nils-herrmann/clean_scirepeval huggingface dataset:
Personalization and Customization Technologies

The article presents a survey of personalization and customization technologies. It starts with an analysis of user profiling and explains how user profiles can be used for generating personalized products and services. The article explains some standard personalization techniques such as rule-based filtering, collaborative filtering, and content-based filtering. Web usage mining is also explained and it is shown how data mining can be used in learning customer behavior and for providing personalized content. Several applications are discussed such as adaptive websites, recommender systems, adaptive web stores, and intelligent agents. The article concludes by summarizing current trends.
MoNet: Deep Motion Exploitation for Video Object Segmentation

In this paper, we propose a novel MoNet model to deeply exploit motion cues for boosting video object segmentation performance from two aspects, i.e., frame representation learning and segmentation refinement. Concretely, MoNet exploits computed motion cue (i.e., optical flow) to reinforce the representation of the target frame by aligning and integrating representations from its neighbors. The new representation provides valuable temporal contexts for segmentation and improves robustness to various common contaminating factors, e.g., motion blur, appearance variation and deformation of video objects. Moreover, MoNet exploits motion inconsistency and transforms such motion cue into foreground/background prior to eliminate distraction from confusing instances and noisy regions. By introducing a distance transform layer, MoNet can effectively separate motion-inconstant instances/regions and thoroughly refine segmentation results. Integrating the proposed two motion exploitation components with a standard segmentation network, MoNet provides new state-of-the-art performance on three competitive benchmark datasets.
WASSA-2017 Shared Task on Emotion Intensity

We present the first shared task on detecting the intensity of emotion felt by the speaker of a tweet. We create the first datasets of tweets annotated for anger, fear, joy, and sadness intensities using a technique called best–worst scaling (BWS). We show that the annotations lead to reliable fine-grained intensity scores (rankings of tweets by intensity). The data was partitioned into training, development, and test sets for the competition. Twenty-two teams participated in the shared task, with the best system obtaining a Pearson correlation of 0.747 with the gold intensity scores. We summarize the machine learning setups, resources, and tools used by the participating teams, with a focus on the techniques and resources that are particularly useful for the task. The emotion intensity dataset and the shared task are helping improve our understanding of how we convey more or less intense emotions through language.
A Bidirectional Bridge Modular Switched-Capacitor-Based Power Electronics Transformer

This paper proposes a new bridge modular switched-capacitor-based ac–ac converter, which is intended to replace conventional transformers in commercial and residential applications, especially for microgrids. The main advantages of the proposed converter lie in high efficiency and power factor, bidirectional operation, and capacitor voltage self-balancing ability. Another distinct merit is its symmetric construction, with which the voltage ripple of charge/pump capacitors can be cancelled out, and the output voltage will be smooth with significant ripple reduction. It is also noteworthy that the symmetric construction ensures that the two symmetric parts can work independently under a simplified control strategy. In addition, the modular configuration contributes to convenient voltage extension. Compared with the reported flying-capacitor switched-capacitor ac–ac converter, the proposed converter requires fewer switches and capacitors per unit of voltage conversion, thus suffering lower voltage stress. To demonstrate the performance of the converter, a prototype with 600-W 220-Vrms high-side voltage, 55-Vrms low-side voltage, and a switching frequency of 100 kHz was designed and fabricated. The relevant experimental results are reported herein. The maximum and rated power efficiency obtained were 95.9% and 94.4%, respectively.
Transponder using SIW based negative and zeroth order resonance dual-band antenna and sub-harmonic self-oscillating mixer

A simple active transponder using a substrate integrated waveguide (SIW) based dual-band antenna and a self-oscillating mixer is proposed. Making use of the negative first mode and zeroth order mode of the SIW based slot antenna, we designed the dual-band antenna operating at around 2.45GHz and 5.85GHz (industrial-scientific-medical bands). Due to the resonance of the diplexer and antenna, the self-oscillating mixer oscillates at around 1.7GHz, providing a second harmonic which can be used to up-convert 2.45GHz input signal into 5.85GHz output. Furthermore, the proposed transponder only needs one DC bias for drain at 1.5V, which should be useful for portable devices with single battery.
Timing Recovery in Digital Synchronous Data Receivers

research interests are in data communications and computer networks. Determination of path lengths in a distributed net-Dr. Pickholtz is a member of Eta Kappa Nu and Sigma Xi. A new class of fast-converging timing recovery methods small loop gains the residual jitter is proportional and convergence time for synchronous digital data receivers is investigated. Starting with a is inversely proportional to the loop gain. The proposed algorithms are worst-case timing offset, convergence with random binary data will simple and economic to implement. They apply to binary or multi-typically occur within 10-20 symbols. The input signal is sampled at level PAM signals as well as to partial response signals. the baud rate; these samples are then processed to derive a suitable control signal to adjust the timing phase. A general method is outlined to obtain near-minimum-variance estimates of the timing offset with respect to a given steady-state sampling criterion. Although we make certain independence assumptions between successive samples and postulate ideal decisions to obtain convenient analytical results, our simulations with a decision-directed reference and baud-to-baud adjustments yield very similar results.
White-box cryptography: practical protection on hostile hosts

