nils-herrmann/clean_scirepeval huggingface dataset:
Improved feature processing for deep neural networks

In this paper, we investigate alternative ways of processing MFCC-based features to use as the input to Deep Neural Networks (DNNs). Our baseline is a conventional feature pipeline that involves splicing the 13-dimensional front-end MFCCs across 9 frames, followed by applying LDA to reduce the dimension to 40 and then further decorrelation using MLLT. Confirming the results of other groups, we show that speaker adaptation applied on the top of these features using feature-space MLLR is helpful. The fact that the number of parameters of a DNN is not strongly sensitive to the input feature dimension (unlike GMM-based systems) motivated us to investigate ways to increase the dimension of the features. In this paper, we investigate several approaches to derive higher-dimensional features and verify their performance with DNN. Our best result is obtained from splicing our baseline 40-dimensional speaker adapted features again across 9 frames, followed by reducing the dimension to 200 or 300 using another LDA. Our final result is about 3% absolute better than our best GMM system, which is a discriminatively trained model.
An Adaptive Data Replication Algorithm

This article addresses the performance of distributed database systems. Specifically, we present an algorithm for dynamic replication of an object in distributed systems. The algorithm is adaptive in the sence that it changes the replication scheme of the object i.e., the set of processors at which the object inreplicated) as changes occur in the read-write patern of the object (i.e., the number of reads and writes issued by each processor). The algorithm continuously moves the replication scheme towards an optimal one. We show that the algorithm can be combined with the concurrency control and recovery mechanisms of ta distributed database management system. The performance of the algorithm is analyzed theoretically and experimentally. On the way we provide a lower bound on the performance of any dynamic replication algorith.
Smart Light-Ultra High Speed Projector for Spatial Multiplexing Optical Transmission

We present a spatial multiplex optical transmission system named the “Smart Light” , which provides multiple data streams to multiple points simultaneously. This system consists of a projector and some devices along with a photo-detector. The projector projects images with invisible information to the devices, and devices receive some data. In this system, the data stream is expandable to a positionbased audio or video stream by using DMDs (Digital Micro-mirror Device) or LEDs (Light Emitting Diode) with unperceivable space-time modulation. First, in a preliminary experiment, we confirmed with a commercially produced XGA grade projector transmitting a million points that the data rate of its path is a few bits per second. Detached devices can receive relative position data and other properties from the projector. Second, we made an LED type high-speed projector to transmit audio streams using modulated light on an object and confirmed the transmission of positionbased audio stream data.
Software-Defined Mobile Cloud: Architecture, services and use cases

Software-Defined Networking (SDN) is an emerging technology which brings flexibility and programmability to networks and introduces new services and features. However, most SDN architectures have been designed for wired infrastructures, especially in the data center space, and primary trends for wireless and mobile SDN are on the access network and the wireless backhaul. In this paper, we propose several designs for SDN-based Mobile Cloud architectures, focusing on Ad hoc networks. We present the required core components to build SDN-based Mobile Cloud, including variations that are required to accommodate different wireless environments, such as mobility and unreliable wireless link conditions. We also introduce several instances of the proposed architectures based on frequency selection of wireless transmission that are designed around different use cases of SDN-based Mobile Cloud. We demonstrate the feasibility of our architecture by implementing SDN-based routing in the mobile cloud and comparing it with traditional Mobile Ad Hoc Network (MANET) routing. The feasibility of our architecture is shown by achieving high packet delivery ratio with acceptable overhead.
Cannabidiol enhances anandamide signaling and alleviates psychotic symptoms of schizophrenia

Cannabidiol is a component of marijuana that does not activate cannabinoid receptors, but moderately inhibits the degradation of the endocannabinoid anandamide. We previously reported that an elevation of anandamide levels in cerebrospinal fluid inversely correlated to psychotic symptoms. Furthermore, enhanced anandamide signaling let to a lower transition rate from initial prodromal states into frank psychosis as well as postponed transition. In our translational approach, we performed a double-blind, randomized clinical trial of cannabidiol vs amisulpride, a potent antipsychotic, in acute schizophrenia to evaluate the clinical relevance of our initial findings. Either treatment was safe and led to significant clinical improvement, but cannabidiol displayed a markedly superior side-effect profile. Moreover, cannabidiol treatment was accompanied by a significant increase in serum anandamide levels, which was significantly associated with clinical improvement. The results suggest that inhibition of anandamide deactivation may contribute to the antipsychotic effects of cannabidiol potentially representing a completely new mechanism in the treatment of schizophrenia.
Query-by-Example: A Data Base Language

