nils-herrmann/clean_scirepeval huggingface dataset:
Learning Rich Features from RGB-D Images for Object Detection and Segmentation

In this paper we study the problem of object detection for RGB-D images using semantically rich image and depth features. We propose a new geocentric embedding for depth images that encodes height above ground and angle with gravity for each pixel in addition to the horizontal disparity. We demonstrate that this geocentric embedding works better than using raw depth images for learning feature representations with convolutional neural networks. Our final object detection system achieves anaverageprecision of 37.3%,which is a56%relative improvement over existing methods. We then focus on the task of instance segmentation where we label pixels belonging to object instances found by our detector. For this task, we propose a decision forest approach that classifies pixels in the detection window as foreground or background using a family of unary and binary tests that query shape and geocentric pose features. Finally, we use the output from our object detectors in an existing superpixel classification framework for semantic scene segmentation and achieve a 24% relative improvement over current state-of-the-art for the object categories that we study.We believe advances such as those represented in this paper will facilitate the use of perception in fields like robotics.
Chemical Similarity Searching

This paper reviews the use of similarity searching in chemical databases. It begins by introducing the concept of similarity searching, differentiating it from the more common substructure searching, and then discusses the current generation of fragment-based measures that are used for searching chemical structure databases. The next sections focus upon two of the principal characteristics of a similarity measure: the coefficient that is used to quantify the degree of structural resemblance between pairs of molecules and the structural representations that are used to characterize molecules that are being compared in a similarity calculation. New types of similarity measure are then compared with current approaches, and examples are given of several applications that are related to similarity searching.
Extending the Lambda Calculus to Express Randomized and Quantumized Algorithms

This paper introduces a formal metalanguage called the lambda-q calculus for the specification of quantum programming languages. This metalanguage is an extension of the lambda calculus, which provides a formal setting for the specification of classical programming languages. As an intermediary step, we introduce a formal metalanguage called the lambdap calculus for the specification of programming languages that allow true random number generation. We demonstrate how selected randomized algorithms can be programmed directly in the lambda-p calculus. We also demonstrate how satisfiability can be efficiently solved in the lambda-q calculus.
Leases: An Efficient Fault-Tolerant Mechanism for Distributed File Cache Consistency

Caching introduces the overhead and complexity of ensuring consistency, reducing some of its performance benefits. In a distributed system, caching must deal with the additional complications of communication and host failures.
Leases are proposed as a time-based mechanism that provides efficient consistent access to cached data in distributed systems. Non-Byzantine failures affect performance, not correctness, with their effect minimized by short leases. An analytic model and an evaluation for file access in the V system show that leases of short duration provide good performance. The impact of leases on performance grows more significant in systems of larger scale and higher processor performance.
Field-split parallel architecture for high performance multi-match packet classification using FPGAs

Multi-match packet classification is a critical function in network intrusion detection systems (NIDS), where all matching rules for a packet need to be reported. Most of the previous work is based on ternary content addressable memories (TCAMs) which are expensive and are not scalable with respect to clock rate, power consumption, and circuit area. This paper studies the characteristics of real-life Snort NIDS rule sets, and proposes a novel SRAM-based architecture. The proposed architecture is called field-split parallel bit vector (FSBV) where some header fields of a packet are further split into bit-level subfields. Unlike previous multi-match packet classification algorithms which suffer from memory explosion, the memory requirement of FSBV is linear in the number of rules. FPGA technology is exploited to provide high throughput and to support dynamic updates. Implementation results show that our architecture can store on a single Xilinx Virtex-5 FPGA the full set of packet header rules extracted from the latest Snort NIDS and sustains 100 Gbps throughput for minimum size (40 bytes) packets. The design achieves 1.25× improvement in throughput while the power consumption is approximately one fourth that of the state-of-the-art solutions.
Visual Semantic Landmark-Based Robust Mapping and Localization for Autonomous Indoor Parking

Autonomous parking in an indoor parking lot without human intervention is one of the most demanded and challenging tasks of autonomous driving systems. The key to this task is precise real-time indoor localization. However, state-of-the-art low-level visual feature-based simultaneous localization and mapping systems (VSLAM) suffer in monotonous or texture-less scenes and under poor illumination or dynamic conditions. Additionally, low-level feature-based mapping results are hard for human beings to use directly. In this paper, we propose a semantic landmark-based robust VSLAM for real-time localization of autonomous vehicles in indoor parking lots. The parking slots are extracted as meaningful landmarks and enriched with confidence levels. We then propose a robust optimization framework to solve the aliasing problem of semantic landmarks by dynamically eliminating suboptimal constraints in the pose graph and correcting erroneous parking slots associations. As a result, a semantic map of the parking lot, which can be used by both autonomous driving systems and human beings, is established automatically and robustly. We evaluated the real-time localization performance using multiple autonomous vehicles, and an repeatability of 0.3 m track tracing was achieved at a 10 kph of autonomous driving.
Confluence: conformity influence in large social networks

