nils-herrmann/clean_scirepeval huggingface dataset:
Polarization and Fake News: Early Warning of Potential Misinformation Targets

Users polarization and confirmation bias play a key role in misinformation spreading on online social media. Our aim is to use this information to determine in advance potential targets for hoaxes and fake news. In this paper, we introduce a general framework for promptly identifying polarizing content on social media and, thus, “predicting” future fake news topics. We validate the performances of the proposed methodology on a massive Italian Facebook dataset, showing that we are able to identify topics that are susceptible to misinformation with 77% accuracy. Moreover, such information may be embedded as a new feature in an additional classifier able to recognize fake news with 91% accuracy. The novelty of our approach consists in taking into account a series of characteristics related to users behavior on online social media, making a first, important step towards the smoothing of polarization and the mitigation of misinformation phenomena.
The Role of Mental Health Professionals in Gender Reassignment Surgeries: Unjust Discrimination or Responsible Care?

Recent literature has raised an important ethical concern relating to the way in which surgeons approach people with gender dysphoria (GD): it has been suggested that referring transsexual patients to mental assessment can constitute a form of unjust discrimination. The aim of this paper is to examine some of the ethical issues concerning the role of the mental health professional in gender reassignment surgeries (GRS). The role of the mental health professional in GRS is analyzed by presenting the Standards of Care by the World Professional Association of Transgender Health, and discussing the principles of autonomy and non-discrimination. Purposes of psychotherapy are exploring gender identity; addressing the negative impact of GD on mental health; alleviating internalized transphobia; enhancing social and peer support; improving body image; promoting resilience; and assisting the surgeons with the preparation prior to the surgery and the patient’s follow-up. Offering or requesting psychological assistance is in no way a form of negative discrimination or an attack to the patient’s autonomy. Contrarily, it might improve transsexual patients’ care, and thus at the most may represent a form of positive discrimination. To treat people as equal does not mean that they should be treated in the same way, but with the same concern and respect, so that their unique needs and goals can be achieved. Offering or requesting psychological assistance to individuals with GD is a form of responsible care, and not unjust discrimination. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .
Coping with compassion fatigue.

Helping others who have undergone a trauma from a natural disaster, accident, or sudden act of violence, can be highly satisfying work. But helping trauma victims can take a toll on even the most seasoned mental health professional. Ongoing exposure to the suffering of those you are helping can bring on a range of signs and symptoms -including anxiety, sleeplessness, irritability, and feelings of helplessness -that can interfere, sometimes significantly, with everyday life and work. In clinicians, including therapists, counselors, and social workers, this response is often referred to as “compassion fatigue” or “secondary post-traumatic stress.”
Analytical Design Method of Multiway Dual-Band Planar Power Dividers With Arbitrary Power Division

In this paper, a novel closed-form design method of generalized Wilkinson power dividers is proposed. By using this method, the power divider could be designed to be arbitrary-way (N-way) with arbitrary power division, and arbitrary dual-band operations in a pure planar structure. A previous dual-band unequal Wilkinson power divider is extended to arbitrary terminal impedances case, thus, it can be used to construct multiway planar power dividers through the combination of the two-section dual-frequency transformers. To obtain three-way (or any odd-way) power dividers with dual-band and unequal power division features, a new developed recombinant structure is employed. This recombinant structure consists of a two-way dual-band unequal power divider/combiner without any isolation structures. Furthermore, the complete design procedures and analytical equations of these proposed generalized power dividers are presented. To verify our proposed design approach in theory, several three-way and four-way power dividers with different dual-band applications and various power divisions are designed and simulated. Finally, a practical three-way power divider operating at both 0.6 and 2.45 GHz with a power dividing ratio of 3:5:1 is fabricated in microstrip technology as a typical example. The measured results of the fabricated power divider verify our proposed idea.
OpenFace: A general-purpose face recognition library with mobile applications

Cameras are becoming ubiquitous in the Internet of Things (IoT) and can use face recognition technology to improve context. There is a large accuracy gap between today’s publicly available face recognition systems and the state-of-the-art private face recognition systems. This paper presents our OpenFace face recognition library that bridges this accuracy gap. We show that OpenFace provides near-human accuracy on the LFW benchmark and present a new classification benchmark for mobile scenarios.  We released OpenFace in October 2015 as an open source library under the Apache 2.0 license. It is available at: http://cmusatyalab.github.io/openface/ This research was supported by the National Science Foundation (NSF) under grant number CNS-1518865. Additional support was provided by Crown Castle, the Conklin Kistler family fund, Google, the Intel Corporation, and Vodafone. NVIDIA’s academic hardware grant provided the Tesla K40 GPU used in all of our experiments. Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and should not be attributed to their employers or funding sources.
Kinematic analysis of electroactive polymer actuators as soft and smart structures with more DoF than inputs

