nils-herrmann/clean_scirepeval huggingface dataset:
Value Based BTB Indexing for indirect jump prediction

History-based branch direction predictors for conditional branches are shown to be highly accurate. Indirect branches however, are hard to predict as they may have multiple targets corresponding to a single indirect branch instruction. We propose the Value Based BTB Indexing (VBBI), a correlation-based target address prediction scheme for indirect jump instructions. For each static hard-to-predict indirect jump instruction, the compiler identifies a ‘hint instruction’, whose output value strongly correlates with the target address of the indirect jump instruction. At run time, multiple target addresses of the indirect jump instruction are stored and subsequently accessed from the BTB at different indices computed using the jump instruction PC and the hint instruction output values. In case the hint instruction has not finished its execution when the jump instruction is fetched, a second and more accurate target address prediction is made when the hint instruction output is available, thus reducing the jump misprediction penalty. We compare our design to the regular BTB design and the best previously proposed indirect jump predictor, the tagged target cache (TTC). Our evaluation shows that the VBBI scheme improves the indirect jump target prediction accuracy by 48% and 18%, compared with the baseline BTB and TTC designs, respectively. This results in average performance improvement of 16.4% over the baseline BTB scheme, and 13% improvement over the TTC predictor. Out of this performance improvement 2% is contributed by target prediction overriding which is accurate 96% of the time.
Towards Monocular Digital Elevation Model (DEM) Estimation by Convolutional Neural Networks - Application on Synthetic Aperture Radar Images

Synthetic aperture radar (SAR) interferometry (InSAR) is performed using repeat-pass geometry. InSAR technique is used to estimate the topographic reconstruction of the earth surface. The main problem of the range-Doppler focusing technique is the nature of the two-dimensional SAR result, affected by the layover indetermination. In order to resolve this problem, a minimum of two sensor acquisitions, separated by a baseline and extended in the crossslant-range, are needed. However, given its multi-temporal nature, these techniques are vulnerable to atmosphere and Earth environment parameters variation in addition to physical platform instabilities. Furthermore, either two radars are needed or an interferometric cycle is required (that spans from days to weeks), which makes real time DEM estimation impossible. In this work, the authors propose a novel experimental alternative to the InSAR method that uses single-pass acquisitions, using a data driven approach implemented by Deep Neural Networks. We propose a fully Convolutional Neural Network (CNN) Encoder-Decoder architecture, training it on radar images in order to estimate DEMs from single pass image acquisitions. Our results on a set of Sentinel images show that this method is able to learn to some extent the statistical properties of the DEM. The results of this exploratory analysis are encouraging and open the way to the solution of single-pass DEM estimation problem with data driven approaches.
A preliminary study on topical cetirizine in the therapeutic management of androgenetic alopecia.

Androgenetic alopecia (AGA) is a common form of scalp hair loss that affects up to 50% of males between 18 and 40 years old. Several molecules are commonly used for the treatment of AGA, acting on different steps of its pathogenesis (Minoxidil, Finasteride, Serenoa repens) and show some side effects. In literature, on the basis of hypertrichosis observed in patients treated with analogues of prostaglandin PGF2a, it was supposed that prostaglandins would have an important role in the hair growth: PGE and PGF2a play a positive role, while PGD2 a negative one.



We carried out a pilot study to evaluate the efficacy of topical cetirizine versus placebo in patients with AGA.



A sample of 85 patients was recruited, of which 67 were used to assess the effectiveness of the treatment with topical cetirizine, while 18 were control patients.



We found that the main effect of cetirizine was an increase in total hair density, terminal hair density and diameter variation from T0 to T1, while the vellus hair density shows an evident decrease. The use of a molecule as cetirizine, with no notable side effects, makes possible a good compliance by patients.



