nils-herrmann/clean_scirepeval huggingface dataset:
Robust perceptual color image hashing using randomized hypercomplex matrix factorizations

Compact representations of color image and video content allow efficient search, retrieval and storage of this content over the Internet and online repositories. However, most of these representations neither take into account the inherent correlation nor the perceptual redundancy of the color information. In this paper, we propose a perceptual hash representation for color images using robust image features. These features, most dominant singular vectors extracted using the quaternion singular value decomposition (QSVD) of pseudorandomly selected overlapping image blocks, are efficiently used for color image search and retrieval applications. Their robustness is guaranteed by the underlying singular vectors. The motivation behind our work is twofold: 1) the ability of the QSVD algorithm to provide the best low-rank approximation of color images in the Frobenius norm sense and 2) compact representations to handle the color components as a single entity. The QSVD algorithm leads to proper modeling of possible geometric attacks as an independent and identically-distributed (i.i.d) quaternionic random noise on the singular vectors. Such modeling simplifies the hash code detector design. Hash code robustness against geometric attacks is evaluated over a large set of test color images where the proposed scheme outperforms existing factorization-based hashing algorithms in terms of lower miss and false alarm probabilities by orders of magnitude. Finally, the improved robustness and performance does not come at the expense of increased computational complexity which is another salient feature of the proposed hashing scheme.
3 D model classification using convolutional neural network

Our goal is to classify 3D models directly using convolutional neural network. Most of existing approaches rely on a set of human-engineered features. We use 3D convolutional neural network to let the network learn the features over 3D space to minimize classification error. We trained and tested over ShapeNet dataset with data augmentation by applying random transformations. We made various visual analysis to find out what the network has learned. We extended our work to extract additional information such as pose of the 3D model.
Regression testing minimization, selection and prioritization: a survey

Regression testing is a testing activity that is performed to provide confidence that changes do not harm the existing behaviour of the software. Test suites tend to grow in size as software evolves, often making it too costly to execute entire test suites. A number of different approaches have been studied to maximize the value of the accrued test suite: minimization, selection and prioritization. Test suite minimization seeks to eliminate redundant test cases in order to reduce the number of tests to run. Test case selection seeks to identify the test cases that are relevant to some set of recent changes. Test case prioritization seeks to order test cases in such a way that early fault detection is maximized. This paper surveys each area of minimization, selection and prioritization technique and discusses open problems and potential directions for future research.
Modified Antipodal Fermi antenna with piecewise-linear approximation

A modified Antipodal Fermi antenna with piecewise-linear approximation is presented in this paper. The performance with wider band (4.8–15 GHz) in return loss responses, moderately high gain (7.6–14.5 dBi), available end-fire (29.8 degree beamwidth and −18.7 dB side lobe level) in radiation patterns are achieved. The current distribution demonstrates the characteristics of transition structure.
A persistent lock-free queue for non-volatile memory

Non-volatile memory is expected to coexist with (or even displace) volatile DRAM for main memory in upcoming architectures. This has led to increasing interest in the problem of designing and specifying durable data structures that can recover from system crashes. Data structures may be designed to satisfy stricter or weaker durability guarantees to provide a balance between the strength of the provided guarantees and performance overhead. This paper proposes three novel implementations of a concurrent lock-free queue. These implementations illustrate algorithmic challenges in building persistent lock-free data structures with different levels of durability guarantees. In presenting these challenges, the proposed algorithmic designs, and the different durability guarantees, we hope to shed light on ways to build a wide variety of durable data structures. We implemented the various designs and compared their performance overhead to a simple queue design for standard (volatile) memory.
How to Choose the Best Pivot Language for Automatic Translation of Low-Resource Languages

Recent research on multilingual statistical machine translation focuses on the usage of pivot languages in order to overcome language resource limitations for certain language pairs. Due to the richness of available language resources, English is, in general, the pivot language of choice. However, factors like language relatedness can also effect the choice of the pivot language for a given language pair, especially for Asian languages, where language resources are currently quite limited. In this article, we provide new insights into what factors make a pivot language effective and investigate the impact of these factors on the overall pivot translation performance for translation between 22 Indo-European and Asian languages. Experimental results using state-of-the-art statistical machine translation techniques revealed that the translation quality of 54.8% of the language pairs improved when a non-English pivot language was chosen. Moreover, 81.0% of system performance variations can be explained by a combination of factors such as language family, vocabulary, sentence length, language perplexity, translation model entropy, reordering, monotonicity, and engine performance.
Switchable Temporal Propagation Network

