nils-herrmann/clean_scirepeval huggingface dataset:
Aadhaar Based Biometric Attendance System Using Wireless Fingerprint Terminals

In every educational institution, attendance for students is compulsory to obtain good knowledge of the lessons taught in the classrooms and also for gaining wisdom as well as the personality development. A biometric attendance system is designed and implemented for efficient real time monitoring as well as transparency in the management of student's genuine attendance using GSM based wireless fingerprint terminals (WFTs). This system can be suitable to deploy at the educational institutions. Each individual has a unique pattern of fingerprints motivates the use of them for biometric authentication and are verified for finding the presence of the students in the institute. In this paper, two different approaches are discussed to authenticate the captured fingerprint in the verification process. The first approach uses data base created by the organization itself and the second approach uses the Aadhaar Central Identification Repository (CIDR). Wireless fingerprint terminals are responsible to capture and store the attendance records of the students in the device data base and updating them to the server data base. SMS Alerts to students and their parents are sent in case of their irregularity, absence or shortage of attendance. The same system can be extended for the use of avoiding malpractice in the examinations as a part of examination monitoring system.
Detecting sentiment embedded in Arabic social media - A lexicon-based approach

Sentiment analysis aims at extracting sentiment embedded mainly in text reviews. The prevalence of semantic web technologies has encouraged users of the web to become authors as well as readers. People write on a wide range of topics. These writings embed valuable information for organizations and industries. This paper introduces a novel framework for sentiment detection in Arabic tweets. The heart of this framework is a sentiment lexicon. This lexicon was built by translating the SentiStrength English sentiment lexicon into Arabic and afterwards the lexicon was expanded using Arabic thesauri. To assess the viability of the suggested framework, the authors have collected and manually annotated a set of 4400 Arabic tweets. These tweets were classified according to their sentiment into positive or negative tweets using the proposed framework. The results reveal that lexicons are helpful for sentiment detection. The overall results are encouraging and open venues for future research.
Network virtualization for cloud computing

Cloud computing enables a transparent access to information technology (IT) services such that the users do not need to know the location and characteristics of the relevant resources. While IT resource virtualization and service abstraction have been widely investigated, data transport within the cloud and its efficient control have not received much attention in the technical literature. In fact, connectivity is, itself, a service that contributes to the overall performance of the cloud. This paper introduces a novel classification of the Network as a Service (NaaS) such that it can be orchestrated with other cloud services. Then, it proposes a network virtualization platform (NVP) as the mediation layer able to provide NaaS to cloud computing by exploiting the functionality provided by control plane (CP)-enabled networks. In particular, the proposed NVP maps the end-point addresses and perceived Quality of Service parameters of a NaaS requests in the parameters characterizing the connectivity as viewed by transport networks using the information obtained from the CP at the boundary of the network. The NVP uses these parameters to fulfill connectivity requests to the CP. Finally, this paper presents a complete design from both the software implementation and network signaling perspective of two use cases in which NaaS is involved as stand-alone facility for the connectivity service provisioning or is combined with other cloud services for a storage service provisioning.
Active Object Perceiver: Recognition-Guided Policy Learning for Object Searching on Mobile Robots

We study the problem of learning a navigation policy for a robot to actively search for an object of interest in an indoor environment solely from its visual inputs. While scene-driven visual navigation has been widely studied, prior efforts on learning navigation policies for robots to find objects are limited. The problem is often more challenging than target scene finding as the target objects can be very small in the view and can be in an arbitrary pose. We approach the problem from an active perceiver perspective, and propose a novel framework that integrates a deep neural network based object recognition module and a deep reinforcement learning based action prediction mechanism. To validate our method, we conduct experiments on both a simulation dataset (AI2-THOR)and a real-world environment with a physical robot. We further propose a new decaying reward function to learn the control policy specific to the object searching task. Experimental results validate the efficacy of our method, which outperforms competing methods in both average trajectory length and success rate.
Long-lived quantum coherence in photosynthetic complexes at physiological temperature.