Businesses often interact with users via web-browsers and applications on mobile devices, and host services on cloud servers they may not own. Such highly-exposed environments employ white-box cryptography (WBC) for security protection. WBC operates on a security model far different from the traditional black-box model. The modern business world includes large commercial segments in which end-users are directly exposed to business application software hosted on web browsers, mobile phones, web-connected tablets, and an increasing number of other devices: the `internet of things' (IoT). Software applications and their communication activities now dominate much of the commercial world, and there have been countless hacks on such software, and on devices hosting it, with targets as diverse as mobile phones, web browser applications, vehicles, and even refrigerators! The business advantages of deploying computational power near the user encourage software migration to exposed network end-points, but this increasing exposure provides an ever growing attack surface. Here, we discuss goals and challenges of white-box cryptography and emerging approaches in a continual attempt to stay at least one step ahead of the attackers. We list some WBC techniques, both traditional and recent, indicating how they might be incorporated into a WBC AES implementation.
Forecasting stock price directional movements using technical indicators: Investigating window size effects on one-step-ahead forecasting

Accurate forecasting of directional changes in stock prices is important for algorithmic trading and investment management. Technical analysis has been successfully used in financial forecasting and recently researchers have explored the optimization of parameters for technical indicators. This study investigates the relationship between the window size used for calculating technical indicators and the accuracy of one-step-ahead (variable steps) forecasting. The directions of the future price movements are predicted using technical analysis and machine learning algorithms. Results show a correlation between window size and forecasting step size for the Support Vector Machines approach but not for the other approaches.
Binding mechanism of PicoGreen to DNA characterized by magnetic tweezers and fluorescence spectroscopy

Fluorescent dyes are broadly used in many biotechnological applications to detect and visualize DNA molecules. However, their binding to DNA alters the structural and nanomechanical properties of DNA and, thus, interferes with associated biological processes. In this work we employed magnetic tweezers and fluorescence spectroscopy to investigate the binding of PicoGreen to DNA at room temperature in a concentration-dependent manner. PicoGreen is an ultrasensitive quinolinium nucleic acid stain exhibiting hardly any background signal from unbound dye molecules. By means of stretching and overwinding single, torsionally constrained, nick-free double-stranded DNA molecules, we acquired force-extension and supercoiling curves which allow quantifying DNA contour length, persistence length and other thermodynamical binding parameters, respectively. The results of our magnetic tweezers single-molecule binding study were well supported through analyzing the fluorescent spectra of stained DNA. On the basis of our work, we could identify a concentration-dependent bimodal binding behavior, where, apparently, PicoGreen associates to DNA as an intercalator and minor-groove binder simultaneously.
Book recommendation system for digital library based on user profiles by using association rule

Due to wide application of management system, information data grows rapidly. On one hand, people have a large number of information resources. On the other hand, the time cost and difficulty of people finding the proper information increases. To tackle the problems, book recommendation is one of the solutions for university libraries which possess huge volumes of books and reading-intensive users. This paper proposes a library book recommendation system based on user profile loaning and apply association rule to create model. The result shows that new association rule algorithm suitable to apply for recommender book in library.
Constraint Programming in OPL

OPL is a modeling language for mathematical programming and combinatorial optimization problems. It is the rst modeling language to combine high-level algebraic and set notations from model-ing languages with a rich constraint language and the ability to specify search procedures and strategies that is the essence of constraint programming. In addition, OPL models can be controlled and composed using OPLSCRIPT, a script language that simpliies the development of applications that solve sequences of models, several instances of the same model, or a combination of both as in column-generation applications. This paper illustrates some of the functionalities of OPL for constraint programming using frequency allocation, sport-scheduling, and job-shop scheduling applications. It also illustrates how OPL models can be composed using OPLSCRIPT on a simple connguration example.
Harvesting Big Data to Enhance Supply Chain Innovation Capabilities : An Analytic Infrastructure Based on Deduction Graph

