nils-herrmann/clean_scirepeval huggingface dataset:
A CMOS voltage reference based on weighted difference of gate-source voltages between PMOS and NMOS transistors for low dropout regulators

A CMOS voltage reference, which takes advantage of weighted difference of the gate-source voltages between a PMOS and an NMOS transistor operating in saturation region, is presented in this paper. The reference has been implemented in a standard 0.6-µm CMOS process (Vthn≈ |Vthp| ≈ 0.9 V@0° C) and gives a temperature coefficient of not greater than 62 ppm/°C from 0 to 100° C without trimming, while consuming a maximum of 9.7 µA with a minimum supply of 1.4 V. The worst-case line regulation is ±0.17 %/V. The occupied chip area is 0.055 mm2. The proposed reference has been applied to a 10-mA CMOS low dropout regulator, and a temperature coefficient of 94 ppm/°C is achieved when the regulator delivers maximum load current.
Sentiment Analysis of Movie Reviews using POS tags and Term Frequencies

Sentiment analysis and opinion mining play an important role in judging and predicting people&apos;s views. Recently, sentiment analysis has focused on assigning positive and negative polarities to opinions. More methods are being devised to find the weightage of a particular expression in a sentence, whether the particular expression gives the sentence a positive, negative or a neutral meaning. Most of the work on sentiment analysis in the past has been carried out by determining the strength of a subjective expression within a sentence using the parts of speech. Sentiment analysis tries to classify opinion sentences in a document on the basis of their polarity as positive or negative, which can be used in various ways and in many applications for example, marketing and contextual advertising, suggestion systems based on the user likes and ratings, recommendation systems etc. This paper presents a novel approach for classification of online movie reviews using parts of speech and machine learning algorithms.
Bootstrapping incremental dialogue systems from minimal data: the generalisation power of dialogue grammars

We investigate an end-to-end method for automatically inducing task-based dialogue systems from small amounts of unannotated dialogue data. It combines an incremental semantic grammar Dynamic Syntax and Type Theory with Records (DS-TTR) with Reinforcement Learning (RL), where language generation and dialogue management are a joint decision problem. The systems thus produced are incremental: dialogues are processed word-by-word, shown previously to be essential in supporting natural, spontaneous dialogue. We hypothesised that the rich linguistic knowledge within the grammar should enable a combinatorially large number of dialogue variations to be processed, even when trained on very few dialogues. Our experiments show that our model can process 74% of the Facebook AI bAbI dataset even when trained on only 0.13% of the data (5 dialogues). It can in addition process 65% of bAbI+, a corpus1 we created by systematically adding incremental dialogue phenomena such as restarts and self-corrections to bAbI. We compare our model with a state-of-theart retrieval model, memn2n (Bordes et al., 2017). We find that, in terms of semantic accuracy, memn2n shows very poor robustness to the bAbI+ transformations even when trained on the full bAbI dataset.
The role of transparency in recommender systems

Recommender Systems act as a personalized decision guides, aiding users in decisions on matters related to personal taste. Most previous research on Recommender Systems has focused on the statistical accuracy of the algorithms driving the systems, with little emphasis on interface issues and the user's perspective. The goal of this research was to examine the role of transprency (user understanding of why a particular recommendation was made) in Recommender Systems. To explore this issue, we conducted a user study of five music Recommender Systems. Preliminary results indicate that users like and feel more confident about recommendations that they perceive as transparent.
FAIR AND BALANCED? QUANTIFYING MEDIA BIAS THROUGH CROWDSOURCED CONTENT ANALYSIS CEREN BUDAK*

It is widely thought that news organizations exhibit ideological bias, but rigorously quantifying such slant has proven methodologically challenging. Through a combination of machinelearning and crowdsourcing techniques, we investigate the selection and framing of political issues in fifteen major US news outlets. Starting with 803,146 news stories published over twelve months, we first used supervised learning algorithms to identify the 14 percent of articles pertaining to political events. We then recruited 749 online human judges to classify a random subset of 10,502 of these political articles according to topic and ideological position. Our analysis yields an ideological ordering of outlets consistent with prior work. However, news outlets are considerably more similar than generally believed. Specifically, with the exception of political scandals, major news organizations present topics in a largely nonpartisan manner, casting neither Democrats nor Republicans in a particularly favorable or unfavorable light. Moreover, again with the exception of political scandals, little evidence exists of systematic differences in story selection, with all major news outlets covering a wide variety of topics with frequency largely unrelated to the outlet’s ideological position. Finally, news organizations express their ideological bias not by directly advocating for a preferred political party, but rather by disproportionately criticizing one side, a convention that further moderates overall differences.  Budak is an assistant professor in the School of Information at the University of Michigan, Ann Arbor, MI, USA. Sharad Goel is an assistant professor in the Management Science and Engineering Department at Stanford University, Palo Alto, CA, USA. Justin M. Rao is a senior researcher at Microsoft Research, Redmond, WA, USA. The authors thank Seth Flaxman, Matthew Salganik, and Sid Suri for comments. Address correspondence to Ceren Budak, University of Michigan, School of Information, 105 S. State St. Ann Arbor, MI 48109-1285, USA; e-mail: cbudak@umich.edu. Public Opinion Quarterly, Vol. 80, Special Issue, 2016, pp. 250–271 doi:10.1093/poq/nfw007
NOBLE – Flexible concept recognition for large-scale biomedical natural language processing