Photosynthetic antenna complexes capture and concentrate solar radiation by transferring the excitation to the reaction center that stores energy from the photon in chemical bonds. This process occurs with near-perfect quantum efficiency. Recent experiments at cryogenic temperatures have revealed that coherent energy transfer--a wave-like transfer mechanism--occurs in many photosynthetic pigment-protein complexes. Using the Fenna-Matthews-Olson antenna complex (FMO) as a model system, theoretical studies incorporating both incoherent and coherent transfer as well as thermal dephasing predict that environmentally assisted quantum transfer efficiency peaks near physiological temperature; these studies also show that this mechanism simultaneously improves the robustness of the energy transfer process. This theory requires long-lived quantum coherence at room temperature, which never has been observed in FMO. Here we present evidence that quantum coherence survives in FMO at physiological temperature for at least 300 fs, long enough to impact biological energy transport. These data prove that the wave-like energy transfer process discovered at 77 K is directly relevant to biological function. Microscopically, we attribute this long coherence lifetime to correlated motions within the protein matrix encapsulating the chromophores, and we find that the degree of protection afforded by the protein appears constant between 77 K and 277 K. The protein shapes the energy landscape and mediates an efficient energy transfer despite thermal fluctuations.
Token-level and sequence-level loss smoothing for RNN language models

Despite the effectiveness of recurrent neural network language models, their maximum likelihood estimation suffers from two limitations. It treats all sentences that do not match the ground truth as equally poor, ignoring the structure of the output space. Second, it suffers from “exposure bias”: during training tokens are predicted given ground-truth sequences, while at test time prediction is conditioned on generated output sequences. To overcome these limitations we build upon the recent reward augmented maximum likelihood approach i.e. sequence-level smoothing that encourages the model to predict sentences close to the ground truth according to a given performance metric. We extend this approach to token-level loss smoothing, and propose improvements to the sequence-level smoothing approach. Our experiments on two different tasks, image captioning and machine translation, show that token-level and sequence-level loss smoothing are complementary, and significantly improve results.
NeuRec: On Nonlinear Transformation for Personalized Ranking

Modeling user-item interaction patterns is an important task for personalized recommendations. Many recommender systems are based on the assumption that there exists a linear relationship between users and items while neglecting the intricacy and non-linearity of real-life historical interactions. In this paper, we propose a neural network based recommendation model (NeuRec) that untangles the complexity of user-item interactions and establish an integrated network to combine non-linear transformation with latent factors. We further design two variants of NeuRec: userbased NeuRec and item-based NeuRec, by focusing on different aspects of the interaction matrix. Extensive experiments on four real-world datasets demonstrated their superior performances on personalized ranking task.
The potentials of Blockchain technology in logistics

Blockchain technology is recently receiving a lot of attention from researchers as well as from many different industries. There are promising application areas for the logistics sector like digital document exchange and tracking of goods, but there is no existing research on these topics. This thesis aims to contribute to the research of information systems in logistics in combination with Blockchain technology.  The purpose of this research is to explore the capabilities of Blockchain technology regarding the concepts of privacy, transparency and trust. In addition, the requirements of information systems in logistics regarding the mentioned concepts are studied and brought in relation to the capabilities of Blockchain technology. The goal is to contribute to a theoretical discussion on the role of Blockchain technology in improving the flow of goods and the flow of information in logistics.  The research is carried out in the form of an explorative case study. Blockchain technology has not been studied previously in a logistics setting and therefore, an inductive research approach is chosen by using thematic analysis. The case study is based on a pilot test which had the goal to facilitate a Blockchain to exchange documents and track shipments.  The findings reflect that the research on Blockchain technology is still in its infancy and that it still takes several years to facilitate the technology in a productive environment. The Blockchain has the capabilities to meet the requirements of information systems in logistics due to the ability to create trust and establish an organisation overarching platform to exchange information.
Multi-niche analysis of dynamics and policies in Dutch renewable energy innovation journeys (1970-2006): hype-cycles, closed networks and technology-focused learning

This article analyses long-term innovation policies and development trajectories of four renewable energy technologies: wind energy, biomass, fuel cells and hydrogen, and photovoltaics. These trajectories and policies are characterised by many costly failures, setbacks, hypedisappointment cycles, tensions, and struggles. Although setbacks and non-linearities are a normal part of innovation journeys, a comparative analysis of four cases shows the recurrence of particular problems. Using Strategic Niche Management as analytical approach, we conclude that major problems exist with regard to learning processes (too much technology-push, focused on R&D), social networks (supply side oriented, narrow, closed) and expectations (hype-disappointment cycles, limited competence to assess promises).
Data restoration and file carving

