nils-herrmann/clean_scirepeval huggingface dataset:
Training with Noise is Equivalent to Tikhonov Regularization

It is well known that the addition of noise to the input data of a neural network during training can, in some circumstances, lead to significant improvements in generalization performance. Previous work has shown that such training with noise is equivalent to a form of regularization in which an extra term is added to the error function. However, the regularization term, which involves second derivatives of the error function, is not bounded below, and so can lead to difficulties if used directly in a learning algorithm based on error minimization. In this paper we show that for the purposes of network training, the regularization term can be reduced to a positive semi-definite form that involves only first derivatives of the network mapping. For a sum-of-squares error function, the regularization term belongs to the class of generalized Tikhonov regularizers. Direct minimization of the regularized error function provides a practical alternative to training with noise.
Deep Semantic Frame-Based Deceptive Opinion Spam Analysis

User-generated content is becoming increasingly valuable to both individuals and businesses due to its usefulness and influence in e-commerce markets. As consumers rely more on such information, posting deceptive opinions, which can be deliberately used for potential profit, is becoming more of an issue. Existing work on opinion spam detection focuses mainly on linguistic features such as n-grams, syntactic patterns, or LIWC. However, deep semantic analysis remains largely unstudied. In this paper, we propose a frame-based deep semantic analysis method for understanding rich characteristics of deceptive and truthful opinions written by various types of individuals including crowdsourcing workers, employees who have expert-level domain knowledge about local businesses, and online users who post on Yelp and TripAdvisor. Using our proposed semantic frame feature, we developed a classification model that outperforms the baseline model and achieves an accuracy of nearly 91%. Also, we performed qualitative analysis of deceptive and truthful review datasets and considered their semantic differences. Finally, we successfully found some interesting features that existing methods were unable to identify.
Sentiment Classification of Social Media Content with Features Generated Using Topic Models

This paper presents a method for using topic distributions generated from topic models as features for performing sentiment analysis on documents. This will be tested in the social media domain, specifically Twitter. The proposed approach allows for the mapping from word space to topic space which allows for less features to be needed and also reduces computational complexity. Multiple machine learning algorithms will be used to test the topic model generated features and a number of different versions of test corpus will be used, including unigrams, bigrams, part-of-speech tagging and adjectives only. The method proposed will also be compared to other notable topic-sentiment methods such as the aspect-sentiment unification model and the joint sentiment/topic model. The results show that using topic distributions can improve the accuracy of classification algorithms, however, the performance can be dependent on the algorithm used and the initial features used. Additionally, we show that using only topics as features outperforms the hybrid topic-sentiment models.
Ethical issues raised by the treatment of gender-variant prepubescent children.

Transgender issues and transgender rights have become increasingly a matter of media attention and public policy debates. Reflecting changes in psychiatric perspectives, the diagnosis of "trans-sexualism" first appeared in the International Statistical Classification of Diseases and Related Health Problems in 1975 and shortly thereafter, in 1980, in the Diagnostic and Statistical Manual of Mental Disorders. Since that time, international standards of care have been developed, and today those standards are followed by clinicians across diverse cultures. In many instances, treatment of older adolescents and adults is covered by national health care systems and, in some cases, by private health insurance. Most recently, the Medicare ban on coverage for gender reassignment surgery was lifted in 2014. In contrast to the relative lack of controversy about treating adolescents and adults, there is no expert clinical consensus regarding the treatment of prepubescent children who meet diagnostic criteria for what was referred to in both DSM-IV-TR and ICD-10 as gender identity disorder in children and now in DSM-5 as gender dysphoria. One reason for the differing attitudes has to do with the pervasive nature of gender dysphoria in older adolescents and adults: it rarely desists, and so the treatment of choice is gender or sex reassignment. On the subject of treating children, however, as the World Professional Association for Transgender Health notes in their latest Standards of Care, gender dysphoria in childhood does not inevitably continue into adulthood, and only 6 to 23 percent of boys and 12 to 27 percent of girls treated in gender clinics showed persistence of their gender dysphoria into adulthood. Further, most of the boys' gender dysphoria desisted, and in adulthood, they identified as gay rather than as transgender. In an effort to clarify best treatment practices for transgender individuals, a recent American Psychiatric Association Task Force on the Treatment of Gender Identity outlined three differing approaches to treating prepubescent gender dysphoric children.
Age Estimation in Short Speech Utterances Based on LSTM Recurrent Neural Networks

