nils-herrmann/clean_scirepeval huggingface dataset:
DiSCern: A diversified citation recommendation system for scientific queries

Performing literature survey for scholarly activities has become a challenging and time consuming task due to the rapid growth in the number of scientific articles. Thus, automatic recommendation of high quality citations for a given scientific query topic is immensely valuable. The state-of-the-art on the problem of citation recommendation suffers with the following three limitations. First, most of the existing approaches for citation recommendation require input in the form of either the full article or a seed set of citations, or both. Nevertheless, obtaining the recommendation for citations given a set of keywords is extremely useful for many scientific purposes. Second, the existing techniques for citation recommendation aim at suggesting prestigious and well-cited articles. However, we often need recommendation of diversified citations of the given query topic for many scientific purposes; for instance, it helps authors to write survey papers on a topic and it helps scholars to get a broad view of key problems on a topic. Third, one of the problems in the keyword based citation recommendation is that the search results typically would not include the semantically correlated articles if these articles do not use exactly the same keywords. To the best of our knowledge, there is no known citation recommendation system in the literature that addresses the above three limitations simultaneously. In this paper, we propose a novel citation recommendation system called DiSCern to precisely address the above research gap. DiSCern finds relevant and diversified citations in response to a search query, in terms of keyword(s) to describe the query topic, while using only the citation graph and the keywords associated with the articles, and no latent information. We use a novel keyword expansion step, inspired by community finding in social network analysis, in DiSCern to ensure that the semantically correlated articles are also included in the results. Our proposed approach primarily builds on the Vertex Reinforced Random Walk (VRRW) to balance prestige and diversity in the recommended citations. We demonstrate the efficacy of DiSCern empirically on two datasets: a large publication dataset of more than 1.7 million articles in computer science domain and a dataset of more than 29,000 articles in theoretical high-energy physics domain. The experimental results show that our proposed approach is quite efficient and it outperforms the state-of-the-art algorithms in terms of both relevance and diversity.
Full Adder Circuits using Static Cmos Logic Style: A Review

This paper presents 1-bit CMOS full adder cell using standard static CMOS logic style. The comparison is taken out using several parameters like number of transistors, delay, power dissipation and power delay product (PDP). The circuits are designed at transistor level using 180 nm and 90nm CMOS technology. Various full adders are presented in this paper like Conventional CMOS (C-CMOS), Complementary pass transistor logic FA (CPL), Double pass transistor logic FA , Transmission gate FA (TGA), Transmission function FA, New 14T,10T, Hybrid CMOS, HPSC, 24T, LPFA (CPL), LPHS, Hybrid Full Adders.
Context-Aware for Smartphone Sensing in Over-the-Counter Market: Ambient Intelligence in Mobile Stock Markets

An Ambient Intelligence (AmI) environment is primary developed using intelligent agents and wireless sensor networks. The intelligent agents could automatically obtain contextual information in real time using Near Field Communication (NFC) technique and wireless ad-hoc networks. In this research, we propose a stock trading and recommendation system with mobile devices (Android platform) interface in the over-the-counter market (OTC) environments. The proposed system could obtain the real-time financial information of stock price through a multi-agent architecture with plenty of useful features. In addition, NFC is used to achieve a context-aware environment allowing for automatic acquisition and transmission of useful trading recommendations and relevant stock information for investors. Finally, AmI techniques are applied to successfully create smart investment spaces, providing investors with useful monitoring tools and investment recommendation.
Automatic Facial Expression Recognition Using Gabor Filter and Expression Analysis

Facial expression extraction is the essential step of facial expression recognition. The paper presents a system that uses 28 facial feature key-points in images detection and Gabor wavelet filter provided with 5 frequencies, 8 orientations. In according to actual demand, It can extract the feature of low quality facial expression image target, and have good robust for automatic facial expression recognition. Experimental results show that the performance of the proposed method achieved excellent average recognition rates, when it is applied to facial expression recognition system.
DeepProbe: Information Directed Sequence Understanding and Chatbot Design via Recurrent Neural Networks

