nils-herrmann/clean_scirepeval huggingface dataset:
ARMDN: Associative and Recurrent Mixture Density Networks for eRetail Demand Forecasting

Accurate demand forecasts can help on-line retail organizations better plan their supply-chain processes. The challenge, however, is the large number of associative factors that result in large, non-stationary shifts in demand, which traditional time series and regression approaches fail to model. In this paper, we propose a Neural Network architecture called AR-MDN, that simultaneously models associative factors, time-series trends and the variance in the demand. We first identify several causal features and use a combination of feature embeddings, MLP and LSTM to represent them. We then model the output density as a learned mixture of Gaussian distributions. The AR-MDN can be trained end-to-end without the need for additional supervision. We experiment on a dataset of an year’s worth of data over tens-of-thousands of products from Flipkart. The proposed architecture yields a significant improvement in forecasting accuracy when compared with existing alternatives.  Srayanta Mukherjee, Devashish Shankar, Atin Ghosh, Nilam Tathawadekar, Pramod Kompalli, Sunita Sarawagi, Krishnendu Chaudhury. AR-MDN: Associative and Recurrent Mixture Density Networks for eRetail Demand Forecasting. PVLDB, 11 (5): xxxxyyyy, 2018.
Compact substrate integrated waveguide based active and passive circularly polarized composite right/left handed leaky wave antennas

Two compact substrate integrated waveguide (SIW) based circularly polarized (CP) composite right/left handed (CRLH) leaky wave antennas (LWAs) are designed and presented. Besides wide-band matching enhancement, the proposed antennas are shortened by 39.8 % along the scanning plane compared to the previous work . Additionally, the current amplitude distribution of the proposed antenna can be controlled by using negative resistance based reflection type amplifiers (RTAs). The compensated uniform distribution yields an enhanced directivity without compromising full space frequency scanning capability.
Cultural humility: measuring openness to culturally diverse clients.

Building on recent theory stressing multicultural orientation, as well as the development of virtues and dispositions associated with multicultural values, we introduce the construct of cultural humility, defined as having an interpersonal stance that is other-oriented rather than self-focused, characterized by respect and lack of superiority toward an individual's cultural background and experience. In 4 studies, we provide evidence for the estimated reliability and construct validity of a client-rated measure of a therapist's cultural humility, and we demonstrate that client perceptions of their therapist's cultural humility are positively associated with developing a strong working alliance. Furthermore, client perceptions of their therapist's cultural humility were positively associated with improvement in therapy, and this relationship was mediated by a strong working alliance. We consider implications for research, practice, and training.
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

We present a class of efficient models called MobileNets for mobile and embedded vision applications. MobileNets are based on a streamlined architecture that uses depthwise separable convolutions to build light weight deep neural networks. We introduce two simple global hyperparameters that efficiently trade off between latency and accuracy. These hyper-parameters allow the model builder to choose the right sized model for their application based on the constraints of the problem. We present extensive experiments on resource and accuracy tradeoffs and show strong performance compared to other popular models on ImageNet classification. We then demonstrate the effectiveness of MobileNets across a wide range of applications and use cases including object detection, finegrain classification, face attributes and large scale geo-localization.
Learning From Positive and Unlabeled Data: A Survey

Learning from positive and unlabeled data or PU learning is the setting where a learner only has access to positive examples and unlabeled data. The assumption is that the unlabeled data can contain both positive and negative examples. This setting has attracted increasing interest within the machine learning literature as this type of data naturally arises in applications such as medical diagnosis and knowledge base completion. This article provides a survey of the current state of the art in PU learning. It proposes seven key research questions that commonly arise in this field and provides a broad overview of how the field has tried to address them.
A Coordinated Approach to Channel Estimation in Large-Scale Multiple-Antenna Systems

This paper addresses the problem of channel estimation in multi-cell interference-limited cellular networks. We consider systems employing multiple antennas and are interested in both the finite and large-scale antenna number regimes (so-called "massive MIMO"). Such systems deal with the multi-cell interference by way of per-cell beamforming applied at each base station. Channel estimation in such networks, which is known to be hampered by the pilot contamination effect, constitutes a major bottleneck for overall performance. We present a novel approach which tackles this problem by enabling a low-rate coordination between cells during the channel estimation phase itself. The coordination makes use of the additional second-order statistical information about the user channels, which are shown to offer a powerful way of discriminating across interfering users with even strongly correlated pilot sequences. Importantly, we demonstrate analytically that in the large-number-of-antennas regime, the pilot contamination effect is made to vanish completely under certain conditions on the channel covariance. Gains over the conventional channel estimation framework are confirmed by our simulations for even small antenna array sizes.
Smart Irrigation Decision Support Based on Fuzzy Logic Using Wireless Sensor Network

