nils-herrmann/clean_scirepeval huggingface dataset:
TWO-WAY COMMUNICATION CHANNELS

input at terminal 2 and Y2 the corresponding output. Once each second, say, new inputs xi and x2 may be chosen from corresponding input alphabets and put into the channel; outputs yi and Y2 may then be observed. These outputs will be related statistically to the inputs and perhaps historically to previous inputs and outputs if the channel has memory. The problem is to communicate in both directions through the channel as effectively as possible. Particularly, we wish to determine what pairs of signalling rates R1 and R2 for the two directions can be approached with arbitrarily small error probabilities.
Treatment for an endosseous implant migrated into the maxillary sinus not causing maxillary sinusitis: case report.

Placement of endosseous implants in the maxilla has been proven to be a reliable treatment modality. If there is lack of supporting bone, the placed implant may not have enough primary stability and may migrate into the maxillary sinus. Displaced implants must be removed. If there are no signs of maxillary sinusitis, augmentation of the resulting alveolar defect can be performed during the same procedure.
Adaptive Computer-Based Training Increases on the Job Performance of X-Ray Screeners

Due to severe terrorist attacks in recent years, aviation security issues have moved into the focus of politicians as well as the general public. Effective screening of passenger bags using state-of-the-art X-ray screening systems is essential to prevent terrorist attacks. The performance of the screening process depends critically on the security personnel, because they decide whether bags are OK or whether they might contain a prohibited item. Screening X-ray images of passenger bags for dangerous and prohibited items effectively and efficiently is a demanding object recognition task. Effectiveness of computer-based training (CBT) on X-ray detection performance was assessed using computer-based tests and on the job performance measures using threat image projection (TIP). It was found that adaptive CBT is a powerful tool to increase detection performance and efficiency of screeners in X-ray image interpretation. Moreover, the results of training could be generalized to the real life situation as shown in the increased detection performance in TIP not only for trained items, but also for new (untrained) items. These results illustrate that CBT is a very useful tool to increase airport security from a human factors perspective.
Interactive Ground Water (IGW)

IGW is a software environment for real-time 2D and 3D groundwater modeling. The software functions as a ‘‘numerical laboratory’’ in which the modeler can freely explore: visually creating an aquifer of desired configurations and then immediately investigating and visualizing the groundwater system. IGW allows the modeler’s thought processes to progress naturally and intuitively with the information visualized, overlaid, and compared at the instant it is required for analysis, providing a real sense of continuous active exploration and engaged problem solving.
Security in Underwater Sensor Network

Underwater wireless sensor network is a novel type of underwater networked system. Due to the characteristics of UWSN and underwater channel, UWSN are vulnerable to malicious attacks. The existing security solutions proposed for WSN cannot be used directly in UWSN. Moreover, most of these solutions are layer wise. In this paper, we studied the threat, attack, and security issues of UWSN. Subsequently, we argue that layered security schemes cannot protect UWSN against blended attacks. Additionally, we presented a novel preliminary conceive, which is a cross-layer, adaptive, selective security scheme.
The design of personal mobile technologies for lifelong learning

This paper sets out a framework for the design of a new genre of educational technology â€” personal (handheld or wearable) computer systems that support learning from any location throughout a lifetime. We set out a theory of lifelong learning mediated by technology and indicate how it can provide requirements for the software, hardware, communications and interface design of a handheld learning resource, or HandLeR. The paper concludes with a description and formative evaluation of a demonstrator system for children aged 7â€“11.
Streaming Hierarchical Video Segmentation

The use of video segmentation as an early processing step in video analysis lags behind the use of image segmentation for image analysis, despite many available video segmentation methods. A major reason for this lag is simply that videos are an order of magnitude bigger than images; yet most methods require all voxels in the video to be loaded into memory, which is clearly prohibitive for even medium length videos. We address this limitation by proposing an approximation framework for streaming hierarchical video segmentation motivated by data stream algorithms: each video frame is processed only once and does not change the segmentation of previous frames. We implement the graph-based hierarchical segmentation method within our streaming framework; our method is the first streaming hierarchical video segmentation method proposed. We perform thorough experimental analysis on a benchmark video data set and longer videos. Our results indicate the graph-based streaming hierarchical method outperforms other streaming video segmentation methods and performs nearly as well as the full-video hierarchical graph-based method.
Optimal Trajectory Control of Burst Mode for LLC Resonant Converter

