Reviewerly/exHarmony huggingface dataset:
Behavioural Equivalence via Modalities for Algebraic Effects

The article investigates behavioural equivalence between programs in a call-by-value functional language extended with a signature of (algebraic) effect-triggering operations. Two programs are considered as being behaviourally equivalent if they enjoy the same behavioural properties. To formulate this, we define a logic whose formulas specify behavioural properties. A crucial ingredient is a collection of modalities expressing effect-specific aspects of behaviour. We give a general theory of such modalities. If two conditions, openness and decomposability , are satisfied by the modalities, then the logically specified behavioural equivalence coincides with a modality-defined notion of applicative bisimilarity, which can be proven to be a congruence by a generalisation of Howe’s method. We show that the openness and decomposability conditions hold for several examples of algebraic effects: nondeterminism, probabilistic choice, global store, and input/output.
Hybrid higher‐order total variation model for multiplicative noise removal

As an important, challenging, and difficult problem in image processing, multiplicative noise removal (MNR) has attracted great attention. To this end, many variational methods have been effectively proposed in the past few decades. Among these variational methods, total variation (TV) and its higher-order extensions are very effective, where the former can preserve sharp edges but cause some undesirable staircase effects and the latter can better reduce the staircase effects but sometimes smooth the image details. To overcome the drawbacks while taking full use of their merits, the authors propose a novel hybrid higher-order TV regularisation model for MNR, in which the novelty of the proposed model consists of combining the image prior information of first-order and second-order derivatives to propose a novel higher-order regulariser, named as hybrid higher-order TV (HHTV). More specifically, a more preferable equivalent formulation of HHTV is derived. Then, they use the derived equivalent formulation to design an efficient alternating iterative algorithm to solve the proposed model. Finally, the experimental results demonstrate that the proposed HHTV method outperforms several state-of-the-art methods in terms of image quality and convergence speed.
Discovering Multiple Co-Clusterings With Matrix Factorization

Clustering is a fundamental data exploration task which aims at discovering the hidden grouping structure in the data. The traditional clustering methods typically compute a single partition. However, there often exist different and equally meaningful clusterings in complex data. To solve this issue, multiple clustering approaches have emerged with the goal of exploring alternative clusterings from different perspectives. Existing solutions to this problem mainly focus on one-way clustering, that is, they cluster either the samples or the features. However, for many practical tasks, it is meaningful and desirable to explore alternative two-way clusterings (or co-clusterings), which capture not only the sample cluster structure but also the feature cluster structure. To tackle this interesting and unresolved task, we introduce an approach, called multiple co-clusterings (MultiCCs), to generate multiple alternative co-clusterings at the same time. MultiCC takes advantage of matrix tri-factorization to seek the co-clustering indicator matrices for samples and features and defines the row and column redundancy quantification terms to enforce diversity among co-clusterings based on these indicator matrices. After that, it integrates matrix tri-factorization and two nonredundancy terms into a unified objective function and gives an alternative optimization procedure to optimize the objective function. Extensive experimental results demonstrate that MultiCC performs significantly better than the existing multiple clustering methods. In addition, MultiCC can find out interesting co-clusters, which cannot be made by those comparing methods.
Analogy Software Effort Estimation Using Ensemble KNN Imputation

Missing data are a serious issue that influences the prediction accuracy of software development effort estimation (SDEE) techniques and especially analogy-based software effort estimation (ASEE). Hence, appropriate handling of missing data is necessary in order to ensure best performance. To deal with this issue K-nearest neighbors (KNN) imputation has been widely used. However, none of the studies investigating KNN imputation in SDEE have addressed the impact of parameter settings on the imputation process given that parameter optimization techniques are often used at the prediction level, as they highly impact the performance of SDEE techniques including ASEE. This paper proposes and evaluates an ensemble KNN imputation technique for ASEE. Thereafter, we compare ASEE performance using ensemble KNN imputation with those using either a grid search based single KNN imputation or KNN imputation without parameter optimization. For the six datasets used for comparison, the ensemble KNN imputation significantly improved ASEE performance compared with KNN imputation without optimization. Moreover, ensemble KNN imputation and grid search-based imputation behaved similarly. Given that grid search is time consuming, the ensemble KNN imputation may be an alternative to deal with missing data in the ASEE process.
A Blockchain-based Random Number Generation Algorithm and the Application in Blockchain Games

