nils-herrmann/clean_scirepeval huggingface dataset:
Evolution of Visual Odometry Techniques

With rapid advancements in the area of mobile robotics and industrial automation, a growing need has arisen towards accurate navigation and localization of moving objects. Camera based motion estimation is one such technique which is gaining huge popularity owing to its simplicity and use of limited resources in generating motion path. In this paper, an attempt is made to introduce this topic for beginners covering different aspects of vision based motion estimation task. The evolution of VO schemes over last few decades is discussed under two broad categories, that is, geometric and non-geometric approaches. The geometric approaches are further detailed under three different classes, that is, feature-based, appearance-based, and a hybrid of feature and appearance based schemes. The non-geometric approach is one of the recent paradigm shift from conventional pose estimation technique and is thus discussed in a separate section. Towards the end, a list of different datasets for visual odometry and allied research areas are provided for a
E-RECRUITMENT: A ROADMAP TOWARDS E- HUMAN RESOURCE MANAGEMENT

The only vital value for an enterprise is the experience, skills, innovativeness and insights of its people. Human resources are the key components in every organization. It represents total knowledge, talent, and attitude, creative ability, aptitude and belief of an individual involved in the affairs of an organization. Management of human resources is an integral part for every concern. It is associated with the people at work and their relationships within and outside the enterprise. Recruitment of efficient staff is one of the important activities as it generates the human capital for the concern. In the recent years, the field of human resource management has undergone numerous technological advancements. Internet has made an impact on the overall functioning of human resource department. HR processes and procedures have been supported by everything from complicated file-folder systems to automation, going from usage multiple systems and databases to a single version of the whole system. It has progressed with frequent innovations viz; Human Resource Information System, Virtual Human Resources and Electronic Human Resource Management (E-HRM) etc. E-HRM means conducting of business transactions by using internet along with other technologies. In other words, E-HRM is a way of implementing HRM strategies, policies and practices in an organization through a directed support of web technology based channels. It influences every area of human resource management. E-Recruitment refers to posting vacancies on the corporate website or on an online recruitment vendors’ website. It allows applicants to send their resumes electronically through an email or in some other electronic format. The e-recruitment methods and systems have helped to reduce much of the routine administrative tasks involved in recruitment. The study tries to identify the overall concept of e-recruitment. It aims at collecting information regarding methods viz; e-mails, corporate websites and commercial job boards etc. of e-recruitment. It includes the general advantages and disadvantages of e-recruitment.
On minimizing budget and time in influence propagation over social networks

In recent years, study of influence propagation in social networks has gained tremendous attention. In this context, we can identify three orthogonal dimensions—the number of seed nodes activated at the beginning (known as budget), the expected number of activated nodes at the end of the propagation (known as expected spread or coverage), and the time taken for the propagation. We can constrain one or two of these and try to optimize the third. In their seminal paper, Kempe et al. constrained the budget, left time unconstrained, and maximized the coverage: this problem is known as Influence Maximization (or MAXINF for short). In this paper, we study alternative optimization problems which are naturally motivated by resource and time constraints on viral marketing campaigns. In the first problem, termed minimum target set selection (or MINTSS for short), a coverage threshold η is given and the task is to find the minimum size seed set such that by activating it, at least η nodes are eventually activated in the expected sense. This naturally captures the problem of deploying a viral campaign on a budget. In the second problem, termed MINTIME, the goal is to minimize the time in which a predefined coverage is achieved. More precisely, in MINTIME, a coverage threshold η and a budget threshold k are given, and the task is to find a seed set of size at most k such that by activating it, at least η nodes are activated in the expected sense, in the minimum possible time. This problem addresses the issue of timing when deploying viral campaigns. Both these problems are NP-hard, which motivates our interest in their approximation. For MINTSS, we develop a simple greedy algorithm and show that it provides a bicriteria approximation. We also establish a generic hardness result suggesting that improving this bicriteria approximation is likely to be hard. For MINTIME, we show that even bicriteria and tricriteria approximations are hard under several conditions. We show, however, that if we allow the budget for number of seeds k to be boosted by a logarithmic factor and allow the coverage to fall short, then the problem can be solved exactly in PTIME, i.e., we can achieve the required coverage within the time achieved by the optimal solution to MINTIME with budget k and coverage threshold η. Finally, we establish the value of the approximation algorithms, by conducting an experimental evaluation, comparing their quality against that achieved by various heuristics.
Stacked Dense U-Nets with Dual Transformers for Robust Face Alignment