Age estimation from speech has recently received increased interest as it is useful for many applications such as user-profiling, targeted marketing, or personalized call-routing. This kind of applications need to quickly estimate the age of the speaker and might greatly benefit from real-time capabilities. Long short-term memory (LSTM) recurrent neural networks (RNN) have shown to outperform state-of-the-art approaches in related speech-based tasks, such as language identification or voice activity detection, especially when an accurate real-time response is required. In this paper, we propose a novel age estimation system based on LSTM-RNNs. This system is able to deal with short utterances (from 3 to 10 s) and it can be easily deployed in a real-time architecture. The proposed system has been tested and compared with a state-of-the-art i-vector approach using data from NIST speaker recognition evaluation 2008 and 2010 data sets. Experiments on short duration utterances show a relative improvement up to 28% in terms of mean absolute error of this new approach over the baseline system.
Stateful Inter-Packet Signal Processing for Wireless Networking

Traditional signal processing design (e.g., frequency offset and channel estimation) at a receiver treats each packet arrival as an independent process to facilitate decoding and interpreting packet data. In this paper, we enhance the performance of this process in the wireless network domain. We propose STAteful inter-Packet signaL procEssing (STAPLE), a framework of stateful signal processing residing between the physical and link layers. STAPLE transforms the signal processing procedure into a lightweight stateful process that caches in a small-sized memory table physical and link layer header fields as packet state information. The similarity of such information among packets serves as prior knowledge to further enhance the reliability of signal processing and thus improve the wireless network performance. We implement STAPLE on USRP X300-series devices with adapted configurations for 802.11a/b/g/n/ac and 802.15.4. The STAPLE prototype is of low processing complexity and does not change any wireless standard specification. Comprehensive experimental results show that the benefit from STAPLE is universal in various wireless networks.
Elman Network with Embedded Memory for System Identification

This paper presents a new recurrent neural network (RNN) structure called ENEM for dynamic system identification. ENEM structure is based on Elman network and NARX neural network. In order to show the performance of ENEM for system identification, the results were also compared to the results of Elman network, Jordan network and their modified models. The identification results of linear and nonlinear systems have shown that the proposed ENEM structure is better than the other results of RNN models.
Design of a 1.8V-input 6.5V-output digital level shifter for trimming application

A 1.8V input, 6.5V output step-up digital level shifter is proposed. It is based on contention mitigated digital level shifter (CMLS) but it uses single supply only instead of two. It uses a complete, stand-alone, and unregulated voltage charge pump circuit that generates a 6.5V output voltage from a 1.8V input. Charge pump output is then used as high-side supply (VDDH) of the step-up level shifter, while the low-side supply (VDDl) is externally-supplied 1.8V. Compared to the conventional level shifter where two separate supply voltages (VDDl and VDDH) are required, the proposed design uses a single supply to shifted-up a digital signals. This technique simplifies the user-end power supply requirement of the system, thus the proposed level shifter design is applicable in many low-power and power-constraint applications such as in mobile and portable devices application. The proposed design is implemented using 0.35-μm TSMC CMOS technology. The schematic and the nestlist have been designed and extracted using LTSPICE tool; while the simulation has been carried out using the HSPICE circuit simulator.
The effects of filtered video on awareness and privacy

Video-based media spaces are designed to support casual interaction between intimate collaborators. Yet transmitting video is fraught with privacy concerns. Some researchers suggest that the video stream be filtered to mask out potentially sensitive information. While a variety of filtering techniques exist, they have not been evaluated for how well they safeguard privacy.
In this paper, we analyze how a blur and a pixelize video filter might impact both awareness and privacy in a media space. Each filter is considered at nine different levels of fidelity, ranging from heavily applied filter levels that mask almost all information, to lightly applied filters that reveal almost everything. We examined how well observers of several filtered video scenes extract particular awareness cues: the number of actors; their posture (moving, standing, seated); their gender; the visible objects (basic to detailed); and how available people look (their busyness, seriousness and approachability). We also examined the privacy-preserving potential of each filter level in the context of common workplace activities. Our results suggest that the blur filter, and to a lesser extent the pixelize filter, have a level suitable for providing awareness information while safeguarding privacy.
Low-Quality Product Review Detection in Opinion Summarization

Product reviews posted at online shopping sites vary greatly in quality. This paper addresses the problem of detecting lowquality product reviews. Three types of biases in the existing evaluation standard of product reviews are discovered. To assess the quality of product reviews, a set of specifications for judging the quality of reviews is first defined. A classificationbased approach is proposed to detect the low-quality reviews. We apply the proposed approach to enhance opinion summarization in a two-stage framework. Experimental results show that the proposed approach effectively (1) discriminates lowquality reviews from high-quality ones and (2) enhances the task of opinion summarization by detecting and filtering lowquality reviews.
Behavioural Finance : A Review and Synthesis

