nils-herrmann/clean_scirepeval huggingface dataset:
A Kinect-Based Wearable Face Recognition System to Aid Visually Impaired Users

In this paper, we introduce a real-time face recognition (and announcement) system targeted at aiding the blind and low-vision people. The system uses a Microsoft Kinect sensor as a wearable device, performs face detection, and uses temporal coherence along with a simple biometric procedure to generate a sound associated with the identified person, virtualized at his/her estimated 3-D location. Our approach uses a variation of the K-nearest neighbors algorithm over histogram of oriented gradient descriptors dimensionally reduced by principal component analysis. The results show that our approach, on average, outperforms traditional face recognition methods while requiring much less computational resources (memory, processing power, and battery life) when compared with existing techniques in the literature, deeming it suitable for the wearable hardware constraints. We also show the performance of the system in the dark, using depth-only information acquired with Kinect's infrared camera. The validation uses a new dataset available for download, with 600 videos of 30 people, containing variation of illumination, background, and movement patterns. Experiments with existing datasets in the literature are also considered. Finally, we conducted user experience evaluations on both blindfolded and visually impaired users, showing encouraging results.
Abstractive Sentence Summarization with Attentive Recurrent Neural Networks

Abstractive Sentence Summarization generates a shorter version of a given sentence while attempting to preserve its meaning. We introduce a conditional recurrent neural network (RNN) which generates a summary of an input sentence. The conditioning is provided by a novel convolutional attention-based encoder which ensures that the decoder focuses on the appropriate input words at each step of generation. Our model relies only on learned features and is easy to train in an end-to-end fashion on large data sets. Our experiments show that the model significantly outperforms the recently proposed state-of-the-art method on the Gigaword corpus while performing competitively on the DUC-2004 shared task.ive Sentence Summarization generates a shorter version of a given sentence while attempting to preserve its meaning. We introduce a conditional recurrent neural network (RNN) which generates a summary of an input sentence. The conditioning is provided by a novel convolutional attention-based encoder which ensures that the decoder focuses on the appropriate input words at each step of generation. Our model relies only on learned features and is easy to train in an end-to-end fashion on large data sets. Our experiments show that the model significantly outperforms the recently proposed state-of-the-art method on the Gigaword corpus while performing competitively on the DUC-2004 shared task.
Ontology-based personalized search and browsing

As the number of Internet users and the number of accessible Web pages grows, it is becoming increasingly difficult for users to find documents that are relevant to their particular needs. Users must either browse through a large hierarchy of concepts to find the information for which they are looking or submit a query to a publicly available search engine and wade through hundreds of results, most of them irrelevant. The core of the problem is that whether the user is browsing or searching, whether they are an eighth grade student or a Nobel prize winner, the identical information is selected and it is presented the same way. In this paper, we report on research that adapts information navigation based on a user profile structured as a weighted concept hierarchy. A user may create his or her own concept hierarchy and use them for browsing Web sites. Or, the user profile may be created from a reference ontology by ‘watching over the user’s shoulder’ while they browse. We show that these automatically created profiles reflect the user’s interests quite well and they are able to produce moderate improvements when applied to search results.
LAYER RECURRENT NEURAL NETWORKS

In this paper, we propose a Layer-RNN (L-RNN) module that is able to learn contextual information adaptively using within-layer recurrence. Our contributions are three-fold: (i) we propose a hybrid neural network architecture that interleaves traditional convolutional layers with L-RNN module for learning longrange dependencies at multiple levels; (ii) we show that a L-RNN module can be seamlessly inserted into any convolutional layer of a pre-trained CNN, and the entire network then fine-tuned, leading to a boost in performance; (iii) we report experiments on the CIFAR-10 classification task, showing that a network with interleaved convolutional layers and L-RNN modules, achieves comparable results (5.39% top1 error) using only 15 layers and fewer parameters to ResNet-164 (5.46%); and on the PASCAL VOC2012 semantic segmentation task, we show that the performance of a pre-trained FCN network can be boosted by 5% (mean IOU) by simply inserting Layer-RNNs.
A Quadrature Radar Topology With Tx Leakage Canceller for 24-GHz Radar Applications

A quadrature radar topology with a Tx leakage canceller is presented. The canceller is composed of four branch-line hybrid couplers, a 90deg delay line, and a Wilkinson combiner. It has high Tx-to-Rx isolation and wide bandwidth characteristics. The isolation is 35.27 dB at 24 GHz and does not drop below 30 dB over a 2-GHz bandwidth. Given that the isolation of the branch-line hybrid coupler is 21.5 dB, the enhancement of the isolation is expected to be 13.77 dB. A quadrature Doppler radar front-end with the canceller was implemented. The noise level in the mixer output is lower by 9 dB compared with that of a conventional quadrature radar. This radar can measure speed as low as 0.76 mm/s, which correspond to a 0.12-Hz Doppler shift. It is also able to detect signs of the Doppler shift from a moving target due to its quadrature topology.
Clustering Based Approach for Customers ’ Classification from Electrical Distribution Systems

