Reviewerly/exHarmony huggingface dataset:
Phishing Attacks and Preventions in Blockchain Based Projects

With the development of Internet technologies, the number of threats and attacks directed at networks and systems is increasing. Attackers invent new ways of attack or improve old ones. One of the most common serious threats is "Phishing", in which cybercriminals attempt to steal user credentials using fake emails or websites or both. Blockchain projects increasingly becomes the target of attacks by intruders due to high investments and gaps in national legislation. After a series of attacks on blockchain projects around the world, the issue of cybersecurity of blockchain became particularly relevant. The article classifies the main types and schemes of phishing attacks on the blockchain, suggests methods of protection against them, examines the development of blockchain protection against phishing attacks.
Jammer Detection based on Artificial Neural Networks

Wireless networks are prone to jamming attacks due to the broadcast nature of the wireless transmission environment. The effect of jamming attacks can be further increased as the jammers can focus their signals on reference signals of the transmitters, to further deteriorate the transmission performance. In this paper, we aim to jointly determine the presence of the jammer, along with its attack characteristics by using neural networks. Two neural network architectures are implemented; deep convolutional neural networks and deep recurrent neural networks. The presence of jammer and the transmitter and the type of the jammer is determined through a diverse set of scenarios that are implemented on software defined radios using orthogonal frequency division multiplexing based signaling. To improve the detection performance, prepossessing techniques are applied. Test results show that the proposed approach can effectively detect and classify the jamming attacks with around 85% accuracy.
Extracting safety information from multi-lingual accident reports using an ontology-based approach

This paper describes an approach to extract meaning from multi-lingual free-text safety incident reports. A sample of 5065 safety incident reports from the Swiss Federal Office of Transport were used in the study. Each report was written in either German, French or Italian natural language. An interactive learning approach between a human and computer software was undertaken to identify key terms in the text that are relevant to discovering meaning. A multi-lingual ontology was created to join meaningful semantic patterns and identify specific classes of safety incident on the railway, including injuries occurring whilst passengers were boarding or alighting from vehicles, falling down stairs, struck by closing doors, or struck by objects such as suitcases. A graph database was used to query the text records via the ontology and identify reports of incidents in each class, regardless of the language used in the report. Fluent speakers of each language – German, French and Italian – reviewed the results to confirm true positive results and detect false positives. The performance of the process varied across languages and incident types, however the overall true positive rate was determined by the fluent speakers to be 98.5%.
Megaphone

We design and implement Megaphone, a data migration mechanism for stateful distributed dataflow engines with latency objectives. When compared to existing migration mechanisms, Megaphone has the following differentiating characteristics: (i) migrations can be subdivided to a configurable granularity to avoid latency spikes, and (ii) migrations can be prepared ahead of time to avoid runtime coordination. Megaphone is implemented as a library on an unmodified timely dataflow implementation, and provides an operator interface compatible with its existing APIs. We evaluate Megaphone on established benchmarks with varying amounts of state and observe that compared to naïve approaches Megaphone reduces service latencies during reconfiguration by orders of magnitude without significantly increasing steady-state overhead.
Self-Adaptive Multiprototype-Based Competitive Learning Approach: A k-Means-Type Algorithm for Imbalanced Data Clustering

Class imbalance problem has been extensively studied in the recent years, but imbalanced data clustering in unsupervised environment, that is, the number of samples among clusters is imbalanced, has yet to be well studied. This paper, therefore, studies the imbalanced data clustering problem within the framework of k-means-type competitive learning. We introduce a new method called self-adaptive multiprototype-based competitive learning (SMCL) for imbalanced clusters. It uses multiple subclusters to represent each cluster with an automatic adjustment of the number of subclusters. Then, the subclusters are merged into the final clusters based on a novel separation measure. We also propose a new internal clustering validation measure to determine the number of final clusters during the merging process for imbalanced clusters. The advantages of SMCL are threefold: 1) it inherits the advantages of competitive learning and meanwhile is applicable to the imbalanced data clustering; 2) the self-adaptive multiprototype mechanism uses a proper number of subclusters to represent each cluster with any arbitrary shape; and 3) it automatically determines the number of clusters for imbalanced clusters. SMCL is compared with the existing counterparts for imbalanced clustering on the synthetic and real datasets. The experimental results show the efficacy of SMCL for imbalanced clusters.
A Comparison of Three Classification Algorithms for Handwritten Digit Recognition