Information extraction and user intention identification is a central topic in modern query understanding and recommendation systems. In this paper, we propose DeepProbe, a generic information-directed interaction framework which is built around an attention-based sequence to sequence (seq2seq) recurrent neural network. DeepProbe can rephrase, evaluate, and even actively ask questions, leveraging the generative ability and likelihood estimation made possible by seq2seq models. DeepProbe makes decisions based on a derived uncertainty (entropy) measure conditioned on user inputs, possibly with multiple rounds of interactions. Three applications, namely a rewritter, a relevance scorer and a chatbot for ad recommendation, were built around DeepProbe, with the first two serving as precursory building blocks for the third. We first use the seq2seq model in DeepProbe to rewrite a user query into one of standard query form, which is submitted to an ordinary recommendation system. Secondly, we evaluate DeepProbe's seq2seq model-based relevance scoring. Finally, we build a chatbot prototype capable of making active user interactions, which can ask questions that maximize information gain, allowing for a more efficient user intention idenfication process. We evaluate first two applications by 1) comparing with baselines by BLEU and AUC, and 2) human judge evaluation. Both demonstrate significant improvements compared with current state-of-the-art systems, proving their values as useful tools on their own, and at the same time laying a good foundation for the ongoing chatbot application.
Deep Reinforcement Learning Attention Selection For Person Re-Identification

Existing person re-identification (re-id) methods assume the provision of accurately cropped person bounding boxes with minimum background noise, mostly by manually cropping. This is significantly breached in practice when person bounding boxes must be detected automatically given a very large number of images and/or videos processed. Compared to carefully cropped manually, auto-detected bounding boxes are far less accurate with random amount of background clutter which can degrade notably person re-id matching accuracy. In this work, we develop a joint learning deep model that optimises person re-id attention selection within any auto-detected person bounding boxes by reinforcement learning of background clutter minimisation subject to re-id label pairwise constraints. Specifically, we formulate a novel unified re-id architecture called Identity DiscriminativE Attention reinforcement Learning (IDEAL) to accurately select re-id attention in auto-detected bounding boxes for optimising re-id performance. Our model can improve re-id accuracy comparable to that from exhaustive human manual cropping of bounding boxes with additional advantages from identity discriminative attention selection that specially benefits re-id tasks beyond human knowledge. Extensive comparative evaluations demonstrate the re-id advantages of the proposed IDEAL model over a wide range of state-of-the-art re-id methods on two auto-detected re-id benchmarks CUHK03 and Market-1501.
See, Hear, and Read: Deep Aligned Representations

We capitalize on large amounts of readily-available, synchronous data to learn a deep discriminative representations shared across three major natural modalities: vision, sound and language. By leveraging over a year of sound from video and millions of sentences paired with images, we jointly train a deep convolutional network for aligned representation learning. Our experiments suggest that this representation is useful for several tasks, such as cross-modal retrieval or transferring classifiers between modalities. Moreover, although our network is only trained with image+text and image+sound pairs, it can transfer between text and sound as well, a transfer the network never observed during training. Visualizations of our representation reveal many hidden units which automatically emerge to detect concepts, independent of the modality.
Stability Study for a Hybrid AC-DC More-Electric Aircraft Power System

The paper deals with the small-signal stability analysis of aircraft ac frequency-wild power systems representing a real ac-dc hybrid distribution architecture with a multiplicity of actuators, aircraft loads, and bus geometries. The dq modelling approach is applied to derive individual power system component models and to constitute the corresponding generalized power system model as a powerful and flexible stability analysis tool. The element models can be interconnected in an algorithmic way according to a variety of the architecture selected. Intensive time-domain simulation and experimental results are used to verify the theoretical results. It is also shown how the proposed approach can be used to predict instability due to possible variations in operating points and system parameters.
A Soft, Controllable, High Force Density Linear Brake Utilizing Layer Jamming

