Reviewerly/exHarmony huggingface dataset:
What Support do Systematic Reviews Provide for Evidence-informed Teaching about Software Engineering Practice?

Background:The adoption of the evidence-based research paradigm by software engineering researchers has created a growing knowledge base provided by the outcomes from systematic reviews.Aim: We set out to identify and catalogue a sample of the knowledge provided by systematic reviews, to determine what support they can provide for an evidence-informed approach to teaching about software engineering practice.Method: We undertook a tertiary study (a mapping study of systematic reviews) covering the period to the end of 2015.We identified and catalogued those reviews that had findings or made recommendations that were considered relevant to teaching about industry practice.Results: We examined a sample of 276 systematic reviews, selecting 49 for which we could clearly identify practice-oriented findings and recommendations that were supported by the data analysis provided in the review.We have classified these against established software engineering education knowledge categories and discuss the extent and forms of knowledge provided for each category.Conclusion: While systematic reviews can provide knowledge that can inform teaching about practice, relatively few systematic reviews present the outcomes in a form suitable for this purpose.Using a suitable format for presenting a summary of outcomes could improve this.Additionally, the increasing number of published systematic reviews suggests that there is a need for greater coordination regarding the cataloguing of their findings and recommendations.
Blockchain, Cryptocurrency, and Cybersecurity

How should managers and executives assess the potential of emerging technologies? First, they should understand the technologies on the list. Next, they should identify the business models and processes to which they might be leveraged. They should understand how mature the technologies are and should “score” the technologies according to their pilot potential. Finally, they should find pilot sponsors and develop pilot project plans with their sponsors.
Children in 2077: Designing Children's Technologies in the Age of Transhumanism

What for and how will we design children's technologies in the transhumanism age, and what stance will we take as designers? This paper aims to answer this question with 13 fictional abstracts from sixteen authors of different countries, institutions and disciplines. Transhumanist thinking envisions enhancing human body and mind by blending human biology with technological augmentations. Fundamentally, it seeks to improve the human species, yet the impacts of such movement are unknown and the implications on children's lives and technologies were not explored deeply. In an age, where technologies such as under-skin chips or brain-machine interfaces can clearly be defined as transhumanist, our aim is to reveal probable pitfalls and benefits of those technologies on children's lives by using the power of design fiction. Thus, main contribution of this paper is to create diverse presentation of provocative research ideas that will foster the discussion on the transhumanist technologies impacting the lives of children in the future.
Centered Partition Processes: Informative Priors for Clustering (with Discussion)

There is a very rich literature proposing Bayesian approaches for clustering starting with a prior probability distribution on partitions. Most approaches assume exchangeability, leading to simple representations in terms of Exchangeable Partition Probability Functions (EPPF). Gibbs-type priors encompass a broad class of such cases, including Dirichlet and Pitman-Yor processes. Even though there have been some proposals to relax the exchangeability assumption, allowing covariate-dependence and partial exchangeability, limited consideration has been given on how to include concrete prior knowledge on the partition. For example, we are motivated by an epidemiological application, in which we wish to cluster birth defects into groups and we have prior knowledge of an initial clustering provided by experts. As a general approach for including such prior knowledge, we propose a Centered Partition (CP) process that modifies the EPPF to favor partitions close to an initial one. Some properties of the CP prior are described, a general algorithm for posterior computation is developed, and we illustrate the methodology through simulation examples and an application to the motivating epidemiology study of birth defects.
Applying Visual Cryptography to Enhance Text Captchas

Nowadays, lots of applications and websites utilize text-based captchas to partially protect the authentication mechanism. However, in recent years, different ways have been exploited to automatically recognize text-based captchas especially deep learning-based ways, such as, convolutional neural network (CNN). Thus, we have to enhance the text captchas design. In this paper, using the features of the randomness for each encoding process in visual cryptography (VC) and the visual recognizability with naked human eyes, VC is applied to design and enhance text-based captcha. Experimental results using two typical deep learning-based attack models indicate the effectiveness of the designed method. By using our designed VC-enhanced text-based captcha (VCETC), the recognition rate is in some degree decreased.
MotherNets: Rapid Deep Ensemble Learning

