nils-herrmann/clean_scirepeval huggingface dataset:
Kernel Codebooks for Scene Categorization

This paper introduces a method for scene categorization by modeling ambiguity in the popular codebook approach. The codebook approach describes an image as a bag of discrete visual codewords, where the frequency distributions of these words are used for image categorization. There are two drawbacks to the traditional codebook model: codeword uncertainty and codeword plausibility. Both of these drawbacks stem from the hard assignment of visual features to a single codeword. We show that allowing a degree of ambiguity in assigning codewords improves categorization performance for three state-of-the-art datasets.
Evolutionary Algorithm for Extractive Text Summarization

Text summarization is the process of automatically creating a compressed version of a given document preserving its information content. There are two types of summarization: extractive and abstractive. Extractive summarization methods simplify the problem of summarization into the problem of selecting a representative subset of the sentences in the original documents. Abstractive summarization may compose novel sentences, unseen in the original sources. In our study we focus on sentence based extractive document summarization. The extractive summarization systems are typically based on techniques for sentence extraction and aim to cover the set of sentences that are most important for the overall understanding of a given document. In this paper, we propose unsupervised document summarization method that creates the summary by clustering and extracting sentences from the original document. For this purpose new criterion functions for sentence clustering have been proposed. Similarity measures play an increasingly important role in document clustering. Here we’ve also developed a discrete differential evolution algorithm to optimize the criterion functions. The experimental results show that our suggested approach can improve the performance compared to sate-of-the-art summarization approaches.
Machine Learning Techniques in ADAS: A Review

What machine learning (ML) technique is used for system intelligence implementation in ADAS (advanced driving assistance system)? This paper tries to answer this question. This paper analyzes ADAS and ML independently and then relate which ML technique is applicable to what ADAS component and why. The paper gives a good grasp of the current state-of-the-art. Sample works in supervised, unsupervised, deep and reinforcement learnings are presented; their strengths and rooms for improvements are also discussed. This forms part of the basics in understanding autonomous vehicle. This work is a contribution to the ongoing research in ML aimed at reducing road traffic accidents and fatalities, and the invocation of safe driving.
Racial discrimination and the stress process.

The unique and combined effects of chronic and daily racial discrimination on psychological distress were examined in a sample of 174 African American doctoral students and graduates. Using a daily process design, 5 models of the stress process were tested. Multilevel random coefficient modeling analyses revealed that chronic exposure to racial discrimination predicted greater daily discrimination and psychological distress. Further, results show that differences in daily discrimination and negative events accounted for meaningful variation in daily distress responses. Finally, findings indicate that daily discrimination and negative events mediated the relationship between chronic discrimination and psychological distress. The study provides support for the need to measure chronic strains as distinctive from daily stressors in the lives of African Americans.
RTL implementation and analysis of fixed priority, round robin, and matrix arbiters for the NoC's routers

Networks-on-Chip (NoC) is an emerging on-chip interconnection centric platform that influences modern high speed communication infrastructure to improve the performance of many-core System-on-Chip (SoCs) designs. The core of each NoCs router involves arbiter and multiplier pairs that need to be carefully co-optimized in order to achieve an overall efficient implementation. Low transmission latency design is one of the most important parameters of NoC design. This paper uses parametric Verilog HDL to implement the designs and compares the performance in terms of power, area, and delay of different types of arbiters using for NoCs routers. The RTL implementation is performed using parametric Verilog HDL and analysis in term of power, area and delay is performed using Xilinx ISE 14.7 and Xpower Analyzer (XPA) with Xpower Estimator (XPE). The target device uses for these implementation is Vertex 6.
Automatic Whole Brain MRI Segmentation of the Developing Neonatal Brain

Magnetic resonance (MR) imaging is increasingly being used to assess brain growth and development in infants. Such studies are often based on quantitative analysis of anatomical segmentations of brain MR images. However, the large changes in brain shape and appearance associated with development, the lower signal to noise ratio and partial volume effects in the neonatal brain present challenges for automatic segmentation of neonatal MR imaging data. In this study, we propose a framework for accurate intensity-based segmentation of the developing neonatal brain, from the early preterm period to term-equivalent age, into 50 brain regions. We present a novel segmentation algorithm that models the intensities across the whole brain by introducing a structural hierarchy and anatomical constraints. The proposed method is compared to standard atlas-based techniques and improves label overlaps with respect to manual reference segmentations. We demonstrate that the proposed technique achieves highly accurate results and is very robust across a wide range of gestational ages, from 24 weeks gestational age to term-equivalent age.
A New Method for Line Segmentation of Handwritten Hindi Text

