Reviewerly/exHarmony huggingface dataset:
Adversarial patch camouflage against aerial detection

Detection of military assets on the ground can be performed by applying deep learning-based object detectors on drone surveillance footage. The traditional way of hiding military assets from sight is camouflage, for example by using camouflage nets. However, large assets like planes or vessels are difficult to conceal by means of traditional camouflage nets. An alternative type of camouflage is the direct misleading of automatic object detectors. Recently, it has been observed that small adversarial changes applied to images of the object can produce erroneous output by deep learning-based detectors. In particular, adversarial attacks have been successfully demonstrated to prohibit person detections in images, requiring a patch with a specific pattern held up in front of the person, thereby essentially camouflaging the person for the detector. Research into this type of patch attacks is still limited and several questions related to the optimal patch configuration remain open. This work makes two contributions. First, we apply patch-based adversarial attacks for the use case of unmanned aerial surveillance, where the patch is laid on top of large military assets, camouflaging them from automatic detectors running over the imagery. The patch can prevent automatic detection of the whole object while only covering a small part of it. Second, we perform several experiments with different patch configurations, varying their size, position, number and saliency. Our results show that adversarial patch attacks form a realistic alter- native to traditional camouflage activities, and should therefore be considered in the automated analysis of aerial surveillance imagery.
Review of the end-of-life solutions in electronics-based smart textiles

Research and development of new products in the smart textile field is growing rapidly because of versatile application areas. There is an extensive focus on the integration of electronics into textiles. However, if different fields are merged, as here, the sustainability and recycling issues might descend into even more complex systems. The article reviews current research and development conducted on the end-of-life (EOL) solutions for electronic textiles (e-textiles). Chosen papers had to be peer-reviewed, written in English, and address the EOL issue for electronic-based smart textiles. The search resulted in 18 publications, which indicates a low amount of research but also the serious lack of legislation and actual solutions emerged in this multidisciplinary field. Three main themes were found: smart textile services, eco-design strategy and educating guidelines. Authors suggest taking urgent actions by preventive steps in combining current electronics and textile waste management systems into one standard for e-textiles.
Deconstructing the Filter Bubble: User Decision-Making and Recommender Systems

We study a model of user decision-making in the context of recommender systems via numerical simulation. Our model provides an explanation for the findings of Nguyen, et. al (2014), where, in environments where recommender systems are typically deployed, users consume increasingly similar items over time even without recommendation. We find that recommendation alleviates these natural filter-bubble effects, but that it also leads to an increase in homogeneity across users, resulting in a trade-off between homogenizing across-user consumption and diversifying within-user consumption. Finally, we discuss how our model highlights the importance of collecting data on user beliefs and their evolution over time both to design better recommendations and to further understand their impact.
Convex combinations of CP-divisible Pauli channels that are not semigroups

We study the memory property of the channels obtained by convex combinations of Markovian channels that are not necessarily quantum dynamical semigroups (QDSs). In particular, we characterize the geometry of the region of (non-)Markovian channels obtained by the convex combination of the three Pauli channels, as a function of deviation from the semigroup form in a family of channels. The regions are highly convex, and interestingly, the measure of the non-Markovian region shrinks with greater deviation from the QDS structure for the considered family, underscoring the counterintuitive nature of (non-)Markovianity under channel mixing.
Detecting Video Game-Specific Bad Smells in Unity Projects

The growth of the video game market, the large proportion of games targeting mobile devices or streaming services, and the increasing complexity of video games trigger the availability of video game-specific tools to assess performance and maintainability problems. This paper proposes UnityLinter, a static analysis tool that supports Unity video game developers to detect seven types of bad smells we have identified as relevant in video game development. Such smell types pertain to performance, maintainability and incorrect behavior problems. After having defined the smells by analyzing the existing literature and discussion forums, we have assessed their relevance with a survey involving 68 participants. Then, we have analyzed the occurrence of the studied smells in 100 open-source Unity projects, and also assessed UnityLinter's accuracy. Results of our empirical investigation indicate that developers well-received performance- and behavior-related issues, while some maintainability issues are more controversial. UnityLinter is, in general, accurate enough in detecting smells (86%-100% precision and 50%-100% recall), and our study shows that the studied smell types occur in 39%-97% of the analyzed projects.
Schema aware iterative Knowledge Graph completion

