nils-herrmann/clean_scirepeval huggingface dataset:
Equidistant prototypes embedding for single sample based face recognition with generic learning and incremental learning

We develop a parameter-free face recognition algorithm which is insensitive to large variations in lighting, expression, occlusion, and age using a single gallery sample per subject. We take advantage of the observation that equidistant prototypes embedding is an optimal embedding that maximizes the minimum one-against-the-rest margin between the classes. Rather than preserving the global or local structure of the training data, our method, called linear regression analysis (LRA), applies least-square regression technique to map gallery samples to the equally distant locations, regardless of the true structure of training data. Further, a novel generic learning method, which maps the intra-class facial differences of the generic faces to the zero vectors, is incorporated to enhance the generalization capability of LRA. Using this novel method, learning based on only a handful of generic classes can largely improve the face recognition performance, even when the generic data are collected from a different database and camera set-up. The incremental learning based on the Greville algorithm makes the mapping matrix efficiently updated from the newly coming gallery classes, training samples, or generic variations. Although it is fairly simple and parameter-free, LRA, combined with commonly used local descriptors, such as Gabor representation and local binary patterns, outperforms the state-of-the-art methods for several standard experiments on the Extended Yale B, CMU PIE, AR, and ∗Corresponding author.  Email address: whdeng@bupt.edu.cn (Weihong Deng)  2014
Combining Numerous Uncorrelated MEMS Gyroscopes for Accuracy Improvement Based on an Optimal Kalman Filter

In this paper, an approach to improve the accuracy of microelectromechanical systems (MEMS) gyroscopes by combining numerous uncorrelated gyroscopes is presented. A Kalman filter (KF) is used to fuse the output signals of several uncorrelated sensors. The relationship between the KF bandwidth and the angular rate input is quantitatively analyzed. A linear model is developed to choose suitable system parameters for a dynamic application of the concept. Simulation and experimental tests of a six-gyroscope array proved that the presented approach was effective to improve the MEMS gyroscope accuracy. The experimental results indicate that six identical gyroscopes with a noise density of 0.11°/s/√Hz and a bias instability of 62°/h can be combined to form a virtual gyroscope with a noise density of 0.03°/s/√Hz and a bias instability of 16.8°/h . The accuracy improvement is better than that of a simple averaging process of the individual sensors.
Two cases of hymenal scars occurred by child rape

Children who have been raped some years back may have hymenal scars. However, medical professionals are not accustomed in assessing these scars because of the lack of experience in performing physical examinations of the external genitalia of children who suffered from rape some years back. Moreover, the importance of physical examination of the victim's external genitalia is sometimes overlooked. Two cases of rape victims with hymenal scars who visited Daegu Child Sexual Abuse Response Center several years after their first sexual abuse along with a literature review are presented here.
Knowledge Collaboration in Online Communities

O communities (OCs) are a virtual organizational form in which knowledge collaboration can occur in unparalleled scale and scope, in ways not heretofore theorized. For example, collaboration can occur among people not known to each other, who share different interests and without dialogue. An exploration of this organizational form can fundamentally change how we theorize about knowledge collaboration among members of organizations. We argue that a fundamental characteristic of OCs that affords collaboration is their fluidity. This fluidity engenders a dynamic flow of resources in and out of the community—resources such as passion, time, identity, social disembodiment of ideas, socially ambiguous identities, and temporary convergence. With each resource comes both a negative and positive consequence, creating a tension that fluctuates with changes in the resource. We argue that the fluctuations in tensions can provide an opportunity for knowledge collaboration when the community responds to these tensions in ways that encourage interactions to be generative rather than constrained. After offering numerous examples of such generative responses, we suggest that this form of theorizing—induced by online communities—has implications for theorizing about the more general case of knowledge collaboration in organizations.
Forecasting Economics and Financial Time Series: ARIMA vs. LSTM

Forecasting time series data is an important subject in economics, business, and finance. Traditionally, there are several techniques to effectively forecast the next lag of time series data such as univariate Autoregressive (AR), univariate Moving Average (MA), Simple Exponential Smoothing (SES), and more notably Autoregressive Integrated Moving Average (ARIMA) with its many variations. In particular, ARIMA model has demonstrated its outperformance in precision and accuracy of predicting the next lags of time series. With the recent advancement in computational power of computers and more importantly developing more advanced machine learning algorithms and approaches such as deep learning, new algorithms are developed to forecast time series data. The research question investigated in this article is that whether and how the newly developed deep learning-based algorithms for forecasting time series data, such as"Long Short-Term Memory (LSTM)", are superior to the traditional algorithms. The empirical studies conducted and reported in this article show that deep learning-based algorithms such as LSTM outperform traditional-based algorithms such as ARIMA model. More specifically, the average reduction in error rates obtained by LSTM is between 84 - 87 percent when compared to ARIMA indicating the superiority of LSTM to ARIMA. Furthermore, it was noticed that the number of training times, known as"epoch"in deep learning, has no effect on the performance of the trained forecast model and it exhibits a truly random behavior.
On the Empirics of Sudden Stops : The Relevance of Balance-Sheet Effects