Conformity is a type of social influence involving a change in opinion or behavior in order to fit in with a group. Employing several social networks as the source for our experimental data, we study how the effect of conformity plays a role in changing users' online behavior. We formally define several major types of conformity in individual, peer, and group levels. We propose Confluence model to formalize the effects of social conformity into a probabilistic model. Confluence can distinguish and quantify the effects of the different types of conformities. To scale up to large social networks, we propose a distributed learning method that can construct the Confluence model efficiently with near-linear speedup. Our experimental results on four different types of large social networks, i.e., Flickr, Gowalla, Weibo and Co-Author, verify the existence of the conformity phenomena. Leveraging the conformity information, Confluence can accurately predict actions of users. Our experiments show that Confluence significantly improves the prediction accuracy by up to 5-10% compared with several alternative methods.
Design and analysis of slotted antenna array for 5G application

A compact 1 × 4 slotted antenna array for 5G application is designed and analyzed. The designed antenna consist of a power divider network and a rectangular shaped slot etched at bottom in ground plane. The dimension of this antenna is 29.9 × 28.7 × 0.13 mm3. The substrate is used as Rogers RO3003 having dielectric constant of 3 and loss tangent (tan δ) of 0.013. Performance analysis of the antenna array is carried out by changing the length of the slots. This shows the improvement in values of return loss, bandwidth and gain. The maximum impedance bandwidth is obtained as 1270 MHz at slot length of 4.01 mm. The directivity and gain of the antenna is improved with the changed length of slots at a constant width 0.45 mm. This antenna array can be used for 5G application.
Joint VM placement and routing for data center traffic engineering

Today's data centers need efficient traffic management to improve resource utilization in their networks. In this work, we study a joint tenant (e.g., server or virtual machine) placement and routing problem to minimize traffic costs. These two complementary degrees of freedom-placement and routing-are mutually-dependent, however, are often optimized separately in today's data centers. Leveraging and expanding the technique of Markov approximation, we propose an efficient online algorithm in a dynamic environment under changing traffic loads. The algorithm requires a very small number of virtual machine migrations and is easy to implement in practice. Performance evaluation that employs the real data center traffic traces under a spectrum of elephant and mice flows, demonstrates a consistent and significant improvement over the benchmark achieved by common heuristics.
Provisioning Software-Defined IoT Cloud Systems

Cloud computing is ever stronger converging with the Internet of Things (IoT) offering novel techniques for IoT infrastructure virtualization and its management on the cloud. However, system designers and operations managers face numerous challenges to realize IoT cloud systems in practice, mainly due to the complexity involved with provisioning large-scale IoT cloud systems and diversity of their requirements in terms of IoT resources consumption, customization of IoT capabilities and runtime governance. In this paper, we introduce the concept of software-defined IoT units - a novel approach to IoT cloud computing that encapsulates fine-grained IoT resources and IoT capabilities in well-defined APIs in order to provide a unified view on accessing, configuring and operating IoT cloud systems. Our software-defined IoT units are the fundamental building blocks of software-defined IoT cloud systems. We present our framework for dynamic, on-demand provisioning and deploying such software-defined IoT cloud systems. By automating provisioning processes and supporting managed configuration models, our framework simplifies provisioning and enables flexible runtime customizations of software-defined IoT cloud systems. We demonstrate its advantages on a real-world IoT cloud system for managing electric fleet vehicles.
Graph SLAM based mapping for AGV localization in large-scale warehouses

The operation of industrial Automated Guided Vehicles (AGV) today requires designated infrastructure and readily available maps for their localization. In logistics, high effort and investment is necessary to enable the introduction of AGVs. Within the SICK AG coordinated EU-funded research project PAN-Robots we aim to reduce the installation time and costs dramatically by semi-automated plant exploration and localization based on natural landmarks. In this paper, we present our current mapping and localization results based on measurement data acquired at the site of our project partner Coca-Cola Iberian Partners in Bilbao, Spain. We evaluate our solution in terms of accuracy of the map, i.e. comparing landmark position estimates with a ground truth map of millimeter accuracy. The localization results are shown based on artificial landmarks as well as natural landmarks (gridmaps) based on the same graph based optimization solution.
The Neural Autoregressive Distribution Estimator

