Reviewerly/exHarmony huggingface dataset:
To tweet or not to tweet

Trolling and social bots have been proven as powerful tactics for manipulating the public opinion and sowing discord among Twitter users. This effort requires substantial content fabrication and account coordination to evade Twitter's detection of nefarious platform use. In this paper we explore an alternative tactic for covert social media interference by inducing misperceptions about genuine, non-trolling content from verified users. This tactic uses a malware that covertly manipulates targeted words, hashtags, and Twitter metrics before the genuine content is presented to a targeted user in a covert man-in-the-middle fashion. Early tests of the malware found that it is capable of achieving a similar goal as trolls and social bots, that is, silencing or provoking social media users to express their opinion in polarized debates on social media. Following this, we conducted experimental tests in controlled settings (N = 315) where the malware covertly manipulated the perception in a Twitter debate on the risk of vaccines causing autism. The empirical results demonstrate that inducing misperception is an effective tactic to silence users on Twitter when debating polarizing issues like vaccines. We used the findings to propose a solution for countering the effect of the malware-induced misperception that could also be used against trolls and social bots on Twitter.
Learning image features with fewer labels using a semi-supervised deep convolutional network

Learning feature embeddings for pattern recognition is a relevant task for many applications. Deep learning methods such as convolutional neural networks can be employed for this assignment with different training strategies: leveraging pre-trained models as baselines; training from scratch with the target dataset; or fine-tuning from the pre-trained model. Although there are separate systems used for learning features from labelled and unlabelled data, there are few models combining all available information. Therefore, in this paper, we present a novel semi-supervised deep network training strategy that comprises a convolutional network and an autoencoder using a joint classification and reconstruction loss function. We show our network improves the learned feature embedding when including the unlabelled data in the training process. The results using the feature embedding obtained by our network achieve better classification accuracy when compared with competing methods, as well as offering good generalisation in the context of transfer learning. Furthermore, the proposed network ensemble and loss function is highly extensible and applicable in many recognition tasks.
Dual factor third‐party biometric‐based authentication scheme using quantum one time passwords<sup>†</sup>

Abstract One Time Passwords (OTP) are being widely used for authentication of user to access a number of services offered online. The security vulnerabilities in classical execution of OTP schemes are addressed using Quantum OTP (QOTP) scheme with user biometrics. This scheme requires sharing of user biometric data with every service provider. The chances of leakage of user biometrics increase with increase in number of entities holding the data. Further, loss of data by any entity compromises user identity for all other services. The work carried out in this article addresses the above‐mentioned challenges by incorporating a trusted third party which holds user biometric data and carries out authentication on its behalf. The proposed model continues leveraging upon inherent security benefits provided by Quantum Entanglement and Quantum Communication and utilizing user biometrics as authentication data. It modifies the QOTP scheme by carrying out mutual authentication for user and server against a particular transaction. The proposed model brings in the advantages of Single Sign On facility in terms of hardware to reduce infrastructure cost and No Secret Sharing of user biometric data with the servers to protect against rogue servers. The security analysis demonstrates the merit of the inherent procedures of the proposed work which leverage on the no‐cloning and quantum cryptography principles.
Opportunities and Challenges of Data-Driven Cybersecurity for Smart Cities

To enhance the quality of life for city residents, ICT (Information and Communication Technology) is used along with additional new technologies to create smart cities. Smart cities also facilitate efficient utilization of the city resources and improve adaptability and sustainability. There are different applications supporting smart cities for smart energy systems, smart water networks, smart transportation systems, smart healthcare and smart public safety and security. While these applications provide several benefits, cybersecurity threats can be a major obstacle in realizing these benefits. In this study, we review the cybersecurity challenges in smart city applications. Then, we investigate using a data-driven cybersecurity approach to protect smart city applications. We identify and discuss the opportunities and challenges of utilizing the data-driven approach. Currently there are research efforts and publications discussing cybersecurity issues in smart city applications. However, none of them focus on data-driven cybersecurity approaches. Therefore, the main contributions of this paper include identifying potential smart city applications, identifying and discussion the benefits and opportunities of using the data-driven approach and outlining the challenges of applying it for securing smart city applications.
Efficient Active Learning by Querying Discriminative and Representative Samples and Fully Exploiting Unlabeled Data