In this paper, we have discussed the new method for Line Segmentation of Handwritten Hindi text. The method is based on header line detection, base line detection and contour following technique. No preprocessing like skew correction, thinning or noise removal has been done on the data. The purpose of this paper is three fold. Firstly, we explained by experiments that this method is suitable for fluctuating lines or variable skew lines of text. Also, we confirm that this method is invariant of non uniform skew between words in a line (non uniform text line skew). Secondly, the contour following after header line detection correctly separates some of the overlapped lines of text. Thirdly, this paper provides a brief review of text line segmentation techniques for handwritten text which can be very useful for the beginners who want to work on text line segmentation.
Throughput-Aware Cooperative Reinforcement Learning for Adaptive Resource Allocation in Device-to-Device Communication

Device-to-device (D2D) communication is an essential feature for the future cellular networks as it increases spectrum efficiency by reusing resources between cellular and D2D users. However, the performance of the overall system can degrade if there is no proper control over interferences produced by the D2D users. Efficient resource allocation among D2D User equipments (UE) in a cellular network is desirable since it helps to provide a suitable interference management system. In this paper, we propose a cooperative reinforcement learning algorithm for adaptive resource allocation, which contributes to improving system throughput. In order to avoid selfish devices, which try to increase the throughput independently, we consider cooperation between devices as promising approach to significantly improve the overall system throughput. We impose cooperation by sharing the value function/learned policies between devices and incorporating a neighboring factor. We incorporate the set of states with the appropriate number of system-defined variables, which increases the observation space and consequently improves the accuracy of the learning algorithm. Finally, we compare our work with existing distributed reinforcement learning and random allocation of resources. Simulation results show that the proposed resource allocation algorithm outperforms both existing methods while varying the number of D2D users and transmission power in terms of overall system throughput, as well as D2D throughput by proper Resource block (RB)-power level combination with fairness measure and improving the Quality of service (QoS) by efficient controlling of the interference level.
SUIT: A Supervised User-Item Based Topic Model for Sentiment Analysis

Probabilistic topic models have been widely used for sentiment analysis. However, most of existing topic methods only model the sentiment text, but do not consider the user, who expresses the sentiment, and the item, which the sentiment is expressed on. Since different users may use different sentiment expressions for different items, we argue that it is better to incorporate the user and item information into the topic model for sentiment analysis. In this paper, we propose a new Supervised User-Item based Topic model, called SUIT model, for sentiment analysis. It can simultaneously utilize the textual topic and latent user-item factors. Our proposed method uses the tensor outer product of text topic proportion vector, user latent factor and item latent factor to model the sentiment label generalization. Extensive experiments are conducted on two datasets: review dataset and microblog dataset. The results demonstrate the advantages of our model. It shows significant improvement compared with supervised topic models and collaborative filtering methods.
Focusing Attention: Towards Accurate Text Recognition in Natural Images

Scene text recognition has been a hot research topic in computer vision due to its various applications. The state of the art is the attention-based encoder-decoder framework that learns the mapping between input images and output sequences in a purely data-driven way. However, we observe that existing attention-based methods perform poorly on complicated and/or low-quality images. One major reason is that existing methods cannot get accurate alignments between feature areas and targets for such images. We call this phenomenon “attention drift”. To tackle this problem, in this paper we propose the FAN (the abbreviation of Focusing Attention Network) method that employs a focusing attention mechanism to automatically draw back the drifted attention. FAN consists of two major components: an attention network (AN) that is responsible for recognizing character targets as in the existing methods, and a focusing network (FN) that is responsible for adjusting attention by evaluating whether AN pays attention properly on the target areas in the images. Furthermore, different from the existing methods, we adopt a ResNet-based network to enrich deep representations of scene text images. Extensive experiments on various benchmarks, including the IIIT5k, SVT and ICDAR datasets, show that the FAN method substantially outperforms the existing methods.
Dynamic current mode logic (DyCML): a new low-power high-performance logic style

This paper introduces a new reduced swing logic style called dynamic current mode logic (DyCML) that reduces both gate and interconnect power dissipation. DyCML circuits combine the advantages of MOS current mode logic (MCML) circuits with those of dynamic logic families to achieve high performance at a low-supply voltage with low-power dissipation. Unlike CML circuits, DyCML gates do not have a static current source, which makes DyCML a good candidate for portable devices and batterypowered systems. Simulation and test results show that DyCML circuits are superior to other logic styles in terms of power and delay. A 16-bit DyCML carry look-ahead adder (CLA), fabricated in 0.6m CMOS technology, attains a delay of 1.24 ns and dissipates 19.2 mW at 400 MHz.
Folded dipole antenna near metal plate

