nils-herrmann/clean_scirepeval huggingface dataset:
The effect of product attractiveness, word of mouth and product quality in elevating the impulsive buying behavior of Garuda Peanut in Surabaya

Consumer behavior is essentially a decision-making processes by consumers either individuals, groups, and organizations that includes the process of choosing, buying, obtaining, use of goods or services. The main question in consumer behavior research is how consumers make a purchase decision. This study indentifies factors that are statistically significant to impulsive buying to Kacang Garuda (Peanut) product of each gender in Surabaya. By using primary data with the population of people ages 18–40, collected with purposive sampling, by spreading questionnaire. Selected object is Garuda Peanut because peanut products are low involvement products that trigger the occurrence of impulsive buying behavior, as well as Garuda Peanut is the market leader for peanut products in Indonesia. This research limits the factors into three, product attractiveness attributed by unique and interesting package, attractive package color, and package size availability, word of mouth attributed by convincing salesman, info from relatives and info from friends, and quality attributed by reliability, conformance quality and durability. The data shows that product attractiveness and quality are significant in increasing the degree of impulsive buying to both gender, but word of mouth applies only to female gender.
A Geometry-Appearance-Based Pupil Detection Method for Near-Infrared Head-Mounted Cameras

This paper presents, for the first time, a novel pupil detection method for near-infrared head-mounted cameras, which relies not only on image appearance to pursue the shape and gradient variation of the pupil contour, but also on structure principle to explore the mechanism of pupil projection. There are three main characteristics in the proposed method. First, in order to complement the pupil projection information, an eyeball center calibration method is proposed to build an eye model. Second, by utilizing the deformation model of pupils under head-mounted cameras and the edge gradients of a circular pattern, we find the best fitting ellipse describing the pupil boundary. Third, an eye-model-based pupil fitting algorithm with only three parameters is proposed to fine-tune the final pupil contour. Consequently, the proposed method extracts the geometry-appearance information, effectively boosting the performance of pupil detection. Experimental results show that this method outperforms the state-of-the-art ones. On a widely used public database (LPW), our method achieves 72.62% in terms of detection rate up to an error of five pixels, which is superior to the previous best one.
Robust end-to-end deep audiovisual speech recognition

Speech is one of the most effective ways of communication among humans. Even though audio is the most common way of transmitting speech, very important information can be fou nd in other modalities, such as vision. Vision is particularly us ef l when the acoustic signal is corrupted. Multi-modal speech recog nition however has not yet found wide-spread use, mostly because th e temporal alignment and fusion of the different information sources is challenging. This paper presents an end-to-end audiovisual speech recog nizer (AVSR), based on recurrent neural networks (RNN) with a conn ectionist temporal classification (CTC)  loss function. CTcreates sparse “peaky” output activations, and we analyze the diffe rences in the alignments of output targets (phonemes or visemes) be tween audio-only, video-only, and audio-visual feature represe ntations. We present the first such experiments on the large vocabulary IB M ViaVoice database, which outperform previously published ap proaches on phone accuracy in clean and noisy conditions.
First-trimester maternal serum PAPP-A and free-beta subunit human chorionic gonadotropin concentrations and nuchal translucency are associated with obstetric complications: a population-based screening study (the FASTER Trial).

The purpose of this study was to determine whether maternal serum levels of pregnancy-associated plasma protein A, free-beta subunit human chorionic gonadotropin, or nuchal translucency size are associated with obstetric complications.



Data were obtained from the First and Second Trimester Evaluation of Risk trial. Pregnancy-associated plasma protein A and free-beta subunit human chorionic gonadotropin levels were analyzed, and nuchal translucency was measured between 10 weeks 3 days and 13 weeks 6 days of gestation in 34,271 pregnancies.