While much work has focused on the design of actuators for inputting energy into robotic systems, less work has been dedicated to devices that remove energy in a controlled manner, especially in the field of soft robotics. Such devices have the potential to significantly modulate the dynamics of a system with very low required input power. In this letter, we leverage the concept of layer jamming, previously used for variable stiffness devices, to create a controllable, high force density, soft layer jamming brake (SLJB). We introduce the design, modeling, and performance analysis of the SLJB and demonstrate variable tensile resisting forces through the regulation of vacuum pressure. Further, we measure and model the tensile force with respect to different layer materials, vacuum pressures, and lengthening velocities, and show its ability to absorb energy during collisions. We hope to apply the SLJB in a number of applications in wearable technology.
Identification of lutein and zeaxanthin oxidation products in human and monkey retinas.

To characterize fully all the major and minor carotenoids and their metabolites in human retina and probe for the presence of the oxidative metabolites of lutein and zeaxanthin.



Carotenoids of a composite of 58 pairs of human retinas and a monkey retina were elucidated by comparing their high-performance liquid chromatography (HPLC)-ultraviolet/visible absorption spectrophotometry (UV/Vis)-mass spectrometry (MS) profile with those of authentic standards prepared by organic synthesis.



In addition to lutein and zeaxanthin, several oxidation products of these compounds were present in the extracts from human retina. A major carotenoid resulting from direct oxidation of lutein was identified as 3-hydroxy-beta, epsilon-caroten-3'-one. Minor carotenoids were identified as: 3'-epilutein, epsilon,epsilon-carotene-3,3'-diol, epsilon,epsilon-carotene-3,3'-dione, 3'-hydroxy-epsilon,epsilon-caroten-3-one, and 2,6-cyclolycopene-1,5-diol. Several of the geometric isomers of lutein and zeaxanthin were also detected at low concentrations. These were as follows: 9-cis-lutein, 9'-cislutein, 13-cis-lutein, 13'-cis-lutein, 9-cis-zeaxanthin, and 13-cis-zeaxanthin. Similar results were also obtained from HPLC analysis of a freshly dissected monkey retina.



Lutein, zeaxanthin, 3'-epilutein, and 3-hydroxy-beta,epsilon-caroten-3'-one in human retina may be interconverted through a series of oxidation-reduction reactions similar to our earlier proposed metabolic transformation of these compounds in humans. The presence of the direct oxidation product of lutein and 3'-epilutein (metabolite of lutein and zeaxanthin) in human retina suggests that lutein and zeaxanthin may act as antioxidants to protect the macula against short-wavelength visible light. The proposed oxidative-reductive pathways for lutein and zeaxanthin in human retina, may therefore play an important role in prevention of age-related macular degeneration and cataracts.
Hot-Carrier-Induced Degradations and Optimizations for Lateral DMOS Transistor With Multiple Floating Poly-Gate Field Plates

The electrical parameters degradations of lateral double-diffused MOS with multiple floating poly-gate field plates under different stress conditions have been investigated experimentally. For the maximum substrate current (<inline-formula> <tex-math notation="LaTeX">${I}_{{\text {submax}}})$ </tex-math></inline-formula> stress, the increased interface states at the bird’s beak mainly result in an on-resistance (<inline-formula> <tex-math notation="LaTeX">${R}_{ \mathrm{\scriptscriptstyle ON}})$ </tex-math></inline-formula> increase at the beginning of the stress, while hot holes injection and trapping into the oxide beneath the edge of real poly-gate turns out to be the dominating degradation mechanism after around 800-s stress, making the <inline-formula> <tex-math notation="LaTeX">${R}_{{ \mathrm{\scriptscriptstyle ON}}}$ </tex-math></inline-formula> decrease. For the maximum operating gate voltage (<inline-formula> <tex-math notation="LaTeX">${V}_{{\text {gmax}}})$ </tex-math></inline-formula> stress, the trapped hot electrons in the channel region bring an increase in threshold voltage (<inline-formula> <tex-math notation="LaTeX">${V}_{{\text {th}}})$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${R}_{{ \mathrm{\scriptscriptstyle ON}}}$ </tex-math></inline-formula>, while the generation of large numbers of interface states at the bird’s beak further dramatically increases the <inline-formula> <tex-math notation="LaTeX">${R}_{{ \mathrm{\scriptscriptstyle ON}}}$ </tex-math></inline-formula>. A novel device structurewith a poly-gate partly recessed into the field oxide has been presented to decrease the hot-carrier-induced degradations.
Deriving physical connectivity from neuronal morphology