The LLC resonant converter has drawn much attention for high efficiency. However, its light-load efficiency still cannot meet the ever increasing requirements. This paper proposes a novel burst-mode control for the LLC resonant converter to improve the light-load efficiency. Based on the state-plane trajectory analysis, during the burst-on time, a three-pulse switching pattern is implemented. By optimizing the first pulse width, the state variables are settled to the steady state of the highest efficiency load in the minimal time, and then, the next two pulse operation follows this steady-state trajectory to achieve high burst efficiency. Moreover, when the load changes, the burst-on time with the optimal three-pulse switching pattern is maintained, but the burst-off time is modulated. As a result, the output-voltage low-frequency ripple minimally keeps constant, and no additional filter is needed. Experimental results validate the proposed techniques.
Preferring the Mighty to the Meek : Toddlers Prefer Novel Dominant Agents

Every human society includes social hierarchies— relationships between individuals and groups of unequal rank or status. Recent research has shown that even preverbal infants represent hierarchical relationships, expecting larger agents and agents from larger groups to win dominance contests. However, to successfully navigate social hierarchies, infants must also integrate information about social rank into their own behavior, such as when deciding which individuals to approach and which to avoid. Here we demonstrate that twoyear-old children (ages 21-31 months) preferred novel dominant agents to subordinates. That is, by the age of 21 months, toddlers not only use phylogenetically stable cues to predict the winner of dominance contests, they also like the dominant agents better. This finding suggests that young children use their ability to infer relative rank to selectively approach dominant individuals.
Emotional Facial Expression Classification for Multimodal User Interfaces

We present a simple and computationally feasible method to perform automatic emotional classification of facial expressions. We propose the use of 10 characteristic points (that are part of the MPEG4 feature points) to extract relevant emotional information (basically five distances, presence of wrinkles and mouth shape). The method defines and detects the six basic emotions (plus the neutral one) in terms of this information and has been fine-tuned with a data-base of 399 images. For the moment, the method is applied to static images. Application to sequences is being now developed. The extraction of such information about the user is of great interest for the development of new multimodal user interfaces.
Simple and Scalable Response Prediction for Display Advertising

Clickthrough and conversation rates estimation are two core predictions tasks in display advertising. We present in this article a machine learning framework based on logistic regression that is specifically designed to tackle the specifics of display advertising. The resulting system has the following characteristics: It is easy to implement and deploy, it is highly scalable (we have trained it on terabytes of data), and it provides models with state-of-the-art accuracy.
Lifelong Machine Learning Systems: Beyond Learning Algorithms

Lifelong Machine Learning, or LML, considers systems that can learn many tasks from one or more domains over its lifetime. The goal is to sequentially retain learned knowledge and to selectively transfer that knowledge when learning a new task so as to develop more accurate hypotheses or policies. Following a review of prior work on LML, we propose that it is now appropriate for the AI community to move beyond learning algorithms to more seriously consider the nature of systems that are capable of learning over a lifetime. Reasons for our position are presented and potential counter-arguments are discussed. The remainder of the paper contributes by defining LML, presenting a reference framework that considers all forms of machine learning, and listing several key challenges for and benefits from LML research. We conclude with ideas for next steps to advance the field.
Spinal and supraspinal factors in human muscle fatigue.

Muscle fatigue is an exercise-induced reduction in maximal voluntary muscle force. It may arise not only because of peripheral changes at the level of the muscle, but also because the central nervous system fails to drive the motoneurons adequately. Evidence for "central" fatigue and the neural mechanisms underlying it are reviewed, together with its terminology and the methods used to reveal it. Much data suggest that voluntary activation of human motoneurons and muscle fibers is suboptimal and thus maximal voluntary force is commonly less than true maximal force. Hence, maximal voluntary strength can often be below true maximal muscle force. The technique of twitch interpolation has helped to reveal the changes in drive to motoneurons during fatigue. Voluntary activation usually diminishes during maximal voluntary isometric tasks, that is central fatigue develops, and motor unit firing rates decline. Transcranial magnetic stimulation over the motor cortex during fatiguing exercise has revealed focal changes in cortical excitability and inhibitability based on electromyographic (EMG) recordings, and a decline in supraspinal "drive" based on force recordings. Some of the changes in motor cortical behavior can be dissociated from the development of this "supraspinal" fatigue. Central changes also occur at a spinal level due to the altered input from muscle spindle, tendon organ, and group III and IV muscle afferents innervating the fatiguing muscle. Some intrinsic adaptive properties of the motoneurons help to minimize fatigue. A number of other central changes occur during fatigue and affect, for example, proprioception, tremor, and postural control. Human muscle fatigue does not simply reside in the muscle.
The merits of sharing a ride

The culture of sharing instead of ownership is sharply increasing in individuals behaviors. Particularly in transportation, concepts of sharing a ride in either carpooling or ridesharing have been recently adopted. An efficient optimization approach to match passengers in real-time is the core of any ridesharing system. In this paper, we model ridesharing as an online matching problem on general graphs such that passengers do not drive private cars and use shared taxis. We propose an optimization algorithm to solve it. The outlined algorithm calculates the optimal waiting time when a passenger arrives. This leads to a matching with minimal overall overheads while maximizing the number of partnerships. To evaluate the behavior of our algorithm, we used NYC taxi real-life data set. Results represent a substantial reduction in overall overheads.
Handwritten digit classification using higher order singular value decomposition

