nils-herrmann/clean_scirepeval huggingface dataset:
Robust parameters for automatic segmentation of speech

Automatic segmentation of speech is an important problem that is useful in speech recognition, synthesis and coding. We explore in this paper, the robust parameter set, weighting function and distance measure for reliable segmentation of noisy speech. It is found that the MFCC parameters, successful in speech recognition. holds the best promise for robust segmentation also. We also explored a variety of symmetric and asymmetric weighting lifters. from which it is found that a symmetric lifter of the form 1 + A sin1/2(πn/L), 0 ≤ n ≤ L − 1, for MFCC dimension L, is most effective. With regard to distance measure, the direct L2 norm is found adequate.
A CMOS time-to-digital converter with better than 10 ps single-shot precision

A high-precision CMOS time-to-digital converter IC has been designed. Time interval measurement is based on a counter and two-level interpolation realized with stabilized delay lines. Reference recycling in the delay line improves the integral nonlinearity of the interpolator and enables the use of a low frequency reference clock. Multi-level interpolation reduces the number of delay elements and registers and lowers the power consumption. The load capacitor scaled parallel structure in the delay line permits very high resolution. An INL look-up table reduces the effect of the remaining nonlinearity. The digitizer measures time intervals from 0 to 204 /spl mu/s with 8.1 ps rms single-shot precision. The resolution of 12.2 ps from a 5-MHz external reference clock is divided by means of only 20 delay elements.
The Eval That Men Do - A Large-Scale Study of the Use of Eval in JavaScript Applications

Transforming text into executable code with a function such as JavaScript’s eval endows programmers with the ability to extend applications, at any time, and in almost any way they choose. But, this expressive power comes at a price: reasoning about the dynamic behavior of programs that use this feature becomes challenging. Any ahead-of-time analysis, to remain sound, is forced to make pessimistic assumptions about the impact of dynamically created code. This pessimism affects the optimizations that can be applied to programs and significantly limits the kinds of errors that can be caught statically and the security guarantees that can be enforced. A better understanding of how eval is used could lead to increased performance and security. This paper presents a large-scale study of the use of eval in JavaScript-based web applications. We have recorded the behavior of 337 MB of strings given as arguments to 550,358 calls to the eval function exercised in over 10,000 web sites. We provide statistics on the nature and content of strings used in eval expressions, as well as their provenance and data obtained by observing their dynamic behavior. eval is evil. Avoid it. eval has aliases. Don’t use them. —
Optimal Coding and Allocation for Perfect Secrecy in Multiple Clouds

For a user to store data in the cloud, using services provided by multiple cloud storage providers (CSPs) is a promising approach to increase the level of data availability and confidentiality, as it is unlikely that different CSPs are out of service at the same time or collude with each other to extract information of a user. This paper investigates the problem of storing data reliably and securely in multiple CSPs constrained by given budgets with minimum cost. Previous works, with variations in problem formulations, typically tackle the problem by decoupling it into sub-problems and solve them separately. While such a decoupling approach is simple, the resultant solution is suboptimal. This paper is the first one which considers the problem as a whole and derives a jointly optimal coding and storage allocation scheme, which achieves perfect secrecy with minimum cost. The analytical result reveals that the optimal coding scheme is the nested maximum-distance-separable code and the optimal amount of data to be stored in the CSPs exhibits a certain structure. The exact parameters of the code and the exact storage amount to each CSP can be determined numerically by simple 2-D search.
Cellulose digestion in primitive hexapods: Effect of ingested antibiotics on gut microbial populations and gut cellulase levels in the firebrat,Thermobia domestica (Zygentoma, Lepismatidae)

Antibiotic feeding studies were conducted on the firebrat,Thermobia domestica (Zygentoma, Lepismatidae) to determine if the insect's gut cellulases were of insect or microbial origin. Firebrats were fed diets containing either nystatin, metronidazole, streptomycin, tetracycline, or an antibiotic cocktail consisting of all four antibiotics, and then their gut microbial populations and gut cellulase levels were monitored and compared with the gut microbial populations and gut cellulase levels in firebrats feeding on antibiotic-free diets. Each antibiotic significantly reduced the firebrat's gut micro-flora. Nystatin reduced the firebrat's viable gut fungi by 89%. Tetracycline and the antibiotic cocktail reduced the firebrat's viable gut bacteria by 81% and 67%, respectively, and metronidazole, streptomycin, tetracycline, and the antibiotic cocktail reduced the firebrat's total gut flora by 35%, 32%, 55%, and 64%, respectively. Although antibiotics significantly reduced the firebrat's viable and total gut flora, gut cellulase levels in firebrats fed antibiotics were not significantly different from those in firebrats on an antibiotic-free diet. Furthermore, microbial populations in the firebrat's gut decreased significantly over time, even in firebrats feeding on the antibiotic-free diet, without corresponding decreases in gut cellulase levels. Based on this evidence, we conclude that the gut cellulases of firebrats are of insect origin. This conclusion implies that symbiont-independent cellulose digestion is a primitive trait in insects and that symbiont-mediated cellulose digestion is a derived condition.
Learning to Generate Time-Lapse Videos Using Multi-stage Dynamic Generative Adversarial Networks