The paper presents the effects on antenna parameters when an antenna is placed horizontally near a metal plate. The plate has finite size and rectangular shape. A folded dipole antenna is used and it is placed symmetrically above the plate. The FEM (finite element method) is used to simulate the dependency of antenna parameters on the size of the plate and the distance between the plate and the antenna. The presence of the metal plate, even a small one if it is at the right distance, causes very big changes in the behaviour of the antenna. The bigger the plate, especially in width, the sharper and narrower are the lobes of the radiation pattern. The antenna height defines how many lobes the radiation pattern has. A number of the antenna parameters, including impedance, directivity and front-to-back ratio, change periodically as the antenna height is increased. The resonant frequency of the antenna also changes under the influence of the metal plate.
Three naive Bayes approaches for discrimination-free classification

In this paper, we investigate how to modify the naive Bayes classifier in order to perform classification that is restricted to be independent with respect to a given sensitive attribute. Such independency restrictions occur naturally when the decision process leading to the labels in the data-set was biased; e.g., due to gender or racial discrimination. This setting is motivated by many cases in which there exist laws that disallow a decision that is partly based on discrimination. Naive application of machine learning techniques would result in huge fines for companies. We present three approaches for making the naive Bayes classifier discrimination-free: (i) modifying the probability of the decision being positive, (ii) training one model for every sensitive attribute value and balancing them, and (iii) adding a latent variable to the Bayesian model that represents the unbiased label and optimizing the model parameters for likelihood using expectation maximization. We present experiments for the three approaches on both artificial and real-life data.
Strip mining on SIMD architectures

Michael Weiss Compass, Inc. 550 Edgewater Dr. Wakefield, MA A compiler for vector processors must strip mine statements to fit the vector register length. The same compiler technology can be applied to SIMD machines, removing the need for a virtual processor mechanism and offering significant advantages over that approach. The different memory organizations of SIMD and vector machines give rise to strip mining differences. SIMD interprocessor communication must be stripped. Array allocation gives SIMD strip mining a global aspect. SIMD loads and stores maybe uniform or ragged. These all contribute to the complexity of the SIMD strip mining problem, and provide opportunities for a compiler to make use of its wider view of the code.
A probabilistic constrained clustering for transfer learning and image category discovery

Neural network-based clustering has recently gained popularity, and in particular a constrained clustering formulation has been proposed to perform transfer learning and image category discovery using deep learning. The core idea is to formulate a clustering objective with pairwise constraints that can be used to train a deep clustering network; therefore the cluster assignments and their underlying feature representations are jointly optimized end-toend. In this work, we provide a novel clustering formulation to address scalability issues of previous work in terms of optimizing deeper networks and larger amounts of categories. The proposed objective directly minimizes the negative log-likelihood of cluster assignment with respect to the pairwise constraints, has no hyper-parameters, and demonstrates improved scalability and performance on both supervised learning and unsupervised transfer learning.
Two Conceptions of the Physical

The debate over physicalism in philosophy of mind can be seen as concerning an inconsistent tetrad of theses: (1) if physicalism is true, a priori physicalism is true; (2) a priori physicalism is false; (3) if physicalism is false, epiphenomenalism is true; (4) epiphenomenalism is false. This paper argues that one may resolve the debate by distinguishing two conceptions of the physical: on the theory-based conception, it is plausible that (2) is true and (3) is false; on the object-based conception, it is plausible that (3) is true and (2) is false. The paper also defends and explores the version of physicalism that results from this strategy. 1. One way to view the contemporary debate in philosophy of mind over physicalism is to see it as being organized around an inconsistent tetrad of theses. These are: (1) If physicalism is true, a priori physicalism is true. (2) A priori physicalism is false. (3) If physicalism is false, epiphenomenalism is true. (4) Epiphenomenalism is false. It is obvious of course that these theses are inconsistent: (1) and (2) entail that physicalism is false, while (3) and (4) entail that it is true. Barring ambiguity, therefore, one thing we know is that one of the theses is false. On the other hand, each of the theses has powerful considerations, or at least what seem initially to be powerful considerations, in its favor. (1) In support of (1) are considerations of supervenience, articulated most clearly in recent times by Frank Jackson and David Chalmers. A priori physicalism is a thesis with two parts. The first part-the physicalist part-is that the mental supervenes with metaphysical necessity on the physical. The second part-the a priori part-is that mental truths are a priori entailed by physical truths. Many philosophers hold that supervenience stands in need of justification or explanation; Jackson and Chalmers argue that the project of justifying or explaining supervenience just is the project of making it plausible that there is an a priori entailment of the mental by the physical. This suggests that the first part of
The String-to-String Correction Problem