A model is presented that allows prediction of the probability for the formation of appositions between the axons and dendrites of any two neurons based only on their morphological statistics and relative separation. Statistics of axonal and dendritic morphologies of single neurons are obtained from 3D reconstructions of biocytin-filled cells, and a statistical representation of the same cell type is obtained by averaging across neurons according to the model. A simple mathematical formulation is applied to the axonal and dendritic statistical representations to yield the probability for close appositions. The model is validated by a mathematical proof and by comparison of predicted appositions made by layer 5 pyramidal neurons in the rat somatosensory cortex with real anatomical data. The model could be useful for studying microcircuit connectivity and for designing artificial neural networks.
Social Commerce as a Driver to Enhance Trust and Intention to Use Cryptocurrencies for Electronic Payments

The deployment of cryptocurrencies in e-commerce has reached a significant number of transactions and continuous increases in monetary circulation; nevertheless, they face two impediments: a lack of awareness of the technological utility, and a lack of trust among consumers. E-commerce carried out through social networks expands its application to a new paradigm called social commerce. Social commerce uses the content generated within social networks to attract new consumers and influence their behavior. The objective of this paper is to analyze the role played by social media in increasing trust and intention to use cryptocurrencies in making electronic payments. It develops a model that combines constructs from social support theory, social commerce, and the technology acceptance model. This model is evaluated using the partial least square analysis. The obtained results show that social commerce increases the trust and intention to use cryptocurrencies. However, mutual support among participants does not generate sufficient trust to adequately promote the perceived usefulness of cryptocurrencies. This research provides a practical tool for analyzing how collaborative relationships that emerge in social media can influence or enhance the adoption of a new technology in terms of perceived trust and usefulness. Furthermore, it provides a significant contribution to consumer behavior research by applying the social support theory to the adoption of new information technologies. These theoretical and practical contributions are detailed in the final section of the paper.
Universal Design of Auditory Graphs: A Comparison of Sonification Mappings for Visually Impaired and Sighted Listeners

Determining patterns in data is an important and often difficult task for scientists and students. Unfortunately, graphing and analysis software typically is largely inaccessible to users with vision impairment. Using sound to represent data (i.e., sonification or auditory graphs) can make data analysis more accessible; however, there are few guidelines for designing such displays for maximum effectiveness. One crucial yet understudied design issue is exactly how changes in data (e.g., temperature) are mapped onto changes in sound (e.g., pitch), and how this may depend on the specific user. In this study, magnitude estimation was used to determine preferred data-to-display mappings, polarities, and psychophysical scaling functions relating data values to underlying acoustic parameters (frequency, tempo, or modulation index) for blind and visually impaired listeners. The resulting polarities and scaling functions are compared to previous results with sighted participants. There was general agreement about polarities obtained with the two listener populations, with some notable exceptions. There was also evidence for strong similarities regarding the magnitudes of the slopes of the scaling functions, again with some notable differences. For maximum effectiveness, sonification software designers will need to consider carefully their intended users’ vision abilities. Practical implications and limitations are discussed.
Efficient Deep Neural Network Serving: Fast and Furious