is a high-level data base management language that provides the user with a convenient and unijied interface to query, update, dejine, and control a data base. W h e n the user performs an operation against the data base, he jills in an example of a solution to that operation in skeleton tables that can be associated with actual tables in the data base. The system is currently being used experimentally for various applications. Query-by-E~ample"~ is a high-level data base management language that provides a convenient and unified style to query, update, define, and control a relational data base. The philosophy of Query-by-Example is to require the user to know very little in order to get started and to minimize the number of concepts that he subsequently has to learn in order to understand and use the whole language. The language syntax is simple, yet it covers a wide variety of complex transactions. This is achieved through the use of the same operations for retrieval, manipulation, definition, and control (to the extent possible). The language operations should mimic, as much as possible, manual table manipulation, thus capturing the simplicity, symmetry and neutrality of the relational The formulation of a transaction should capture the user's thought process, thereby providing freedom to formulate a transaction. The system should allow the user to create and drop tables dynamically from the data base; it must also provide the user with a dynamic capability for defining control statements and security features. The architecture of the Query-by-Example language addresses all the requirements just mentioned. The results of various psychological studies of the language7 show that it requires less than three hours of instruction for nonprogrammers to acquire the skill to make fairly complicated queries. Such queries would otherwise require the user to know first order predicate calculus. Other nonprocedural languages that deal with the same topic are
An MIMO Rectangular Dielectric Resonator Antenna for 4G Applications

A multiple-input-multiple-output (MIMO) rectangular dielectric resonator antenna (RDRA) for 2.6-GHz Long Term Evolution (LTE) applications is investigated and presented. Two orthogonal modes of the RDRA are excited by using two different feed mechanisms: coplanar waveguide (CPW) and coaxial probe. The measured impedance bandwidth for port 1 and port 2 is 47% (2.09-3.38 GHz) and 25% (2.40-3.09 GHz), respectively. The measured correlation coefficient is 0.03 with nearly 10 dB diversity gain at frequency 2.6 GHz. The MIMO RDRA gives isolation of above 20 dB over the operating frequency. The gain of 4.97 dBi is obtained for port 1 and 4.51 dBi for port 2 at 2.6 GHz. The S-parameters, isolation, gain, correlation coefficient, and diversity gain of the MIMO RDRA are studied, and reasonable agreement between the measured and simulated results is observed .
65nW CMOS temperature sensor for ultra-low power microsystems

We propose a temperature sensor using a novel process-invariant temperature sensing element and voltage to current converter for battery-operated ultra-low power micro systems. By introducing a new temperature-to-voltage sensing element that outputs only 75mV, the sensor achieves ultra-low power. The sensor was implemented in 180nm CMOS process and uses 0.09mm2 of area. Measurements from test chips show 65nW power consumption, the lowest reported to date, with an inaccuracy of +1.3°C /-1.4°C across 0°C to 100°C after 2-point calibration.
MATLAB based defect detection and classification of printed circuit board

A variety of ways has been established to detect defects found on printed circuit boards (PCB). In previous studies, defects are categories into seven groups with a minimum of one defect and up to a maximum of 4 defects in each group. Using Matlab image processing tools this research separates two of the existing groups containing two defects each into four new groups containing one defect each by processing synthetic images of bare through-hole single layer PCBs.
Presentation attack detection algorithm for face and iris biometrics

Biometric systems are vulnerable to the diverse attacks that emerged as a challenge to assure the reliability in adopting these systems in real-life scenario. In this work, we propose a novel solution to detect a presentation attack based on exploring both statistical and Cepstral features. The proposed Presentation Attack Detection (PAD) algorithm will extract the statistical features that can capture the micro-texture variation using Binarized Statistical Image Features (BSIF) and Cepstral features that can reflect the micro changes in frequency using 2D Cepstrum analysis. We then fuse these features to form a single feature vector before making a decision on whether a capture attempt is a normal presentation or an artefact presentation using linear Support Vector Machine (SVM). Extensive experiments carried out on a publicly available face and iris spoof database show the efficacy of the proposed PAD algorithm with an Average Classification Error Rate (ACER) = 10.21% on face and ACER = 0% on the iris biometrics.
Classification of human emotion from EEG using discrete wavelet transform

In this paper, we summarize the human emotion recognition using different set of electroencephalogram (EEG) channels using discrete wavelet transform. An audio-visual induction based protocol has been designed with more dynamic emotional content for inducing discrete emotions (disgust, happy, surprise, fear and neutral). EEG signals are collected using 64 electrodes from 20 subjects and are placed over the entire scalp using International 10-10 system. The raw EEG signals are preprocessed using Surface Laplacian (SL) filtering method and decomposed into three different frequency bands (alpha, beta and gamma) using Discrete Wavelet Transform (DWT). We have used “db4” wavelet function for deriving a set of conventional and modified energy based features from the EEG signals for classifying emotions. Two simple pattern classification methods, K Nearest Neighbor (KNN) and Linear Discriminant Analysis (LDA) methods are used and their performances are compared for emotional states classification. The experimental results indicate that, one of the proposed features (ALREE) gives the maximum average classification rate of 83.26% using KNN and 75.21% using LDA compared to those of conventional features. Finally, we present the average classification rate and subsets of emotions classification rate of these two different classifiers for justifying the performance of our emotion recognition system.
CATRA: interactive measuring and modeling of cataracts