I provide a synthesis of the Behavioural finance literature over the past two decades. I review the literature in three parts, namely, (i) empirical and theoretical analyses of patterns in the cross-section of average stock returns, (ii) studies on trading activity, and (iii) research in corporate finance. Behavioural finance is an exciting new field because it presents a number of normative implications for both individual investors and CEOs. The papers reviewed here allow us to learn more about these specific implications.
Qualitative Case Study Guidelines

Although widely used, the qualitative case study method is not well understood. Due to conflicting epistemological presuppositions and the complexity inherent in qualitative case-based studies, scientific rigor can be difficult to demonstrate, and any resulting findings can be difficult tojustify. For that reason, this paper discusses methodological problems associated with qualitative case-based research and offers guidelines for overcoming them. Due to its nearly universal acceptance, Yin’s six-stage case study process is adopted and elaborated on. Moreover, additional principles from the wider methodological literature are integrated and explained. Finally, some modifications to the dependencies between the six case study stages are suggested. It is expected that following the guidelines presented in this paper may facilitate the collection of the most relevant data in the most efficient and effective manner, simplify the subsequent analysis, as well as enhance the validity of the resulting findings. The paper should be of interest to students (honour, masters, doctoral), academics, and practitioners involved with conducting and reviewing qualitative case-based studies.
Algorithm for 3D Point Cloud Denoising

The raw data of point cloud produced by 3D scanning tools contains additive noise from various sources. This paper proposes a method for 3D unorganized point cloud denoising by making full use of the depth information of unorganized points and space analytic geometry theory, applying over-domain average method for 2D image of image denoising theory to 3D point data. The point cloud noises are filtered by using irregular polyhedron based on the limited local neighborhoods. The experiment shows that the proposed method successfully removes noise from point cloud with the features of the scattered point model reserved. Furthermore, the presented algorithm excels in its simplicity both in implementation and operation.
Translation techniques in cross-language information retrieval

Cross-language information retrieval (CLIR) is an active sub-domain of information retrieval (IR). Like IR, CLIR is centered on the search for documents and for information contained within those documents. Unlike IR, CLIR must reconcile queries and documents that are written in different languages. The usual solution to this mismatch involves translating the query and/or the documents before performing the search. Translation is therefore a pivotal activity for CLIR engines. Over the last 15 years, the CLIR community has developed a wide range of techniques and models supporting free text translation. This article presents an overview of those techniques, with a special emphasis on recent developments.
Goal-based trajectory analysis for unusual behaviour detection in intelligent surveillance

In a typical surveillance installation, a human operator has to constantly monitor a large array of video feeds for suspicious behaviour. As the number of cameras increases, information overload makes manual surveillance increasingly difficult, adding to other confounding factors such as human fatigue and boredom. The objective of an intelligent vision-based surveillance system is to automate the monitoring and event detection components of surveillance, alerting the operator only when unusual behaviour or other events of interest are detected. While most traditional methods for trajectory-based unusual behaviour detection rely on low-level trajectory features such as flow vectors or control points, this paper builds upon a recently introduced approach that makes use of higher-level features of intentionality. Individuals in the scene are modelled as intentional agents, and unusual behaviour is detected by evaluating the explicability of the agent’s trajectory with respect to known spatial goals. The proposed method extends the original goal-based approach in three ways: first, the spatial scene structure is learned in a training phase; second, a region transition model is learned to describe normal movement patterns between spatial regions; and third, classification of trajectories in progress is performed in a probabilistic framework using particle filtering. Experimental validation on three published third-party datasets demonstrates the validity of the proposed approach.
Critical Success Factors for Online Distance Learning in Higher Education : A Review of the Literature

The aim of this paper is to specify the Critical Success Factors (CSFs) for Online Distance Learning (ODL) in Higher Education (HE). Research methodology was analyzing and synthesizing the literature review. The literatures were reviewed to determine items relevant to online learning success as implementation, criteria and indicator. A total of 19 papers, published during 2000-2012, were selected from Chulalongkorn University reference databases. Data analysis method was using one of the popular analysis techniques for qualitative research works or the content analysis. The results on the CSFs for ODL can be grouped into 5 factors: 1) institutional management, 2) learning environment, 3) instructional design, 4) services support and 5) course evaluation. Each of these 5 factors includes several important elements that can assist to enhance efficiency of online learning courses in higher education institutions. It is a concrete approach to lead functions of an online institute or course in all levels to the same directions for achieving the success of the institute‘s vision, and make staffs and executives know what they have to do for the success of online distance learning.
Cloudlets: at the leading edge of mobile-cloud convergence

