nils-herrmann/clean_scirepeval huggingface dataset:
Catadioptric Sensors that Approximate Wide-Angle Perspective Projections

We presenttwo families of reflective surfacesthat are capableof providing a wide field of view, andyet still approximatea perspecti ve projectionto a high degree.These surfacesarederivedby consideringaplaneperpendicular to theaxisof a surfaceof revolutionandfinding theequations governingthedistortionof theimageof theplanein thissurface. We thenview this relationasa differentialequation and prescribethe distortion term to be linear. By choosing appropriateinitial conditionsfor the differentialequation andsolvingit numerically, wederivethesurfaceshape andobtaina preciseestimateasto what degreethe resulting sensorcanapproximatea perspecti ve projection.Thus thesesurfacesactascomputational sensors, allowing for a wide-angleperspecti ve view of a scenewithout processing the imagein software. The applicationsof sucha sensor shouldbe numerous,including surveillance,roboticsand traditionalphotography . Recently, many researchersin the roboticsand vision communityhave begun to considervisual sensorsthat are ableto obtainwidefieldsof view. Suchdevicesarethenatural solution to variousdifficulties encounteredwith conventionalimagingsystems. Thetwo mostcommonmeansof obtainingwidefieldsof view arefish-eye lensesandreflectivesurfaces,alsoknown ascatoptrics.Whencatoptricsarecombinedwith conventional lenssystems,known asdioptrics,the resultingsensors are known as catadioptrics. The possibleusesof thesesystemsincludeapplicationssuchasrobotcontroland surveillance. In this paperwe will consideronly catadioptric basedsensors.Oftensuchsystemsconsistof a camera pointingataconvex mirror, asin figure(1). How to interpretand make useof the visual information obtainedby suchsystems,e.g. how they shouldbe usedto control robots,is not at all obvious. Thereareinfinitely many differentshapesthat a mirror canhave, and at leasttwo differentcameramodels(perspecti ve and orthographicprojection)with which to combineeachmirror. Convex mirror
Zero-Shot Detection

As we move towards large-scale object detection, it is unrealistic to expect annotated training data for all object classes at sufficient scale, and so methods capable of unseen object detection are required. We propose a novel zero-shot method based on training an end-to-end model that fuses semantic attribute prediction with visual features to propose object bounding boxes for seen and unseen classes. While we utilize semantic features during training, our method is agnostic to semantic information for unseen classes at test-time. Our method retains the efficiency and effectiveness of YOLO  for objects seen during training, while improving its performance for novel and unseen objects. The ability of state-of-art detection methods to learn discriminative object features to reject background proposals also limits their performance for unseen objects. We posit that, to detect unseen objects, we must incorporate semantic information into the visual domain so that the learned visual features reflect this information and leads to improved recall rates for unseen objects. We test our method on PASCAL VOC and MS COCO dataset and observed significant improvements on the average precision of unseen classes.
A Framework for Research in Gamified Mobile Guide Applications using Embodied Conversational Agents (ECAs)

Mobile Guides are mobile applications that provide players with local and locationbased services (LBS), such as navigation assistance, where and when they need them most. Advances in mobile technologies in recent years have enabled the gamification of these applications, opening up new opportunities to transfer education and culture through game play. However, adding traditional game elements such as PBLs (points, badges, and leaderboards) alone cannot ensure that the intended learning outcomes will be met, as the player’s cognitive resources are shared between the application and the surrounding environment. This distribution of resources prevents players from easily immersing themselves into the educational scenario. Adding artificial conversational characters (ECAs) that simulate the social norms found in real-life human-to-human guide scenarios has the potential to address this problem and improve the player’s experience and learning of cultural narratives . Although significant progress has been made towards creating game-like mobile guides with ECAs (, ), there is still a lack of a unified framework that enables researchers and practitioners to investigate the potential effects of such applications to players and how to approach the concepts of player experience, cognitive accessibility and usability in this context. This paper presents a theoretically-well supported research framework consisted of four key components: differences in players, different features of the gamified task, aspects of how the ECA looks, sound or behaves and different mobile environments. Furthermore, it provides based on this framework a working definition of what player experience, cognitive accessibility and usability are in the context of game-like mobile guide applications. Finally, a synthesis of the results of six empirical studies conducted within this research framework is discussed and a series of design guidelines for the effective gamification of mobile guide applications using ECAs are presented. Results show that an ECA can positively affect the quality of the player’s experience, but it did not elicit better player retention of cultural narratives and navigation of routes.
Pose Guided RGBD Feature Learning for 3D Object Pose Estimation