Our results have shown that topical cetirizine 1% is responsible for a significant improvement of the initial framework of AGA.
Adversarial Attribute-Image Person Re-identification

While attributes have been widely used for person re-identification (Re-ID) which aims at matching the same person images across disjoint camera views, they are used either as extra features or for performing multi-task learning to assist the image-image matching task. However, how to find a set of person images according to a given attribute description, which is very practical in many surveillance applications, remains a rarely investigated cross-modality matching problem in person Re-ID. In this work, we present this challenge and leverage adversarial learning to formulate the attribute-image cross-modality person Re-ID model. By imposing a semantic consistency constraint across modalities as a regularization, the adversarial learning enables to generate imageanalogous concepts of query attributes for matching the corresponding images at both global level and semantic ID level. We conducted extensive experiments on three attribute datasets and demonstrated that the regularized adversarial modelling is so far the most effective method for the attributeimage cross-modality person Re-ID problem.
Design, Control and Human Testing of an Active Knee Rehabilitation Orthotic Device

This paper presents a novel, smart and portable active knee rehabilitation orthotic device (AKROD) designed to train stroke patients to correct knee hyperextension during stance and stiff-legged gait (defined as reduced knee flexion during swing). The knee brace provides variable damping controlled in ways that foster motor recovery in stroke patients. A resistive, variable damper, electro-rheological fluid (ERF) based component is used to facilitate knee flexion during stance by providing resistance to knee buckling. Furthermore, the knee brace is used to assist in knee control during swing, i.e. to allow patients to achieve adequate knee flexion for toe clearance and adequate knee extension in preparation to heel strike. The detailed design of AKROD, the first prototype built, closed loop control results and initial human testing are presented here
ONYCHOMADESIS FOLLOWING HAND-FOOT-MOUTH DISEASE – OBSERVATIONS ON FIVE CASES

In 2015, an outbreak of hand, foot and mouth disease (HFMD) occurred in Romania and many patients presented with onychomadesis following HFMD. We report on five children (3 girls and 2 boys, aged between 3 and 12 years) who were consulted for onychomadesis involving the fingernails and toenails, all of them being diagnosed with HFMD 4-6 weeks ago. We observed no relationship between onychomadesis and the severity of the previous HFMD in our patients. In all of our cases, treatment for onychomadesis was not necessary; nail changes were temporary with spontaneous normal regrowth.
Effective Communication in Requirements Elicitation: A Comparison of Methodologies

The elicitation or communication of user requirements comprises an early and critical but highly error-prone stage in system development. Socially oriented methodologies provide more support for user involvement in design than the rigidity of more traditional methods, facilitating the degree of user–designer communication and the ‘capture’ of requirements. A more emergent and collaborative view of requirements elicitation and communication is required to encompass the user, contextual and organisational factors. From this accompanying literature in communication issues in requirements elicitation, a four-dimensional framework is outlined and used to appraise comparatively four different methodologies seeking to promote a closer working relationship between users and designers. The facilitation of communication between users and designers is subject to discussion of the ways in which communicative activities can be ‘optimised’ for successful requirements gathering, by making recommendations based on the four dimensions to provide fruitful considerations for system designers.
Exploring the handshake in employment interviews.

The authors examined how an applicant's handshake influences hiring recommendations formed during the employment interview. A sample of 98 undergraduate students provided personality measures and participated in mock interviews during which the students received ratings of employment suitability. Five trained raters independently evaluated the quality of the handshake for each participant. Quality of handshake was related to interviewer hiring recommendations. Path analysis supported the handshake as mediating the effect of applicant extraversion on interviewer hiring recommendations, even after controlling for differences in candidate physical appearance and dress. Although women received lower ratings for the handshake, they did not on average receive lower assessments of employment suitability. Exploratory analysis suggested that the relationship between a firm handshake and interview ratings may be stronger for women than for men.
Ethiopian Banknote Recognition and Fake Detection Using Support Vector Machine

Currency recognition is an important task in numerous automated payment services and used to categorize the banknotes of different nation. The importance of automatic methods for currency recognition has been increasing in the time being because of circulation of fake notes is increased in today's economy. This recognition system contains basic image processing techniques such like image acquisition, image preprocesses, extract features and classification using support vector machine. Basically camera or scanner used for image acquisition. The images of currency processed using a variety of preprocessing techniques and different features of the image extracted using local binary pattern technique, once the features are extracted it is important to recognize the currency using effective classifier called Support vector machine and Finally a prototype able to recognize Ethiopian paper currency with accuracy of 98% shows high performance classification model for paper currency recognition and also verify the validity of given banknotes with average accuracy of 93% rate.
PhaseNet: Discretized Phase Modeling with Deep Neural Networks for Audio Source Separation

