Reviewerly/exHarmony huggingface dataset:
Risk Perceptions on Social Media Use in Norway

Social media are getting more and more ingrained into everybody’s lives. With people’s more substantial presence on social media, threat actors exploit the platforms and the information that people share there to deploy and execute various types of attacks. This paper focuses on the Norwegian population, exploring how people perceive risks arising from the use of social media, focusing on the analysis of specific indicators such as age, sexes and differences among the users of distinct social media platforms. For data collection, a questionnaire was structured and deployed towards the users of multiple social media platforms (total n = 329). The analysis compares risk perceptions of using the social media platforms Facebook (n = 288), Twitter (n = 134), Reddit (n = 189) and Snapchat (n = 267). Furthermore, the paper analyses the differences between the sexes and between the digital natives and non-natives. Our sample also includes sufferers of ID theft (n = 50). We analyse how account compromise occurs and how suffering ID theft changes behaviour and perception. The results show significant discrepancies in the risk perception among the social media platform users across the examined indicators, but also explicit variations on how this affects the associated usage patterns. Based on the results, we propose a generic risk ranking of social media platforms, activities, sharing and a threat model for SoMe users. The results show the lack of a unified perception of risk on social media, indicating the need for targeted security awareness enhancement mechanisms focusing on this topic.
Lightweight Deep Learning for Malaria Parasite Detection Using Cell-Image of Blood Smear Images

Malaria is an infectious disease that is caused by the plasmodium parasite which is a single-celled group. This disease is usually spread employing an infected female anopheles mosquito. Recent statistics show that in 2017 there were only around 219 million recorded cases and about 435,000 deaths were reported due to this disease and more than 40% of the global population is at risk. Despite this, many image processing fused with machine learning algorithms were developed by researchers for the early detection of malaria using blood smear images. This research used a new CNN model using transfer learning for classifying segmented infected and Uninfected red blood cells. The experimental results show that the proposed architecture success to detect malaria with an accuracy of 98.85%, sensitivity of 98.79%, and a specificity of 98.90% with the highest speed and smallest input size among all previously used CNN models.
A Pointer Type Instrument Intelligent Reading System Design Based on Convolutional Neural Networks

The pointer instrument has the advantages of being simple, reliable, stable, easy to maintain, having strong anti-interference properties, and so on, which has long occupied the main position of electrical and electric instruments. Though the pointer instrument structure is simple, it is not convenient for real-time reading of measurements. In this paper, a RK3399 microcomputer was used for real-time intelligent reading of a pointer instrument using a camera. Firstly, a histogram normalization transform algorithm was used to optimize the brightness and enhance the contrast of images; then, the feature recognition algorithm You Only Look Once 3rd (YOLOv3) was used to detect and capture the panel area in images; and Convolutional Neural Networks were used to read and predict the characteristic images. Finally, predicted results were uploaded to a server. The system realized automatic identification, numerical reading, an intelligent online reading of pointer data, which has high feasibility and practical value. The experimental results show that the recognition rate of this system was 98.71% and the reading accuracy was 97.42%. What is more, the system can accurately locate the pointer-type instrument area and read corresponding values with simple operating conditions. This achievement meets the demand of real-time readings for analog instruments.
Catching the Fastest Boomerangs

In this paper we describe a new tool to search for boomerang distinguishers. One limitation of the MILP model of Liu et al. is that it handles only one round for the middle part while Song et al. have shown that dependencies could affect much more rounds, for instance up to 6 rounds for SKINNY. Thus we describe a new approach to turn an MILP model to search for truncated characteristics into an MILP model to search for truncated boomerang characteristics automatically handling the middle rounds. We then show a new CP model to search for the best possible instantiations to identify good boomerang distinguishers. Finally we systematized the method initiated by Song et al. to precisely compute the probability of a boomerang. As a result, we found many new boomerang distinguishers up to 24 rounds in the TK3 model. In particular, we improved by a factor 230 the probability of the best known distinguisher against 18-round SKINNY-128/256.
One-Shot Manipulation of Dynamical Quantum Resources

We develop a unified framework to characterize one-shot transformations of dynamical quantum resources in terms of resource quantifiers, establishing universal conditions for exact and approximate transformations in general resource theories. Our framework encompasses all dynamical resources represented as quantum channels, including those with a specific structure---such as boxes, assemblages, and measurements---thus immediately applying in a vast range of physical settings. For the particularly important manipulation tasks of distillation and dilution, we show that our conditions become necessary and sufficient for broad classes of important theories, enabling an exact characterization of these tasks and establishing a precise connection between operational problems and resource monotones based on entropic divergences. We exemplify our results by considering explicit applications to quantum communication, where we obtain exact expressions for one-shot quantum capacity and simulation cost assisted by no-signaling, separability-preserving, and positive partial transpose--preserving codes; as well as to nonlocality, contextuality, and measurement incompatibility, where we present operational applications of a number of relevant resource measures.
Vehicle detection in aerial images based on lightweight deep convolutional network