Blockchain technology has developed rapidly and has been applied in various areas. Blockchain technology has the features of decentralization, transparency, autonomy and tamper resistance etc. The blockchain, with its unique features, can address some problems in traditional areas. In traditional games area, especially the gambling game, we learn that there is a strong requirement for fairness and random numbers. But traditional games are centralized and cannot meet this requirement. Hence, we propose a blockchain-based random number generation algorithm that can provide a “true random number”. This algorithm allows all the participants to take part in the generation of a random number, which ensures that the random number will not be manipulated by anyone. We as well design a blockchain game platform adopting this random number generation algorithm. Traditional games can be deployed on this platform with a simple adaptation. Finally, we deploy a poker game on the platform to verify the algorithm and performance. The experimental results show that the algorithm is effective and the platform has good performance.
Gender Classification Based on Asian Faces using Deep Learning

For the past few years, gender classification has been an active area of study and researchers have been putting a lot of effort to contribute quality research in this area. There is a big potential field of study as it can be used in monitoring, surveillance and human-computer interaction. However, there is still a lack of the performance of existing methods on real live images. The rise of deep learning algorithm has been showing a spectacular increase in performance lately. Many difficult tasks involving computer vision, speech recognition, and natural language processing are easily solved with deep learning. Therefore, the approach to deep learning notably growing and this also happens to be on image classification. Gender classification is an important subject in the face recognition process. This paper shows the results of classifying gender using Convolutional Neural Network based Deep Learning architectures using Tensorflow’s Deep Learning framework. We have used models provided by Keras with weights pre-trained on ImageNet. We have made a comparison of the different type of models which includes VGG16, ResNet-50, and MobileNet. Our own database consists of Asian faces inclusive of Malaysians and some Caucasians. Our trained model on a database consisting of 1000 images shows that VGG-16 delivered the highest recognition accuracy.
A Risk and Comfort Optimizing Motion Planning Scheme for Merging Scenarios

Motion planning for merging scenarios accounting for measurement and prediction uncertainties is a major challenge on the way to autonomous driving. Classical methods subdivide the motion planning into behavior and trajectory planning, thus narrowing down the solution set. Hence, in complex merging scenarios, no suitable solution might be found. In this work, we present a planning scheme that solves behavior and trajectory planning together by exploring all possible decision options. A safety strategy is implemented and the risk of violating a safety constraint is minimized as well as the jerk to feature a risk and comfort optimal trajectory. To mitigate the injection of noise into the actual trajectory, a new analytical trajectory generation method is derived and its optimality is proven. The decision capability is evaluated through Monte-Carlo simulation. Furthermore, the calculation time is evaluated showing the real-time capability of our approach.
Hybrid A-based Curvature Continuous Path Planning in Complex Dynamic Environments

With the progress of autonomous driving technology in recent years, motion planning has been an issue in the navigation of self-driving cars. To achieve an optimal path that meets the requirements of both smoothness and safety, vehicle kinematics and dynamics constraints should be considered. This paper proposes a novel motion planning method based on Hybrid A* for real-time and curvature-contentious path planning with local post smoothing in complex dynamic environments: (1)our method introduces parametric clothoid curves precomputed offline as basic motion primitives for rapid online planning; (2)the path obtained using our method is G <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -continuous (i.e., curvature continuous) and does not have a considerable effect on the search time consumption, while also considering possible collisions and motion constraints of nonholonomic car-like vehicles; (3) the node re-expansion issue of conventional Hybrid A* is discussed and resolved by the proposed quintic spine-based local smoothing approach for complete path continuity. Hence, post smoothing and collision checking for the overall resulting path. Simulation and on-road tests have been performed to evaluate the efficiency of the proposed method. The method can be widely implemented in numerous complex scenarios.
An efficient task scheduling in a cloud computing environment using hybrid Genetic Algorithm - Particle Swarm Optimization (GA-PSO) algorithm

Cloud computing provides the computational machines as a support of the clients utilizing cloud organize. In cloud computing, the user inputs are executed with required machines to convey the administrations. Numerous task scheduling methods are utilized to plan the client tasks to the machines. In this paper, another successful hybrid task scheduling is proposed to minimize the total execution time using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithms. In hybrid Genetic Algorithm - Particle Swarm Optimization (GA-PSO) algorithm, PSO helped GA to obtain better results compare to a standard genetic algorithm, Min-Min, and Max-Min algorithms results.
Generalizable Adversarial Examples Detection Based on Bi-model Decision Mismatch

