nils-herrmann/clean_scirepeval huggingface dataset:
"8 Amazing Secrets for Getting More Clicks": Detecting Clickbaits in News Streams Using Article Informality

Clickbaits are articles with misleading titles, exaggerating the content on the landing page. Their goal is to entice users to click on the title in order to monetize the landing page. The content on the landing page is usually of low quality. Their presence in user homepage stream of news aggregator sites (e.g., Yahoo news, Google news) may adversely impact user experience. Hence, it is important to identify and demote or block them on homepages. In this paper, we present a machine-learning model to detect clickbaits. We use a variety of features and show that the degree of informality of a webpage (as measured by different metrics) is a strong indicator of it being a clickbait. We conduct extensive experiments to evaluate our approach and analyze properties of clickbait and non-clickbait articles. Our model achieves high performance (74.9% F-1 score) in predicting clickbaits.
There's No Free Lunch, Even Using Bitcoin: Tracking the Popularity and Profits of Virtual Currency Scams

We present the first empirical analysis of Bitcoin-based scams: operations established with fraudulent intent. By amalgamating reports gathered by voluntary vigilantes and tracked in online forums, we identify 192 scams and categorize them into four groups: Ponzi schemes, mining scams, scam wallets and fraudulent exchanges. In 21% of the cases, we also found the associated Bitcoin addresses, which enables us to track payments into and out of the scams. We find that at least $11 million has been contributed to the scams from 13 000 distinct victims. Furthermore, we present evidence that the most successful scams depend on large contributions from a very small number of victims. Finally, we discuss ways in which the scams could be countered.
Hierarchical relational models for document networks

We develop the relational topic model (RTM), a hierarchical model of both network structure and node attributes. We focus on document networks, where the attributes of each document are its words, i.e., discrete observations taken from a fixed vocabulary. For each pair of documents, the RTM models their link as a binary random variable that is conditioned on their contents. The model can be used to summarize a network of documents, predict links between them, and predict words within them. We derive efficient inference and estimation algorithms based on variational methods that take advantage of sparsity and scale with the number of links. We evaluate the predictive performance of the RTM for large networks of scientific abstracts, web documents, and geographically tagged news.
Robust Partitioning Policies of Multiprocessor Systems

Various techniques for multiprogramming parallel multiprocessor systems have been proposed recently as a way to improve performance. A natural approach is to divide the set of processing elements into independent partitions, and simultaneously execute a diierent parallel program in each partition. Several issues arise, including the determination of the optimal number of programs allowed to execute simultaneously (i.e., the number of partitions) and the corresponding partition sizes. This can be done statically, dynamically, or adaptively, depending on the system and workload characteristics. In this paper several adaptive partitioning policies are evaluated. Their behavior, as well as the behavior of static policies, is investigated using real parallel programs. The policy applicability to actual systems is addressed, and implementation results of the proposed policies on an iPSC/2 hypercube system are reported. The concept of robustness (i.e., the ability to perform well on a wide range of workload types over a wide range of arrival rates) is presented and quantiied. Relative rankings of the policies are obtained, depending on the speciic work-load characteristics. A trade-oo is shown between potential performance and the amount of knowledge of the workload characteristics required to select the best policy. A policy that performs best when such knowledge of workload parallelism and/or arrival rate is not available is proposed as the most robust of those analyzed.
An ultra low power 1V, 220nW temperature sensor for passive wireless applications

This work presents a low power temperature sensor that is suitable for passive wireless systems. The test chip is fabricated with a 0.18 mum CMOS technology and the total area is 0.05 mm2. With temperature inaccuracy of -1.6degC/+3degC from 0degC to 100degC, the temperature sensor consumes only 220 nW at 1V under room temperature. The data conversion rate is 100 sample/s with an output resolution of 0.3degC, which is sufficient for most sensor applications.
StackSync: bringing elasticity to dropbox-like file synchronization

The design of elastic file synchronization services like Dropbox is an open and complex issue yet not unveiled by the major commercial providers, as it includes challenges like fine-grained programmable elasticity and efficient change notification to millions of devices. In this paper, we propose a novel architecture for file synchronization which aims to solve the above two major challenges. At the heart of our proposal lies ObjectMQ, a lightweight framework for providing programmatic elasticity to distributed objects using messaging. The efficient use of indirect communication: i) enables programmatic elasticity based on queue message processing, ii) simplifies change notifications offering simple unicast and multicast primitives; and iii) provides transparent load balancing based on queues.
 Our reference implementation is StackSync, an open source elastic file synchronization Cloud service developed in the context of the FP7 project CloudSpaces. StackSync supports both predictive and reactive provisioning policies on top of ObjectMQ that adapt to real traces from the Ubuntu One service. The feasibility of our approach has been extensively validated with an open benchmark, including commercial synchronization services like Dropbox or OneDrive.
A sensor independent probabilistic fusion system for driver assistance systems