In this paper we present two algorithms for handwritten digit classification based on the higher order singular value decomposition (HOSVD). The first algorithm uses HOSVD for construction of the class models and achieves classification results with error rate lower than 6%. The second algorithm uses the HOSVD for tensor approximation simultaneously in two modes. Classification results for the second algorithm are almost down at 5% even though the approximation reduces the original training data with more than 98% before the construction of the class models. The actual classification in the test phase for both algorithms is conducted by solving a series least squares problems. Considering computational amount for the test presented the second algorithm is twice as efficient as the first one.
Acupuncture for posttraumatic stress disorder: a randomized controlled pilot trial.

The purpose of the study was to evaluate the potential efficacy and acceptability of accupuncture for posttraumatic stress disorder (PTSD). People diagnosed with PTSD were randomized to either an empirically developed accupuncture treatment (ACU), a group cognitive-behavioral therapy (CBT), or a wait-list control (WLC). The primary outcome measure was self-reported PTSD symptoms at baseline, end treatment, and 3-month follow-up. Repeated measures MANOVA was used to detect predicted Group X Time effects in both intent-to-treat (ITT) and treatment completion models. Compared with the WLC condition in the ITT model, accupuncture provided large treatment effects for PTSD (F [1, 46] = 12.60; p < 0.01; Cohen's d = 1.29), similar in magnitude to group CBT (F [1, 47] = 12.45; p < 0.01; d = 1.42) (ACU vs. CBT, d = 0.29). Symptom reductions at end treatment were maintained at 3-month follow-up for both interventions. Accupuncture may be an efficacious and acceptable nonexposure treatment option for PTSD. Larger trials with additional controls and methods are warranted to replicate and extend these findings.
On the Influence of Fingerprint Area in Partial Fingerprint Recognition

Conventional algorithms for fingerprint recognition are mainly based on minutiae information. However, the small number of minutiae in partial fingerprints is still a challenge in fingerprint matching. In fingerprint recognition systems, there are frequently appeared partial fingerprints, such as incompletely touching in fingerprint scanning or latent fingerprints. In this paper, we studied the influence of the fingerprint area in partial fingerprint recognition. First, a simulation scheme was proposed to construct a serial of partial fingerprints with different area. Then, the influence of the fingerprint area in partial fingerprint recognition is studied. By comparing the performance of partial fingerprint recognition with different fingerprint area, some useful conclusions can be drawn: (1) The decrease of the fingerprint area degrades the performance of partial fingerprint recognition; (2) When the fingerprint area decreases, the genuine matching scores will decrease, whereas the imposter matching scores will increase; (3) When the area of partial fingerprints is smaller than 20,000 pixels (about fifth of the normal full fingerprints), the performance of partial fingerprint recognition becomes very poor; (4) The threshold value of a given false accept rate increases when the area of partial fingerprints decrease a lot, but it remains almost the same if the area of partial fingerprints decrease not so much, e.g., greater than 50,000 pixels (about half of the normal full fingerprint). These observations can be helpful in improving the performance of partial fingerprint recognition in the future.
CATS: Collection and Analysis of Tweets Made Simple

Twitter presents an unparalleled opportunity for researchers from various ï¬elds to gather valuable and genuine textual data from millions of people. However, the collection pro-cess, as well as the analysis of these data require different kinds of skills (e.g. programing, data mining) which can be an obstacle for people who do not have this background. In this paper we present CATS, an open source, scalable, Web application designed to support researchers who want to carry out studies based on tweets. The purpose of CATS is twofold: (i) allow people to collect tweets (ii) enable them to analyze these tweets thanks to efï¬cient tools (e.g. event detection, named-entity recognition, topic modeling, word-clouds). What is more, CATS relies on a distributed imple-mentation which can deal with massive data streams.
Making Passwords Secure and Usable

To date, system research has focused on designing security mechanisms to protect systems access although their usability has rarely been investigated. This paper reports a study in which users’ perceptions of password mechanisms were investigated through questionnaires and interviews. Analysis of the questionnaires shows that many users report problems, linked to the number of passwords and frequency of password use. In-depth analysis of the interview data revealed that the degree to which users conform to security mechanisms depends on their perception of security levels, information sensitivity and compatibility with work practices. Security mechanisms incompatible with these perceptions may be circumvented by users and thereby undermine system security overall.
Pathophysiological distortions in time perception and timed performance.