The paper presents a clustering based approach for consumers’ classification in representative categories characterized by typical load profiles. For determination of the consumption categories, every customer must be characterized by the following primary information: daily (monthly) energy consumption, average load and peak load. The used database contains the daily load curves corresponding to the small customers from a rural distribution system from Romania. The obtained results it demonstrated that the proposed approach can be used with the success in building specific tariff structures of the customers or in the optimal operation and planning of distribution systems.
Building Verifiable Trusted Path on Commodity x86 Computers

A trusted path is a protected channel that assures the secrecy and authenticity of data transfers between a user's input/output (I/O) device and a program trusted by that user. We argue that, despite its incontestable necessity, current commodity systems do not support trusted path with any significant assurance. This paper presents a hyper visor-based design that enables a trusted path to bypass an untrusted operating-system, applications, and I/O devices, with a minimal Trusted Computing Base (TCB). We also suggest concrete I/O architectural changes that will simplify future trusted-path system design. Our system enables users to verify the states and configurations of one or more trusted-paths using a simple, secret less, hand-held device. We implement a simple user-oriented trusted path as a case study.
Learning Compact Face Representation: Packing a Face into an int32

This paper addresses the problem of producing very compact representation of a face image for large-scale face search and analysis tasks. In tradition, the compactness of face representation is achieved by a dimension reduction step after representation extraction. However, the dimension reduction usually degrades the discriminative ability of the original representation drastically. In this paper, we present a deep learning framework which optimizes the compactness and discriminative ability jointly. The learnt representation can be as compact as 32 bit (same as the int32) and still produce highly discriminative performance (91.4% on LFW benchmark). Based on the extreme compactness, we show that traditional face analysis tasks (e.g. gender analysis) can be effectively solved by a Look-Up-Table approach given a large-scale face data set.
FeTa: A DCA Pruning Algorithm with Generalization Error Guarantees

Recent DNN pruning algorithms have succeeded in reducing the number of parameters in fully connected layers, often with little or no drop in classification accuracy. However, most of the existing pruning schemes either have to be applied during training or require a costly retraining procedure after pruning to regain classification accuracy. We start by proposing a cheap pruning algorithm for fully connected DNN layers based on difference of convex functions (DC) optimisation, that requires little or no retraining. We then provide a theoretical analysis for the growth in the Generalization Error (GE) of a DNN for the case of bounded perturbations to the hidden layers, of which weight pruning is a special case. Our pruning method is orders of magnitude faster than competing approaches, while our theoretical analysis sheds light to previously observed problems in DNN pruning. Experiments on commnon feedforward neural networks validate our results.
LTE-V for Sidelink 5G V2X Vehicular Communications: A New 5G Technology for Short-Range Vehicle-to-Everything Communications

This article provides an overview of the long-term evolution-vehicle (LTE-V) standard supporting sidelink or vehicle-to-vehicle (V2V) communications using LTE's direct interface named PC5 in LTE. We review the physical layer changes introduced under Release 14 for LTE-V, its communication modes 3 and 4, and the LTE-V evolutions under discussion in Release 15 to support fifth-generation (5G) vehicle-to-everything (V2X) communications and autonomous vehicles' applications. Modes 3 and 4 support direct V2V communications but differ on how they allocate the radio resources. Resources are allocated by the cellular network under mode 3. Mode 4 does not require cellular coverage, and vehicles autonomously select their radio resources using a distributed scheduling scheme supported by congestion control mechanisms. Mode 4 is considered the baseline mode and represents an alternative to 802.11p or dedicated shortrange communications (DSRC). In this context, this article also presents a detailed analysis of the performance of LTE-V sidelink mode 4, and proposes a modification to its distributed scheduling.
Monocular Localization of a moving person onboard a Quadrotor MAV

In this paper, we propose a novel method to recover the 3D trajectory of a moving person from a monocular camera mounted on a quadrotor micro aerial vehicle (MAV). The key contribution is an integrated approach that simultaneously performs visual odometry (VO) and persistent tracking of a person automatically detected in the scene. All computation pertaining to VO, detection and tracking runs onboard the MAV from a front-facing monocular RGB camera. Given the gravity direction from an inertial sensor and the knowledge of the individual's height, a complete 3D trajectory of the person within the reconstructed scene can be estimated. When the ground plane is detected from the triangulated 3D points, the absolute metric scale of the trajectory and the 3D map is also recovered. Our extensive indoor and outdoor experiments show that the system can localize a person moving naturally within a large area. The system runs at 17 frames per second on the onboard computer. A walking person was successfully tracked for two minutes and an accurate trajectory was recovered over a distance of 140 meters with our system running onboard.
Multirate Multimodal Video Captioning