Smart Irrigation Decision Support (SIDS) system based on fuzzy logic using Wireless Sensor Network (WSN) is considered for precision water irrigation. WSN consists of limited-energy sensor nodes which are equipped with sensing, wireless communication and processing capabilities. SIDS aims to measure the agricultural parameters including the soil temperature and soil moisture using the sensor nodes. The rate of soil moisture reduction is calculated from the current soil moisture reading and the previous one. These soil temperate and the rate of soil moisture reduction are employed as input parameters for fuzzy logic controller to produce the amount of irrigation time as output parameter. The fuzzy logic rules and linguistic values for the input and output parameters of fuzzy logic are carefully selected with the guide of agricultural experts including the farmers. The experimental results show that by using the SIDS system, the amount of irrigation time is precisely calculated based on the measured agricultural parameters. In addition, the water irrigation utilization is improved.
CSI: Community-Level Social Influence Analysis

Modeling how information propagates in social networks driven by peer influence, is a fundamental research question towards understanding the structure and dynamics of these complex networks, as well as developing viral marketing applications. Existing literature studies influence at the level of individuals, mostly ignoring the existence of a community structure in which multiple nodes may exhibit a common influence pattern. In this paper we introduce CSI, a model for analyzing information propagation and social influence at the granularity of communities. CSI builds over a novel propagation model that generalizes the classic Independent Cascade model to deal with groups of nodes (instead of single nodes) influence. Given a social network and a database of past information propagation, we propose a hierarchical approach to detect a set of communities and their reciprocal influence strength. CSI provides a higher level and more intuitive description of the influence dynamics, thus representing a powerful tool to summarize and investigate patterns of influence in large social networks. The evaluation on various datasets suggests the effectiveness of the proposed approach in modeling information propagation at the level of communities. It further enables to detect interesting patterns of influence, such as the communities that play a key role in the overall diffusion process, or that are likely to start information cascades.
Generative Semantic Manipulation with Mask-Contrasting GAN

Despite the promising results on paired/unpaired image-to-image translation achieved by Generative Adversarial Networks (GANs), prior works often only transfer the low-level information (e.g. color or texture changes), but fail to manipulate high-level semantic meanings (e.g., geometric structure or content) of different object regions. On the other hand, while some researches can synthesize compelling real-world images given a class label or caption, they cannot condition on arbitrary shapes or structures, which largely limits their application scenarios and interpretive capability of model results. In this work, we focus on a more challenging semantic manipulation task, aiming at modifying the semantic meaning of an object while preserving its own characteristics (e.g. viewpoints and shapes), such as cow→sheep, motor→ bicycle, cat→dog. To tackle such large semantic changes, we introduce a contrasting GAN (contrast-GAN) with a novel adversarial contrasting objective which is able to perform all types of semantic translations with one category-conditional generator. Instead of directly making the synthesized samples close to target data as previous GANs did, our adversarial contrasting objective optimizes over the distance comparisons between samples, that is, enforcing the manipulated data be semantically closer to the real data with target category than the input data. Equipped with the new contrasting objective, a novel mask-conditional contrast-GAN architecture is proposed to enable disentangle image background with object semantic changes. Extensive qualitative and quantitative experiments on several semantic manipulation tasks on ImageNet and MSCOCO dataset show considerable performance gain by our contrast-GAN over other conditional GANs.
Condylomata acuminata and human genital cancer.

1. Almeida, J. D., Oriel, J. D., and Stannard, L. M. Characterization of the Virus Found in Human Genital Warts.  225-232, 1969. 2. Bauer, K. M., and Friederich H. C. Peniscarcinom auf dem Boden vorbehandelter Condylomata acuminata.  225-231, 1965. 4. Domaniewski, J., and Gustowski, A. Planoepithelial, Spinocellular Cancer of the Vulva, Originating from Condylomata Acuminata.  239-242, 1968. 5. Dunn, A. E. G., and Ogilvie, M. M.  282-291,1968. 6. Frenkel, N., Roizman, B., Cassai, E., and Nahmias, A. J. DNA Fragment of Herpes Simplex 2 and Its Transcription in Human Cervical Cancer Tissue.  1972. 7. Friedberg, M. J., and Serlin, O. Condyloma acuminatum: Its Association with Malignancy. Diseases Colon Rectum, 6: 352-355, 1963. 8. Goldberg, R. J., and Gravell, M. A Search for Herpes Simplex Virus Type 2 Markers in Cervical Carcinoma. . Hautarzt, 25: 443-447, 1974. 10. Kerl, H., and Pickel, H. Maligne Umwandlung yon Condylomata Acuminata der Vulva.  Women with Cervical Cancer.  1970. 13. Oriel, J. D., and Almeida, J. D., Demonstration of Virus Particles in Human Genital Warts.  Rawls, W. E., Tompkins, W. A. F., and Melnick, J. L. The Association of Herpesvirus Type 2 and Carcinoma of the Uterine Cervix.  Transformation of Condyloma Acuminatum.  Wolf, F. In die Tiefe wuchernde spitze Condylome und Carcinom in demselben Penis-Tumor. Z. Haut-Geschlechlskrankh., 13: 142-144, 1952. 19. zur Hausen, H. Oncogenic Herpes Viruses.  Acta, 417: 25-53,  I. Nucleic Acid Hybridizations with Complementary RNA of Human Wart Virus.  1974. 21. zur Hausen; H., Schulte-Holthausen, H., Wolf, H., DOrries, K., and Egger, H. Attempts to Detect Virus-specific DNA Sequences in Human Tumors: I1. Nucleic Acid Hybridizations with Complementary RNA of Human Herpes Group Viruses.
An efficient probabilistic occupancy map-based people localization approach

