nils-herrmann/clean_scirepeval huggingface dataset:
Tune and mix: learning to rank using ensembles of calibrated multi-class classifiers

In subset ranking, the goal is to learn a ranking function that approximates a gold standard partial ordering of a set of objects (in our case, a set of documents retrieved for the same query). The partial ordering is given by relevance labels representing the relevance of documents with respect to the query on an absolute scale. Our approach consists of three simple steps. First, we train standard multi-class classifiers (AdaBoost.MH and multi-class SVM) to discriminate between the relevance labels. Second, the posteriors of multi-class classifiers are calibrated using probabilistic and regression losses in order to estimate the Bayes-scoring function which optimizes the Normalized Discounted Cumulative Gain (NDCG). In the third step, instead of selecting the best multi-class hyperparameters and the best calibration, we mix all the learned models in a simple ensemble scheme. Our extensive experimental study is itself a substantial contribution. We compare most of the existing learning-to-rank techniques on all of the available large-scale benchmark data sets using a standardized implementation of the NDCG score. We show that our approach is competitive with conceptually more complex listwise and pairwise methods, and clearly outperforms them as the data size grows. As a technical contribution, we clarify some of the confusing results related to the ambiguities of the evaluation tools, and propose guidelines for future studies.
Interpersonal emotion regulation: Implications for affiliation, perceived support, relationships, and well-being.

People often recruit social resources to manage their emotions, a phenomenon known as interpersonal emotion regulation (IER). Despite its importance, IER's psychological structure remains poorly understood. We propose that two key dimensions describe IER: (a) individuals' tendency to pursue IER in response to emotional events, and (b) the efficacy with which they perceive IER improves their emotional lives. To probe these dimensions, we developed the Interpersonal Regulation Questionnaire (IRQ), a valid and reliable measure of individual differences in IER. Factor analyses of participants' responses confirmed tendency and efficacy as independent dimensions of IER (Study 1; N = 285), and demonstrated independence between how individuals engage with IER in response to negative, versus positive, emotion. In Study 2 (N = 347), we found that individuals high in IER tendency and efficacy are more emotionally expressive, empathetic, and socially connected. Two subsequent studies highlighted behavioral consequences of IER dimensions: people high in IER tendency sought out others more often following experimentally induced emotion (Study 3; N = 400), and individuals high in IER efficacy benefitted more from social support after real-world emotional events (Study 4; N = 787). Finally, a field study of social networks in freshman dormitories revealed that individuals high in IER tendency and efficacy developed more supportive relationships during the first year of college (Study 5; N = 193). These data (a) identify distinct dimensions underlying IER, (b) demonstrate that these dimensions can be stably measured and separated from related constructs, and (c) reveal their implications for relationships and well-being.
The challenges of M 2 M massive access in wireless cellular networks

The next generation of communication systems, which is commonly referred to as 5G, is expected to support, besides the traditional voice and data services, new communication paradigms, such as Internet of Things (IoT) and Machine-to-Machine (M2M) services, which involve communication between Machine-Type Devices (MTDs) in a fully automated fashion, thus, without or with minimal human intervention. Although the general requirements of 5G systems are progressively taking shape, the technological issues raised by such a vision are still partially unclear. Nonetheless, general consensus has been reached upon some specific challenges, such as the need for 5G wireless access networks to support massive access by MTDs, as a consequence of the proliferation of M2M services. In this paper, we describe the main challenges raised by the M2M vision, focusing in particular on the problems related to the support of massive MTD access in current cellular communication systems. Then we analyze the most common approaches proposed in the literature to enable the coexistence of conventional and M2M services in the current and next generation of cellular wireless systems. We finally conclude by pointing out the research challenges that require further investigation in order to provide full support to the M2M paradigm.
Earpod: eyes-free menu selection using touch input and reactive audio feedback

We present the design and evaluation of earPod: an eyes-free menu technique using touch input and reactive auditory feedback. Studies comparing earPod with an iPod-like visual menu technique on reasonably-sized static menus indicate that they are comparable in accuracy. In terms of efficiency (speed), earPod is initially slower, but outperforms the visual technique within 30 minutes of practice. Our results indicate that earPod is potentially a reasonable eyes-free menu technique for general use, and is a particularly exciting technique for use in mobile device interfaces.
Common mode noise cancellation for electrically non-contact ECG measurement system on a chair.