In this paper we examine the effects of using object poses as guidance to learning robust features for 3D object pose estimation. Previous works have focused on learning feature embeddings based on metric learning with triplet comparisons and rely only on the qualitative distinction of similar and dissimilar pose labels. In contrast, we consider the exact pose differences between the training samples, and aim to learn embeddings such that the distances in the pose label space are proportional to the distances in the feature space. However, since it is less desirable to force the pose-feature correlation when objects are symmetric, we discuss the use of weights that reflect object symmetry when measuring the pose distances. Furthermore, end-to-end pose regression is investigated and is shown to further boost the discriminative power of feature learning, improving pose recognition accuracies. Experimental results show that the features that are learnt guided by poses, are significantly more discriminative than the ones learned in the traditional way, outperforming state-of-the-art works. Finally, we measure the generalisation capacity of pose guided feature learning in previously unseen scenes containing objects under different occlusion levels, and we show that it adapts well to novel tasks.
A Vectorial Semantics Approach to Personality Assessment

Personality assessment and, specifically, the assessment of personality disorders have traditionally been indifferent to computational models. Computational personality is a new field that involves the automatic classification of individuals' personality traits that can be compared against gold-standard labels. In this context, we introduce a new vectorial semantics approach to personality assessment, which involves the construction of vectors representing personality dimensions and disorders, and the automatic measurements of the similarity between these vectors and texts written by human subjects. We evaluated our approach by using a corpus of 2468 essays written by students who were also assessed through the five-factor personality model. To validate our approach, we measured the similarity between the essays and the personality vectors to produce personality disorder scores. These scores and their correspondence with the subjects' classification of the five personality factors reproduce patterns well-documented in the psychological literature. In addition, we show that, based on the personality vectors, we can predict each of the five personality factors with high accuracy.
Personality & Emotional States: Understanding Users' Music Listening Needs

Music plays an important part in people’s lives to regulate their emotions throughout the day. We conducted an online user study to investigate how the emotional state relates to the use of emotionally laden music. We found among 359 participants that they in general prefer emotionally laden music that correspond with their emotional state. However, when looking at personality traits, different patterns emerged. We found that when in a negative emotional state, those who scored high on openness, extraversion, and agreeableness tend to cheer themselves up with happy music, while those who scored high on neuroticism tend to increase their worry with sad music. With our results we show general patterns of music usage, but also individual differences. Our results contribute to the improvement of applications such as recommender systems in order to provide tailored recommendations based on users’ personality and emotional state.
Autonomous tracking of vehicle rear lights and detection of brakes and turn signals

Automatic detection of vehicle alert signals is extremely critical in autonomous vehicle applications and collision avoidance systems, as these detection systems can help in the prevention of deadly and costly accidents. In this paper, we present a novel and lightweight algorithm that uses a Kalman filter and a codebook to achieve a high level of robustness. The algorithm is able to detect braking and turning signals of the vehicle in front both during the daytime and at night (daytime detection being a major advantage over current research), as well as correctly track a vehicle despite changing lanes or encountering periods of no or low-visibility of the vehicle in front. We demonstrate that the proposed algorithm is able to detect the signals accurately and reliably under different lighting conditions.
Fine-grained classification of identity document types with only one example

In this paper, we tackle the task of recognizing types of partly very similar identity documents using state-of-the-art visual recognition approaches. Given a scanned document, the goal is to identify the country of issue, the type of document, and its version. Whereas recognizing the individual parts of a document with known standardized layout can be done reliably, identifying the type of a document and therefore also its layout is a challenging problem due to the large variety of documents. In our paper, we develop and evaluate different techniques for this application including feature representations based on recent achievements with convolutional neural networks. On a dataset with 74 different classes and using only one training image per class, our best approach achieves a mean class-wise accuracy of 97.7%.
Text sentiment analysis based on long short-term memory

With the rapid development of Internet and big explosion of text data, it has been a very significant research subject to extract valuable information from text ocean. To realize multi-classification for text sentiment, this paper promotes a RNN language model based on Long Short Term Memory (LSTM), which can get complete sequence information effectively. Compared with the traditional RNN language model, LSTM is better in analyzing emotion of long sentences. And as a language model, LSTM is applied to achieve multi-classification for text emotional attributes. So though training different emotion models, we can know which emotion the sentence belongs to by using these emotion models. And numerical experiments show that it can produce better accuracy rate and recall rate than the conventional RNN.
Equivalent-Circuit Analysis of a Broadband Printed Dipole With Adjusted Integrated Balun and an Array for Base Station Applications