Recent success of Knowledge Graph has spurred widespread interests in methods for the problem of Knowledge Graph completion. However, efforts to understand the quality of the candidate triples from these methods, in particular from the schema aspect, have been limited. Indeed, most existing Knowledge Graph completion methods do not guarantee that the expanded Knowledge Graphs are consistent with the ontological schema of the initial Knowledge Graph. In this work, we challenge the silver standard method, by proposing the notion of schema-correctness. A fundamental challenge is how to make use of different types of Knowledge Graph completion methods together to improve the production of schema-correct triples. To address this, we analyse the characteristics of different methods and propose a schema aware iterative approach to Knowledge Graph completion. Our main findings are: (i) Some popular Knowledge Graph completion methods have surprisingly low schema-correctness ratio; (ii) Different types of Knowledge Graph completion methods can work with each other to help overcame individual limitations; (iii) Some iterative sequential combinations of Knowledge Graph completion methods have significantly better schema-correctness and coverage ratios than other combinations; (iv) All the MapReduce based iterative methods outperform involved single-pass methods significantly over the tested Knowledge Graphs in terms of productivity of schema-correct triples. Our findings and infrastructure can help further work on evaluating Knowledge Graph completion methods, more fine-grained approaches for schema aware iterative knowledge graph completion, as well as new approximate reasoning approaches based Knowledge Graph completion methods.
A Crime Script Analysis of Counterfeit Identity Document Procurement Online

Over the last two decades, researchers explored various aspects of the operational practices of online illicit market operations through the Open and Dark Web for various physical and digital goods. Far less work has considered the presence of counterfeit identity documents for sale within these markets, or the process of advertising, purchasing, producing, selling, and delivering these materials. This study utilized a qualitative crime script analysis of 19 vendors advertising counterfeit documents on the Open and Dark Web, focusing on the advertising, actualization, and delivery of various products. The pricing for various document types and the locations they claim to reflect citizenship of were examined, along with the variations dependent on where the product was advertised. The findings demonstrated that the market for identity documents shared common practices to other online markets, highlighting the value of crime script analyses to understand the distribution of goods through illicit markets generally.
When to Stop Reviewing in Technology-Assisted Reviews

Technology-Assisted Reviews (TAR) aim to expedite document reviewing (e.g., medical articles or legal documents) by iteratively incorporating machine learning algorithms and human feedback on document relevance. Continuous Active Learning (CAL) algorithms have demonstrated superior performance compared to other methods in efficiently identifying relevant documents. One of the key challenges for CAL algorithms is deciding when to stop displaying documents to reviewers. Existing work either lacks transparency—it provides an ad-hoc stopping point, without indicating how many relevant documents are still not found, or lacks efficiency by paying an extra cost to estimate the total number of relevant documents in the collection prior to the actual review. In this article, we handle the problem of deciding the stopping point of TAR under the continuous active learning framework by jointly training a ranking model to rank documents, and by conducting a “greedy” sampling to estimate the total number of relevant documents in the collection. We prove the unbiasedness of the proposed estimators under a with-replacement sampling design, while experimental results demonstrate that the proposed approach, similar to CAL, effectively retrieves relevant documents; but it also provides a transparent, accurate, and effective stopping point.
Rule-Based Machine Translation from Tunisian Dialect to Modern Standard Arabic

This paper aims to present a machine translation system capable of translating Tunisian Dialect (TD) text to Modern Standard Arabic (MSA) using a rule-based approach. Having adopted such a classical approach can give us better translation mainly in terms of morphology and syntax. Moreover, it also allows us to automate the translation task to build a training dataset (parallel corpus), which can be exploited whether for hybridization with the statistical approach or even for the newly developed neural-network method. To do that, our work is based on a free open source platform, named Apertium, which provides a whole environment that includes all required tools to develop an RBMT system. Therefore, the translation process starts by given TD input text, where the data are passed through three big parts, which are respectively morphological analysis and disambiguation, lexical and structural transfer, and morphological generation and spelling corrections to get the translated text in MSA. In fact, each of these stages is a challenge. It should be noted that we have made a great effort to create the required resources from scratch starting by the two monolingual morphological dictionaries for TD and MSA, the bilingual lexical dictionary to map TD words to their equivalents in MSA, and finally setting up more than 500 rules for disambiguation and structural transfer. The proposed system is evaluated by three metrics: WER, TER and BLEU score. As a result, we respectively reach 23.28%, 23.85% and 55.22, which are promising scores.
Combining textual features to detect cyberbullying in social media posts

