor of a social group (university) gives more importance in terms of vocabulary.
Twinning Automata and Regular Expressions for String Static Analysis

In this paper we formalize $$\textsc {Tarsis}$$ , a new abstract domain based on the abstract interpretation theory that approximates string values through finite state automata. The main novelty of $$\textsc {Tarsis}$$ is that it works over an alphabet of strings instead of single characters. On the one hand, such an approach requires a more complex and refined definition of the widening operator, and the abstract semantics of string operators. On the other hand, it is in position to obtain strictly more precise results than state-of-the-art approaches. We implemented a prototype of $$\textsc {Tarsis}$$ , and we applied it to some case studies taken from some of the most popular Java libraries manipulating string values. The experimental results confirm that $$\textsc {Tarsis}$$ is in position to obtain strictly more precise results than existing analyses.
Object Detection, Distributed Cloud Computing and Parallelization Techniques for Autonomous Driving Systems

Autonomous vehicles are increasingly becoming a necessary trend towards building the smart cities of the future. Numerous proposals have been presented in recent years to tackle particular aspects of the working pipeline towards creating a functional end-to-end system, such as object detection, tracking, path planning, sentiment or intent detection, amongst others. Nevertheless, few efforts have been made to systematically compile all of these systems into a single proposal that also considers the real challenges these systems will have on the road, such as real-time computation, hardware capabilities, etc. This paper reviews the latest techniques towards creating our own end-to-end autonomous vehicle system, considering the state-of-the-art methods on object detection, and the possible incorporation of distributed systems and parallelization to deploy these methods. Our findings show that while techniques such as convolutional neural networks, recurrent neural networks, and long short-term memory can effectively handle the initial detection and path planning tasks, more efforts are required to implement cloud computing to reduce the computational time that these methods demand. Additionally, we have mapped different strategies to handle the parallelization task, both within and between the networks.
Blockchain and Government Transformation

In today’s digital age, transactions are made increasingly in the virtual universe. Some do all financial transactions online, but some do not even want to think about this possibility since the consideration is unreliable. To make online financial transactions more reliable, many technologies have already been tested. Since passwords, tokens, physical code generation accessories, there have been many attempts, being the most elaborate, the block-chain. Governments, especially in developed countries, often find it difficult to gain the trust of their citizens, especially when it comes to evidence of service provision and improvement of existing services. In developing countries, blockchain requests from governments would be useful in eliminating some important problems, such as corruption, while ensuring more effective deployment and distribution of resources. The adoption of such technologies can also help to facilitate better use of resources. In this paper, we will analyze the impact of the application of blockchain technology on e-government.
Characterizing a snapshot of perceptual experience.

What can we perceive in a single glance of the visual world? Although this question appears rather simple, answering it has been remarkably difficult and controversial. Traditionally, researchers have tried to infer the nature of perceptual experience by examining how many objects and what types of objects are not fully encoded within a scene (e.g., failing to notice a bowl disappearing/changing). Here, we took a different approach and asked how much we could alter an entire scene before observers noticed those global alterations. Surprisingly, we found that observers could fixate on a scene for hundreds of milliseconds yet routinely fail to notice drastic changes to that scene (e.g., scrambling the periphery so no object can be identified, putting the center of 1 scene on the background of another scene). In addition, we also found that as observers allocate more attention to their periphery, their ability to notice these changes to a scene increases. Together, these results show that although a single snapshot of perceptual experience can be remarkably impoverished, it is also not a fixed constant and is likely to be continuously changing from moment to moment depending on attention. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
An Anonymous Distributed Electronic Voting System Using Zerocoin

Electronic voting systems have several necessarily requirements, such as anonymity that others cannot link voters with votes, the fairness that votes should not be leaked before the tally, and the accuracy that eligible voters' votes should be counted correctly. There have been proposed various methods to realize the requirements. Some of them require anonymous communication paths as necessary elements to satisfy the anonymity requirement for a practical electronic voting system. Cruz et al. proposed an electronic voting system that does not need anonymous communication paths, using Bitcoin, a cryptographic currency, as a distributed database. However, Bitcoin is a system in which all data are disclosed, and anyone can check what exchanges a person of a certain ID (Bitcoin address) has done. It is possible to reveal which voter made what vote, and hence(anonymity is not satisfied. In this paper, we propose a method to solve the above problem by using Bitcoin mixing. We propose an electronic voting system using Zerocoin, a variant of Bitcoin, as a database, in which zero knowledge proof is used instead of blind signature for mixing.
An Adaptive Opposition-Based Learning Selection: The Case for Jaya Algorithm

