nils-herrmann/clean_scirepeval huggingface dataset:
Fast segmentation of 3D point clouds for ground vehicles

This paper describes a fast method for segmentation of large-size long-range 3D point clouds that especially lends itself for later classification of objects. Our approach is targeted at high-speed autonomous ground robot mobility, so real-time performance of the segmentation method plays a critical role. This is especially true as segmentation is considered only a necessary preliminary for the more important task of object classification that is itself computationally very demanding. Efficiency is achieved in our approach by splitting the segmentation problem into two simpler subproblems of lower complexity: local ground plane estimation followed by fast 2D connected components labeling. The method's performance is evaluated on real data acquired in different outdoor scenes, and the results are compared to those of existing methods. We show that our method requires less runtime while at the same time yielding segmentation results that are better suited for later classification of the identified objects.
MACE prediction of acute coronary syndrome via boosted resampling classification using electronic medical records

Major adverse cardiac events (MACE) of acute coronary syndrome (ACS) often occur suddenly resulting in high mortality and morbidity. Recently, the rapid development of electronic medical records (EMR) provides the opportunity to utilize the potential of EMR to improve the performance of MACE prediction. In this study, we present a novel data-mining based approach specialized for MACE prediction from a large volume of EMR data.



The proposed approach presents a new classification algorithm by applying both over-sampling and under-sampling on minority-class and majority-class samples, respectively, and integrating the resampling strategy into a boosting framework so that it can effectively handle imbalance of MACE of ACS patients analogous to domain practice. The method learns a new and stronger MACE prediction model each iteration from a more difficult subset of EMR data with wrongly predicted MACEs of ACS patients by a previous weak model.



We verify the effectiveness of the proposed approach on a clinical dataset containing 2930 ACS patient samples with 268 feature types. While the imbalanced ratio does not seem extreme (25.7%), MACE prediction targets pose great challenge to traditional methods. As these methods degenerate dramatically with increasing imbalanced ratios, the performance of our approach for predicting MACE remains robust and reaches 0.672 in terms of AUC. On average, the proposed approach improves the performance of MACE prediction by 4.8%, 4.5%, 8.6% and 4.8% over the standard SVM, Adaboost, SMOTE, and the conventional GRACE risk scoring system for MACE prediction, respectively.



We consider that the proposed iterative boosting approach has demonstrated great potential to meet the challenge of MACE prediction for ACS patients using a large volume of EMR.
Intent-based recommendation for B2C e-commerce platforms

Recommendation systems are a well-established component of business-to-customer (B2C) e-commerce websites. The goal of a recommendation system is to increase sales by accurately predicting additional items that a customer will buy, but has not yet considered, during his or her online shopping session. Some companies, such as Amazon.com, consider the efficacy of their recommendation systems a critical competitive advantage. As competition intensifies, many online retailers seek more effective methods to anticipate customers’ needs and desires during their online experience. In this paper, we present a novel approach for increasing the conversion rate from browsing to buying customers. Our method makes a behavior-based inference of a customer’s propensity to purchase from a product category, and then executes a dynamic recommendation plan that is conditioned on the propensity levels. The approach uses a hidden Markov model, classic recommendation algorithms, and business rules.
Development of a Matlab software for analysis of heart rate variability

The analysis of heart rate variability (HRV) signals is an important tool for studying the autonomic nervous system, as it allows the evaluation of the balanc e between the sympathetic and parasympathetic influences o n heart rhythm. This paper presents a tool for analysis of HRV called ECGLab, which was developed in Matlab language in order to help research on HRV by making the analysis process faster and easier. The software obtains the HRV signal by using an automatic QRS detection algorithm. The user can inspect the ECG and correct mistakes in the detection process, and also identify ectopic beats. Im porting RR intervals from previously typed ASCII files is also possible. Some of the most popular HRV analysis techni ques were implemented: statistical and time series analysis, spectral analysis (using FFT, auto-regressive and Lomb methods), Poincaré plot analysis and sequential trend analysis.
Entrapment of the sciatic nerve at the linea aspera: A case report and literature review

Nontraumatic, non-neoplastic sciatic nerve entrapment at the level of the thigh is extremely rare. In its course, in proximity of the linea aspera, the nerve is exposed to unexpected neuropathic syndromes associated with bone disorders.



A 67-year-old woman presented with a painful, neuropathic syndrome of the sciatic nerve, not resulting from any trauma and persisting for approximately 2 years. Imaging studies of the thigh showed a delimited zone of hyperostosis in the proximal third of the femoral diaphysis. The symptoms dramatically resolved after the patient underwent neurolysis of the tract of the nerve adjoining to the linea aspera. At the clinical checkup 2 years later, the patient remained free of pain.