The widespread use of vision-based video surveillance systems has inspired many research efforts on people localization. One of the current main trends in this field is based on probabilistic occupancy map (POM) obtained from multiple camera views. Although the POM-based approaches are robust against noisy foregrounds and can achieve great localization accuracy, they require high computation complexity. In this paper, two enhancement schemes are proposed to improve the efficiency of the POM-based people localization: (i) quick screening of potential people locations, and (ii) timely termination of iterations for occupancy probability estimation. Experimental results show that the proposed approach achieves up to 7.25 times speed-up compared to the standard POM-based approach, while delivering comparable people localization accuracy.
Star Ratings versus Sentiment Analysis -- A Comparison of Explicit and Implicit Measures of Opinions

A typical trade-off in decision making is between the cost of acquiring information and the decline in decision quality caused by insufficient information. Consumers regularly face this trade-off in purchase decisions. Online product/service reviews serve as sources of product/service related information. Meanwhile, modern technology has led to an abundance of such content, which makes it prohibitively costly (if possible at all) to exhaust all available information. Consumers need to decide what subset of available information to use. Star ratings are excellent cues for this decision as they provide a quick indication of the tone of a review. However there are cases where such ratings are not available or detailed enough. Sentiment analysis -text analytic techniques that automatically detect the polarity of text- can help in these situations with more refined analysis. In this study, we compare sentiment analysis results with star ratings in three different domains to explore the promise of this technique.
A one-shot deviation principle for stability in matching problems

This paper considers marriage problems, roommate problems with nonempty core, and college admissions problems with responsive preferences. Jackson and Watts (2002) and Klaus et al. (2010) applied stochastic stability analysis à la Kandori et al. (1993) to those problems and showed that all stable matchings are stochastically stable. We revisited them with stochastic stability analysis with a class of noisy best responses including logit and probit choice rules. All stochastically stable matchings are shown to be contained in the set of matchings which are most robust to one-shot deviation, i.e. matchings having the lowest probability of the most likely deviation.
Adaptive Execution of Compiled Queries

Compiling queries to machine code is a very efficient way for executing queries. One often overlooked problem with compilation is the time it takes to generate machine code. Even with fast compilation frameworks like LLVM, generating machine code for complex queries often takes hundreds of milliseconds. Such durations can be a major disadvantage for workloads that execute many complex, but quick queries. To solve this problem, we propose an adaptive execution framework, which dynamically switches from interpretation to compilation. We also propose a fast bytecode interpreter for LLVM, which can execute queries without costly translation to machine code and dramatically reduces the query latency. Adaptive execution is fine-grained, and can execute code paths of the same query using different execution modes. Our evaluation shows that this approach achieves optimal performance in a wide variety of settings—low latency for small data sets and maximum throughput for large data sizes.
Automatic Sublingual Vein Feature Extraction System