Vehicle detection in aerial images is an interesting and challenging task. Traditional methods are based on sliding-window search and handcrafted features, which limits the representation power and has heavy computational costs. Recent research have shown that deep-learning algorithms are widely used in the field of object detection. However, the deep-learning algorithms still face many difficulties and challenges in the object detection under the aerial scene. Meanwhile, the high computational cost of detection algorithms lead to low-detection efficiency. In this study, we build a fast and accurate lightweight detection framework for vehicle detection in aerial scenes. The proposed detection method improves the expressive ability of detection network and significantly reduces the amount of calculations in the model. Meanwhile, setting suitable anchor boxes according to the size of the object vehicles have been introduced in our model, which also effectively improves the performance of the detection. In addition, we have published a new aerial vehicle image dataset and verified the effectiveness of our method. In the Munich dataset and our dataset, our method achieves 85.8% and 91.2% of the mean average precision (mAP), and its detection time is 1.78 and 0.048 s on Nvidia Titan XP. Our results show that the proposed framework achieves significant improvement over several alternatives and state-of-the-art schemes with higher accuracy and less detection time.
Adapting Behavior via Intrinsic Reward: A Survey and Empirical Study

Learning about many things can provide numerous benefits to a reinforcement learning system. For example, learning many auxiliary value functions, in addition to optimizing the environmental reward, appears to improve both exploration and representation learning. The question we tackle in this paper is how to sculpt the stream of experience—how to adapt the learning system’s behavior—to optimize the learning of a collection of value functions. A simple answer is to compute an intrinsic reward based on the statistics of each auxiliary learner, and use reinforcement learning to maximize that intrinsic reward. Unfortunately, implementing this simple idea has proven difficult, and thus has been the focus of decades of study. It remains unclear which of the many possible measures of learning would work well in a parallel learning setting where environmental reward is extremely sparse or absent. In this paper, we investigate and compare different intrinsic reward mechanisms in a new bandit-like parallel-learning testbed. We discuss the interaction between reward and prediction learners and highlight the importance of introspective prediction learners: those that increase their rate of learning when progress is possible, and decrease when it is not. We provide a comprehensive empirical comparison of 14 different rewards, including well-known ideas from reinforcement learning and active learning. Our results highlight a simple but seemingly powerful principle: intrinsic rewards based on the amount of learning can generate useful behavior, if each individual learner is introspective.
Blockchain-Based Application for Certification Management

Blockchain technology will bring a disruption in plenty of industries and businesses. Recently it proved the robustness, immutability, auditability, in many crucial practical applications. The blockchain structure offers traceability of actions, alterations, alerts, which is an important property of a system needed for development of sustainable technologies. A crucial part of the blockchain technology regarding the optimization of the processes is the smart contract. It is a self-executable computer code, open and transparent, encoding the terms of a regular contract. It is able to automate the processes, thus decreasing the human-factor mistakes or counterfeits. In this paper, we are presenting the feasibility of the blockchain technology in the certification processes, with an application developed for university diploma certification. The example is easily transferable in other areas and business models such as logistics, supply chain management, or other segments where certification is essential.
F2Net: Learning to Focus on the Foreground for Unsupervised Video Object Segmentation

Although deep learning based methods have achieved great progress in unsupervised video object segmentation, difficult scenarios (e.g., visual similarity, occlusions, and appearance changing) are still no well-handled. To alleviate these issues, we propose a novel Focus on Foreground Network (F2Net), which delves into the intra-inter frame details for the foreground objects and thus effectively improve the segmentation performance. Specifically, our proposed network consists of three main parts: Siamese Encoder Module, Center Guiding Appearance Diffusion Module, and Dynamic Information Fusion Module. Firstly, we take a siamese encoder to extract the feature representations of paired frames (reference frame and current frame). Then, a Center Guiding Appearance Diffusion Module is designed to capture the inter-frame feature (dense correspondences between reference frame and current frame), intra-frame feature (dense correspondences in current frame), and original semantic feature of current frame. Different from the Anchor Diffusion Network, we establish a Center Prediction Branch to predict the center location of the foreground object in current frame and leverage the center point information as spatial guidance prior to enhance the inter-frame and intra-frame feature extraction, and thus the feature representation considerably focus on the foreground objects. Finally, we propose a Dynamic Information Fusion Module to automatically select relatively important features through three aforementioned different level features. Extensive experiments on DAVIS, Youtube-object, and FBMS datasets show that our proposed F2Net achieves the state-of-the-art performance with significant improvement.
Toward Better Structure and Constraint to Mine Negative Sequential Patterns