Color propagation. For color propagation considered in the paper, we train both the basic and switchable TPN networks, for 10 epochs, on the COCO  dataset, and then fine-tune them on the ACT [2] dataset for 4 epochs. For both networks we keep the number of training epochs the same to ensure a fair comparison. To pre-train our networks on the COCO image-based dataset, we sample two sets of similarity transformation parameters from s ∈ [0.9, 1.1], θ ∈ [−15, 15] and dx ∈ [−0.1, 0.1] × b, where b = min(H,W ) and use them to create two geometrically transformed versions of each training image. Then we randomly crop 256× 256 sized patches from the same location of the two transformed images. While fine-tuning the models on the ACT video-based dataset, we skip the step of applying geometric transformations to the frames and simply generate a pair of 256× 256 sized patches by randomly cropping from the same location of two different video frames. Additionally, we randomly sample the pairs of video frames to use together, on-the-fly, during training.
Enhancing credit scoring model performance by a hybrid scoring matrix

Competition of the consumer credit market in Taiwan has become severe recently. Therefore, most financial institutions actively develop credit scoring models based on assessments of the credit approval of new customers and the credit risk management of existing customers. This study uses a genetic algorithm for feature selection and decision trees for customer segmentation. Moreover, it utilizes logistic regression to build the application and credit bureau scoring models where the two scoring models are combined for constructing the scoring matrix. The scoring matrix undergoes more accurate risk judgment and segmentation to further identify the parts required enhanced management or control within a personal loan portfolio. The analytical results demonstrate that the predictive ability of the scoring matrix outperforms both the application and credit bureau scoring models. Regarding the K-S value, the scoring matrix increases the prediction accuracy compared to the application and credit bureau scoring models by 18.40 and 5.70%, respectively. Regarding the AUC value, the scoring matrix increases the prediction accuracy compared to the application and credit bureau scoring models by 10.90 and 6.40%, respectively. Furthermore, this study applies the scoring matrix to the credit approval decisions for corresponding risk groups to strengthen bank’s risk management practices.
OctoPocus: a dynamic guide for learning gesture-based command sets

We describe OctoPocus, an example of a dynamic guide that combines on-screen feedforward and feedback to help users learn, execute and remember gesture sets. OctoPocus can be applied to a wide range of single-stroke gestures and recognition algorithms and helps users progress smoothly from novice to expert performance. We provide an analysis of the design space and describe the results of two experi-ments that show that OctoPocus is significantly faster and improves learning of arbitrary gestures, compared to con-ventional Help menus. It can also be adapted to a mark-based gesture set, significantly improving input time compared to a two-level, four-item Hierarchical Marking menu.
Performance comparison of FPGA, GPU and CPU in image processing

Many applications in image processing have high inherent parallelism. FPGAs have shown very high performance in spite of their low operational frequency by fully extracting the parallelism. In recent micro processors, it also becomes possible to utilize the parallelism using multi-cores which support improved SIMD instructions, though programmers have to use them explicitly to achieve high performance. Recent GPUs support a large number of cores, and have a potential for high performance in many applications. However, the cores are grouped, and data transfer between the groups is very limited. Programming tools for FPGA, SIMD instructions on CPU and a large number of cores on GPU have been developed, but it is still difficult to achieve high performance on these platforms. In this paper, we compare the performance of FPGA, GPU and CPU using three applications in image processing; two-dimensional filters, stereo-vision and k-means clustering, and make it clear which platform is faster under which conditions.
Evaluation on State of Charge Estimation of Batteries With Adaptive Extended Kalman Filter by Experiment Approach

An accurate State-of-Charge (SoC) estimation plays a significant role in battery systems used in electric vehicles due to the arduous operation environments and the requirement of ensuring safe and reliable operations of batteries. Among the conventional methods to estimate SoC, the Coulomb counting method is widely used, but its accuracy is limited due to the accumulated error. Another commonly used method is model-based online iterative estimation with the Kalman filters, which improves the estimation accuracy in some extent. To improve the performance of Kalman filters in SoC estimation, the adaptive extended Kalman filter (AEKF), which employs the covariance matching approach, is applied in this paper. First, we built an implementation flowchart of the AEKF for a general system. Second, we built an online open-circuit voltage (OCV) estimation approach with the AEKF algorithm so that we can then get the SoC estimate by looking up the OCV-SoC table. Third, we proposed a robust online model-based SoC estimation approach with the AEKF algorithm. Finally, an evaluation on the SoC estimation approaches is performed by the experiment approach from the aspects of SoC estimation accuracy and robustness. The results indicate that the proposed online SoC estimation with the AEKF algorithm performs optimally, and for different error initial values, the maximum SoC estimation error is less than 2% with close-loop state estimation characteristics.
CAN - Ethernet gateway for automotive applications

