Reviewerly/exHarmony huggingface dataset:
Pizza versus Pinsa: On the Perception and Measurability of Unit Test Code Quality

Test cases are an essential asset to evaluate software quality. The research community has provided various alternatives to help developers assessing the quality of tests, like code or mutation coverage. Despite the effort spent so far, however, little is known on how practitioners perceive unit test code quality and whether the existing metrics reflect their perception. This paper aims at addressing this gap of knowledge. We first conduct semi-structured interviews and surveys with practitioners to establish a taxonomy of relevant factors for unit test quality and collect a dataset of tests rated by developers based on their perceived quality. Then, we devise a statistical model to measure how the metrics available in literature reflect the perceived quality of test cases. The findings of our study show that readability and maintainability are the key aspects for developers to diagnose the outcome of test cases and drive debugging activities. On the contrary, code coverage metrics are necessary but not sufficient to evaluate the capability of tests. Finally, we discover that available metrics are effective in characterizing poor-quality tests, while limited in distinguishing high-quality ones.
User preference-aware navigation for mobile robot in domestic via defined virtual area

Human-aware robot navigation is the key to facilitate the deployment of mobile robots into human–robot shared environments. Although many efforts have been made in this regard, almost all of the existing works focus on the constraints such as user comfort and social rules, while ignoring to consider user preferences. And it is expected that the mobile robot is able to navigate in the user-desired areas according to user preferences, e.g., navigating at low speed in the bedroom. To this end, this paper studies the problem of allowing users to control the robot’s working areas to ensure robot aware navigation based on their preferences. To address the intrusiveness of additional facilities or devices to users, we propose a non-intrusive solution by deploying the virtual areas that are non-physical areas but are respected by the mobile robot while performing navigation tasks. Farther, an interactive interface is developed to support the common (non-expert) users to flexibly define the desired areas and specify the navigation behavior of the robot according to their preferences. The proposed solution is fully evaluated through extensive experiments. Experimental results are presented to demonstrate the validity of our approach, and show that the mobile robot can change its navigation behavior in user-defined areas according to user preferences. Supplementary video can be available at https://youtu.be/oXtbNavLmMk.
Fast Fusion-Based Dehazing With Histogram Modification and Improved Atmospheric Illumination Prior

Haze can seriously affect the visible and visual quality of outdoor optical sensor systems, e.g., driving assistance, remote sensing, and video surveillance. Single image dehazing is an intractable problem due to its ill-posed nature. The main idea of the paper is combining multi-scale fusion strategy and prior knowledge, thereby presenting balanced image contrast enhancement and intrinsic color preservation, efficiently. The atmospheric illumination prior (AIP) has been proved that haze mainly degrades the contrast of the luminance channel rather than chrominance channels in YCrCb colorspace. To this end, we firstly identify and remove the color veil (unbalanced color channel) with the white balance algorithm, to reduce the influence of unbalanced color channels neglected by the AIP. Considering the new observation that hazy regions exhibit low contrast with high-intensity pixels, the dense and mild haze are enhanced by a set of histogram modification techniques, respectively. Then, with the derived inputs, multi-scale fusion based on Laplacian decomposition strategy is proposed to blend visual contrast only in the luminance channel. Without relying on complex enhancement algorithms and only dealing with one channel, the proposed method is attractive for real-time applications. Moreover, The proposed method can be directly applied to the video sequences frame by frame, alleviating visual artifacts. The simulation results show that our method is comparative to and even better than the more complex state-of-the-art techniques.
A distributed density estimation algorithm and its application to naive Bayes classification