Electrically non-contact ECG measurement system on a chair can be applied to a number of various fields for continuous health monitoring in daily life. However, the body is floated electrically for this system due to the capacitive electrodes and the floated body is very sensitive to the external noises or motion artifacts which affect the measurement system as the common mode noise. In this paper, the Driven-Seat-Ground circuit similar to the Driven-Right-Leg circuit is proposed to reduce the common mode noise. The analysis of this equivalent circuit is performed and the output signal waveforms are compared between with Driven-Seat-Ground and with capacitive ground. As the results, the Driven-Seat-Ground circuit improves significantly the properties of the fully capacitive ECG measurement system as the negative feedback.
Batch Reinforcement Learning for Smart Home Energy Management

Smart grids enhance power grids by integrating electronic equipment, communication systems and computational tools. In a smart grid, consumers can insert energy into the power grid. We propose a new energy management system (called RLbEMS) that autonomously defines a policy for selling or storing energy surplus in smart homes. This policy is achieved through Batch Reinforcement Learning with historical data about energy prices, energy generation, consumer demand and characteristics of storage systems. In practical problems, RLbEMS has learned good energy selling policies quickly and effectively. We obtained maximum gains of 20.78% and 10.64%, when compared to a Naı̈ve-greedy policy, for smart homes located in Brazil and in the USA, respectively. Another important result achieved by RLbEMS was the reduction of about 30% of peak demand, a central desideratum for smart grids.
A Review on Data Mining in Banking Sector

The banking industry has undergone various changes in the way they conduct the business and focus on modern technologies to compete the market. The banking industry has started realizing the importance of creating the knowledge base and its utilization for the benefits of the bank in the area of strategic planning to survive in the competitive market. In the modern era, the technologies are advanced and it facilitates to generate, capture and store data are increased enormously. Data is the most valuable asset, especially in financial industries. The value of this asset can be evaluated only if the organization can extract the valuable knowledge hidden in raw data. The increase in the huge volume of data as a part of day to day operations and through other internal and external sources, forces information technology industries to use technologies like data mining to transform knowledge from data. Data mining technology provides the facility to access the right information at the right time from huge volumes of raw data. Banking industries adopt the data mining technologies in various areas especially in customer segmentation and profitability, Predictions on Prices/Values of different investment products, money market business, fraudulent transaction detections, risk predictions, default prediction on pricing. It is a valuable tool which identifies potentially useful information from large amount of data, from which organization can gain a clear advantage over its competitors. This study shows the significance of data mining technologies and its advantages in the banking and financial sectors.
Studying the Effects of Task Notification Policies on Participation and Outcomes in On-the-go Crowdsourcing

Recent years have seen the growth of physical crowdsourcing systems (e.g., Uber; TaskRabbit) that motivate large numbers of people to provide new and improved physical tasking and delivery services on-demand. In these systems, opportunistically relying on people to make convenient contributions may lead to incomplete solutions, while directing people to do inconvenient tasks requires high incentives. To increase people’s willingness to participate and reduce the need to incentivize participation, we study on-the-go crowdsourcing as an alternative approach that suggests tasks along people’s existing routes that are conveniently on their way. We explore as a first step in this paper the design of task notification policies that decide when, where, and to whom to suggest tasks. Situating our work in the context of practical problems such as package delivery and lost-and-found searches, we conducted controlled experiments that show how small changes in task notification policy can influence individual participation and actions in significant ways that in turn affect system outcomes. We discuss the implications of our findings on the design of future on-the-go crowdsourcing technologies.
Attention-based BiLSTM Neural Networks for Sentiment Classification of Short Texts

Sentiment analysis of short texts such as single sentence has been a research hotspot of natural language processing (NLP). There still exists the challenge of effectively handling the problem with limited contextual information and semantic features. Hence, in this paper, an attentionbased bidirectional LSTM neural network (AB-BiLSTM) is proposed to solve the problem. The proposed model can attend the qualitative and informative parts and learn semantic features from both directions of a sentence to perform sentiment analysis of short texts. The proposed model mainly contributes to take advantage of the attention mechanism to capture the informative parts of a sentence without any syntactic features and lexicon features. The model is conducted on the Stanford Sentiment Treebank dataset and the Movie Review Data provided by Cornell University for single sentence sentiment analysis of binary classification. The experimental results indicate that the proposal in this study has superior performances over the existing methods, without taking the attention mechanism into account.
Smart Agents in Industrial Cyber–Physical Systems

Future industrial systems can be realized using the cyber-physical systems (CPSs) that advocate the coexistence of cyber and physical counterparts in a network structure to perform the system's functions in a collaborative manner. Multiagent systems share common ground with CPSs and can empower them with a multitude of capabilities in their efforts to achieve complexity management, decentralization, intelligence, modularity, flexibility, robustness, adaptation, and responsiveness. This work surveys and analyzes the current state of the industrial application of agent technology in CPSs, and provides a vision on the way agents can effectively enable emerging CPS challenges.
The prevalence of comorbid depression in adults with diabetes: a meta-analysis.