Taking a photo outside, can we predict the immediate future, e.g., how would the cloud move in the sky? We address this problem by presenting a generative adversarial network (GAN) based two-stage approach to generating realistic time-lapse videos of high resolution. Given the first frame, our model learns to generate long-term future frames. The first stage generates videos of realistic contents for each frame. The second stage refines the generated video from the first stage by enforcing it to be closer to real videos with regard to motion dynamics. To further encourage vivid motion in the final generated video, Gram matrix is employed to model the motion more precisely. We build a large scale time-lapse dataset, and test our approach on this new dataset. Using our model, we are able to generate realistic videos of up to 128 Ã— 128 resolution for 32 frames. Quantitative and qualitative experiment results demonstrate the superiority of our model over the state-of-the-art models.
An End-to-End Goal-Oriented Dialog System with a Generative Natural Language Response Generation

Recently advancements in deep learning allowed the development of endto-end trained goal-oriented dialog systems. Although these systems already achieve good performance, some simplifications limit their usage in real-life scenarios. In this work, we address two of these limitations: ignoring positional information and a fixed number of possible response candidates. We propose to use positional encodings in the input to model the word order of the user utterances. Furthermore, by using a feedforward neural network, we are able to generate the output word by word and are no longer restricted to a fixed number of possible response candidates. Using the positional encoding, we were able to achieve better accuracies in the Dialog bAbI Tasks and using the feedforward neural network for generating the response, we were able to save computation time and space consumption.
Fault Diagnostics in industrial application domains using data mining and artificial intelligence technologies and frameworks

Fault Diagnostics and Prognostics has been an increasing interest in recent years, as a result of the increased degree of automation and the growing demand for higher performance, efficiency, reliability and safety in industrial systems. On-line fault detection and isolation methods have been developed for automated processes. These methods include data mining methodologies, artificial intelligence methodologies or combinations of the two. Data Mining is the statistical approach of extracting knowledge from data. Artificial Intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs. Activities in AI include searching, recognizing patterns and making logical inferences. This paper focuses on the various techniques used for Fault Diagnostics and Prognostics in Industry application domains.
Optimization of a Drum Type Washing Machine By Analytical and Computational Assessment

Eco Efficient systems are the need of the hour in the market. This paper proposes an idea for the optimization of a washing machine in terms of reduction in drum vibration, power consumption and water consumption. The complete model of the washing machine is modeled in Solid-Works 2010 and its modified model in terms of eco-efficiency with respect to water consumption is proposed in this research. Also in this research, a mathematical model is formulated for reducing the drum vibration and an improved drum design is proposed to further reduce the vibrations. Based on the values obtained from the mathematical model, the Finite Element Anlaysis of the old and the new model is performed in Solid-Works Cosmos software and it is observed that the drum displacement reduced to a considerable extent in the new model. By replacing the electromechanically controlled motor by a three phase induction motor, the power consumption (mainly speed, torque and power) is also reduced. Thus in nutshell, the water consumption, drum vibration and the power consumption is reduced which led to an overall optimization of the washing machine.
Predicting Thorax Diseases with NIH Chest X-Rays

The use of diagnostic imaging has increased dramatically in recent years. A substantial number are chest xrays used to diagnose a plethora of conditions. These diagnoses are still primarily done by radiologists manually poring over each scan, with no automated triaging or assistance. We aim to use deep learning to predict thorax disease categories using chest x-rays and their metadata with greater than first-pass specialist accuracy. Our problem can be cast as a multiclass image classification problem with 15 different labels. The paper provides a proof of concept of an automated chest x-ray diagnosis system by utilizing the NIH dataset. Deep learning is used to improve the multiclass classification accuracy of thorax disease classification, measured against a baseline of softmax regression.
Modeling the IEC 61000-4-4 EFT Injection Clamp