Women with pregnancy-associated plasma protein A of < or =5th percentile were significantly more likely to experience spontaneous fetal loss at < or =24 weeks of gestation, low birth weight, preeclampsia, gestational hypertension, preterm birth ( P < .001) and stillbirth, preterm premature rupture of membranes, and placental abruption ( P < .02). Nuchal translucency at > or =99th percentile and free-beta subunit human chorionic gonadotropin at < or =1st percentile were associated with an increased risk of spontaneous loss at < or =24 weeks of gestation (adjusted odds ratios, 3.90, 3.62, respectively; P < .001).



Low pregnancy-associated plasma protein A levels in the first trimester were associated strongly with a number of adverse pregnancy outcomes. Low free-beta subunit human chorionic gonadotropin levels and large nuchal translucency were both associated with early fetal loss.
A security analysis of smartphone data flow and feasible solutions for lawful interception

Smartphones providing proprietary encryption schemes, albeit offering a novel paradigm to privacy, are becoming a bone of contention for certain sovereignties. These sovereignties have raised concerns about their security agencies not having any control on the encrypted data leaving their jurisdiction and the ensuing possibility of it being misused by people with malicious intents. Such smartphones have typically two types of customers, independent users who use it to access public mail servers and corporates/enterprises whose employees use it to access corporate emails in an encrypted form. The threat issues raised by security agencies concern mainly the enterprise servers where the encrypted data leaves the jurisdiction of the respective sovereignty while on its way to the global smartphone router. In this paper, we have analyzed such email message transfer mechanisms in smartphones and proposed some feasible solutions, which, if accepted and implemented by entities involved, can lead to a possible win-win situation for both the parties, viz., the smartphone provider who does not want to lose the customers and these sovereignties who can avoid the worry of encrypted data leaving their jurisdiction.
Semi-Supervised Novelty Detection

A common setting for novelty detection assumes that labeled xamples from the nominal class are available, but that labeled examples of novelties are un available. The standard (inductive) approach is to declare novelties where the nominal density is l ow, which reduces the problem to density level set estimation. In this paper, we consider the setting where an unlabeled and possibly contaminated sample is also available at learning tim e. We argue that novelty detection in this semi-supervised setting is naturally solved by a gener al r duction to a binary classification problem. In particular, a detector with a desired false posi tive rate can be achieved through a reduction to Neyman-Pearson classification. Unlike the induc tive approach, semi-supervised novelty detection (SSND) yields detectors that are optimal (e.g., s tatistically consistent) regardless of the distribution on novelties. Therefore, in novelty detectio n, unlabeled data have a substantial impact on the theoretical properties of the decision rule. We valid ate the practical utility of SSND with an extensive experimental study. We also show that SSND provides distribution-free, learnin g-theoretic solutions to two well known problems in hypothesis testing. First, our results pr ovide a general solution to the general two-sample problem, that is, the problem of determining whe ther two random samples arise from the same distribution. Second, a specialization of SSND coi ncides with the standard p-value approach to multiple testing under the so-called random effec ts model. Unlike standard rejection regions based on thresholded p-values, the general SSND framework allows for adaptation t o arbitrary alternative distributions in multiple dimensions.
Valkyrie: NASA's First Bipedal Humanoid Robot

In December 2013, sixteen teams from around the world gathered at Homestead Speedway near Miami, FL to participate in the DARPA Robotics Challenge (DRC) Trials, an aggressive robotics competition, partly inspired by the aftermath of the Fukushima Daiichi reactor incident. While the focus of the DRC Trials is to advance robotics for use in austere and inhospitable environments, the objectives of the DRC are to progress the areas of supervised autonomy and mobile manipulation for everyday robotics. NASA’s Johnson Space Center led a team comprised of numerous partners to develop Valkyrie, NASA’s first bipedal humanoid robot. Valkyrie is a 44 degree-of-freedom, series elastic actuator-based robot that draws upon over 18 years of humanoid robotics design heritage. Valkyrie’s application intent is aimed at not only responding to events like Fukushima, but also advancing human spaceflight endeavors in extraterrestrial planetary settings. This paper presents a brief system overview, detailing Valkyrie’s mechatronic subsystems, followed by a summarization of the inverse kinematics-based walking algorithm employed at the Trials. Next, the software and control architectures are highlighted along with a description of the operator interface tools. Finally, some closing remarks are given about the competition and a vision of future work is provided.
WiFi-based indoor positioning

