nils-herrmann/clean_scirepeval huggingface dataset:
DeepTAM: Deep Tracking and Mapping

We present a system for keyframe-based dense camera tracking and depth map estimation that is entirely learned. For tracking, we estimate small pose increments between the current camera image and a synthetic viewpoint. This significantly simplifies the learning problem and alleviates the dataset bias for camera motions. Further, we show that generating a large number of pose hypotheses leads to more accurate predictions. For mapping, we accumulate information in a cost volume centered at the current depth estimate. The mapping network then combines the cost volume and the keyframe image to update the depth prediction, thereby effectively making use of depth measurements and image-based priors. Our approach yields state-of-the-art results with few images and is robust with respect to noisy camera poses. We demonstrate that the performance of our 6 DOF tracking competes with RGB-D tracking algorithms.We compare favorably against strong classic and deep learning powered dense depth algorithms.
The CRISPR-associated DNA-cleaving enzyme Cpf1 also processes precursor CRISPR RNA

CRISPR–Cas systems that provide defence against mobile genetic elements in bacteria and archaea have evolved a variety of mechanisms to target and cleave RNA or DNA. The well-studied types I, II and III utilize a set of distinct CRISPR-associated (Cas) proteins for production of mature CRISPR RNAs (crRNAs) and interference with invading nucleic acids. In types I and III, Cas6 or Cas5d cleaves precursor crRNA (pre-crRNA) and the mature crRNAs then guide a complex of Cas proteins (Cascade-Cas3, type I; Csm or Cmr, type III) to target and cleave invading DNA or RNA. In type II systems, RNase III cleaves pre-crRNA base-paired with trans-activating crRNA (tracrRNA) in the presence of Cas9 (refs 13, 14). The mature tracrRNA–crRNA duplex then guides Cas9 to cleave target DNA. Here, we demonstrate a novel mechanism in CRISPR–Cas immunity. We show that type V-A Cpf1 from Francisella novicida is a dual-nuclease that is specific to crRNA biogenesis and target DNA interference. Cpf1 cleaves pre-crRNA upstream of a hairpin structure formed within the CRISPR repeats and thereby generates intermediate crRNAs that are processed further, leading to mature crRNAs. After recognition of a 5′-YTN-3′ protospacer adjacent motif on the non-target DNA strand and subsequent probing for an eight-nucleotide seed sequence, Cpf1, guided by the single mature repeat-spacer crRNA, introduces double-stranded breaks in the target DNA to generate a 5′ overhang. The RNase and DNase activities of Cpf1 require sequence- and structure-specific binding to the hairpin of crRNA repeats. Cpf1 uses distinct active domains for both nuclease reactions and cleaves nucleic acids in the presence of magnesium or calcium. This study uncovers a new family of enzymes with specific dual endoribonuclease and endonuclease activities, and demonstrates that type V-A constitutes the most minimalistic of the CRISPR–Cas systems so far described.
Obfuscation of abstract data-types

An obfuscation is a behaviour-preserving program transformation whose aim is to make a program “harder to understand”. Obfuscations are applied to make reverse engineering of a program more difficult. Two concerns about an obfuscation are whether it preserves behaviour (i.e. it is correct) and the degree to which it maintains efficiency. Obfuscations are applied mainly to objectoriented programs but constructing proofs of correctness for such obfuscations is a challenging task. It is desirable to have a workable definition of obfuscation which is more rigorous than the metric-based definition of Collberg et al. and overcomes the impossibility result of Barak et al. for their strong cryptographic definition. We present a fresh approach to obfuscation by obfuscating abstract datatypes allowing us to develop structure-dependent obfuscations that would otherwise (traditionally) not be available. We regard obfuscation as data refinement enabling us to produce equations for proving correctness and we model the datatype operations as functional programs making our proofs easy to construct. For case studies, we examine different data-types exploring different areas of computer science. We consider lists letting us to capture array-based obfuscations, sets reflecting specification based software engineering, trees demonstrating standard programming techniques and as an example of numerical methods we consider matrices. Our approach has the following benefits: obfuscations can be proved correct; obfuscations for some operations can be derived and random obfuscations can be produced (so that different program executions give rise to different obfuscations). Accompanying the approach is a new definition of obfuscation based on measuring the effectiveness of an obfuscation. Furthermore in our case studies the computational complexity of each obfuscated operations is comparable with the complexity of the unobfuscated version. Also, we give an example of how our approach can be applied to implement imperative obfuscations.
The beauty of simplicity