Natural language processing (NLP) applications are increasingly important in biomedical data analysis, knowledge engineering, and decision support. Concept recognition is an important component task for NLP pipelines, and can be either general-purpose or domain-specific. We describe a novel, flexible, and general-purpose concept recognition component for NLP pipelines, and compare its speed and accuracy against five commonly used alternatives on both a biological and clinical corpus. NOBLE Coder implements a general algorithm for matching terms to concepts from an arbitrary vocabulary set. The system’s matching options can be configured individually or in combination to yield specific system behavior for a variety of NLP tasks. The software is open source, freely available, and easily integrated into UIMA or GATE. We benchmarked speed and accuracy of the system against the CRAFT and ShARe corpora as reference standards and compared it to MMTx, MGrep, Concept Mapper, cTAKES Dictionary Lookup Annotator, and cTAKES Fast Dictionary Lookup Annotator. We describe key advantages of the NOBLE Coder system and associated tools, including its greedy algorithm, configurable matching strategies, and multiple terminology input formats. These features provide unique functionality when compared with existing alternatives, including state-of-the-art systems. On two benchmarking tasks, NOBLE’s performance exceeded commonly used alternatives, performing almost as well as the most advanced systems. Error analysis revealed differences in error profiles among systems. NOBLE Coder is comparable to other widely used concept recognition systems in terms of accuracy and speed. Advantages of NOBLE Coder include its interactive terminology builder tool, ease of configuration, and adaptability to various domains and tasks. NOBLE provides a term-to-concept matching system suitable for general concept recognition in biomedical NLP pipelines.
High performance CMOS dual supply level shifter for a 0.5V input and 1V output in standard 1.2V 65nm technology process

This paper presents the design of a highly efficient CMOS level shifter qc-level shifter. Unlike many recent level shifters, the proposed qc-level shifter does not use bootstrap capacitors to minimize active area. When implemented on a 65nm CMOS technology, under the large capacitive loading condition (2pF), qc-level shifter has a lower active area (94%), and energy-delay product (21.4%) than the reference bootstrap level shifter circuit (ts-level shifter). In comparison to a conventional shifter (c-level shifter)the corresponding reductions are 9.5% and 55%, respectively. Also qc-level shifter has very small effective input capacitance in comparison with ts-level shifter as it does not need a bootstrap capacitor connected to its input.
Understanding and Predicting Graded Search Satisfaction

Understanding and estimating satisfaction with search engines is an important aspect of evaluating retrieval performance. Research to date has modeled and predicted search satisfaction on a binary scale, i.e., the searchers are either satisfied or dissatisfied with their search outcome. However, users' search experience is a complex construct and there are different degrees of satisfaction. As such, binary classification of satisfaction may be limiting. To the best of our knowledge, we are the first to study the problem of understanding and predicting graded (multi-level) search satisfaction. We ex-amine sessions mined from search engine logs, where searcher satisfaction was also assessed on multi-point scale by human annotators. Leveraging these search log data, we observe rich and non-monotonous changes in search behavior in sessions with different degrees of satisfaction. The findings suggest that we should predict finer-grained satisfaction levels. To address this issue, we model search satisfaction using features indicating search outcome, search effort, and changes in both outcome and effort during a session. We show that our approach can predict subtle changes in search satisfaction more accurately than state-of-the-art methods, affording greater insight into search satisfaction. The strong performance of our models has implications for search providers seeking to accu-rately measure satisfaction with their services.
Network virtualization: state of the art and research challenges

Recently network virtualization has been pushed forward by its proponents as a long-term solution to the gradual ossification problem faced by the existing Internet and proposed to be an integral part of the next-generation networking paradigm. By allowing multiple heterogeneous network architectures to cohabit on a shared physical substrate, network virtualization provides flexibility, promotes diversity, and promises security and increased manageability. However, many technical issues stand in the way of its successful realization. This article investigates the past and the state of the art in network virtualization along with the future challenges that must be addressed to realize a viable network virtualization environment.
Direct Torque Control of an IPM-Synchronous Motor Drive at Very Low Speed Using a Sliding-Mode Stator Flux Observer