Previous research on audio source separation based on deep neural networks (DNNs) mainly focuses on estimating the magnitude spectrum of target sources and typically, phase of the mixture signal is combined with the estimated magnitude spectra in an ad-hoc way. Although recovering target phase is assumed to be important for the improvement of separation quality, it can be difficult to handle the periodic nature of the phase with the regression approach. Unwrapping phase is one way to eliminate the phase discontinuity, however, it increases the range of value along with the times of unwrapping, making it difficult for DNNs to model. To overcome this difficulty, we propose to treat the phase estimation problem as a classification problem by discretizing phase values and assigning class indices to them. Experimental results show that our classificationbased approach 1) successfully recovers the phase of the target source in the discretized domain, 2) improves signal-todistortion ratio (SDR) over the regression-based approach in both speech enhancement task and music source separation (MSS) task, and 3) outperforms state-of-the-art MSS.
MATISSE 2.0: A Large-Scale Multi-agent Simulation System for Agent-Based ITS

In this paper we present MATISSE 2.0, a microscopic multi-agent based simulation system for the specification and execution of simulation scenarios for Agent-based intelligent Transportation Systems (ATS). In MATISSE, each smart traffic element (e.g., vehicle, intersection control device) is modeled as a virtual agent which continuously senses its surroundings and communicates and collaborates with other agents. MATISSE incorporates traffic control strategies such as contraflow operations and dynamic traffic sign changes. Experimental results show the ability of MATISSE 2.0 to simulate traffic scenarios with thousands of agents on a single PC.
Full-System Simulation of Java Workloads with RISC-V and the Jikes Research Virtual Machine

Managed languages such as Java, JavaScript or Python account for a large portion of workloads, both in cloud data centers and on mobile devices. It is therefore unsurprising that there is an interest in hardware-software co-design for these languages. However, existing research infrastructure is often unsuitable for this kind of research: managed languages are sensitive to fine-grained interactions that are not captured by high-level architectural models, yet are also too long-running and irregular to be simulated using cycle-accurate software simulators. Open-source hardware based on the RISC-V ISA provides an opportunity to solve this problem, by running managed workloads on RISC-V systems in FPGA-based full-system simulation. This approach achieves both the accuracy and simulation speeds required for managed workloads, while enabling modification and design-space exploration for the underlying hardware. A crucial requirement for this hardware-software research is a managed runtime that can be easily modified. The Jikes Research Virtual Machine (JikesRVM) is a Java Virtual Machine that was developed specifically for this purpose, and has become the gold standard in managed-language research. In this paper, we describe our experience of porting JikesRVM to the RISC-V infrastructure. We discuss why this combined setup is necessary, and how it enables hardware-software research for managed languages that was infeasible with previous infrastructure.
Children's Oncology Group (COG) Nutrition Committee.

The Children's Oncology Group (COG) Nutrition Committee was established to further the knowledge of nutrition in children with cancer by education and the conduct of clinical trials. A survey of COG institutions revealed lack of conformity in evaluation and categorization of nutritional status, and criteria for nutritional intervention. The Committee subsequently established specific categories of malnutrition (Underweight and Overweight) based on ideal body weight or body mass index. An algorithm was developed as a guideline for nutritional intervention as well as references and resources for determining estimated needs. The Committee embarked on concepts for clinical trials of nutritional interventions. The first pilot study, evaluating the feasibility of using an immunoneutraceutical precursor for glutathione production, has been completed. This study showed weight gain and improvement in glutathione status. A pilot trial of proactive enteral feeding for patients at high risk of malnutrition has commenced. The Committee believes that nutrition is relevant to all aspects of cancer control. The paucity of nutritional investigation in children with cancer needs to be rectified.
Social norms and energy conservation

This paper evaluates a series of programs, run by a company called OPOWER, to send Home Energy Report letters to residential utility customers comparing their energy use to that of their neighbors. Using data from randomized natural eld experiments at 600,000 treatment and control households across the United States, I estimate that the programs reduce energy consumption by 1.1 to 2.8 percent relative to baseline. The program provides additional evidence that non-price interventions can substantially and cost e¤ectively change consumer behavior: the e¤ect is equivalent to that of a short-run electricity price increase of 17 to 28 percent, and the cost e¤ectiveness compares favorably to that of traditional energy conservation programs. I show that there is substantial treatment e¤ect heterogeneity and implement an optimal variable selection algorithm to illustrate how "proling," or using a statistical decision rule to target the program at households with higher expected Conditional Average Treatment E¤ects, could further improve cost e¤ectiveness by 43 percent.  Social norms, energy demand, randomized eld experiments, proling.                             I thank, without implicating, Ian Ayres, Bob Cialdini, Tyler Curtis, Rajeev Dehejia, Kenneth Gillingham, Larry Goulder, Michael Greenstone, Matt Harding, Kosuke Imai, Seema Jayachandran, Karthik Kalyanaraman, Alex Kaufman, Ogi Kavazovic, Alex Laskey, Aprajit Mahajan, Sendhil Mullainathan, Dave Rapson, Todd Rogers, Eldar Shar, Joe Shapiro, Lan Shi, Marc Solomon, Dmitry Taubinsky, and seminar participants at Harvard, Stanford, and the Environmental Defense Fund for helpful conversations related to this project. This is a revision of the original July 2009 working paper (Allcott 2009).
Intelligent plagiarism detection mechanism using semantic technology: A different approach

