nils-herrmann/clean_scirepeval huggingface dataset:
An Output-Capacitorless Low-Dropout Regulator With Direct Voltage-Spike Detection

An output-capacitorless low-dropout regulator (LDO) with a direct voltage-spike detection circuit is presented in this paper. The proposed voltage-spike detection is based on capacitive coupling. The detection circuit makes use of the rapid transient voltage at the LDO output to increase the bias current momentarily. Hence, the transient response of the LDO is significantly enhanced due to the improvement of the slew rate at the gate of the power transistor. The proposed voltage-spike detection circuit is applied to an output-capacitorless LDO implemented in a standard 0.35-¿m CMOS technology (where VTHN ¿ 0.5 V and VTHP ¿ -0.65 V). Experimental results show that the LDO consumes 19 ¿A only. It regulates the output at 0.8 V from a 1-V supply, with dropout voltage of 200 mV at the maximum output current of 66.7 mA. The voltage spike and the recovery time of the LDO with the proposed voltage-spike detection circuit are reduced to about 70 mV and 3 ¿s, respectively, whereas they are more than 420 mV and 30 ¿s for the LDO without the proposed detection circuit.
Fine-Grained Plant Classification Using Convolutional Neural Networks for Feature Extraction

We present an overview of the QUT plant classification system submitted to LifeCLEF 2014. This system uses generic features extracted from a convolutional neural network previously used to perform general object classification. We examine the effectiveness of these features to perform plant classification when used in combination with an extremely randomised forest. Using this system, with minimal tuning, we obtained relatively good results with a score of 0.249 on the test set of LifeCLEF 2014.
Gamification in E-Commerce - A Survey Based on the Octalysis Framework

Relying on social connections, online recommendation engines and other enabling technologies, consumers have constantly been increasing expectations and seek experiential value in online shopping. Since customers have more places and ways to shop than ever before, retailers – in order to be successful – must find ways to make online shopping pleasant and enjoyable. They have begun to enhance the online customer experience by incorporating game elements into their business processes, making online shopping not just attractive with innovative products and low prices, but also fun. This concept is known as gamification – a trending topic in both academia and business – and generally defined as the use of game thinking and elements in non-game contexts. In our study, we used a state-of-the-art framework (Octalysis) to analyze a sample of retailers from different industries operating on the European market. Based on an octagonal shape, Octalysis comprises 8 core drives that seek to explain the influence of certain gamification techniques on consumer motivation. Our study focused on determining (a) each retailer’s position in the octagon and (b) whether retailers in the same sector target the same core drives. Further, we suggest guidelines for academics and practitioners seeking to convert results into more and better ideas for online shopping.
Neural Systems for Visual Orienting and Their Relationships to Spatial Working Memory

We investigated neural correlates of human visual orienting using event-related functional magnetic resonance imaging (fMRI). When subjects voluntarily directed attention to a peripheral location, we recorded robust and sustained signals uniquely from the intraparietal sulcus (IPs) and superior frontal cortex (near the frontal eye field, FEF). In the ventral IPs and FEF only, the blood oxygen level dependent signal was modulated by the direction of attention. The IPs and FEF also maintained the most sustained level of activation during a 7-sec delay, when subjects maintained attention at the peripheral cued location (working memory). Therefore, the IPs and FEF form a dorsal network that controls the endogenous allocation and maintenance of visuospatial attention. A separate right hemisphere network was activated by the detection of targets at unattended locations. Activation was largely independent of the target's location (visual field). This network included among other regions the right temporo-parietal junction and the inferior frontal gyrus. We propose that this cortical network is important for reorienting to sensory events.
Spatial Decision Support Systems: A coming of age

Decision Support Systems (DDS) have developed to exploit Information Technology (IT) to assist decision-makers in a wide variety of fields. The need to use spatial data in many of these diverse fields has led to increasing interest in the development of Spatial Decision Support Systems (SDSS) based around the Geographic Information System (GIS) technology. The paper examines the relationship between SDSS and GIS and suggests that SDSS is poised for further development owing to improvement in technology and the greater availability of spatial data.
ParaGraphE: A Library for Parallel Knowledge Graph Embedding

Knowledge graph embedding aims at translating the knowledge graph into numerical representations by transforming the entities and relations into continuous low-dimensional vectors. Recently, many methods [1, 5, 3, 2, 6] have been proposed to deal with this problem, but existing single-thread implementations of them are time-consuming for large-scale knowledge graphs. Here, we design a unified parallel framework to parallelize these methods, which achieves a significant time reduction without influencing the accuracy. We name our framework as ParaGraphE, which provides a library for parallel knowledge graph embedding. The source code can be downloaded from https: //github.com/LIBBLE/LIBBLE-MultiThread/tree/master/ParaGraphE.
RADIO OVER FIBER FOR WIRELESS COMMUNICATION

