nils-herrmann/clean_scirepeval huggingface dataset:
A Novel Approach for Data-Driven Automatic Site Recommendation and Selection

This paper presents a novel, generic, and automatic method for data-driven site selection. Site selection is one of the most crucial and important decisions made by any company. Such a decision depends on various factors of sites, including socioeconomic, geographical, ecological, as well as specific requirements of companies. The existing approaches for site selection (commonly used by economists) are manual, subjective, and not scalable, especially to Big Data. The presented method for site selection is robust, efficient, scalable, and is capable of handling challenges emerging in Big Data. To assess the effectiveness of the presented method, it is evaluated on real data (collected from Federal Statistical Office of Germany) of around 200 influencing factors which are considered by economists for site selection of Supermarkets in Germany (Lidl, EDEKA, and NP). Evaluation results show that there is a big overlap (86.4 %) between the sites of existing supermarkets and the sites recommended by the presented method. In addition, the method also recommends many sites (328) for supermarket where a store should be opened.
Simulation of Automotive Radar Target Lists using a Novel Approach of Object Representation

The development of radar signal processing algorithms for target tracking and higher-level automotive applications is mainly done based on real radar data. A data basis has to be acquired during cost-expensive and time-consuming test runs. For a comparably simple application like the adaptive cruise control (ACC), the variety of significant traffic situations can sufficiently be covered by test runs. But for more advanced applications like intersection assistance, the effort for the acquisition of a representative set of radar data will be unbearable. In this paper, we propose a way of simulating radar target lists in a realistic but computationally undemanding way, which will allow to significantly reduce the amount of real radar data needed
Patch Autocorrelation Features: a translation and rotation invariant approach for image classification

The autocorrelation is often used in signal processing as a tool for finding repeating patterns in a signal. In image processing, there are various image analysis techniques that use the autocorrelation of an image in a broad range of applications from texture analysis to grain density estimation. This paper provides an extensive review of two recently introduced and related frameworks for image representation based on autocorrelation, namely Patch Autocorrelation Features (PAF) and Translation and Rotation Invariant Patch Autocorrelation Features (TRIPAF). The PAF approach stores a set of features obtained by comparing pairs of patches from an image. More precisely, each feature is the euclidean distance between a particular pair of patches. The proposed approach is successfully evaluated in a series of handwritten digit recognition experiments on the popular MNIST data set. However, the PAF approach has limited applications, because it is not invariant to affine transformations. More recently, the PAF approach was extended to become invariant to image transformations, including (but not limited to) translation and rotation changes. In the TRIPAF framework, several features are extracted from each image patch. Based on these features, a vector of similarity values is computed between each pair of patches. Then, the similarity vectors are clustered together such that the spatial offset between the patches of each pair is roughly the same. Finally, the mean and the standard deviation of each similarity value are computed for each group of similarity vectors. These statistics are concatenated to obtain the TRIPAF feature vector. The TRIPAF vector essentially records information about the repeating patterns within an image at various spatial offsets. After presenting the two approaches, several optical character recognition and texture classification experiments are conducted to evaluate the two approaches. Results are reported on the MNIST (98.93%), the Brodatz (96.51%), and the UIUCTex (98.31%) data sets. Both PAF and TRIPAF are fast to compute and produce compact representations in practice, while reaching accuracy levels similar to other state-of-the-art methods.
Development of an ANN-Based Linearization Technique for the VCO Thermistor Circuit

This paper presents the development of an artificial neural network (ANN)-based linearization technique for the voltage controlled oscillator (VCO) thermistor circuit. VCO thermistor circuit exhibits a linear analog temperature-frequency relation over a temperature range of 0 °C-100 °C with good response and reasonable error. An ANN-based direct modeling technique is used for a nonlinearity estimation of VCO thermistor circuit to further improve the linearity. The performance of the developed technique has been experimentally verified. A linearity of approximately ±0.2% and sensitivity of 5 kHz/°C are achieved over a temperature range of 0 °C-100 °C, with a high thermal stability. A notable feature of the developed technique is linearity and sensitivity of the temperature measurement, which are quite high. The efficacy of the technique is established through simulation studies and its practicality is demonstrated on a prototype unit.
Content matters: A study of hate groups detection based on social networks analysis and web mining

