nils-herrmann/clean_scirepeval huggingface dataset:
Digital forensics investigations in the Cloud

The essentially infinite storage space offered by Cloud Computing is quickly becoming a problem for forensics investigators in regards to evidence acquisition, forensic imaging and extended time for data analysis. It is apparent that the amount of stored data will at some point become impossible to practically image for the forensic investigators to complete a full investigation. In this paper, we address these issues by determining the relationship between acquisition times on the different storage capacities, using remote acquisition to obtain data from virtual machines in the cloud. A hypothetical case study is used to investigate the importance of using a partial and full approach for acquisition of data from the cloud and to determine how each approach affects the duration and accuracy of the forensics investigation and outcome. Our results indicate that the relation between the time taken for image acquisition and different storage volumes is not linear, owing to several factors affecting remote acquisition, especially over the Internet. Performing the acquisition using cloud resources showed a considerable reduction in time when compared to the conventional imaging method. For a 30GB storage volume, the least time was recorded for the snapshot functionality of the cloud and dd command. The time using this method is reduced by almost 77 percent. FTK Remote Agent proved to be most efficient showing an almost 12 percent reduction in time over other methods of acquisition. Furthermore, the timelines produced with the help of the case study, showed that the hybrid approach should be preferred to complete approach for performing acquisition from the cloud, especially in time critical scenarios.
Design of a Ternary Memory Cell Using CNTFETs

This paper presents a novel design of a ternary memory cell using carbon nanotube field-effect transistors (CNTFETs). Ternary logic is a promising alternative to conventional binary logic because it allows simplicity and energy efficiency in modern digital design due to the reduced circuit overhead in interconnects and chip area. In this paper, a novel design of a ternary memory cell based on CNTFETs is proposed; this cell uses a transmission gate for the write operation and a buffer for the read operation to make them separate. Chirality of the CNTFETs is utilized for threshold voltage control, thus avoiding the use of additional power supplies. Extensive simulation results using SPICE are reported to show that the two memory operations of the proposed ternary cell perform correctly at 0.9 V power supply. The static noise margin and read/write delay of the proposed ternary memory cell are also very good; by utilizing the latest CNTFET layout design tools, it is shown that the proposed ternary memory cell achieves a significant saving in area (41.6%) compared with its CMOS ternary counterpart at 32 nm.
A simple structure of LLC resonant DC-DC converter for multi-output applications

Multi-output LLC resonant topologies with asymmetrical structures dedicating to multi-output power supply systems, having limitation of lowest load, are presented in this paper. According to output power level, different configuration of secondary output end can be flexibly adapted. Consequently, the presented topology is a favorable candidate for such application as whose outputs are not symmetrical, i.e. compared with auxiliary output, power level of main output is comparatively higher. In addition, all switches of the very topology operate under the condition of ZVS and therefore it is applicable to such occasion as high frequency, high power density. Asymmetrical operating principle and ZVS operation are analyzed in detail, moreover key parameter design considerations are presented. Finally, the analysis above is verified both by simulation and experiment.
A Real Time System for Robust 3D Voxel Reconstruction of Human Motions

We present a multi-PC/camera system that can perform 3D reconstruction and ellipsoids fitting of moving humans in real time. The system consists of five cameras. Each camera is connected to a PC which locally extracts the silhouettes of the moving person in the image captured by the camera. The five silhouette images are then sent, via local network, to a host computer to perform 3D voxel-based reconstruction by an algorithm called SPOT. Ellipsoids are then used to fit the reconstructed data. By using a simple and user-friendly interface, the user can display and observe, in real time and from any view-point, the 3D models of the moving human body. With a rate of higher than 15 frames per second, the system is able to capture nonintrusively sequence of human motions.
Cross-scenario clothing retrieval and fine-grained style recognition

In this paper, we propose a new approach for cross-scenario clothing retrieval and fine-grained clothing style recognition. The query clothing photos captured by cameras or other mobile devices are filled with noisy background while the product clothing images online for shopping are usually presented in a pure environment. We tackle this problem by two steps. Firstly, a hierarchical super-pixel merging algorithm based on semantic segmentation is proposed to obtain the intact query clothing item. Secondly, aiming at solving the problem of clothing style recognition in different scenarios, we propose sparse coding based on domain-adaptive dictionary learning to improve the accuracy of the classifier and adaptability of the dictionary. In this way, we obtain fine-grained attributes of the clothing items and use the attributes matching score to re-rank the retrieval results further. The experiment results show that our method outperforms the state-of-the-art approaches. Furthermore, we build a well labeled clothing dataset, where the images are selected from 1.5 billion product clothing images.
Pre-implementation attitudes and organizational readiness for implementing an Enterprise Resource Planning system