Active learning is an important learning paradigm in machine learning and data mining, which aims to train effective classifiers with as few labeled samples as possible. Querying discriminative (informative) and representative samples are the state-of-the-art approach for active learning. Fully utilizing a large amount of unlabeled data provides a second chance to improve the performance of active learning. Although there have been several active learning methods proposed by combining with semisupervised learning, fast active learning with fully exploiting unlabeled data and querying discriminative and representative samples is still an open question. To overcome this challenging issue, in this article, we propose a new efficient batch mode active learning algorithm. Specifically, we first provide an active learning risk bound by fully considering the unlabeled samples in characterizing the informativeness and representativeness. Based on the risk bound, we derive a new objective function for batch mode active learning. After that, we propose a wrapper algorithm to solve the objective function, which essentially trains a semisupervised classifier and selects discriminative and representative samples alternately. Especially, to avoid retraining the semisupervised classifier from scratch after each query, we design two unique procedures based on the path-following technique, which can remove multiple queried samples from the unlabeled data set and add the queried samples into the labeled data set efficiently. Extensive experimental results on a variety of benchmark data sets not only show that our algorithm has a better generalization performance than the state-of-the-art active learning approaches but also show its significant efficiency.
Practical Performance Analysis of a Bifacial PV Module and System

Bifacial photovoltaic (PV) modules can take advantage of rear-surface irradiance, enabling them to produce more energy compared with monofacial PV modules. However, the performance of bifacial PV modules depends on the irradiance at the rear side, which is strongly affected by the installation setup and environmental conditions. In this study, we experiment with a bifacial PV module and a bifacial PV system by varying the size of the reflective material, vertical installation, temperature mismatch, and concentration of particulate matter (PM), using three testbeds. From our analyses, we found that the specific yield increased by 1.6% when the reflective material size doubled. When the PV module was installed vertically, the reduction of power due to the shadow effect occurred, and thus the maximum current was 14.3% lower than the short-circuit current. We also observed a maximum average surface temperature mismatch of 2.19 °C depending on the position of the modules when they were composed in a row. Finally, in clear sky conditions, when the concentration of PM 10 changed by 100 µg/m3, the bifacial gain increased by 4%. In overcast conditions, when the concentration of PM 10 changed by 100 µg/m3, the bifacial gain decreased by 0.9%.
Sketch-guided Deep Portrait Generation

Generating a realistic human class image from a sketch is a unique and challenging problem considering that the human body has a complex structure that must be preserved. Additionally, input sketches often lack important details that are crucial in the generation process, hence making the problem more complicated. In this article, we present an effective method for synthesizing realistic images from human sketches. Our framework incorporates human poses corresponding to locations of key semantic components (e.g., arm, eyes, nose), seeing that its a strong prior for generating human class images. Our sketch-image synthesis framework consists of three stages: semantic keypoint extraction, coarse image generation, and image refinement. First, we extract the semantic keypoints using Part Affinity Fields (PAFs) and a convolutional autoencoder. Then, we integrate the sketch with semantic keypoints to generate a coarse image of a human. Finally, in the image refinement stage, the coarse image is enhanced by a Generative Adversarial Network (GAN) that adopts an architecture carefully designed to avoid checkerboard artifacts and to generate photo-realistic results. We evaluate our method on 6,300 sketch-image pairs and show that our proposed method generates realistic images and compares favorably against state-of-the-art image synthesis methods.
Virtual environments as medium for laypeople to communicate and collaborate in urban design

Regarding laypeople’s active participation with artefacts in the early stage of urban design, there is a certain difference between conventional urban design process and participatory urban design process. The design artefacts used in the conventional urban design process do not allow laypeople to take part actively in the early stages of the design process. Similarly, in the participatory design process, the generated design ideas remain hidden in assumption due to the lack of associated information of the artefacts and the participants perform as individual actors. The research speculates that a virtual immersive participatory design instrument can reduce the gap, where the participants can act together as a unit to produce authentic design outcomes. An Immersive Virtual Environment (IVE) assisted design experiment set-up has developed for laypeople to engage in a shared and enhanced communicative platform. The article reports the procedure of developing the instrument and discusses them in terms of design communication, participation and expert’s role. It concludes with a reflection of how laypeople as co-designers can use IVE instruments to design their neighbourhood meaningfully.
A Framework for Building Uncertainty Wrappers for AI/ML-Based Data-Driven Components

