nils-herrmann/clean_scirepeval huggingface dataset:
A generative probability model of joint label fusion for multi-atlas based brain segmentation

Automated labeling of anatomical structures in medical images is very important in many neuroscience studies. Recently, patch-based labeling has been widely investigated to alleviate the possible mis-alignment when registering atlases to the target image. However, the weights used for label fusion from the registered atlases are generally computed independently and thus lack the capability of preventing the ambiguous atlas patches from contributing to the label fusion. More critically, these weights are often calculated based only on the simple patch similarity, thus not necessarily providing optimal solution for label fusion. To address these limitations, we propose a generative probability model to describe the procedure of label fusion in a multi-atlas scenario, for the goal of labeling each point in the target image by the best representative atlas patches that also have the largest labeling unanimity in labeling the underlying point correctly. Specifically, sparsity constraint is imposed upon label fusion weights, in order to select a small number of atlas patches that best represent the underlying target patch, thus reducing the risks of including the misleading atlas patches. The labeling unanimity among atlas patches is achieved by exploring their dependencies, where we model these dependencies as the joint probability of each pair of atlas patches in correctly predicting the labels, by analyzing the correlation of their morphological error patterns and also the labeling consensus among atlases. The patch dependencies will be further recursively updated based on the latest labeling results to correct the possible labeling errors, which falls to the Expectation Maximization (EM) framework. To demonstrate the labeling performance, we have comprehensively evaluated our patch-based labeling method on the whole brain parcellation and hippocampus segmentation. Promising labeling results have been achieved with comparison to the conventional patch-based labeling method, indicating the potential application of the proposed method in the future clinical studies.
Reliability Comparison of ZrO2-Based DRAM High-k Dielectrics Under DC and AC Stress

In this paper, the time dependent dielectric breakdown behavior is investigated for production type crystalline ZrO2-based thin films under dc and ac stress. Constant voltage stress measurements over six decades in time show that the voltage acceleration of time-to-breakdown follows the conventional exponential law. The effects of ac stress on time-to-breakdown are studied in detail by changing the experimental parameters including stress voltage, base voltage, and frequency. In general, ac stressing gives rise to a gain in lifetime, which may result from less overall charge trapping. This trap dynamic was investigated by dielectric absorption measurements. Overall, the typical DRAM refresh of the capacitor leads to the most critical reliability concern.
Analysis and taxonomy of column header categories for web tables

We describe a component of a document analysis system for constructing ontologies for domain-specific web tables imported into Excel. This component automates extraction of the Wang Notation for the column header of a table. Using column-header specific rules for XY cutting we convert the geometric structure of the column header to a linear string denoting cell attributes and directions of cuts. The string representation is parsed by a context-free grammar and the parse tree is further processed to produce an abstract data-type representation (the Wang notation tree) of each column category. Experiments were carried out to evaluate this scheme on the original and edited column headers of Excel tables drawn from a collection of 200 used in our earlier work. The transformed headers were obtained by editing the original column headers to conform to the format targeted by our grammar. Forty-four original headers and their reformatted versions were submitted as input to our software system. Our grammar was able to parse and the extract Wang notation tree for all the edited headers, but for only four of the original headers. We suggest extensions to our table grammar that would enable processing a larger fraction of headers without manual editing.
Short-Range Optical Wireless Communications

It is commonly agreed that the next generation of wireless communication systems, usually referred to as 4G systems, will not be based on a single access technique but it will encompass a number of different complementary access technologies. The ultimate goal is to provide ubiquitous connectivity, integrating seamlessly operations in most common scenarios, ranging from fixed and low-mobility indoor environments in one extreme to high-mobility cellular systems in the other extreme. Surprisingly, perhaps the largest installed base of short-range wireless communications links are optical, rather than RF, however. Indeed, ‘point and shoot’ links corresponding to the Infra-Red Data Association (IRDA) standard are installed in 100 million devices a year, mainly digital cameras and telephones. In this paper we argue that optical wireless communications (OW) has a part to play in the wider 4G vision. An introduction to OW is presented, together with scenarios where optical links can enhance the performance of wireless networks.
Predicting Fluctuations in Cryptocurrency Transactions Based on User Comments and Replies

This paper proposes a method to predict fluctuations in the prices of cryptocurrencies, which are increasingly used for online transactions worldwide. Little research has been conducted on predicting fluctuations in the price and number of transactions of a variety of cryptocurrencies. Moreover, the few methods proposed to predict fluctuation in currency prices are inefficient because they fail to take into account the differences in attributes between real currencies and cryptocurrencies. This paper analyzes user comments in online cryptocurrency communities to predict fluctuations in the prices of cryptocurrencies and the number of transactions. By focusing on three cryptocurrencies, each with a large market size and user base, this paper attempts to predict such fluctuations by using a simple and efficient method.
Urdu Nastaleeq Optical Character Recognition