The paper analyzes the test setup required by the International Electrotechnical Commission (IEC) 61000-4-4 to evaluate the immunity of electronic equipment to electrical fast transients (EFTs), and proposes an electrical model of the capacitive coupling clamp, which is employed to add disturbances to nominal signals. The study points out limits on accuracy of this model, and shows how it can be fruitfully employed to predict the interference waveform affecting nominal system signals through computer simulations.
Javert: fully automatic mining of general temporal properties from dynamic traces

Program specifications are important for many tasks during software design, development, and maintenance. Among these, temporal specifications are particularly useful. They express formal correctness requirements of an application's ordering of specific actions and events during execution, such as the strict alternation of acquisition and release of locks. Despite their importance, temporal specifications are often missing, incomplete, or described only informally. Many techniques have been proposed that mine such specifications from execution traces or program source code. However, existing techniques mine only simple patterns, or they mine a single complex pattern that is restricted to a particular set of manually selected events. There is no practical, automatic technique that can mine general temporal properties from execution traces.
 In this paper, we present Javert, the first general specification mining framework that can learn, fully automatically, complex temporal properties from execution traces. The key insight behind Javert is that real, complex specifications can be formed by composing instances of small generic patterns, such as the alternating pattern ((ab)) and the resource usage pattern ((ab c)). In particular, Javert learns simple generic patterns and composes them using sound rules to construct large, complex specifications. We have implemented the algorithm in a practical tool and conducted an extensive empirical evaluation on several open source software projects. Our results are promising; they show that Javert is scalable, general, and precise. It discovered many interesting, nontrivial specifications in real-world code that are beyond the reach of existing automatic techniques.
A protocol for data exploration to avoid common statistical problems

1. While teaching statistics to ecologists, the lead authors of this paper have noticed common statistical problems. If a random sample of their work (including scientific papers) produced before doing these courses were selected, half would probably contain violations of the underlying assumptions of the statistical techniques employed. 2. Some violations have little impact on the results or ecological conclusions; yet others increase type I or type II errors, potentially resulting in wrong ecological conclusions. Most of these violations can be avoided by applying better data exploration. These problems are especially troublesome in applied ecology, where management and policy decisions are often at stake. 3. Here, we provide a protocol for data exploration; discuss current tools to detect outliers, heterogeneity of variance, collinearity, dependence of observations, problems with interactions, double zeros in multivariate analysis, zero inflation in generalized linear modelling, and the correct type of relationships between dependent and independent variables; and provide advice on how to address these problems when they arise. We also address misconceptions about normality, and provide advice on data transformations. 4. Data exploration avoids type I and type II errors, among other problems, thereby reducing the chance of making wrong ecological conclusions and poor recommendations. It is therefore essential for good quality management and policy based on statistical analyses.
Processing complex aggregate queries over data streams

Recent years have witnessed an increasing interest in designing algorithms for querying and analyzing streaming data (i.e., data that is seen only once in a fixed order) with only limited memory. Providing (perhaps approximate) answers to queries over such continuous data streams is a crucial requirement for many application environments; examples include large telecom and IP network installations where performance data from different parts of the network needs to be continuously collected and analyzed.In this paper, we consider the problem of approximately answering general aggregate SQL queries over continuous data streams with limited memory. Our method relies on randomizing techniques that compute small "sketch" summaries of the streams that can then be used to provide approximate answers to aggregate queries with provable guarantees on the approximation error. We also demonstrate how existing statistical information on the base data (e.g., histograms) can be used in the proposed framework to improve the quality of the approximation provided by our algorithms. The key idea is to intelligently partition the domain of the underlying attribute(s) and, thus, decompose the sketching problem in a way that provably tightens our guarantees. Results of our experimental study with real-life as well as synthetic data streams indicate that sketches provide significantly more accurate answers compared to histograms for aggregate queries. This is especially true when our domain partitioning methods are employed to further boast the accuracy of the final estimates.
A comparative study of epidemiological investigations of malaria outbreaks and related deaths in two districts of Jharkhand during the same prewinter season using shoe-leather epidemiology

Following news of deaths in two districts of Jharkhand (West Singhbum and Garhwa) in November 2016, epidemiological investigations were contemplated to investigate any current outbreak of falciparum malaria and deaths attributed to it.



The epidemiological investigations, verbal autopsy of suspected deaths attributed to malaria and keys interviews were conducted in the 2nd and 4th week of November 2016 in Khuntpani and Dhurki block of West Singhbum and Garhwa districts, respectively, following a strict protocol.



