Reviewerly/exHarmony huggingface dataset:
Telecommunication Compatibility Evaluation for Co-existing Quantum Key Distribution in Homogenous Multicore Fiber

Quantum key distribution (QKD) is regarded as an alternative to traditional cryptography methods for securing data communication by quantum mechanics rather than computational complexity. Towards the massive deployment of QKD, embedding it with the telecommunication system is crucially important. Homogenous optical multi-core fibers (MCFs) compatible with spatial division multiplexing (SDM) are essential components for the next-generation optical communication infrastructure, which provides a big potential for co-existence of optical telecommunication systems and QKD. However, the QKD channel is extremely vulnerable due to the fact that the quantum states can be annihilated by noise during signal propagation. Thus, investigation of telecom compatibility for QKD co-existing with high-speed classical communication in SDM transmission media is needed. In this paper, we present analytical models of the noise sources in QKD links over heterogeneous MCFs. Spontaneous Raman scattering and inter-core crosstalk are experimentally characterized over spans of MCFs with different refractive index profiles, emulating shared telecom traffic conditions. Lower bounds for the secret key rates and quantum bit error rate (QBER) due to different core/wavelength allocation are obtained to validate intra- and inter-core co-existence of QKD and classical telecommunication.
Automatic segmentation system for liver tumors based on the multilevel thresholding and electromagnetism optimization algorithm

Abstract In this article, we propose an automated segmentation system for liver tumors using magnetic resonance imaging and computed tomography. The proposed system is based on the algorithm of multilevel thresholding with electromagnetism optimization (EMO). The system starts with visualizing a patient's digital communication in medicine (DICOM) abdominal data set in three views. Two‐stage active contour segmentation methods that integrate region‐based local and global techniques using the active geodesic contour technique are proposed to segment the liver. To increase the accuracy and speed of segmentation for liver images, we identify the optimal threshold of the image segmentation method based on EMO with Otsu and Kapur algorithms. EMO offers interesting search capabilities while keeping a low computational cost. The proposed system was tested using a set of five DICOM data sets. All images were of the same size and stored in JPEG format (512 × 512 pixels). Experimental results illustrate that the proposed system outperforms state‐of‐the‐art methods such as the watershed algorithm. The average sensitivity, specificity, and accuracy of the segmented liver using the active contour model were 97.05%, 99.88%, and 98.47%, respectively. Moreover, the average sensitivity, specificity, and accuracy of the segmented liver tumor results were 94.15%, 99.57%, and 96.86%, respectively.
Combination of multi‐scale and residual learning in deep CNN for image denoising

To better restore a clean image from a noise observation under high noise levels, the authors propose an image denoising network based on the combination of multi-scale and residual learning. Instead of using filters with different large sizes in traditional multi-scale schemes, they arrange multi-layer convolutions with the filters of the same size to speed up the model. Some dilated convolutions of different rates are combined with the common convolutions to enrich the extracted features in multi-layer convolutions. Furthermore, they cascade the multi-layer convolutions with residual blocks to improve the performance of image denoising. Their extensive evaluations on several challenging datasets demonstrate that the proposed model outperforms the state-of-art methods under all different noise levels in terms of peak signal-to-noise ratio, and the visual effects achieved by the proposed model are also better than the competing methods.
How Are Performance Issues Caused and Resolved?-An Empirical Study from a Design Perspective

Empirical experience regarding how real-life performance issues are caused and resolved can provide valuable insights for practitioners to effectively and efficiently prevent, detect, and fix performance issues. Prior work shows that most performance issues have their roots in poor architectural decisions. This paper contributes a large scale empirical study of 192 real-life performance issues, with an emphasis on software design. First, this paper contributes a holistic view of eight common root causes and typical resolutions that recur in different projects, and surveyed existing literature, in particular, tools, that can detect and fix each type of performance issue. Second, this study is first-of-its-kind to investigate performance issues from a design perspective. In the 192 issues, 33% required design-level optimization, i.e. simultaneously revising a group of related source files for resolving the issues. We reveal four design-level optimization patterns, which have shown different prevalence in resolving different root causes. Finally, this study investigated the Return on Investment for addressing performance issues, to help practitioners choose between localized or design-level optimization resolutions, and to prioritize issues due to different root causes.
Machine &amp; Deep Learning Techniques for Detection of Fake Reviews: A Survey