The diagnosis of sciatic nerve entrapment at the linea aspera may present considerable difficulties. The clinical history and physical examination sometimes motivate the exploration and neurolysis of the nerve at this site.
Syntactically Guided Neural Machine Translation

We investigate the use of hierarchical phrase-based SMT lattices in end-to-end neural machine translation (NMT). Weight pushing transforms the Hiero scores for complete translation hypotheses, with the full translation grammar score and full ngram language model score, into posteriors compatible with NMT predictive probabilities. With a slightly modified NMT beam-search decoder we find gains over both Hiero and NMT decoding alone, with practical advantages in extending NMT to very large input and output vocabularies.
Compressive Sensing Image Sensors-Hardware Implementation

The compressive sensing (CS) paradigm uses simultaneous sensing and compression to provide an efficient image acquisition technique. The main advantages of the CS method include high resolution imaging using low resolution sensor arrays and faster image acquisition. Since the imaging philosophy in CS imagers is different from conventional imaging systems, new physical structures have been developed for cameras that use the CS technique. In this paper, a review of different hardware implementations of CS encoding in optical and electrical domains is presented. Considering the recent advances in CMOS (complementary metal-oxide-semiconductor) technologies and the feasibility of performing on-chip signal processing, important practical issues in the implementation of CS in CMOS sensors are emphasized. In addition, the CS coding for video capture is discussed.
Self-compassion , Achievement Goals , and Coping with Academic Failure

s International, 59, 1911. Turner, J. E., Husman, J., & Schallert, D. L. (2002). The importance of students’ goals in their emotional experience of academic failure: Investigating the precursors and consequences of shame. Educational Psychologist, 37, 79 – 89. Urdan, T. (1997). Achievement goal theory: Past studies, future directions. In M. Maehr & P. Pintrich (Eds.), Handbook of motivation and cognition (pp. 350 – 378).  (1989). White bears and other unwanted thoughts: Suppression, obsession, and the psychology of mental control. New York, NY: Viking/Penguin. Wegner, D. M., & Zanakos, S. (1994). Chronic thought suppression. Journal of Personality, 62, 615 – 640. Williams, G. C., & Deci, E. L. (1996). Internalization of biopsychosocial values by medical students: A test of self-determination theory. Journal of Personality and Social Psychology, 70, 767 – 779. Wolters, C., Yu, S., & Pintrich, P. (1996). The relation between goal orientation and students’ motivational beliefs and self-regulated learning. Learning and Individual differences, 8, 211 – 238. Zeidner, M. (1995). Adaptive coping with test situations: A review of the literature.  Self-compassion, Achievement Goals, and Academic Failure 287
A Survey of Visualization Construction User Interfaces

We have systematically surveyed the publications on visualization construction user interfaces that have been published in 12 major Visualization and HCI venues. We found six different visualization construction approaches (visual builder, visualization spreadsheet, textual programming, visual dataflow programming, template editor, and shelf configuration). The approaches differ in their flexibility, whether they support presentation or exploration tasks, and the spatial, temporal, and conceptual distance between the user interface (UI) and the visualization. Our results provide guidance to designers of visualization construction UIs.
From random block corruption to privilege escalation: A filesystem attack vector for rowhammer-like attacks

Rowhammer demonstrated that non-physical hardwareweakness-based attacks can be devastating. In a recent paper, Cai et al.  propose that similar attacks can be performed on MLC NAND flash. In this paper, we discuss the requirements for a successful, full-system, local privilege escalation attack on such media, and show a filesystem based attack vector. We demonstrate the filesystem layer of this attack, showing that a random block corruption of a carefully chosen block is sufficient to achieve privilege escalation. In particular, to motivate the assumptions of this filesystem-level attack, we show the attack primitive that an attacker can obtain by making use of cell-to-cell interference is quite weak, and therefore requires a carefully crafted attack at the OS layer for successful exploitation.
Real time implementation of hybrid personal tracking system for anomaly detection

The major threats witnessed by mankind in recent years, demands very accurate and trustworthy personal tracking system (PTS). Using the advancement in the current technology, it becomes a promising solution to meet the above requirement. In this research, an attempt is made to integrate both GPS and GSM technologies to detect and track the position of the target (mankind). Initially, the position of the target is tracked by the authorized caretaker using GPS technology. Whenever the GPS system is unable to track the target, an alternative GSM technology is activated and the information about the concerned individual is obtained. The proposed hybrid tracking system is implemented in real-time using a customized embedded device.
Self-organization of speech sound inventories in the framework of complex networks