In this contribution we present a probabilistic fusion framework for implementing a sensor independent measurement fusion. All interfaces are using probabilistic descriptions of measurement and existence uncertainties. We introduce several extensions to already existing algorithms: the support for association of multiple measurements to the same object is introduced, which reduces the effects of split segments in the data preprocessing step of high-resolution sensors like laser scanners. Furthermore, we present an approach for integrating explicit object birth models. We also developed extensions to speed up the algorithm which lead to real-time performance with fragmented data. We show the application of the framework in an automotive multi-target multi-sensor environment by fusing laser scanner and video. The algorithms were evaluated using real-world data in our research vehicle.
Knowledge Representation Learning with Entities, Attributes and Relations

Distributed knowledge representation (KR) encodes both entities and relations in a lowdimensional semantic space, which has significantly promoted the performance of relation extraction and knowledge reasoning. In many knowledge graphs (KG), some relations indicate attributes of entities (attributes) and others indicate relations between entities (relations). Existing KR models regard all relations equally, and usually suffer from poor accuracies when modeling one-to-many and many-to-one relations, mostly composed of attribute. In this paper, we distinguish existing KGrelations into attributes and relations, and propose a new KR model with entities, attributes and relations (KR-EAR). The experiment results show that, by special modeling of attribute, KR-EAR can significantly outperform state-of-the-art KR models in prediction of entities, attributes and relations. The source code of this paper can be obtained from https://github.com/thunlp/KR-EAR.
The Velo gripper: A versatile single-actuator design for enveloping, parallel and fingertip grasps

We design, optimize and demonstrate the behavior of a tendon-driven robotic gripper performing parallel, enveloping and fingertip grasps. The gripper consists of two fingers, each with two links, and is actuated using a single active tendon. During unobstructed closing, the distal links remain parallel, for parallel grasps. If the proximal links are stopped by contact with an object, the distal links start flexing, creating a stable enveloping grasp. We optimize the route of the active flexor tendon and the route and stiffness of a passive extensor tendon in order to achieve this behavior. We show how the resulting gripper can also execute fingertip grasps for picking up small objects off a flat surface, using contact with the surface to its advantage through passive adaptation. Finally, we introduce a method for optimizing the dimensions of the links in order to achieve enveloping grasps of a large range of objects, and apply it to a set of common household objects.
Do we really understand tacit knowledge ?

This paper advances the claim that tacit knowledge has been greatly misunderstood in management studies. Nonaka and Takeuchi’s widely adopted interpretation of tacit knowledge as knowledge awaiting “translation” or “conversion” into explicit knowledge is erroneous: contrary to Polanyi’s argument, it ignores the essential ineffability of tacit knowledge. In the paper I show why the idea of focussing on a set of tacitly known particulars and “converting” them into explicit knowledge is unsustainable. However, the ineffability of tacit knowledge does not mean that we cannot discuss the skilled performances in which we are involved. We can discuss them provided we stop insisting on “converting” tacit knowledge and, instead, start recursively drawing our attention to how we draw each other’s attention to things. Instructive forms of talk help us re-orientate ourselves to how we relate to others and the world around us, thus enabling us to talk and act differently. Following Wittgenstein and Shotter, I argue that we can command a clearer view of our skilled performances if we “re-mind” ourselves of how we do things, so that distinctions, which we had previously not noticed, and features, which had previously escaped our attention, may be brought forward. We cannot operationalise tacit knowledge but we can find new ways of talking, fresh forms of interacting and novel ways of distinguishing and connecting. Tacit knowledge cannot be “captured”, “translated”, or “converted” but only displayed and manifested, in what we do. New knowledge comes about not when the tacit becomes explicit, but when our skilled performance is punctuated in new ways through social interaction. Presented to Knowledge Economy and Society Seminar, LSE Department of Information Systems, 14 June 2002
Mobile Robots Navigation in Indoor Environments Using Kinect Sensor