Facial landmark localisation in images captured in-the-wild is an important and challenging problem. The current state-of-the-art revolves around certain kinds of Deep Convolutional Neural Networks (DCNNs) such as stacked U-Nets and Hourglass networks. In this work, we innovatively propose stacked dense U-Nets for this task. We design a novel scale aggregation network topology structure and a channel aggregation building block to improve the model’s capacity without sacrificing the computational complexity and model size. With the assistance of deformable convolutions inside the stacked dense U-Nets and coherent loss for outside data transformation, our model obtains the ability to be spatially invariant to arbitrary input face images. Extensive experiments on many in-the-wild datasets, validate the robustness of the proposed method under extreme poses, exaggerated expressions and heavy occlusions. Finally, we show that accurate 3D face alignment can assist pose-invariant face recognition where we achieve a new stateof-the-art accuracy on CFP-FP (98.514%).
Deep Learning Methods on Recommender System: A Survey of State-of-the-art

The advancement in technology accelerated and opened availability of various alternatives to make a choice in every domain. In the era of big data it is a tedious and time consuming task to evaluate the features of a large amount of information provided to make a choice. One solution to ease this overload problem is building recommender system that can process a large amount of data and support users’ decision making ability. In this paper different traditional recommendation techniques, deep learning approaches for recommender system and survey of deep learning techniques on recommender system are presented. A variety of techniques have been proposed to perform recommendation, including content based, collaborative and hybrid recommenders. Due to the limitation of the traditional recommendation methods in obtaining accurate result a deep learning approach is introduced both for collaborative and content based
Deep Voice: Real-time Neural Text-to-Speech

We present Deep Voice, a production-quality text-to-speech system constructed entirely from deep neural networks. Deep Voice lays the groundwork for truly end-to-end neural speech synthesis. The system comprises five major building blocks: a segmentation model for locating phoneme boundaries, a grapheme-tophoneme conversion model, a phoneme duration prediction model, a fundamental frequency prediction model, and an audio synthesis model. For the segmentation model, we propose a novel way of performing phoneme boundary detection with deep neural networks using connectionist temporal classification (CTC) loss. For the audio synthesis model, we implement a variant of WaveNet that requires fewer parameters and trains faster than the original. By using a neural network for each component, our system is simpler and more flexible than traditional text-tospeech systems, where each component requires laborious feature engineering and extensive domain expertise. Finally, we show that inference with our system can be performed faster than real time and describe optimized WaveNet inference kernels on both CPU and GPU that achieve up to 400x speedups over existing implementations.
A Blockchain Research Framework - What We (don't) Know, Where We Go from Here, and How We Will Get There

While blockchain technology is commonly considered potentially disruptive in various regards, there is a lack of understanding where and how blockchain technology is effectively applicable and where it has mentionable practical effects. This issue has given rise to critical voices that judge the technology as over-hyped. Against this backdrop, this study adapts an established research framework to structure the insights of the current body of research on blockchain technology, outline the present research scope as well as disregarded topics, and sketch out multidisciplinary research approaches. The framework differentiates three groups of activities (design and features, measurement and value, management and organization) at four levels of analysis (users and society, intermediaries, platforms, firms and industry). The review shows that research has predominantly focused on technological questions of design and features, while neglecting application, value creation, and governance. In order to foster substantial blockchain research that addresses meaningful questions, this study identifies several avenues for future studies. Given the breadth of open questions, it shows where research can benefit from multidisciplinary collaborations and presents data sources as starting points for empirical investigations.
Learning Grounded Meaning Representations with Autoencoders

In this paper we address the problem of grounding distributional representations of lexical meaning. We introduce a new model which uses stacked autoencoders to learn higher-level embeddings from textual and visual input. The two modalities are encoded as vectors of attributes and are obtained automatically from text and images, respectively. We evaluate our model on its ability to simulate similarity judgments and concept categorization. On both tasks, our approach outperforms baselines and related models.
Supervised Deep Features for Software Functional Clone Detection by Exploiting Lexical and Syntactical Information in Source Code