Presently, review sites are frequently confronted with the spread of wrong information, this could be done by an individual spammer or group spammers who compose fake reviews to either advertise or demean certain products that are available. This paper focuses on the detection of these fake reviews using sentiment analysis. Various data pre-processing techniques are used to convert the reviews to the proper format for analysis and for detection. The proposed methodology is to analyze reviews by making use of neural networks such as LSTM, Bi-LSTM and GRNN, and the activation functions in them namely ReLU, Sigmoid, TanH and comparing them to find the optimal model for analysis.
Silicon erasable waveguides and directional couplers by germanium ion implantation for configurable photonic circuits

A novel technique for realization of configurable/one-time programmable (OTP) silicon photonic circuits is presented. Once the proposed photonic circuit is programmed, its signal routing is retained without the need for additional power consumption. This technology can potentially enable a multi-purpose design of photonic chips for a range of different applications and performance requirements, as it can be programmed for each specific application after chip fabrication. Therefore, the production costs per chip can be reduced because of the increase in production volume, and rapid prototyping of new photonic circuits is enabled. Essential building blocks for the configurable circuits in the form of erasable directional couplers (DCs) were designed and fabricated, using ion implanted waveguides. We demonstrate permanent switching of optical signals between the drop port and through the port of the DCs using a localized post-fabrication laser annealing process. Proof-of-principle demonstrators in the form of generic 1×4 and 2×2 programmable switching circuits were fabricated and subsequently programmed.
Time hacking: how technologies mediate time

Technologies change users’ existing social, cultural, and material practices by providing new opportunities for reflecting on and managing their lives. As technological advancements pervade our private and professional lives, users are tempted to see them as ‘magic bullets’ that can help them become more organized and efficient. In this paper, we introduce the term ‘time hacking’ to capture the various ways technologies mediate users’ time perception and perspective. We will use the examples of virtual assistants like Siri and Alexa and the Quantified Self Movement to illustrate how people feel that they are capable of hacking time by using devices and programs. Imagining tools as neutral entities that help them better manage their lives in a world that seems increasingly sped up, users are often blind to the multifarious ways these technologies, and the companies that produce them, shape what they attend to and how they make sense of information. The concept of time hacking helps us examine what narratives users construct and share about timesaving tools and how users’ perception of and perspective about time changes in response to emerging technologies. Most importantly, time hacking can help to explain the allure of timesaving technologies, why users might be enthusiastic about taking them up and integrating them into their lives.
Failure Characterization and Prediction of Scheduling Jobs in Google Cluster Traces

In cloud computing, all services including infrastructure, platform, and software experience failures due to their large scale and heterogeneity nature. These failures can lead to job failure execution that may cause performance deterioration and resource waste. Most studies have focused mainly on failure analysis and characterization while there is limited research has been done on failure prediction. In this paper, the overall aim is to develop a failure prediction framework that can early detect failed jobs, and the real advantages of this framework are to decrease the resources waste and to increase the performance of cloud applications. Our failure analysis and prediction are based on Google cluster traces. We have developed a failure prediction model for job failure execution based on applying different machine learning algorithms and selecting the best accurate model. Moreover, we evaluate the model performance using different types of evaluation metrics to ensure that the proposed prediction model provides the highest accuracy of predicted values. Finally, we apply different feature selection techniques to improve the accuracy of our proposed model. Our evaluation results show that our model has achieved a high rate of precision, recall, and f1-score.
Generating Fake Data Using GANs for Anonymizing Healthcare Data

EDITH is a project aiming to orchestrate an ecosystem of manipulation of reliable and safe data, applied to the field of health, proposing the creation of digital twins for personalised healthcare. This paper elaborates on a first approach about using Generative Adversarial Networks (GANs) for the generation of fake data, with the objective of anonymizing users information in the health sector. This is intended to create valuable data that can be used both, in educational and research areas, while avoiding the risk of a sensitive data leakage. Meanwhile GANs are mainly exploited on images and video frames, we are proposing to process raw data in the form of an image, so it can be managed through a GAN, then decoded back to the original data domain. The performance of this prototype has been demonstrated. Moreover, a novel research pathway has been opened so further developments are expected.
Challenges of Designing Computer Vision-Based Pedestrian Detector for Supporting Autonomous Driving

