nils-herrmann/clean_scirepeval huggingface dataset:
The PHQ-9: A New Depression Diagnostic and Severity Measure

and treatable mental disorders presenting in general medical as well as specialty settings. There are a number of case-finding instruments for detecting depression in primary care, ranging from 2 to 28 items.1,2 Typically these can be scored as continuous measures of depression severity and also have established cutpoints above which the probability of major depression is substantially increased. Scores on these various measures tend to be highly correlated3, with little evidence that one measure is superior to any other.1,2,4
Development of Wireless In-Wheel Motor Using Magnetic Resonance Coupling

In-wheel motors (IWMs) in electric vehicles are particularly important for motion control. A conventional IWM is powered from a battery aboard the vehicle via cables. Since power cables and signal cables of an IWM are exposed to harsh environments, they can possibly become disconnected by high acceleration or vibration. In order to overcome this problem, the wireless-in wheel motor (W-IWM) has been proposed. The risk of disconnection would disappear if the cables of the IWM are removed. One way to implement wireless power transfer is by utilizing the magnetic resonance coupling method. However, motion of the W-IWM, and thus, a misalignment between the wheel and the vehicle, leads to variations in the secondary-side voltage provided. To account for this, this paper discusses two new control methods. One proposed method maintains the secondary voltage using a hysteresis comparator. The other proposed method estimates the secondary inverter output power, applying it to a feedforward controller in order to keep the secondary dc-link voltage constant. Experimental results show that these methods can drive a W-IWM effectively with high efficiency.
Deep neural networks for acoustic emotion recognition: Raising the benchmarks

Deep Neural Networks (DNNs) denote multilayer artificial neural networks with more than one hidden layer and millions of free parameters. We propose a Generalized Discriminant Analysis (GerDA) based on DNNs to learn discriminative features of low dimension optimized with respect to a fast classification from a large set of acoustic features for emotion recognition. On nine frequently used emotional speech corpora, we compare the performance of GerDA features and their subsequent linear classification with previously reported benchmarks obtained using the same set of acoustic features classified by Support Vector Machines (SVMs). Our results impressively show that low-dimensional GerDA features capture hidden information from the acoustic features leading to a significantly raised unweighted average recall and considerably raised weighted average recall.
Simulation of Static and Dynamic Wrinkles of Skin

Wrinkles are an extremely important contribution for enhancing the realism of human figure models. In this paper, we present an approach to generate static and dynamic wrinkles on human skin. For the static model, we consider micro and macro structures of the skin surface geometry. For the wrinkle dynamics, an approach using a biomechanical skin model is employed. The tile texture patterns in the micro structure of skin surface are created using planar Delaunay triangulation. Functions of barycentric coordinates are applied to simulate the curved ridges. The visible (macro) flexure lines which may form wrinkles are predefined edges on the micro structure. These lines act as constraints for the hierarchical triangulation process. Furtheremore, the dynamics of expressive wrinkles --controlling their depth and fold-is modeled according to the principal strain of the deformed skin surface. Bump texture mapping is used for skin rendering.
IPA: A New Class of Power Attacks

We present Inferential Power Analysis (IPA), a new class of attacks based on power analysis. An IPA attack has two stages: a profiling stage and a key extraction stage. In the profiling stage, intratrace differencing, averaging, and other statistical operations are performed on a large number of power traces to learn details of the implementation, leading to the location and identification of key bits. In the key extraction stage, the key is obtained from a very few power traces; we have successfully extracted keys from a single trace. Compared to differential power analysis, IPA has the advantages that the attacker does not need either plaintext or ciphertext, and that, in the key extraction stage, a key can be obtained from a small number of traces.
No Bot Expects the DeepCAPTCHA! Introducing Immutable Adversarial Examples, With Applications to CAPTCHA Generation

Recent advances in deep learning (DL) allow for solving complex AI problems that used to be considered very hard. While this progress has advanced many fields, it is considered to be bad news for Completely Automated Public Turing tests to tell Computers and Humans Apart (CAPTCHAs), the security of which rests on the hardness of some learning problems. In this paper, we introduce DeepCAPTCHA, a new and secure CAPTCHA scheme based on adversarial examples, an inherit limitation of the current DL networks. These adversarial examples are constructed inputs, either synthesized from scratch or computed by adding a small and specific perturbation called adversarial noise to correctly classified items, causing the targeted DL network to misclassify them. We show that plain adversarial noise is insufficient to achieve secure CAPTCHA schemes, which leads us to introduce immutable adversarial noise—an adversarial noise that is resistant to removal attempts. In this paper, we implement a proof of concept system, and its analysis shows that the scheme offers high security and good usability compared with the best previously existing CAPTCHAs.
Shadow detection for moving humans using gradient-based background subtraction

