nils-herrmann/clean_scirepeval huggingface dataset:
An optimum VLSI design of a 16-BIT ALU

The key parameters for the performance measure of any VLSI design are logic delay, power consumption and chip area. This paper describes the VLSI design of a 16 Bit ALU and design is optimized in terms of Speed, Power Consumption and Chip Area. Different logic families are used in the design for various logic modules. The choice of logic families for each module is determined by considering speed and power consumption as the important parameters offered by each logic family. The adder circuit being the most important module used by the arithmetic operations of an ALU, detailed analysis of the variety of adder circuit configurations are carried out and the best suited configuration for the ALU design i.e. Carry Skip Adder configuration is used to design the optimum ALU. Finally a 16 bit Arithmetic Logic unit is designed using mixed logic families such as CMOS for basic logic functions, pseudo-NMOS for AND logic and Pass Transistor logic for multiplexers, in order to optimize the overall performance of the design.  DSCH is used to validate the design at gate level implementation and IC Layout editor Microwind is used to implement the chip level design.
Enhanced Fraud Miner : Credit Card Fraud Detection using Clustering Data Mining Techniques

This paper aimed to build unified pattern per customer not only represent normal behavior but also Fraud pattern that’s represented previously and confirmed as fraud transactions that’s facilitate studding fraudsters behavior. An enhancement for the proposed algorithm of Fraud Miner has been proposed. This enhancement involves introducing LINGO clustering Data mining algorithm by replacing Apriori algorithm used in Fraud Miner for Frequently Pattern creation and facilitate summarize customer previous behavior either within his Legal or Fraud transactions. Using this algorithm provide more chance for easily fraud detection as the fraudsters always behaving same as customer behaviors instead of study fraudster behavior the customer frequent behavior will be identified from his legal or previously confirmed transactions being fraud. A performance comparison with other algorithms has been carried out.
Evolution of Trajectories: A Novel Representation for Deep Action Recognition

Achieving high classification accuracy remains a challenge for human action recognition approaches that are based on convolutional neural networks (CNN). CNN-based action recognition methods on resource-constrained edge systems are currently unable to train on whole videos due to infeasible computational and memory requirements. On the other hand, approaches that utilize video-level supervision with sparse-sampling designs run the risk of learning local features shared with multiple similar classes. Additionally, features captured by motion estimation algorithms for temporal stream CNN's are already reduced in dimension, increasing the possibility of label mismatch in methods that rely on short-term features. To address the aforementioned points, we design a novel temporal representation to capture a 1) long-term interval of motion and 2) integrate the trajectory of motion captured therein. We compare our approach with current motion representations and demonstrate its efficacy for examples containing local features with high inter-class similarity. We implement our representation as part of two and three-stream CNN's and conduct experiments on one of the popular and most difficult action recognition datasets: HMDB51. Our results show that, for methods employing sparse-sampling designs, our approach surpasses the current state-of-the-art approaches achieving 71.76% on HMDB51.
Precision Passive-Charge-Sharing SAR ADC: Analysis, Design, and Measurement Results

This paper presents a passive-charge-sharing successive approximation register (SAR) analog-to-digital converter (ADC) that achieves 16-bit linearity. It is known that on-chip passive charge sharing suffers from poor linearity due to the unregulated reference voltage during bit trials. This paper gives a detailed analysis for the reasons of poor linearity for passive-charge-sharing SAR ADCs and proposes solutions. The proposed ADC architecture with one-reference-cap-per-bit and short switches addresses the issue of signal-dependent reference voltage droop during SAR ADC bit trials and orthogonalizes the bit weights. The calibration technique presented further calibrates the ADC to 16-bit linearity. In addition, the proposed architecture maximizes SNR by sampling on to the digital-to-analog converter (DAC) capacitor array, the first reported in this type of SAR ADC. This paper provides the foundation of building a precision SAR ADC out of on-chip reference capacitors. Measurement result from a prototype test chip shows ±0.8 LSB (16-bit level) integrated non-linearity at 1 MSPS.
[Characteristics of children and adolescents with gender dysphoria referred to the Hamburg Gender Identity Clinic].