The sound inventories of the world’s languages show a considerable extent of symmetry. It has been postulated that this symmetry is a reflection of the human physiological, cognitive and societal factors. There have been a large number of linguistically motivated studies in order to explain the selforganization of these inventories that arguably leads to the emergence of this symmetry. A few computational models in order to explain especially the structure of the smaller vowel inventories have also been proposed in the literature. However, there is a need for a single unified computational framework for studying the self-organization of the vowel as well as other inventories of complex utterances like consonants and syllables. In this thesis, we reformulate this problem in the light of statistical mechanics and present complex network representations of these inventories. The central objective of the thesis is to study and explain the self-organization and emergence of the consonant inventories. Nevertheless, in order to demonstrate the versatility of our modeling methodology, we further apply it to investigate and detect certain interesting properties of the vowel inventories. Two types of networks are considered a language-consonant bipartite network and a consonant-consonant co-occurrence network. The networks are constructed from the UCLA Phonological Segment Inventory Database (UPSID). From the systematic analysis of these networks we find that the occur-
Designing a Desktop Virtual Reality-based Learning Environment with Emotional Consideration

Many studies related to the use of virtual reality in education are focused on the cognitive aspects with little consideration given to the emotional domain. Thus, the present study aims to uncover the salient linkages between learners’ emotions and design elements of a desktop virtual reality-based learning environment by employing Kansei Engineering concepts. A courseware related to the teaching of road safety skills to young learners was designed and developed to be used as a case for the study. Ten specimens of the courseware, which highlights different design elements, were presented to 90 students from three randomly selected secondary schools. They were required to rate their feelings towards the specimens using the provided checklist that consists of thirty words related to emotions. The gathered data were then analysed using Principal Component Analysis and Partial Least Squares analysis. The results revealed that the most influential design elements in inducing positive emotions are environment richness and coaching. Ultimately, the uncovered linkages could be used to inform future design of emotionally sound desktop VR-learning environments.
The zone of proximal development in Vygotsky’s analysis of learning and instruction

What kind of instruction is optimal for a particular child? Without doubt, this question is immediately comprehensible to any committed teacher in virtually any country in the world, and most of them are likely to want concrete answers to the question, not only as a theoretical puzzle, but in relation to their immediate practices. If one were to look to scientific psychology and educational research for advice in relation to this practical problem, what would the answer(s) look like?
Machine Learning Strategies for Time Series Forecasting

The increasing availability of large amounts of historical data and the need of performing accurate forecasting of future behavior in several scientific and applied domains demands the definition of robust and efficient techniques able to infer from observations the stochastic dependency between past and future. The forecasting domain has been influenced, from the 1960s on, by linear statistical methods such as ARIMA models. More recently, machine learning models have drawn attention and have established themselves as serious contenders to classical statistical models in the forecasting community. This chapter presents an overview of machine learning techniques in time series forecasting by focusing on three aspects: the formalization of one-step forecasting problems as supervised learning tasks, the discussion of local learning techniques as an effective tool for dealing with temporal data and the role of the forecasting strategy when we move from one-step to multiple-step forecasting.
Quad trees a data structure for retrieval on composite keys

The quad tree is a data structure appropriate for storing information to be retrieved on composite keys. We discuss the specific case of two-dimensional retrieval, although the structure is easily generalised to arbitrary dimensions. Algorithms are given both for staightforward insertion and for a type of balanced insertion into quad trees. Empirical analyses show that the average time for insertion is logarithmic with the tree size. An algorithm for retrieval within regions is presented along with data from empirical studies which imply that searching is reasonably efficient. We define an optimized tree and present an algorithm to accomplish optimization in n log n time. Searching is guaranteed to be fast in optimized trees. Remaining problems include those of deletion from quad trees and merging of quad trees, which seem to be inherently difficult operations.
Have biopesticides come of age?