Cast shadows cause serious problems in the functionality of vision-based applications, such as video surveillance, traffic monitoring and various other applications. Accurate detection and removal of cast shadows is a challenging task. Common shadow detection techniques normally use color information, which is not a reliable base in every scenario. This paper presents a novel scheme for real time detection of cast shadows using contour like structures of objects, which are obtained by gradient-based background subtraction. The scheme does not use any color information. Two basic rules are followed for shadow detection. The first rule is that shadows do not change the texture of the background. The second rule is a cast shadow lies outside the boundary of an object and has a relatively small common boundary with the object. Experimental results show the performance of the proposed scheme. Objective evaluation shows that the algorithm classifies 90 percent of the pixels of the objects and their shadow correctly.
Ultra-Wideband Bandpass Filter With Improved Upper Stopband Performance Using Defected Ground Structure

A novel ultra-wideband (UWB) bandpass filter (BPF) with improved upper stopband performance using a defected ground structure (DGS) is presented in this letter. The proposed BPF is composed of seven DGSs that are positioned under the input and output microstrip line and coupled double step impedance resonator (CDSIR). By using CDSIR and open loop defected ground structure (OLDGS), we can achieve UWB BPF characteristics, and by using the conventional CDGSs under the input and output microstrip line, we can improve the upper stopband performance. Simulated and measured results are found in good agreement with each other, showing a wide passband from 3.4 to 10.9 GHz, minimum insertion loss of 0.61 dB at 7.02 GHz, a group delay variation of less than 0.4 ns in the operating band, and a wide upper stopband with more than 30 dB attenuation up to 20 GHz. In addition, the proposed UWB BPF has a compact size (0.27¿g ~ 0.29¿g , ¿g : guided wavelength at the central frequency of 6.85 GHz).
A Classification-Based Perspective on GAN Distributions

A fundamental, and still largely unanswered, question in the context of Generative Adversarial Networks (GANs) is whether GANs are actually able to capture the key characteristics of the datasets they are trained on. The current approaches to examining this issue require significant human supervision, such as visual inspection of sampled images, and often offer only fairly limited scalability. In this paper, we propose new techniques that employ a classification–based perspective to evaluate synthetic GAN distributions and their capability to accurately reflect the essential properties of the training data. These techniques require only minimal human supervision and can easily be scaled and adapted to evaluate a variety of state-of-the-art GANs on large, popular datasets. Our analysis indicates that GANs have significant problems in reproducing the more distributional properties of the training dataset. In particular, when seen through the lens of classification, the diversity of GAN data is orders of magnitude less than that of the original data.
Enhancing Light Blob Detection for Intelligent Headlight Control Using Lane Detection

In this paper, we propose an enhanced method for detecting light blobs (LBs) for intelligent headlight control (IHC). The main function of the IHC system is to automatically convert high-beam headlights to low beam when vehicles are found in the vicinity. Thus, to implement the IHC, it is necessary to detect preceding or oncoming vehicles. Generally, this process of detecting vehicles is done by detecting LBs in the images. Previous works regarding LB detection can largely be categorized into two approaches by the image type they use: low-exposure (LE) images or autoexposure (AE) images. While they each have their own strengths and weaknesses, the proposed method combines them by integrating the use of the partial region of the AE image confined by the lane detection information and the LE image. Consequently, the proposed method detects headlights at various distances and taillights at close distances using LE images while handling taillights at distant locations by exploiting the confined AE images. This approach enhances the performance of detecting the distant LBs while maintaining low false detections.
Mixed Pooling for Convolutional Neural Networks

Convolutional Neural Network (CNN) is a biologically inspired trainable architecture that can learn invariant features for a number of applications. In general, CNNs consist of alternating convolutional layers, non-linearity layers and feature pooling layers. In this work, a novel feature pooling method, named as mixed pooling, is proposed to regularize CNNs, which replaces the deterministic pooling operations with a stochastic procedure by randomly using the conventional max pooling and average pooling methods. The advantage of the proposed mixed pooling method lies in its wonderful ability to address the overfitting problem encountered by CNN generation. Experimental results on three benchmark image classification datasets demonstrate that the proposed mixed pooling method is superior to max pooling, average pooling and some other state-of-the-art works known in the literature.
BrainNet: A Multi-Person Brain-to-Brain Interface for Direct Collaboration Between Brains