More and more software-intensive systems include components that are data-driven in the sense that they use models based on artificial intelligence (AI) or machine learning (ML). Since the outcomes of such models cannot be assumed to always be correct, related uncertainties must be understood and taken into account when decisions are made using these outcomes. This applies, in particular, if such decisions affect the safety of the system. To date, however, hardly any AI-/ML-based model provides dependable estimates of the uncertainty remaining in its outcomes. In order to address this limitation, we present a framework for encapsulating existing models applied in data-driven components with an uncertainty wrapper in order to enrich the model outcome with a situation-aware and dependable uncertainty statement. The presented framework is founded on existing work on the concept and mathematical foundation of uncertainty wrappers. The application of the framework is illustrated using pedestrian detection as an example, which is a particularly safety-critical feature in the context of autonomous driving. The Brier score and its components are used to investigate how the key aspects of the framework (scoping, clustering, calibration, and confidence limits) can influence the quality of uncertainty estimates.
Unconformity-Type Uranium Systems: A Comparative Review and Predictive Modelling of Critical Genetic Factors

A review of descriptive and genetic models is presented for unconformity-type uranium deposits with particular attention given to spatial representations of key process components of the mineralising system and their mappable expressions. This information formed the basis for the construction of mineral potential models for the world’s premier unconformity-style uranium provinces, the Athabasca Basin in Saskatchewan, Canada (&gt;650,000 t U3O8), and the NW McArthur Basin in the Northern Territory, Australia (&gt;450,000 t U3O8). A novel set of ‘edge’ detection routines was used to identify high-contrast zones in gridded geophysical data in support of the mineral potential modelling. This approach to geophysical data processing and interpretation offers a virtually unbiased means of detecting potential basement structures under cover and at a range of scales. Fuzzy logic mineral potential mapping was demonstrated to be a useful tool for delineating areas that have high potential for hosting economic uranium concentrations, utilising all knowledge and incorporating all relevant spatial data available for the project area. The resulting models not only effectively ‘rediscover’ the known uranium mineralisation but also highlight several other areas containing all of the mappable components deemed critical for the accumulation of economic uranium deposits. The intelligence amplification approach to mineral potential modelling presented herein is an example of augmenting expert-driven conceptual targeting with the powerful logic and rationality of modern computing. The result is a targeting tool that captures the current status quo of geospatial and exploration information and conceptual knowledge pertaining to unconformity-type uranium systems. Importantly, the tool can be readily updated once new information or knowledge comes to hand. As with every targeting tool, these models should not be utilised in isolation, but as one of several inputs informing exploration decision-making. Nor should they be regarded as ‘treasure maps’, but rather as pointers towards areas of high potential that are worthy of further investigation.
Color-compensated multi-scale exposure fusion based on physical features

The dynamic range captured by ordinary digital cameras is much smaller than that in natural scenes, so a single image cannot fully capture the real scene details when shooting high dynamic scenes. Traditional multi-exposure image fusion algorithms often cause inconsistencies in local brightness and corresponding scenes, which easily leads to problems such as color distortion and loss of details. Based on this, a novel multi-scale exposure fusion approach based on physical features is presented in this paper. Specifically, we first employ a novel Retinex model to get the illumination maps of original images and adopt the obtained illuminations map to construct the exposure maps. Then, a feature descriptor named patterns of oriented edge magnitudes (POEM) is introduced to extract local contrast from source images. Moreover, the extracted feature is combined to construct a weight map, and the weight term is integrated with the Laplacian pyramid algorithm to obtain an initial fusion image. Finally, simple detail enhancement and color compensation operations are performed to get the final fusion result. The numerous experiments have revealed that the proposed approach yields comparative and even better results in comparison with some state-of-the-art techniques both in subjective and objective evaluation.
A survey on Symmetric and Asymmetric Key based Image Encryption

Image Encryption is a technique where an algorithm along with a set of characters called key encrypts the data into cipher text. The cipher text can be converted back into plaintext by decryption. This technique is employed for the security of data such that confidentiality, integrity and authenticity of data is maintained. In today's era security of information has become a crucial task, unauthorized access and use of data has become a noticeable issue. To provide the security required, there are several algorithms to suit the purposes. While the use and transferring of images has become easy and faster due to technological advancements especially wireless sensor network, image destruction and illegitimate use has become a potential threat. Different transfer mediums and various uses of images require different and appropriately suiting encryption approaches. Hence, in this paper we discuss the types of image encryption techniques. We have also discussed several encryption algorithms, their advantages and suitability.
Knowledge Society Failure? Barriers in the Use of ICTs and Further Teacher Education in the Czech Republic