The quintessence of the diagnosis in traditional Chinese medicine is syndrome differentiation and treatment. Syndrome differentiation consists of four methods: observing, hearing as well as smelling, asking, and touching. The examination of the observing is the most important procedure in the method of "tongue." In recent years, numerous medical studies have identified the close relations between sublingual veins and human organs. Therefore, sublingual pathological symptoms, as well as demographical information of patients, imply pathological changes in the organs, and early diagnosis is beneficial for early treatment. However, the diagnosis of sublingual pathological symptoms is usually influenced by the doctor's subjective interpretation, experience, and environmental factors. The results can easily be limited by subjective factors such as knowledge, experience, mentality, diagnostic techniques, color perception and interpretation. Different doctors may make different judgments on the same tongue, presenting less than desirable repeatability. Therefore, assisting doctors' diagnoses with scientific methods and standardizing the differentiating process to obtain reliable diagnoses and enhance the clinical applicability of Chinese medicine is an important issue. In its wake, this study aims to construct an Automatic Sublingual Vein Feature Extraction System based on image processing technologies to allow objective and quantified computer readings. The extraction of sublingual vein features mainly captures the back of the tongue and extract the sublingual vein area for feature expression analysis. Firstly, the patient's back of the tongue is photographed and color-graded to compensate for color distortion, and then the tongue-back area is extracted. This study extracts tongue-back imagery by analyzing the RGB color expression of the back of the tongue, lips, teeth and skin, translating it into the HSI color space easily perceived by the human eye, along with skin area removal, rectangle detection, teeth area removal, black area removal and control point detection. The captured tongue-back image goes through histogram equalization and hue shift to enhance color contrast. Sublingual veins are extracted through analyzing RGB color component shift, hues, saturation and brightness. Then the sublingual vein color information and positioning are used to differentiate hues, lengths and branches. Thinning analysis is used to determine the presence of varicose veins. At the same time, the surrounding features of sublingual veins, such as columnar vein, bubbly vein, petechiae and bloodshot, are extracted. The information regarding features and lingual vein conditions are integrated and analyzed for doctors' clinical reference. This study utilizes 199 lingual images for statistic testing and three lingual diagnostic experts in Chinese medicine for lingual reading. The accuracy for the extractions are: tongue back 86%, sublingual vein 80%, varicose veins 90%, branches 87%, and the accuracy rates for columnar veins and bubbly veins are 87%, 88% and 73% respectively.
Parallel computations for controlling an arm.

In order to control a reaching movement of the arm and body, several different computational problems must be solved. Some parallel methods that could be implemented in networks of neuron-like processors are described. Each method solves a different part of the overall task. First, a method is described for finding the torques necessary to follow a desired trajectory. The methods is more economical and more versatile than table look-up and requires very few sequential steps. Then a way of generating an internal representation of a desired trajectory is described. This method shows the trajectory one piece at a time by applying a large set of heuristic rules to a "motion blackboard" that represents the static and dynamic parameters of the state of the body at the current point in the trajectory. The computations are simplified by expressing the positions, orientations, and motions of parts of the body in terms of a single, non-accelerating, world-based frame of reference, rather than in terms of the joint-angles or an egocentric frame based on the body itself.
Implementation of FPGA based PID Controller for DC Motor Speed Control System

In this paper, the implementation of software module using ‘VHDL’ for Xilinx FPGA (XC3S400) based PID controller for DC motor speed control system is presented. The tools used for building and testing the software modules are Xilinx ISE 9.2i and ModelSim XE III 6.3c. Before verifying the design on FPGA the complete design is simulated using Modelsim Simulation tool. A test bench is written where, the set speed can be changed for the motor. It is observed that the motor speed gradually changes to the set speed and locks to the set speed. IndexTerms— Field Programmable Gate Array (FPGA), Proportional-Integral-Derivative (PID) controller, Very High Speed Integrated Circuit Hardware Description Language (VHDL), Pulse Width Modulation (PWM).
A RFID case-based logistics resource management system for managing order-picking operations in warehouses

In the supply chain, a warehouse is an essential component for linking the chain partners. It is necessary to allocate warehouse resources efficiently and effectively to enhance the productivity and reduce the operation costs of the warehouse. Therefore, warehouse management systems (WMSs) have been developed for handling warehouse resources and monitoring warehouse operations. However, it is difficult to update daily operations of inventory level, locations of forklifts and stock keeping units (SKUs) in realtime by using the bar-code-based or manual-based warehouse management systems. In this paper, RFID technology is adopted to facilitate the collection and sharing of data in a warehouse. Tests are performed for evaluating the reading performance of both the active and passive RFID apparatus. With the help of the testing results, the efficient radio frequency cover ranges of the readers are examined for formulating a radio frequency identification case-based logistics resource management system (R-LRMS). The capabilities of R-LRMS are demonstrated in GSL Limited. Three objectives are achieved: (i) a simplification of RFID adoption procedure, (ii) an improvement in the visibility of warehouse operations and (iii) an enhancement of the productivity of the warehouse. The successful case example proved the feasibility of R-LRMS in real working practice.
Is FLIP enough? Or should we use the FLIPPED model instead?

Yunglung Chen, Yuping Wang, Kinshuk, Nian-Shing Chen Department of Information Management, National Sun Yat-sen University, No. 70, Lienhai Rd., Kaohsiung 80424, Taiwan School of Languages and Linguistics, Griffith University, Nathan, 4111, Australia School of Computing and Information System, Athabasca University, Athabasca, Canada henryylchen@gmail.com, y.wang@griffith.edu.au, kinshuk@athabascau.ca, nschen@mis.nsysu.edu.tw