We present BrainNet which, to our knowledge, is the first multi-person non-invasive direct brain-to-brain interface for collaborative problem solving. The interface combines electroencephalography (EEG) to record brain signals and transcranial magnetic stimulation (TMS) to deliver information noninvasively to the brain. The interface allows three human subjects to collaborate and solve a task using direct brain-to-brain communication. Two of the three subjects are designated as “Senders” whose brain signals are decoded using real-time EEG data analysis. The decoding process extracts each Sender’s decision about whether to rotate a block in a Tetris-like game before it is dropped to fill a line. The Senders’ decisions are transmitted via the Internet to the brain of a third subject, the “Receiver,” who cannot see the game screen. The Senders’ decisions are delivered to the Receiver’s brain via magnetic stimulation of the occipital cortex. The Receiver integrates the information received from the two Senders and makes a decision using an EEG interface about either turning the block or keeping it in the same position. A second round of the game provides an additional chance for the Senders to evaluate the Receiver’s decision and send feedback to the Receiver’s brain, and for the Receiver to rectify a possible incorrect decision made in the first round. We evaluated the performance of BrainNet in terms of (1) Group-level performance during the game; (2) True/False positive rates of subjects’ decisions; (3) Mutual information between subjects. Five groups, each with three human subjects, successfully used BrainNet to perform the Tetris task, with an average accuracy of 81.25%. Furthermore, by varying the information reliability of the Senders by artificially injecting noise into one Sender’s signal, we investigated how the Receiver learns to integrate noisy signals in order to make a correct decision. We found that Receivers are able to learn which Sender is more reliable based solely on the information transmitted to their brains. Our results raise the possibility of future brain-to-brain interfaces that enable cooperative problem solving by humans using a “social network” of connected brains.
A Review of SCADA Anomaly Detection Systems

The security of critical infrastructures is decreasing due to the apparition of new cyber threats against Supervisory Control and Data Acquisition (SCADA) systems. The evolution they have experienced; the use of standard hardware and software components or the increase of interconnected devices in order to reduce costs and improve efficiency, have contributed to this. This work reviews the research effort done towards the development of anomaly detection for these specific systems. SCADA systems have a number of peculiarities that make anomaly detection perform better than in traditional information and communications technology (ICT) networks. SCADA communications are deterministic, and their operation model is often cyclical. Based on this premise, modeling normal behavior by mining specific features gets feasible.
SNIF: a simple nude image finder

The lack of control of the content published is broadly regarded as a positive aspect of the Web, assuring freedom of speech to its users. On the other hand, there is also a lack of control of the content accessed by users when browsing Web pages. In some situations this lack of control may be undesired. For instance, parents may not desire their children to have access to offensive content available on the Web. In particular, accessing Web pages with nude images is among the most common problem of this sort. One way to tackle this problem is by using automated offensive image detection algorithms which can filter undesired images. Recent approaches on nude image detection use a combination of features based on color, texture, shape and other low level features in order to describe the image content. These features are then used by a classifier which is able to detect offensive images accordingly. In this paper we propose SNIF - simple nude image finder - which uses a color based feature only, extracted by an effective and efficient algorithm for image description, the border/interior pixel classification (BIC), combined with a machine learning technique, namely support vector machines (SVM). SNIF uses a simpler feature model when compared to previously proposed methods, which makes it a fast image classifier. The experiments carried out depict that the proposed method, despite its simplicity, is capable to identify up to 98% of nude images from the test set. This indicates that SNIF is as effective as previously proposed methods for detecting nude images.
Helical antenna for handheld UHF RFID reader

In this paper, we describe a compact high gain circularly polarized antenna for handheld UHF RFID reader. The antenna is an axial mode helix operating in a backfire mode with ground plane on top and reflector on the bottom. The described antenna has small footprint (85 mm in diameter) and delivers maximum linear gain ≫ 6 dBil, standing wave ratio ≪ 2, and axial ratio ≪ 2 dB in 60 MHz band centered around 895 MHz (approximate bandwidth 7%). The antenna can be easily tuned to cover any desired portion of the global UHF RFID band (860–960 MHz). The antenna is an attractive solution for handheld readers to maximize tag read range while providing circular polarization. We also review existing antenna solutions for handheld readers and discuss link budget and forward and reverse link tradeoffs.
BitWhisper: Covert Signaling Channel between Air-Gapped Computers Using Thermal Manipulations

It has been assumed that the physical separation ('air-gap') of computers provides a reliable level of security, such that should two adjacent computers become compromised, the covert exchange of data between them would be impossible. In this paper, we demonstrate BitWhisper, a method of bridging the air-gap between adjacent compromised computers by using their heat emissions and built-in thermal sensors to create a covert communication channel. Our method is unique in two respects: it supports bidirectional communication, and it requires no additional dedicated peripheral hardware. We provide experimental results based on the implementation of the Bit-Whisper prototype, and examine the channel's properties and limitations. Our experiments included different layouts, with computers positioned at varying distances from one another, and several sensor types and CPU configurations (e.g., Virtual Machines). We also discuss signal modulation and communication protocols, showing how BitWhisper can be used for the exchange of data between two computers in a close proximity (positioned 0-40 cm apart) at an effective rate of 1-8 bits per hour, a rate which makes it possible to infiltrate brief commands and exfiltrate small amount of data (e.g., passwords) over the covert channel.
Marcatili E A J. Dielectric rectangular waveguide and directional coupler for integrated optics. Bell Syst. Tech. J. 48:2071-102, 1969