The study investigates barriers of Czech secondary school teachers in the use of Information and Communication Technologies (ICTs) in teaching and in further education in ICTs. The Czech Republic is used as an example of a post-communist country with an evident gap between the digital competencies in general and teachers’ didactic skills necessary for their implementation in teaching. To explore how teachers deal with the challenges of digitalization of the education system, an online questionnaire was created to analyze the use of ICTs in teaching, as well as barriers teachers encounter when using digital technologies and when participating in further education. The results indicate that respondents have average understanding of ICTs and use them rather occasionally in teaching. Data show gender and age differences in the use of various digital means and in the barriers, the importance of social support in using ICTs in teaching, and also the necessity to eliminate prevalent gender stereotypes. Systematic further education that focuses on lowering computer anxiety and the distrust in new technologies might be also a major key in successful digital transformation of schools.
Deep Learning Model for Spatial Interpolation of Real-Time Seismic Intensity

Abstract Spatial distribution of seismic intensity plays an important role in emergency response during and immediately after an earthquake. In this study, we propose a deep learning model to predict the seismic intensity based on only the observation records at the seismic stations in a surrounding area. The deep learning model is trained using the observation records at both the input and target stations, and no geological information is used. Once the model is developed, for example, using the data from a temporal seismic array, the model can spatially interpolate the seismic intensity from the sparse layout of the seismic stations. The model consists of long short-term memory cells, which are well-established neural network components for time series analysis. We used observed seismograms in 1996 through 2019 at the Kyoshin Network (K-NET) and Kiban–Kyoshin Network (KiK-net) stations located in the northeastern part of Japan. In our deep learning model, approximately 85% of validation data is successfully classified into seismic intensity scales, which is better than adopting either the maximum or weighted average of the input data. We also apply the deep learning model to earthquake early warning (EEW). The model can predict the seismic intensity accurately and provides a long warning time. We concluded that our approach is a possible future solution for increasing the accuracy of EEW.
Evaluation of a Hybrid of Hand Gesture and Controller Inputs in Virtual Reality

Controller and hand gesture recognition are both common input mechanisms in immersive environments. We evaluate the effect of combining them as a hybrid input to perform game-like tasks. To explore the possible design space, three tests were conducted sequentially. First, a design study was conducted to investigate which of the tasks were most likely to be performed with hybrid inputs. Next, we implemented the four highest-ranked tasks and invited 22 participants to perform these tasks with different input combinations. From examining execution times and reviewing feedback from participants, we provide observations and discussions concerning the usage of a hybrid input. A follow-up experiment was designed to refine these observations. The results indicate that task execution using hybrid inputs is as efficient as bimanual controllers when assigning actions to the appropriate input. Finally, a few guidelines are provided suggesting how to consider possible scenarios for this hybrid input.
TIES

Since its inception, Facebook has become an integral part of the online social community. People rely on Facebook to connect with others and build communities. As a result, it is paramount to protect the integrity of such a large network in a fast and scalable manner. In this paper, we present our efforts to protect various social media entities at Facebook from people who try to abuse our platform. We present a novel Temporal Interaction EmbeddingS (TIES) model that is designed to capture rogue social interactions and flag them for further suitable actions. TIES is a supervised, deep learning, production ready model at Facebook-scale networks. Prior works on integrity problems are mostly focused on capturing either only static or certain dynamic features of social entities. In contrast, TIES can capture both these variant behaviors in a unified model owing to the recent strides made in the domains of graph embedding and deep sequential pattern learning. To show the real-world impact of TIES, we present a few applications especially for preventing spread of misinformation, fake account detection, and reducing ads payment risks in order to enhance Facebook platform's integrity.
Assessing Building Damage by Learning the Deep Feature Correspondence of Before and After Aerial Images

The damage caused by a natural disaster such as a hurricane, not only impacts human lives but can also be detrimental to the city's infrastructure and potentially cause the loss of historical buildings and essential records. Delivering an effective response requires quick and precise analyses concerning the impact of a disastrous event. With the current technological developments to acquire massive volumes of data and the recent advances in artificial intelligence and machine learning, now more than ever, disaster information integration and fusion have the potential to deliver enhanced situational awareness tools for humanitarian assistance and disaster relief efforts. Given the aerial images of a residential building taken before and after a natural disaster, recent applications of Convolutional Neural Networks (CNNs) work well when differentiating two types of damage (i.e., whether the structure is intact or destroyed) but underperform when trying to differentiate more damage levels. According to our findings: (1) including enough surrounding context provides essential visual clues that help the model better predict the building's level of damage and (2) learning the correspondence between the features extracted from pre-and post-imagery boosts the performance compared to a simple concatenation. We propose a two-stream CNN architecture that overcomes the difficulties of classifying the buildings at four damage levels and evaluate its performance on a curated, fully-labeled dataset assembled from open sources.