Reviewerly/exHarmony huggingface dataset:
Open Relation Extraction for Chinese Noun Phrases

Relation Extraction (RE) aims at harvesting relational facts from texts. A majority of existing research targets at knowledge acquisition from sentences, where subject-verb-object structures are usually treated as the signals of existence of relations. In contrast, relational facts expressed within noun phrases are highly implicit. Previous works mostly relies on human-compiled assertions and textual patterns in English to address noun phrase-based RE. For Chinese, the corresponding task is non-trivial because Chinese is a highly analytic language with flexible expressions. Additionally, noun phrases tend to be incomplete in grammatical structures, where clear mentions of predicates are often missing. In this article, we present an unsupervised Noun Phrase-based Open RE system for the Chinese language (NPORE), which employs a three-layer data-driven architecture. The system contains three components, i.e., Modifier-sensitive Phrase Segmenter, Candidate Relation Generator and Missing Relation Predicate Detector. It integrates with a graph clique mining algorithm to chunk Chinese noun phrases, considering how relations are expressed. We further propose a probabilistic method with knowledge priors and a hypergraph-based random walk process to detect missing relation predicates. Experiments over Chinese Wikipedia show NPORE outperforms state-of-the-art, capable of extracting 55.2 percent more relations than the most competitive baseline, with a comparable precision at 95.4 percent.
Predatory Conversation Detection

Providing a safe environment for children in online networks can be challenged by the anonymous nature of the internet. One of the worst forms of cyber-security issues is child grooming, where sexual predators seek contact with minors to abuse them. Various types of organizations such as chat providers and law enforcement are inclined to find online predatory conversations in order to protect the children. This paper proposes a study on predatory conversation detection using Natural Language Processing. We analyzed the different types of features of online grooming data considering various characteristics of online conversation, such as psycho-linguistic patterns. Our experiments with online communication showed an accuracy of 0.98 in automatically classifying the conversations into predatory conversations and non-predatory conversations. Best results are obtained by using linear SVM on 1-gram features when removing stop words as well as by using multinomial Naïve Bayes on 1-gram features when not removing stop words.
Online deception and situations conducive to the progression of non-payment fraud

Adopting the criminal event perspective, we explore how online fraudsters make use of urgency cues in their interactions with potential victims throughout the progression of an online nonpayment fraud attempt. Integrating claims from the 'Interpersonal-Deception Theory' with situational explanations of crime, we investigate whether fraudsters' presentations of verbal cues of urgency during the early stages of a criminal event are followed by a consistent presentation of verbal and non-verbal urgency cues. To answer this question, we posted a large number of 'for-sale' advertisements over a classified-ad website and interacted with online fraudsters and legitimate users who responded to our ads over email. Our findings highlight the relevance of the criminal event perspective in guiding research on targets and offenders in cyberspace.
Enhancing Interaction with Augmented Reality through Mid-Air Haptic Feedback: Architecture Design and User Feedback

Nowadays, Augmented-Reality (AR) head-mounted displays (HMD) deliver a more immersive visualization of virtual contents, but the available means of interaction, mainly based on gesture and/or voice, are yet limited and obviously lack realism and expressivity when compared to traditional physical means. In this sense, the integration of haptics within AR may help to deliver an enriched experience, while facilitating the performance of specific actions, such as repositioning or resizing tasks, that are still dependent on the user’s skills. In this direction, this paper gathers the description of a flexible architecture designed to deploy haptically enabled AR applications both for mobile and wearable visualization devices. The haptic feedback may be generated through a variety of devices (e.g., wearable, graspable, or mid-air ones), and the architecture facilitates handling the specificity of each. For this reason, within the paper, it is discussed how to generate a haptic representation of a 3D digital object depending on the application and the target device. Additionally, the paper includes an analysis of practical, relevant issues that arise when setting up a system to work with specific devices like HMD (e.g., HoloLens) and mid-air haptic devices (e.g., Ultrahaptics), such as the alignment between the real world and the virtual one. The architecture applicability is demonstrated through the implementation of two applications: (a) Form Inspector and (b) Simon Game, built for HoloLens and iOS mobile phones for visualization and for UHK for mid-air haptics delivery. These applications have been used to explore with nine users the efficiency, meaningfulness, and usefulness of mid-air haptics for form perception, object resizing, and push interaction tasks. Results show that, although mobile interaction is preferred when this option is available, haptics turn out to be more meaningful in identifying shapes when compared to what users initially expect and in contributing to the execution of resizing tasks. Moreover, this preliminary user study reveals some design issues when working with haptic AR. For example, users may be expecting a tailored interface metaphor, not necessarily inspired in natural interaction. This has been the case of our proposal of virtual pressable buttons, built mimicking real buttons by using haptics, but differently interpreted by the study participants.
Limited Gradient Descent: Learning With Noisy Labels