The transmission properties of a guide consisting of a dielectric rod of rectangular cross-section surrounded by dielectrics of smaller refractive indices are determined. This guide is the basic component in a new technology called integrated optical circuitry. The directional coupler, a particularly useful device, made of two of those guides closely spaced is also analyzed.
Large-scale physiological waveform retrieval via locality-sensitive hashing

We propose a fast, scalable locality-sensitive hashing method for the problem of retrieving similar physiological waveform time series. When compared to the naive k-nearest neighbor search, the method vastly speeds up the retrieval time of similar physiological waveforms without sacrificing significant accuracy. Our result shows that we can achieve 95% retrieval accuracy or better with up to an order of magnitude of speed-up. The extra time required in advance to create the optimal data structure is recovered when query quantity equals 15% of the repository, while the method incurs a trivial additional memory cost. We demonstrate the effectiveness of this method on an arterial blood pressure time series dataset extracted from the ICU physiological waveform repository of the MIMIC-II database.
Anti-forensics with a small army of exploits

Anti-forensics (AF) is a multi-headed demon with a range of weapons in its arsenal. Sarah Hilley looks at a set of hell-raising attacks directed at prominent forensic tools. Major forensic programs have started to attract unwanted attention from hackers aka security researchers of a type that have plagued mainstream software developers for years. This report focuses on the development of the Metasploit Anti-Forensic Investigation Arsenal (MAFIA).
Multiple Instance Learning Networks for Fine-Grained Sentiment Analysis

We consider the task of fine-grained sentiment analysis from the perspective of multiple instance learning (MIL). Our neural model is trained on document sentiment labels, and learns to predict the sentiment of text segments, i.e. sentences or elementary discourse units (EDUs), without segment-level supervision. We introduce an attention-based polarity scoring method for identifying positive and negative text snippets and a new dataset which we call SpoT (as shorthand for Segment-level POlariTy annotations) for evaluating MIL-style sentiment models like ours. Experimental results demonstrate superior performance against multiple baselines, whereas a judgement elicitation study shows that EDU-level opinion extraction produces more informative summaries than sentence-based alternatives.
Integrated Task and Interrupt Management for Real-Time Systems

Real-time scheduling algorithms like RMA or EDF and their corresponding schedulability test have proven to be powerful tools for developing predictable real-time systems. However, the traditional interrupt management model presents multiple inconsistencies that break the assumptions of many of the real-time scheduling tests, diminishing its utility. In this article, we analyze these inconsistencies and present a model that resolves them by integrating interrupts and tasks in a single scheduling model. We then use the RMA theory to calculate the cost of the model and analyze the circumstances under which it can provide the most value. This model was implemented in a kernel module. The portability of the design of our module is discussed in terms of its independence from both the hardware and the kernel. We also discuss the implementation issues of the model over conventional PC hardware, along with its cost and novel optimizations for reducing the overhead. Finally, we present our experimental evaluation to show evidence of its temporal determinism and overhead.
Joint Optimization of User-desired Content in Multi-document Summaries by Learning from User Feedback

In this paper, we propose an extractive multi-document summarization (MDS) system using joint optimization and active learning for content selection grounded in user feedback. Our method interactively obtains user feedback to gradually improve the results of a state-of-the-art integer linear programming (ILP) framework for MDS. Our methods complement fully automatic methods in producing highquality summaries with a minimum number of iterations and feedbacks. We conduct multiple simulation-based experiments and analyze the effect of feedbackbased concept selection in the ILP setup in order to maximize the user-desired content in the summary.
DeepMood: Modeling Mobile Phone Typing Dynamics for Mood Detection

The increasing use of electronic forms of communication presents new opportunities in the study of mental health, including the ability to investigate the manifestations of psychiatric diseases unobtrusively and in the setting of patients' daily lives. A pilot study to explore the possible connections between bipolar affective disorder and mobile phone usage was conducted. In this study, participants were provided a mobile phone to use as their primary phone. This phone was loaded with a custom keyboard that collected metadata consisting of keypress entry time and accelerometer movement. Individual character data with the exceptions of the backspace key and space bar were not collected due to privacy concerns. We propose an end-to-end deep architecture based on late fusion, named DeepMood, to model the multi-view metadata for the prediction of mood scores. Experimental results show that 90.31% prediction accuracy on the depression score can be achieved based on session-level mobile phone typing dynamics which is typically less than one minute. It demonstrates the feasibility of using mobile phone metadata to infer mood disturbance and severity.