The string-to-string correction problem is to determine the distance between two strings as measured by the minimum cost sequence of “edit operations” needed to change the one string into the other. The edit operations investigated allow changing one symbol of a string into another single symbol, deleting one symbol from a string, or inserting a single symbol into a string. An algorithm is presented which solves this problem in time proportional to the product of the lengths of the two strings. Possible applications are to the problems of automatic spelling correction and determining the longest subsequence of characters common to two strings.
Investigating the Impact of Data Volume and Domain Similarity on Transfer Learning Applications

Transfer learning allows practitioners to recognize and apply knowledge learned in previous tasks (source task) to new tasks or new domains (target task), which share some commonality. The two important factors impacting the performance of transfer learning models are: (a) the size of the target dataset, and (b) the similarity in distribution between source and target domains. Thus far, there has been little investigation into just how important these factors are. In this paper, we investigate the impact of target dataset size and source/target domain similarity on model performance through a series of experiments. We find that more data is always beneficial, and model performance improves linearly with the log of data size, until we are out of data. As source/target domains differ, more data is required and fine tuning will render better performance than feature extraction. When source/target domains are similar and data size is small, fine tuning and feature extraction renders equivalent performance. Our hope is that by beginning this quantitative investigation on the effect of data volume and domain similarity in transfer learning we might inspire others to explore the significance of data in developing more accurate statistical models.
Learning Generalized Linear Models Over Normalized Data

Enterprise data analytics is a booming area in the data management industry. Many companies are racing to develop toolkits that closely integrate statistical and machine learning techniques with data management systems. Almost all such toolkits assume that the input to a learning algorithm is a single table. However, most relational datasets are not stored as single tables due to normalization. Thus, analysts often perform key-foreign key joins before learning on the join output. This strategy of learning after joins introduces redundancy avoided by normalization, which could lead to poorer end-to-end performance and maintenance overheads due to data duplication. In this work, we take a step towards enabling and optimizing learning over joins for a common class of machine learning techniques called generalized linear models that are solved using gradient descent algorithms in an RDBMS setting. We present alternative approaches to learn over a join that are easy to implement over existing RDBMSs. We introduce a new approach named factorized learning that pushes ML computations through joins and avoids redundancy in both I/O and computations. We study the tradeoff space for all our approaches both analytically and empirically. Our results show that factorized learning is often substantially faster than the alternatives, but is not always the fastest, necessitating a cost-based approach. We also discuss extensions of all our approaches to multi-table joins as well as to Hive.
Fractional Order Differentiator Based Filter For Edge Detection of Low Contrast Underwater Images

Underwater images usually suffer from degraded visibility. Light attenuates and scatters in water resulting in low contrast and haziness in the scenes. Therefore, the main problems to be dealt with in underwater environment are poor contrast, non-uniform lighting, haziness and blurring. Hence, in order to study underwater images, it becomes utmost important to extract the invisible or unclear edges. This paper presents an edge detection method by using fractional order differentiation (FOD) approach. As texture plays a major role in low-level image analysis, therefore texture based image enhancement is very important. In order to attain texture enhancement in images, an algorithm based on the Grünwald-Letnikov (G–L) fractional order derivative is proposed. Considering the G-L based fractional differential operator’s basic definition and implementation, a filter is devised and its applicability for texture enhancement is analyzed. Legendre polynomials based FOD has been used to design the filter. Further High Pass Filters (HPF) and Low Pass Filters (LPF) with the concept of intensity factor (γ) are designed. Next, a multiplication operation is performed in the pre processing stage. At last, Sobel’s method for edge detection is applied on the resultant pre processed image. The algorithm is experimented on various underwater images. The quality of the resultant images are evaluated by checking their respective Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR) values. Further, the results are compared with another approach based on Riemann Liouville (R-L) fractional differential operator. The analysis of tests proves that the proposed method displays better results for detecting edges of low contrast underwater images and reveals more information than Histogram Equalization method and R-L method based on MSE and PSNR values.  Fractional Order Differentiator, Legendre polynomials, Grunwald Letnikov fractional derivative, MSE, PSNR, Sobel Edge Detector, Underwater Images.