Electroactive polymer (EAP) actuators have been attracting the attention of researchers due to their muscle-like behaviour and unusual properties. Several modelling methods have been proposed to understand their mechanical, chemical, electrical behaviours or `electro-chemo-mechanical' behaviour. However, estimating the whole shape or configuration of the EAP actuators has always been challenging due to their highly non-linear bending behaviour. This paper reports on an effective method to estimate the whole shape deflection of the EAP actuators by employing a so-called backbone approach. Tri-layer configured polypyrrole (PPy) based EAP actuators were used as a soft and smart structure with more degrees of freedom than its input. After deriving the inverse kinematic model of the actuator, its complete shape is estimated by solving the inverse kinematic model with an angle optimization (AngleOPT) method. The experimental results and numerical results have demonstrated the effectiveness of the method in estimating the highly non-linear bending behaviour of the PPy actuators and applicability of this modelling approach to other EAP actuators.
The art of UHF RFID antenna design: impedance-matching and size-reduction techniques

Radio-frequency identification technology, based on the reader/tag paradigm, is quickly permeating several aspects of everyday life. The electromagnetic research mainly concerns the design of tag antennas having high efficiency and small size, and suited to complex impedance matching to the embedded electronics. Starting from the available but fragmented open literature, this paper presents a homogeneous survey of relevant methodologies for the design of UHF passive tag antennas. Particular care is taken to illustrate, within a common framework, the basic concepts of the most-used design layouts. The design techniques are illustrated by means of many noncommercial examples.
FROM SIMPLE ASSOCIATIONS TO SYSTEMATIC REASONING : A Connectionist representation of rules , variables , and dynamic bindings using temporal synchrony

Human agents draw a variety of inferences effortlessly, spontaneously, and with remarkable efficiency — as though these inferences are a reflex response of their cognitive apparatus. Furthermore, these inferences are drawn with reference to a large body of background knowledge. This remarkable human ability seems paradoxical given the results about the complexity of reasoning reported by researchers in artificial intelligence. It also poses a challenge for cognitive science and computational neuroscience: How can a system of simple and slow neuron-like elements represent a large body of systematic knowledge and perform a range of inferences with such speed? We describe a computational model that is a step toward addressing the cognitive science challenge and resolving the artificial intelligence paradox. We show how a connectionist network can encode millions of facts and rules involving n-ary predicates and variables, and perform a class of inferences in a few hundred msec. Efficient reasoning requires the rapid representation and propagation of dynamic bindings. Our model achieves this by i) representing dynamic bindings as the synchronous firing of appropriate nodes, ii) rules as interconnection patterns that direct the propagation of rhythmic activity, iii) and long-term facts as temporal pattern-matching sub-networks. The model is consistent with recent neurophysiological findings which suggest that synchronous activity occurs in the brain and may play a representational role in neural information processing. The model also makes specific predictions about the nature of reflexive reasoning that are psychologically significant. It identifies constraints on the form of rules that may participate in such reasoning and relates the capacity of the working memory underlying reflexive reasoning to biological parameters such as the lowest frequency at which nodes can sustain synchronous oscillations and the coarseness of synchronization.
Single Image 3D Interpreter Network

Understanding 3D object structure from a single image is an important but difficult task in computer vision, mostly due to the lack of 3D object annotations in real images. Previous work tackles this problem by either solving an optimization task given 2D keypoint positions, or training on synthetic data with ground truth 3D information. In this work, we propose 3D INterpreter Network (3D-INN), an endto-end framework which sequentially estimates 2D keypoint heatmaps and 3D object structure, trained on both real 2D-annotated images and synthetic 3D data. This is made possible mainly by two technical innovations. First, we propose a Projection Layer, which projects estimated 3D structure to 2D space, so that 3D-INN can be trained to predict 3D structural parameters supervised by 2D annotations on real images. Second, heatmaps of keypoints serve as an intermediate representation connecting real and synthetic data, enabling 3D-INN to benefit from the variation and abundance of synthetic 3D objects, without suffering from the difference between the statistics of real and synthesized images due to imperfect rendering. The network achieves state-of-the-art performance on both 2D keypoint estimation and 3D structure recovery. We also show that the recovered 3D information can be used in other vision applications, such as 3D rendering and image retrieval.
Line Graphs of Weighted Networks for Overlapping Communities