Implementing an Enterprise Resource Planning (ERP) system is expensive and time consuming. A substantial cost is associated with pre-implementation involvement and training designed to encourage acceptance and effective implementation of the system. The results of this study indicate that, contrary to conventional wisdom, extensive organizational investments in shaping pre-implementation attitudes do not always achieve the desired effects. Despite extensive time, money and effort, length of time with the firm and position had a greater impact on attitudes toward ERP capabilities, value, acceptance and timing than high levels of pre-implementation involvement.
ADASECANT: Robust Adaptive Secant Method for Stochastic Gradient

Stochastic gradient algorithms have been the main focus of large-scale learning problems and led to important successes in machine learning. The convergence of SGD depends on the careful choice of learning rate and the amount of the noise in stochastic estimates of the gradients. In this paper, we propose a new adaptive learning rate algorithm, which utilizes curvature information for automatically tuning the learning rates. The information about the element-wise curvature of the loss function is estimated from the local statistics of the stochastic first order gradients. We further propose a new variance reduction technique to speed up the convergence. In our experiments with deep neural networks, we obtained better performance compared to the popular stochastic gradient algorithms.
Knowledge Representation and Ontologies

Knowledge representation and reasoning aims at designing computer systems that reason about a machine-interpretable representation of the world. Knowledge-based systems have a computational model of some domain of interest in which symbols serve as surrogates for real world domain artefacts, such as physical objects, events, relationships, etc. . The domain of interest can cover any part of the real world or any hypothetical system about which one desires to represent knowledge for computational purposes. A knowledge-based system maintains a knowledge base, which stores the symbols of the computational model in the form of statements about the domain, and it performs reasoning by manipulating these symbols. Applications can base their decisions on answers to domain-relevant questions posed to a knowledge base.
iCAM06: A refined image appearance model for HDR image rendering

A new image appearance model, designated iCAM06, was developed for High-Dynamic-Range (HDR) image rendering. The model, based on the iCAM framework, incorporates the spatial processing models in the human visual system for contrast enhancement, photoreceptor light adaptation functions that enhance local details in highlights and shadows, and functions that predict a wide range of color appearance phenomena. Evaluation of the model proved iCAM06 to have consistently good HDR rendering performance in both preference and accuracy making iCAM06 a good candidate for a general-purpose tone-mapping operator with further potential applications to a wide-range of image appearance research and practice.
Credit Risk Analysis in Peer-to-Peer Lending System

This research paper aims to analyze the credit risk involved in peer-to-peer (P2P) lending system of “LendingClub” Company. The P2P system allows investors to get significantly higher return on investment as compared to bank deposit, but it comes with a risk of the loan and interest not being repaid. Ensemble machine learning algorithms and preprocessing techniques are used to explore, analyze and determine the factors which play crucial role in predicting the credit risk involved in “LendingClub” publicly available 2013-2015 loan applications dataset. A loan is considered “good” if it's repaid with interest and on time. The algorithms are optimized to favor the potential good loans whilst identifying defaults or risky credits.
jFuzzyLogic: a Java Library to Design Fuzzy Logic Controllers According to the Standard for Fuzzy Control Programming

Fuzzy Logic Controllers are a specific model of Fuzzy Rule Based Systems suitable for engineering applications for which classic control strategies do not achieve good results or for when it is too difficult to obtain a mathematical model. Recently, the International Electrotechnical Commission has published a standard for fuzzy control programming in part 7 of the IEC 61131 norm in order to offer a well defined common understanding of the basic means with which to integrate fuzzy control applications in control systems. In this paper, we introduce an open source Java library called jFuzzyLogic which offers a fully functional and complete implementation of a fuzzy inference system according to this standard, providing a programming interface and Eclipse plugin to easily write and test code for fuzzy control applications. A case study is given to illustrate the use of jFuzzyLogic.
An Intelligent Heart Disease Prediction System Using K-Means Clustering and Naïve Bayes Algorithm