In this paper we show how discussion about design quality or even usability is often discussion about aesthetics. First, we introduce some definitions of beauty in aesthetics. Then, we introduce how the beauty of the Web design affects the feeling of online trust, and bring forth observations about this design quality as an aesthetic notion. As a result, we present how simplicity and beauty affect the user's experience and interpretation of the design, and contemplate on how this perception may vary according to cultural background, age, and the amount of user experience. We will also envision some future trends for the aesthetics of user interfaces.
Detecting friendly, flirtatious, awkward, and assertive speech in speed-dates

Automatically detecting human social intentions and attitudes from spoken conversation is an important task for speech processing nd social computing. We describe a system for detecting interpersonal stance: whether a speaker is flirtatious, friendly, awkward, or ssertive. We make use of a new spoken corpus of over 1000 4-min speed-dates. Participants rated themselves and their interlocutors or these interpersonal stances, allowing us to build detectors for style both as interpreted by the speaker and as perceived by the earer. We use lexical, prosodic, and dialog features in an SVM classifier to detect very clear styles (the strongest 10% in each stance) ith up to 75% accuracy on previously seen speakers (50% baseline) and up to 59% accuracy on new speakers (48% baseline). feature analysis suggests that flirtation is marked by joint focus on the woman as a target of the conversation, awkwardness by ecreased speaker involvement, and friendliness by a conversational style including other-directed laughter and appreciations. Our ork has implications for our understanding of interpersonal stance, their linguistic expression, and their automatic extraction.
Capacitive Biopotential Measurement for Electrophysiological Signal Acquisition: A Review

A conventional clinical biopotential measurement system requires the use of wet electrodes that are in contact with skin using conductive gel. Those systems can acquire high-quality signals but may not be feasible for a long-term purpose due to the skin irritation and allergic contact dermatitis. The emerging of human-centered monitoring calls for alternative solutions. Recent technological advances enable capacitive measurement of electrophysiological measurement, which can acquire signals through cloth or even with an air gap; however, difficulties still exist. This paper systemically reviews the recent progress in capacitive measurement from electrode design, analog front end to high-level system architecture, aiming to provide comprehensive and practical instructions for entire system design. The current development serves as the solid fundamental, raising new solutions for future improvement. In addition, the challenges and the strategies are highlighted in order to demonstrate a technical guide. Some perspectives based on our experience are also discussed hoping to inspire new idea and technique in this area.
Aiming to Know You Better Perhaps Makes Me a More Engaging Dialogue Partner

There have been several attempts to define a plausible motivation for a chit-chat dialogue agent that can lead to engaging conversations. In this work, we explore a new direction where the agent specifically focuses on discovering information about its interlocutor. We formalize this approach by defining a quantitative metric. We propose an algorithm for the agent to maximize it. We validate the idea with human evaluation where our system outperforms various baselines. We demonstrate that the metric indeed correlates with the human judgments of engagingness.
Coherent intrinsic images from photo collections

An intrinsic image is a decomposition of a photo into an illumination layer and a reflectance layer, which enables powerful editing such as the alteration of an object's material independently of its illumination. However, decomposing a single photo is highly under-constrained and existing methods require user assistance or handle only simple scenes. In this paper, we compute intrinsic decompositions using several images of the same scene under different viewpoints and lighting conditions. We use multi-view stereo to automatically reconstruct 3D points and normals from which we derive relationships between reflectance values at different locations, across multiple views and consequently different lighting conditions. We use robust estimation to reliably identify reflectance ratios between pairs of points. From these, we infer constraints for our optimization and enforce a coherent solution across multiple views and illuminations. Our results demonstrate that this constrained optimization yields high-quality and coherent intrinsic decompositions of complex scenes. We illustrate how these decompositions can be used for image-based illumination transfer and transitions between views with consistent lighting.
Collaborative matrix factorization with multiple similarities for predicting drug-target interactions

We address the problem of predicting new drug-target interactions from three inputs: known interactions, similarities over drugs and those over targets. This setting has been considered by many methods, which however have a common problem of allowing to have only one similarity matrix over drugs and that over targets. The key idea of our approach is to use more than one similarity matrices over drugs as well as those over targets, where weights over the multiple similarity matrices are estimated from data to automatically select similarities, which are effective for improving the performance of predicting drug-target interactions. We propose a factor model, named Multiple Similarities Collaborative Matrix Factorization(MSCMF), which projects drugs and targets into a common low-rank feature space, which is further consistent with weighted similarity matrices over drugs and those over targets. These two low-rank matrices and weights over similarity matrices are estimated by an alternating least squares algorithm. Our approach allows to predict drug-target interactions by the two low-rank matrices collaboratively and to detect similarities which are important for predicting drug-target interactions. This approach is general and applicable to any binary relations with similarities over elements, being found in many applications, such as recommender systems. In fact, MSCMF is an extension of weighted low-rank approximation for one-class collaborative filtering. We extensively evaluated the performance of MSCMF by using both synthetic and real datasets. Experimental results showed nice properties of MSCMF on selecting similarities useful in improving the predictive performance and the performance advantage of MSCMF over six state-of-the-art methods for predicting drug-target interactions.
A robust model for paper reviewer assignment