Label noise may affect the generalization of classifiers, and the effective learning of main patterns from samples with noisy labels is an important challenge.Recent studies have shown that deep neural networks tend to prioritize the learning of simple patterns over the memorization of noise patterns.This suggests a possible method to search for the best generalization that learns the main pattern until the noise begins to be memorized.Traditional approaches often employ a clean validation set to find the best stop timing of learning, i.e., early stopping.However, the generalization performance of such methods relies on the quality of validation sets.Further, in practice, a clean validation set is sometimes difficult to obtain.To solve this problem, we propose a method that can estimate the optimal stopping timing without a clean validation set, called limited gradient descent.We modified the labels of a few samples in a noisy dataset to obtain false labels and to create a reverse pattern.By monitoring the learning progress of the noisy and reverse samples, we can determine the stop timing of learning.In this paper, we also theoretically provide some necessary conditions on learning with noisy labels.Experimental results on CIFAR-10 and CIFAR-100 datasets demonstrate that our approach has a comparable generalization performance to methods relying on a clean validation set.Thus, on the noisy Clothing-1M dataset, our approach surpasses methods that rely on a clean validation set.
Moore's Law: A Hard Act to Follow

Moore's law was the exponential improvement path for semiconductors that fueled the computer revolution and enormous worldwide growth. It's a hard act to follow, but there are three new followon trends that seem just as interesting.
An Evolutionary Approach to Compact DAG Neural Network Optimization

Neural networks are the cutting edge of artificial intelligence, demonstrated to reliably outperform other techniques in machine learning. Within the domain of neural networks, many different classes of architectures have been developed for various tasks in specific subfields, as well as a multitude of diversity in the way of activation functions, loss functions, and other such hyperparameters. These networks are often large and computationally expensive to train and deploy, restricting their utility. Furthermore, the fundamental theory behind the effectiveness of particular network architectures and hyperparameters are often not well understood, and as such, practitioners frequently resort to trial-and-error techniques to optimize their model performance. To address these concerns, we propose the use of compact directed acyclic graph neural networks (DAG-NNs) and an evolutionary approach for automating the optimization of their structure and parameters. Our experimental results demonstrate that our approach consistently outperforms conventional neural networks, even while employing fewer nodes.
Classification of Normal Versus Malignant Cells in B-ALL White Blood Cancer Microscopic Images

Identification of malignant cells is an important aspect of cancer diagnosis and in determining the degree of progression. Blood disorders, such as leukemia, are most often detected only in advanced stages, when the number of cancer cells is much higher than the number of normal cells. Differentiation of cancer cells from normal blood cells is challenging due to their acute morphological similarity. Early diagnosis of leukemia relies on the accurate classification of malignant cells versus normal cells in microscopic images of blood cells. Computer-aided cell classification has gained popularity as an efficient technique for the diagnosis of leukemia. Our approach to classification involves finetuning of a VGG16 neural network with batch normalization to classify the images of malignant vis-à-vis normal cells.
Towards Smart LMS to Improve Learning Outcomes Students Using LenoBot with Natural Language Processing

In recent years, various online learning models called Massive Online Open Courses (MOOC) have been mediated by smart and effective computers that play an important role in the acquisition of knowledge for learning. Discussion forums, also referred to as a means to discuss all learning materials and conference systems, are also a means to discuss the material as a substitute for face-to-face meetings between students and lecturers. This phenomenon is also found in online learning. Unfortunately, online learning is not supported by 24-hour real-time lecturer responses. Therefore, the purpose of this study is to identify, analyze needs, and design Chatbots that can be used as role models to develop LMS as an intelligent academic information system, especially to support 24 hours of interactive learning processes in all subjects. This study uses the Natural Language Processing approach to generate responses such as daily conversations. The analysis is done and used to design chatbots that can maximize LMS work that is smart and can help student learning activities, especially around the most frequently asked questions. After being tested on several subjects in the Information System Engineering group, it is known that Chatbot can interact with students like having daily conversations. In the future, the results of this study are used as supporting systems for recommendations on smart LMS.
Understanding Consumer Purchase Intention in a Blockchain Technology for Food Traceability and Transparency context