Recently, several indoor localization solutions based on WiFi, Bluetooth, and UWB have been proposed. Due to the limitation and complexity of the indoor environment, the solution to achieve a low-cost and accurate positioning system remains open. This article presents a WiFibased positioning technique that can improve the localization performance from the bottleneck in ToA/AoA. Unlike the traditional approaches, our proposed mechanism relaxes the need for wide signal bandwidth and large numbers of antennas by utilizing the transmission of multiple predefined messages while maintaining high-accuracy performance. The overall system structure is demonstrated by showing localization performance with respect to different numbers of messages used in 20/40 MHz bandwidth WiFi APs. Simulation results show that our WiFi-based positioning approach can achieve 1 m accuracy without any hardware change in commercial WiFi products, which is much better than the conventional solutions from both academia and industry concerning the trade-off of cost and system complexity.
On flowering patterns of alien species: 2. Robinia pseudoacacia, R. × ambigua, and R. neomexicana

A comparative study of the structure of the flower in three species of Robinia L., R. pseudoacacia, R. × ambigua, and R. neomexicana, was carried out. The widely naturalized R. pseudoacacia, as compared to the two other species, has the smallest sizes of flower organs at all stages of development. Qualitative traits that describe each phase of the flower development were identified. A set of microscopic morphological traits of the flower (both quantitative and qualitative) was analyzed. Additional taxonomic traits were identified: shape of anthers, size and shape of pollen grains, and the extent of pollen fertility.
A cued-recall and emotion classification graphical password authentication scheme

This paper presents an alternative visual authentication scheme with two secure layers for desktops or laptops. The first layer is a recognition-based scheme that addresses human factors for protection against bots by recognizing a Captcha and images with specific patterns. The second layer uses a clicked based Cued-Recall graphical password scheme for authentication, it also exploits emotions perceived by humans and use them as decision factor. The proposed authentication system is effective against brute-force, online guessing and relay attacks. We believe that the perception of security is enhaced using human emotions as main decision factor. The proposed scheme usability was tested using the Computer System Usability Questionnaires, results showed that it is highly usable and could improve the security level on ATM machines.
Knowledge Management Frameworks : A Review of Conceptual Foundations and a KMF for IT-based Organizations

In the new economic era, knowledge has become the primary source of wealth and consequently, the term knowledge economy or knowledge age. Rapid technological advancements and innovations have narrowed the gap between competing organizations such that the collective knowledge of employees is regarded as the key factor in producing innovative and competitive products or services. Organizations, since the early 1 0s, have been forced to rethink the way they manage their intangible assets, which are in form of knowledge resources and therefore the need for knowledge management. Many organisations use knowledge management frameworks as a model that initiates and strengthens knowledge management activities in the context of achieving organisational excellence. However, different knowledge management frameworks do not fully address knowledge management activities across the organisation, such that each of them addresses certain knowledge management elements, while leaving others unattended to. The paper examined 21 knowledge management frameworks guided by three themes as knowledge management activities, knowledge management resources and knowledge management enablers (or influences) on knowledge management. A matrix was developed to capture the individual components advanced by each author with respect to knowledge management activities, resources and influences. Based on the matrices for activities, resources and influences, the individual components were harmonised and integrated in terms of relationships in the context of knowledge management. The findings are that knowledge management activities are socially enacted activities that support individual and collective knowledge. The activities vary depending on which of the knowledge resources the organization aims at improving. Since each organization has a different focus, knowledge management activities take place in different contexts. These activities have been summarized as knowledge acquisition, creation, repository, sharing, use and evaluation. The organization should consciously choose which of these activities they intend to support in order to identify appropriate organizational variables and technology to enable them have effect. Based on findings, a new knowledge management framework has been proposed to guide practitioners and researchers when conducting knowledge management.
Stochastic Gradient/Mirror Descent: Minimax Optimality and Implicit Regularization