This paper presents the development of a perception system for indoor environments to allow autonomous navigation for surveillance mobile robots. The system is composed by two parts. The first part is a reactive navigation system in which a mobile robot moves avoiding obstacles in environment, using the distance sensor Kinect. The second part of this system uses a artificial neural network (ANN) to recognize different configurations of the environment, for example, path ahead, left path, right path and intersections. The ANN is trained using data captured by the Kinect sensor in indoor environments. This way, the robot becomes able to perform a topological navigation combining internal reactive behavior to avoid obstacles and the ANN to locate the robot in the environment, in a deliberative behavior. The topological map is represented by a graph which represents the configuration of the environment, where the hallways (path ahead) are the edges and locations (left path and intersection, for example) are the vertices. The system also works in the dark, which is a great advantage for surveillance systems. The experiments were performed with a Pioneer P3-AT robot equipped with a Kinect sensor in order to validate and evaluate this approach. The proposed method demonstrated to be a promising approach to autonomous mobile robots navigation.
Delusion and Deception in Large Infrastructure Projects : TWO MODELS FOR EXPLAINING AND PREVENTING EXECUTIVE DISASTER

T he Economist recently reported that infrastructure spending is the largest it is ever been as a share of world GDP. With $22 trillion in projected investments over the next ten years in emerging economies alone, the magazine calls it the “biggest investment boom in history.” The efficiency of infrastructure planning and execution is therefore particularly important at present. Unfortunately, the private sector, the public sector, and private/public sector partnerships have a dismal record of delivering on large infrastructure cost and performance promises. Consider the following typical examples.
Variable selection using random forests

This paper proposes, focusing on random forests, the increa singly used statistical method for classification and regre ssion problems introduced by Leo Breiman in 2001, to investigate two classi cal issues of variable selection. The first one is to find impor tant variables for interpretation and the second one is more rest rictive and try to design a good prediction model. The main co tribution is twofold: to provide some insights about the behavior of th e variable importance index based on random forests and to pr opose a strategy involving a ranking of explanatory variables usi ng the random forests score of importance and a stepwise asce nding variable introduction strategy.
Face Recognition in Hyperspectral Images

Hyperspectral cameras provide useful discriminants for human face recognition that cannot be obtained by other imaging methods.We examine the utility of using near-infrared hyperspectral images for the recognition of faces over a database of 200 subjects. The hyperspectral images were collected using a CCD camera equipped with a liquid crystal tunable filter to provide 31 bands over the near-infrared (0.7 m-1.0 m). Spectral measurements over the near-infrared allow the sensing of subsurface tissue structure which is significantly different from person to person, but relatively stable over time. The local spectral properties of human tissue are nearly invariant to face orientation and expression which allows hyperspectral discriminants to be used for recognition over a large range of poses and expressions. We describe a face recognition algorithm that exploits spectral measurements for multiple facial tissue types. We demonstrate experimentally that this algorithm can be used to recognize faces over time in the presence of changes in facial pose
A Hybrid Knowledge-Guided Detection Technique for Screening of Infectious Pulmonary Tuberculosis From Chest Radiographs

Tuberculosis (TB) is a deadly infectious disease and the presence of cavities in the upper lung zones is a strong indicator that the disease has developed into a highly infectious state. Currently, the detection of TB cavities is mainly conducted by clinicians observing chest radiographs. Diagnoses performed by radiologists are labor intensive and very often there is insufficient health care personnel available, especially in remote communities. After assessing existing approaches, we propose an automated segmentation technique, which takes a hybrid knowledge-based Bayesian classification approach to detect TB cavities automatically. We apply gradient inverse coefficient of variation and circularity measures to classify detected features and confirm true TB cavities. By comparing with nonhybrid approaches and the classical active contour techniques for feature extraction in medical images, experimental results demonstrate that our approach achieves high accuracy with a low false positive rate in detecting TB cavities.
ACCNet: Actor-Coordinator-Critic Net for "Learning-to-Communicate" with Deep Multi-agent Reinforcement Learning

Communication is a critical factor for the big multi-agent world to stay organized and productive. Typically, most previous multi-agent “learning-to-communicate” studies try to predefine the communication protocols or use technologies such as tabular reinforcement learning and evolutionary algorithm, which cannot generalize to the changing environment or large collection of agents directly. In this paper, we propose an Actor-Coordinator-Critic Net (ACCNet) framework for solving multi-agent “learning-to-communicate” problem. The ACCNet naturally combines the powerful actor-critic reinforcement learning technology with deep learning technology. It can learn the communication protocols even from scratch under partially observable environments. We demonstrate that the ACCNet can achieve better results than several baselines under both continuous and discrete action space environments. We also analyse the learned protocols and discuss some design considerations.
Abstractive text summarization using LSTM-CNN based deep learning