A printed dipole with an integrated balun features a broad operating bandwidth. The feed point of conventional balun structures is fixed at the top of the integrated balun, which makes it difficult to match to a 50-Omega feed. In this communication, we demonstrate that it is possible to directly match with the 50-Omega feed by adjusting the position of the feed point of the integrated balun. The printed dipole with the hereby presented adjustable integrated balun maintains the broadband performance and exhibits flexibility for the matching to different impedance values, which is extremely important for the design of antenna arrays since the mutual coupling between antenna elements commonly changes the input impedance of each single element. An equivalent-circuit analysis is presented for the understanding of the mechanism of the impedance match. An eight-element linear antenna array is designed as a benchmarking topology for broadband wireless base stations.
An investigation into problematic smartphone use: The role of narcissism, anxiety, and personality factors

Over the last decade, worldwide smartphone usage has greatly increased. Alongside this growth, research on the influence of smartphones on human behavior has also increased. However, a growing number of studies have shown that excessive use of smartphones can lead to detrimental consequences in a minority of individuals. This study examines the psychological aspects of smartphone use particularly in relation to problematic use, narcissism, anxiety, and personality factors.  A sample of 640 smartphone users ranging from 13 to 69 years of age (mean = 24.89 years, SD = 8.54) provided complete responses to an online survey including modified DSM-5 criteria of Internet Gaming Disorder to assess problematic smartphone use, the Spielberger State-Trait Anxiety Inventory, the Narcissistic Personality Inventory, and the Ten-Item Personality Inventory.  The results demonstrated significant relationships between problematic smartphone use and anxiety, conscientiousness, openness, emotional stability, the amount of time spent on smartphones, and age. The results also demonstrated that conscientiousness, emotional stability, and age were independent predictors of problematic smartphone use.  The findings demonstrate that problematic smartphone use is associated with various personality factors and contributes to further understanding the psychology of smartphone behavior and associations with excessive use of smartphones.
A low-noise high-sensitivity readout circuit for MEMS capacitive sensors

This paper presents a differential low-noise highresolution switched-capacitor readout circuit that is intended for capacitive sensors. Amplitude modulation/demodulation and correlated double sampling are used to minimize the adverse effects of the amplifier offset and flicker (1/f) noise and improve the sensitivity of the readout circuit. In order to simulate the response of the readout circuit, a Verilog-A model is used to model the variable sense capacitor. The interface circuit is designed and laid out in a 0.8 µm CMOS process. Postlayout simulation results show that the readout interface is able to linearly resolve sense capacitance variation from 2.8 aF to 0.3 fF with a sensitivity of 7.88 mV/aF from a single 5V supply (the capacitance-to-voltage conversion is approximately linear for capacitance changes from 0.3 fF to~1.2 fF). The power consumption of the circuit is 9.38 mW.
Relational Leadership Theory : Exploring the social processes of leadership and organizing

Relational leadership is a relatively new term in the leadership literature, and because of this, its meaning is open to interpretation. In the present article I describe two perspectives of relational leadership: an entity perspective that focuses on identifying attributes of individuals as they engage in interpersonal relationships, and a relational perspective that views leadership as a process of social construction through which certain understandings of leadership come about and are given privileged ontology. These approaches can be complementary, but their implications for study and practice are quite different. After reviewing leadership research relative to these two perspectives I offer Relational Leadership Theory (RLT) as an overarching framework for the study of leadership as a social influence process through which emergent coordination (e.g., evolving social order) and change (e.g., new approaches, values, attitudes, behaviors, ideologies) are constructed and produced. This framework addresses relationships both as an outcome of investigation (e.g., How are leadership relationships produced?) and a context for action (e.g., How do relational dynamics contribute to structuring?). RLT draws from both entity and relational ontologies and methodologies to more fully explore the relational dynamics of leadership and organizing.
Skip-Gram - Zipf + Uniform = Vector Additivity

In recent years word-embedding models have gained great popularity due to their remarkable performance on several tasks, including word analogy questions and caption generation. An unexpected “sideeffect” of such models is that their vectors often exhibit compositionality, i.e., adding two word-vectors results in a vector that is only a small angle away from the vector of a word representing the semantic composite of the original words, e.g., “man” + “royal” = “king”. This work provides a theoretical justification for the presence of additive compositionality in word vectors learned using the Skip-Gram model. In particular, it shows that additive compositionality holds in an even stricter sense (small distance rather than small angle) under certain assumptions on the process generating the corpus. As a corollary, it explains the success of vector calculus in solving word analogies. When these assumptions do not hold, this work describes the correct non-linear composition operator. Finally, this work establishes a connection between the Skip-Gram model and the Sufficient Dimensionality Reduction (SDR) framework of Globerson and Tishby: the parameters of SDR models can be obtained from those of Skip-Gram models simply by adding information on symbol frequencies. This shows that SkipGram embeddings are optimal in the sense of Globerson and Tishby and, further, implies that the heuristics commonly used to approximately fit Skip-Gram models can be used to fit SDR models.
Design of a Broadband and Multiband Planar Inverted-F Antenna