Automatic expert assignment is a common problem encountered in both industry and academia. For example, for conference program chairs and journal editors, in order to collect "good" judgments for a paper, it is necessary for them to assign the paper to the most appropriate reviewers. Choosing appropriate reviewers of course includes a number of considerations such as expertise and authority, but also diversity and avoiding conflicts. In this paper, we explore the expert retrieval problem and implement an automatic paper-reviewer recommendation system that considers aspects of expertise, authority, and diversity. In particular, a graph is first constructed on the possible reviewers and the query paper, incorporating expertise and authority information. Then a Random Walk with Restart (RWR)  model is employed on the graph with a sparsity constraint, incorporating diversity information. Extensive experiments on two reviewer recommendation benchmark datasets show that the proposed method obtains performance gains over state-of-the-art reviewer recommendation systems in terms of expertise, authority, diversity, and, most importantly, relevance as judged by human experts.
Hydrogenated Amorphous Silicon Thin-Film Transistor-Based Optical Pixel Sensor With High Sensitivity Under Ambient Illumination

This letter develops an optical pixel sensor that is based on hydrogenated amorphous silicon thin-film transistors. Exploiting the photo sensitivity of the photo TFTs and combining different color filters, the proposed sensor can sense an optical input signal of a specified color under high ambient illumination conditions. Measurements indicate that the proposed pixel sensor effectively reacts to the optical input signal under light intensities from 873 to 12,910 lux, proving that the sensor is highly reliable under strong ambient illumination.
Static Analysis for Security

source-code security analysis with static analysis tools. Since ITS4's release in early 2000 (www.cigital.com/its4/), the idea of detecting security problems through source code has come of age. ITS4 is extremely simple—the tool basically scans through a file looking for syntactic matches based on several simple " rules " that might indicate possible security vulnera-bilities (for example, use of str-cpy() should be avoided). Much better approaches exist. Programmers make little mistakes all the time—a missing semicolon here, an extra parenthesis there. Most of the time, these gaffes are inconsequential; the compiler notes the error, the programmer fixes the code, and the development process continues. This quick cycle of feedback and response stands in sharp contrast to what happens with most security vulner-abilities, which can lie dormant (sometimes for years) before discovery. The longer a vulnerability lies dormant, the more expensive it can be to fix, and adding insult to injury, the programming community has a long history of repeating the same security-related mistakes. The promise of static analysis is to identify many common coding problems automatically before a program is released. Static analysis tools examine the text of a program statically, without attempting to execute it. Theoretically , they can examine either a pro-gram's source code or a compiled form of the program to equal benefit , although the problem of decoding the latter can be difficult. We'll focus on source code analysis here because that's where the most mature technology exists. Manual auditing, a form of static analysis, is very time-consuming , and to do it effectively, human code auditors must first know what security vulnerabilities look like before they can rigorously examine the code. Static analysis tools compare favorably to manual audits because they're faster, which means they can evaluate programs much more frequently, and they encapsulate security knowledge in a way that doesn't require the tool operator to have the same level of security expertise as a human auditor. Just as a programmer can rely on a compiler to consistently enforce the finer points of language syntax, the operator of a good static analysis tool can successfully apply that tool without being aware of the finer points of security bugs. Testing for security vulnerabili-ties is complicated by the fact that they often exist in hard-to-reach states or crop up in unusual circumstances. Static analysis tools can peer into more of a program's dark corners with less fuss than …
EMPLOYEE TRAINING AND DEVELOPMENT AND THE LEARNING ORGANIZATION UDC 331 . 363

The global competition and swiftness of changes emphasize the importance of human capital within organizations, as well as the swiftness and ways of knowledge gaining of that capital. In the economy where uncertainty is the only certainty, knowledge is becoming a reliable source of sustained competitive advantage. Knowledge is becoming basic capital and the trigger of development. Previously built on foundations of possessing specific resources and low costs, present day competition is based on knowledge possessing and efficient knowledge management. Modern organizations, therefore use their resources (money, time, energy, information, etc.) for permanent training and advancement of their employees. Organizations which are constantly creating new knowlegde, extending it through the entire organization and implementing it quickly inside the new technologies, develop good products and excellent services. These activities determine the company as a learning organization with constant innovation being its sole business. These are organizations which realize that learning and new knowledge are becoming the key of success, and that education is crucial for abundance.
Accelerating the Lee-Seung Algorithm for Nonnegative Matrix Factorization