Text Summarization (ATS), which is the task of constructing summary sentences by merging facts from different source sentences and condensing them into a shorter representation while preserving information content and overall meaning. It is very difficult and time consuming for human beings to manually summarize large documents of text. In this paper, we propose an LSTM-CNN based ATS framework (ATSDL) that can construct new sentences by exploring more fine-grained fragments than sentences, namely, semantic phrases. Different from existing abstraction based approaches, ATSDL is composed of two main stages, the first of which extracts phrases from source sentences and the second generates text summaries using deep learning. Experimental results on the datasets CNN and DailyMail show that our ATSDL framework outperforms the state-of-the-art models in terms of both semantics and syntactic structure, and achieves competitive results on manual linguistic quality evaluation.
Unethical computer using behavior scale: A study of reliability and validity on Turkish university students

This study was carried out in a Turkish university with 216 undergraduate students of computer technology as respondents. The study aimed to develop a scale (UECUBS) to determine the unethical computer use behavior. A factor analysis of the related items revealed that the factors were can be divided under five headings; intellectual property, social impact, safety and quality, net integrity and information integrity.
ICFHR2014 Competition on Handwritten Document Image Binarization (H-DIBCO 2014)

Document image binarization is an important step in the document image analysis and recognition pipeline. H-DIBCO 2014 is the International Document Image Binarization Competition which is dedicated to handwritten document images organized in conjunction with ICFHR 2014 conference. The objective of the contest is to identify current advances in handwritten document image binarization using meaningful evaluation performance measures. This paper reports on the contest details including the evaluation measures used as well as the performance of the 7 submitted methods along with a short description of each method.
Classifying Semantic Relations in Bioscience Texts

A crucial step toward the goal of automatic extraction of propositionalinformationfrom naturallanguagetext is the identificationof semanticrelations betweenconstituentsin sentences.We examinethe problemof distinguishing amongsevenrelationtypesthatcanoccurbetweentheentities“treatment”and “disease” in biosciencetext, and the problemof identifyingsuchentities.We comparefive generati ve graphicalmodels anda neuralnetwork, usinglexical, syntactic,andsemanticfeatures,finding that thelatterhelpachieve high classificationaccuracy.
From gender identity disorder to gender identity creativity: true gender self child therapy.

True gender self child therapy is based on the premise of gender as a web that weaves together nature, nurture, and culture and allows for a myriad of healthy gender outcomes. This article presents concepts of true gender self, false gender self, and gender creativity as they operationalize in clinical work with children who need therapeutic supports to establish an authentic gender self while developing strategies for negotiating an environment resistant to that self. Categories of gender nonconforming children are outlined and excerpts of a treatment of a young transgender child are presented to illustrate true gender self child therapy.
Artificial Intelligence Safety and Cybersecurity: a Timeline of AI Failures

In this work, we present and analyze reported failures of artificially intelligent systems and extrapolate our analysis to future AIs. We suggest that both the frequency and the seriousness of future AI failures will steadily increase. AI Safety can be improved based on ideas developed by cybersecurity experts. For narrow AIs safety failures are at the same, moderate, level of criticality as in cybersecurity, however for general AI, failures have a fundamentally different impact. A single failure of a superintelligent system may cause a catastrophic event without a chance for recovery. The goal of cybersecurity is to reduce the number of successful attacks on the system; the goal of AI Safety is to make sure zero attacks succeed in bypassing the safety mechanisms. Unfortunately, such a level of performance is unachievable. Every security system will eventually fail; there is no such thing as a 100% secure system.
imMens: Real-time Visual Querying of Big Data

Data analysts must make sense of increasingly large data sets, sometimes with billions or more records. We present methods for interactive visualization of big data, following the principle that perceptual and interactive scalability should be limited by the chosen resolution of the visualized data, not the number of records. We first describe a design space of scalable visual summaries that use data reduction methods (such as binned aggregation or sampling) to visualize a variety of data types. We then contribute methods for interactive querying (e.g., brushing & linking) among binned plots through a combination of multivariate data tiles and parallel query processing. We implement our techniques in imMens, a browser-based visual analysis system that uses WebGL for data processing and rendering on the GPU. In benchmarks imMens sustains 50 frames-per-second brushing & linking among dozens of visualizations, with invariant performance on data sizes ranging from thousands to billions of records.
Compact wideband 3dB branch line coupler with multiple symmetric PI section

This paper presents the design of a miniaturized 3dB branch line coupler (BLC) for wideband applications. Multiple symmetric PI sections are used in this design to replace the quarter-wavelength transmission lines of the conventional coupler. The design formulas are derived and verified with the simulated and measured results. The circuit area of the proposed BLC is only 20.3% of the conventional one. The fractional bandwidth is greater than 75% at the center frequency of 0.9 GHz.