We consider the problem of learning a density function from observations of an unknown underlying model in a distributed setting, where the observations are partitioned into different sites. Applying commonly used density estimation methods such as Gaussian Mixture Model (GMM) or Kernel Density Estimation (KDE) to distributed data leads to an extensive amount of communication. A familiar approach to address this issue is to sample a small subset of data and collect them into a central node to run the density estimation algorithms on them. In this paper, we follow an alternative to the sub-sampling approach by proposing the nested Log-Poly model. This model provides an accurate density estimation from a small sized statistic of the entire data. In distributed settings, it transfers the small sized statistics from the client nodes to a central node. The estimation process is then run in the central node. The proposed model can be used in different learning tasks such as classification in supervised learning and clustering in unsupervised learning. However, the properties of nested Log-Poly make it a suitable model for one-dimensional density estimations in the distributed settings. This makes Log-Poly a good choice for naive Bayes classifier, where one-dimensional density estimation is required for every feature conditioned on the class label. We provide a theoretical analysis of the efficiency of our model in estimating a wide range of probability density functions. Our experiments show that nested Log-Poly outperforms the state of the art density estimators on several synthetic datasets. We compare the accuracy and the communication load of naive Bayes classifier using nested Log-Poly and other related density estimators on several real datasets. The experimental outcomes depict that nested Log-Poly has less communication load, while maintaining a competitive classification accuracy compared to similar methods that use the entire data. Moreover, we present a comprehensive comparison between nested Log-Poly and validated KDE with sub-sampling, in terms of the number of communicated variables and the number of bytes transferred between the clients and the central node. Nested Log-Poly provides comparable accuracy with the validated KDE with sub-sampling, while communicating fewer variables. However, our method needs to compute and transmit the variables with a high precision in order to accurately capture the details of the underlying distributions.
Privacy preserving and data publication for vehicular trajectories with differential privacy

In the Vehicular Ad-hoc Networks, an enormous number of Location-based Services could be given to clients as per their development characteristics. Then, protection might be split when clients distribute certain vehicular trajectories information to the servers. Location-based Services collect large amounts of the Vehicular Ad-hoc Networks trajectories data, and if this data is released directly without any processing, it leaks the vehicles privacy. Nowadays, many scientists have encouraged different technologies to protect privacy, but how to use it rationally for Location-based Service is also a challenge. This path is continuous in time and space. Still, mostly the existing approaches only acknowledge a single position of the moving object at a particular time. They do not find the entire path, which may destroy the integrity of the space–time of the trajectory of the vehicle. However, existing work cannot fully guarantee the privacy of the vehicle's trajectories because randomly selected noise does not contribute to the dissemination of meaningful path data, and people must hide access to sensitive areas. In this paper, a Differential Privacy and generalization based anonymization approach is proposed to protect the privacy of the sensitive vehicular trajectories. Privacy analysis shows that this scheme is achieved the Differential Privacy. The experiments with vehicular trajectories data shows that the system has good data feasibility and can be applied on large vehicular trajectories datasets. In the results firstly, to compute the Dump Ratio and CAVG experiment to check the efficiency of the method. Results shows the histogram of DPPS, PPDP and NTDP and the proposed strategy on the four datasets, where DPPS, PPDP and NTDP have lower accessibility contrasted with the proposed technique. The accuracy, precision and the recall rate of proposed method is also achieved. The impact of privacy budget values on Relative Average Error, Mean Absolute Error, Standard Deviation are also examined.
Detecting Phishing SMS Based on Multiple Correlation Algorithms

The SMS phishing is another method where the phisher operates the SMS as a medium to communicate with the victims and this method is identified as smishing (SMS + phishing). Researchers promoted several anti-phishing methods where the correlation algorithm is applied to explore the relevancy of the features since there are numerous features in the features corpus. The correlation algorithm assesses the rank of the features that is the highest rank leads to the more relevant to the appropriate assignment. Therefore, this paper analyses four rank correlation algorithms particularly Pearson rank correlation, Spearman's rank correlation, Kendall rank correlation, and Point biserial rank correlation with a machine-learning algorithm to determine the best features set for detecting Smishing messages. The result of the investigation reveals that the AdaBoost classifier offered better accuracy. Further analysis shows that the classifier with the ranking algorithm that is Kendall rank correlation appeared superior accuracy than the other correlation algorithms. The inferred of this experiment confirms that the ranking algorithm was able to reduce the dimension of features with 61.53% and presented an accuracy of 98.40%.
Inflation and Finance-Growth Nexus in Sub-Saharan Africa

The study aims at determining the effect of inflation on finance-growth nexus on 23 Sub-Saharan Africa countries with 5-year averaged data set over the period 1982–2016. A non-dynamic threshold approach is utilized. Strong evidence of inflation threshold of 31% beyond which the impact of financial development on economic growth turns negative from positive is obtained. There is also inflation threshold of 13% obtained when financial development is represented by private credit by deposit money banks and other financial institutions (% of GDP) but with an even higher positive impact of financial development on economic growth beyond the identified inflation threshold. This supports the view that financial development can only become inversely related to growth at much higher-inflation level of above 31%, below which there is robust positive finance-growth nexus. It is, therefore, recommended to always keep inflation levels at lower levels, preferably below 31%, which would ensure sustainable development through financial development.
Monocular Vision-Based Calibration Method for Determining Frequency Characteristics of Low-Frequency Vibration Sensors