The demand for broadband services has driven research on millimeter-wave frequency band communications for wireless access network due its spectrum availability, and compact size of radio frequency devices. The millimeter-wave signals are affected by losses along the transmission as well as atmospheric attenuation. One of the solution to overcome these problems is by using low-attenuation, electromagnetic interference-free optical fiber. Radio-over-Fiber (ROF) is considered to be cost-effective, practical and relatively flexible system configuration for long-haul transport of mill metric frequency band wireless signals using multicarrier modulation OFDM.
End-to-End Training of Hybrid CNN-CRF Models for Semantic Segmentation using Structured Learning

In this work we tackle the problem of semantic image segmentation with a combination of convolutional neural networks (CNNs) and conditional random fields (CRFs). The CRF takes contrast sensitive weights in a local neighborhood as input (pairwise interactions) to encourage consistency (smoothness) within the prediction and align our segmentation boundaries with visual edges. We model unary terms with a CNN which outperforms non data driven models. We approximate the CRF inference with a fixed number of iterations of a linearprogramming relaxation based approach. We experiment with training the combined model end-to-end using a discriminative formulation (structured support vector machine) and applying stochastic subgradient descend to it. Our proposed model achieves an intersection over union score of 62.4 in the test set of the cityscapes pixel-level semantic labeling task which is comparable to state-of-the-art models.
Data-Dependent Stability of Stochastic Gradient Descent

We establish a data-dependent notion of algorithmic stability for Stochastic Gradient Descent (SGD), and employ it to develop novel generalization bounds. This is in contrast to previous distribution-free algorithmic stability results for SGD which depend on the worst-case constants. By virtue of the data-dependent argument, our bounds provide new insights into learning with SGD on convex and non-convex problems. In the convex case, we show that the bound on the generalization error depends on the risk at the initialization point. In the non-convex case, we prove that the expected curvature of the objective function around the initialization point has crucial influence on the generalization error. In both cases, our results suggest a simple data-driven strategy to stabilize SGD by pre-screening its initialization. As a corollary, our results allow us to show optimistic generalization bounds that exhibit fast convergence rates for SGD subject to a vanishing empirical risk and low noise of stochastic gradient.
HEMD: a highly efficient random forest-based malware detection framework for Android

Mobile phones are rapidly becoming the most widespread and popular form of communication; thus, they are also the most important attack target of malware. The amount of malware in mobile phones is increasing exponentially and poses a serious security threat. Google’s Android is the most popular smart phone platforms in the world and the mechanisms of permission declaration access control cannot identify the malware. In this paper, we proposed an ensemble machine learning system for the detection of malware on Android devices. More specifically, four groups of features including permissions, monitoring system events, sensitive API and permission rate are extracted to characterize each Android application (app). Then an ensemble random forest classifier is learned to detect whether an app is potentially malicious or not. The performance of our proposed method is evaluated on the actual data set using tenfold cross-validation. The experimental results demonstrate that the proposed method can achieve a highly accuracy of 89.91%. For further assessing the performance of our method, we compared it with the state-of-the-art support vector machine classifier. Comparison results demonstrate that the proposed method is extremely promising and could provide a cost-effective alternative for Android malware detection.
Analysis and QoE evaluation of cloud gaming service adaptation under different network conditions: The case of NVIDIA GeForce NOW

Cloud gaming represents a highly interactive service whereby game logic is rendered in the cloud and streamed as a video to end devices. While benefits include the ability to stream high-quality graphics games to practically any end user device, drawbacks include high bandwidth requirements and very low latency. Consequently, a challenge faced by cloud gaming service providers is the design of algorithms for adapting video streaming parameters to meet the end user system and network resource constraints. In this paper, we conduct an analysis of the commercial NVIDIA GeForce NOW game streaming platform adaptation mechanisms in light of variable network conditions. We further conduct an empirical user study involving the GeForce NOW platform to assess player Quality of Experience when such adaptation mechanisms are employed. The results provide insight into limitations of the currently deployed mechanisms, as well as aim to provide input for the proposal of designing future video encoding adaptation strategies.
Event Embeddings for Semantic Script Modeling