In recent years, with rapid growth of social networking websites, users are very active in these platforms and large amount of data are aggregated. Among those social networking websites, Facebook is the most popular website that has most users. However, in Facebook, the abusing problem is a very critical issue, such as Hate Groups. Therefore, many researchers are devoting on how to detect potential hate groups, such as using the techniques of social networks analysis. However, we believe content is also a very important factors for hate groups detection. Thus, in this paper, we will propose an architecture to for hate groups detection which is based on the technique of Social Networks Analysis and Web Mining (Text Mining; Natural Language Processing). From the experiment result, it shows that content plays an critical role for hate groups detection and the performance is better than the system that just applying social networks analysis.
Multiclass and Binary SVM Classification: Implications for Training and Classification Users

Support vector machines (SVMs) have considerable potential for supervised classification analyses, but their binary nature has been a constraint on their use in remote sensing. This typically requires a multiclass analysis be broken down into a series of binary classifications, following either the one-against-one or one-against-all strategies. However, the binary SVM can be extended for a one-shot multiclass classification needing a single optimization operation. Here, an approach for one-shot multi- class classification of multispectral data was evaluated against approaches based on binary SVM for a set of five-class classifications. The one-shot multiclass classification was more accurate (92.00%) than the approaches based on a series of binary classifications (89.22% and 91.33%). Additionally, the one-shot multi- class SVM had other advantages relative to the binary SVM-based approaches, notably the need to be optimized only once for the parameters C and 7 as opposed to five times for one-against-all and ten times for the one-against-one approach, respectively, and used fewer support vectors, 215 as compared to 243 and 246 for the binary based approaches. Similar trends were also apparent in results of analyses of a data set of larger dimensionality. It was also apparent that the conventional one-against-all strategy could not be guaranteed to yield a complete confusion matrix that can greatly limit the assessment and later use of a classification derived by that method.
Effective text classification using multi-level fuzzy neural network

Nowadays, large volumes of text data are being produced in real time due to expansion of communication. It is necessary to organize this data for exploitation and extraction of useful information. Text classification based on the topic is one of the efficient solutions to this problem. Efficient algorithms are applied for text classification if they address high dimensional data. In this paper, a novel neural network classifier is adapted to classify the texts and compared to some similar techniques. To this end, firstly preprocessing is conducted on the words, which includes five steps. After that, the feature extraction phase is applied consisting of three steps, with the Principle Component Analysis (PCA) method used for dimensionality reduction. Finally, a supervised learning method, namely the Multi-Level Fuzzy min-max neural network classifier (MLF), is used for text classification. The simulation results of the experiments, compared to three other principle methods, indicate that MLF method has a high accuracy of 94% for the Reuters-21578 dataset and 95% for the 20 newsgroup dataset. In addition, the execution time of the proposed method is less than other supervised learning algorithms.
Classifying ITIL Processes; A Taxonomy under Tool Support Aspects

Providing IT services to customers with better, guaranteed quality has been the aim of many diverse efforts, undertaken under the common denominaor "IT Service Management". Lately, more organizational approaches to this issue have been gaining popularity, especially the guidelines of the IT Infrastructure Library (ITIL) for IT Service Management business processes. But just like with most other business processes, implementing ITIL processes in an efficient way involves building or procuring IT tools that can support them. On this aspect, ITIL itself offers only minimal guidance. This paper addresses basic issues of supporting ITIL with process-oriented tools such as workflow management systems. It discusses the need for workflow management support of service management processes to achieve service level compliance, and presents criteria for determining which IT Service Management processes can and should be supported by workflow management systems. The IT Service Management processes defined by ITIL are evaluated and divided into four basic process classes according to their suitability for workflow management, thereby laying a foundation to future top-down approaches for comprehensive ITIL tool support.
Speech recognition for vocalized and subvocal modes of production using surface EMG signals from the neck and face

We report automatic speech recognition accuracy for individual words using eleven surface electromyographic (sEMG) recording locations on the face and neck during three speaking modes: vocalized, mouthed, and mentally rehearsed. An HMM based recognition system was trained and tested on a 65 word vocabulary produced by 9 American English speakers in all three speaking modes. Our results indicate high sEMG-based recognition accuracy for the vocalized and mouthed speaking modes (mean rates of 92.1% and 86.7% respectively), but an inability to conduct recognition on mentally rehearsed speech due to a lack of sufficient sEMG activity.
Approach and Avoidance Motivation

In this article, we introduce this special issue by establishing a conceptual foundation for the distinction between approach and avoidance motivation. We do so primarily by explicating several reasons why the approach–avoidance distinction should be viewed as fundamental and basic to the study of human behavior. In addition, we compare and contrast the “approach–avoidance” designation with other designations that have been used in the motivational literature to cover the same or similar conceptual ground. Finally, we conclude by briefly overviewing the other contributions to this special issue, specifically highlighting how they make use of the approach–avoidance distinction.
Design and Analysis of Stacked Power Amplifier in Series-Input and Series-Output Configuration