Using a sample of 32 developed and developing countries we analyze the empirical characteristics of Sudden Stops in capital flows and the relevance of balance-sheet effects in the likelihood of their occurrence. We find that large real exchange rate (RER) fluctuations accompanied by Sudden Stops are basically an emerging market (EM) phenomenon. Sudden Stops seem to come in bunches, grouping together countries that are different in many respects. However, countries are similar in that they remain vulnerable to large RER fluctuations. This may be the case because countries are forced to make large adjustments in the absorption of tradable goods, and/or because the size of dollar liabilities in the banking system (i.e., domestic liability dollarization, or DLD) is large. Openness, understood as a large supply of tradable goods that reduces leverage over the current account deficit, in combination with DLD, is a key determinant of the probability of Sudden Stops. The relationship between Openness and DLD in the determination of the probability of Sudden Stops is highly non-linear, implying that the interaction of high current account leverage and high dollarization may be a dangerous cocktail.
A Theory of Regular Queries

A major theme in relational database theory is navigating the tradeoff between expressiveness and tractability for query languages, where the query-containment problem is considered a benchmark of tractability. The query class UCQ, consisting off unions of conjunctive queries, is a fragment of first-order logic that has a decidable query containment problem, but its expressiveness is limited. Extending UCQ with recursion yields Datalog, an expressive query language that has been studied extensively and has recently become popular in application areas such as declarative networking. Unfortunately, Datalog has an undecidable query containment problem. Identifying a fragment of Datalog that is expressive enough for applications but has a decidable query-containment problem has been an open problem for several years.
 In the area of graph databases, there has been a similar search for a query language that combines expressiveness and tractability. Because of the need to navigate along graph paths of unspecified length, transitive closure has been considered a fundamental operation. Query classes of increasing complexity -- using the operations of disjunction, conjunction, projection, and transitive closure -- have been studied, but the classes lacked natural closure properties. The class RQ of regular queries has emerged only recently as a natural query class that is closed under all of its operations and has a decidable query-containment problem.
 RQ turned out to be a fragment of Datalog where recursion can be used only to express transitive closure. Furthermore, it turns out that applying this idea to Datalog, that is, restricting recursion to the expression of transitive closure, does yield the long-sought goal -- an expressive fragment of Datalog with a decidable query-optimization problem.
Equivalent-circuit models for split-ring resonators and complementary split-ring resonators coupled to planar transmission lines

In this paper, a new approach for the development of planar metamaterial structures is developed. For this purpose, split-ring resonators (SRRs) and complementary split-ring resonators (CSRRs) coupled to planar transmission lines are investigated. The electromagnetic behavior of these elements, as well as their coupling to the host transmission line, are studied, and analytical equivalent-circuit models are proposed for the isolated and coupled SRRs/CSRRs. From these models, the stopband/passband characteristics of the analyzed SRR/CSRR loaded transmission lines are derived. It is shown that, in the long wavelength limit, these stopbands/passbands can be interpreted as due to the presence of negative/positive values for the effective /spl epsiv/ and /spl mu/ of the line. The proposed analysis is of interest in the design of compact microwave devices based on the metamaterial concept.
Hello! My name is... Buffy'' -- Automatic Naming of Characters in TV Video

We investigate the problem of automatically labelling appearances of characters in TV or film material. This is tremendously challenging due to the huge variation in imaged appearance of each character and the weakness and ambiguity of available annotation. However, we demonstrate that high precision can be achieved by combining multiple sources of information, both visual and textual. The principal novelties that we introduce are: (i) automatic generation of time stamped character annotation by aligning subtitles and transcripts; (ii) strengthening the supervisory information by identifying when characters are speaking; (iii) using complementary cues of face matching and clothing matching to propose common annotations for face tracks. Results are presented on episodes of the TV series “Buffy the Vampire Slayer”.
CIVSched: A Communication-Aware Inter-VM Scheduling Technique for Decreased Network Latency between Co-Located VMs