Software clone detection, aiming at identifying out code fragments with similar functionalities, has played an important role in software maintenance and evolution. Many clone detection approaches have been proposed. However, most of them represent source codes with hand-crafted features using lexical or syntactical information, or unsupervised deep features, which makes it difficult to detect the functional clone pairs, i.e., pieces of codes with similar functionality but differing in both syntactical and lexical level. In this paper, we address the software functional clone detection problem by learning supervised deep features. We formulate the clone detection as a supervised learning to hash problem and propose an end-to-end deep feature learning framework called CDLH for functional clone detection. Such framework learns hash codes by exploiting the lexical and syntactical information for fast computation of functional similarity between code fragments. Experiments on software clone detection benchmarks indicate that the CDLH approach is effective and outperforms the state-of-the-art approaches in software functional clone detection.
Automatic Extraction of Femur Contours from Hip X-Ray Images

Extraction of bone contours from x-ray images is an important first step in computer analysis of medical images. It is more complex than the segmentation of CT and MR images because the regions delineated by bone contours are highly nonuniform in intensity and texture. Classical segmentation algorithms based on homogeneity criteria are not applicable. This paper presents a model-based approach for automatically extracting femur contours from hip x-ray images. The method works by first detecting prominent features, followed by registration of the model to the x-ray image according to these features. Then the model is refined using active contour algorithm to get the accurate result. Experiments show that this method can extract the contours of femurs with regular shapes, despite variations in size, shape and orientation.
An Empirical Study of Namecoin and Lessons for Decentralized Namespace Design

Secure decentralized namespaces have recently become possible due to cryptocurrency technology. They enable a censorship-resistant domainname system outside the control of any single entity, among other applications. Namecoin, a fork of Bitcoin, is the most prominent example. We initiate the study of decentralized namespaces and the market for names in such systems. Our extensive empirical analysis of Namecoin reveals a system in disrepair. Indeed, our methodology for detecting “squatted” and otherwise inactive domains reveals that among Namecoin’s roughly 120,000 registered domain names, a mere 28 are not squatted and have nontrivial content. Further, we develop techniques for detecting transfers of domains in the Namecoin block chain and provide evidence that the market for domains is thin-to-nonexistent. We argue that the state of the art in mechanism design for decentralized namespace markets is lacking. We propose a model of utility of different names to different participants, and articulate desiderata of a decentralized namespace in terms of this utility function. We use this model to explore the design space of mechanisms and analyze the trade-offs.
Ceramic Laminate Veneers : Materials Advances and Selection

Porcelain laminate veneers have been a common treatment strategy in dental clinics. It is a conservative method for treatment of esthetic and functional problems in anterior region of oral cavity. Wide range of dental ceramics is now available on market for fabrication of laminate veneers. Clinician should have enough knowledge regarding the composition and properties of these materials in order to be able to choose the appropriate one according to clinical situations.
STRUCTURED WIKI WITH ANNOTATION FOR KNOWLEDGE MANAGEMENT: AN APPLICATION TOCULTURAL HERITAGE

In this paper, we highlight how semantic wikis can be relevant solutions for building cooperative data driven applications in domains characterized by a rapid evolution of knowledge. We will point out the semantic capabilities of annotated databases and structured wikis to provide better quality of content, to support complex queries and finally to carry on different type of users. Then we compare database application development with wiki for domains that encompass evolving knowledge. We detail the architecture of WikiBridge, a semantic wiki, which integrates templates forms and allows complex annotations as well as consistency checking. We describe the archaeological CARE project, and explain the conceptual modeling approach. A specific section is dedicated to ontology design, which is the compulsory foundational knowledge for the application. We finally report related works of the semantic wiki use for archaeological projects.
The YouTube-8M Kaggle Competition: Challenges and Methods

We took part in the YouTube-8M Video Understanding Challenge hosted on Kaggle, and achieved the 10th place within less than one month’s time. In this paper, we present an extensive analysis and solution to the underlying machine-learning problem based on frame-level data, where major challenges are identified and corresponding preliminary methods are proposed. It’s noteworthy that, with merely the proposed strategies and uniformlyaveraging multi-crop ensemble was it sufficient for us to reach our ranking1. We also report the methods we believe to be promising but didn’t have enough time to train to convergence. We hope this paper could serve, to some extent, as a review and guideline of the YouTube-8M multi-label video classification benchmark, inspiring future attempts and research.
Physical Design Obfuscation of Hardware: A Comprehensive Investigation of Device and Logic-Level Techniques