Given the increasing demand for counselling in gender dysphoria in childhood in Germany, there is a definite need for empirical data on characteristics and developmental trajectories of this clinical group. This study aimed to provide a first overview by assessing demographic characteristics and developmental trajectories of a group of gender variant boys and girls referred to the specialised Gender Identity Clinic in Hamburg. Data were extracted from medical charts, transcribed and analysed using qualitative content analysis methods. Categories were set up by inductive-deductive reasoning based on the patients' parents' and clinicians' information in the files. Between 2006 and 2010, 45 gender variant children and adolescents were seen by clinicians; 88.9% (n = 40) of these were diagnosed with gender identity disorder (ICD-10). Within this group, the referral rates for girls were higher than for boys (1:1.5). Gender dysphoric girls were on average older than the boys and a higher percentage of girls was referred to the clinic at the beginning of adolescence (> 12 years of age). At the same time, more girls reported an early onset age. More girls made statements about their (same-sex) sexual orientation during adolescence and wishes for gender confirming medical interventions. More girls than boys revealed self-mutilation in the past or present as well as suicidal thoughts and/or attempts. Results indicate that the presentation of clinically referred gender dysphoric girls differs from the characteristics boys present in Germany; especially with respect to the most salient age differences. Therefore, these two groups require different awareness and individual treatment approaches.
BioNumbers—the database of key numbers in molecular and cell biology

BioNumbers (http://www.bionumbers.hms.harvard.edu) is a database of key numbers in molecular and cell biology--the quantitative properties of biological systems of interest to computational, systems and molecular cell biologists. Contents of the database range from cell sizes to metabolite concentrations, from reaction rates to generation times, from genome sizes to the number of mitochondria in a cell. While always of importance to biologists, having numbers in hand is becoming increasingly critical for experimenting, modeling, and analyzing biological systems. BioNumbers was motivated by an appreciation of how long it can take to find even the simplest number in the vast biological literature. All numbers are taken directly from a literature source and that reference is provided with the number. BioNumbers is designed to be highly searchable and queries can be performed by keywords or browsed by menus. BioNumbers is a collaborative community platform where registered users can add content and make comments on existing data. All new entries and commentary are curated to maintain high quality. Here we describe the database characteristics and implementation, demonstrate its use, and discuss future directions for its development.
RY-ON WITH CLOTHING REGION

We propose a virtual try-on method based on generative adversarial networks (GANs). By considering clothing regions, this method enables us to reflect the pattern of clothes better than Conditional Analogy GAN (CAGAN), an existing virtual try-on method based on GANs. Our method first obtains the clothing region on a person by using a human parsing model learned with a large-scale dataset. Next, using the acquired region, the clothing part is removed from a human image. A desired clothing image is added to the blank area. The network learns how to apply new clothing to the area of people’s clothing. Results demonstrate the possibility of reflecting a clothing pattern. Furthermore, an image of the clothes that the person is originally wearing becomes unnecessary during testing. In experiments, we generate images using images gathered from Zaland (a fashion E-commerce site).
When two choices are not enough: Balancing at scale in Distributed Stream Processing

Carefully balancing load in distributed stream processing systems has a fundamental impact on execution latency and throughput. Load balancing is challenging because real-world workloads are skewed: some tuples in the stream are associated to keys which are significantly more frequent than others. Skew is remarkably more problematic in large deployments: having more workers implies fewer keys per worker, so it becomes harder to “average out” the cost of hot keys with cold keys. We propose a novel load balancing technique that uses a heavy hitter algorithm to efficiently identify the hottest keys in the stream. These hot keys are assigned to d ≥ 2 choices to ensure a balanced load, where d is tuned automatically to minimize the memory and computation cost of operator replication. The technique works online and does not require the use of routing tables. Our extensive evaluation shows that our technique can balance real-world workloads on large deployments, and improve throughput and latency by 150% and 60% respectively over the previous state-of-the-art when deployed on Apache Storm.
Color Demosaicing by Estimating Luminance and Opponent Chromatic Signals in the Fourier Domain

We propose a new method for color demosaicing based on a mathematical model of spatial multiplexing of color. We demonstrate that a one-color per pixel image can be written as the sum of luminance and chrominance. In case of a regular arrangement of colors, such as with the Bayer color filter array (CFA), luminance and chrominance are well localized in the spatial frequency domain. Our algorithm is based on selecting the luminance and chrominance signal in the Fourier domain. This simple and efficient algorithm gives good results, comparable with the Bayesian approach to demosaicing. Additionally, this model allows us to demonstrate that the Bayer CFA is the most optimal arrangement of three colors on a square grid. Visual artifacts of the reconstruction can be clearly explained as aliasing between luminance and chrominance. Finally, this framework also allows us to control the trade-off between algorithm efficiency and quality in an explicit manner.
A formal study of information retrieval heuristics