Server consolidation in cloud computing environments makes it possible for multiple servers or desktops to run on a single physical server for high resource utilization, low cost, and reduced energy consumption. However, the scheduler in the virtual machine monitor (VMM), such as Xen credit scheduler, is agnostic about the communication behavior between the guest operating systems (OS). The aforementioned behavior leads to increased network communication latency in consolidated environments. In particular, the CPU resources management has a critical impact on the network latency between co-located virtual machines (VMs) when there are CPUand I/O-intensive workloads running simultaneously. This paper presents the design and implementation of a communication-aware inter-VM scheduling (CIVSched) technique that takes into account the communication behavior between inter-VMs running on the same virtualization platform. The CIVSched technique inspects the network packets transmitted between local co-resident domains to identify the target VM and process that will receive the packets. Thereafter, the target VM and process are preferentially scheduled by the VMM and the guest OS. The cooperation of these two schedulers makes the network packets to be timely received by the target application. Experimental results on the Xen virtualization platform depict that the CIVSched technique can reduce the average response time of network traffic by approximately 19 percent for the highly consolidated environment, while keeping the inherent fairness of the VMM scheduler.
Impact of social media usage on students academic performance in Saudi Arabia

Social media is a popular method for communication amongst university students in Saudi Arabia. However excessive social media use can raise questions about whether academic performance is affected. This research explores this question by conducting a survey on university students in Saudi Arabia in regards to social media usage and their academic performance. The survey also explored which social network is the most popular amongst Saudi students, what students thought about their social media usage and factors besides social media usage which negatively affect academic performance. The survey received 108 responses and descriptive statistics including normality tests i.e. scatter plots were used to examine the relationship between the average number of hours students spent of social media a week and GPA scores of the students. The results demonstrated that there was no linear relationship between social media usage in a week and GPA score. Students highlighted that besides social media use, time management is a factor which affects students ‘studies negatively. The findings of the paper can be used to propose the effective plans for improving the academic performance of the students in such a way that a balance in the leisure, information exchange and academic performance can be maintained.
A Deep Policy Inference Q-Network for Multi-Agent Systems

We present DPIQN, a deep policy inference Q-network that targets multi-agent systems composed of controllable agents, collaborators, and opponents that interact with each other. We focus on one challenging issue in such systems—modeling agents with varying strategies—and propose to employ “policy features” learned from raw observations (e.g., raw images) of collaborators and opponents by inferring their policies. DPIQN incorporates the learned policy features as a hidden vector into its own deep Q-network (DQN), such that it is able to predict better Q values for the controllable agents than the state-of-the-art deep reinforcement learningmodels. We further propose an enhanced version of DPIQN, called deep recurrent policy inference Q-network (DRPIQN), for handling partial observability. Both DPIQN and DRPIQN are trained by an adaptive training procedure, which adjusts the network’s attention to learn the policy features and its own Q-values at different phases of the training process. We present a comprehensive analysis of DPIQN and DRPIQN, and highlight their effectiveness and generalizability in various multi-agent settings. Our models are evaluated in a classic soccer game involving both competitive and collaborative scenarios. Experimental results performed on 1 vs. 1 and 2 vs. 2 games show that DPIQN and DRPIQN demonstrate superior performance to the baseline DQN and deep recurrent Q-network (DRQN) models. We also explore scenarios in which collaborators or opponents dynamically change their policies, and show that DPIQN and DRPIQN do lead to better overall performance in terms of stability and mean scores.
Bidirectional Joint Representation Learning with Symmetrical Deep Neural Networks for Multimodal and Crossmodal Applications

Common approaches to problems involving multiple modalities (classification, retrieval, hyperlinking, etc.) are early fusion of the initial modalities and crossmodal translation from one modality to the other. Recently, deep neural networks, especially deep autoencoders, have proven promising both for crossmodal translation and for early fusion via multimodal embedding. In this work, we propose a flexible crossmodal deep neural network architecture for multimodal and crossmodal representation. By tying the weights of two deep neural networks, symmetry is enforced in central hidden layers thus yielding a multimodal representation space common to the two original representation spaces. The proposed architecture is evaluated in multimodal query expansion and multimodal retrieval tasks within the context of video hyperlinking. Our method demonstrates improved crossmodal translation capabilities and produces a multimodal embedding that significantly outperforms multimodal embeddings obtained by deep autoencoders, resulting in an absolute increase of 14.14 in precision at 10 on a video hyperlinking task.
Analysis, Design and Realization of a Novel Directive Ultrawideband Antenna