Handwritten digits recognition is considered as a core to a diversity of emerging application. It is used widely by computer vision and machine learning researchers for performing practical applications such as computerized bank check numbers reading. However, executing a computerized system to carry out certain types of duties is not easy and it is a challenging matter. Recognizing the numeral handwriting of a person from another is a hard task because each individual has a unique handwriting way. The selection of the classifiers and the number of features play a vast role in achieving best possible accuracy of classification. This paper presents a comparison of three classification algorithms namely Naive Bayes (NB), Multilayer Perceptron (MLP) and K_Star algorithm based on correlation features selection (CFS) using NIST handwritten dataset. The objective of this comparison is to find out the best classifier among the three ones that can give an acceptable accuracy rate using a minimum number of selected features. The accuracy measurement parameters are used to assess the performance of each classifier individually, which are precision, recall and F-measure. The results show that K_Star algorithm gives better recognition rate than NB and MLPas it reached the accuracy of 82.36%.
ADLAuth: Passive Authentication Based on Activity of Daily Living Using Heterogeneous Sensing in Smart Cities

The Internet of Things is a rapidly growing paradigm for smart cities that provides a way of communication, identification, and sensing capabilities among physically distributed devices. With the evolution of the Internet of Things (IoTs), user dependence on smart systems and services, such as smart appliances, smartphone, security, and healthcare applications, has been increased. This demands secure authentication mechanisms to preserve the users' privacy when interacting with smart devices. This paper proposes a heterogeneous framework "ADLAuth" for passive and implicit authentication of the user using either a smartphone's built-in sensor or wearable sensors by analyzing the physical activity patterns of the users. Multiclass machine learning algorithms are applied to users' identity verification. Analyses are performed on three different datasets of heterogeneous sensors for a diverse number of activities. A series of experiments have been performed to test the effectiveness of the proposed framework. The results demonstrate the better performance of the proposed scheme compared to existing work for user authentication.
Exploring chat-based communication in the EFL class: computer and mobile environments

This study investigates the use of computer-mediated and mobile-mediated chat-based communication in an EFL course and seeks to explore differences between both modes of communication in order to better understand their potential to foster language development. Factors such as engagement, timing, participation, addressivity, speakership roles, type/retype messages, average message length and students’ perceptions are evaluated in both environments. A total of 64 Spanish students taking a B1 English course at the University of Almería were studied. According to treatment type, subjects were divided in two groups, one in which the WhatsApp application was used to represent mobile chat-based communication and another using Facebook to represent computer chat-based conversation. Two chat groups were created, one on each application, where 32 students in each group participated in daily interaction over four months. Pre and post surveys analysing students’ engagement were given to participants in both groups. Messages were tracked and samples of both types of communication were used to explore differences between the two environments. An end-of-course survey was also used to evaluate students’ perceptions of the interaction as well as each of the contexts under investigation. Findings highlighted significant differences between cognitive engagement and emotional engagement in the computer and mobile environments. Furthermore, differences between the two environments were also found regarding timing, perceived value of the interaction, use and participation.
Linking and Layout: Exploring the Integration of Text and Visualization in Storytelling

Abstract Modern web technologies are enabling authors to create various forms of text visualization integration for storytelling. This integration may shape the stories' flow and thereby affect the reading experience. In this paper, we seek to understand two text visualization integration forms: (i) different text and visualization spatial arrangements (layout), namely, vertical and slideshow; and (ii) interactive linking of text and visualization (linking). Here, linking refers to a bidirectional interaction mode that explicitly highlights the explanatory visualization element when selecting narrative text and vice versa. Through a crowdsourced study with 180 participants, we measured the effect of layout and linking on the degree to which users engage with the story (user engagement), their understanding of the story content (comprehension), and their ability to recall the story information (recall). We found that participants performed significantly better in comprehension tasks with the slideshow layout. Participant recall was better with the slideshow layout under conditions with linking versus no linking. We also found that linking significantly increased user engagement. Additionally, linking and the slideshow layout were preferred by the participants. We also explored user reading behaviors with different conditions.
Social Recommendation with Optimal Limited Attention