Direct torque control of interior permanent-magnet synchronous motors is known to deliver fast torque and flux dynamic responses. However, the poor flux estimation at very low speeds has been its largest drawback. A multitude of flux observers have been proposed for flux estimation, but most of them fail to fare in the low-speed region. This paper proposes a sliding-mode stator flux observer for improved flux estimation at very low speeds. Unlike conventional flux observers, this observer does not require any speed adaptation mechanism and is immune to speed estimation error. A novel stator resistance estimator is incorporated into the sensorless drive to compensate the effects of stator resistance variation. Global asymptotic stability of both the flux observer and stator resistance estimator is guaranteed by the Lyapunov stability analysis. DC-offset effects are mitigated by introducing a small integral component in the observer gains. Simulation and experimental results at very low speeds, including 0 and 2 r/min, without signal injection confirm the effectiveness of the proposed method.
- 9359 ( Volume-5 , Issue-4 ) Automated Question Paper Generation System

n today’s age, education is the most important way of achieving success. When we discuss education, it is imperative to mention tests and examination. Examinations prepare students in their quest for knowledge. So, having a proper examination paper and format is quite necessary. Now the traditional method of generating question paper has been manual. In this method certain officials chalk out the question paper. But this method can be ineffective at times owing to bias, repetition and security concerns. We have proposed an Automated process of Question Paper Generation which is fast, streamlined, randomized and secure. Every task performed by this system is automated so that storage space, bias and security is not a concern anymore. Furthermore, we have proposed a new algorithm which ensures total randomization of questions and avoids repetitions. The proposed system can be helpful to many educational institutes and NGO based institutes.  question paper generation; paperless; automation; randomization; information communication technology.
Grade Prediction with Temporal Course-wise Influence

There is a critical need to develop new educational technology applications that analyze the data collected by universities to ensure that students graduate in a timely fashion (4 to 6 years); and they are well prepared for jobs in their respective fields of study. In this paper, we present a novel approach for analyzing historical educational records from a large, public university to perform next-term grade prediction; i.e., to estimate the grades that a student will get in a course that he/she will enroll in the next term. Accurate next-term grade prediction holds the promise for better student degree planning, personalized advising and automated interventions to ensure that students stay on track in their chosen degree program and graduate on time. We present a factorization-based approach called Matrix Factorization with Temporal Course-wise Influence that incorporates course-wise influence effects and temporal effects for grade prediction. In this model, students and courses are represented in a latent “knowledge” space. The grade of a student on a course is modeled as the similarity of their latent representation in the “knowledge” space. Course-wise influence is considered as an additional factor in the grade prediction. Our experimental results show that the proposed method outperforms several baseline approaches and infer meaningful patterns between pairs of courses within academic programs.
The Effectiveness of Intrinsic and Extrinsic Motivations : A Study of Malaysian Amway Company ’ s Direct Sales Forces

This research utilizes the survey questionnaire data collected in May/June 2010 from 200 Amway Company’s direct sales forces in Klang Valley areas (Malaysia) to analyze the effectiveness of intrinsic and extrinsic motivation in influencing job satisfaction. The research will be analyzed using the well-established correlation analysis, regression analysis, independent sample t-test, and one-way ANOVA. There are four major findings. First, there is a relationship between intrinsic and extrinsic motivations with job satisfaction. According to the correlation value, intrinsic motivation compared to extrinsic motivation tends to contribute more in job satisfaction. Second, there are significant and positive relationship between intrinsic and extrinsic motivations and job satisfaction. Both of the intrinsic and extrinsic motivation is identified as the predictor for job satisfaction. In other words, they are both significantly contribute in better job satisfaction. Third, there is no difference between gender and intrinsic and extrinsic motivations. Hence, gender is not a factor that affects both of the intrinsic and extrinsic motivations. Lastly, the result indicates that there is a difference between age and intrinsic and extrinsic motivations. Therefore, age is the factor that influences on both intrinsic and extrinsic motivations. Last but not least, the results have demonstrated the effectiveness of intrinsic and extrinsic motivations in influencing job satisfaction among the Amway Company’s direct sales forces, as well as establishing appropriate intrinsic motivations may promote higher job satisfaction.
Vascular structures in dermoscopy*

Dermoscopy is an aiding method in the visualization of the epidermis and dermis. It is usually used to diagnose melanocytic lesions. In recent years, dermoscopy has increasingly been used to diagnose non-melanocytic lesions. Certain vascular structures, their patterns of arrangement and additional criteria may demonstrate lesion-specific characteristics. In this review, vascular structures and their arrangements are discussed separately in the light of conflicting views and an overview of recent literature.
The Concentric Malar Lift: Malar and Lower Eyelid Rejuvenation