Modern applications of artificial neural networks have yielded remarkable performance gains in a wide range of tasks. However, recent studies have discovered that such modelling strategy is vulnerable to Adversarial Examples, i.e. examples with subtle perturbations often too small and imperceptible to humans, but that can easily fool neural networks. Defense techniques against adversarial examples have been proposed, but ensuring robust performance against varying or novel types of attacks remains an open problem. In this work, we focus on the detection setting, in which case attackers become identifiable while models remain vulnerable. Particularly, we employ the decision layer of independently trained models as features for posterior detection. The proposed framework does not require any prior knowledge of adversarial examples generation techniques, and can be directly employed along with unmodified off-the-shelf models. Experiments on the standard MNIST and CIFAR10 datasets deliver empirical evidence that such detection approach generalizes well across not only different adversarial examples generation methods but also quality degradation attacks. Non-linear binary classifiers trained on top of our proposed features can achieve a high detection rate (>90%) in a set of white-box attacks and maintain such performance when tested against unseen attacks.
Multi-task Network for Panoptic Segmentation in Automated Driving

In this paper, we tackle the newly introduced panoptic segmentation task. Panoptic segmentation unifies semantic and instance segmentation and leverages the capabilities of these complementary tasks by providing pixel and instance level classification. Current state-of-the-art approaches employ either separate networks for each task or a single network for both task and post processing heuristics fuse the outputs into the final panoptic segmentation. Instead, our approach solves all three tasks including panoptic segmentation with an end-to-end learnable fully convolutional neural network. We build upon the Mask R-CNN framework with a shared backbone and individual network heads for each task. Our semantic segmentation head uses multi-scale information from the Feature Pyramid Network, while the panoptic head learns to fuse the semantic segmentation logits with variable number of instance segmentation logits. Moreover, the panoptic head refines the outputs of the network, improving the semantic segmentation results. Experimental results on the challenging Cityscapes dataset demonstrate that the proposed solution achieves significant improvements for both panoptic segmentation and semantic segmentation.
Motivating Information Security Policy Compliance: Insights from Perceived Organizational Formalization

Psychological and behavioral characteristics are among the most important factors that instigate information security incidents. Although many previous studies have discussed the influencing factors of information security policy compliance behavior in an organization, few have considered the influence of organizational structures. In this study, the mechanism by which information security policy compliance behavioral intention is formed was studied by integrating the theory of planned behavior (TPB) and perceived organizational formalization. Data analysis was performed using the structural equation modeling (SEM) with data obtained from a survey of 261 company employees. The empirical results reveal that perceived organizational formalization significant affected cognitive processes theorized by TPB, behavioral habits, and deterrent certainty. This study suggests that formalized rules, procedures, and communications should be designed to improve employee information security policy compliance behavioral habits and intentions.
A Novel Approach of Object Detection Using Point Feature Matching Technique for Colored Images

For computer vision, image matching is an essential trait which includes scene or object recognition. Detection using point feature method is much effective technique to detect a specific target instead of other objects or within clutter scene in an image. It is done by comparing correspondence points and analyzing between cluttered scene image and a target object in image. This paper presents novel SURF algorithm that is used for extracting, describing, and matching objects in colored images. The algorithm works on finding correspondence points between a target and reference images and detecting a particular object. Speeded-up robust features (SURF) algorithm is used in this study which can detect objects for unique feature matches and which has non-repeating patterns. This approach of detection can robustly find specified objects between colored cluttered images and provide constriction to other achieving near real-time performance.
Smart Agriculture: An Open Field For Smart Contracts

Smart agriculture is increasingly becoming a paramount financial sector with important implications on a global scale. The real time weather and soil status monitoring as well as the desired higher food quality are major drivers behind this technological, ecological, and financial trend. This work explores the enticing prospect of combining IoT and smart contract technologies with smart agriculture in order to deliver not only higher quality agricultural products, but also improving the associated supply chain and agricultural logistics, thus resulting in multiple benefits for all the parties involved. Emphasis is placed on deriving similarity metrics for tuples describing soil and climate conditions based on numerical and possibly categorical data. Moreover, a sample implementation of one such metric is given in Solidity, a high level language for formulating smart contracts designed for the Ethereum Virtual Machine is also provided as a concrete example. Finally, aspects of agricultural asset digitization, a crucial step for smart contracts relying on physical objects are also discussed.
Learning Permutation Invariant Representations Using Memory Networks

Many real-world tasks such as classification of digital histopathology images and 3D object detection involve learning from a set of instances. In these cases, only a group of instances or a set, collectively, contains meaningful information and therefore only the sets have labels, and not individual data instances. In this work, we present a permutation invariant neural network called Memory-based Exchangeable Model (MEM) for learning universal set functions. The MEM model consists of memory units that embed an input sequence to high-level features enabling it to learn inter-dependencies among instances through a self-attention mechanism. We evaluated the learning ability of MEM on various toy datasets, point cloud classification, and classification of whole slide images (WSIs) into two subtypes of the lung cancer—Lung Adenocarcinoma, and Lung Squamous Cell Carcinoma. We systematically extracted patches from WSIs of the lung, downloaded from The Cancer Genome Atlas (TCGA) dataset, the largest public repository of WSIs, achieving a competitive accuracy of 84.84% for classification of two sub-types of lung cancer. The results on other datasets are promising as well, and demonstrate the efficacy of our model.
Disparagement humour and gendered racism on social media in Brazil