Semantic scripts is a conceptual representation which defines how events are organized into higher level activities. Practically all the previous approaches to inducing script knowledge from text relied on count-based techniques (e.g., generative models) and have not attempted to compositionally model events. In this work, we introduce a neural network model which relies on distributed compositional representations of events. The model captures statistical dependencies between events in a scenario, overcomes some of the shortcomings of previous approaches (e.g., by more effectively dealing with data sparsity) and outperforms count-based counterparts on the narrative cloze task.
Entering the Digital Era - The Impact of Digital Technology-related M&As on Business Model Innovations of Automobile OEMs

Digital technologies have reached the sphere of industrial-age, primarily physical industries, thus forcing incumbent firms to digitally innovate their business models. Employing a longitudinal dataset of the world’s largest automobile manufacturers from 2000 to 2013, we found empirical evidence of a positive effect of digital technology– related mergers and acquisitions (M&As) on digital business model innovativeness. Moreover, this effect is enhanced by previous non-digital M&A experience, a diversified M&A history, as well as early experience with digital technology–related M&As. Consequently, our findings reveal that OEMs acquiring complementary and heterogeneous external knowledge on digital technologies and possessing the absorptive capacity to integrate as well as commercialize this type of knowledge are better prepared to master the digital transformation of their business. Furthermore, we find indications of a positive influence of digital business model innovations on the expected future firm performance of automobile manufacturers, thus substantiating the importance of digital transformation.
Emotions from Text: Machine Learning for Text-based Emotion Prediction

In addition to information, text contains attitudinal, and more specifically, emotional content. This paper explores the text-based emotion prediction problemempirically, using supervised machine learning with the SNoW learning architecture. The goal is to classify the emotional affinity of sentences in the narrative domain of children’s fairy tales, for subsequent usage in appropriate expressive rendering of text-to-speech synthesis. Initial experiments on a preliminary data set of 22 fairy tales show encouraging results over a na ı̈ve baseline and BOW approach for classification of emotional versus non-emotional contents, with some dependency on parameter tuning. We also discuss results for a tripartite model which covers emotional valence, as well as feature set alternations. In addition, we present plans for a more cognitively sound sequential model, taking into consideration a larger set of basic emotions.
The Research on Software Metrics and Software Complexity Metrics

The process of software development, including documentation, design, program, test, and maintenance can be measured statistically. Therefore the quality of software can be monitored efficiently. Software metrics is very important in research of software engineering and it has developed gradually. In this paper, software metrics definition were given and the history of and the types of software metrics were overviewed. Software complexity measuring is the important constituent of software metrics and it is concerning the cost of software development and maintenance. In order to improve the software quality and the project controllability, it is necessary to control the software complexity by measuring the related aspects. This paper respectively expounds McCabe methods and C & K metric method for examples of complexity metrics
Building Manipuri-English Machine Readable Dictionary by Implementing Ontology

Any system that hopes to process natural languages as people do must have information about words, their meaning, concept, relative words in another language and meaningful sentences are composed of meaningful words. Traditionally information is provided through electronic dictionaries. But these dictionary entries evolved for the convenience of human readers, not for machines. So, machine readable electronic dictionary becomes the central resources for Natural Language applications. Dictionaries and other lexical resources are not yet widely available in electronic form for Manipuri language. And there is no Manipuri-English machine readable dictionary that can provide both of lexical resources and conceptual information. This paper describes the process for developing Manipuri-English dictionary by implementing ontology. This implementation should provide a more effective combination of traditional Manipuri-English bilingual lexicographic information and their conceptual information.
Mass spectrometry in grape and wine chemistry. Part I: polyphenols.

Mass spectrometry, had and still has, a very important role for research and quality control in the viticulture and enology field, and its analytical power is relevant for structural studies on aroma and polyphenolic compounds. Polyphenols are responsible for the taste and color of wine, and confer astringency and structure to the beverage. The knowledge of the anthocyanic structure is very important to predict the aging attitude of wine, and to attempt to resolve problems about color stability. Moreover, polyphenols are the main compounds related to the benefits of wine consumption in the diet, because of their properties in the treatment of circulatory disorders such as capillary fragility, peripheral chronic venous insufficiency, and microangiopathy of the retina. Liquid Chromatography-Mass Spectrometry (LC-MS) techniques are nowadays the best analytical approach to study polyphenols in grape extracts and wine, and are the most effective tool in the study of the structure of anthocyanins. The MS/MS approach is a very powerful tool that permits anthocyanin aglycone and sugar moiety characterization. LC-MS allows the characterization of complex structures of grape polyphenols, such as procyanidins, proanthocyanidins, prodelphinidins, and tannins, and provides experimental evidence for structures that were previously only hypothesized. The matrix-assisted-laser-desorption-ionization-time-of-flight (MALDI-TOF) technique is suitable to determine the presence of molecules of higher molecular weight with high accuracy, and it has been applied with success to study procyanidin oligomers up to heptamers in the reflectron mode, and up to nonamers in the linear mode. The levels of resveratrol in wine, an important polyphenol well-known for its beneficial effects, have been determined by SPME and LC-MS, and the former approach led to the best results in terms of sensitivity.
Future Trends in Standardization of Herbal Drugs