In this paper, we develop the idea to partition the edges of a weighted graph in order to uncover overlapping communities of its nodes. Our approach is based on the construction of different types of weighted line graphs, i.e. graphs whose nodes are the links of the original graph, that encapsulate differently the relations between the edges. Weighted line graphs are argued to provide an alternative, valuable representation of the system’s topology, and are shown to have important applications in community detection, as the usual node partition of a line graph naturally leads to an edge partition of the original graph. This identification allows us to use traditional partitioning methods in order to address the longstanding problem of the detection of overlapping communities. We apply it to the analysis of different social and geographical networks.
DSCarver: decompose-and-spiral-carve for subtractive manufacturing

We present an automatic algorithm for subtractive manufacturing of freeform 3D objects using high-speed machining (HSM) via CNC. A CNC machine operates a cylindrical cutter to carve off material from a 3D shape stock, following a tool path, to "expose" the target object. Our method decomposes the input object's surface into a small number of patches each of which is fully accessible and machinable by the CNC machine, in continuous fashion, under a fixed cutter-object setup configuration. This is achieved by covering the input surface with a minimum number of accessible regions and then extracting a set of machinable patches from each accessible region. For each patch obtained, we compute a continuous, space-filling, and iso-scallop tool path which conforms to the patch boundary, enabling efficient carving with high-quality surface finishing. The tool path is generated in the form of connected Fermat spirals, which have been generalized from a 2D fill pattern for layered manufacturing to work for curved surfaces. Furthermore, we develop a novel method to control the spacing of Fermat spirals based on directional surface curvature and adapt the heat method to obtain iso-scallop carving. We demonstrate automatic generation of accessible and machinable surface decompositions and iso-scallop Fermat spiral carving paths for freeform 3D objects. Comparisons are made to tool paths generated by commercial software in terms of real machining time and surface quality.
AN EFFICIENT IMPLEMENTATION OF APRIORI ALGORITHM BASED ON HADOOP-MAPREDUCE MODEL

Finding frequent itemsets is one of the most important fields of data mining. Apriori algorithm is the most established algorithm for finding frequent itemsets from a transactional dataset; however, it needs to scan the dataset many times and to generate many candidate itemsets. Unfortunately, when the dataset size is huge, both memory use and computational cost can still be very expensive. In addition, single processor’s memory and CPU resources are very limited, which make the algorithm performance inefficient. Parallel and distributed computing are effective strategies for accelerating algorithms performance. In this paper, we have implemented an efficient MapReduce Apriori algorithm (MRApriori) based on HadoopMapReduce model which needs only two phases (MapReduce Jobs) to find all frequent k-itemsets, and compared our proposed MRApriori algorithm with current two existed algorithms which need either one or k phases (k is maximum length of frequent itemsets) to find the same frequent k-itemsets. Experimental results showed that the proposed MRApriori algorithm outperforms the other two algorithms.
Risk Assessment Uncertainties in Cybersecurity Investments

When undertaking cybersecurity risk assessments, it is important to be able to assign numeric values to metrics to compute the final expected loss that represents the risk that an organization is exposed to due to cyber threats. Even if risk assessment is motivated by real-world observations and data, there is always a high chance of assigning inaccurate values due to different uncertainties involved (e.g., evolving threat landscape, human errors) and the natural difficulty of quantifying risk. Existing models empower organizations to compute optimal cybersecurity strategies given their financial constraints, i.e., available cybersecurity budget. Further, a general game-theoretic model with uncertain payoffs (probability-distribution-valued payoffs) shows that such uncertainty can be incorporated in the game-theoretic model by allowing payoffs to be random. This paper extends previous work in the field to tackle uncertainties in risk assessment that affect cybersecurity investments. The findings from simulated examples indicate that although uncertainties in cybersecurity risk assessment lead, on average, to different cybersecurity strategies, they do not play a significant role in the final expected loss of the organization when utilising a game-theoretic model and methodology to derive these strategies. The model determines robust defending strategies even when knowledge regarding risk assessment values is not accurate. As a result, it is possible to show that the cybersecurity investments’ tool is capable of providing effective decision support.
Increasing fruit and vegetable intake and decreasing fat and sugar intake in families at risk for childhood obesity.

The goal of this study was to evaluate the effect of a parent-focused behavioral intervention on parent and child eating changes and on percentage of overweight changes in families that contain at least one obese parent and a non-obese child.



Families with obese parents and non-obese children were randomized to groups in which parents were provided a comprehensive behavioral weight-control program and were encouraged to increase fruit and vegetable intake or decrease intake of high-fat/high-sugar foods. Child materials targeted the same dietary changes as their parents without caloric restriction.



Changes over 1 year showed that treatment influenced targeted parent and child fruit and vegetable intake and high-fat/high-sugar intake, with the Increase Fruit and Vegetable group also decreasing their consumption of high-fat/high-sugar foods. Parents in the increased fruit and vegetable group showed significantly greater decreases in percentage of overweight than parents in the decreased high-fat/high-sugar group.