This paper discusses the Urdu script characteristics, Urdu Nastaleeq and a simple but a novel and robust technique to recognize the printed Urdu script without a lexicon. Urdu being a family of Arabic script is cursive and complex script in its nature, the main complexity of Urdu compound/connected text is not its connections but the forms/shapes the characters change when it is placed at initial, middle or at the end of a word. The characters recognition technique presented here is using the inherited complexity of Urdu script to solve the problem. A word is scanned and analyzed for the level of its complexity, the point where the level of complexity changes is marked for a character, segmented and feeded to Neural Networks. A prototype of the system has been tested on Urdu text and currently achieves 93.4% accuracy on the average.  Cursive Script, OCR, Urdu.
EigenRank: a ranking-oriented approach to collaborative filtering

A recommender system must be able to suggest items that are likely to be preferred by the user. In most systems, the degree of preference is represented by a rating score. Given a database of users' past ratings on a set of items, traditional collaborative filtering algorithms are based on predicting the potential ratings that a user would assign to the unrated items so that they can be ranked by the predicted ratings to produce a list of recommended items. In this paper, we propose a collaborative filtering approach that addresses the item ranking problem directly by modeling user preferences derived from the ratings. We measure the similarity between users based on the correlation between their rankings of the items rather than the rating values and propose new collaborative filtering algorithms for ranking items based on the preferences of similar users. Experimental results on real world movie rating data sets show that the proposed approach outperforms traditional collaborative filtering algorithms significantly on the NDCG measure for evaluating ranked results.
Face Recognition and Micro-expression Recognition Based on Discriminant Tensor Subspace Analysis Plus Extreme Learning Machine

In this paper, a novel recognition algorithm based on discriminant tensor subspace analysis (DTSA) and extreme learning machine (ELM) is introduced. DTSA treats a gray facial image as a second order tensor and adopts two-sided transformations to reduce dimensionality. One of the many advantages of DTSA is its ability to preserve the spatial structure information of the images. In order to deal with micro-expression video clips, we extend DTSA to a high-order tensor. Discriminative features are generated using DTSA to further enhance the classification performance of ELM classifier. Another notable contribution of the proposed method includes significant improvements in face and micro-expression recognition accuracy. The experimental results on the ORL, Yale, YaleB facial databases and CASME micro-expression database show the effectiveness of the proposed method.
Ball on plate balancing system for multi-discipline educational purposes

The paper presents a ball on plate balancing system useful for various educational purposes. A touch-screen placed on the plate is used for ball's position sensing and two servomotors are employed for balancing the plate in order to control ball's Cartesian coordinates. The design of control embedded systems is demonstrated for different control algorithms in compliance with FreeRTOS real time operating system and dsPIC33 microcontroller. On-line visualizations useful for system monitoring are provided by a PC host application connected with the embedded application. The measurements acquired during real-time execution and the parameters of the system are stored in specific data files, as support for any desired additional analysis. Taking into account the properties of this controlled system (instability, fast dynamics) and the capabilities of the embedded architecture (diversity of the involved communication protocols, diversity of employed hardware components, usage of an open source real time operating system), this educational setup allows a good illustration of numerous theoretical and practical aspects related to system engineering and applied informatics.
The Mass Production of BSI CMOS Image Sensors

Numerous pixel design and process improvements have been driven by the need to maintain or improve image quality at an ever smaller pixel size. A major development in imager technology is the mass production of Backside Illumination (BSI) technology for image sensors at low cost and high yield. The Omnivision-TSMC R&D alliance has been focused on the development of a low cost BSI technology which enables high performance at the 1.4μm pixel node. We report the performance of two BSI products both in mass production, using the same BSI 1.4μm pixel design and process. For the 1.4μm pixel, peak quantum efficiencies of 43.8%, 53.6%, 51.6% has been achieved in the red, green and blue channels respectively, with low crosstalk, excellent Gb/Gr performance, no lag, no FPN, 2.3e total read noise, dark current of 27 e/sec at 50°C, and low white pixel defect density. For 1.75μm pixel products, the baseline BSI process has been re-optimized to achieve peak QE of 53%, 60.2%, and 60.4% in the red, green, and blue channels, respectively. This modified process also meets mass production targets for the three 1.75μm BSI products. Our reliability testing has found no reliability issue associated with OmniBSITM architecture.
Toward learning better metrics for sequence generation training with policy gradient

