nils-herrmann/clean_scirepeval huggingface dataset:
Uplink contention based SCMA for 5G radio access

Fifth generation (5G) wireless networks are expected to support very diverse applications and terminals. Massive connectivity with a large number of devices is an important requirement for 5G networks. Current LTE system is not able to efficiently support massive connectivity, especially on the uplink (UL). Among the issues that arise due to massive connectivity is the cost of signaling overhead and latency. In this paper, an uplink contention-based sparse code multiple access (SCMA) design is proposed as a solution. First, the system design aspects of the proposed multiple-access scheme are described. The SCMA parameters can be adjusted to provide different levels of overloading, thus suitable to meet the diverse traffic connectivity requirements. In addition, the system-level evaluations of a small packet application scenario are provided for contention-based UL SCMA. SCMA is compared to OFDMA in terms of connectivity and drop rate under a tight latency requirement. The simulation results demonstrate that contention-based SCMA can provide around 2.8 times gain over contention-based OFDMA in terms of supported active users. The uplink contention-based SCMA scheme can be a promising technology for 5G wireless networks for data transmission with low signaling overhead, low delay, and support of massive connectivity.
The effect of training distinct neurofeedback protocols on aspects of cognitive performance.

The use of neurofeedback as an operant conditioning paradigm has disclosed that participants are able to gain some control over particular aspects of their electroencephalogram (EEG). Based on the association between theta activity (4-7 Hz) and working memory performance, and sensorimotor rhythm (SMR) activity (12-15 Hz) and attentional processing, we investigated the possibility that training healthy individuals to enhance either of these frequencies would specifically influence a particular aspect of cognitive performance, relative to a non-neurofeedback control-group. The results revealed that after eight sessions of neurofeedback the SMR-group were able to selectively enhance their SMR activity, as indexed by increased SMR/theta and SMR/beta ratios. In contrast, those trained to selectively enhance theta activity failed to exhibit any changes in their EEG. Furthermore, the SMR-group exhibited a significant and clear improvement in cued recall performance, using a semantic working memory task, and to a lesser extent showed improved accuracy of focused attentional processing using a 2-sequence continuous performance task. This suggests that normal healthy individuals can learn to increase a specific component of their EEG activity, and that such enhanced activity may facilitate semantic processing in a working memory task and to a lesser extent focused attention. We discuss possible mechanisms that could mediate such effects and indicate a number of directions for future research.
Acoustic Scene Classification: Classifying environments from the sounds they produce

In this article, we present an account of the state of the art in acoustic scene classification (ASC), the task of classifying environments from the sounds they produce. Starting from a historical review of previous research in this area, we define a general framework for ASC and present different implementations of its components. We then describe a range of different algorithms submitted for a data challenge that was held to provide a general and fair benchmark for ASC techniques. The data set recorded for this purpose is presented along with the performance metrics that are used to evaluate the algorithms and statistical significance tests to compare the submitted methods.
Rigid body motion from range image sequences

An algorithm is described for recovering the six degrees of freedom of motion of a vehicle from a sequence of range images of a static environment taken by a range camera rigidly attached to the vehicle. The technique utilizes a least-squares minimization of the difference between the measured rate of change of elevation at a point and the rate predicted by the so-called elevation rate constmint equation. It is assumed that most of the surface is smooth enough so that local tangent planes can be constructed, and that the motion between frames is smaller than the size of most features in the range image. This method does not depend on the determination of correspondences between isolated high-level features in the range images. The algorithm has been successfully applied to data obtained from the range imager on the Autonomous Land Vehicle (ALV). Other sensors on the ALV provide an initial approximation to the motion between frames. It was found that the outputs of the vehicle sensors themselves are not suitable for accurate motion recovery because of errors in dead reckoning resulting from such problems as wheel slippage. The sensor measurements are used only to approximately register range data. The algorithm described here then recovers the difference between the true motion and that estimated from the sensor outputs. s 1991
The Driving Forces of Customer Loyalty: A Study of Internet Service Providers in Hong Kong

In this study we examine the driving forces of customer loyalty in the broadband market in Hong Kong. We developed and empirically tested a model to examine the antecedents of customer loyalty towards Internet service providers (ISPs) in Hong Kong. Structural equation modeling (SEM) was used to evaluate the proposed model. A total of 737 valid returns were obtained through a questionnaire survey. The results show that customer satisfaction, switching cost, and price perception are antecedents that lead directly to customer loyalty, with customer satisfaction exerting the greatest influence. Although we found that service quality significantly influences customer satisfaction, which in turn leads to customer loyalty, we did not find a direct relationship between service quality and customer loyalty. Our results also reveal that corporate image is not related to customer loyalty. Our empirical investigation suggests that investing huge resources in building corporate image can indeed be a risky strategy for ISPs.
IA-32 Execution Layer: a two-phase dynamic translator designed to support IA-32 applications on Itanium-based systems