We introduce an interactive method to assess cataracts in the human eye by crafting an optical solution that measures the perceptual impact of forward scattering on the foveal region. Current solutions rely on highly-trained clinicians to check the back scattering in the crystallin lens and test their predictions on visual acuity tests. Close-range parallax barriers create collimated beams of light to scan through sub-apertures, scattering light as it strikes a cataract. User feedback generates maps for opacity, attenuation, contrast and sub-aperture point-spread functions. The goal is to allow a general audience to operate a portable high-contrast light-field display to gain a meaningful understanding of their own visual conditions. User evaluations and validation with modified camera optics are performed. Compiled data is used to reconstruct the individual's cataract-affected view, offering a novel approach for capturing information for screening, diagnostic, and clinical analysis.
Integrating Wireless Sensor Networks with Cloud Computing

Wireless Sensor Networks (WSN) has been a focus for research for several years. WSN enables novel and attractive solutions for information gathering across the spectrum of endeavour including transportation, business, health-care, industrial automation, and environmental monitoring. Despite these advances, the exponentially increasing data extracted from WSN is not getting adequate use due to the lack of expertise, time and money with which the data might be better explored and stored for future use. The next generation of WSN will benefit when sensor data is added to blogs, virtual communities, and social network applications. This transformation of data derived from sensor networks into a valuable resource for information hungry applications will benefit from techniques being developed for the emerging Cloud Computing technologies. Traditional High Performance Computing approaches may be replaced or find a place in data manipulation prior to the data being moved into the Cloud. In this paper, a novel framework is proposed to integrate the Cloud Computing model with WSN. Deployed WSN will be connected to the proposed infrastructure. Users request will be served via three service layers (IaaS, PaaS, SaaS) either from the archive, archive is made by collecting data periodically from WSN to Data Centres (DC), or by generating live query to corresponding sensor network.
Ambiguities in Visual Tracking of Articulated Objects Using Two- and Three-Dimensional Models

Three-dimensional (3D) kinematic models are widely-used in videobased figure tracking. We show that these models can suffer from singularities when motion is directed along the viewing axis of a single camera. The single camera case is important because it arises in many interesting applications, such as motion capture from movie footage, video surveillance, and vision-based user-interfaces. We describe a novel two-dimensional scaled prismatic model (SPM) for figure registration. In contrast to 3D kinematic models, the SPM has fewer singularity problems and does not require detailed knowledge of the 3D kinematics. We fully characterize the singularities in the SPM and demonstrate tracking through singularities using synthetic and real examples. We demonstrate the application of our model to motion capture from movies. Fred Astaire is tracked in a clip from the film “Shall We Dance”. We also present the use of monocular hand tracking in a 3D user-interface. These results demonstrate the benefits of the SPM in tracking with a single source of video.
Physical Human-Robot Interaction: Mutual Learning and Adaptation

Close physical interaction between robots and humans is a particularly challenging aspect of robot development. For successful interaction and cooperation, the robot must have the ability to adapt its behavior to the human counterpart. Based on our earlier work, we present and evaluate a computationally efficient machine learning algorithm that is well suited for such close-contact interaction scenarios. We show that this algorithm helps to improve the quality of the interaction between a robot and a human caregiver. To this end, we present two human-in-the-loop learning scenarios that are inspired by human parenting behavior, namely, an assisted standing-up task and an assisted walking task.
Physically-Based Rendering for Indoor Scene Understanding Using Convolutional Neural Networks

Indoor scene understanding is central to applications such as robot navigation and human companion assistance. Over the last years, data-driven deep neural networks have outperformed many traditional approaches thanks to their representation learning capabilities. One of the bottlenecks in training for better representations is the amount of available per-pixel ground truth data that is required for core scene understanding tasks such as semantic segmentation, normal prediction, and object boundary detection. To address this problem, a number of works proposed using synthetic data. However, a systematic study of how such synthetic data is generated is missing. In this work, we introduce a large-scale synthetic dataset with 500K physically-based rendered images from 45K realistic 3D indoor scenes. We study the effects of rendering methods and scene lighting on training for three computer vision tasks: surface normal prediction, semantic segmentation, and object boundary detection. This study provides insights into the best practices for training with synthetic data (more realistic rendering is worth it) and shows that pretraining with our new synthetic dataset can improve results beyond the current state of the art on all three tasks.
A Hole-filling Algorithm Using Non-uniform Rational B-splines