In this paper, we present a simple log-periodic-dipole-array (LPDA) solution that allows us to achieve good ultrawideband (UWB) performances. The antenna has been manufactured and the measurements agree well with the theoretical predictions. The antenna presents an average gain of 8 dB and a return loss better than -10 dB over the band from 4.2 to 10.6 GHz. Both the measured antenna transfer function and the computed effect on pulse transmission show good performances in comparison with already known UWB antennas.
Validation of the Korean Version of Schedule of Fatigue and Anergia: General Physician Questionnaire

The Schedule of Fatigue and Anergy/General Physician (SOFA/GP) was developed to screen for prolonged fatigue in the primary care setting. We aimed to evaluate the reliability and validity of the Korean version of the SOFA/GP (SOFA/GP-K), which is adapted from the original English version. We performed translation and back translation, and after conducting a pilot study, we tested the final version of the questionnaire for its reliability and validity in a Korean primary care setting. Two hundred participants that visited a health examination center in a university hospital completed the survey between September and November 2012. A second survey was performed within 2 weeks of the primary survey to test for reliability. We evaluated concurrent validity between the SOFA/GP-K score, the Fatigue Severity Scale (FSS), and Brief Fatigue Index (BFI) scores. The Spearman correlation coefficient between SOFA/GP-K and FSS was 0.71 and 0.61 between SOFA/GP-K and BFI. Internal consistency of SOFA/GP-K was observed (Cronbach's alpha = 0.82) and construct validity was confirmed by factor analysis. The Kappa scores for test-retest reliability for each survey item were between 0.28 and 0.64. The SOFA/GP-K is a valid and reliable questionnaire for screening fatigue in a primary care setting.
Digital Nudging

Digital nudging is the use of user-interface design elements to guide people’s behavior in digital choice environments. Digital choice environments are user interfaces – such as web-based forms and ERP screens – that require people to make judgments or decisions. Humans face choices every day, but the outcome of any choice is influenced not only by rational deliberations of the available options but also by the design of the choice environment in which information is presented, which can exert a subconscious influence on the outcome. In other words, ‘‘what is chosen often depends upon how the choice is presented’’ (Johnson et al. 2012, p. 488) such that the ‘‘choice architecture alters people’s behavior in a predictable way’’ (Thaler and Sunstein 2008, p. 6). Even simple modifications of the choice environment in which options are presented can influence people’s choices and ‘‘nudge’’ them into behaving in particular ways. In fact, there is no neutral way to present choices. For example, Johnson and Goldstein (2003) showed that simply changing default options (from opt-in to opt-out) in the context of organ donation nearly doubled the percentage of people who consent to being organ donors. Many choices are made in online environments. As the design of digital choice environments always (either deliberately or accidentally) influences people’s choices, understanding the effects of digital nudges in these environments can help designers lead users to the most desirable choice. For example, the mobile payment app Square nudges people into giving tips by setting the default to ‘‘tipping’’ so that customers must actively select a ‘‘no tipping’’ option if they choose not to give a tip. Using this simple nudge has raised tip amounts, especially where little or no tipping has been common (Carr 2013). These examples show that simply changing the default option affects the outcome.
Word Embeddings and Their Use In Sentence Classification Tasks

This paper has two parts. In the first part we discuss word embeddings. We discuss the need for them, some of the methods to create them, and some of their interesting properties. We also compare them to image embeddings and see how word embedding and image embedding can be combined to perform different tasks. In the second part we implement a convolutional neural network trained on top of pre-trained word vectors. The network is used for several sentence-level classification tasks, and achieves state-of-art (or comparable) results, demonstrating the great power of pre-trainted word embeddings over random ones.
Towards AI-powered personalization in MOOC learning

Massive Open Online Courses (MOOCs) represent a form of large-scale learning that is changing the landscape of higher education. In this paper, we offer a perspective on how advances in artificial intelligence (AI) may enhance learning and research on MOOCs. We focus on emerging AI techniques including how knowledge representation tools can enable students to adjust the sequence of learning to fit their own needs; how optimization techniques can efficiently match community teaching assistants to MOOC mediation tasks to offer personal attention to learners; and how virtual learning companions with human traits such as curiosity and emotions can enhance learning experience on a large scale. These new capabilities will also bring opportunities for educational researchers to analyse students’ learning skills and uncover points along learning paths where students with different backgrounds may require different help. Ethical considerations related to the application of AI in MOOC education research are also discussed.