Data reveals that in 2017 in Brazil, there were 63,698 reported cases of hate speech on the internet, with one-third of these being related to racist discourses. Another study reveals that 81 per cent of the victims of racism on Facebook in Brazil are upwardly mobile black women. These discourses are often conveyed through disparagement humour, which offers the abusers the convenient excuse that their comments are simply harmless jests. This study develops a critical discourse analysis of a selection of Facebook posts, aiming to understand their embedded messages. The findings suggest, first, that racist disparagement humour is employed to challenge black women's upward social mobility and disqualify their achievements. Secondly, they reinforce deep-seated ideologies regarding differentiated symbolic social spaces for blacks and whites in Brazil. Finally, major social media platforms represent the contemporary arena for the manifestation, dissemination, and reinforcement of racist ideologies.
Learning Unknown Groundings for Natural Language Interaction with Mobile Robots

Our goal is to enable robots to understand or “ground” natural language instructions in the context of their perceived workspace. Contemporary models learn a probabilistic correspondence between input phrases and semantic concepts (or groundings) such as objects, regions or goals for robot motion derived from the robot’s world model. Crucially, these models assume a fixed and a priori known set of object types as well as phrases and train probable correspondences offline using static language-workspace corpora. Hence, model inference fails when an input command contains unknown phrases or references to novel object types that were not seen during the training. We introduce a probabilistic model that incorporates a notion of unknown groundings and learns a correspondence between an unknown phrase and an unknown object that cannot be classified into known visual categories. Further, we extend the model to “hypothesize” known or unknown object groundings in case the language utterance references an object that exists beyond the robot’s partial view of its workspace. When the grounding for an instruction is unknown or hypothetical, the robot performs exploratory actions to gather new observations and find the referenced objects beyond the current view. Once an unknown grounding is associated with percepts of a new object, the model is adapted and trained online using accrued visual-linguistic observations to reflect the new knowledge gained for interpreting future utterances. We evaluate the model quantitatively using a corpus from a user study and report experiments on a mobile platform in a workspace populated with objects from a standardized dataset. A video of the experimental demonstration is available at: https://youtu.be/XFLNdaUKgW0 .
LoPrO: Location Privacy-preserving Online auction scheme for electric vehicles joint bidding and charging

Developments in the Internet of Things and cyber–physical systems have enabled intelligent solutions to alleviate peak loads, and transmission and storage concerns in MicroGrids (MGs) through the deployment of Electrical Vehicles (EVs), as well as provide a platform for EV energy exchange. Nonetheless, EV participation depends upon the willingness of the owners, who can enter and leave the market in a random fashion with multiple demands. Clever participants may be tempted to misrepresent their information to achieve a greater personal reward. Also, utilizing location information from users will make them susceptible to privacy leakage, but is necessary for the appropriate allocation of services. To resolve these challenges, we propose a Location Privacy-preserving Online (LoPrO) scheme, which can allocate electricity and charging stations in MicroGrids to EVs when the energy supply is limited. In our scheme, the optimal decision of winner determination and the electricity and charging station allocations are made by the auctioneer, without prior knowledge of EV arrival and departure, and EV location information privacy is differentially guaranteed by leveraging the Laplace mechanism. Through theoretical analysis, we prove that LoPrO scheme achieves the properties of incentive compatibility, individual rationality, and ϵ-differential privacy. The results of our experimental evaluation also demonstrate that LoPrO achieves better performance with respect to EV utility, buyer satisfaction ratio, electricity allocation efficiency and EV State-of-Charge (SoC), in comparison with existing schemes. In terms of privacy preservation, LoPrO is capable of protecting EV location information with low probability of leakage, and computation overhead is reasonable.
A GPU Implementation of the Sparse Deep Neural Network Graph Challenge

This paper presents a CUDA implementation of the latest addition to the Graph Challenge, the inference computation on a collection of large sparse deep neural networks. A single Tesla V100 can compute the inference at 3.7 TeraEdges/s. Using the managed memory API available in CUDA allows for simple and efficient distribution of these computations across a multiGPU NVIDIA DGX-2 server.