nils-herrmann/clean_scirepeval huggingface dataset:
Singularities Affect Dynamics of Learning in Neuromanifolds

The parameter spaces of hierarchical systems such as multilayer perceptrons include singularities due to the symmetry and degeneration of hidden units. A parameter space forms a geometrical manifold, called the neuromanifold in the case of neural networks. Such a model is identified with a statistical model, and a Riemannian metric is given by the Fisher information matrix. However, the matrix degenerates at singularities. Such a singular structure is ubiquitous not only in multilayer perceptrons but also in the gaussian mixture probability densities, ARMA time-series model, and many other cases. The standard statistical paradigm of the Cramr-Rao theorem does not hold, and the singularity gives rise to strange behaviors in parameter estimation, hypothesis testing, Bayesian inference, model selection, and in particular, the dynamics of learning from examples. Prevailing theories so far have not paid much attention to the problem caused by singularity, relying only on ordinary statistical theories developed for regular (nonsingular) models. Only recently have researchers remarked on the effects of singularity, and theories are now being developed. This article gives an overview of the phenomena caused by the singularities of statistical manifolds related to multilayer perceptrons and gaussian mixtures. We demonstrate our recent results on these problems. Simple toy models are also used to show explicit solutions. We explain that the maximum likelihood estimator is no longer subject to the gaussian distribution even asymptotically, because the Fisher information matrix degenerates, that the model selection criteria such as AIC, BIC, and MDL fail to hold in these models, that a smooth Bayesian prior becomes singular in such models, and that the trajectories of dynamics of learning are strongly affected by the singularity, causing plateaus or slow manifolds in the parameter space. The natural gradient method is shown to perform well because it takes the singular geometrical structure into account. The generalization error and the training error are studied in some examples.
Reversible Jump MCMC Simulated Annealing for Neural Networks

We propose a novel reversible jump Markov chain Monte Carlo (MCMC) simulated an­ nealing algorithm to optimize radial basis function (RBF) networks. This algorithm enables us to maximize the joint posterior distribution of the network parameters and the number of basis functions. It performs a global search in the joint space of the pa­ rameters and number of parameters, thereby surmounting the problem of local minima. We also show that by calibrating a Bayesian model, we can obtain the classical AIC, BIC and MDL model selection criteria within a penalized likelihood framework. Finally, we show theoretically and empirically that the algorithm converges to the modes of the full posterior distribution in an efficient way.
Improved lane detection based on past vehicle trajectories

Knowing where the host lane lies is paramount to the effectiveness of many advanced driver assistance systems (ADAS), such as lane keep assist (LKA) and adaptive cruise control (ACC). This paper presents an approach for improving lane detection based on the past trajectories of vehicles. Instead of expensive high-precision map, we use the vehicle trajectory information to provide additional lane-level spatial support of the traffic scene, and combine it with the visual evidence to improve each step of the lane detection procedure, thereby overcoming typical challenges of normal urban streets. Such an approach could serve as an Add-On to enhance the performance of existing lane detection systems in terms of both accuracy and robustness. Experimental results in various typical but challenging scenarios show the effectiveness of the proposed system.
The Situational Eight DIAMONDS: a taxonomy of major dimensions of situation characteristics.

Taxonomies of person characteristics are well developed, whereas taxonomies of psychologically important situation characteristics are underdeveloped. A working model of situation perception implies the existence of taxonomizable dimensions of psychologically meaningful, important, and consequential situation characteristics tied to situation cues, goal affordances, and behavior. Such dimensions are developed and demonstrated in a multi-method set of 6 studies. First, the "Situational Eight DIAMONDS" dimensions Duty, Intellect, Adversity, Mating, pOsitivity, Negativity, Deception, and Sociality (Study 1) are established from the Riverside Situational Q-Sort (Sherman, Nave, & Funder, 2010, 2012, 2013; Wagerman & Funder, 2009). Second, their rater agreement (Study 2) and associations with situation cues and goal/trait affordances (Studies 3 and 4) are examined. Finally, the usefulness of these dimensions is demonstrated by examining their predictive power of behavior (Study 5), particularly vis-à-vis measures of personality and situations (Study 6). Together, we provide extensive and compelling evidence that the DIAMONDS taxonomy is useful for organizing major dimensions of situation characteristics. We discuss the DIAMONDS taxonomy in the context of previous taxonomic approaches and sketch future research directions.
Concurrent Design Analysis of High-Linearity SP10T Switch With 8.5 kV ESD Protection