A three-dimensional (3D) geometric model obtained from a 3D device or other approaches is not necessarily watertight due to the presence of geometric deficiencies. These inadequacies must be repaired to create a valid surface mesh on the model as a pre-process of computational engineering analyses. This procedure has been a tedious and labor-intensive step, as there are many kinds of deficiencies that can make the geometry to be nonwatertight, such as gaps and holes. It is still challenging to repair discrete surface models based on available geometric information. The focus of this paper is to develop a new automated method for patching holes on the surface models in order to achieve watertightness. It describes a numerical algorithm utilizing Non-Uniform Rational B-Splines (NURBS) surfaces to generate smooth triangulated surface patches for topologically simple holes on discrete surface models. The Delaunay criterion for point insertion and edge swapping is used in this algorithm to improve the outcome. Surface patches are generated based on existing points surrounding the holes without altering them. The watertight geometry produced can be used in a wide range of engineering applications in the field of computational engineering simulation studies.
Evaluation of Haar Cascade Classifiers Designed for Face Detection

In the past years a lot of effort has been made in the field of face detection. The human face contains important features that can be used by vision-based automated systems in order to identify and recognize individuals. Face location, the primary step of the vision-based automated systems, finds the face area in the input image. An accurate location of the face is still a challenging task. Viola-Jones framework has been widely used by researchers in order to detect the location of faces and objects in a given image. Face detection classifiers are shared by public communities, such as OpenCV. An evaluation of these classifiers will help researchers to choose the best classifier for their particular need. This work focuses of the evaluation of face detection classifiers minding facial landmarks. Face datasets, face detection, facial landmarking, haar wavelets, Viola-Jones detectors.
LIE EXPERTS ' BELIEFS ABOUT NONVERBAL INDICATORS OF DECEPTION

. Beliefs about behavioral clues to deception were investigated in 212 people, consisting of prisoners, police detectives, patrol police officers, prison guards, customs officers, and college students. Previous studies, mainly conducted with college students as subjects, showed that people have some incorrect beliefs about behavioral clues to deception. It was hypothesized that prisoners would have the best notion about clues of deception, due to the fact that they receive the most adequate feedback about successful deception strategies. The results supported this hypothesis. In deception research a distinction is usually made between actual and perceived indicators of deception (DePaulo, Stone, & Lassiter, 1985, Zuckerman, DePaulo, & Rosenthai, 1981). Actual indicators of deception consist of nonverbal behaviors which have been found to be associated with deception. Perceived indicators of deception are nonverbal behaviors that observers associate with deception, regardless of whether such behavior is a manifestation of actual deception. Meta:analyses of actual indicators of deception (DePaulo, 1992; DePaulo et al., 1985;  1989; Vrij, 1991; Zuckerman & Driver, 1985; Zuckerman et al., 1981) provide empirical evidence that deceiving others is correlated with several nonverbal behaviors, such as an increase in speech disturbances (both more ahs and non-ahs), a higher-pitched voice, a slower speech rate, a longer latency period, and a decrease in leg/foot movements and hand/arm movements. The emotional, cognitive, and control approaches have been offered to explain these findings. In the emotional approach (Knapp, Hart, & Dennis, 1974; KOhnken, 1989; Riggio & Friedman, 1983) it is emphasized that deceiving causes physiological reactions, such as high blood pressure, increased heart rate, and increased respiration rate. The physiological reaction is the conseAddress correspondence to Aldert Vrij, University of Portsmouth, Department of Psychology, King Charles Street, Portsmouth PO1 2ER, United Kingdom.
Energy adaptive MAC protocol for wireless sensor networks with RF energy transfer

Recently, various energy harvesting techniques from ambient environments were proposed as alternative methods for powering sensor nodes, which convert the ambient energy from environments into electricity to power sensor nodes. However, those techniques are not applicable to the wireless sensor networks (WSNs) in the environment with no ambient energy source. To overcome this problem, an RF energy transfer method was proposed to power wireless sensor nodes. However, the RF energy transfer method also has a problem of unfairness among sensor nodes due to the significant difference between their energy harvesting rates according to their positions. In this paper, we propose a medium access control (MAC) protocol for WSNs based on RF energy transfer. The proposed MAC protocol adaptively manages the duty cycle of sensor nodes according to their the amount of harvested energy as well as the contention time of sensor nodes considering fairness among them. Through simulations, we show that our protocol can achieve a high degree of fairness, while maintaining duty cycle of sensor nodes appropriately according to the amount of their harvested energy.