This paper presents the design and practical implementation of a gateway interface between two networks, CAN and Ethernet. Data packets detected over a network will be routed to the other network, with the possibility of using various filters. Since there is no standard yet for this purpose, the paper also proposes a method to pack one or more CAN messages in an Ethernet frame. For testing purposes of the interface gateway and its implementation a series of graphical software tools running on a PC client have been developed. These tools are able to send and decode messages using the specified packaging. At the end of the paper a graphical application demonstrates the functionality of the gateway implementation and the benefits of the proposed CAN-Ethernet routing method.
Pairwise FastText Classifier for Entity Disambiguation

For the Australasian Language Technology Association (ALTA) 2016 Shared Task, we devised Pairwise FastText Classifier (PFC), an efficient embedding-based text classifier, and used it for entity disambiguation. Compared with a few baseline algorithms, PFC achieved a higher F1 score at 0.72 (under the team name BCJR). To generalise the model, we also created a method to bootstrap the training set deterministically without human labelling and at no financial cost. By releasing PFC and the dataset augmentation software to the public, we hope to invite more collaboration.
A compact, 37% fractional bandwidth millimeter-wave phase shifter using a wideband lange coupler for 60-GHz and E-band systems

A wideband 57.7–84.2 GHz Phase Shifter is presented using a compact Lange coupler to generate in-phase and quadrature signal. The Lange coupler is followed by two balun transformers that provide the IQ vector modulation with differential I and Q signals. The implemented Phase Shifter demonstrates an average 6-dB insertion loss and 5-dB gain variation. The measured average rms phase and gain errors are 7 degrees and 1 dB, respectively. The phase shifter is implemented in GlobalFoundries 45-nm SOI CMOS technology using a trap-rich substrate. The chip area is 385 μm × 285 μm and the Phase Shifter consumes less than 17 mW. To the best of authors knowledge, this is the first phase shifter that covers both 60 GHz band and E-band frequencies with a fractional bandwidth of 37%.
Cross-Validation and the Estimation of Conditional Probability Densities

Many practical problems, especially some connected with forecasting, require nonparametric estimation of conditional densities from mixed data. For example, given an explanatory data vector X for a prospective customer, with components that could include the customer’s salary, occupation, age, sex, marital status, and address, a company might wish to estimate the density of the expenditure, Y , that could be made by that person, basing the inference on observations of (X,Y) for previous clients. Choosing appropriate smoothing parameters for this problem can be tricky, not in the least because plug-in rules take a particularly complex form in the case of mixed data. An obvious difficulty is that there exists no general formula for the optimal smoothing parameters. More insidiously, and more seriously, it can be difficult to determine which components of X are relevant to the problem of conditional inference. For example, if the jth component of X is independent of Y , then that component is irrelevant to estimating the density of Y given X, and ideally should be dropped before conducting inference. In this article we show that cross-validation overcomes these difficulties. It automatically determines which components are relevant and which are not, through assigning large smoothing parameters to the latter and consequently shrinking them toward the uniform distribution on the respective marginals. This effectively removes irrelevant components from contention, by suppressing their contribution to estimator variance; they already have very small bias, a consequence of their independence of Y . Cross-validation also yields important information about which components are relevant; the relevant components are precisely those that cross-validation has chosen to smooth in a traditional way, by assigning them smoothing parameters of conventional size. Indeed, cross-validation produces asymptotically optimal smoothing for relevant components, while eliminating irrelevant components by oversmoothing. In the problem of nonparametric estimation of a conditional density, cross-validation comes into its own as a method with no obvious peers.
An Insight Extraction System on BioMedical Literature with Deep Neural Networks

Mining biomedical text offers an opportunity to automatically discover important facts and infer associations among them. As new scientific findings appear across a large collection of biomedical publications, our aim is to tap into this literature to automate biomedical knowledge extraction and identify important insights from them. Towards that goal, we develop a system with novel deep neural networks to extract insights on biomedical literature. Evaluation shows our system is able to provide insights with competitive accuracy of human acceptance and its relation extraction component outperforms previous work.
Story generation after TALE-SPIN