We describe a new approach for modeling the distribution of high-dimensional vectors of discrete variables. This model is inspired by the restricted Boltzmann machine (RBM), which has been shown to be a powerful model of such distributions. However, an RBM typically does not provide a tractable distribution estimator, since evaluating the probability it assigns to some given observation requires the computation of the so-called partition function, which itself is intractable for RBMs of even moderate size. Our model circumvents this difficulty by decomposing the joint distribution of observations into tractable conditional distributions and modeling each conditional using a non-linear function similar to a conditional of an RBM. Our model can also be interpreted as an autoencoder wired such that its output can be used to assign valid probabilities to observations. We show that this new model outperforms other multivariate binary distribution estimators on several datasets and performs similarly to a large (but intractable) RBM.
Childhood Adversity and Factors Determining Resilience among Undergraduate Students

Childhood adversity experiences, when not properly managed may result to anti-social behaviour, health risk or psychological problems like severe depression, suicidal behavior in undergraduate students. Hence the study examined childhood adversity experiences and its factors determining resilience among undergraduate students in Oyo State. The study adopted a descriptive survey research; multi-stage random sampling technique was used to select 341 undergraduate students of Ladoke Akintola University of Technology, Ogbomosho and Oyo State. Two research instruments were used to test childhood adversity scale and factors determining resilience (protective family, birth order, community protective factors). The study formulated four research hypotheses which were tested with Pearson Product moment correlation analysis and multiple regression analysis at coefficient level of 0.05. The result of the analysis revealed that level of Adverse Childhood Experience (ACE) among respondents is slightly low with half of the respondents between the ages of 18 years to 20 years. The prominent ACE identified by respondents is physical assaults, home with incidence of substance abuse, victim of sexual abuse, and humiliation from parents. The level of resilience among the respondents is moderate. There was a positive significant relationship between childhood adversity and lifetime resilience undergraduate student in Oyo State (r = 0.272), protective family factor was also associated with lifetime resilience undergraduate student in Oyo State (r = 0.018) there was a significant relationship between birth order and lifetime resilience undergraduate student in Oyo State. (r = 0.794) there was a significant relationship between community protective factor and lifetime resilience undergraduate student o in Oyo State (r = 0.835). The magnitude of relative contribution showed protective family factor (β = 0.987), had significant relative contribution; community protective factor (β = 0.762), had significant relative contribution; childhood adversity (β = 0.724), had significant relative contribution; and then birth order (β = 0.687), had significant relative contribution to resilience of the undergraduate students. Educational institutions should tackle issues on educator-student relationships through various channels, especially social work, counseling units and students affairs department. There is need for initial psychosocial assessment for fresher to test resilience and adverse childhood experiences, mental health service should be contextualize in health care service for undergraduate students, the parents should be educated on the impact of childhood adversity on wellbeing and academic performance of undergraduate students in Nigeria.
Crowdfunding : tapping the right crowd

The basic idea of crowdfunding is to raise external finance from a large audience (the “crowd”), where each individual provides a very small amount, instead of soliciting a small group of sophisticated investors. The paper develops a model that associates crowdfunding with pre-ordering and price discrimination, and studies the conditions under which crowdfunding is preferred to traditional forms of external funding. Compared to traditional funding, crowdfunding has the advantage of offering an enhanced experience to some consumers and, thereby, of allowing the entrepreneur to practice menu pricing and extract a larger share of the consumer surplus; the disadvantage is that the entrepreneur is constrained in his/her choice of prices by the amount of capital that he/she needs to raise: the larger this amount, the more prices have to be twisted so as to attract a large number of “crowdfunders” who pre-order, and the less profitable the menu pricing scheme.
Bandit Structured Prediction for Learning from Partial Feedback in Statistical Machine Translation

We present an approach to structured prediction from bandit feedback, called Bandit Structured Prediction, where only the value of a task loss function at a single predicted point, instead of a correct structure, is observed in learning. We present an application to discriminative reranking in Statistical Machine Translation (SMT) where the learning algorithm only has access to a 1 − BLEU loss evaluation of a predicted translation instead of obtaining a gold standard reference translation. In our experiment bandit feedback is obtained by evaluating BLEU on reference translations without revealing them to the algorithm. This can be thought of as a simulation of interactive machine translation where an SMT system is personalized by a user who provides single point feedback to predicted translations. Our experiments show that our approach improves translation quality and is comparable to approaches that employ more informative feedback in learning.
Autonomous reinforcement learning on raw visual input data in a real world application

We propose a learning architecture, that is able to do reinforcement learning based on raw visual input data. In contrast to previous approaches, not only the control policy is learned. In order to be successful, the system must also autonomously learn, how to extract relevant information out of a high-dimensional stream of input information, for which the semantics are not provided to the learning system. We give a first proof-of-concept of this novel learning architecture on a challenging benchmark, namely visual control of a racing slot car. The resulting policy, learned only by success or failure, is hardly beaten by an experienced human player.
Development and Evaluation of a Bluetooth EKG Monitoring Sensor