Nonoccurring behavior (NOB) studies have attracted the growing attention of scholars as a crucial part of behavioral science. As an effective method to discover both NOB and occurring behaviors (OB), negative sequential pattern (NSP) mining is successfully used in analyzing medical treatment and abnormal behavior patterns. At this time, NSP mining is still an active and challenging research domain. Most of the algorithms are inefficient in practice. Briefly, the key weaknesses of NSP mining are: 1) an inefficient positive sequential pattern (PSP) mining process, 2) a strict constraint of negative containment, and 3) the lack of an effective Negative Sequential Candidate (NSC) generation method. To address these weaknesses, we propose a highly efficient algorithm with improved techniques, named sc-NSP, to mine NSP efficiently. We first propose an improved PrefixSpan algorithm in the PSP mining process, which connects to a bitmap storage structure instead of the original structure. Second, sc-NSP loosens the frequency constraint and exploits the NSC generation method of positive and negative sequential patterns mining (PNSP) (a classic NSP mining method). Furthermore, a novel pruning strategy is designed to reduce the computational complexity of sc-NSP. Finally, sc-NSP obtains the support of NSC by using the most efficient bitwise-based calculation operation. Theoretical analyses show that sc-NSP performs particularly well on data sets with a large number of elements and items in sequence. Comparison and extensive experiments along with case studies on health data show that sc-NSP is 10 times more efficient than other state-of-the-art methods, and the number of NSPs obtained is 5 times greater than other methods.
Language Model-Driven Chatbot for Business to Address Marketing and Selection of Products

Artificial Intelligence has been increasingly gaining acceptance across advanced functions in numerous fields and industries. This includes marketing, customer support, and leads generation in healthcare, transportation, education, and off late in e-commerce. Machine learning as a subset of artificial intelligence techniques provides various algorithms that enable machines to learn from historical data and make realtime predictions on numbers and texts. Most of the businesses nowadays are trying to increase their reach and making sure that they are available to cater to the customers when they need help. This also enables the companies to market and respond to the queries of potential customers on a realtime basis. Chatter robots or chatbot is one such application of machine learning which allows the business to provide round the clock support to customers and potential leads for marketing questions. Most of the business fail to venture in the domain of hosting chatbot on the website as they do not have enough conversational data with them to train the machine learning algorithm and wait for years to collect enough sample. With the proposed language model-driven chatbots, businesses starting fresh in the domain of the hosting this application can use the user-generated content on social media to fuel the backend framework for the chatbots and start hosting the application.
Cross-Domain Grouping and Alignment for Domain Adaptive Semantic Segmentation

Existing techniques to adapt semantic segmentation networks across source and target domains within deep convolutional neural networks (CNNs) deal with all the samples from the two domains in a global or category-aware manner. They do not consider an inter-class variation within the target domain itself or estimated category, providing the limitation to encode the domains having a multi-modal data distribution. To overcome this limitation, we introduce a learnable clustering module, and a novel domain adaptation framework, called cross-domain grouping and alignment. To cluster the samples across domains with an aim to maximize the domain alignment without forgetting precise segmentation ability on the source domain, we present two loss functions, in particular, for encouraging semantic consistency and orthogonality among the clusters. We also present a loss so as to solve a class imbalance problem, which is the other limitation of the previous methods. Our experiments show that our method consistently boosts the adaptation performance in semantic segmentation, outperforming the state-of-the-arts on various domain adaptation settings.
Advanced Assistive Maintenance Based on Augmented Reality and 5G Networking

Internet of Things (IoT) applications play a relevant role in today’s industry in sharing diagnostic data with off-site service teams, as well as in enabling reliable predictive maintenance systems. Several interventions scenarios, however, require the physical presence of a human operator: Augmented Reality (AR), together with a broad-band connection, represents a major opportunity to integrate diagnostic data with real-time in-situ acquisitions. Diagnostic information can be shared with remote specialists that are able to monitor and guide maintenance operations from a control room as if they were in place. Furthermore, integrating heterogeneous sensors with AR visualization displays could largely improve operators’ safety in complex and dangerous industrial plants. In this paper, we present a complete setup for a remote assistive maintenance intervention based on 5G networking and tested at a Vodafone Base Transceiver Station (BTS) within the Vodafone 5G Program. Technicians’ safety was improved by means of a lightweight AR Head-Mounted Display (HDM) equipped with a thermal camera and a depth sensor to foresee possible collisions with hot surfaces and dangerous objects, by leveraging the processing power of remote computing paired with the low latency of 5G connection. Field testing confirmed that the proposed approach can be a viable solution for egocentric environment understanding and enables an immersive integration of the obtained augmented data within the real scene.
Building an Internet-Based Knowledge Ontology for Trademark Protection