As mobile computing and cloud computing converge, the sensing and interaction capabilities of mobile devices can be seamlessly fused with compute-intensive and data-intensive processing in the cloud. Cloudlets are important architectural components in this convergence, representing the middle tier of a mobile device - cloudlet - cloud hierarchy. We show how cloudlets enable a new genre of applications called cognitive assistance applications that augment human perception and cognition. We describe a plug-and-play architecture for cognitive assistance, and a proof of concept using Google Glass.
Hierarchical Document Clustering: A Review

As text documents are largely increasing in the internet, the process of grouping similar documents for versatile applications have put the eye of researchers in this area. However most clustering methods suffer from challenges in dealing with problems of high dimensionality, scalability, accuracy and meaningful cluster labels. This paper presents a review on all these well known methods of document clustering. Hierarchical document clustering method is explained in detail. Study shows that hierarchical document clustering performs well but still there is a scope to improve above mentioned problems.
Long-Time Exposure to Violent Video Games Does Not Show Desensitization on Empathy for Pain: An fMRI Study

As a typical form of empathy, empathy for pain refers to the perception and appraisal of others' pain, as well as the corresponding affective responses. Numerous studies investigated the factors affecting the empathy for pain, in which the exposure to violent video games (VVGs) could change players' empathic responses to painful situations. However, it remains unclear whether exposure to VVG influences the empathy for pain. In the present study, in terms of the exposure experience to VVG, two groups of participants (18 in VVG group, VG; 17 in non-VVG group, NG) were screened from nearly 200 video game experience questionnaires. And then, the functional magnetic resonance imaging data were recorded when they were viewing painful and non-painful stimuli. The results showed that the perception of others' pain were not significantly different in brain regions between groups, from which we could infer that the desensitization effect of VVGs was overrated.
Information Systems and Healthcare XVI: Physician Adoption of Electronic Medical Records: Applying the UTAUT Model in a Healthcare Context

This study applies the Unified Theory of Acceptance and Use of Technology (UTAUT) to the phenomenon of physician adoption of electronic medical records (EMR) technology. UTAUT integrates eight theories of individual acceptance into one comprehensive model designed to assist in understanding what factors either enable or hinder technology adoption and use. As such, it provides a useful lens through which to view what is currently taking place in the healthcare industry regarding EMR adoption. This is mutually beneficial to both the healthcare and MIS communities, as UTAUT offers valuable practical insight to the healthcare industry in explaining why EMR technology has not been more widely adopted as well as what prescriptions may facilitate future adoption, while offering the MIS community the opportunity to strengthen existing theory through an illustration of its application.
BSI : B LOOM FILTER-BASED S

Resource management and search is very important yet challenging in large-scale distributed systems like P2Pnetworks. Most existing P2P systems rely on indexing to efficiently route queries over the network. However, searches based on such indices face two key issues. First, majority of existing search schemes often rely on simply keyword based indices that can only support exact string based matches without taking into account the meaning of words. Second it is difficult, if not impossible, to devise query based indexing schemes that can represent all possible concept combinations without resulting in exponential index sizes. To address these problems, we present BSI, a novel P2P indexing and query routing strategy to support semantic based content searches. The BSI indexing structure captures the semantic content of documents using a reference ontology. Our indexing scheme can efficiently handle multi -concept queries by maintaining summary level information for each individual concept and concept combinations using a novel space-efficient Two-level Semantic Bloom Filter(TSBF) data structure. By using TSBFs to represent a large document and query base, BSI significantly reduces the communication cost and storage cost of indices. Furthermore, We devise a low-overhead mechanism to allow peers to dynamically estimate the relevance strength of a peer for multi-concept queries with high accuracy solely based on TSBFs. We also propose a routing index compression mechanism to observe peers’ dynamic storage limitations with minimal loss of information by exploiting a reference ontology structure. Based on the proposed index structure, we design a novel query routing algorithm that exploits semantic based information to route queries to semantically relevant peers. Performance evaluation demonstrates that our proposed approach can improve the search recall of unstructured P2P systems up to 383.71% while keeping the communication cost at a low level compared to state-of-art search mechanism OSQR [7].