Biopesticides based on living microbes and their bioactive compounds have been researched and promoted as replacements for synthetic pesticides for many years. However, lack of efficacy, inconsistent field performance and high cost have generally relegated them to niche products. Recently, technological advances and major changes in the external environment have positively altered the outlook for biopesticides. Significant increases in market penetration have been made, but biopesticides still only make up a small percentage of pest control products. Progress in the areas of activity spectra, delivery options, persistence of effect and implementation have contributed to the increasing use of biopesticides, but technologies that are truly transformational and result in significant uptake are still lacking.
Efficiency of low power audio amplifiers and loudspeakers

In this paper we look at the load presented to audio amplifiers by real transducers. We consider the power losses in Class-AB and Class-D amplifier topologies, and determine that in order to predict efficiency it is necessary to consider the amplifier/transducer combination. The ability of the class-D amplifier to recycle quadrature load current offers new ways to improve efficiency.  Class-D amplifiers are beginning to become viable alternatives to the Class-AB amplifier because of the reducing cost of suitable devices. There is particular interest in developing them for low power applications where their intrinsic efficiency advantages are important. The operation of Class-D amplifiers is very different to Class-AB amplifiers and this has implications for other components in the audio chain. Conventionally the performance of audio amplifiers is considered using a pure resistive load of four or eight ohms. The origin of this lies in the nominal impedance given to electro-magnetic loudspeakers. The true impedance of an electro-magnetic loudspeaker will vary over the operating frequency range, but the variability between individual real transducers makes the definition of a more realistic ‘bench-mark’ load impractical. Other audio transducers also have a complex impedance. Transducers which utilise piezo electric elements have a input impedance that is highly reactive. The load presented to the amplifier is almost entirely capacitive. The power output and efficiency of an amplifier are dependent load impedance and so the resistive load performance may not resemble to the operation of the amplifier in real situations. In this paper we will look at the true load presented to amplifiers by the transducer and derive a more accurate measure of overall efficiency. GENERAL AMPLIFIER EFFICIENCY The electrical efficiency of an amplifier is defined as the ratio of the power developed in the load to the power drawn from the DC supply. Using simple linear analysis we can determine the efficiency of amplifier output stages and the dependence of the efficiency on load parameters. In this section the following symbols are used; Vo Output voltage Vs Supply rail voltage RL Load resistance Ibias Class AB quiescent bias current φ Load phase angle Zload Load impedance L Class D filter inductance Rin Resistance of filter inductor RDson ‘On’ state resistance of switching devices fs Class D Switching frequency Class-AB resistive case A simple Class-AB output stage is shown in figure 1. A complementary pair of output devices operate over their linear region to amplify the signal. When the devices are operated in the linear region there will be current flowing through them whilst there is a voltage across them, this will give rise to power dissipation, and hence reduce efficiency. The devices also need a quiescent bias to reduce crossover distortion as one device takes over from the other.
A PCell design methodology for automatic layout generation of spiral inductor using skill script

With advancement in technology, higher level integration is coming to fore which demands components with high performance and efficiency in time to market. In VLSI domain, layout design is quite a lengthy process. Spiral inductor being one of the crucial components in RFIC design needs to depict high performance and design flexibility. Implementation of parameterized cells (PCells) for modeling on-chip spiral inductors overcomes this issue. PCells design proves quite flexible and less time consuming compared to classical design approach of inductor. In this paper, an efficient PCell design technique for automatic layout generation is presented for on chip spiral inductor using Cadence SKILL scripts. To support the concept defined, PCell design for automatic layout generation of square, hexagonal and octagonal spiral inductor are designed using SKILL script in Virtuoso. The spiral inductor characteristics are further validated through EM simulations using ADS Momentum.
Methodology for customer relationship management

Customer relationship management (CRM) is a customer-focused business strategy that dynamically integrates sales, marketing and customer care service in order to create and add value for the company and its customers. This change towards a customer-focused strategy is leading to a strong demand for CRM solutions by companies. However, in spite of companies interest in this new management model, many CRM implementations fail. One of the main reasons for this lack of success is that the existing methodologies being used to approach a CRM project are not adequate, since they do not satisfactorily integrate and complement the strategic and technological aspects of CRM. This paper describes a formal methodology for directing the process of developing and implementing a CRM System that considers and integrates various aspects, such as defining a customer strategy, re-engineering customer-oriented business processes, human resources management, the computer system, management of change and continuous improvement.
An Architecture for Local Energy Generation, Distribution, and Sharing

The United States electricity grid faces significant problems resulting from fundamental design principles that limit its ability to handle the key energy challenges of the 21st century. We propose an innovative electric power architecture, rooted in lessons learned from the Internet and microgrids, which addresses these problems while interfacing gracefully into the current grid to allow for non-disruptive incremental adoption. Such a system, which we term a "Local" grid, is controlled by intelligent power switches (IPS), and can consist of loads, energy sources, and energy storage. The desired result of the proposed architecture is to produce a grid network designed for distributed renewable energy, prevalent energy storage, and stable autonomous systems. We will describe organizing principles of such a system that ensure well-behaved operation, such as requirements for communication and energy transfer protocols, regulation and control schemes, and market-based rules of operation.