Global online sales for products, where many are substantially identical or deceptively similar, are the cause of a growing number of trademark (TM) infringement lawsuits. This research proposes an intelligent trademark legal precedent recommendation system to assist trademark owners to find relevant past cases, laws, and judgments to form legal arguments to defend against infringement. Judicial precedent and applicable laws from the USA are used to construct an ontology of trademark litigation knowledge. The ontology is used to analyze potential infringement cases with similar laws and precedents used to resolve previous legal disputes. The analysis provides a basis for proceeding with legal action necessary to protect a company's brand equity when arguing potential trademark infringement. Using the Python programming language, the precedent-based recommendation system provides a means for continuously updating trademark case data and assists TM owners to quickly identify similar cases to support infringement allegations.
A Simple Chaotic Flow with Hyperbolic Sinusoidal Function and Its Application to Voice Encryption

In this article, a new chaotic system with hyperbolic sinusoidal function is introduced. This chaotic system provides a new category of chaotic flows which gives better perception of chaotic attractors. In the proposed chaotic flow with hyperbolic sinusoidal function, according to the changes of parameters of the system, the self-excited attractor and two forms of hidden attractors are occurred. Dynamic behavior of the offered chaotic flow is studied through eigenvalues, bifurcation diagrams, phase portraits, and spectrum of Lyapunov exponents. Moreover, the existence of double-scroll attractors in real word is considered via the Orcard-PSpice software through an electronic execution of the new chaotic flow and illustrative results between the numerical simulation and Orcard-PSpice outcomes are obtained. Lastly, random number generator (RNG) design is completed with the new chaos. Using the new RNG design, a novel voice encryption algorithm is suggested and voice encryption use and encryption analysis are performed.
Bridging the Gap in Multilingual Semantic Role Labeling: a Language-Agnostic Approach

Recent research indicates that taking advantage of complex syntactic features leads to favorable results in Semantic Role Labeling.Nonetheless, an analysis of the latest state-of-the-art multilingual systems reveals the difficulty of bridging the wide gap in performance between highresource (e.g., English) and low-resource (e.g., German) settings.To overcome this issue, we propose a fully language-agnostic model that does away with morphological and syntactic features to achieve robustness across languages.Our approach outperforms the state of the art in all the languages of the CoNLL-2009 benchmark dataset, especially whenever a scarce amount of training data is available.Our objective is not to reject approaches that rely on syntax, rather to set a strong and consistent language-independent baseline for future innovations in Semantic Role Labeling.We release our model code and checkpoints at https://github.
Knowledge Graph Embedding with Atrous Convolution and Residual Learning

Knowledge graph embedding is an important task and it will benefit lots of downstream applications.Currently, deep neural networks based methods achieve state-of-the-art performance.However, most of these existing methods are very complex and need much time for training and inference.To address this issue, we propose a simple but effective atrous convolution based knowledge graph embedding method.Compared with existing state-of-the-art methods, our method has following main characteristics.First, it effectively increases feature interactions by using atrous convolutions.Second, to address the original information forgotten issue and vanishing/exploding gradient issue, it uses the residual learning method.Third, it has simpler structure but much higher parameter efficiency.We evaluate our method on six benchmark datasets with different evaluation metrics.Extensive experiments show that our model is very effective.On these diverse datasets, it achieves better results than the compared state-of-theart methods on most of evaluation metrics.The source codes of our model could be found at https://github.com/neukg/AcrE.
QANom: Question-Answer driven SRL for Nominalizations

We propose a new semantic scheme for capturing predicate-argument relations for nominalizations, termed QANom.This scheme extends the QA-SRL formalism (He et al., 2015), modeling the relations between nominalizations and their arguments via natural language question-answer pairs.We construct the first QANom dataset using controlled crowdsourcing, analyze its quality and compare it to expertly annotated nominal-SRL annotations, as well as to other QA-driven annotations.In addition, we train a baseline QANom parser for identifying nominalizations and labeling their arguments with question-answer pairs.Finally, we demonstrate the extrinsic utility of our annotations for downstream tasks using both indirect supervision and zero-shot settings.