Plagiarism of digital documents seems a serious problem in today's era. Plagiarism refers to the use of someone's data, language and writing without proper acknowledgment of the original source. Plagiarism of another author's original work is one of the biggest problems in publishing, science, and education. Plagiarism can be of different types. This paper presents a different approach for measuring semantic similarity between words and their meanings. Existing systems are based on the traditional approach. For detecting plagiarism, traditional methods focus on text matching according to keywords but fail to detect intelligent plagiarism using semantic web. We have suggested new strategies for detecting the plagiarism in the user document using the semantic web. In paper we have proposed architecture and algorithms to better detection of copy case using semantic search, it can improve the performance of copy case detection system. It analyzes the user document. After the implementation of this technique, the accuracy of plagiarism detection system will surely increase.
A Qualitative Investigation of Gamification: Motivational Factors in Online Gamified Services and Applications

Gamification is commonly employed in designing interactive systems to enhance user engagement and motivations, or to trigger behavior change processes. Although some quantitative studies have been recently conducted aiming at measuring the effects of gamification on users’ behaviors and motivations, there is a shortage of qualitative studies able to capture the subjective experiences of users, when using gamified systems. The authors propose to investigate how users are engaged by the most common gamification techniques, by conducting a diary study followed by a series of six focus groups. From the findings gathered, they conclude the paper identifying some implications for the design of interactive systems that aim at supporting intrinsic motivations to engage their users. A Qualitative Investigation of Gamification: Motivational Factors in Online Gamified Services and Applications
Synthesis of Biodiesel from Waste Cooking Oil

In the present study biodiesel was synthesized from Waste Cook Oil (WCO) by three-step method and regressive analyzes of the process was done. The raw oil, containing 1.9wt% Free Fatty Acid (FFA) and viscosity was 47.6mm/s. WCO was collected from local restaurant of Sylhet city in Bangladesh. Transesterification method gives lower yield than three-step method. In the three-step method, the first step is saponification of the oil followed by acidification to produce FFA and finally esterification of FFA to produce biodiesel. In the saponification reaction, various reaction parameters such as oil to sodium hydroxide molar ratio and reaction time were optimized and the oil to NaOH molar ratio was 1:2, In the esterification reaction, the reaction parameters such as methanol to FFA molar ratio, catalyst concentration and reaction temperature were optimized. Silica gel was used during esterification reaction to adsorb water produced in the reaction. Hence the reaction rate was increased and finally the FFA was reduced to 0.52wt%. A factorial design was studied for esterification reaction based on yield of biodiesel. Finally various properties of biodiesel such as FFA, viscosity, specific gravity, cetane index, pour point, flash point etc. were measured and compared with biodiesel and petro-diesel standard. The reaction yield was 79%.
LEAN , AGILE & LEAGILE SUPPLY CHAIN : A COMPARATIVE STUDY

Lean and agile principles are two most buzzing words around the corporate in the past few decades. The industrial sectors throughout the world are upgrading to these principles to enhance their performance lowering their operating costs. Though, these principles have been proven to be efficient in handling supply chains, a more robust strategy is developed inheriting the salient features of both lean and agile principles. The synergy of the two principles, leagile, takes advantage of leanness by eliminating non-value added time and agility by additional reduction of value-added time via production technology breakthroughs. Though, all the three are directly focused on core competency, the suitability of each of them must be evaluated before implementation. The objective of this study is to perform a comparative analysis of the agile, lean and leagile supply chain which specifies the competitive features of the three strategies and their competency. In addition to this, different frameworks of leagile supply chain are presented suitable for different manufacturing industry. Key wordssupply chain; lean; agile; le-agile; decoupling