The affected villages were Argundi and Korba-Pahariya and their adjacent tolas in Khuntpani and Dhurki block. Undoubtedly, there was the continuous transmission of falciparum malaria in both the regions in October and November 2016. The total cases (according to case definitions) were 1002, of them, 338 and 12 patients were positive for Plasmodium falciparum positive (Pf +ve) and Plasmodium vivax positive (Pv +ve), respectively, in the affected areas of Khuntpani block. In Dhurki block, out of the total of 631 patients fulfilling the case definition, 65 patients were PF +ve and 23 Pv +ve. Comparing to the last year, there is remarkably high number of falciparum cases. Verbal autopsy of deceased individuals showed that malaria might be one of the strongly probable diagnoses, but not conclusively.



According to epidemiological investigation, verbal autopsy and key interviews conducted, it may be concluded that there is a definite outbreak of falciparum malaria in the area and environment is congenial for malaria and other tropical diseases.
Design and Simulation of Multi Channel UART for Serial Communication

UART (Universal Asynchronous Receiver Transmitter) is used for serial communication. It is used for long distance and low cost process for transfer of data between pc and its devices. In general a UART operated with specific baud rate. To meet the complex communication demands it is not sufficient. To overcome this difficulty a multi channel UART is proposed in this paper. And the whole design is simulated with modelsim and synthesized with Xilinx software
Reverse Engineering CAPTCHAs

CAPTCHAs are automated Turing tests used to determine if the end-user is human and not an automated program. Users are asked to read and answer Visual CAPTCHAs, which often appear as bitmaps of text characters, in order to gain access to a low-cost resource such as webmail or a blog. CAPTCHAs are generated by software and the structure of a CAPTCHA gives hints to its implementation. Thus due to these properties of image processing and image composition, the process that creates CAPTCHAs can often be reverse engineered. Once the implementation strategy of a family of CAPTCHAs has been reverse engineered the CAPTCHA instances may be solved automatically by leveraging weaknesses in the creation process or by comparing a CAPTCHA's output against itself. In this paper, we present a case study where we reverse engineer and solve real-world CAPTCHAs using simple image processing techniques such as bitmap comparison, thresholding, fill-flood segmentation, dilation, and erosion. We present black-box and white-box methodologies for reverse engineering and solving CAPTCHAs. As well we provide an open source toolkit for solving CAPTCHAs that we have used with a success rates of 99, 95, 61, 30%, and 27% on hundreds of CAPTCHAs from five real-world examples.
FaceForensics: A Large-scale Video Dataset for Forgery Detection in Human Faces

With recent advances in computer vision and graphics, it is now possible to generate videos with extremely realistic synthetic faces, even in real time. Countless applications are possible, some of which raise a legitimate alarm, calling for reliable detectors of fake videos. In fact, distinguishing between original and manipulated video can be a challenge for humans and computers alike, especially when the videos are compressed or have low resolution, as it often happens on social networks. Research on the detection of face manipulations has been seriously hampered by the lack of adequate datasets. To this end, we introduce a novel face manipulation dataset of about half a million edited images (from over 1000 videos). The manipulations have been generated with a state-of-the-art face editing approach. It exceeds all existing video manipulation datasets by at least an order of magnitude. Using our new dataset, we introduce benchmarks for classical image forensic tasks, including classification and segmentation, considering videos compressed at various quality levels. In addition, we introduce a benchmark evaluation for creating indistinguishable forgeries with known ground truth; for instance with generative refinement models.
Suction helps in a pinch: Improving underwater manipulation with gentle suction flow

Pinching is an important capability for mobile robots handling small items or tools. Successful pinching requires force-closure and, in underwater applications, gentle suction flow at the fingertips can dramatically improve the handling of light objects by counteracting the negative effects of water lubrication and enhancing friction. In addition, monitoring the flow gives a measure of suction-engagement and can act as a binary tactile sensor. Although a suction system adds complexity, elastic tubes can double as passive spring elements for desired finger kinematics.
Ultrathin silicon solar microcells for semitransparent, mechanically flexible and microconcentrator module designs.

The high natural abundance of silicon, together with its excellent reliability and good efficiency in solar cells, suggest its continued use in production of solar energy, on massive scales, for the foreseeable future. Although organics, nanocrystals, nanowires and other new materials hold significant promise, many opportunities continue to exist for research into unconventional means of exploiting silicon in advanced photovoltaic systems. Here, we describe modules that use large-scale arrays of silicon solar microcells created from bulk wafers and integrated in diverse spatial layouts on foreign substrates by transfer printing. The resulting devices can offer useful features, including high degrees of mechanical flexibility, user-definable transparency and ultrathin-form-factor microconcentrator designs. Detailed studies of the processes for creating and manipulating such microcells, together with theoretical and experimental investigations of the electrical, mechanical and optical characteristics of several types of module that incorporate them, illuminate the key aspects.