Calibration is required to determine the frequency characteristics of vibration sensors to ensure their measurement accuracy in engineering applications. Thus, a monocular vision-based calibration method for low-frequency vibration sensors is investigated in this study. A sub-pixel edge detection method which based on Gaussian curve fitting is applied to extract the edges of motion sequence images in order to accurately measure the excitation acceleration of the sensors. Because the motion sequence images and the output signal of the sensors cannot be collected synchronously, it is very difficult to align the excitation acceleration signal obtained from the images and the output signal in the time domain. Although the misalignment only has a negligible effect on the magnitude frequency characteristic calibration, it dramatically decreases the calibration accuracy of the phase frequency characteristic, especially with the increasing of the frequency. A time-spatial synchronization technique is proposed to accurately calibrate the phase frequency characteristic by determining the phase of the excitation acceleration signal at a specific spatial position and that of the output signal at the corresponding time. Finally, both the magnitude and phase frequency characteristics are simultaneously calibrated by the investigated method with a flexible and low-cost vision system. The experimental results compared with laser interferometry show that the investigated method accomplishes the high-accuracy magnitude and phase frequency characteristics calibration in a broad low-frequency range. Its calibration accuracy is superior to that of laser interferometry when the frequency is less than 0.3 Hz, and is equivalent at other frequencies.
The Role of the Teacher and AI in Education

What is it like being a lecturer or teacher? 1 The classic image is an old, wise, and powerful stoic with oceans of knowledge: a bit like Yoda in Star Wars or Dumbledore in Harry Potter. With the rise of new technology in general and artificial intelligence (AI) in particular, teachers are no longer the sole source of knowledge. Does this mean that the teacher will soon be replaced by technology? Will AI take over?This chapter considers the risks and benefits of AI in higher education. It argues that AI will not, should not, and, indeed, cannot replace the teacher, because of what is (for now at least) unique to the teacher: namely, her humanity. The idea is simple: to secure good education into the future, one must take advantage of teachers' uniquely human expertise. State-of-the-art AI applications cannot be bodily present in the same way as human teachers, nor teach existential reflection, norms and values, or a sense of self, history, and society.
Result-Based Re-computation for Error-Tolerant Classification by a Support Vector Machine

Support Vector Machines (SVMs) are widely used in Machine Learning (ML) to perform classification. For a given element, an SVM computes a value using a kernel function and several support vectors to determine its class. Unfortunately, the computational units can be affected by externally induced phenomena such as soft errors; therefore, an erroneous result can be obtained. This is an issue when an SVM is used in safety-critical applications in which a change of the classification result is not acceptable. To ensure that errors do not change the classification result, traditional protection schemes can be used. For example, if the SVM computation is done on a processor, calculations can be executed twice, and the results get compared. If they are different, the error is detected, and the calculation can be done for a third time, voting can then be utilized to obtain the most likely result. However, this approach incurs in a large cost for computing resources and may not be acceptable when an SVM is used in resource constrained platforms such as Internet of Things (IoT) devices. In this paper, Result-based Re-computation (RBR) is proposed; RBR is an efficient technique to protect SVMs from errors in the kernel function, which is the most complex part in the SVM implementation. RBR is based on the observation that the SVM result is a sum of kernel terms to detect the terms that can modify the classification result and only these terms must be re-computed. The evaluation results using several publicly available datasets show that compared to a traditional protection scheme, the proposed RBR reduces up to 95.58% of the re-computation needed to protect an SVM against errors. Impact Statement-Error tolerance is critical in safety-critical Artificial Intelligence (AI) applications due to their dramatic consequences and safety implications. However, conventional protection solutions always incur in a large computational cost that could become prohibitive in resource-constrained platforms and application domains such as Internet of Things devices. The Algorithm-based Error Tolerance (ABET) approach for Support Vector Machines (SVMs) proposed in this paper has a significant advantage in terms of low computational demands. This advantage makes it very attractive in practice. The proposed algorithm reduces computational time with up to 95.58%, when compared to a classic protection scheme. This saving will promote the use of ABET approaches in industry, especially in embedded low-power systems.
Hybrid microblog recommendation with heterogeneous features using deep neural network