Cyberbullying has become prevalent in social media communication. To create a safe space for cyber communication, an effective cyberbullying detection method is needed. This study focuses on using combination of textual features to detect cyberbullying across social media platforms. Lexicon enhanced rule-based method was applied to detect cyberbullying on Facebook comments. The resulting algorithm was evaluated using performance measures of accuracy, precision, recall, and F1 Score, and showed promising performance with average recall of 95.981%.
Application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4A

Abstract Background The classification of Breast Imaging Reporting and Data System 4A (BI-RADS 4A) lesions is mostly based on the personal experience of doctors and lacks specific and clear classification standards. The development of artificial intelligence (AI) provides a new method for BI-RADS categorisation. We analysed the ultrasonic morphological and texture characteristics of BI-RADS 4A benign and malignant lesions using AI, and these ultrasonic characteristics of BI-RADS 4A benign and malignant lesions were compared to examine the value of AI in the differential diagnosis of BI-RADS 4A benign and malignant lesions. Methods A total of 206 lesions of BI-RADS 4A examined using ultrasonography were analysed retrospectively, including 174 benign lesions and 32 malignant lesions. All of the lesions were contoured manually, and the ultrasonic morphological and texture features of the lesions, such as circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, margin lobulation, energy, entropy, grey mean, internal calcification and angle between the long axis of the lesion and skin, were calculated using grey level gradient co-occurrence matrix analysis. Differences between benign and malignant lesions of BI-RADS 4A were analysed. Results Significant differences in margin lobulation, entropy, internal calcification and ALS were noted between the benign group and malignant group ( P = 0.013, 0.045, 0.045, and 0.002, respectively). The malignant group had more margin lobulations and lower entropy compared with the benign group, and the benign group had more internal calcifications and a greater angle between the long axis of the lesion and skin compared with the malignant group. No significant differences in circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, energy, and grey mean were noted between benign and malignant lesions. Conclusions Compared with the naked eye, AI can reveal more subtle differences between benign and malignant BI-RADS 4A lesions. These results remind us carefully observation of the margin and the internal echo is of great significance. With the help of morphological and texture information provided by AI, doctors can make a more accurate judgment on such atypical benign and malignant lesions.
Advantages and Limitations of Fully on-Chip CNN FPGA-Based Hardware Accelerator

Convolution Neural Networks are a class of deep neural networks commonly used in audio and video elaborations. Their implementation on the edge represents a complex task due to the limited computational power and low power consumption requirement that characterize these applications. In this paper, a fully on-chip Convolutional Neural Network Field Programmable Gate Array-based hardware accelerator is presented. This approach allows to reduce power consumption due to off-chip memory accesses and aims to reduce design time. Advantages and limitations of the proposed architecture are discussed and a trade-off analysis is provided to give intuitions about the feasibility of this method.
“It’s on its way”: Chatbots applied for online food delivery services, social or task-oriented interaction style?

Research has yet to explore the spread of chatbots into the foodservice delivery sector and its impact on the customer's experience, especially in a moment when the Internet seems to be the tool to meet needs associated with being physically apart. In order to fill this gap, the present study addresses the implications that chatbots' interaction styles have for younger consumers using this channel for conversational online food delivery (OFD) services. Specifically, this study provides theoretical and practical insights into whether the conversational design of chatbots can influence social, affective, and behavioral intent outcomes. The study adopts an experimental design to investigate the effects of a social- versus task-oriented interaction style chatbot on the level of social presence and trust (social outcomes), perceived enjoyment (affective outcome), and intention to use the conversational OFD service in the future (behavioral intent outcome). Findings from a sample of 171 participants show that the interaction with the chatbot set up with a social-oriented interaction style increased users' perception of social presence and perceived enjoyment, while it did not have any direct and significant effect on trust and intention to use. The study further supports the role of social presence, trust, and perceived enjoyment as mediators between the chatbot's interaction style and the intention to use the conversational OFD service.
An NMPC Approach using Convex Inner Approximations for Online Motion Planning with Guaranteed Collision Avoidance

Even though mobile robots have been around for decades, trajectory optimization and continuous time collision avoidance remain subject of active research. Existing methods trade off between path quality, computational complexity, and kinodynamic feasibility. This work approaches the problem using a nonlinear model predictive control (NMPC) framework, that is based on a novel convex inner approximation of the collision avoidance constraint. The proposed Convex Inner ApprOximation (CIAO) method finds kinodynamically feasible and continuous time collision free trajectories, in few iterations, typically one. For a feasible initialization, the approach is guaranteed to find a feasible solution, i.e. it preserves feasibility. Our experimental evaluation shows that CIAO outperforms state of the art baselines in terms of planning efficiency and path quality. Experiments show that it also efficiently scales to high-dimensional systems. Furthermore real-world experiments demonstrate its capability of unifying trajectory optimization and tracking for safe motion planning in dynamic environments.
Interpretability-Guided Content-Based Medical Image Retrieval