Empirical studies of information retrieval methods show that good retrieval performance is closely related to the use of various retrieval heuristics, such as TF-IDF weighting. One basic research question is thus what exactly are these "necessary" heuristics that seem to cause good retrieval performance. In this paper, we present a formal study of retrieval heuristics. We formally define a set of basic desirable constraints that any reasonable retrieval function should satisfy, and check these constraints on a variety of representative retrieval functions. We find that none of these retrieval functions satisfies all the constraints unconditionally. Empirical results show that when a constraint is not satisfied, it often indicates non-optimality of the method, and when a constraint is satisfied only for a certain range of parameter values, its performance tends to be poor when the parameter is out of the range. In general, we find that the empirical performance of a retrieval formula is tightly related to how well it satisfies these constraints. Thus the proposed constraints provide a good explanation of many empirical observations and make it possible to evaluate any existing or new retrieval formula analytically.
Multi-View Representation Learning: A Survey from Shallow Methods to Deep Methods

Recently, multi-view representation learning has become a rapidly growing direction in machine learning and data mining areas. This paper introduces several principles for multi-view representation learning: correlation, consensus, and complementarity principles. Consequently, we first review the representative methods and theories of multi-view representation learning based on correlation principle, especially on canonical correlation analysis (CCA) and its several extensions. Then from the viewpoint of consensus and complementarity principles we investigate the advancement of multi-view representation learning that ranges from shallow methods including multi-modal topic learning, multi-view sparse coding, and multi-view latent space Markov networks, to deep methods including multi-modal restricted Boltzmann machines, multi-modal autoencoders, and multi-modal recurrent neural networks. Further, we also provide an important perspective from manifold alignment for multi-view representation learning. Overall, this survey aims to provide an insightful overview of theoretical basis and state-of-the-art developments in the field of multi-view representation learning and to help researchers find the most appropriate tools for particular applications.
Data Mining in Higher Education

Higher Education targets to develop complex theoretical, abstract and analytical reasoning capabilities in the alumni. This objective can be accomplished addressing four major steps: Theoretical foundation, practice, communication and assessment (Petry, 2002). Theoretical background and practical exercise comprise the basic knowledge building process at initial stages. Assessment guides the alumni through higher complexity studies permitting the student to identify the weak points in the knowledge building where further theory study and/or practice is required. Theoretical foundation and problem-solving practice are well known aspects in Higher Education. High-quality materials in printed and electronic format, sometimes including multimedia –audio, video or computer graphics–, sometimes delivered through the Internet are readily available today. Teaching-aids as computer-feed overhead projectors or electronic blackboards in the classroom are common place and facilitate the knowledgebuilding process. Moreover, computers in the classroom are a powerful tool linking theory and problem-solving practice in engineering studies (Beyerlein et al., 1993). On the other hand, the assessment process has been evolving slowly in the last decades. Pen-and-paper examination techniques have been translated to the computer-enabled classroom as software applications that present an exam in the screen and record the student answers. This process can be seen as an external assessment targeting to evaluate the skills of the student in order to give a pass/fail on a given subject. The external evaluation can be useful for the student in order to know the skill level, but usually fails short when the student wants to now “what’s wrong”, i.e. to know not only what question was missed but also what knowledge areas the student is finding difficulties. Weak areas identification cannot be done from a single question or set of questions. It requires the assessment process, examination or similar, to be considered as a whole. Student attention time or time spent thinking on a specific question –relative to the other question, as some students think faster than othersclearly indicates the areas where difficulties hide, by example. The pattern followed when answering the exam questions, by example, is another useful indicator. The student will try to answer first the questions he feels more comfortable with. Dubitation on the answer –change the answer several times– is another useful parameter. All these parameters and many others can be compiled and processed by the unprecedented analytic processing capabilities of modern data mining techniques. Collaborative assessment appears as a the natural evolution from the individual learning to the collaborative learning, which was proposed as a suitable technique to speed-up development of analytical reasoning as different approaches are continuously suggested
Ultra-stretchable and skin-mountable strain sensors using carbon nanotubes-Ecoflex nanocomposites.

Super-stretchable, skin-mountable, and ultra-soft strain sensors are presented by using carbon nanotube percolation network-silicone rubber nanocomposite thin films. The applicability of the strain sensors as epidermal electronic systems, in which mechanical compliance like human skin and high stretchability (ϵ > 100%) are required, has been explored. The sensitivity of the strain sensors can be tuned by the number density of the carbon nanotube percolation network. The strain sensors show excellent hysteresis performance at different strain levels and rates with high linearity and small drift. We found that the carbon nanotube-silicone rubber based strain sensors possess super-stretchability and high reliability for strains as large as 500%. The nanocomposite thin films exhibit high robustness and excellent resistance-strain dependency for over ~1380% mechanical strain. Finally, we performed skin motion detection by mounting the strain sensors on different parts of the body. The maximum induced strain by the bending of the finger, wrist, and elbow was measured to be ~ 42%, 45% and 63%, respectively.
Extension of phase correlation to subpixel registration