Approximate nonnegative matrix factorization is an emerging technique with a wide spectrum of potential applications in data analysis. Currently, the most-used algorithms for this problem are those proposed by Lee and Seung [7]. In this paper we present a variation of one of the Lee-Seung algorithms with a notably improved performance. We also show that algorithms of this type do not necessarily converge to local minima.
Managing Churn to Maximize Profits

Working papers are in draft form. This working paper is distributed for purposes of comment and discussion only. It may not be reproduced without permission of the copyright holder. Copies of working papers are available from the author. Please do not copy or distribute without explicit permission of the authors.  Customer defection or churn is a widespread phenomenon that threatens firms across a variety of industries with dramatic financial consequences. To tackle this problem, companies are developing sophisticated churn management strategies. These strategies typically involve two steps – ranking customers based on their estimated propensity to churn, and then offering retention incentives to a subset of customers at the top of the churn ranking. The implicit assumption is that this process would maximize firm's profits by targeting customers who are most likely to churn. However, current marketing research and practice aims at maximizing the correct classification of churners and non-churners. Profit from targeting a customer depends on not only a customer's propensity to churn, but also on her spend or value, her probability of responding to retention offers, as well as the cost of these offers. Overall profit of the firm also depends on the number of customers the firm decides to target for its retention campaign. We propose a predictive model that accounts for all these elements. Our optimization algorithm uses stochastic gradient boosting, a state-of-the-art numerical algorithm based on stage-wise gradient descent. It also determines the optimal number of customers to target. The resulting optimal customer ranking and target size selection leads to, on average, a 115% improvement in profit compared to current methods. Remarkably, the improvement in profit comes along with more prediction errors in terms of which customers will churn. However, the new loss function leads to better predictions where it matters the most for the company's profits. For a company like Verizon Wireless, this translates into a profit increase of at least $28 million from a single retention campaign, without any additional implementation cost.
Autonomous Crowdsourcing through Human-Machine Collaborative Learning

In this paper, we introduce a general iterative human-machine collaborative method for training crowdsource workers: the classifier (i.e., the machine) selects the highest quality examples for training the crowdsource workers (i.e., the humans). Then, the latter annotate the lower quality examples such that the classifier can be re-trained with more accurate examples. This process can be iterated several times. We tested our approach on two different tasks, Relation Extraction and Community Question Answering, which are also in two different languages, English and Arabic, respectively. Our experimental results show a significant improvement for creating Gold Standard data over distant supervision or just crowdsourcing without worker training. At the same time, our method approach the performance than state-of-the-art methods using expensive Gold Standard for training workers
From Security to Assurance in the Cloud: A Survey

The cloud computing paradigm has become a mainstream solution for the deployment of business processes and applications. In the public cloud vision, infrastructure, platform, and software services are provisioned to tenants (i.e., customers and service providers) on a pay-as-you-go basis. Cloud tenants can use cloud resources at lower prices, and higher performance and flexibility, than traditional on-premises resources, without having to care about infrastructure management. Still, cloud tenants remain concerned with the cloud’s level of service and the nonfunctional properties their applications can count on. In the last few years, the research community has been focusing on the nonfunctional aspects of the cloud paradigm, among which cloud security stands out. Several approaches to security have been described and summarized in general surveys on cloud security techniques. The survey in this article focuses on the interface between cloud security and cloud security assurance. First, we provide an overview of the state of the art on cloud security. Then, we introduce the notion of cloud security assurance and analyze its growing impact on cloud security approaches. Finally, we present some recommendations for the development of next-generation cloud security and assurance solutions.
Brain Tumour Extraction from Mri Images Using Matlab

Automatic detection of tumors in medical images is motivated by the necessity of high accuracy when dealing with a human life. Also, the computer assistance is demanded in medical institutions due to the fact that it could improve the results of humans in such a domain where the false negative cases must be at a very low rate. Processing of Magnetic Resonance Imaging (MRI) images is one of the techniques to diagnose the brain tumor. This paper describes the strategy to detect and extract brain tumor from patient’s MRI scan images. First it takes the name and age of a person and then MRI brain image is used for tumor detection process. It includes pre-processing, segmentation, morphological operation, watershed segmentation and calculation of the tumor area and determination of the tumor location. The user interface of the application is developed using Graphical User Interface (GUI) developing environment of Matrix Laboratory (MATLAB).