This document proposes to make a contribution to the conceptualization and implementation of data recovery techniques through the abstraction of recovery methodologies and aspects that influence the process, relating human motivation to research needs, whether these are for the Auditing or computer science, allowing to generate classification of recovery techniques in the absence of the metadata provided by the filesystem, in this sense have been proposed to file carving techniques as a solution option. Finally, it is revealed that while many file carving techniques are being implemented in other tools, they are still in the research phase.
Multilevel image thresholding using elephant herding optimization algorithm

Digital images are widely used and numerous application in different scientific fields use digital image processing algorithms where image segmentation is a common task. Thresholding represents one technique for solving that task and Kapur's and Otsu's methods are well known criteria often used for selecting thresholds. Finding optimal threshold values represents a hard optimization problem and swarm intelligence algorithms have been successfully used for solving such problems. In this paper we adjusted recent elephant herding optimization algorithm for multilevel thresholding by Kapur's and Otsu's method. Performance was tested on standard benchmark images and compared with four other swarm intelligence algorithms. Elephant herding optimization algorithm outperformed other approaches from literature and it was more robust.
Rectennas for microwave power transmission

Microwave power transmission (MPT) has had a long history before the more recent movement toward wireless power transmission (WPT). MPT can be applied not only to beam-type point-to-point WPT but also to an energy harvesting system fed from distributed or broadcasting radio waves. The key technology is the use of a rectenna, or rectifying antenna, to convert a microwave signal to a DC signal with high efficiency. In this paper, various rectennas suitable for MPT are discussed, including various rectifying circuits, frequency rectennas, and power rectennas.
Spent Resources : Self-Regulatory Resource Availability Affects Impulse Buying

This research investigated impulse buying as resulting from the depletion of a common—but limited—resource that governs self-control. In three investigations, participants’ self-regulatory resources were depleted or not; later, impulsive spending responses were measured. Participants whose resources were depleted, relative to participants whose resources were not depleted, felt stronger urges to buy, were willing to spend more, and actually did spend more money in unanticipated buying situations. Participants having depleted resources reported being influenced equally by affective and cognitive factors and purchased products that were high on each factor at equal rates. Hence, self-regulatory resource availability predicts whether people can resist impulse buying temptations.
Gather-Excite : Exploiting Feature Context in Convolutional Neural Networks

While the use of bottom-up local operators in convolutional neural networks (CNNs) matches well some of the statistics of natural images, it may also prevent such models from capturing contextual long-range feature interactions. In this work, we propose a simple, lightweight approach for better context exploitation in CNNs. We do so by introducing a pair of operators: gather, which efficiently aggregates feature responses from a large spatial extent, and excite, which redistributes the pooled information to local features. The operators are cheap, both in terms of number of added parameters and computational complexity, and can be integrated directly in existing architectures to improve their performance. Experiments on several datasets show that gather-excite can bring benefits comparable to increasing the depth of a CNN at a fraction of the cost. For example, we find ResNet-50 with gather-excite operators is able to outperform its 101-layer counterpart on ImageNet with no additional learnable parameters. We also propose a parametric gather-excite operator pair which yields further performance gains, relate it to the recently-introduced Squeeze-and-Excitation Networks, and analyse the effects of these changes to the CNN feature activation statistics.
Evaluation of knowledge management tools using AHP

This paper presents an application of the analytic hierarchy process (AHP) used to select the most appropriate tool to support knowledge management (KM). This method adopts a multi-criteria approach that can be used to analyse and compare KM tools in the software market. The method is based on pairwise comparisons between several factors that affect the selection of the most appropriate KM tool. An AHP model is formulated and applied to a real case of assisting decision-makers in a leading communications company in Hong Kong to evaluate a suitable KM tool. We believe that the application shown can be of use to managers and that, because of its ease of implementation, others can benefit from this approach.
Comparing Clusterings – an information based distance

This paper proposes an information theoretic criterion for comparing two partitions, or clusterings, of the same data set. The criterion, called variation of information (VI), measures the amount of information lost and gained in changing from clustering C to clustering C′. The basic properties of VI are presented and discussed. We focus on two kinds of properties: (1) those that help one build intuition about the new criterion (in particular, it is shown the VI is a true metric on the space of clusterings), and (2) those that pertain to the comparability of VI values over different experimental conditions. As the latter properties have rarely been discussed explicitly before, other existing comparison criteria are also examined in their light. Finally we present the VI from an axiomatic point of view, showing that it is the only “sensible” criterion for comparing partitions that is both aligned to the lattice and convexely additive. As a consequence, we prove an impossibility result for comparing partitions: there is no criterion for comparing partitions that simultaneoulsly satisfies the above two desirable properties and is bounded.
Example-based Acquisition of Fine-grained Collocation Resources