A novel planar inverted-F antenna (PIFA) is designed in this paper. Compared to the previous PIFA, the proposed PIFA can enhance bandwidths and achieve multi-band which is loaded with a T-shaped ground plane and etched slots on ground plane and a rectangular patch. It covered 4 service bands, including GSM900, DCS1800, PCS1900 and ISM2450 under the criteria -7 dB return loss for the first band and -10 dB for the last bands. Process of designing and calculation of parameters are presented in detail. The simulation results showed that each band has good characteristics and the bandwidth has been greatly expanded.
Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems

A new efficient optimization method, called ‘Teaching–Learning-Based Optimization (TLBO)’, is proposed in this paper for the optimization of mechanical design problems. This method works on the effect of influence of a teacher on learners. Like other nature-inspired algorithms, TLBO is also a population-based method and uses a population of solutions to proceed to the global solution. The population is considered as a group of learners or a class of learners. The process of TLBO is divided into two parts: the first part consists of the ‘Teacher Phase’ and the second part consists of the ‘Learner Phase’. ‘Teacher Phase’ means learning from the teacher and ‘Learner Phase’ means learning by the interaction between learners. The basic philosophy of the TLBO method is explained in detail. To check the effectiveness of the method it is tested on five different constrained benchmark test functions with different characteristics, four different benchmark mechanical design problems and six mechanical design optimization problems which have real world applications. The effectiveness of the TLBO method is compared with the other populationbased optimization algorithms based on the best solution, average solution, convergence rate and computational effort. Results show that TLBO is more effective and efficient than the other optimization methods for the mechanical design optimization problems considered. This novel optimization method can be easily extended to other engineering design optimization problems.
Learning Control in Robotics

Recent trends in robot learning are to use trajectory-based optimal control techniques and reinforcement learning to scale complex robotic systems. On the one hand, increased computational power and multiprocessing, and on the other hand, probabilistic reinforcement learning methods and function approximation, have contributed to a steadily increasing interest in robot learning. Imitation learning has helped significantly to start learning with reasonable initial behavior. However, many applications are still restricted to rather lowdimensional domains and toy applications. Future work will have to demonstrate the continual and autonomous learning abilities, which were alluded to in the introduction.
Detection and Resolution of Rumours in Social Media: A Survey

Despite the increasing use of social media platforms for information and news gathering, its unmoderated nature often leads to the emergence and spread of rumours, i.e., items of information that are unverified at the time of posting. At the same time, the openness of social media platforms provides opportunities to study how users share and discuss rumours, and to explore how to automatically assess their veracity, using natural language processing and data mining techniques. In this article, we introduce and discuss two types of rumours that circulate on social media: long-standing rumours that circulate for long periods of time, and newly emerging rumours spawned during fast-paced events such as breaking news, where reports are released piecemeal and often with an unverified status in their early stages. We provide an overview of research into social media rumours with the ultimate goal of developing a rumour classification system that consists of four components: rumour detection, rumour tracking, rumour stance classification, and rumour veracity classification. We delve into the approaches presented in the scientific literature for the development of each of these four components. We summarise the efforts and achievements so far toward the development of rumour classification systems and conclude with suggestions for avenues for future research in social media mining for the detection and resolution of rumours.
A Four-Channel 94-GHz SiGe-Based Digital Beamforming FMCW Radar

This paper presents a multi-channel frequency-modulated continuous-wave (FMCW) radar sensor operating in the frequency range from 91 to 97 GHz. The millimeter-wave radar sensor utilizes an SiGe chipset comprising a single signal-generation chip and multiple monostatic transceiver (TRX) chips, which are based on a 200-GHz fT HBT technology. The front end is built on an RF soft substrate in chip-on-board technology and employs a nonuniformly distributed antenna array to improve the angular resolution. The synthesis of ten virtual antennas achieved by a multiple-input multiple-output technique allows the virtual array aperture to be maximized. The fundamental-wave voltage-controlled oscillator achieves a single-sideband phase noise of -88 dBc/Hz at 1-MHz offset frequency. The TX provides a saturated output power of 6.5 dBm, and the mixer within the TRX achieves a gain and a double sideband noise figure of 11.5 and 12 dB, respectively. Possible applications include radar sensing for range and angle detection, material characterization, and imaging.