IA-32 Execution Layer (IA-32 EL) is a newtechnology that executes IA-32 applications onIntel® Itanium® processor family systems.Currently, support for IA-32 applications onItanium-based platforms is achieved usinghardware circuitry on the Itanium processors.This capability will be enhanced with IA-32EL-software that will ship with Itanium-basedoperating systems and will convert IA-32instructions into Itanium instructions viadynamic translation.In this paper, we describeaspects of the IA-32 Execution Layertechnology, including the general two-phasetranslation architecture and the usage of asingle translator for multiple operatingsystems.The paper provides details of someof the technical challenges such as preciseexception, emulation of FP, MMXTM, and Intel®Streaming SIMD Extension instructions, andmisalignment handling.Finally, the paperpresents some performance results.
Metrics that matter

Within NASA, there is an increasing awareness that software is of growing importance to the success of missions. Much data has been collected, and many theories have been advanced on how to reduce or eliminate errors in code. However, learning requires experience. This article documents a new NASA initiative to build a centralized repository of software defect data; in particular, it documents one specific case study on software metrics. Software metrics are used as a basis for prediction of errors in code modules, but there are many different metrics available. McCabe is one of the more popular tools used to produce metrics, but, as will be shown in this paper, other metrics can be more significant.
Support Vector Regression

− Instead of minimizing the observed training error, Support Vector Regression (SVR) attempts to minimize the generalization error bound so as to achieve generalized performance. The idea of SVR is based on the computation of a linear regression function in a high dimensional feature space where the input data are mapped via a nonlinear function. SVR has been applied in various fields – time series and financial (noisy and risky) prediction, approximation of complex engineering analyses, convex quadratic programming and choices of loss functions, etc. In this paper, an attempt has been made to review the existing theory, methods, recent developments and scopes of SVR.
A 30 Year Retrospective on Dennard's MOSFET Scaling Paper

The MOSFET scaling principles for obtaining simultaneous improvements in transistor density, switching speed, and power dissipation described by Robert H. Dennard and others in "Design of Ion-implanted MOSFETs with Very Small Physical Dimensions" (1974 ) became a roadmap for the semiconductor industry to provide systematic and predictable transistor improvements. New technology generations emerging approximately every three years during the 1970's and 1980's and appearing every other year starting in the mid-1990's, promise to continue although we face growing challenges.
Free Space Computation Using Stochastic Occupancy Grids and Dynamic Programming

The computation of free space available in an environment is an essential task for many intelligent automotive and robotic applications. This paper proposes a new approach, which builds a stochastic occupancy grid to address the free space problem as a dynamic programming task. Stereo measurements are integrated over time reducing disparity uncertainty. These integrated measurements are entered into an occupancy grid, taking into account the noise properties of the measurements. In order to cope with real-time requirements of the application, three occupancy grid types are proposed. Their applicabilities and implementations are also discussed. Experimental results with real stereo sequences show the robustness and accuracy of the method. The current implementation of the method runs on off-the-shelf hardware at 20 Hz.
An agent for optimizing airline ticket purchasing

Buying airline tickets is an ubiquitous task in which it is difficult for humans to minimize cost due to insufficient information. Even with historical data available for inspection (a recent addition to some travel reservation websites), it is difficult to assess how purchase timing translates into changes in expected cost. To address this problem, we introduce an agent which is able to optimize purchase timing on behalf of customers. We provide results that demonstrate the method can perform much closer to the optimal purchase policy than existing decision theoretic approaches for this domain.
Movie genre classification via scene categorization

This paper presents a method for movie genre categorization of movie trailers, based on scene categorization. We view our approach as a step forward from using only low-level visual feature cues, towards the eventual goal of high-level seman- tic understanding of feature films. Our approach decom- poses each trailer into a collection of keyframes through shot boundary analysis. From these keyframes, we use state-of- the-art scene detectors and descriptors to extract features, which are then used for shot categorization via unsuper- vised learning. This allows us to represent trailers using a bag-of-visual-words (bovw) model with shot classes as vo- cabularies. We approach the genre classification task by mapping bovw temporally structured trailer features to four high-level movie genres: action, comedy, drama or horror films. We have conducted experiments on 1239 annotated trailers. Our experimental results demonstrate that exploit- ing scene structures improves film genre classification com- pared to using only low-level visual features.
Development of a Wearable-Sensor-Based Fall Detection System