Today, firms can access to big data (tweets, videos, click streams, and other unstructured sources) to extract new ideas or understanding about their products, customers, and markets. Thus, managers increasingly view data as an important driver of innovation and a significant source of value creation and competitive advantage. To get the most out of the big data (in combination with a firm’s existing data), a more sophisticated way of handling, managing, analysing and interpreting data is necessary. However, there is a lack of data analytics techniques to assist firms to capture the potential of innovation afforded by data and to gain competitive advantage. This research aims to address this gap by developing and testing an analytic infrastructure based on the deduction graph technique. The proposed approach provides an analytic infrastructure for firms to incorporate their own competence sets with other firms. Case studies results indicate that the proposed data analytic approach enable firms to utilise big data to gain competitive advantage by enhancing their supply chain innovation capabilities.
Real-time Human Pose and Shape Estimation for Virtual Try-On Using a Single Commodity Depth Camera

We present a system that allows the user to virtually try on new clothes. It uses a single commodity depth camera to capture the user in 3D. Both the pose and the shape of the user are estimated with a novel real-time template-based approach that performs tracking and shape adaptation jointly. The result is then used to drive realistic cloth simulation, in which the synthesized clothes are overlayed on the input image. The main challenge is to handle missing data and pose ambiguities due to the monocular setup, which captures less than 50 percent of the full body. Our solution is to incorporate automatic shape adaptation and novel constraints in pose tracking. The effectiveness of our system is demonstrated with a number of examples.
Solvable and unsolvable problems Universal Turing Machine

Recent advances in constant-time technology and atomic epistemologies do not necessarily obviate the need for thin clients. In fact, few information theorists would disagree with the unfortunate unification of Moore’s Law and flipflop gates, which embodies the private principles of cyberinformatics. We construct a homogeneous tool for analyzing kernels (TOTA), confirming that IPv4 and kernels are entirely incompatible.
Presenting the facts about smoking to adolescents: effects of an autonomy-supportive style.

To test the self-determination model of health-related behavior by examining whether the degree to which adolescents experience an appeal to not smoke as autonomy supportive would affect their autonomous motivation for not smoking and, in turn, their behavior of either refraining from smoking or smoking less, and to validate the measures of perceived autonomy support and autonomous motivation for not smoking.



Two studies of physicians presenting information about not smoking using 2 message styles, 1 of which was designed to be more autonomy supportive. The preliminary study involved nonrandomized assignment to message style and only immediate assessment of perceptions, motivation, and behavior, while the primary study involved randomized assignment and 4-month longitudinal assessments.



Nearly 400 ninth- through 12th-grade students at 2 suburban high schools in upstate New York.



Adolescents' perceptions of the presentations' autonomy supportiveness of the presenters, as well as adolescents' autonomous motivation for not smoking and their self-reports of smoking. The primary study also assessed change in students' autonomous motivation and change in their self-reported smoking during 4 months.



In both studies, the measures were reliable and valid. Students perceived significantly (P = .04 and P<.001, respectively) greater autonomy support in the "It's Your Choice" presentation, after controlling for whether the students were smokers. Perceived autonomy supportiveness of the presentation was positively correlated with autonomous reasons for not smoking in the preliminary study and with increases in autonomous motivation for not smoking in the primary study. Change in autonomous reasons for not smoking significantly (P<.001) predicted reduction in smoking during 4 months.



When adolescents perceived messages about not smoking as autonomy supportive, they had more autonomous motivation for not smoking, and that, in turn, predicted a decrease in their self-reports of smoking.
Simulation and evaluation of urban bus-networks using a multiagent approach

Evolution of public road transportation systems requires analysis and planning tools to improve service quality. A wide range of road transportation simulation tools exist with a variety of applications in planning, training and demonstration. However, few simulation models take into account traveler behaviors and vehicle operation specific to public transportation. We present in this paper a bus network simulation tools which include these specificities and allows to analyze and evaluate a bus network at diverse space and time scales. We adopt a multiagent approach to describe the global system operation as behaviors of numerous autonomous entities such as buses and travelers.
Adopting the Internet of Things technologies in health care systems

Internet of Things based health care systems play a significant role in Information and Communication Technologies and has contribution in development of medical information systems. The developing of IoT-based health care systems must ensure and increase the safety of patients, the quality of life and other health care activities. The tracking, tracing and monitoring of patients and health care actors activities are challenging research directions. In this paper we propose a general architecture of a health care system for monitoring of patients at risk in smart Intensive Care Units. The system advices and alerts in real time the doctors/medical assistants about the changing of vital parameters or the movement of the patients and also about important changes in environmental parameters, in order to take preventive measures.
Students' perceptions of using Facebook as an interactive learning resource at university