The emergence of deep neural networks (DNNs) as a state-of-the-art machine learning technique has enabled a variety of artificial intelligence applications for image recognition, speech recognition and translation, drug discovery, and machine vision. These applications are backed by large DNN models running in serving mode on a cloud computing infrastructure to process client inputs such as images, speech segments, and text segments. Given the compute-intensive nature of large DNN models, a key challenge for DNN serving systems is to minimize the request response latencies. This paper characterizes the behavior of different parallelism techniques for supporting scalable and responsive serving systems for large DNNs. We identify and model two important properties of DNN workloads: 1) homogeneous request service demand and 2) interference among requests running concurrently due to cache/memory contention. These properties motivate the design of serving deep learning systems fast (SERF), a dynamic scheduling framework that is powered by an interference-aware queueing-based analytical model. To minimize response latency for DNN serving, SERF quickly identifies and switches to the optimal parallel configuration of the serving system by using both empirical and analytical methods. Our evaluation of SERF using several well-known benchmarks demonstrates its good latency prediction accuracy, its ability to correctly identify optimal parallel configurations for each benchmark, its ability to adapt to changing load conditions, and its efficiency advantage (by at least three orders of magnitude faster) over exhaustive profiling. We also demonstrate that SERF supports other scheduling objectives and can be extended to any general machine learning serving system with the similar parallelism properties as above.
Development and psychometric analysis of the Duchenne muscular dystrophy Functional Ability Self-Assessment Tool (DMDSAT)

The objective of this study was to describe the development and initial psychometric analysis of the UK English version of the Duchenne muscular dystrophy Functional Ability Self-Assessment Tool (DMDSAT), a patient-reported outcome (PRO) scale designed to measure functional ability in patients with Duchenne muscular dystrophy (DMD). Item selection was made by neuromuscular specialists and a Rasch analysis was performed to understand the psychometric properties of the DMDSAT. Instrument scores were also linked to cost of illness and health-related quality of life data. The administered version, completed by 186 UK patient-caregivers pairs, included eight items in four domains: Arm function, Mobility, Transfers, and Ventilation status. These items together successfully operationalized functional ability in DMD, with excellent targeting and reliability (Person Separation Index: 0.95; Cronbach's α: 0.93), stable item locations, and good fit to the Rasch model (mean person/item fit residual: -0.21/-0.44, SD: 0.32/1.28). Estimated item difficulty was in excellent agreement with clinical opinion (Spearman's ρ: 0.95) and instrument scores mapped well onto health economic outcomes. We show that the DMDSAT is a PRO instrument fit for purpose to measure functional ability in ambulant and non-ambulant patients with DMD. Rasch analysis augments clinical expertise in the development of robust rating scales.
Robust Controller for Tremor Suppression at Musculoskeletal Level in Human Wrist

Tremor is a rhythmical and involuntary oscillatory movement of a body part and it is one of the most common movement disorders. Orthotic devices have been under investigation as a noninvasive tremor suppression alternative to medication or surgery. The challenge in musculoskeletal tremor suppression is estimating and attenuating the tremor motion without impeding the patient's intentional motion. In this research a robust tremor suppression algorithm was derived for patients with pathological tremor in the upper limbs. First the motion in the tremor frequency range is estimated using a high-pass filter. Then, by applying the backstepping method the appropriate amount of torque is calculated to drive the output of the estimator toward zero. This is equivalent to an estimation of the tremor torque. It is shown that the arm/orthotic device control system is stable and the algorithm is robust despite inherent uncertainties in the open-loop human arm joint model. A human arm joint simulator, capable of emulating tremorous motion of a human arm joint was used to evaluate the proposed suppression algorithm experimentally for two types of tremor, Parkinson and essential. Experimental results show 30-42 dB (97.5-99.2%) suppression of tremor with minimal effect on the intentional motion.