The stacked-device power-combining technique is a proven method to increase the output power and load impedance of a power amplifier (PA) simultaneously. The series-input configuration is physically realizable for multicell stacked device configuration in monolithic circuits. The series-input and series-output stack configuration is rigorously analyzed and proven to increase both the input impedance and output impedance simultaneously, easing the matching circuit designs in high PAs. The effects of asymmetry of the input feed and amplifier cells due to distributed effects and process variation on the performance of the stack amplifier are discussed. A four-cell HBT amplifier operating at 5-6 GHz is demonstrated to validate the circuit concept.
Adapting NLP and Corpus Analysis Techniques to Structured Imagery Analysis in Classical Chinese Poetry

This paper describes some pioneering work as a joint research project between City University of Hong Kong and Yuan Ze University in Taiwan to adapt language resources and technologies in order to set up a computational framework for the study of the creative language employed in classical Chinese poetry. In particular, it will first of all describe an existing ontology of imageries found in poems written during the Tang and the Song dynasties (7 –14 century AD). It will then propose the augmentation of such imageries into primary, complex, extended and textual imageries. A rationale of such a structured approach is that while poets may use a common dichotomy of primary imageries, creative language use is to be found in the creation of complex and compound imageries. This approach will not only support analysis of inter-poets stylistic similarities and differences but will also effectively reveal intra-poet stylistic characteristics. This article will then describe a syntactic parser designed to produce parse trees that will eventually enable the automatic identification of possible imageries and their subsequent structural analysis and classification. Finally, a case study will be presented that investigated the syntactic properties found in two lyrics written by two stylistically different lyric writers in the Song Dynasty.
Generic Taxonomy of Social Engineering Attack

Social engineering is a type of attack that allows unauthorized access to a system to achieve specific objective. Commonly, the purpose is to obtain information for social engineers. Some successful social engineering attacks get victims’ information via human based retrieval approach, example technique terms as dumpster diving or shoulder surfing attack to get access to password. Alternatively, victims’ information also can be stolen using technical-based method such as from pop-up windows, email or web sites to get the password or other sensitive information. This research performed a preliminary analysis on social engineering attack taxonomy that emphasized on types of technical-based social engineering attack. Results from the analysis become a guideline in proposing a new generic taxonomy of Social Engineering Attack (SEA).
Putrefactive rigor: apparent rigor mortis due to gas distension.

Artifacts due to decomposition may cause confusion for the initial death investigator, leading to an incorrect suspicion of foul play. Putrefaction is a microorganism-driven process that results in foul odor, skin discoloration, purge, and bloating. Various decompositional gases including methane, hydrogen sulfide, carbon dioxide, and hydrogen will cause the body to bloat. We describe 3 instances of putrefactive gas distension (bloating) that produced the appearance of inappropriate rigor, so-called putrefactive rigor. These gases may distend the body to an extent that the extremities extend and lose contact with their underlying support surface. The medicolegal investigator must recognize that this is not true rigor mortis and the body was not necessarily moved after death for this gravity-defying position to occur.
REMAINING LIFE PREDICTION OF ELECTRONIC PRODUCTS USING LIFE CONSUMPTION MONITORING APPROACH

Various kinds of failures may occur in electronic products because of their life cycle environmental conditions including temperature, humidity, shock and vibration. Failure mechanism models are available to estimate the time to failure for most of these failures. Hence if the life cycle environment of a product can be determined, it is possible to assess the amount of damage induced and predict when the product might fail. This paper presents a life consumption monitoring methodology to determine the remaining life of a product. A battery powered data recorder is used to monitor the temperature, shock and vibration loads on a printed circuit board assembly placed under the hood of an automobile. The recorded data is used in conjunction with physics-of-failure models to determine the damage accumulation in the solder joints due to temperature and vibration loading. The remaining life of the solder joints of the test board is then obtained from the damage accumulation information. 1. Reliability Prediction Reliability is defined as the ability of a product to perform as intended (i.e., without failure and within specified performance limits) for a specified time, in its life cycle application environment. Over time, technology improvements have lead to higher I/O counts in components and circuit cards of electronic products. This has resulted in significant downsizing of interconnect thicknesses, making them more vulnerable in field applications. At the same time, increase in warranties and severe liabilities of electronic product failure have compelled manufacturers to predict the reliability of their products in field applications. An efficient reliability prediction scheme can be used for many purposes including  • Logistics support (e.g., forecast warranty and life cycle costs, spare parts provisioning, availability)
Environmental Detectives--The Development of an Augmented Reality Platform for Environmental Simulations