The threat of hardware reverse engineering is a growing concern for a large number of applications. A main defense strategy against reverse engineering is hardware obfuscation. In this paper, we investigate physical obfuscation techniques, which perform alterations of circuit elements that are difficult or impossible for an adversary to observe. The examples of such stealthy manipulations are changes in the doping concentrations or dielectric manipulations. An attacker will, thus, extract a netlist, which does not correspond to the logic function of the device-under-attack. This approach of camouflaging has garnered recent attention in the literature. In this paper, we expound on this promising direction to conduct a systematic end-to-end study of the VLSI design process to find multiple ways to obfuscate a circuit for hardware security. This paper makes three major contributions. First, we provide a categorization of the available physical obfuscation techniques as it pertains to various design stages. There is a large and multidimensional design space for introducing obfuscated elements and mechanisms, and the proposed taxonomy is helpful for a systematic treatment. Second, we provide a review of the methods that have been proposed or in use. Third, we present recent and new device and logic-level techniques for design obfuscation. For each technique considered, we discuss feasibility of the approach and assess likelihood of its detection. Then we turn our focus to open research questions, and conclude with suggestions for future research directions.
Mining Educational Data Using Classification to Decrease Dropout Rate of Students

In the last two decades, number of Higher Education Institutions (HEI) grows rapidly in India. Since most of the institutions are opened in private mode therefore, a cut throat competition rises among these institutions while attracting the student to got admission. This is the reason for institutions to focus on the strength of students not on the quality of education. This paper presents a data mining application to generate predictive models for engineering student’s dropout management. Given new records of incoming students, the predictive model can produce short accurate prediction list identifying students who tend to need the support from the student dropout program most. The results show that the machine learning algorithm is able to establish effective predictive model from the existing student dropout data.  Data Mining, Machine Learning Algorithms, Dropout Management and Predictive Models
Line-sweep: Cross-ratio for wide-baseline matching and 3D reconstruction

We propose a simple and useful idea based on cross-ratio constraint for wide-baseline matching and 3D reconstruction. Most existing methods exploit feature points and planes from images. Lines have always been considered notorious for both matching and reconstruction due to the lack of good line descriptors. We propose a method to generate and match new points using virtual lines constructed using pairs of keypoints, which are obtained using standard feature point detectors. We use cross-ratio constraints to obtain an initial set of new point matches, which are subsequently used to obtain line correspondences. We develop a method that works for both calibrated and uncalibrated camera configurations. We show compelling line-matching and large-scale 3D reconstruction.
Supervised random walks: predicting and recommending links in social networks

Predicting the occurrence of links is a fundamental problem in networks. In the link prediction problem we are given a snapshot of a network and would like to infer which interactions among existing members are likely to occur in the near future or which existing interactions are we missing. Although this problem has been extensively studied, the challenge of how to effectively combine the information from the network structure with rich node and edge attribute data remains largely open.
 We develop an algorithm based on Supervised Random Walks that naturally combines the information from the network structure with node and edge level attributes. We achieve this by using these attributes to guide a random walk on the graph. We formulate a supervised learning task where the goal is to learn a function that assigns strengths to edges in the network such that a random walker is more likely to visit the nodes to which new links will be created in the future. We develop an efficient training algorithm to directly learn the edge strength estimation function.
 Our experiments on the Facebook social graph and large collaboration networks show that our approach outperforms state-of-the-art unsupervised approaches as well as approaches that are based on feature extraction.
Biometric Template Security

Biometric recognition offers a reliable solution to the problem of user authentication in identity management systems. With the widespread deployment of biometric systems in various applications, there are increasing concerns about the security and privacy of biometric technology. Public acceptance of biometrics technology will depend on the ability of system designers to demonstrate that these systems are robust, have low error rates, and are tamper proof. We present a high-level categorization of the various vulnerabilities of a biometric system and discuss countermeasures that have been proposed to address these vulnerabilities. In particular, we focus on biometric template security which is an important issue because, unlike passwords and tokens, compromised biometric templates cannot be revoked and reissued. Protecting the template is a challenging task due to intrauser variability in the acquired biometric traits. We present an overview of various biometric template protection schemes and discuss their advantages and limitations in terms of security, revocability, and impact on matching accuracy. A template protection scheme with provable security and acceptable recognition performance has thus far remained elusive. Development of such a scheme is crucial as biometric systems are beginning to proliferate into the core physical and information infrastructure of our society.