From being associated with the financial services primarily, blockchain is now affecting other industries as well. Blockchain is attracting interest as a way to track and monitor food through the supply network, ensuring its origin. Consumers are increasingly demanding to know where their purchases come from and how they are made. In food industry, consumers can confirm the provenance of their purchases by blockchain. Despite some blockchain research on innovation adoption in fintech contexts, little is known about how consumers evaluate blockchain in food traceability and transparency. This study explores consumers' motivations to purchase food with blockchain code and takes the Unified Theory of Acceptance and Use of Technology 2 (UTA UT2) as a reference and develops an extended model by considering perceived trust, since the capabilities of blockchain technology for food traceability and transparency. The results show that blockchain technology has the capability to build trust and affect purchase intention. Results also highlight purchase intention is significantly and positively influenced by performance expectancy, effort expectancy, and habit. The results further show that technographic can significantly enhance intention to the use of blockchain technology to trace food The research provides an insight to practitioners and researchers on how to increase intention towards blockchain.
Investigating Low Level Features in CNN for Traffic Sign Detection and Recognition

Understanding traffic signs is one of the basic requirements for a self-driving car to drive autonomously in real world scenario. It needs to be able to achieve this task with an error rate similar or lower than a human. Currently, in the automotive industry, most vision based algorithms still rely heavily on the geometry and color of traffic signs to detect and classify them. Although these approaches are suitable for highway scenario where there is not much background objects, they are not robust enough to handle the vast and diverse amount of objects that can be found in urban scenario. Inspired by the performance achieved by Convolutional Neural Network (CNN) based object detector, we would like to investigate the feasibility of using CNN for the task of traffic sign detection from camera images.In this paper, we will be focusing on two specific issues of our task, the low interclass variation of traffic signs and the small size of the traffic signs in images. To tackle these issues, we propose a new architecture that splits the detection and classification branches of a CNN based object detector. This architecture exploits low level features for classification while it uses high level features for detection in a single forward pass. With this modification, we were able to improve the average precision of the detection results by 5% to 19% on public datasets.
Sym-Parameterized Dynamic Inference for Mixed-Domain Image Translation

Recent advances in image-to-image translation have led to some ways to generate multiple domain images through a single network. However, there is still a limit in creating an image of a target domain without a dataset on it. We propose a method to expand the concept of `multi-domain' from data to the loss area, and to combine the characteristics of each domain to create an image. First, we introduce a sym-parameter and its learning method that can mix various losses and can synchronize them with input conditions. Then, we propose Sym-parameterized Generative Network (SGN) using it. Through experiments, we confirmed that SGN could mix the characteristics of various data and losses, and it is possible to translate images to any mixed-domain without ground truths, such as 30% Van Gogh and 20% Monet and 40% snowy.
Clustering of Top 50 Latin American Universities in SIR, QS, ARWU, and Webometrics Rankings

Every year, international rankings of the universities are published reflecting their positioning through the evaluation of academic and research quality criteria established in their methodologies. The positioning of Latin American universities in the rankings reflects the ability to research, generate results, and publish scientific output to be cited, as well as evidence of academic excellence, capacity for innovation and social linkage. The present study presents the results of a descriptive analysis based on clusters of eighty-five Latin American universities located in the first fifty positions of ARWU, SIR Scimago, QS and Webometrics rankings, according to their most recent editions published during the year 2019, highlighting the universities located in Brazil, Chile, Mexico, Argentina and Colombia, countries that host 90% of the best positioned universities in Latin America.
Experimental Insights from the Rogues Gallery

The Rogues Gallery is a new deployment for understanding next-generation hardware with a focus on unorthodox and uncommon technologies. This testbed project was initiated in 2017 in response to Rebooting Computing efforts and initiatives. The Gallery's focus is to acquire new and unique hardware (the rogues) from vendors, research labs, and start-ups and to make this hardware widely available to students, faculty, and industry collaborators within a managed data center environment. By exposing students and researchers to this set of unique hardware, we hope to foster cross-cutting discussions about hardware designs that will drive future performance improvements in computing long after the Moore's Law era of cheap transistors ends. We have defined an initial vision of the infrastructure and driving engineering challenges for such a testbed in a separate document, so here we present highlights of the first one to two years of post-Moore era research with the Rogues Gallery and give an indication of where we see future growth for this testbed and related efforts.
cARe