To estimate the odds and prevalence of clinically relevant depression in adults with type 1 or type 2 diabetes. Depression is associated with hyperglycemia and an increased risk for diabetic complications; relief of depression is associated with improved glycemic control. A more accurate estimate of depression prevalence than what is currently available is needed to gauge the potential impact of depression management in diabetes.



MEDLINE and PsycINFO databases and published references were used to identify studies that reported the prevalence of depression in diabetes. Prevalence was calculated as an aggregate mean weighted by the combined number of subjects in the included studies. We used chi(2) statistics and odds ratios (ORs) to assess the rate and likelihood of depression as a function of type of diabetes, sex, subject source, depression assessment method, and study design.



A total of 42 eligible studies were identified; 20 (48%) included a nondiabetic comparison group. In the controlled studies, the odds of depression in the diabetic group were twice that of the nondiabetic comparison group (OR = 2.0, 95% CI 1.8-2.2) and did not differ by sex, type of diabetes, subject source, or assessment method. The prevalence of comorbid depression was significantly higher in diabetic women (28%) than in diabetic men (18%), in uncontrolled (30%) than in controlled studies (21%), in clinical (32%) than in community (20%) samples, and when assessed by self-report questionnaires (31%) than by standardized diagnostic interviews (11%).



The presence of diabetes doubles the odds of comorbid depression. Prevalence estimates are affected by several clinical and methodological variables that do not affect the stability of the ORs.
Synthetic aperture radar at millimeter wavelength for UAV surveillance applications

The airborne monitoring of scenes using unmanned aircrafts is becoming increasingly important. Several types of airborne sensors - in the optical, infrared or millimeter wave spectrum - are available for the different platforms. Beside the all-weather suitability of the sensors, the deployment scenarios, often also demand for the ability to look through dust clouds, smoke, and fog. The only sensor, which is capable to cope with such environmental restrictions and is able to deliver high-resolution images, is the synthetic aperture radar (SAR). In this paper we focus on miniaturized SAR systems which were developed and optimized for utilization in a UA V (unmanned aerial vehicle) with a low loading capacity. This not only requires a compact and light radar sensor but the processing also has to cope with the unstable flight conditions of a small aircraft. Therefore, a high-precision inertial measurement unit (IMU) and motion compensating SAR-algorithms are needed. Thanks to the utilization of a high transmit frequency of either 35 GHz or 94 GHz, the sensors are suitable for the detection of small-scale objects. A very high resolution of 15 cm × 15 cm can be achieved when using modern FMCW (frequency modulated continuous wave) generation with a high bandwidth (up to 1 GHz) in combination with small antennas.
On Human Motion Prediction Using Recurrent Neural Networks

Human motion modelling is a classical problem at the intersection of graphics and computer vision, with applications spanning human-computer interaction, motion synthesis, and motion prediction for virtual and augmented reality. Following the success of deep learning methods in several computer vision tasks, recent work has focused on using deep recurrent neural networks (RNNs) to model human motion, with the goal of learning time-dependent representations that perform tasks such as short-term motion prediction and long-term human motion synthesis. We examine recent work, with a focus on the evaluation methodologies commonly used in the literature, and show that, surprisingly, state of the art performance can be achieved by a simple baseline that does not attempt to model motion at all. We investigate this result, and analyze recent RNN methods by looking at the architectures, loss functions, and training procedures used in state-of-the-art approaches. We propose three changes to the standard RNN models typically used for human motion, which results in a simple and scalable RNN architecture that obtains state-of-the-art performance on human motion prediction.
Reverse Engineering Flash EEPROM Memories Using Scanning Electron Microscopy

In this article, a methodology to extract Flash EEPROM memory contents is presented. Samples are first backside prepared to expose the tunnel oxide of floating gate transistors. Then, a Scanning Electron Microscope (SEM) in the so called Passive Voltage Contrast (PVC) mode allows distinguishing ‘0’ and ‘1’ bit values stored in individual memory cell. Using SEM operator-free acquisition and standard image processing technique we demonstrate the possible automating of such technique over a full memory. The presented fast, efficient and low cost technique is successfully implemented on 0.35μm technology node microcontrollers and on a 0.21μm smart card type integrated circuit. The technique is at least two orders of magnitude faster than state-of-the-art Scanning Probe Microscopy (SPM) methods. Without adequate protection an adversary could obtain the full memory array content within minutes. The technique is a first step for reverse engineering secure embedded systems.
A comparative analysis of software architecture recovery techniques