Nowadays people work on computers for hours and hours they don’t have time to take care of themselves. Due to hectic schedules and consumption of junk food it affects the health of people and mainly heart. So to we are implementing an heart disease prediction system using data mining technique Naïve Bayes and k-means clustering algorithm. It is the combination of both the algorithms. This paper gives an overview for the same. It helps in predicting the heart disease using various attributes and it predicts the output as in the prediction form. For grouping of various attributes it uses k-means algorithm and for predicting it uses naïve bayes algorithm. Index Terms —Data mining, Comma separated files, naïve bayes, k-means algorithm, heart disease.
A 1 W 104 dB SNR Filter-Less Fully-Digital Open-Loop Class D Audio Amplifier With EMI Reduction

This paper presents the design and implementation of a high-performance fully-digital PWM DAC and switching output stage which can drive a speaker in portable devices, including cellular phones. Thanks to the quaternary pulse-width modulation scheme, filter-less implementation are possible. A pre-modulation DSP algorithm eliminates the harmonic distortion inherent to the employed modulation process, and an oversampling noise shaper reduces the modulator clock speed to facilitate the hardware implementation while keeping high-fidelity quality. Radiated electromagnetic field emission of the class D amplifier is reduced thanks to a clock spreading technique with only a minor impact on audio performance characteristics. Clock jitter effects on the audio amplifier performance are presented, showing very low degradation for jitter value up to a few nanoseconds. The digital section works with a 1.2 V power supply voltage, while the output switching stage and its driver are supplied from a high-efficiency DC-DC converter either at 3.6 V or 5 V. An output power of 0.5 W at 3.6 V and 1 W at 5 V over an 8 Ω load with efficiency (digital section included) of about 79% and 81%, respectively, has been achieved. The total harmonic distortion (THD) at maximum output level is about 0.2%, while the dynamic range is 104 dB A-weighted. The active area is about 0.94 mm2 in a 0.13 μm single-poly, five-metal, N-well digital CMOS technology with double-oxide option (0.5 μm minimum length).
Automatic Exudate Detection from Non-dilated Diabetic Retinopathy Retinal Images Using Fuzzy C-means Clustering

Exudates are the primary sign of Diabetic Retinopathy. Early detection can potentially reduce the risk of blindness. An automatic method to detect exudates from low-contrast digital images of retinopathy patients with non-dilated pupils using a Fuzzy C-Means (FCM) clustering is proposed. Contrast enhancement preprocessing is applied before four features, namely intensity, standard deviation on intensity, hue and a number of edge pixels, are extracted to supply as input parameters to coarse segmentation using FCM clustering method. The first result is then fine-tuned with morphological techniques. The detection results are validated by comparing with expert ophthalmologists' hand-drawn ground-truths. Sensitivity, specificity, positive predictive value (PPV), positive likelihood ratio (PLR) and accuracy are used to evaluate overall performance. It is found that the proposed method detects exudates successfully with sensitivity, specificity, PPV, PLR and accuracy of 87.28%, 99.24%, 42.77%, 224.26 and 99.11%, respectively.
Differential Evolution Training Algorithm for Feed-Forward Neural Networks

An evolutionary optimization method over continuous search spaces, differential evolution, has recently been successfully applied to real world and artificial optimization problems and proposed also for neural network training. However, differential evolution has not been comprehensively studied in the context of training neural network weights, i.e., how useful is differential evolution in finding the global optimum for expense of convergence speed. In this study, differential evolution has been analyzed as a candidate global optimization method for feed-forward neural networks. In comparison to gradient based methods, differential evolution seems not to provide any distinct advantage in terms of learning rate or solution quality. Differential evolution can rather be used in validation of reached optima and in the development of regularization terms and non-conventional transfer functions that do not necessarily provide gradient information.
Freesound technical demo

Freesound is an online collaborative sound database where people with diverse interests share recorded sound samples under Creative Commons licenses. It was started in 2005 and it is being maintained to support diverse research projects and as a service to the overall research and artistic community. In this demo we want to introduce Freesound to the multimedia community and show its potential as a research resource. We begin by describing some general aspects of Freesound, its architecture and functionalities, and then explain potential usages that this framework has for research applications.
Logo Detection in Document Images Based on Boundary Extension of Feature Rectangles

A new method of logo detection in document images is proposed in this paper. It is based on the boundary extension of feature rectangles of which the definition is also given in this paper. This novel method takes advantage of a layout assumption that logos have background (white spaces) surrounding it in a document. Compared with other logo detection methods, this new method has the advantage that it is independent on logo shapes and very fast. After the logo candidates are detected, a simple decision tree is used to reduce the false positive from the logo candidate pool. We have tested our method on a public image database involving logos. Experiments show that our method is more precise and robust than the previous methods and is well qualified as an effective assistance in document retrieval.
Knowledge Representation via Joint Learning of Sequential Text and Knowledge Graphs