Stochastic descent methods (of the gradient and mirror varieties) have become increasingly popular in optimization. In fact, it is now widely recognized that the success of deep learning is not only due to the special deep architecture of the models, but also due to the behavior of the stochastic descent methods used, which play a key role in reaching “good” solutions that generalize well to unseen data. In an attempt to shed some light on why this is the case, we revisit some minimax properties of stochastic gradient descent (SGD) for the square loss of linear models—originally developed in the 1990’s—and extend them to general stochastic mirror descent (SMD) algorithms for general loss functions and nonlinear models. In particular, we show that there is a fundamental identity which holds for SMD (and SGD) under very general conditions, and which implies the minimax optimality of SMD (and SGD) for sufficiently small step size, and for a general class of loss functions and general nonlinear models. We further show that this identity can be used to naturally establish other properties of SMD (and SGD), namely convergence and implicit regularization for over-parameterized linear models (in what is now being called the “interpolating regime”), some of which have been shown in certain cases in prior literature. We also argue how this identity can be used in the so-called “highly over-parameterized” nonlinear setting (where the number of parameters far exceeds the number of data points) to provide insights into why SMD (and SGD) may have similar convergence and implicit regularization properties for deep learning.
General-purpose molecular dynamics simulations on GPU-based clusters

We present a GPU implementation of LAMMPS, a widely-used parallel molecular dynamics (MD) software package, and show 5x to 13x single node speedups versus the CPU-only version of LAMMPS. This new CUDA package for LAMMPS also enables multi-GPU simulation on hybrid heterogeneous clusters, using MPI for inter-node communication, CUDA kernels on the GPU for all methods working with particle data, and standard LAMMPS C++ code for CPU execution. Cell and neighbor list approaches are compared for best performance on GPUs, with thread-peratom and block-per-atom neighbor list variants showing best performance at low and high neighbor counts, respectively. Computational performance results of GPU-enabled LAMMPS are presented for a variety of materials classes (e.g. biomolecules, polymers, metals, semiconductors), along with a speed comparison versus other available GPU-enabled MD software. Finally, we show strong and weak scaling performance on a CPU/GPU cluster using up to 128 dual GPU nodes.
Leaching capacity of metals-metalloids and recovery of valuable materials from waste LCDs.

The purpose of Directive 2012/19/EU which is related to WEEE (Waste Electrical and Electronic Equipment), also known as "e-waste", is to contribute to their sustainable production and consumption that would most possibly be achieved by their recovery, recycling and reuse. Under this perspective, the present study focused on the recovery of valuable materials, metals and metalloids from LCDs (Liquid Crystal Displays). Indium (In), arsenic (As) and stibium (Sb) were selected to be examined for their Leaching Capacity (R) from waste LCDs. Indium was selected mainly due to its rarity and preciousness, As due to its high toxicity and wide use in LCDs and Sb due to its recent application as arsenic's replacement to improve the optimal clarity of a LCD screen. The experimental procedure included disassembly of screens along with removal and recovery of polarizers via thermal shock, cutting, pulverization and digestion of the shredded material and finally leaching evaluation of the aforementioned elements. Leaching tests were conducted under various temperatures, using various solid:liquid (S/L) ratios and solvents (acid mixtures), to determine the optimal conditions for obtaining the maximum leaching capacities. The examined elements exhibited different leaching behaviors, mainly due to the considerable diversity in their inherent characteristic properties. Indium demonstrated the highest recovery percentages (approximately 60%), while the recovery of As and Sb was unsuccessful, obtaining poor leaching percentages (0.16% and 0.5%, respectively).
Frankfurt microsurgery course: the first 175 trainees