Designing a metric manually for unsupervised sequence generation tasks, such as text generation, is essentially difficult. In a such situation, learning a metric of a sequence from data is one possible solution. The previous study, SeqGAN, proposed the framework for unsupervised sequence generation, in which a metric is learned from data, and a generator is optimized with regard to the learned metric with policy gradient, inspired by generative adversarial nets (GANs) and reinforcement learning. In this paper, we make two proposals to learn better metric than SeqGAN’s: partial reward function and expert-based reward function training. The partial reward function is a reward function for a partial sequence of a certain length. SeqGAN employs a reward function for completed sequence only. By combining long-scale and short-scale partial reward functions, we expect a learned metric to be able to evaluate a partial correctness as well as a coherence of a sequence, as a whole. In expert-based reward function training, a reward function is trained to discriminate between an expert (or true) sequence and a fake sequence that is produced by editing an expert sequence. Expert-based reward function training is not a kind of GAN frameworks. This makes the optimization of the generator easier. We examine the effect of the partial reward function and expert-based reward function training on synthetic data and real text data, and show improvements over SeqGAN and the model trained with MLE. Specifically, whereas SeqGAN gains 0.42 improvement of NLL over MLE on synthetic data, our best model gains 3.02 improvement, and whereas SeqGAN gains 0.029 improvement of BLEU over MLE, our best model gains 0.250 improvement.
Determinants of repurchase intention in online group-buying: The perspectives of DeLone & McLean IS success model and trust

The purpose of this study is to propose a theoretical model to examine the antecedents of repurchase intention in online group-buying by integrating the perspective of DeLone & McLean IS success model and the literature of trust. The model was tested using the data collected from 253 customers of a group-buying website in Taiwan. The results show that satisfaction with website, satisfaction with sellers, and perceived quality of website have positive influences on repurchase intention, while perceived quality of website and perceived quality of sellers have significant impacts on satisfaction with website and satisfaction with sellers, respectively. The results also show that trust in website has positive influences on perceived quality of website and satisfaction with website, whereas trust in sellers influence perceived quality of sellers and satisfaction with sellers significantly. Finally, the results show that perceived size of website has positive influence on trust in website, while reputation of website and reputation of sellers significantly affect trust in website and trust in sellers, respectively. The implications for theory and practice and suggestions for future research are also discussed.
A leakage reduction methodology for distributed MTCMOS

Multithreshold CMOS (MTCMOS) circuits reduce standby leakage power with low delay overhead. Most MTCMOS designs cut off the power to large blocks of logic using large sleep transistors. Locally distributing sleep devices has remained less popular even though it has several advantages described in this paper. However, locally placed sleep devices are only feasible if sneak leakage currents are prevented. This paper makes two contributions to leakage reduction. First, we examine the causes of sneak leakage paths and propose a design methodology that enables local insertion of sleep devices for sequential and combinational circuits. A set of design rules allows designers to prevent most sneak leakage paths. A fabricated 0.13-/spl mu/m, dual V/sub T/ test chip employs our methodology to implement a low-power FPGA architecture with gate-level sleep FETs and over 8/spl times/ measured standby current reduction. Second, we describe the implementation and benefits of local sleep regions in our design and examine the interfacing issues for this technique. Local sleep regions reduce leakage in unused circuit components at a local level while the surrounding circuits remain active. Measured results show that local sleep regions reduce leakage in active configurable logic blocks (CLBs) on our chip by up to 2.2/spl times/ (measured) based on configuration.
Under-optimized smart contracts devour your money

Smart contracts are full-fledged programs that run on blockchains (e.g., Ethereum, one of the most popular blockchains). In Ethereum, gas (in Ether, a cryptographic currency like Bitcoin) is the execution fee compensating the computing resources of miners for running smart contracts. However, we find that under-optimized smart contracts cost more gas than necessary, and therefore the creators or users will be overcharged. In this work, we conduct the first investigation on Solidity, the recommended compiler, and reveal that it fails to optimize gas-costly programming patterns. In particular, we identify 7 gas-costly patterns and group them to 2 categories. Then, we propose and develop GASPER, a new tool for automatically locating gas-costly patterns by analyzing smart contracts' bytecodes. The preliminary results on discovering 3 representative patterns from 4,240 real smart contracts show that 93.5%, 90.1% and 80% contracts suffer from these 3 patterns, respectively.
Effects of Code Obfuscation on Android App Similarity Analysis