Over the years, opposition-based Learning (OBL) technique has been proven to effectively enhance the convergence of meta-heuristic algorithms. The fact that OBL is able to give alternative candidate solutions in one or more opposite directions ensures good exploration and exploitation of the search space. In the last decade, many OBL techniques have been established in the literature including the Standard-OBL, General-OBL, Quasi Reflection-OBL, Centre-OBL and Optimal-OBL. Although proven useful, much existing adoption of OBL into meta-heuristic algorithms has been based on a single technique. If the search space contains many peaks with potentially many local optima, relying on a single OBL technique may not be sufficiently effective. In fact, if the peaks are close together, relying on a single OBL technique may not be able to prevent entrapment in local optima. Addressing this issue, assembling a sequence of OBL techniques into meta-heuristic algorithm can be useful to enhance the overall search performance. Based on a simple penalized and reward mechanism, the best performing OBL is rewarded to continue its execution in the next cycle, whilst poor performing one will miss cease its current turn. This paper presents a new adaptive approach of integrating more than one OBL techniques into Jaya Algorithm, termed OBL-JA. Unlike other adoptions of OBL which use one type of OBL, OBL-JA uses several OBLs and their selections will be based on each individual performance. Experimental results using the combinatorial testing problems as case study demonstrate that OBL-JA shows very competitive results against the existing works in term of the test suite size. The results also show that OBL-JA performs better than standard Jaya Algorithm in most of the tested cases due to its ability to adapt its behaviour based on the current performance feedback of the search process.
BatchSizer

GPU accelerators can deliver significant improvement for DNN processing; however, their performance is limited by internal and external parameters. A well-known parameter that restricts the performance of various computing platforms in real-world setups, including GPU accelerators, is the power cap imposed usually by an external power controller. A common approach to meet the power cap constraint is using the Dynamic Voltage Frequency Scaling (DVFS) technique. However, the functionally of this technique is limited and platform-dependent. To improve the performance of DNN inference on GPU accelerators, we propose a new control knob, which is the size of input batches fed to the GPU accelerator in DNN inference applications. After evaluating the impact of this control knob on power consumption and performance of GPU accelerators and DNN inference applications, we introduce the design and implementation of a fast and lightweight runtime system, called BatchSizer. This runtime system leverages the new control knob for managing the power consumption of GPU accelerators in the presence of the power cap. Conducting several experiments using a modern GPU and several DNN models and input datasets, we show that our BatchSizer can significantly surpass the conventional DVFS technique regarding performance (up to 29%), while successfully meeting the power cap.
IB-M: A Flexible Framework to Align an Interpretable Model and a Black-box Model

Both interpretation and accuracy are very important for a predictive model in real applications, but most of previous works, no matter interpretable models or black-box models, cannot simultaneously achieve both of them, resulting in a tradeoff between model interpretation and model accuracy. To break this trade-off, in this paper, we propose a flexible framework, named IB-M, to align an Interpretable model and a Blackbox Model for simultaneously optimizing model interpretation and model accuracy. Generally, we think most of samples that are well-clustered or away from the true decision boundary can be easily interpreted by an interpretable model. Removing those samples can help to learn a more accurate black-box model by focusing on the left samples around the true decision boundary. Inspired by this, we propose a data re-weighting based framework to align an interpretable model and a blackbox model, letting them focus on the samples what they are good at, hence, achieving both interpretation and accuracy. We implement our IB-M framework for a real medical problem of ultrasound thyroid nodule diagnosis. Extensive experiments demonstrate that our proposed framework and algorithm can achieve a more interpretable and more accurate diagnosis than a single interpretable model and a single black-box model.