Many automated techniques of varying accuracy have been developed to help recover the architecture of a software system from its implementation. However, rigorously assessing these techniques has been hampered by the lack of architectural “ground truths”. Over the past several years, we have collected a set of eight architectures that have been recovered from open-source systems and independently, carefully verified. In this paper, we use these architectures as ground truths in performing a comparative analysis of six state-of-the-art software architecture recovery techniques. We use a number of metrics to assess each technique for its ability to identify a system's architectural components and overall architectural structure. Our results suggest that two of the techniques routinely outperform the rest, but even the best of the lot has surprisingly low accuracy. Based on the empirical data, we identify several avenues of future research in software architecture recovery.
Design and Optimization of Multipath Ring Oscillators

Multipath ring oscillators have been used to generate multiple clock phases and higher oscillation frequencies. This paper presents a modified linear analysis to find an accurate expression for the oscillation frequency and phase noise. The analysis shows that for the same power consumption, the number of phases can be increased without decreasing the oscillation frequency or degrading the phase noise. The expression further indicates factors that determine the oscillation frequency and shows that in principle, the maximum oscillation frequency can exceed that of a three-stage ring oscillator. The analytical results can be used to find the optimum coupling structure of the oscillator to achieve a desired operating frequency and phase noise performance.
A Comprehensive Survey of Neighborhood-Based Recommendation Methods

Among collaborative recommendation approaches, methods based on nearest-neighbors still enjoy a huge amount of popularity, due to their simplicity, their efficiency, and their ability to produce accurate and personalized recommendations. This chapter presents a comprehensive survey of neighborhood-based methods for the item recommendation problem. In particular, the main benefits of such methods, as well as their principal characteristics, are described. Furthermore, this document addresses the essential decisions that are required while implementing a neighborhood-based recommender system, and gives practical information on how to make such decisions. Finally, the problems of sparsity and limited coverage, often observed in large commercial recommender systems, are discussed, and a few solutions to overcome these problems are presented.
A New Deep Learning-Based Food Recognition System for Dietary Assessment on An Edge Computing Service Infrastructure

Literature has indicated that accurate dietary assessment is very important for assessing the effectiveness of weight loss interventions. However, most of the existing dietary assessment methods rely on memory. With the help of pervasive mobile devices and rich cloud services, it is now possible to develop new computer-aided food recognition system for accurate dietary assessment. However, enabling this future Internet of Things-based dietary assessment imposes several fundamental challenges on algorithm development and system design. In this paper, we set to address these issues from the following two aspects: (1) to develop novel deep learning-based visual food recognition algorithms to achieve the best-in-class recognition accuracy; (2) to design a food recognition system employing edge computing-based service computing paradigm to overcome some inherent problems of traditional mobile cloud computing paradigm, such as unacceptable system latency and low battery life of mobile devices. We have conducted extensive experiments with real-world data. Our results have shown that the proposed system achieved three objectives: (1) outperforming existing work in terms of food recognition accuracy; (2) reducing response time that is equivalent to the minimum of the existing approaches; and (3) lowering energy consumption which is close to the minimum of the state-of-the-art.
A Framework for White Blood Cell Segmentation in Microscopic Blood Images Using Digital Image Processing

Evaluation of blood smear is a commonly clinical test these days. Most of the time, the hematologists are interested on white blood cells (WBCs) only. Digital image processing techniques can help them in their analysis and diagnosis. For example, disease like acute leukemia is detected based on the amount and condition of the WBC. The main objective of this paper is to segment the WBC to its two dominant elements: nucleus and cytoplasm. The segmentation is conducted using a proposed segmentation framework that consists of an integration of several digital image processing algorithms. Twenty microscopic blood images were tested, and the proposed framework managed to obtain 92% accuracy for nucleus segmentation and 78% for cytoplasm segmentation. The results indicate that the proposed framework is able to extract the nucleus and cytoplasm region in a WBC image sample.
Effects of non-negligible cable mass on the static behavior of large workspace cable-driven parallel mechanisms

Cable-driven robots are currently extensively studied. Generally, for this type of manipulators, cables are considered to be massless and inextensible. But for large working volume applications, their mass cannot be neglected. Based on a well-known model which describes the profile of a cable under the action of its own weight, the inverse and forward kinematics of minimally constrained cable-driven manipulators can be numerically computed. This paper studies the effects of taking cable mass into account by comparison to classical massless cable model. It highlights the real effects of such a model on cable lengths to reach a given position. The effects on cable tensions are also studied.