This paper discusses concurrent design and analysis of the first 8.5 kV electrostatic discharge (ESD) protected single-pole ten-throw (SP10T) transmit/receive (T/R) switch for quad-band (0.85/0.9/1.8/1.9 GHz) GSM and multiple-band WCDMA smartphones. Implemented in a 0.18 μm SOI CMOS, this SP10T employs a series-shunt topology for the time-division duplex (TDD) transmitting (Tx) and receiving (Rx), and frequency-division duplex (FDD) transmitting/receiving (TRx) branches to handle the high GSM transmitter power. The measured P0.1 dB, insertion loss and Tx-Rx isolation in the lower/upper bands are 36.4/34.2 dBm, 0.48/0.81 dB and 43/40 dB, respectively, comparable to commercial products with no/little ESD protection in high-cost SOS and GaAs technologies. Feed-forward capacitor (FFC) and AC-floating bias techniques are used to further improve the linearity. An ESD-switch co-design technique is developed that enables simultaneous whole-chip design optimization for both ESD protection and SP10T circuits.
Enhancing Semantic Segmentation for Robotics: The Power of 3-D Entangled Forests

We present a novel, fast, and compact method to improve semantic segmentation of three-dimensional (3-D) point clouds, which is able to learn and exploit common contextual relations between observed structures and objects. Introducing 3-D Entangled Forests (3-DEF), we extend the concept of entangled features for decision trees to 3-D point clouds, enabling the classifier not only to learn, which labels are likely to occur close to each other, but also in which specific geometric configuration. Operating on a plane-based representation of a point cloud, our method does not require a final smoothing step and achieves state-of-the-art results on the NYU Depth Dataset in a single inference step. This compactness in turn allows for fast processing times, a crucial factor to consider for online applications on robotic platforms. In a thorough evaluation, we demonstrate the expressiveness of our new 3-D entangled feature set and the importance of spatial context in the scope of semantic segmentation.
Look me in the Eyes: A Survey of Eye and Gaze Animation for Virtual Agents and Artificial Systems

A person’s emotions and state of mind are apparent in their face and eyes. As a Latin proverb states: “The face is the portrait of the mind; the eyes, its informers.”. This presents a huge challenge for computer graphics researchers in the generation of artificial entities that aim to replicate the movement and appearance of the human eye, which is so important in human-human interactions. This State of the Art Report provides an overview of the efforts made on tackling this challenging task. As with many topics in Computer Graphics, a cross-disciplinary approach is required to fully understand the workings of the eye in the transmission of information to the user. We discuss the movement of the eyeballs, eyelids, and the head from a physiological perspective and how these movements can be modelled, rendered and animated in computer graphics applications. Further, we present recent research from psychology and sociology that seeks to understand higher level behaviours, such as attention and eye-gaze, during the expression of emotion or during conversation, and how they are synthesised in Computer Graphics and
Teaching and learning human-computer interaction: past, present, and future

hundreds of specialized apps? Who could have anticipated the power of our everyday devices to capture our every moment and movement? Cameras, GPS tracking, sensors—a phone is no longer just a phone; it is a powerful personal computing device loaded with access to interactive services that you carry with you everywhere you go. In response to these technological changes, user populations have diversified and grown. Once limited to workplaces and used only by experts, interactive computational devices and applications are now widely available for everyday use, anywhere, anytime by any and all of us. Though complex institutional infrastructures and communications networks still provide the backbone of our digital communications world, HCI research has strongly affected the marketability of these new technologies and networked systems. Human-computer interaction is a discipline concerned with the design, evaluation, and implementation of interactive computing systems for human use and with the study of major phenomena surrounding them. —Thomas T. Hewett et al., 1992
Hand-Drawn Face Sketch Recognition by Humans and a PCA-Based Algorithm for Forensic Applications

Because face sketches represent the original faces in a very concise yet recognizable form, they play an important role in criminal investigations, human visual perception, and face biometrics. In this paper, we compared the performances of humans and a principle component analysis (PCA)-based algorithm in recognizing face sketches. A total of 250 sketches of 50 subjects were involved. All of the sketches were drawn manually by five artists (each artist drew 50 sketches, one for each subject). The experiments were carried out by matching sketches in a probe set to photographs in a gallery set. This study resulted in the following findings: 1) A large interartist variation in terms of sketch recognition rate was observed; 2) fusion of the sketches drawn by different artists significantly improved the recognition accuracy of both humans and the algorithm; 3) human performance seems mildly correlated to that of PCA algorithm; 4) humans performed better in recognizing the caricature-like sketches that show various degrees of geometrical distortion or deviation, given the particular data set used; 5) score level fusion with the sum rule worked well in combining sketches, at least for a small number of artists; and 6) the algorithm was superior with the sketches of less distinctive features, while humans seemed more efficient in handling tonality (or pigmentation) cues of the sketches that were not processed with advanced transformation functions.
A current-mode non-inverting CMOS buck-boost DC-DC converter