When encountering a dubious diagnostic case, radiologists typically search in public or internal databases for similar cases that would help them in their decision-making process. This search represents a massive burden to their workflow, as it considerably reduces their time to diagnose new cases. It is, therefore, of utter importance to replace this manual intensive search with an automatic content-based image retrieval system. However, general content-based image retrieval systems are often not helpful in the context of medical imaging since they do not consider the fact that relevant information in medical images is typically spatially constricted. In this work, we explore the use of interpretability methods to localize relevant regions of images, leading to more focused feature representations, and, therefore, to improved medical image retrieval. As a proof-of-concept, experiments were conducted using a publicly available Chest X-ray dataset, with results showing that the proposed interpretability-guided image retrieval translates better the similarity measure of an experienced radiologist than state-of-the-art image retrieval methods. Furthermore, it also improves the class-consistency of top retrieved results, and enhances the interpretability of the whole system, by accompanying the retrieval with visual explanations.
Digital and information literacy inside and outside Spanish primary education schools

This paper addresses the use of digital and information literacy with primary pupils and its relationship with the development of literacy inside and outside school. Information and Communication Technologies have created new opportunities for reading and writing texts in social spaces that have modified the way children learn. This study was a non-experimental, explanatory design. 1540 Spanish primary school pupils completed a self-report questionnaire based on the literacy, digital and information events developed by pupils. The data obtained were analyzed through a Categorical Principal Component Analysis (CATPCA) that identified two components related to the events “inside school” and “outside school”. These components were later used as variables to classify the socioeconomic status and type of school, curricular preferences, as well as age, sex and year of study. The results obtained show two spaces for literacy: one was promoted inside school and was based on printed texts; the other developed outside school and favoured digital and information literacy. This work concludes with the need to establish bridges which connect digital competence inside and outside school through the creation of a third literacy space.
VALID: A Comprehensive Virtual Aerial Image Dataset

Aerial imagery plays an important role in land-use planning, population analysis, precision agriculture, and unmanned aerial vehicle tasks. However, existing aerial image datasets generally suffer from the problem of inaccurate labeling, single ground truth type, and few category numbers. In this work, we implement a simulator that can simultaneously acquire diverse visual ground truth data in the virtual environment. Based on that, we collect a comprehensive Virtual AeriaL Image Dataset named VALID, consisting of 6690 high-resolution images, all annotated with panoptic segmentation on 30 categories, object detection with oriented bounding box, and binocular depth maps, collected in 6 different virtual scenes and 5 various ambient conditions (sunny, dusk, night, snow and fog). To our knowledge, VALID is the first aerial image dataset that can provide panoptic level segmentation and complete dense depth maps. We analyze the characteristics of VALID and evaluate state-of-the-art methods for multiple tasks to provide reference baselines. The experiment results demonstrate that VALID is well presented and challenging. The dataset is available at https://sites.google.com/view/valid-dataset/.
Interpreting cloud computer vision pain-points

Intelligent services are becoming increasingly more pervasive; application developers want to leverage the latest advances in areas such as computer vision to provide new services and products to users, and large technology firms enable this via RESTful APIs. While such APIs promise an easy-to-integrate on-demand machine intelligence, their current design, documentation and developer interface hides much of the underlying machine learning techniques that power them. Such APIs look and feel like conventional APIs but abstract away data-driven probabilistic behaviour---the implications of a developer treating these APIs in the same way as other, traditional cloud services, such as cloud storage, is of concern. The objective of this study is to determine the various pain-points developers face when implementing systems that rely on the most mature of these intelligent services, specifically those that provide computer vision. We use Stack Overflow to mine indications of the frustrations that developers appear to face when using computer vision services, classifying their questions against two recent classification taxonomies (documentation-related and general questions). We find that, unlike mature fields like mobile development, there is a contrast in the types of questions asked by developers. These indicate a shallow understanding of the underlying technology that empower such systems. We discuss several implications of these findings via the lens of learning taxonomies to suggest how the software engineering community can improve these services and comment on the nature by which developers use them.