Cognitive impairment such as memory loss, an impaired executive function and decreasing motivation can gradually undermine instrumental activities of daily living (IADL). With an older growing population, previous works have explored assistive technologies (ATs) to automate repetitive components of therapy and thereby increase patients' autonomy and reduce dependence on carers. While most ATs were built around screens and projection-based augmented reality (AR), the potential of head-mounted displays (HMDs) for therapeutic assistance is still under-explored. As a contribution to this effort we present cARe, an HMD-based AR framework that uses in-situ instructions and a guidance mechanism to assist patients with manual tasks. In a case study with six geriatric patients, we investigated the prototype's feasibility during a cooking task in comparison to a regular paper-based recipe. Qualitative and quantitative results indicate that cARe has potential to offer assistance to older individuals with declining cognitive function in their day-to-day tasks and increase their independence in an enjoyable way.
WooKey

The work presented in this paper takes place in the design initiatives that have emerged to thwart BadUSB threats [59]. Though many attempts were focusing on the host side, by enhancing the operating system's USB sub-module robustness [78, 80] or by adding a proxy between the host and the device [6, 53], we have chosen to focus our efforts on the device side. More specifically, our work presents the WooKey platform, a custom highly secure USB thumb drive with mass storage capabilities, designed for user data encryption and protection, and embedding a full-fledged set of in-depth defenses. The device encloses a firmware with a secure DFU (Device Firmware Update) implementation using up-to-date cryptography as well as an external and extractable authentication token based on a secure element. The runtime software security is built upon EwoK, an innovative open source microkernel designed for microcontrollers with advanced security and performance in mind. Finally, another strength of the project is its core guiding principle: provide an open source and open hardware platform [3] using off-the-shelf components.
Improving the security of image manipulation detection through one-and-a-half-class multiple classification

Protecting image manipulation detectors against perfect knowledge attacks requires the adoption of detector architectures which are intrinsically difficult to attack. In this paper, we do so, by exploiting a recently proposed multiple-classifier architecture combining the improved security of 1-Class (1C) classification and the good performance ensured by conventional 2-Class (2C) classification in the absence of attacks. The architecture, also known as 1.5-Class (1.5C) classifier, consists of one 2C classifier and two 1C classifiers run in parallel followed by a final 1C classifier. In our system, the first three classifiers are implemented by means of Support Vector Machines (SVM) fed with SPAM features. The outputs of such classifiers are then processed by a final 1C SVM in charge of making the final decision. Particular care is taken to design a proper strategy to train the SVMs the 1.5C classifier relies on. This is a crucial task, due to the difficulty of training the two 1C classifiers at the front end of the system. We assessed the performance of the proposed solution with regard to three manipulation detection tasks, namely image resizing, median filtering and contrast enhancement. As a result the security improvement allowed by the 1.5C architecture with respect to a conventional 2C solution is confirmed, with a performance loss in the absence of attacks that remains at a negligible level.
Continuously Non-malleable Secret Sharing for General Access Structures

We study leakage-resilient continuously non-malleable secret sharing, as recently introduced by Faonio and Venturi (CRYPTO 2019). In this setting, an attacker can continuously tamper and leak from a target secret sharing of some message, with the goal of producing a modified set of shares that reconstructs to a message related to the originally shared value. Our contributions are two fold. Prior to our work, there was no secret sharing scheme achieving continuous non-malleability against joint tampering, and the only known scheme for independent tampering was tailored to threshold access structures.
MF-SORT: Simple Online and Realtime Tracking with Motion Features

Multiple object tracking (MOT) plays a key role in video analysis. On MOT, DeepSORT (Simple Online and Realtime Tracking with a deep association metric) performs effectively by combining features of appearance and motion for estimating data association. However, computing with multiple features are time consuming. In certain applications, cameras are static, such as pedestrian surveillance, sports video analysis and so on. Here, without camera movement the motion trajectories of objects are generally possible to estimate. The introduction of more features cannot improve the performance of object tracking discriminatively. Furthermore, the time cost rises evidently. To address this problem, we propose a novel Simple Online and Realtime Tracking with motion features (MF-SORT). By focusing on the motion features of the objects during data association, the proposed scheme is able to take a trade-off between performance and efficiency. The experimental results on the MOT Challenge benchmark and MOT-SOCCER (newly established in this work) demonstrate that the proposed method is much faster than DeepSORT with the comparable accuracy.
New Blockchain Adoptation For Automotive Security by Using Systematic Innovation

This paper deals with the security matters in automotive by using the Blockchain technology and an innovative thinking skill which has been applied to develop the new prototype design from the initial idea. The Blockchain Governance Game and Fog Computing are adopted to build the new highly secure decentralized service architecture. These relatively new technologies make possible to design the Blockchain based on the automotive security. Although the paper is condensed and does not contain the details, it will be the good guideline for the direction of future research. The thinking skill called Systematic Innovation is the core to solve this problems in the proper Blockchain security network design and well demonstrated in this paper.