Automatically describing videos with natural language is a crucial challenge of video understanding. Compared to images, videos have specific spatial-temporal structure and various modality information. In this paper, we propose a Multirate Multimodal Approach for video captioning. Considering that the speed of motion in videos varies constantly, we utilize a Multirate GRU to capture temporal structure of videos. It encodes video frames with different intervals and has a strong ability to deal with motion speed variance. As videos contain different modality cues, we design a particular multimodal fusion method. By incorporating visual, motion, and topic information together, we construct a well-designed video representation. Then the video representation is fed into a RNN-based language model for generating natural language descriptions. We evaluate our approach for video captioning on "Microsoft Research - Video to Text" (MSR-VTT), a large-scale video benchmark for video understanding. And our approach gets great performance on the 2nd MSR Video to Language Challenge.
Extrinsic dexterity: In-hand manipulation with external forces

“In-hand manipulation” is the ability to reposition an object in the hand, for example when adjusting the grasp of a hammer before hammering a nail. The common approach to in-hand manipulation with robotic hands, known as dexterous manipulation , is to hold an object within the fingertips of the hand and wiggle the fingers, or walk them along the object's surface. Dexterous manipulation, however, is just one of the many techniques available to the robot. The robot can also roll the object in the hand by using gravity, or adjust the object's pose by pressing it against a surface, or if fast enough, it can even toss the object in the air and catch it in a different pose. All these techniques have one thing in common: they rely on resources extrinsic to the hand, either gravity, external contacts or dynamic arm motions. We refer to them as “extrinsic dexterity”. In this paper we study extrinsic dexterity in the context of regrasp operations, for example when switching from a power to a precision grasp, and we demonstrate that even simple grippers are capable of ample in-hand manipulation. We develop twelve regrasp actions, all open-loop and hand-scripted, and evaluate their effectiveness with over 1200 trials of regrasps and sequences of regrasps, for three different objects (see video ). The long-term goal of this work is to develop a general repertoire of these behaviors, and to understand how such a repertoire might eventually constitute a general-purpose in-hand manipulation capability.
Interactive Digital Learning Materials for Kindergarten Students in Bangladesh

Traditional education system for preschool children is not updated in Bangladesh. Generally, parents and teachers are tried to teach children by introducing first alphabet and numbers in the form of text books. Sometimes it is quite difficult for teachers to teach play group about their first learning when they are not interested with it. The pedagogy of teaching and learning is changed with the proliferation of communication technology and it is necessary to develop interactive learning materials for children that may improve their learning, catching, and memorizing capabilities. Perhaps, one of the most important innovations in the age of technology is multimedia and its application. It is imperative to create high quality and realistic leaning environment for children. Interactive learning materials can be easier to understand and deal with their first learning. We developed some interactive learning materials in the form of video for playgroup using multimedia application tools. This study investigated the impact of student’s abilities to acquire new knowledge or skills through interactive learning materials. We visited one kindergartens (Nursery schools), interviewed class teachers about their teaching methods and level of students’ ability of recognizing English alphabets, pictures etc. The course teachers were provided interactive learning materials to show their playgroups for a number of sessions. The video included English alphabets with related words and pictures, and motivational funs. We noticed that almost all children were very interested to interact with their leaning video. The students were assesses individually and asked to recognize the alphabets, and pictures. The students adapted with their first alphabets very quickly. However, there were individual differences in their cognitive development. This interactive multimedia can be an alternative to traditional pedagogy for teaching playgroups.  Multimedia, interactive learning materials, IT, Kindergarten, Education system
Design principles for sensemaking support systems in environmental sustainability transformations

Stefan Seidel, University of Liechtenstein, Fuerst-Franz-Josef-Strasse 23, 9490 Vaduz, Liechtenstein. Tel: +423 265 1303; E-mail: stefan.seidel@uni.li  This paper reports on the results of a design science research (DSR) study that develops design principles for information systems (IS) that support organisational sensemaking in environmental sustainability transformations. We identify initial design principles based on salient affordances required in organisational sensemaking and revise them through three rounds of developing, demonstrating and evaluating a prototypical implementation. Through our analysis, we learn how IS can support essential sensemaking practices in environmental sustainability transformations, including experiencing disruptive ambiguity through the provision of environmental data, noticing and bracketing, engaging in an open and inclusive communication and presuming potential alternative environmentally responsible actions. We make two key contributions: First, we provide a set of theory-inspired design principles for IS that support sensemaking in sustainability transformations, and revise them empirically using a DSR method. Second, we show how the concept of affordances can be used in DSR to investigate how IS can support organisational practices. While our findings are based on the investigation of the substantive context of environmental sustainability transformation, we suggest that they might be applicable in a broader set of contexts of organisational sensemaking and thus for a broader class of sensemaking support systems.  (2017). doi:10.1057/s41303-017-0039-0
Mathematical methods in solutions of the problems from the Third International Students' Olympiad in Cryptography