TALE-SPIN, the laat major AI attempt at atory generation, approached the problem of making up atoriea primarily from the perspective of an impartial world simulator. AUTHOR ia a program (under development) which generates atoriea as a creative reaaoner in purauit of her own narrative goals. It is thus intended to aimulate an author's mind aa ahe makes up a story, rather than the world as things happen in it. The four major forces driving the atory generation process, according to the AUTHOR model, are (1) author intentionality, (2) conceptual reformulation, (3) reminding, and (4) the opportunity enhancement metagoal. 1. World Simulation Based Story Generation TALE-SPIN [Meehan, 1976] is a program which made up Aesop-fable-like conceptual atoriea, which it then expressed in natural language. Each atory TALE-SPIN made up waa based on a model of the storyworld, including the characters in it and their goala, peraonalities, and interperaonal relatione. Storiea were apun by working out consequences of the model. Thus, central to TALE-SPIN waa the idea of world simulation. The three levels TALE-SPIN was most concerned with simulating were: 1. character intentionality-e.g., if Joe Bear was hungry, he would try to get something to eat 2. social conatrainta-e.g., if Joe Bear believed Irving Bird to be hia friend, he could aak an outright favor; if Joe Bear believed their relation to be a bit cooler, he would feel obliged to offer something in return (auch aa a worm) 3. phyaical cauaality-e.g., if Irving Bird ate a worm, the worm would cease to exist; after "fooling around", Aa oppoaed to Sheldon Klein's ayetem which only produced atory texts and which ia incapable, in principle, of understanding the atoriea it "makes up" [Klein et el., 1976]. characters felt "wiped out" World simulation, however, fails to reflect the proctaa that an author goes through in making up a atory-storyworlds are developed by authors aa needed, frequently as post hoc justification for eventa that the author has already decided ahe wants as part of the story. As illustration, let us consider the question of why, in the Star Wars aequel, "The Empire Strikes Back", Princess Leia and Solo go to visit Landeau. If one asks a reader (or viewer) this question, the anawer will be along the lines of, "They needed a place to hide from imperial stormtroopers until they got the hyperdrive fixed, and Landeau was an old buddy of Solo's, and ..." • …
Handling non-functional requirements for big data and IOT projects in Scrum

Agile methodologies are gaining popularity at a lightning pace in the software industry. Prominent agile methodologies, such as Scrum and Extreme Programming (XP), help quickly deliver a system that meets Functional Requirements (FRs), while adapting to the changes in the customer requirements and feedback. However, Non-Functional Requirements (NFRs) by and large have been either mostly ignored or introduced and retrofit late in the software development cycle, oftentimes leading to project failures. With the recent, increasing shifts towards cloud and emphasis in big data in the software industry, NFRs, such as security and performance, have become more critical than ever before. This research proposes a novel approach to handling security and performance NFRs for projects involving big data and cloud, using Scrum. An industrial case study, conducted over a span of 9 months, shows that the approach helps deal with security and performance requirements individually as well as conflicts between them in an agile methodology.
Generating timeline summaries with social media attention

Timeline generation is an important research task which can help users to have a quick understanding of the overall evolution of one given topic. Previous methods simply split the time span into fixed, equal time intervals without studying the role of the evolutionary patterns of the underlying topic in timeline generation. In addition, few of these methods take users’ collective interests into considerations to generate timelines. We consider utilizing social media attention to address these two problems due to the facts: 1) social media is an important pool of real users’ collective interests; 2) the information cascades generated in it might be good indicators for boundaries of topic phases. Employing Twitter as a basis, we propose to incorporate topic phases and user’s collective interests which are learnt from social media into a unified timeline generation algorithm.We construct both one informativeness-oriented and three interestingness-oriented evaluation sets over five topics.We demonstrate that it is very effective to generate both informative and interesting timelines. In addition, our idea naturally leads to a novel presentation of timelines, i.e., phase based timelines, which can potentially improve user experience.
CASH DEMAND FORECASTING FOR ATM USING NEURAL NETWORKS AND SUPPORT VECTOR REGRESSION ALGORITHMS

In this paper two different methods are used to forecast the daily cash demand for automatic teller machines (ATM). The first method is based on flexible artificial neural network (ANN). The generalization properties of this ANN were improved using special adaptive regularization term. The second forecasting method employs the support vector regression (SPR) algorithm. Performed simulation studies and experimental tests showed tolerable forecasting capacities using the both proposed methods. Despite the today's overenthusiastic beliefs about the capabilities of SPR, our investigation showed however that for this application slightly better result can be achieved using forecasting method based on flexible ANN. At this stage the forecasting schema based on flexible ANN is in the implementing phase for intelligent cash management in ATM network.
Polynomial Eigenvalue Solutions to the 5-pt and 6-pt Relative Pose Problems

In this paper we provide new fast and simple solutions to two important minimal problems in computer vision, the five-point relative pose problem and the six-point focal length problem. We show that these two problems can easily be formulated as polynomial eigenvalue problems of degree three and two and solved using standard efficient numerical algorithms. Our solutions are somewhat more stable than state-of-the-art solutions by Nister and Stewenius and are in some sense more straightforward and easier to implement since polynomial eigenvalue problems are well studied with many efficient and robust algorithms available. The quality of the solvers is demonstrated in experiments 1.