With the development of mobile Internet, microblog has become one of the most popular social platforms. The enormous user-generated microblogs have caused the problem of information overload, which makes users difficult to find the microblogs they actually need. Hence, how to provide users with accurate microblogs has become a hot and urgent issue. In this paper, we propose an approach of hybrid microblog recommendation, which is developed on a framework of deep neural network with a group of heterogeneous features as its input. Specifically, two new recommendation strategies are first constructed in terms of the extended user-interest tags and user interest topics, respectively. These two strategies additionally with the collaborative filtering are employed together to obtain the candidate microblogs for final recommendation. Then, we propose the heterogeneous features related to personal interests of users, interest in authors and microblog quality to describe the candidate microblogs. Finally, a deep neural network with multiple hidden layers is designed to predict and rank the microblogs. Extensive experiments conducted on the datasets of Sina Weibo and Twitter indicate that our proposed approach significantly outperforms the state-of-the-art methods. The code and the two datasets of this paper are publicly available at GitHub.
Entropy Minimisation Framework for Event-Based Vision Model Estimation

We propose a novel Entropy Minimisation (EMin) framework for event-based vision model estimation. The framework extends previous event-based motion compensation algorithms to handle models whose outputs have arbitrary dimensions. The main motivation comes from estimating motion from events directly in 3D space (e.g. events augmented with depth), without projecting them onto an image plane. This is achieved by modelling the event alignment according to candidate parameters and minimising the resultant dispersion. We provide a family of suitable entropy loss functions and an efficient approximation whose complexity is only linear with the number of events (e.g. the complexity does not depend on the number of image pixels). The framework is evaluated on several motion estimation problems, including optical flow and rotational motion. As proof of concept, we also test our framework on 6-DOF estimation by performing the optimisation directly in 3D space.
Smart Attendance Management System Based on Face Recognition Using CNN

Convolutional Neural Networks has been playing a significant role in many applications including surveillance, object detection, object tracking, etc. Extensive research is recorded for face recognition using CNNs, which is a key aspect of surveillance applications. In most recent times, the Face Recognition technique is widely used in University automation systems, Smart Entry management systems, etc. In this paper, a novel CNN architecture for face recognition system is proposed including the process of collecting face data of students. Experimentally it is shown that the proposed CNN architecture provides 99% accuracy. Further, the proposed CNN framework is used to develop a “Smart Attendance Management System (SAMS)“, which is a web-based application, to provide attendance of students using face recognition, in realtime. The proposed application is easy to deploy and maintain.
Prior guided conditional generative adversarial network for single image dehazing

Single image dehazing is an important problem as the existence of haze degrades the quality of the image and hinders most high-level computer vision tasks. Previous methods solve this problem using various low-level statistics priors or learning on synthetic data sets with CNN. In practice, the low-level priors are not always held in various scenes. And many CNN based methods directly estimate the transmission maps and atmospheric lights from huge synthetic data. However, without the guidance or constraints of priors may lead to over-dehazed or under-dehazed results. To address these issues, we propose a prior guided conditional generative adversarial network, an end-to-end model that generates realistic clean images using hazy image input and dehazed image based on the traditional prior-based method. The proposed generator extracted the feature with a parameters-shared encoder, and the clear image is recovered by decoding multi-scale features, which are fused and enhanced by the proposed attention-based feature aggregation block. And two-scale discriminators are adopted to supervise the generator to recover more image details with a combination of perceptual loss and adversarial loss. Our algorithm can efficiently combine the prior-based and CNN based image dehazing method and remove the weakness of each other. Experimental results on synthetic datasets and real-world images demonstrate our model can generate more perceptually appealing dehazing results, and provide superior performance compared with the state-of-the-art methods.
Complex Network Representation of the Structure-Mechanical Property Relationships in Elastomers with Heterogeneous Connectivity

The complicated structure-property relationships of materials have recently been described using a methodology of data science that is recognized as the fourth paradigm in materials science. In network polymers or elastomers, the manner of connection of the polymer chains among the crosslinking points has a significant effect on the material properties. In this study, we quantitatively evaluate the structural heterogeneity of elastomers at the mesoscopic scale based on complex network, one of the methods used in data science, to describe the elastic properties. It was determined that a unified parameter with topological and spatial information universally describes some parameters related to the stresses. This approach enables us to uncover the role of individual crosslinking points for the stresses, even in complicated structures. Based on the data science, we anticipate that the structure-property relationships of heterogeneous materials can be interpretatively represented using this type of "white box" approach.