Distortions in time perception and timed performance are presented by a number of different neurological and psychiatric conditions (e.g. Parkinson's disease, schizophrenia, attention deficit hyperactivity disorder and autism). As a consequence, the primary focus of this review is on factors that define or produce systematic changes in the attention, clock, memory and decision stages of temporal processing as originally defined by Scalar Expectancy Theory. These findings are used to evaluate the Striatal Beat Frequency Theory, which is a neurobiological model of interval timing based upon the coincidence detection of oscillatory processes in corticostriatal circuits that can be mapped onto the stages of information processing proposed by Scalar Timing Theory.
Automatic information extraction from semi-structured Web pages by pattern discovery

The World Wide Web is now undeniably the richest and most dense source of information; yet, its structure makes it difficult to make use of that information in a systematic way. This paper proposes a pattern discovery approach to the rapid generation of information extractors that can extract structured data from semi-structured Web documents. Previous work in wrapper induction aims at learning extraction rules from user-labeled training examples, which, however, can be expensive in some practical applications. In this paper, we introduce IEPAD (an acronym for Information Extraction based on PAttern Discovery), a system that discovers extraction patterns from Web pages without user-labeled examples. IEPAD applies several pattern discovery techniques, including PAT-trees, multiple string alignments and pattern matching algorithms. Extractors generated by IEPAD can be generalized over unseen pages from the same Web data source. We empirically evaluate the performance of IEPAD on an information extraction task from 14 real Web data sources. Experimental results show that with the extraction rules discovered from a single page, IEPAD achieves 96% average retrieval rate, and with less than five example pages, IEPAD achieves 100% retrieval rate for 10 of the sample Web data sources. D 2002 Elsevier Science B.V. All rights reserved.
On the Connection of Deep Fusion to Ensembling

In this paper, we provide a systematic study to the prevailing ResNet architecture by showing a connection from a general deeply-fused net view to ensembling. We start by empirically demonstrating the resemblance between the expanded form of the deeply-fused net and an ensemble of neural networks. Our empirical results uncover that the deepest network among the ensemble components does not contribute the most significantly to the overall performance and instead it provides a manner to introduce many layers and thus guarantee the ensemble size. Guided by the above study and observation, we develop a new deeply-fused network that combines two networks in a merge-and-run fusion manner. It is less deep than a ResNet with the same number of parameters but yields an ensemble of the same number of more-capable component networks, thus improving the classification accuracy. We evaluate the proposed network on the standard recognition tasks. Our approach demonstrates consistent improvements over the ResNet with the comparable setup, and achieves the state-of-the-art results (e.g., 3.57% testing error on CIFAR-10, 19.00% on CIFAR100, 1.51% on SVHN).
Commitment to organizational change: extension of a three-component model.

Three studies were conducted to test the application of a three-component model of workplace commitment (J. P. Meyer & N.  J. P. Meyer & L. Herscovitch, 2001) in the context of employee commitment to organizational change. Study 1, conducted with 224 university students, provided preliminary evidence for the validity of newly developed Affective, Continuance, and Normative Commitment to Change Scales. Studies 2 and 3, conducted with hospital nurses (N = 157 and 108, respectively), provided further support for the validity of the three Commitment to Change Scales, and demonstrated that (a) commitment to a change is a better predictor of behavioral support for a change than is organizational commitment, (b) affective and normative commitment to a change are associated with higher levels of support than is continuance commitment, and (c) the components of commitment combine to predict behavior.
B5. Ultra wideband patch antenna and power amplifier transmitter front end

An ultra wideband (UWB) transmitter front end (RFE) consisting of UWB circular patch antenna and an UWB GaAs common source (CS) power amplifier (PA) for the frequency range 3 to 10 GHz is presented. The patch antenna covers most of the UWB range and has an area of 30×30 squared mm. The UWB patch antenna has a return loss below 10 dB from 3 to 9 GHz, simulated with a port of 50 Ohm impedance. Since the PA and antenna both have variable impedance, the matching is performed simultaneously. The antenna impedance is directly matched to the PA using optimization algorithms. The transmitted power from the antenna is simulated with a variation around 2 dB over the entire frequency range. The PA has a gain varying from 15 to 30 dB to compensate for the antenna impedance variation.
A 20 W/Channel Class-D Amplifier With Near-Zero Common-Mode Radiated Emissions

A class-D amplifier that employs a new modulation scheme and associated output stage to achieve true filter-less operation is presented. It uses a new type of BD modulation that keeps the output common-mode constant, thereby removing a major contributor to radiated emissions, typically an issue for class-D amplifiers. The amplifier meets the FCC class B standard for radiated emissions without any LC filtering. It can accomplish this without any degradation to audio performance and while retaining high efficiency. THD+N is 0.19% at 1 kHz while supplying 5 W to an 8 Ohm load from a 12 V supply. Efficiency is 90% while providing 10 W under the same supply and load conditions. The new output stage occupies 1.8 mm2 per channel using the high voltage devices of a 0.25 ¿m BCD process.