Social recommendation has been playing an important role in suggesting items to users through utilizing information from social connections. However, most existing approaches do not consider the attention factor causing the constraint that people can only accept a limited amount of information due to the limited strength of mind, which has been discovered as an intrinsic physiological property of human by social science. We address this issue by resorting to the concept of limited attention in social science and combining it with machine learning techniques in an elegant way. When introducing the idea of limited attention into social recommendation, two challenges that fail to be solved by existing methods appear: i) how to develop a mathematical model which can optimally choose a subset of friends for each user such that these friends' preferences can best influence the target user, and ii) how can the model learn an optimal attention for each of these selected friends. To tackle these challenges, we first propose to formulate the problem of optimal limited attention in social recommendation. We then develop a novel algorithm through employing an EM-style strategy to jointly optimize users' latent preferences, optimal number of their best influential friends and the corresponding attentions. We also give a rigorous proof to guarantee the algorithm's optimality. The proposed model is capable of efficiently finding an optimal number of friends whose preferences have the best impact on target user as well as adaptively learning an optimal personalized attention towards every selected friend w.r.t. the best recommendation accuracy. Extensive experiments on real-world datasets demonstrate the superiority of our proposed model over several state-of-the-art algorithms.
collabdraw

Sketching is one of the most accessible techniques for communicating our ideas quickly and for collaborating in real time. Here we present a web-based environment for collaborative sketching of everyday visual concepts. We explore the integration of an artificial agent, instantiated as a recurrent neural network, who is both cooperative and responsive to actions performed by its human collaborator. To evaluate the quality of the sketches produced in this environment, we conducted an experimental user study and found that sketches produced collaboratively carried as much semantically relevant information as those produced by humans on their own. Further control analyses suggest that the semantic information in these sketches were indeed the product of collaboration, rather than attributable to the contributions of the human or the artificial agent alone. Taken together, our findings attest to the potential of systems enabling real-time collaboration between humans and machines to create novel and meaningful content.
Discovering Maximal Motif Cliques in Large Heterogeneous Information Networks

We study the discovery of cliques (or "complete" subgraphs) in heterogeneous information networks (HINs). Existing clique-finding solutions often ignore the rich semantics of HINs. We propose motif clique, or m-clique, which redefines subgraph completeness with respect to a given motif. A motif, essentially a small subgraph pattern, is a fundamental building block of an HIN. The m-clique concept is general and allows us to analyse "complete" subgraphs in an HIN with respect to desired high-order connection patterns. We further investigate the maximal m-clique enumeration problem (MMCE), which finds all maximal m-cliques not contained in any other m-cliques. Because MMCE is NP-hard, developing an accurate and efficient solution for MMCE is not straightforward. We thus present the META algorithm, which employs advanced pruning strategies to effectively reduce the search space. We also design fast techniques to avoid generating duplicated maximal m-clique instances. Our extensive experiments on large real and synthetic HINs show that META is highly effective and efficient.
Reconsidering the Voice Principle with Non-native Language Speakers

Researchers have suggested that use of pedagogical agents speaking with a human voice increases social perception and enables deeper learning when compared against computer-generated voice. However, recent research (Craig & Schroeder, 2017) found modern computer voice was as effective as human voice in certain social measures, and can outperformed human voice in particular learning outcomes. This research aimed to study whether two human voice conditions (strong-prosodic and weak-prosodic) produced consistent measures when compared against modern computer voice and each other in social perception and retention measures with non-native speakers. The human weak-prosodic voice was rated significantly higher on four of seven scale items compared to modern computer voice. However, no significant differences were found in the retention of information. These results show that non-native speakers prefer human voice with less prosodic elements, and factors behind voice are more complicated than just categorizing it as either human or computer.
WiCAR

In-car human activity recognition is playing a critical role in detecting distracted driving and improving human-car interaction. Among multiple sensing technologies, WiFi-based in-car activity recognition exhibits unique advantages since it does not rely on visible light, avoids privacy leaks and is cost-efficient with integrated WiFi signals in cars. Existing WiFi-based recognition systems mostly focus on the relatively stable indoor space, which only yield reasonably good performance in limited situations. Based on our field studies, the in-car activity recognition, however, is much more complicated suffering from more impact factors. First, the external moving objects and the surrounding WiFi signals can cause various disturbances to the in-car activity sensing. Second, considering the compact in-car space, different car models can also lead to different multipath distortions. Moreover, different people can also perform activities in different shapes. Such extraneous information related to specific driving conditions, car models and human subjects is implicitly contained for training and prediction, inevitably leading to poor recognition performance for new environment and people.
Keeping and Discarding Personal Data