In recent years, aiming to improve deriving safety and supporting autonomous driving, pedestrian detection has attracted considerable attention from both industry and academic. Moreover, by taking advantage of the powerful computational capacity of GPU and high-level feature learning ability of the deep convolutional neural network, tremendous image/video-based pedestrian detection methods have been proposed. However, most of the existing approaches are designed relying on the computer vision-based target detection techniques. Accordingly, the evaluation criteria they consider in the design are often from the computer vision research field. Therefore, these existing methods tend to focus on the improvement of accuracy and ignore some of the special requirements that need to be considered in the field of autonomous driving. In this paper, we will analyze and summarize the features of the state-of-the-art pedestrian detection methods in detail. Then, by considering the practical application scenarios of autonomous driving techniques, we further discuss the open challenges of designing a practical pedestrian detection method for supporting autonomous deriving.
Ecologies of Contestation in Participatory Design

How do various forms of contestation and agonism in collective social contexts challenge and transform Participatory Design (PD)? Under what conditions does agonism lead to productive outcomes, expand participation and social inclusion? In this paper, we highlight key insights and issues emerging from three case studies, where design practitioners engaged in PD projects for urban and cultural transformation in New York City and Cambridge. Wide-ranging interviews and participatory workshops reveal how PD is transformed by different “ecologies” inherent in the socio-cultural conditions, power relations, design constraints, and intrinsic values of practitioners grappling with contestation and seeking to engage agonistic pluralism.
A Simulated Annealing based Energy-Efficient VM Placement Policy in Cloud Computing

Employing cloud computing to capture the benefits of the cloud by performing optimization on the various parameters which is meet the changing requirement is one of the challenging tasks. The mapping between the virtual machine(VMs) to the task and virtual machine to the physical machine is known as VM placement problem and it is necessary that it consumes less energy and high resource utilization. Nowadays, both task and resources become heterogeneous which will create more complicated problem in cloud computing. In this work, we proposed a VM placement algorithm which minimizes the power consumption by deprecating the physically active machine and also reduce the makespan. We have tested our proposed algorithm in CloudSim simulator, and the result showed that our proposed work is performed better result than the existing standard work.
BeCAPTCHA-Mouse: Synthetic mouse trajectories and improved bot detection

We first study the suitability of behavioral biometrics to distinguish between computers and humans, commonly named as bot detection. We then present BeCAPTCHA-Mouse, a bot detector based on: i) a neuromotor model of mouse dynamics to obtain a novel feature set for the classification of human and bot samples; and ii) a learning framework involving real and synthetically generated mouse trajectories. We propose two new mouse trajectory synthesis methods for generating realistic data: a) a function-based method based on heuristic functions, and b) a data-driven method based on Generative Adversarial Networks (GANs) in which a Generator synthesizes human-like trajectories from a Gaussian noise input. Experiments are conducted on a new testbed also introduced here and available in GitHub: BeCAPTCHA-Mouse Benchmark; useful for research in bot detection and other mouse-based HCI applications. Our benchmark data consists of 15,000 mouse trajectories including real data from 58 users and bot data with various levels of realism. Our experiments show that BeCAPTCHA-Mouse is able to detect bot trajectories of high realism with 93% of accuracy in average using only one mouse trajectory. When our approach is fused with state-of-the-art mouse dynamic features, the bot detection accuracy increases relatively by more than 36%, proving that mouse-based bot detection is a fast, easy, and reliable tool to complement traditional CAPTCHA systems.
Adopting an African Standpoint in HCI4D: A Provocation

While studies in HCI4D have been advanced by the shift of perspective from developmental studies to a range of other discourses, current analytical concepts for understanding the sociality of society in Africa have arguably led to some misinterpretations of the place of technology. This provocation suggests that an 'African Standpoint' based on a combination of various standpoint positionalities and the Wittgensteinian approach of Winch can offer conceptual and analytical sensitivities for articulating social relations, transnational engagements and the conceptualisation of technological innovation. This provides an approach for seeing and accounting for things as they are - right here, right there and right now - and not some idealised conception of an African reality
The Significance of Global Vectors Representation in Sarcasm Analysis