With the adoption of 2G and 3G cellular network technologies, mobile phones now have the bandwidth capability to stream data back to monitoring stations in real-time. Our paper describes the design and evaluation of a Bluetooth electrocardiogram sensor that transmits medical data to a cell phone. This data is displayed and stored on the phone. Future development of the system will relay this data over a cellular GPRS network. The current system provides a low cost and lightweight alternative to existing EKG event monitors. The final GPRS connected system will provide continuous monitoring of a patient 's heart anywhere cellular coverage is available
On inverse kinematics with fractional calculus

In this paper an application of the fractional calculus to path control is studied. The integer-order derivative and integral are replaced with the fractional-order ones in order to solve the inverse kinematics problem. The proposed algorithm is a modification of the existing one. In order to maintain the accuracy and to lower the memory requirements a history limit and varying order for derivation are proposed. An impact of the parameters upon a generated path is studied. Obtained results prove that using the fractional calculus may improve well known methods of solving the inverse kinematics problem.
A Static C + + Object-Oriented Programming ( SCOOP ) Paradigm Mixing Benefits of Traditional OOP and Generic Programming

Object-oriented and generic programming are both supported in C++. OOP provides high expressiveness whereas GP leads to more efficient programs by avoiding dynamic typing. This paper presents SCOOP, a new paradigm which enables both classical OO design and high performance in C++ by mixing OOP and GP. We show how classical and advanced OO features such as virtual methods, multiple inheritance, argument covariance, virtual types and multimethods can be implemented in a fully statically typed model, hence without run-time overhead.
Recent Research in Cooperative Control of Multivehicle

This paper presents a survey of recent research in cooperative control of multivehicle systems, using a common mathematical framework to allow different methods to be described in a unified way. The survey has three primary parts: an overview of current applications of cooperative control, a summary of some of the key technical approaches that have been explored, and a description of some possible future directions for research. Specific technical areas that are discussed include formation control, cooperative tasking, spatiotemporal planning, and consensus.
Local Search Yields a PTAS for k-Means in Doubling Metrics

The most well known and ubiquitous clustering problem encountered in nearly every branch of science is undoubtedly k-MEANS: given a set of data points and a parameter k, select k centres and partition the data points into k clusters around these centres so that the sum of squares of distances of the points to their cluster centre is minimized. Typically these data points lie in Euclidean space Rd for some d ≥ 2. k-MEANS and the first algorithms for it were introduced in the 1950's. Over the last six decades, hundreds of papers have studied this problem and different algorithms have been proposed for it. The most commonly used algorithm in practice is known as Lloyd-Forgy, which is also referred to as "the" k-MEANS algorithm, and various extensions of it often work very well in practice. However, they may produce solutions whose cost is arbitrarily large compared to the optimum solution. Kanungo et al. [2004] analyzed a very simple local search heuristic to get a polynomial-time algorithm with approximation ratio 9 + ε for any fixed ε > 0 for k-Umeans in Euclidean space. Finding an algorithm with a better worst-case approximation guarantee has remained one of the biggest open questions in this area, in particular whether one can get a true PTAS for fixed dimension Euclidean space. We settle this problem by showing that a simple local search algorithm provides a PTAS for k-MEANS for Rd for any fixed d. More precisely, for any error parameter ε > 0, the local search algorithm that considers swaps of up to ρ = dO(d) · ε-O(d/ε) centres at a time will produce a solution using exactly k centres whose cost is at most a (1+ε)-factor greater than the optimum solution. Our analysis extends very easily to the more general settings where we want to minimize the sum of q'th powers of the distances between data points and their cluster centres (instead of sum of squares of distances as in k-MEANS) for any fixed q ≥ 1 and where the metric may not be Euclidean but still has fixed doubling dimension.
Representation Learning for Large-Scale Dynamic Networks

Representation leaning on networks aims to embed networks into a low-dimensional vector space, which is useful in many tasks such as node classification, network clustering, link prediction and recommendation. In reality, most real-life networks constantly evolve over time with various kinds of changes to the network structure, e.g., creation and deletion of edges. However, existing network embedding methods learn the representation vectors for nodes in a static manner, which are not suitable for dynamic network embedding. In this paper, we propose a dynamic network embedding approach for large-scale networks. The method incrementally updates the embeddings by considering the changes of the network structures and is able to dynamically learn the embedding for networks with millions of nodes within a few seconds. Extensive experimental results on three real large-scale networks demonstrate the efficiency and effectiveness of our proposed methods.