This paper presents a current-mode control non-inverting buck-boost converter. The proposed circuit is controlled by the current mode and operated in three operation modes which are buck, buck-boost, and boost mode. The operation mode is automatically determined by the ratio between the input and output voltages. The proposed circuit is simulated by HSPICE with 0.5 um standard CMOS parameters. Its input voltage range is 2.5–5 V, and the output voltage range is 1.5–5 V. The maximum efficiency is 92% when it operates in buck mode.
The Study of Biometrics Technology Applied in Attendance Management System

Research on “How to create a fair, convenient attendance management system”, is being pursued by academics and government departments fervently. This study is based on the biometric recognition technology. The hand geometry machine captures the personal hand geometry data as the biometric code and applies this data in the attendance management system as the attendance record. The attendance records that use this technology is difficult to replicate by others. It can improve the reliability of the attendance records and avoid fraudulent issues that happen when you use a register. This research uses the social survey method-questionnaire to evaluate the theory and practice of introducing biometric recognition technology-hand geometry capturing into the attendance management system.
Fact Checking: Task definition and dataset construction

In this paper we introduce the task of fact checking, i.e. the assessment of the truthfulness of a claim. The task is commonly performed manually by journalists verifying the claims made by public figures. Furthermore, ordinary citizens need to assess the truthfulness of the increasing volume of statements they consume. Thus, developing fact checking systems is likely to be of use to various members of society. We first define the task and detail the construction of a publicly available dataset using statements fact-checked by journalists available online. Then, we discuss baseline approaches for the task and the challenges that need to be addressed. Finally, we discuss how fact checking relates to mainstream natural language processing tasks and can stimulate further research.
A Comparison of Modeling Units in Sequence-to-Sequence Speech Recognition with the Transformer on Mandarin Chinese

The choice of modeling units is critical to automatic speech recognition (ASR) tasks. Conventional ASR systems typically choose context-dependent states (CD-states) or contextdependent phonemes (CD-phonemes) as their modeling units. However, it has been challenged by sequence-to-sequence attention-based models, which integrate an acoustic, pronunciation and language model into a single neural network. On English ASR tasks, previous attempts have already shown that the modeling unit of graphemes can outperform that of phonemes by sequence-to-sequence attention-based model. In this paper, we are concerned with modeling units on Mandarin Chinese ASR tasks using sequence-to-sequence attention-based models with the Transformer. Five modeling units are explored including context-independent phonemes (CI-phonemes), syllables, words, sub-words and characters. Experiments on HKUST datasets demonstrate that the lexicon free modeling units can outperform lexicon related modeling units in terms of character error rate (CER). Among five modeling units, character based model performs best and establishes a new state-of-the-art CER of 26.64% on HKUST datasets without a hand-designed lexicon and an extra language model integration, which corresponds to a 4.8% relative improvement over the existing best CER of 28.0% by the joint CTC-attention based encoder-decoder network.
Are Microgrids the Future of Energy?: DC Microgrids from Concept to Demonstration to Deployment

Microgrids are defined as groups of energy resources, both renewable and/or conventional, and loads located and interconnected in a specific physical area that appear as a single entity to the alternating-current (ac) electric grid. The use of distributed resources to power local loads combined with the capability to operate independently of the ac grid makes microgrids a technically feasible option to address the concerns of sustainability, resilience, and energy efficiency. Furthermore, microgrids can operate while completely separated from the grid, representing a lower-cost option to provide electrical power to regions in developing countries where conventional ac grids are not available or are too unreliable. When connected to the ac grid, microgrids appear as controlled entities within the power system that, instead of being a burden to the ac grid power-management system, represent a resource capable of supporting the grid. Energy storage as the element responsible for balancing generation with load is critical to the success of the microgrid concept, and it is more important as larger penetration of renewable resources is present in the microgrid. Accelerated improvements in performance and cost of energy-storage technologies during the last five years are making microgrids an economically viable option for power systems in the very near future .
Plant Image Retrieval Using Color, Shape and Texture Features

We present a content-based image retrieval system for plant image retrieval, intended especially for the house plant identification problem. A plant image consists of a collection of overlapping leaves and possibly flowers, which makes the problem challenging. We studied the suitability of various wellknown color, shape and texture features for this problem, as well as introducing some new texture matching techniques and shape features. Feature extraction is applied after segmenting the plant region from the background using the max-flow min-cut technique. Results on a database of 380 plant images belonging to 78 different types of plants show promise of the proposed new techniques and the overall system: in 55% of the queries, the correct plant image is retrieved among the top-15 results. Furthermore, the accuracy goes up to 73% when a 132-image subset of well-segmented plant images are considered.
A COMPENDIUM OF PATTERN RECOGNITION TECHNIQUES IN FACE , SPEECH AND LIE DETECTION