The mathematical problems and their solutions of the Third International Students’ Olympiad in Cryptography NSUCRYPTO’2016 are presented. We consider mathematical problems related to the construction of algebraic immune vectorial Boolean functions and big Fermat numbers, problems about secrete sharing schemes and pseudorandom binary sequences, biometric cryptosystems and the blockchain technology, etc. Two open problems in mathematical cryptography are also discussed and a solution for one of them proposed by a participant during the Olympiad is described. It was the first time in the Olympiad history.
Vibration parameter estimation using FMCW radar

Vibration sensing is essential in many applications. Traditional vibration sensors are contact based. With the advance of low-cost and highly integrated CMOS radars, another class of non-contact vibration sensors is emerging. In this paper, we present detailed analysis on obtaining vibration parameters using frequency modulated continuous wave (FMCW) radars. We establish the Cramer Rao lower bounds (CRLB) of the parameter estimation problem and propose an estimation algorithm that achieves the bounds in simulations. These analysis show that vibration sensing using FMCW radars can easily achieve sub-Hertz frequency accuracy and micrometer level amplitude accuracy.
Data acquisition and quality analysis of 3-dimensional fingerprints

This paper introduces a new technology of non-contact 3D fingerprint capture and processing for higher quality fingerprint data acquisition. The system relies on a real-time 3D sensor using structured light illumination (SLI), which generates both texture and detailed ridge depth information. The high resolution 3D scans are then converted into flat equivalent images. As a result, many limitations imposed upon conventional fingerprint capture and processing can be reduced by the unobtrusiveness of this approach and the extra depth information acquired. The image quality is evaluated and analyzed using NIST fingerprint image software. A comparison is performed between the unraveled 3D fingerprints and their 2D plain counterparts in terms of fingerprint quality.
Even swaps: a rational method for making trade-offs.

Making wise trade-offs is one of the most important and difficult challenges in decision making. Needless to say, the more alternative you're considering and the more objectives you're pursuing, the more trade-offs you'll need to make. The sheer volume of trade-offs, however, is not what makes decision making so hard. It's the fact that each objective has its own basis of comparison, from precise numbers (34% versus 38%) to relationships (high versus low) to descriptive terms (red versus blue). You're not just trading off apples and oranges; you're trading off apples and oranges and elephants. How do you make trade-offs when comparing widely disparate things? In the past, decision makers have relied mostly on instinct, common sense, and guesswork. They've lacked a clear, rational, and easy-to-use trade-off methodology. To help fill that gap, the authors have developed a system-which they call even swaps-that provides a practical way of making trade-offs among a range of objectives across a range of alternatives. The even-swap method will not make complex decisions easy; you'll still have to make hard choices about the values you set and the trades you make. What it does provide is a reliable mechanisms for making trades and a coherent framework in which to make them. By simplifying and codifying the mechanical elements of trade-offs, the even-swap method lets you focus all your mental energy on the most important work of decision making: deciding the real value to your company of different courses of action.
A fusion approach for managing multi-granularity linguistic term sets in decision making

The aim of this paper is to present a fusion approach of multi-granularity linguistic information for managing information assessed in di erent linguistic term sets (multi-granularity linguistic term sets) together with its application in a decision making problem with multiple information sources, assuming that the linguistic performance values given to the alternatives by the di erent sources are represented in linguistic term sets with di erent granularity and=or semantic. In this context, a decision process based on two steps is proposed with a view to obtaining the solution set of alternatives. First, the fusion of the multi-granularity linguistic performance values is carried out in order to obtain collective performance evaluations. In this step, on the one hand, the multi-granularity linguistic information is made uniform using a linguistic term set as the uniform representation base, the basic linguistic term set. On the other hand, the collective performance evaluations of the alternatives are obtained by means of an aggregation operator, being fuzzy sets on the basic linguistic term set. Second, the choice of the best alternative(s) from the collective performance evaluations is performed. To do that, a fuzzy preference relation is computed from the collective performance evaluations using a ranking method of pairs of fuzzy sets in the setting of Possibility Theory, applied to fuzzy sets on the basic linguistic term set. Then, a choice degree may be applied on the preference relation in order to rank the alternatives. ? 2000 Elsevier Science B.V. All rights reserved.