Sarcasm detection is an unending drawback in natural language processing, which brings a hindrance in a progression of finding the correct state of people's sentiment. Many approaches has been applied for sarcasm detection in text such as machine learning based, lexicon based, and deep learning based approach by employing different word embedding representation schemes. The traditional word embedding schemes for learning vector representation such as bag-of-words, skip gram and Continuous bag-of-word has successfully done well in extracting discriminative syntactic and semantic features by employing vector arithmetic. However, these models failed to capture the word context in the text. In addition, skip gram and CBOW do not work openly on corpus co-occurrence statistics but rather examine the context window over the whole corpus, which fail to consider the important recurrence in the data. In order to address the aforementioned limitations, this study investigates the word vector model (GloVe) that integrate two major model families; Global statistics of matrix factorization technique and local context window based approach. The model is able to build a word representation that do not only learn semantics and grammar information, but also captures the context of the word and information of global corpus statistics. We analyzed the predictive performance of the representation by investigating sarcasm identification in tweets by employing a machine learning approach. The experiment results produced a better accuracy, which shows the significance of GloVe embedding in sarcasm classification.
A new binary level set model using L0 regularizer for image segmentation

In this paper, a new model of image segmentation is proposed by considering L0 regularizer term. A new energy function is formulated by utilizing Local Gaussian Distribution model based on binary level set function followed by introducing a L0 gradient regularizer as regularizing term. Instead of zero level set, the binary level set function is applied to differentiate between foreground and background regions. The regularization function is used to calculate the interfaces between foreground sub-regions and regularization term L0 which helps to evolve the curve. The proposed energy function is solved using minimization algorithm to achieve promising results in terms of segmentation accuracy. The different sets of experiments are performed on real and medical images. The proposed model provides higher segmentation accuracy results in less computational time compared to the other state-of-the-art models. Further, the results are found to be superior as compared to other existing models in terms of robustness to noise and intensity inhomogeneity.
Applying forces to generate cartograms: a fast and flexible transformation framework

Automatic production of contiguous area cartograms has become practical with computer algorithms, particularly applying forces to rubber-sheets and simulating diffusion processes. Reformulating the existing force-based and rubber-sheet methods, this article presents a fast and flexible force-based computational framework for general space transformation with solid physical and mathematical foundations. When being applied to cartogram production, this framework guarantees topological integrity, allows flexible force generation, achieves fast convergence, and avoids extreme shape deformation. Benchmarked against the recently published fast flow-based diffusion method using five datasets of various volume, compactness, and complexity, this force-based framework is faster, reduces more shape deformation, and can produce cartograms with distinctive slim or inflated styles. Additionally, the force-based framework is robust and can handle complicated datasets, whereas the flow-based method produces errors from them. Although the flow-based diffusion method often reduces cartogram size error more than the force-based (typical weighted mean error is below 0.1% versus 1%), both are outstanding with such low levels of error and their cartograms generally register little visual differences. Overall, the force-based transformation framework provides a fast, flexible, and robust alternative to the diffusion method for cartogram production.
How OTT Services Can Match the Quality of Broadcast

Recent events, such as the 2018 FIFA World Cup, demonstrated that live over-the-top (OTT) experiences have some serious limitations, including low quality on TV sets and a delay of more than 30 sec compared with broadcast services. If the industry does not solve those problems, they could halt the development of live OTT offerings. This article analyzes the current challenges involved with delivering live OTT services and will describe solutions that are available today, such as content-aware encoding, artificial intelligence, and low-latency common media application format (CMAF). In addition, this article will present new concepts that can be deployed as part of an end-to-end OTT solution to further optimize quality of experience for OTT services on existing infrastructure [i.e., headend, content delivery network (CDN), and devices].
The Fedorov–Groth law revisited: complexity analysis using mineralogical data

The Fedorov–Groth law points out that, on average, chemical simplicity corresponds to higher symmetry, and chemically complex compounds usually have lower symmetry than chemically simple compounds. Using mineralogical data, it is demonstrated that the Fedorov–Groth law is valid and statistically meaningful, when chemical complexity is expressed as the amount of Shannon chemical information per atom and the degree of symmetry as the order of the point group of a mineral.
SAWIT—Security Awareness Improvement Tool in the Workplace

Cyberattacks are becoming increasingly sophisticated and severe, and an organization’s protection depends on its weakest member. Although users are aware of the risks in cyberspace, most of them do not follow best practices, and there is a need for permanent structured training. The majority of previous training programs concentrated on technically educated users, but the organization is only as secure as the most vulnerable link in it. The paper presents SAWIT, a new Web tool, created with the goal of improving security awareness among employees. It is an innovative artificial intelligence framework aimed at improving the cyber security knowledge of employees by using collaborative learning and assessment within the specified knowledge transformation model.