Code obfuscation is a technique to transform a program into an equivalent one that is harder to be reverse engineered and understood. On Android, well-known obfuscation techniques are shrinking, optimization, renaming, string encryption, control flow transformation, etc. On the other hand, adversaries may also maliciously use obfuscation techniques to hide pirated or stolen software. If pirated software were obfuscated, it would be difficult to detect software theft. To detect illegal software transformed by code obfuscation, one possible approach is to measure software similarity between original and obfuscated programs and determine whether the obfuscated version is an illegal copy of the original version. In this paper, we analyze empirically the effects of code obfuscation on Android app similarity analysis. The empirical measurements were done on five different Android apps with DashO obfuscator. Experimental results show that similarity measures at bytecode level are more effective than those at source code level to analyze software similarity.
The Scyther Tool: Verification, Falsification, and Analysis of Security Protocols

With the rise of the Internet and other open networks, a large number of security protocols have been developed and deployed in order to provide secure communication. The analysis of such security protocols has turned out to be extremely difficult for humans, as witnessed by the fact that many protocols were found to be flawed after deployment. This has driven the research in formal analysis of security protocols. Unfortunately, there are no effective approaches yet for constructing correct and efficient protocols, and work on concise formal logics that might allow one to easily prove that a protocol is correct in a formal model, is still ongoing. The most effective approach so far has been automated falsification or verification of such protocols with state-of-the-art tools such as ProVerif  or the Avispa tools . These tools have shown to be effective at finding attacks on protocols (Avispa) or establishing correctness of protocols (ProVerif). In this paper we present a push-button tool, called Scyther, for the verification, the falsification, and the analysis of security protocols. Scyther can be freely downloaded, and provides a number of novel features not offered by other tools, as well as state-of-the-art performance. Novel features include the possibility of unbounded verification with guaranteed termination, analysis of infinite sets of traces in terms of patterns, and support for multi-protocol analysis. Scyther is based on a pattern refinement algorithm, providing concise representations of (infinite) sets of traces. This allows the tool to assist in the analysis of classes of attacks and possible protocol behaviours, or to prove correctness for an unbounded number of protocol sessions. The tool has been successfully applied in both research and teaching.
Jype - a program visualization and programming exercise tool for Python

Based on research into learning programming and a review of program visualization research, we designed an educational software tool that aims to target students' apparent fragile knowledge of elementary programming which manifests as difficulties in tracing and writing even simple programs. Most existing tools build on a single supporting technology and focus on one aspect of learning. For example, visualization tools support the development of a conceptual-level understanding of how programs work, and automatic assessment tools give feedback on submitted tasks. We implemented a combined tool that closely integrates programming tasks with visualizations of program execution and thus lets students practice writing code and more easily transition to visually tracing it in order to locate programming errors. In this paper we present Jype, a web-based tool that provides an environment for visualizing the line-by-line execution of Python programs and for solving programming exercises with support for immediate automatic feedback and an integrated visual debugger. Moreover, the debugger allows stepping back in the visualization of the execution as if executing in reverse. Jype is built for Python, when most research in programming education support tools revolves around Java.
A Novel Dual-Band Circularly Polarized Antenna Based on Electromagnetic Band-Gap Structure

In this letter, a dual-band polarization-dependent electromagnetic band-gap (PDEBG) surface is designed as a reflector of a dual-band dipole antenna to realize dual-band circular polarization. Experimental and numerical results verify the concept of the low-profile dual-band circularly polarized antenna. The antenna features 10-dB return-loss bandwidths of 13.4% and 3.2% and 3-dB axial-ratio bandwidths of 2.4% and 3.5% in low and high frequency bands, respectively. The proposed antenna can present the same or different circular polarization in two bands based on different designs of the PDEBG.
GPS based bus tracking system

In this fast life, everyone is in hurry to reach their destinations. In this case waiting for the buses is not reliable. People who rely on the public transport their major concern is to know the real time location of the bus for which they are waiting for and the time it will take to reach their bus stop. This information helps people in making better travelling decisions. This paper gives the major challenges in the public transport system and discuses various approaches to intelligently manage it. Current position of the bus is acquired by integrating GPS device on the bus and coordinates of the bus are sent by either GPRS service provided by GSM networks or SMS or RFID. GPS device is enabled on the tracking device and this information is sent to centralized control unit or directly at the bus stops using RF receivers. This system is further integrated with the historical average speeds of each segment. This is done to improve the accuracy by including the factors like volume of traffic, crossings in each segment, day and time of day. People can track information using LEDs at bus stops, SMS, web application or Android application. GPS coordinates of the bus when sent to the centralized server where various arrival time estimation algorithms are applied using historical speed patterns.