In this paper, we have derived analytic expressions for the phase correlation of downsampled images. We have shown that for downsampled images the signal power in the phase correlation is not concentrated in a single peak, but rather in several coherent peaks mostly adjacent to each other. These coherent peaks correspond to the polyphase transform of a filtered unit impulse centered at the point of registration. The analytic results provide a closed-form solution to subpixel translation estimation, and are used for detailed error analysis. Excellent results have been obtained for subpixel translation estimation of images of different nature and across different spectral bands.
Three Decades of Driver Assistance Systems: Review and Future Perspectives

This contribution provides a review of fundamental goals, development and future perspectives of driver assistance systems. Mobility is a fundamental desire of mankind. Virtually any society strives for safe and efficient mobility at low ecological and economic costs. Nevertheless, its technical implementation significantly differs among societies, depending on their culture and their degree of industrialization. A potential evolutionary roadmap for driver assistance systems is discussed. Emerging from systems based on proprioceptive sensors, such as ABS or ESC, we review the progress incented by the use of exteroceptive sensors such as radar, video, or lidar. While the ultimate goal of automated and cooperative traffic still remains a vision of the future, intermediate steps towards that aim can be realized through systems that mitigate or avoid collisions in selected driving situations. Research extends the state-of-the-art in automated driving in urban traffic and in cooperative driving, the latter addressing communication and collaboration between different vehicles, as well as cooperative vehicle operation by its driver and its machine intelligence. These steps are considered important for the interim period, until reliable unsupervised automated driving for all conceivable traffic situations becomes available. The prospective evolution of driver assistance systems will be stimulated by several technological, societal and market trends. The paper closes with a view on current research fields.
Optimizing F-Measure with Support Vector Machines

Support vector machines (SVMs) are regularly used for classification of unbalanced data by weighting more heavily the error contribution from the rare class. This heuristic technique is often used to learn classifiers with high F-measure, although this particular application of SVMs has not been rigorously examined. We provide significant and new theoretical results that support this popular heuristic. Specifically, we demonstrate that with the right parameter settings SVMs approximately optimize F-measure in the same way that SVMs have already been known to approximately optimize accuracy. This finding has a number of theoretical and practical implications for using SVMs in F-measure optimization.
Region growing method for the analysis of functional MRI data

Existing analytical techniques for functional magnetic resonance imaging (fMRI) data always need some specific assumptions on the time series. In this article, we present a new approach for fMRI activation detection, which can be implemented without any assumptions on the time series. Our method is based on a region growing method, which is very popular for image segmentation. A comparison of performance on fMRI activation detection is made between the proposed method and the deconvolution method and the fuzzy clustering method with receiver operating characteristic (ROC) methodology. In addition, we examine the effectiveness and usefulness of our method on real experimental data. Experimental results show that our method outperforms over the deconvolution method and the fuzzy clustering method on a number of aspects. These results suggest that our region growing method can serve as a reliable analysis of fMRI data.
Why Don't Men Ever Stop to Ask for Directions? Gender, Social Influence, and Their Role in Technology Acceptance and Usage Behavior

Masalah Begitu banyak kemajuan telah terjadi pada kemampuan perangkat keras maupun perangkat lunak untuk membantu kehidupan manusia. Meskipun begitu, masih banyak sekali masalah sistem yang tidak digunakan dengan maksimal. Oleh karena itu, salah satu isu yang sangat penting adalah bagaimana cara untuk mengerti kondisi-kondisi di mana sistem informasi diterima atau tidak diterima di dalam suatu organisasi. Sejumlah model diajukan untuk meneliti masalah ini, termasuk Technology Acceptance Model (TAM). Sayangnya dalam konteks penelitian TAM, faktor pengaruh sosial dan gender masih sangat sedikit disinggung. Padahal terdapat bukti kuat di luar domain sistem informasi bahwa kedua hal tersebut memegang peranan kunci dalam perilaku manusia untuk menggunakan teknologi. Sejauh ini, penelitian-penelitian yang ada hanya membahas perbedaan persepsi antargender, bukannya perbedaan gender yang menjadi dasar seorang manusia membuat keputusan tentang teknologi.
259 Second ring-down time and 4.45 million quality factor in 5.5 kHz fused silica birdbath shell resonator

The fused silica birdbath (BB) resonator is an axisymmetric 3D shell resonator that could be used in high-performance MEMS gyroscopes. We report a record quality factor (0 for a 5-mm-radius resonator, which is expected to reduce gyroscope bias drift. We present measurement results for two sizes of resonators with long vibratory decay time constants (τ), high Qs, and low damping asymmetry (Δτ−1) between their n = 2 wine-glass (WG) modes. We find a reduction in damping for larger resonators and a correlation between higher Q and lower as well as evidence of a lower bound on Q for resonators with low damping asymmetry.