Today, most technology users have to deal with a growing amount of personal data across many devices and online platforms. There is a growing need for tools that can help people decide more intentionally what data to keep or discard. We created five design concepts in the form of video prototypes to probe on alternative design strategies for supporting users. Automation, aggressiveness, and temporality were key dimensions we explored. We conducted interviews with 16 participants using the concepts as a starting point for discussion. Participants had a range of reactions: some wanted to retain full control over keeping and discarding decisions, while others welcomed more automatic tools. We identify common ground in the need for a contextual and nuanced approach in design. We use these results to outline and reflect on possible future design directions for personalization, automation, new keeping or discarding actions, and privacy.
Adaptive RBF-AR Models Based on Multi-Innovation Least Squares Method

In the previous work, the parameters of radial basis function network based autoregressive (RBF-AR) models are estimated offline and no longer updated afterward. In this letter, an adaptive learning algorithm is proposed for the RBF-AR models. The proposed strategy is that the nonlinear parameters are previously determined by an off-line variable projection method; and once new samples are available, the linear parameters are updated. The linear adaptive algorithm adopted in this letter is the multi-innovation least squares method, due to its high performance. The simulation results show that with the adaption of the linear parameters, the prediction performance of the RBF-AR models may be significantly improved, which demonstrates the effectiveness of the proposed algorithm.
Beyond majority: Label ranking ensembles based on voting rules

Label ranking is a machine learning task that deals with mapping an instance to a ranking of labels, representing the labels' ordered relevance to the instance. Three recent studies have suggested the use of ensembles to improve the performance of simple label ranking models. However, none of them has explicitly inspected the question of how should the results obtained by simple models be aggregated into a single combined output. While classification tasks and regression tasks typically employ trivial aggregation techniques (i.e., majority voting and averaging, respectively), the case of label ranking tasks is not straightforward. The contribution of this paper is twofold. First, we propose to apply voting rules, typically used in the field of social choice, as the aggregation technique for label ranking ensembles. Our evaluation reveals that there is no single rule that consistently outperforms all other voting rules, and that under different settings different voting rules perform the best. Second, we propose a novel aggregation method for label ranking ensembles that learns the best voting rule to be used in a given setting. An extensive evaluation of the proposed method on semi-synthetic as well as real-world datasets shows that it obtains prediction performance that is significantly higher than that of the aggregation techniques currently used by state-of-the-art label ranking ensembles.
Automatic Short Answer Grading via Multiway Attention Networks

Automatic short answer grading (ASAG), which autonomously score student answers according to reference answers, provides a cost-effective and consistent approach to teaching professionals and can reduce their monotonous and tedious grading workloads. However, ASAG is a very challenging task due to two reasons: (1) student answers are made up of free text which requires a deep semantic understanding; and (2) the questions are usually open-ended and across many domains in K-12 scenarios. In this paper, we propose a generalized end-to-end ASAG learning framework which aims to (1) autonomously extract linguistic information from both student and reference answers; and (2) accurately model the semantic relations between free-text student and reference answers in open-ended domain. The proposed ASAG model is evaluated on a large real-world K-12 dataset and can outperform the state-of-the-art baselines in terms of various evaluation metrics.
Unpaired photo-to-caricature translation on faces in the wild

Recently, image-to-image translation has been made much progress owing to the success of conditional Generative Adversarial Networks (cGANs). And some unpaired methods based on cycle consistency loss such as DualGAN, CycleGAN and DiscoGAN are really popular. However, it’s still very challenging for translation tasks with the requirement of high-level visual information conversion, such as photo-to-caricature translation that requires satire, exaggeration, lifelikeness and artistry. We present an approach for learning to translate faces in the wild from the source photo domain to the target caricature domain with different styles, which can also be used for other high-level image-to-image translation tasks. In order to capture global structure with local statistics while translation, we design a dual pathway model with one coarse discriminator and one fine discriminator. For generator, we provide one extra perceptual loss in association with adversarial loss and cycle consistency loss to achieve representation learning for two different domains. Also the style can be learned by the auxiliary noise input. Experiments on photo-to-caricature translation of faces in the wild show considerable performance gain of our proposed method over state-of-the-art translation methods as well as its potential real applications. Our code is available at https://github.com/zhengziqiang/P2C.