This research was supported with a grant from Microsoft MIT iCampus as a part of the Games-to-Teach Project. The authors would like to thank the PIs of the Games-to-Teach Project, Randy Hinrichs at Microsoft Research and Henry Jenkins, Director MIT Comparative Media Studies for their support of this project, as well as Kodjo Hesse, Gunnar Harboe, and Walter Holland for their hard work in the development of Environmental Detectives.
Grapheme-to-phoneme conversion using Long Short-Term Memory recurrent neural networks

Grapheme-to-phoneme (G2P) models are key components in speech recognition and text-to-speech systems as they describe how words are pronounced. We propose a G2P model based on a Long Short-Term Memory (LSTM) recurrent neural network (RNN). In contrast to traditional joint-sequence based G2P approaches, LSTMs have the flexibility of taking into consideration the full context of graphemes and transform the problem from a series of grapheme-to-phoneme conversions to a word-to-pronunciation conversion. Training joint-sequence based G2P require explicit grapheme-to-phoneme alignments which are not straightforward since graphemes and phonemes don't correspond one-to-one. The LSTM based approach forgoes the need for such explicit alignments. We experiment with unidirectional LSTM (ULSTM) with different kinds of output delays and deep bidirectional LSTM (DBLSTM) with a connectionist temporal classification (CTC) layer. The DBLSTM-CTC model achieves a word error rate (WER) of 25.8% on the public CMU dataset for US English. Combining the DBLSTM-CTC model with a joint n-gram model results in a WER of 21.3%, which is a 9% relative improvement compared to the previous best WER of 23.4% from a hybrid system.
Multi-target Tracking by Lagrangian Relaxation to Min-cost Network Flow

We propose a method for global multi-target tracking that can incorporate higher-order track smoothness constraints such as constant velocity. Our problem formulation readily lends itself to path estimation in a trellis graph, but unlike previous methods, each node in our network represents a candidate pair of matching observations between consecutive frames. Extra constraints on binary flow variables in the graph result in a problem that can no longer be solved by min-cost network flow. We therefore propose an iterative solution method that relaxes these extra constraints using Lagrangian relaxation, resulting in a series of problems that ARE solvable by min-cost flow, and that progressively improve towards a high-quality solution to our original optimization problem. We present experimental results showing that our method outperforms the standard network-flow formulation as well as other recent algorithms that attempt to incorporate higher-order smoothness constraints.
Cloud computing security threats and responses

Cloud computing is one of today's most exciting technologies due to its ability to reduce costs associated with computing while increasing flexibility and scalability for computer processes. During the past few years, cloud computing has grown from being a promising business idea to one of the fastest growing parts of the IT industry. IT organizations have expresses concern about critical issues (such as security) that exist with the widespread implementation of cloud computing. These types of concerns originate from the fact that data is stored remotely from the customer's location; in fact, it can be stored at any location. Security, in particular, is one of the most argued-about issues in the cloud computing field; several enterprises look at cloud computing warily due to projected security risks. The risks of compromised security and privacy may be lower overall, however, with cloud computing than they would be if the data were to be stored on individual machines instead of in a so — called "cloud" (the network of computers used for remote storage and maintenance). Comparison of the benefits and risks of cloud computing with those of the status quo are necessary for a full evaluation of the viability of cloud computing. Consequently, some issues arise that clients need to consider as they contemplate moving to cloud computing for their businesses. In this paper I summarize reliability, availability, and security issues for cloud computing (RAS issues), and propose feasible and available solutions for some of them.
A View of Fog Computing from Networking Perspective

With smart devices, particular smartphones, becoming our everyday companions, the ubiquitous mobile Internet and computing applications pervade people’s daily lives. With the surge demand on high-quality mobile services at anywhere, how to address the ubiquitous user demand and accommodate the explosive growth of mobile traffics is the key issue of the next generation mobile networks. The Fog computing is a promising solution towards this goal. Fog computing extends cloud computing by providing virtualized resources and engaged location-based services to the edge of the mobile networks so as to better serve mobile traffics. Therefore, Fog computing is a lubricant of the combination of cloud computing and mobile applications. In this article, we outline the main features of Fog computing and describe its concept, architecture and design goals. Lastly, we discuss some of the future research issues from the networking perspective.