Reviewerly/exHarmony huggingface dataset:
Discovering Anomalies by Incorporating Feedback from an Expert

Unsupervised anomaly detection algorithms search for outliers and then predict that these outliers are the anomalies. When deployed, however, these algorithms are often criticized for high false-positive and high false-negative rates. One main cause of poor performance is that not all outliers are anomalies and not all anomalies are outliers. In this article, we describe the Active Anomaly Discovery (AAD) algorithm, which incorporates feedback from an expert user that labels a queried data instance as an anomaly or nominal point. This feedback is intended to adjust the anomaly detector so that the outliers it discovers are more in tune with the expert user’s semantic understanding of the anomalies. The AAD algorithm is based on a weighted ensemble of anomaly detectors. When it receives a label from the user, it adjusts the weights on each individual ensemble member such that the anomalies rank higher in terms of their anomaly score than the outliers. The AAD approach is designed to operate in an interactive data exploration loop. In each iteration of this loop, our algorithm first selects a data instance to present to the expert as a potential anomaly and then the expert labels the instance as an anomaly or as a nominal data point. When it receives the instance label, the algorithm updates its internal model and the loop continues until a budget of B queries is spent. The goal of our approach is to maximize the total number of true anomalies in the B instances presented to the expert. We show that the AAD method performs well and in some cases doubles the number of true anomalies found compared to previous methods. In addition we present approximations that make the AAD algorithm much more computationally efficient while maintaining a desirable level of performance.
Automatic Attendance Management System based on Deep One-Shot Learning

Due to the positive relationship between the presence of students in classes and their performance, student attendance assessment is considered essential within the classroom environment, even as a tiring and time-consuming task. We proposed a solution for student attendance control using face recognition with deep one-shot learning and evaluated our approach in different conditions and image capturing devices to confirm that such a pipeline may work in a real-world setting. For better results regarding the high number of false negatives that often occur in uncontrolled environments, we also proposed a face detection stage using HOG and a CNN with Max-Margin Object Detection based features. We achieved accuracy and F1 scores of 97% and 98.4% with an iPhone 7 camera, 91.9% and 94.8% with a Moto G camera, and 51.2% and 61.1% with a WebCam respectively. These experiments reinforce the effectiveness and availability of this approach to the student attendance assessment problem since the recognition pipeline can be either made available for embedded processing with limited computational resources (smartphones), or offered as “Software as a Service” tool.
Securing Car Data and Analytics using Blockchain

Automakers in collaboration with technology industries are swiftly innovating and transforming the automobile industry. The current trend of connected cars relies on retrieving various kinds of vehicular data since there is a huge demand from associated entities included insurance companies, vehicle buyers/sellers and government authorities. Currently, the data collection is done either manual or unsupervised that poses trust, legitimacy and accuracy issues such as duplicated or falsified vehicular data records, tampered safety checks and meddled driving history, etc. Hence, a strong tool that can protect the vehicular data; log the changes for audit purposes and eventually build the trust in the system is necessary. We propose the use of blockchain technology for these needs. The proposed solution involves connecting an IoT module to a car data port to collect rich telemetric data; analyze the driving behaviors on various fronts and storing the outcomes to a blockchain. For various good reasons we use the Ethereum blockchain in this study. However, other blockchain deployments can also be utilized. If adopted by the stakeholders, the proposed solution can provide a trusted, transparent and easily accessible platform to auto buyers/sellers, insurance agencies, vehicle dealers, law enforcements and vehicle history providers.
Trajectory Tracking for Aerial Robots: an Optimization-Based Planning and Control Approach

In this work, we present an optimization-based trajectory tracking solution for multirotor aerial robots given a geometrically feasible path. A trajectory planner generates a minimum-time kinematically and dynamically feasible trajectory that includes not only standard restrictions such as continuity and limits on the trajectory, constraints in the waypoints, and maximum distance between the planned trajectory and the given path, but also restrictions in the actuators of the aerial robot based on its dynamic model, guaranteeing that the planned trajectory is achievable. Our novel compact multi-phase trajectory definition, as a set of two different kinds of polynomials, provides a higher semantic encoding of the trajectory, which allows calculating an optimal solution but following a predefined simple profile. A Model Predictive Controller ensures that the planned trajectory is tracked by the aerial robot with the smallest deviation. Its novel formulation takes as inputs all the magnitudes of the planned trajectory (i.e. position and heading, velocity, and acceleration) to generate the control commands, demonstrating through in-lab real flights an improvement of the tracking performance when compared with a controller that only uses the planned position and heading. To support our optimization-based solution, we discuss the most commonly used representations of orientations, as well as both the difference as well as the scalar error between two rotations, in both tridimensional and bidimensional spaces SO(3) and SO(2). We demonstrate that quaternions and error-quaternions have some advantages when compared to other formulations.
QUANTIFY: A Framework for Resource Analysis and Design Verification of Quantum Circuits