Facebook is a popular platform that may facilitate learning activities at university. In this study, students’ perceptions of using ‘Facebook pages’ within individual university subject offerings were evaluated. Individual ‘Facebook pages’ were developed for four university courses and used to provide information relevant to the courses and allow opportunities for student interaction. An initial questionnaire administered in the first lecture of semester indicated that nearly all students (n=161, 93.1%) possessed an active Facebook account. Most students (n=135, 78.0%) anticipated that a Facebook page would facilitate their learning, by increased interaction with students and instructors, and notifications for course information. A second questionnaire was completed in the final lecture of semester indicating that 81.9% of students engaged with the course Facebook page at some stage. However, perceptions of the effectiveness of the page as a learning tool were variable, with only 51% of students stating that it was effective. Despite this, the majority of students (n=110, 76.4%) recommended using Facebook in future courses. This preliminary evaluation of Facebook as a learning aid suggests that it has the potential to promote collaborative and cooperative learning, but further research is required, specifically to understand if and how it can enhance learning outcomes.
CMiner: Opinion Extraction and Summarization for Chinese Microblogs

Sentiment analysis of microblog texts has drawn lots of attention in both the academic and industrial fields. However, most of the current work only focuses on polarity classification. In this paper, we present an opinion mining system for Chinese microblogs called CMiner. Instead of polarity classification, CMiner focuses on more complicated opinion mining tasks - opinion target extraction and opinion summarization. Novel algorithms are developed for the two tasks and integrated into the end-to-end system. CMiner can help to effectively understand the users' opinion towards different opinion targets in a microblog topic. Specially, we develop an unsupervised label propagation algorithm for opinion target extraction. The opinion targets of all messages in a topic are collectively extracted based on the assumption that similar messages may focus on similar opinion targets. In addition, we build an aspect-based opinion summarization framework for microblog topics. After getting the opinion targets of all the microblog messages in a topic, we cluster the opinion targets into several groups and extract representative targets and summaries for each group. A co-ranking algorithm is proposed to rank both the opinion targets and microblog sentences simultaneously. Experimental results on a benchmark dataset show the effectiveness of our system and the algorithms.
An ultra wide band power divider for antenna array feeding network

This paper presents a design procedure for a physically small, equal phase and equal power, 1-to-4 ultra-wideband Wilkinson power divider is presented. Initially, a 1-to-2 divider was designed and optimized for the full 3.1 GHz to 10.6 GHz range. The 1-to-4 divider was realized using three 1 to 2 dividers, and further optimized for full band insertion loss, return loss, and isolation. The circuits were constructed using a 0.75mm thick Rogers RO3035 substrate, and experimentally validated.
Bounded Rationality, Ambiguity, and the Engineering of Choice

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Lymphomatoid papulosis and associated lymphomas: a retrospective case series of 84 patients.

To determine incidence and risk factors for developing lymphoma in patients with lymphomatoid papulosis (LyP), and to identify putative triggers amenable to treatment.



The prognostic effect of severity of LyP, viral infection by history or serology, beta-2-microglobulin level, lactic dehydrogenase (LDH) level, and CD4 : CD8 ratio were evaluated using logistic regression models. Responses to prophylactic or palliative treatment were assessed.



In total, 84 patients (38 men, 46 women, median age at diagnosis 48.5 years) were identified. Of these, 34 (40%) were also diagnosed with one or more lymphomas: 16 (19%) had mycosis fungoides, 15 (17%) had primary cutaneous anaplastic large-cell lymphoma (pcALCL), 2 had both MF and pcALCL, and 1 had MF with large cell transformation and 1 had peripheral T-cell lymphoma. Of the 61 people presenting with LyP alone, only 11 (18%) subsequently developed lymphoma, with a median onset of 17.6 years (95% CI: 4, not obtained). Men were 2.5 times more likely than women to develop lymphoma (P = 0.04). Exposure to Epstein-Barr virus (EBV) was associated with an increase in incidence of 4.8 times (P = 0.16). Treatment for a putative infectious trigger resulted in improvement for 15 of 24 patients (63%).



Referral bias may explain the higher (40%) incidence of lymphoma in this population of LyP patients, compared with the 10-20% incidence commonly cited in the literature. In the subset of patients presenting with LyP alone, only 18% later developed lymphoma. Male patients or patients with prior EBV infection may have a higher risk for developing lymphoma, and some patients improved with treatment of putative infectious triggers.