Fall detection is a major challenge in the public healthcare domain, especially for the elderly as the decline of their physical fitness, and timely and reliable surveillance is necessary to mitigate the negative effects of falls. This paper develops a novel fall detection system based on a wearable device. The system monitors the movements of human body, recognizes a fall from normal daily activities by an effective quaternion algorithm, and automatically sends request for help to the caregivers with the patient's location.
Flink: Semantic Web technology for the extraction and analysis of social networks

We present the Flink system for the extraction, aggregation and visualization of online social networks. Flink employs semantic technology for reasoning with personal information extracted from a number of electronic information sources including web pages, emails, publication archives and FOAF profiles. The acquired knowledge is used for the purposes of social network analysis and for generating a webbased presentation of the community. We demonstrate our novel method to social science based on electronic data using the example of the Semantic Web research community.
Challenges in multimodal gesture recognition

This paper surveys the state of the art on multimodal gesture recognition and introduces the JMLR special topic on gesture recognition 2011-2015. We began right at the start of the KinectT Mrevolution when inexpensive infrared cameras providing image depth recordings became available. We published papers using this technology and other more conventional methods, including regular video cameras, to record data, thus providing a good overview of uses of machine learning and computer vision using multimodal data in this area of application. Notably, we organized a series of challenges and made available several datasets we recorded for that purpose, including tens of thousands of videos, which are available to conduct further research. We also overview recent state of the art works on gesture recognition based on a proposed taxonomy for gesture recognition, discussing challenges and future lines of research.
Integrating Programming by Example and Natural Language Programming

We motivate the integration of programming by example and natural language programming by developing a system for specifying programs for simple text editing operations based on regular expressions. The programs are described with unconstrained natural language instructions, and providing one or more examples of input/output. We show that natural language allows the system to deduce the correct program much more often and much faster than is possible with the input/output example(s) alone, showing that natural language programming and programming by example can be combined in a way that overcomes the ambiguities that both methods suffer from individually and, at the same time, provides a more natural interface to the user.
Gratitude and hedonic and eudaimonic well-being in Vietnam war veterans.

Little information exists on the contribution of psychological strengths to well-being in persons with post-traumatic stress disorder (PTSD). Data from other populations suggest that gratitude, defined as the positive experience of thankfulness for being the recipient of personal benefits, may have salutary effects on everyday functioning. We investigated whether dispositional gratitude predicted daily hedonic and eudaimonic well-being in combat veterans with and without PTSD. We also examined associations between daily gratitude and daily well-being across time. Veterans with PTSD, compared to those without PTSD, exhibited significantly lower dispositional gratitude; no differences were found on daily gratitude. Dispositional gratitude predicted greater daily positive affect, percentage of pleasant days over the assessment period, daily intrinsically motivating activity, and daily self-esteem over and above effects attributable to PTSD severity and dispositional negative and positive affect in the PTSD group but not the non-PTSD group. Daily gratitude was uniquely associated with each dimension of daily well-being in both groups. Although preliminary, these results provide support for the further investigation of gratitude in trauma survivors.
Fuse: A Reproducible, Extendable, Internet-Scale Corpus of Spreadsheets

Spreadsheets are perhaps the most ubiquitous form of end-user programming software. This paper describes a corpus, called Fuse, containing 2,127,284 URLs that return spreadsheets (and their HTTP server responses), and 249,376 unique spreadsheets, contained within a public web archive of over 26.83 billion pages. Obtained using nearly 60,000 hours of computation, the resulting corpus exhibits several useful properties over prior spreadsheet corpora, including reproducibility and extendability. Our corpus is unencumbered by any license agreements, available to all, and intended for wide usage by end-user software engineering researchers. In this paper, we detail the data and the spreadsheet extraction process, describe the data schema, and discuss the trade-offs of Fuse with other corpora.
Representing Verbs with Rich Contexts: an Evaluation on Verb Similarity

Several studies on sentence processing suggest that the mental lexicon keeps track of the mutual expectations between words. Current DSMs, however, represent context words as separate features, which causes the loss of important information for word expectations, such as word order and interrelations. In this paper, we present a DSM which addresses the issue by defining verb contexts as joint dependencies. We test our representation in a verb similarity task on two datasets, showing that joint contexts are more efficient than single dependencies, even with a relatively small amount of training data.
Can long passwords be secure and usable?