Ensembles of deep neural networks significantly improve generalization accuracy. However, training neural network ensembles requires a large amount of computational resources and time. State-of-the-art approaches either train all networks from scratch leading to prohibitive training cost that allows only very small ensemble sizes in practice, or generate ensembles by training a monolithic architecture, which results in lower model diversity and decreased prediction accuracy. We propose MotherNets to enable higher accuracy and practical training cost for large and diverse neural network ensembles: A MotherNet captures the structural similarity across some or all members of a deep neural network ensemble which allows us to share data movement and computation costs across these networks. We first train a single or a small set of MotherNets and, subsequently, we generate the target ensemble networks by transferring the function from the trained MotherNet(s). Then, we continue to train these ensemble networks, which now converge drastically faster compared to training from scratch. MotherNets handle ensembles with diverse architectures by clustering ensemble networks of similar architecture and training a separate MotherNet for every cluster. MotherNets also use clustering to control the accuracy vs. training cost tradeoff. We show that compared to state-of-the-art approaches such as Snapshot Ensembles, Knowledge Distillation, and TreeNets, MotherNets provide a new Pareto frontier for the accuracy-training cost tradeoff. Crucially, training cost and accuracy improvements continue to scale as we increase the ensemble size (2 to 3 percent reduced absolute test error rate and up to 35 percent faster training compared to Snapshot Ensembles). We verify these benefits over numerous neural network architectures and large data sets.
Driver Drowsiness Detection Using Convolutional Neural Networks

The presented paper deals with automatic detection of driver drowsiness. Detecting the driver's drowsiness behind the steering wheel and then alerting him may reduce road accidents. Drowsiness in this case is captured using an autocamera, whereby, based on the captured image, the neural network recognizes whether the driver is awake or tired. The convolutional neural network (CNN) technology has been used as a component of a neural network, where each frame is evaluated separately and the average of the last 20 frames is evaluated, which corresponds to approximately one second in the training and test dataset. First, we analyze methods of image segmentation, and develop a model based on convolutional neural networks. Using an annotated dataset of more than 2000 image slices we train and test the segmentation network to extract the driver emotional status from the images.
Exploring the Hand and Finger-Issued Behaviors Toward Natural Authentication

As wearables, such as smartwatches and data gloves, become ubiquitous in our daily lives, it is possible to capture users' behaviors as they go, and then to authenticate them using biometrics. A variety of behaviors have been exploited to authenticate users in a plethora of scenarios. However, there is still a lack of systematic investigations on what types of behaviors present better performance for authentication, and therefore, limiting our understanding of the custom design of behavioral “passwords” to fit people's needs. This paper is designed to help answer this question. First, we design a collection of behaviors, which is used to explore the effect of different types of behaviors on authentication. This collection involves hand and finger behaviors since they are common and natural human actions; it also reasonably covers both basic movements and complex behaviors, two major categories of behaviors. We evaluated authentication performance along the dimensions of behaviors, users (sensitivity) and classification models (independence). A valuable finding shows that finger-issued movements present almost equivalent performance with complex behaviors. Therefore, it suggests applying them to the wearable and mobile devices as an alternative to complex behaviors, and could considerably reduce requirements for computational resources compared to complex behaviors. Furthermore, they extend the applications of behavioral authentication to a wider array of scenarios, like performing behavioral authentication such as moving fingers in one's pocket, which is common for wearable and mobile applications.
Greater Control and Transparency in Personal Data Processing

Although the European General Data Protection Regulation affords data subjects more control over how theirpersonal data is stored and processed, there is a need for technical solutions to support these legal rights.In this position paper we assess the level of control, transparency and compliance offered by three differentapproaches (i.e., defacto standard, SPECIAL, Solid). We propose a layered decentralised architecture based oncombining SPECIAL and Solid. Finally, we introduce our usage control framework, which we use to compareand contrast the level of control and compliance offered by the four different approaches.
Covert Communication via the QR Code Image by a Data Hiding Technique Based on Module Shape Adjustments

A novel data hiding method for covert communication via the QR code image based on adjustments of the shapes of the QR code modules using image processing techniques is proposed. A module block consisting of two module pairs is taken as the unit for message-bit embedding by adjusting the vertical and horizontal internal boundaries in the module pairs. Eliminations of the resulting boundary line zigzaggedness and irregular overlapping and holing phenomena in the module block are also carried out. The resulting stego-image with secret bits embedded can be scanned by a barcode reader to obtain the facial data recorded in the QR code while a program implementing the message extraction process of the proposed method can process the stego-image to extract the hidden secret message. Good experimental results, tests of stego-image readability by QR code scanners and resistance to noise attacks, and a comparison with other methods from the viewpoint of data embedding rate show the feasibility and superiority of the proposed method for real covert communication applications.
Cloud motion estimation from small-scale irradiance sensor networks: General analysis and proposal of a new method