Textual information is considered as significant supplement to knowledge representation learning (KRL). There are two main challenges for constructing knowledge representations from plain texts: (1) How to take full advantages of sequential contexts of entities in plain texts for KRL. (2) How to dynamically select those informative sentences of the corresponding entities for KRL. In this paper, we propose the Sequential Text-embodied Knowledge Representation Learning to build knowledge representations from multiple sentences. Given each reference sentence of an entity, we first utilize recurrent neural network with pooling or long short-term memory network to encode the semantic information of the sentence with respect to the entity. Then we further design an attention model to measure the informativeness of each sentence, and build text-based representations of entities. We evaluate our method on two tasks, including triple classification and link prediction. Experimental results demonstrate that our method outperforms other baselines on both tasks, which indicates that our method is capable of selecting informative sentences and encoding the textual information well into knowledge representations.
When do stereotypes come to mind and when do they color judgment? A goal-based theoretical framework for stereotype activation and application.

The authors describe a theoretical framework for understanding when people interacting with a member of a stereotyped group activate that group's stereotype and apply it to that person. It is proposed that both stereotype activation and stereotype application during interaction depend on the strength of comprehension and self-enhancement goals that can be satisfied by stereotyping one's interaction partner and on the strength of one's motivation to avoid prejudice. The authors explain how these goals can promote and inhibit stereotype activation and application, and describe diverse chronic and situational factors that can influence the intensity of these goals during interaction and, thereby, influence stereotype activation and application. This approach permits integration of a broad range of findings on stereotype activation and application.
DeepNL: a Deep Learning NLP pipeline

We present the architecture of a deep learning pipeline for natural language processing. Based on this architecture we built a set of tools both for creating distributional vector representations and for performing specific NLP tasks. Three methods are available for creating embeddings: feedforward neural network, sentiment specific embeddings and embeddings based on counts and Hellinger PCA. Two methods are provided for training a network to perform sequence tagging, a window approach and a convolutional approach. The window approach is used for implementing a POS tagger and a NER tagger, the convolutional network is used for Semantic Role Labeling. The library is implemented in Python with core numerical processing written in C++ using parallel linear algebra library for efficiency and scalability.
Quantization based Fast Inner Product Search

We propose a quantization based approach for fast approximate Maximum Inner Product Search (MIPS). Each database vector is quantized in multiple subspaces via a set of codebooks, learned directly by minimizing the inner product quantization error. Then, the inner product of a query to a database vector is approximated as the sum of inner products with the subspace quantizers. Different from recently proposed LSH approaches to MIPS, the database vectors and queries do not need to be augmented in a higher dimensional feature space. We also provide a theoretical analysis of the proposed approach, consisting of the concentration results under mild assumptions. Furthermore, if a small set of held-out samples from the query distribution is given at the training time, we propose a modified codebook learning procedure which further improves the accuracy. Experimental results on a variety of datasets including those arising from deep neural networks show that the proposed approach significantly outperforms the existing state-of-the-art.
Sentence Ordering and Coherence Modeling using Recurrent Neural Networks

Modeling the structure of coherent texts is a key NLP problem. The task of coherently organizing a given set of sentences has been commonly used to build and evaluate models that understand such structure. We propose an end-to-end unsupervised deep learning approach based on the set-to-sequence framework to address this problem. Our model strongly outperforms prior methods in the order discrimination task and a novel task of ordering abstracts from scientific articles. Furthermore, our work shows that useful text representations can be obtained by learning to order sentences. Visualizing the learned sentence representations shows that the model captures high-level logical structure in paragraphs. Our representations perform comparably to state-of-the-art pre-training methods on sentence similarity and paraphrase detection tasks.
Authentication handover and privacy protection in 5G hetnets using software-defined networking

Recently, densified small cell deployment with overlay coverage through coexisting heterogeneous networks has emerged as a viable solution for 5G mobile networks. However, this multi-tier architecture along with stringent latency requirements in 5G brings new challenges in security provisioning due to the potential frequent handovers and authentications in 5G small cells and HetNets. In this article, we review related studies and introduce SDN into 5G as a platform to enable efficient authentication hand-over and privacy protection. Our objective is to simplify authentication handover by global management of 5G HetNets through sharing of userdependent security context information among related access points. We demonstrate that SDN-enabled security solutions are highly efficient through its centralized control capability, which is essential for delay-constrained 5G communications.