In recent years more people throughout world are turning to use medicinal plant products in healthcare system. World wide need of alternative medicine has resulted in growth of natural product markets and interest in traditional systems of medicine. Herbal drug technology is used for converting botanicals materials into medicines, where standardization and quality control with proper integration of modern scientific techniques and traditional knowledge is important. In order to prove constant composition of herbal preparations, adequate analytical methods the have to be applied such as photometric analysis, thin layer chromatography [TLC], high performance liquid chromatography [HPLC], and gas chromatography [GC], DNA Fingerprinting.
Solutions to Homework 8-Continuous-Time Markov Chains

A) CTMC states. Since we assume that c, d and Xmax are integers, while the premiums that the customers pay are worth 1, every integer between 0 and Xmax is achievable. Accordingly, given our assumptions every cash flow consists of an integer-valued amount of money. Thus, the CTMC cannot reach any non-integer state. We are obviously assuming that the initial amount of cash X(0) is also an integer. Consequently, the state space of the CTMC consists of every nonnegative integer number between 0 and Xmax.
Osteochondritis dissecans of the capitellum: current concepts.

Osteochondritis dissecans (OCD) of the capitellum is an uncommon disorder seen primarily in the adolescent overhead athlete. Unlike Panner disease, a self-limiting condition of the immature capitellum, OCD is multifactorial and likely results from microtrauma in the setting of cartilage mismatch and vascular susceptibility. The natural history of OCD is poorly understood, and degenerative joint disease may develop over time. Multiple modalities aid in diagnosis, including radiography, MRI, and magnetic resonance arthrography. Lesion size, location, and grade determine management, which should attempt to address subchondral bone loss and articular cartilage damage. Early, stable lesions are managed with rest. Surgery should be considered for unstable lesions. Most investigators advocate arthroscopic débridement with marrow stimulation. Fragment fixation and bone grafting also have provided good short-term results, but concerns persist regarding the healing potential of advanced lesions. Osteochondral autograft transplantation appears to be promising and should be reserved for larger, higher grade lesions. Clinical outcomes and return to sport are variable. Longer-term follow-up studies are necessary to fully assess surgical management, and patients must be counseled appropriately.
Using Supervised Bigram-based ILP for Extractive Summarization

In this paper, we propose a bigram based supervised method for extractive document summarization in the integer linear programming (ILP) framework. For each bigram, a regression model is used to estimate its frequency in the reference summary. The regression model uses a variety of indicative features and is trained discriminatively to minimize the distance between the estimated and the ground truth bigram frequency in the reference summary. During testing, the sentence selection problem is formulated as an ILP problem to maximize the bigram gains. We demonstrate that our system consistently outperforms the previous ILP method on different TAC data sets, and performs competitively compared to the best results in the TAC evaluations. We also conducted various analysis to show the impact of bigram selection, weight estimation, and ILP setup.
Continuous Relaxation Training of Discrete Latent Variable Image Models

Despite recent improvements in training methodology, discrete latent variable models have failed to achieve the performance and popularity of their continuous counterparts. Here, we evaluate several approaches to training large-scale image models on CIFAR-10 using a probabilistic variant of the recently proposed Vector Quantized VAE architecture. We find that biased estimators such as continuous relaxations provide reliable methods for training these models while unbiased score-function-based estimators like VIMCO struggle in high-dimensional discrete spaces. Furthermore, we observe that the learned discrete codes lie on low-dimensional manifolds, indicating that discrete latent variables can learn to represent continuous latent quantities. Our findings show that continuous relaxation training of discrete latent variable models is a powerful method for learning representations that can flexibly capture both continuous and discrete aspects of natural data.
AUTOMATIC BABY CRADLE AND MONITORING FOR INFANT CARE

This paper presents a design of an Automatic Baby Cradle System which gives a reliable and efficient baby monitoring system that can play a significant role in providing better infant care. This system monitor parameters such as baby cry, environment temperature, moisture condition, and using cloud this information is accessed by parents to initiate the proper control actions. The system architecture consists of sensors for monitoring vital parameters, dc motor for cradle movement, cloud where data is stored and a sound buzzer all controlled by a single Arduino Mega microcontroller core.