Midface rejuvenation surgery is most challenging. The margin of error for the lower lid is on the order of 0.5 mm, and the cosmetic result can sometimes look unnatural. A minimally invasive technique for malar and lower lid lift is proposed. Two incisions are used: the standard subciliary lower eyelid incision and one on the lateral part of the upper eyelid. Through these incisions a skin flap lower eyelid dissection and a subperiosteal malar dissection are performed. The arcus marginalis itself is not transected as is the case when the malar area is entered from the lower eyelid. Rather, a subperiosteral release of the arcus marginalis is performed through a muscle-splitting incision at the lateral canthus. Eyelid malposition is avoided because the muscles, vessels, and nerves converging toward the medial canthus are not interrupted. The subperiosteal dissection of the arcus marginalis extends to the medial canthus and also releases the insertion of the orbicularis oculi superior malar part. Consequently, all the attachments of the tear trough are released. Two subperiosteal suspensions connect the central part of the nasolabial volume and, more laterally, the central part of the malar area to the inferolateral orbital rim. The elevation of the malar volume resulting from these suspensions is concentric with the orbit. A final third suspension vertically connects the orbicularis oculi muscle with the underlying periosteum to the bone of the lateral orbital rim. Significant skin excess is removed from the lower eyelid. Complete disinsertion of the tear trough attachments combined with the malar elevation treats the entire palpebromalar groove. The lifted fat volume fills the space resulting from the subperiosteal disinsertion. A safer, more natural and more reliable result is achieved because the vectors of traction with this technique are exactly opposite those of the midface aging process, and because a very stable fixation is created between the lifted malar periosteum and the malar and latero-orbital rim bones.
Analysis on DNA based Cryptography to Secure Data Transmission

The biological research in the field of information technology paves the exploitation of storing capabilities, parallelism and also in conservative cryptography which enhances the security features for data transmission. DNA is the gene information which encodes information of all living beings. Though the DNA computing has its application in the field of huge information storage, massive parallel processing, low energy consumption which have been proposed and proved by the researchers and soon the molecular computer can replace the existing silicon computer and it exploits the world smallest computer. The combination of DNA molecules can be interpreted as a result to give a solution to a specific problem. The DNA strands can be replicated 500 times per second with greater accuracy. It can also be used in the field of cryptography based upon the vast parallelism which is used to break the existing cryptographic approach. This paper analysis an existing approach to the DNA computing method and DNA based cryptographic approach which provides the clear idea and limitations of existing research works.
TextCatcher: a method to detect curved and challenging text in natural scenes

In this paper, we propose a text detection algorithm which is hybrid and multi-scale. First, it relies on a connected component-based approach: After the segmentation of the image, a classification step using a new wavelet descriptor spots the letters. A new graph modeling and its traversal procedure allow to form candidate text areas. Second, a texture-based approach discards the false positives. Finally, the detected text areas are precisely cut out and a new binarization step is introduced. The main advantage of our method is that few assumptions are put forward. Thus, “challenging texts” like multi-sized, multi-colored, multi-oriented or curved text can be localized. The efficiency of TextCatcher has been validated on three different datasets: Two come from the ICDAR competition, and the third one contains photographs we have taken with various daily life texts. We present both qualitative and quantitative results.
Efficient and Low Latency Detection of Intruders in Mobile Active Authentication

Active authentication (AA) refers to the problem of continuously verifying the identity of a mobile device user for the purpose of securing the device. We address the problem of quickly detecting intrusions with lower false detection rates in mobile AA systems with higher resource efficiency. Bayesian and MiniMax versions of the quickest change detection (QCD) algorithms are introduced to quickly detect intrusions in mobile AA systems. These algorithms are extended with an update rule to facilitate low-frequency sensing which leads to low utilization of resources. Effectiveness of the proposed framework is demonstrated using three publicly available unconstrained face and touch gesture-based AA datasets. It is shown that the proposed QCD-based intrusion detection methods can perform better than many state-of-the-art AA methods in terms of latency and low false detection rates. Furthermore, it is shown that employing the proposed resource-efficient extension further improves the performance of the QCD-based setup.
Toward Personalised Gamification for Learning Environments

Many learning environments are deserted by the learners, even if they are effective. Gamification is a growing approach used to raise learners’ motivation by adding game elements in their environment, but it still pays few attention to the individual differences among learners’ motivations. This paper presents a gamification system designed to be plugged on various learning environments. It can be automatically personalised, based on an analysis of the interaction traces.