Small-scale PV generation is particularly affected by the irradiance variability produced by cloud shadows. Accurate predictions of the clouds passing over the PV field are thus necessary for the optimized management and integration of this renewable power source. Two main elements are required to perform these predictions: the local irradiance field and the cloud motion vector. Irradiance sensor networks are positioning as a promising data source at the spatial and temporal scales of the problem, avoiding the irradiance inference problems and costs of image-based instrumentation. This paper proposes a method to infer the cloud shadow motion vector from small-scale irradiance sensor networks data. The method does not require specific network configuration or layout, and the algorithm is computationally simple: graphical solutions are obtained by aggregating mean absolute errors in a diagram/matrix with each element representing a possible displacement of the cloud shadows. The validation is conducted with a fractal cloud model that allows the generation of irradiance time series according to an arbitrary cloud motion vector. The most correlated pair and the linear cloud edge methods are used for benchmarking purposes. Gridded and non-gridded sensors layouts are tested with number of sensors ranging from 9 to 100, monitored areas from 100 to 10000 m2, and sampling periods from 0.3 to 3.3 s. The results show the superiority of the proposed method with a reduction of 50–90% of the root mean square errors respect to the benchmark methods in 75% of the tests. Additionally, the proposed method maintains similar performance as sampling rate decreases, while the benchmark methods exhibit worsening results.
Issues of Dialectal Saudi Twitter Corpus

Text mining research relies heavily on the availability of a suitable corpus. This paper presents a dialectal Saudi corpus that contains 207452 tweets generated by Saudi Twitter users. In addition, a comparison between the Saudi tweets dataset, Egyptian Twitter corpus and Arabic top news raw corpus (representing Modern Standard Arabic (MSA) in various aspects, such as the differences between formal and colloquial texts was carried out. Moreover, investigation into the issues and phenomena, such as shortening, concatenation, colloquial language, compounding, foreign language, spelling errors and neologisms on this type of dataset was performed.
The Weakest Link of Certificate Transparency: Exploring the TLS/HTTPS Configurations of Third-Party Monitors

In order to enhance the accountability of certification authorities (CAs), certificate transparency (CT) is proposed to record CA-signed certificates in public logs. A certificate is accepted by CT-compliant browsers only if it is recorded in the logs, so that any fraudulent certificate will be detected by the domain owner in the public-visible logs. In practice, third-party monitors fetch certificates in the public logs to provide certificate search services, and the domain owner regularly searches all certificates issued for its domain from the third-party monitors to watch for suspicious ones among them. In this paper, we study the links of the CT framework among CAs, browsers, domain owners (or websites), third-party monitors, and log servers, and then analyze the security designs of each link. As an essential link of the CT framework which is proposed against TLS man-in-the-middle (MitM) attacks, the services of a thirdparty monitor shall be protected well against such attacks. We explore the TLS/HTTPS configurations of 8 well-known monitors and find that there are vulnerabilities of TLS MitM attacks. Thus, the attackers might first launch the MitM attacks on the very limited number of third-party monitors on the Internet and return manipulated certificate search results to domain owners, to conceal fraudulent certificates. The overall security guarantees of CT are jeopardized due to the weak protections of third-party monitors.
Augmented Reality and Television: Dimensions and Themes

Commercialisation of augmented reality (AR) devices has led to their growing application in domestic environments and leisure activities. One such domain is that of television, where AR is one of several technologies driving innovation (c.f. Internet broadcasting, second screen devices). We conduct a systematic literature review to quantify research at the intersection of AR and broadcast television. We identify six common themes and a set of cross-cutting design decisions. We distill this information into a design space incorporating six dimensions: abstraction, interaction, time, display, context and editorial control. We provide methods to operationalise the dimensions to enable research and development of novel concepts, and through this generate six design guidelines to shape future activity at the intersection of AR and television.
Analyzing concept drift: A case study in the financial sector

In this paper, we present a method for exploratory data analysis of streaming data based on probabilistic graphical models (latent variable models). This method is illustrated by concept drift tracking, using financial client data from a European regional bank. For this particular setting, the anal yzed data spans the period from April 2007 to March 2014 and therefore starts before the beginning of the financial crisis of 2008. The implied changes in the economic climate during this period manifests itself as concept drift in the underlying data generating distribution. We explore and analyze this financial client data using a probabilistic graphical modeling framework that provides an explicit representation of concept drift as an integral part of the model. We show how learning these types of models from data provides additional insight into the hidden mechanisms governing the drift in the domain. We present an iterative approach for identifying disparate factors that jointly account for the drift in the domain. This includes a semantic characterization of one of the main influencing drift factors. Based on the experiences and results obtained from analyzing the financial data, we discuss the applicability of the framework within a more general context.
Design and simulation a video steganography system by using FFT­turbo code methods for copyrights application