Collocations such as heavy rain or make [a] decision, are combinations of two elements where one (the base) is freely chosen, while the choice of the other (collocate) is restricted, depending on the base. Collocations present difficulties even to advanced language learners, who usually struggle to find the right collocate to express a particular meaning, e.g., both heavy and strong express the meaning ‘intense’, but while rain selects heavy, wind selects strong. Lexical Functions (LFs) describe the meanings that hold between the elements of collocations, such as ‘intense’, ‘perform’, ‘create’, ‘increase’, etc. Language resources with semantically classified collocations would be of great help for students, however they are expensive to build, since they are manually constructed, and scarce. We present an unsupervised approach to the acquisition and semantic classification of collocations according to LFs, based on word embeddings in which, given an example of a collocation for each of the target LFs and a set of bases, the system retrieves a list of collocates for each base and LF.
Prenatal exposure to maternal depression and cortisol influences infant temperament.

Accumulating evidence indicates that prenatal maternal and fetal processes can have a lasting influence on infant and child development. Results from animal models indicate that prenatal exposure to maternal stress and stress hormones has lasting consequences for development of the offspring. Few prospective studies of human pregnancy have examined the consequences of prenatal exposure to stress and stress hormones.



In this study the effects of prenatal maternal psychosocial (anxiety, depression, and perceived stress) and endocrine (cortisol) indicators of stress on infant temperament were examined in a sample of 247 full-term infants. Maternal salivary cortisol and psychological state were evaluated at 18-20, 24-26, and 30-32 weeks of gestation and at 2 months postpartum. Infant temperament was assessed with a measure of negative reactivity (the fear subscale of the Infant Temperament Questionnaire) at 2 months of age.



Elevated maternal cortisol at 30-32 weeks of gestation, but not earlier in pregnancy, was significantly associated with greater maternal report of infant negative reactivity. Prenatal maternal anxiety and depression additionally predicted infant temperament. The associations between maternal cortisol and maternal depression remained after controlling for postnatal maternal psychological state.



These data suggest that prenatal exposure to maternal stress has consequences for the development of infant temperament.
Robust speech recognition using DNN-HMM acoustic model combining noise-aware training with spectral subtraction

Recently, acoustic models based on deep neural notworks (DNNs) have been introduced and showed dramatic improvements over acoustic models based on GMM in a variety of tasks. In this paper, we considered the improvement of noise robustness of DNN. Inspired by Missing Feature Theory and static noise aware training, we proposed an approach that uses a noise-suppressed acoustic feature and estimated noise information as input of DNN. We used simple Spectral Subtraction as noise-suppression. As noise estimation, we used estimation per utterance or frame. In noisy speech recognition experiments, we compared the proposed method with other methods and the proposed method showed the superior performance than the other approaches. For noise estimation per utterance with log Mel Filterbank, we obtained 28.6% word error rate reduction compared with multi condition training, 5.9% reduction compared with noise adaptive training.
Video-to-Video Synthesis

We study the problem of video-to-video synthesis, whose goal is to learn a mapping function from an input source video (e.g., a sequence of semantic segmentation masks) to an output photorealistic video that precisely depicts the content of the source video. While its image counterpart, the image-to-image translation problem, is a popular topic, the video-to-video synthesis problem is less explored in the literature. Without modeling temporal dynamics, directly applying existing image synthesis approaches to an input video often results in temporally incoherent videos of low visual quality. In this paper, we propose a video-to-video synthesis approach under the generative adversarial learning framework. Through carefully-designed generators and discriminators, coupled with a spatio-temporal adversarial objective, we achieve high-resolution, photorealistic, temporally coherent video results on a diverse set of input formats including segmentation masks, sketches, and poses. Experiments on multiple benchmarks show the advantage of our method compared to strong baselines. In particular, our model is capable of synthesizing 2K resolution videos of street scenes up to 30 seconds long, which significantly advances the state-of-the-art of video synthesis. Finally, we apply our method to future video prediction, outperforming several competing systems. Code, models, and more results are available at our website.