Quantum resource analysis is crucial for designing quantum circuits as well as assessing the viability of arbitrary (error-corrected) quantum computations. To this end, we introduce QUANTIFY, which is an open-source framework for the quantitative analysis of quantum circuits. It is based on Google Cirq and is developed with Clifford+T circuits in mind, and it includes the necessary methods to handle Toffoli+H and more generalised controlled quantum gates, too. Key features of QUANTIFY include: (1) analysis and optimisation methods which are compatible with the surface code, (2) choice between different automated (mixed polarity) Toffoli gate decompositions, (3) semi-automatic quantum circuit rewriting and quantum gate insertion methods that take into account known gate commutation rules, and (4) novel optimiser types that can be combined with different verification methods (e.g. truth table or circuit invariants like number of wires). For benchmarking purposes QUANTIFY includes quantum memory and quantum arithmetic circuits. Experimental results show that the framework's performance scales to circuits with thousands of qubits.
Learning from Humans to Generate Communicative Gestures for Social Robots

Non-verbal behaviors play an essential role in human-human interaction, allowing people to convey their intention and attitudes, and affecting social outcomes. Of particular importance in the context of human-robot interaction is that the communicative gestures are expected to endow social robots with the capability of emphasizing its speech, describing something, or showing its intention. In this paper, we propose an approach to learn the relation between human behaviors and natural language based on a Conditional Generative Adversarial Network (CGAN). We demonstrated the validity of our model through a public dataset. The experimental results indicated that the generated human-like gestures correctly convey the meaning of input sentences. The generated gestures were transformed into the target robot's motion, being the robot's personalized communicative gestures, which showed significant improvements over the baselines and could be widely accepted and understood by the general public.
A Clinical Prediction Model in Health Time Series Data Based on Long Short-Term Memory Network Optimized by Fruit Fly Optimization Algorithm

Aiming the problems that the clinical data of different patients is difficult for reasonable representation and the time interval between medical events is different, which lead to the difficulty of clinical prediction, a clinical prediction model based on the long short-term memory (LSTM) network optimized by fruit fly optimization algorithm in health time series data is proposed. First, FastText method is used to represent the interpretable vector of medical events, which can extract the concept relationship rich in medical information more effectively. Then, considering the strong dependence of clinical data on time stamp, LSTM network is used to model clinical events for better extraction of long-term and short-term information, so as to improve the prediction performance of the model. Finally, the fruit fly optimization algorithm is used to find the optimal super parameters of LSTM network, which can improve the training efficiency and prediction precision of the network. Experimental results on MIMIC datasets show that the prediction precision, Recall@k and MAP@k of the proposed model are better than those of other models. The validity of the model is proved.
A comparison of forecasting models for the resource usage of MapReduce applications

In this paper, we construct forecasting models (multivariate long short-term memory recurrent neural networks and multiple linear regression) for the resource usage prediction of four MapReduce applications and applications executed within the Spark framework. We have evaluated the impact of a sample size to prediction accuracy. Also, we propose a phase modelling approach for read/write-intensive applications. Our results show that models based on long short-term memory recurrent neural networks exhibit a higher accuracy than multiple linear regression models and the intensive characteristics of a resource are closely related to the prediction accuracy of forecasting models. We investigated the hyperparameter tuning of such models and showed that a randomly initialised, shallow, well-tuned network may outperform deeper models that use stacked autoencoder initialisation. Furthermore, multivariate long short-term memory recurrent neural network models are more sensitive to sample size than multiple linear regression models. We show that an LSTM model trained in a specific machine may be used to predict the resource usage in another machine.
Multimodal exponential families of circular distributions with application to daily peak hours of <b>PM2.5</b> level in a large city