These results suggest that focusing on increasing intake of healthy foods may be a useful approach for nutritional change in obese parents and their children.
Distributed Hessian-Free Optimization for Deep Neural Network

Training deep neural network is a high dimensional and a highly non-convex optimization problem. In this paper, we revisit Hessian-free optimization method for deep networks with negative curvature direction detection. We also develop its distributed variant and demonstrate superior scaling potential to SGD, which allows more efficiently utilizing larger computing resources thus enabling large models and faster time to obtain desired solution. We show that these techniques accelerate the training process for both the standard MNIST dataset and also the TIMIT speech recognition problem, demonstrating robust performance with upto an order of magnitude larger batch sizes. This increased scaling potential is illustrated with near l inear speed-up on upto 32 CPU nodes for a simple 4-layer network.
X-Ray PoseNet: 6 DoF Pose Estimation for Mobile X-Ray Devices

Precise reconstruction of 3D volumes from X-ray projections requires precisely pre-calibrated systems where accurate knowledge of the systems geometric parameters is known ahead. However, when dealing with mobile X-ray devices such calibration parameters are unknown. Joint estimation of the systems calibration parameters and 3d reconstruction is a heavily unconstrained problem, especially when the projections are arbitrary. In industrial applications, that we target here, nominal CAD models of the object to be reconstructed are usually available. We rely on this prior information and employ Deep Learning to learn the mapping between simulated X-ray projections and its pose. Moreover, we introduce the reconstruction loss in addition to the pose loss to further improve the reconstruction quality. Finally, we demonstrate the generalization capabilities of our method in case where poses can be learned on instances of the objects belonging to the same class, allowing pose estimation of unseen objects from the same category, thus eliminating the need for the actual CAD model. We performed exhaustive evaluation demonstrating the quality of our results on both synthetic and real data.
A Novel Overactuated Quadrotor Unmanned Aerial Vehicle: Modeling, Control, and Experimental Validation

Standard quadrotor unmanned aerial vehicles (UAVs) possess a limited mobility because of their inherent underactuation, that is, availability of four independent control inputs (the four propeller spinning velocities) versus the 6 degrees of freedom parameterizing the quadrotor position/orientation in space. Thus, the quadrotor pose cannot track arbitrary trajectories in space (e.g., it can hover on the spot only when horizontal). Because UAVs are more and more employed as service robots for interaction with the environment, this loss of mobility due to their underactuation can constitute a limiting factor. In this paper, we present a novel design for a quadrotor UAV with tilting propellers which is able to overcome these limitations. Indeed, the additional set of four control inputs actuating the propeller tilting angles is shown to yield full actuation to the quadrotor position/orientation in space, thus allowing it to behave as a fully actuated flying vehicle. We then develop a comprehensive modeling and control framework for the proposed quadrotor, and subsequently illustrate the hardware and software specifications of an experimental prototype. Finally, the results of several simulations and real experiments are reported to illustrate the capabilities of the proposed novel UAV design.
A Geometric Approach to Active Learning for Convolutional Neural Networks

Convolutional neural networks (CNNs) have been successfully applied to many recognition and learning tasks using a universal recipe; training a deep model on a very large dataset of supervised examples. However, this approach is rather restrictive in practice since collecting a large set of labeled images is very expensive. One way to ease this problem is coming up with smart ways for choosing images to be labelled from a very large collection (i.e. active learning) In this paper, we first show that existing active learning heuristics are not effective for CNNs even in an oracle setting. Our counterintuitive empirical results make us question these heuristics and inspire us to come up with a simple but effective method, choosing a set of images to label such that they cover the set of unlabeled images as closely as possible. We further present a theoretical justification for this geometric heuristic by giving a bound over the generalization error of CNNs. Our experiments show that the proposed method significantly outperforms existing approaches in image classification experiments by a large margin.
Lean design and analysis of a milk-run delivery system: Case study

Multiple discrete event simulation models are developed to represent a milk-run delivery system in an automobile emissions system production facility as part of a logistics system overhaul. The aim of this study is to analyze resupply configurations and variability in key model inputs in order to make recommendations based on supply train utilization and workstation starvation. This study includes three experiments that compare optimized routing, recommended routing, and on-demand resupply systems. Sensitivity analyses are conducted to measure the effects of various factors such as number of supply trains, travel speeds, and load and unload times to find the best combination of input parameters. The results of the proposed simulation models demonstrated potential impacts of a milk-run delivery framework on pull systems with limited transport capabilities, but diminished improvements on systems with multiple supply trains.