Lie is a false statement made with deliberate intent to deceive, this is intentional untruth. People use different technologies of lie detection as Pattern recognition is a science to discover if an individual is telling the truth or lying. Patterns can describe some characteristics of liars, in this work to face and speech specifically. Face recognition take patttern of face and speech recognition of voice or speech to text. So this paper pretends realize a compendium on lie detection techniques and pattern recognition face and speech. It permits to review the actual state of the tecnhologies realized in these recognitions. Also It presents an analysis of tecnhologies using some of these techniques to resum the result.
Friction and damping of a compliant foot based on granular jamming for legged robots

Moving away from simple foot designs of current quadruped robots towards a more bio-inspired approach, a novel foot design was implemented on the quadruped robot Oncilla. These feet mimic soft paw-pads of dogs and cats with high traction and soft underlying tissue. Consisting of a granular medium enclosed in a flexible membrane, they can be set to different pressure/vacuum conditions. Tests of general properties such as friction force, damping and deformation were completed by proof of concept tests on the robot. These included flat ground locomotion as well as ascending a slope with different inclination. Comparison tests with the previous feet were performed as well, showing that the new feet have a high friction and strong damping properties. Additionally, the speed of flat ground locomotion is comparable to the maximum speed of the robot with the previous feet while retaining the desired trotting gait. These are promising aspects for legged locomotion. The jamming of granular media previously has been used to create a universal gripper which in the future also opens up opportunities to use the feet both in locomotion and simple object manipulation (although the manipulation is not tested here).
Semantic and letter fluency in Spanish-English bilinguals.

Spanish-English bilinguals and English monolinguals completed 12 semantic, 10 letter, and 2 proper name fluency categories. Bilinguals produced fewer exemplars than monolinguals on all category types, but the difference between groups was larger (and more consistent) on semantic categories. Bilinguals and monolinguals produced the same number of errors across all category types. The authors discuss 2 accounts of the similarities and differences between groups and the interaction with category type, including (a) cross-language interference and (b) relatively weak connections in the bilingual lexical system because of reduced use of words specific to each language. Surprisingly, bilinguals' fluency scores did not improve when they used words in both languages. This result suggests that voluntary language switching incurs a processing cost.
A systematic review on clustering and routing techniques based upon LEACH protocol for wireless sensor networks

In recent years, Wireless Sensor Networks (WSNs) have emerged as a new powerful technology used in many applications such as military operations, surveillance system, Intelligent Transport Systems (ITS) etc. These networks consist of many Sensor Nodes (SNs), which are not only used for monitoring but also capturing the required data from the environment. Most of the research proposals on WSNs have been developed keeping in view of minimization of energy during the process of extracting the essential data from the environment where SNs are deployed. The primary reason for this is the fact that the SNs are operated on battery which discharges quickly after each operation. It has been found in literature that clustering is the most common technique used for energy aware routing in WSNs. The most popular protocol for clustering in WSNs is Low Energy Adaptive Clustering Hierarchy (LEACH) which is based on adaptive clustering technique. This paper provides the taxonomy of various clustering and routing techniques in WSNs based upon metrics such as power management, energy management, network lifetime, optimal cluster head selection, multihop data transmission etc. A comprehensive discussion is provided in the text highlighting the relative advantages and disadvantages of many of the prominent proposals in this category which helps the designers to select a particular proposal based upon its merits over the others. & 2012 Elsevier Ltd. All rights reserved.
Utilizing QR Code and Mobile Phones for Blinds and Visually Impaired People

In this paper a barcode-based system to help the visually impaired and blind people identify objects in the environment is introduced. The system is based on the idea of utilizing QR codes (two-dimensional barcode) affixed to an object and scanned using a camera phone equipped with QR reader software. The reader decodes the barcode to a URL and directs the phone’s browser to fetch an audio file from the Web that contains a verbal description of the object. Our proposed system is expected to be useful in real-time interaction with different environments and to further illustrate the potential of our work, two scenarios are presented.
Depth Estimation Using Monocular Camera

The paper presents a novel approach for distance estimation using a single camera as input. In particular we discuss a method for depth estimation using camera parameters and also image geometry. The system has been implemented for specific camera parameters. This is a novel concept which uses road geometry and point of contact on road. Using a single forward facing camera located typically near the rear view mirror, our method can estimate distance of any vehicle on the road.  Depth, dead zone, optical axis, oblique distance, vanishing point, field of view