In this paper, we propose two multimodal circular distributions which are suitable for modeling circular data sets with two or more modes. Both distributions belong to the regular exponential family of distributions and are considered as extensions of the von Mises distribution. Hence, they possess the highly desirable properties, such as the existence of non-trivial sufficient statistics and optimal inferences for their parameters. Fine particulates (PM2.5) are generally emitted from activities such as industrial and residential combustion and from vehicle exhaust. We illustrate the utility of our proposed models using a real data set consisting of fine particulates (PM2.5) pollutant levels in Houston region during Fall season in 2019. Our results provide a strong evidence that its diurnal pattern exhibits four modes; two peaks during morning and evening rush hours and two peaks in between.
EasyCloud: a Rule based Toolkit for Multi-platform Cloud/Edge Service Management

In order to satisfy the demand for collective and collaborative use of the various Cloud/Edge computing platforms available, the Cloud/Edge interoperability is necessary. Unfortunately, due to the specific solutions provided by the major Cloud/Edge providers, right now it is difficult to fully exploit different Clouds/Edges concurrently. In this paper, we aim to fill this gap by proposing EasyCloud, an easy and effective toolkit and user interface able to not only interact with multiple and different Cloud/Edge platforms at the same time but also to provide a rule-based engine where the user can specify what to do in real-time when the workload of the services running on the Clouds/Edges becomes underutilized (e.g., switch-off the service to save money) or overutilized (e.g., switch-on new computational resources to overcome the increased workload). EasyCloud is currently used by researchers, educators, and students with success and its source code is publicly available.
On Performance Improvement Of Reversible Data Hiding With Contrast Enhancement

Abstract Reversible data hiding (RDH) with contrast enhancement (RDH-CE) is a special type of RDH in improving the subjective visual perception by enhancing the image contrast during the process of data embedding. In RDH-CE, data hiding is achieved via pairwise histogram expansion, and the embedding rate can be increased by performing multiple cycles of histogram expansions. However, when embedding rate gets high, human visible image degradation is observed. Previous work designed an upper bound of the embedding level for RDH-CE, which effectively avoids image over-sharping but offers limited embedding capacity. In this paper, a better tunable bound is designed to enhance the embedding capacity of RDH-CE by exploiting the characteristics of histogram distribution. Furthermore, the objective distortion introduced by histogram pre-shifting is minimized when the embedding level is no more than the upper bound, and the human visible degradation is minimized when the embedding level exceeds the limitation of the proposed upper bound. Experimental results validate that the proposed method provides appropriate upper bound of the embedding level, increases the effective embedding capacity and offers better image contrast.
CBUA: A Probabilistic, Predictive, and Practical Approach for Evaluating Test Suite Effectiveness

Knowing the effectiveness of a test suite is essential for many activities such as assessing the test adequacy of code and guiding the generation of new test cases. Mutation testing is a commonly used defect injection technique for evaluating the effectiveness of a test suite. However, it is usually computationally expensive, as a large number of mutants (buggy versions) are needed to be generated from a production code under test and executed against the test suite. In order to reduce the expensive testing cost, recent studies proposed to use supervised models to predict the effectiveness of a test suite without executing the test suite against the mutants. Nonetheless, the training of such a supervised model requires labeled data, which still depends on the costly mutant execution. Furthermore, existing models are based on traditional supervised learning techniques, which assume that the training and testing data come from the same distribution. But, in practice, software systems are subject to considerable concept drifts, i.e., the same distribution assumption usually does not hold. This can lead to inaccurate predictions of a learned supervised model on the target code as time progresses. To tackle these problems, in this paper, we propose a Coverage-Based Unsupervised Approach (CBUA) for evaluating the effectiveness of a test suite. Given a production code under test, the corresponding mutants, and a test suite, CBUA first collects the coverage information of the mutated statements in the target production code under the execution of the test suite. Then, CBUA employs coverage to estimate the probability of each mutant being alive. As such, a mutation score is computed to evaluate the test suite effectiveness and the predicted labels (i.e., killed or alive) are obtained. The whole process only requires a one-time execution of the test suite against the target production code, without involving any mutant execution and any training data. CBUA can ensure the score monotonicity property (i.e., adding test cases to a test suite does not decrease its mutation score), which may be violated by a supervised approach. The experimental results show that CBUA is very competitive with the state-of-the-art supervised approaches in prediction accuracy. In particular, CBUA is shown to be more effective in finding mutants that are covered but not killed by a test suite, which is helpful in identifying the weaknesses in the current test suite and generating new test cases accordingly. Since CBUA is an easy-to-implement approach with a low cost, we suggest that it should be used as a baseline approach for comparison when any novel prediction approach is proposed in future studies.
The concept of ‘sharing’ in Chinese social media: origins, transformations and implications