To encourage strong passwords, system administrators employ password-composition policies, such as a traditional policy requiring that passwords have at least 8 characters from 4 character classes and pass a dictionary check. Recent research has suggested, however, that policies requiring longer passwords with fewer additional requirements can be more usable and in some cases more secure than this traditional policy. To explore long passwords in more detail, we conducted an online experiment with 8,143 participants. Using a cracking algorithm modified for longer passwords, we evaluate eight policies across a variety of metrics for strength and usability. Among the longer policies, we discover new evidence for a security/usability tradeoff, with none being strictly better than another on both dimensions. However, several policies are both more usable and more secure that the traditional policy we tested. Our analyses additionally reveal common patterns and strings found in cracked passwords. We discuss how system administrators can use these results to improve password-composition policies.
Two-Sided Markets : An Overview

The paper offers an introduction and a road map to the burgeoning literature on two-sided markets. In many industries, platforms court two (or more) sides that use the platform to interact with each other. The platforms’ usage or variable charges impact the two sides’ willingness to trade, and thereby their net surpluses from potential interactions; the platforms’ membership or fixed charges in turn determine the end-users’ presence on the platform. The platforms’ fine design of the structure of variable and fixed charges is relevant only if the two sides do not negotiate away the corresponding usage and membership externalities. The paper first focuses on usage charges and provides conditions for the allocation of the total usage charge (e.g., the price of a call or of a payment card transaction) between the two sides not to be neutral; the failure of the Coase theorem is necessary but not sufficient for two-sidedness. Second, the paper builds a canonical model integrating usage and membership externalities. This model allows us to unify and compare the results obtained in the two hitherto disparate strands of the literature emphasizing either form of externality; and to place existing membership (or indirect) externalities models on a stronger footing by identifying environments in which these models can accommodate usage pricing. We also obtain general results on usage pricing of independent interest. Finally, the paper reviews some key economic insights on platform price and non-price strategies.
Modeling shape and textural variations in aging faces

We propose a two fold approach towards modeling facial aging in adults. Firstly, we develop a shape transformation model that is formulated as a physically-based parametric muscle model that captures the subtle deformations facial features undergo with age. The model implicitly accounts for the physical properties and geometric orientations of the individual facial muscles. Next, we develop an image gradient based texture transformation function that characterizes facial wrinkles and other skin artifacts often observed during different ages. Facial growth statistics (both in terms of shape and texture) play a crucial role in developing the aforementioned transformation models. From a database that comprises of pairs of age separated face images of many individuals, we extract age-based facial measurements across key fiducial features and further, study textural variations across ages. We present experimental results that illustrate the applications of the proposed facial aging model in tasks such as face recognition and facial appearance prediction across aging.
What is the Jeopardy Model? A Quasi-Synchronous Grammar for QA

This paper presents a syntax-driven approach to question answering, specifically the answer-sentence selection problem for short-answer questions. Rather than using syntactic features to augment existing statistical classifiers (as in previous work), we build on the idea that questions and their (correct) answers relate to each other via loose but predictable syntactic transformations. We propose a probabilistic quasi-synchronous grammar, inspired by one proposed for machine translation (D. Smith and Eisner, 2006), and parameterized by mixtures of a robust nonlexical syntax/alignment model with a(n optional) lexical-semantics-driven log-linear model. Our model learns soft alignments as a hidden variable in discriminative training. Experimental results using the TREC dataset are shown to significantly outperform strong state-of-the-art baselines.
Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms

Noninvasive electroencephalogram (EEG) recordings provide for easy and safe access to human neocortical processes which can be exploited for a brain-computer interface (BCI). At present, however, the use of BCIs is severely limited by low bit-transfer rates. We systematically analyze and develop two recent concepts, both capable of enhancing the information gain from multichannel scalp EEG recordings: 1) the combination of classifiers, each specifically tailored for different physiological phenomena, e.g., slow cortical potential shifts, such as the premovement Bereitschaftspotential or differences in spatio-spectral distributions of brain activity (i.e., focal event-related desynchronizations) and 2) behavioral paradigms inducing the subjects to generate one out of several brain states (multiclass approach) which all bare a distinctive spatio-temporal signature well discriminable in the standard scalp EEG. We derive information-theoretic predictions and demonstrate their relevance in experimental data. We will show that a suitably arranged interaction between these concepts can significantly boost BCI performances.