Protecting information on various communication media is considered an essential requirement in the present information transmission technology. So, there is a continuous search around different modern techniques that may be used to protect the data from the attackers. Steganography is one of those techniques that can be used to maintain the copyright by employing it to cover the publisher logo image inside the video frames. Nowadays, most of the popular known of the Video-Steganography methods become a conventional technique to the attacker, so there is a requirement for a modern and smart strategy to protect the copyright of the digital video file. Where this proposed system goal to create a hybrid system that combines the properties of Cryptography and Steganography work to protect the copyright hidden data from different attack types with maintaining of characteristics of the original video (quality and resolution). In this article, a modern Video-Steganography method is presented by employing the benefits of TC (Turbo code) to encrypt the pixels of logo image and Least two Significant Bit Technique procedure to embed the encryption pixels inside the frames of the video file. The insertion is performed in the frequency domain by applying the Fast Fourier Transform (FFT)on the video frames. The examination of the suggested architecture is done by terms of Structural Similarity Index, MSE (mean squared error), and PSNR (peak signal-to-noise ratio) by comparing between an original and extracted logo as well as between original and Steganographic video (averaged overall digital frames in the video). The simulation results show that this method proved high security, robustness, capacity and produces a substantial performance enhancement over the present known ways with fewer distortions in the quality of the video
Building the executive system of autonomous aerial robots using the Aerostack open-source framework

A variety of open-source software tools are currently available to help building autonomous mobile robots. These tools have proven their effectiveness in developing different types of robotic systems, but there are still needs related to safety and efficiency that are not sufficiently covered. This article describes recent advances in the Aerostack software framework to address part of these needs, which may become critical in the case of aerial robots. The article describes a software tool that helps to develop the executive system, an important component of the control architecture whose characteristics significantly affect the quality of the final autonomous robotic system. The presented tool uses an original solution for execution control that aims at simplifying mission specification and protecting against errors, considering also the efficiency needs of aerial robots. The effectiveness of the tool was evaluated by building an experimental autonomous robot. The results of the evaluation show that it provides significant benefits about usability and reliability with acceptable development effort and computational cost. The tool is based on Robot Operating System and it is publicly available as part of the last release of the Aerostack software framework (version 3.0).
Bayesian Differential Privacy for Machine Learning

Traditional differential privacy is independent of the data distribution. However, this is not well-matched with the modern machine learning context, where models are trained on specific data. As a result, achieving meaningful privacy guarantees in ML often excessively reduces accuracy. We propose Bayesian differential privacy (BDP), which takes into account the data distribution to provide more practical privacy guarantees. We also derive a general privacy accounting method under BDP, building upon the well-known moments accountant. Our experiments demonstrate that in-distribution samples in classic machine learning datasets, such as MNIST and CIFAR-10, enjoy significantly stronger privacy guarantees than postulated by DP, while models maintain high classification accuracy.
Dynamic OverCloud: Realizing Microservices-Based IoT-Cloud Service Composition over Multiple Clouds

With the expansion of cloud-leveraged Information and Communications Technology (ICT) convergence trend, cloud-native computing is starting to be the de-facto paradigm together with MSA(Microservices Architecture)-based service composition for agility and efficiency. Moreover, by bridging the Internet of Things (IoT) and cloud together, a variety of cloud applications are explosively emerging. As an example, the so-called IoT-Cloud services, which are cloud-leveraged inter-connected services with distributed IoT devices, dynamically utilize geographically-distributed multiple clouds since mobile IoT devices can selectively connect to the near-by cloud resources for low-latency and high-throughput connectivity. In comparison, most public cloud providers may cause vendor lock-in problems that limit the inter-operable service compositions. Thus, in this paper, we propose a new overlay approach to address the above limitations, denoted as Dynamic OverCloud, which is a specially-arranged razor-thin overlay layer that provides users with an inter-operable and visibility-supported environment for MSA-based IoT-Cloud service composition over the existing multiple clouds. Then, we design a software framework that dynamically builds the proposed concept. We also describe a detailed implementation of the software framework with workflows. Finally, we verify its feasibility by realizing a smart energy IoT-Cloud service with the suggested operation lifecycle.