In this article we present an analysis of the concepts of fenxiang and gongxiang – the Mandarin words for 'sharing'– in the context of Chinese social media. We do so through an interrogation of the words fenxiang and gongxiang as used by Chinese social media companies. Using the Internet Archive's Wayback Machine, we created screenshots of 32 Chinese social network sites between 2000 and 2018 and tracked changes in the usage of fenxiang and gongxiang over time. The Mandarin translations in some ways operate like the English word, 'sharing'. Fenxiang has the meaning of participating in social media, and gongxiang refers to technological aspects of sharing, while also conveying a sense of harmony. However, the interpersonal relations implied by fenxiang, and the political order implied by gongxiang, are quite different from those conveyed by 'sharing'. Together, fenxiang and gongxiang construct a convergence of micro-level interpersonal harmony and macro-level social harmony. Thus, the language of sharing becomes the lens through which to observe the subtlety, complexity and idiosyncrasies of the Chinese internet. This article offers a new heuristic for understanding Chinese social media, while also pointing to an important facet of the discursive construction of Chinese social media. This implies a continuing need to de-westernize research into the internet and to identify cultural-specific meanings of social media.
Exploring HTTPS security inconsistencies: A cross-regional perspective

If two or more identical HTTPS clients, located at different geographic locations (regions), make an HTTPS request to the same domain (e.g. example.com), on the same day, will they receive the same HTTPS security guarantees in response? Our results give evidence that this is not always the case. We conduct scans for the top 250000 most visited domains on the Internet, from clients located at five different regions: Australia, Brazil, India, the UK, and the US. Our scans gather data from both application (URLs and HTTP headers) and transport (servers’ selected TLS version, ciphersuite, and certificate) layers. Overall, we find that HTTPS inconsistencies at the application layer are higher than those at the transport layer. We also find that HTTPS security inconsistencies are strongly related to URLs and IPs diversity among regions, and to a lesser extent to the presence of redirections. Further manual inspection shows that there are several reasons behind URLs diversity among regions such as downgrading to the plain-HTTP protocol, using different subdomains, different TLDs, or different home page documents. Furthermore, we find that downgrading to plain-HTTP is related to websites’ regional blocking. We also provide attack scenarios that show how an attacker can benefit from HTTPS security inconsistencies, and introduce a new attack scenario which we call the “region confusion” attack. Finally, based on our analysis and observations, we provide discussion, which include some recommendations such as the need for testing tools for domain administrators and users that help to mitigate and detect regional domains’ inconsistencies, standardising regional domains format with the same-origin policy (of domains) in mind, standardising secure URL redirections, and avoid redirections whenever possible.
Minimizing Forking in Blockchain-Based IoT Networks

Blockchain is a special type of data structure in which data, usually termed as transactions, is linked together using cryptographic techniques. Because of these modular features, the applications of blockchain are being investigated in myriad domains such as cybersecurity, finance, transportation, and Internet-of-things (IoT). Although the applications of blockchain in IoT networks have much potential, many research challenges need the attention of researchers from both academia and industry. One of the open research challenges is the frequent occurrence of forking events in blockchain-based IoT networks. The forking of a blockchain can not only lead to potential security attacks but also poses excessive overhead in the largescale IoT networks. To tackle this inefficiency of blockchain-based IoT networks, this paper employs deep learning to minimize forking by reducing the transmission delays. Specifically, a deep neural network is used to reduce the transmission delay by improving the transmission rate. The deep learning approach is also compared with other benchmark techniques to show the superiority of the proposed method. We anticipate that this foundational work on blockchain-based IoT networks would pave the way for researchers to upscale the study into real-world implementations.
Generating High-Resolution Climate Prediction through Generative Adversarial Network

Downscaling technology is always used for high-resolution climate prediction, and this technology can generate small-scale regional climate prediction from large-scale climate output information. Inspired by image super resolution, we propose to apply the super-resolution models to downscaling technology. However, some unpleasant artifacts always appear in the high-resolution climate images generated by exiting super-resolution models. To further eliminate these unpleasant artifacts, we innovatively apply the super-resolution generative adversarial network (SRGAN) to climate prediction. SRGAN adopts a perceptual loss function which consists of an adversarial loss and a content loss, which can recover more high-frequency details in the generated high-resolution images. Besides, we propose a method to fuse climate data with related meteorological factors to generate climate images, this measure can improve the accuracy of climate prediction. Finally, extensive experimental results on climate datasets show that SRGAN performs better than most super-resolution approaches in climate prediction.