Microsurgery courses, taught external to surgical training programs, are essential for acquiring the high level of technical skill required for clinical proficiency. The Frankfurt microsurgery course is a 5-day, intensive course that teaches arterial and venous anastomosis using end-to-end, end-to-side, one-way-up, continuous-suture, and vessel graft techniques. During the course, the instructor records the level of skill (in-course data) achieved by each trainee by assessing anastomosis completion and patency. Demographic information is also collected. Post-course trainees are invited to complete an online survey (post-course data) to get their opinions of the courses’ effectiveness. The in-course “skill achievement” and post-course “course effectiveness” data are presented below. In-course data: 94.8 and 59.9% of participants completed patent end-to-end arterial and venous anastomoses, respectively, while 85.4% performed a patent end-to-side anastomosis. 96.1 and 57.1% of participants who attempted arterial and venous anastomoses using the one-way-up technique were successful, as were 90.9% of those attempting continuous-suture technique. Patent venous grafts were performed by 54.7% of participants. All respondents indicated significant improvement of their microsurgical skills after taking the course. 66.7% of respondents considered the full-time presence of the instructor to be the most valuable aspect of the course. All respondents would highly recommend the course to colleagues. The microcourse significantly increased trainees’ clinical microsurgery skills, confidence, and the number of clinical cases they perform. Of all the anastomosis techniques taught, venous anastomosis and grafting were the most difficult to learn. The presence of a full-time experienced instructor was most important.
A high temperature, double-sided cooling SiC power electronics module

A high temperature, wire-bondless power electronics module with a double-sided cooling capability is proposed and successfully fabricated. In this module, a low-temperature co-fired ceramic (LTCC) substrate was used as the dielectric and chip carrier. Conducting vias were created on the LTCC carrier to realize the interconnection. The absent of a base plate reduced the overall thermal resistance and also improved the fatigue life by eliminating a large-area solder layer. Nano silver paste was used to attach power devices to the DBC substrate as well as to pattern the gate connection. Finite element simulations were used to compare the thermal performance to several reported double-sided power modules. Electrical measurements of a SiC MOSFET and SiC diode switching position demonstrated the functionality of the module.
Dietary nitrate does not enhance running performance in elite cross-country skiers.

The objective of this study is to examine the effects of acute ingestion of dietary nitrate on endurance running performance in highly trained cross-country skiers. Dietary nitrate has been shown to reduce the oxygen cost of submaximal exercise and improve tolerance of high-intensity exercise, but it is not known if this holds true for highly trained endurance athletes.



Ten male junior cross-country skiers (V˙O(2max)) ≈ 70 mL·kg·min) each completed two trials in a randomized, double-blind design. Participants ingested potassium nitrate (614-mg nitrate) or a nitrate-free placebo 2.5 h before two 5-min submaximal tests on a treadmill at 10 km·h (≈55% of V˙O(2max)) and 14 km·h (≈75% of V˙O(2max)), followed by a 5-km running time trial on an indoor track.



Plasma nitrite concentrations were higher after nitrate supplementation (325 ± 95 nmol·L) compared with placebo (143 ± 59 nmol·L, P < 0.001). There was no significant difference in 5-km time-trial performance between nitrate (1005 ± 53 s) and placebo treatments (996 ± 49 s, P = 0.12). The oxygen cost of submaximal running was not significantly different between placebo and nitrate trials at 10 km·h (both 2.84 ± 0.34 L·min) and 14 km·h (3.89 ± 0.39 vs. 3.77 ± 0.62 L·min).



Acute ingestion of dietary nitrate may not represent an effective strategy for reducing the oxygen cost of submaximal exercise or for enhancing endurance exercise performance in highly trained cross-country skiers.