nils-herrmann/clean_scirepeval huggingface dataset:
Approximation Algorithms for the Multiple Knapsack Problem with Assignment Restrictions

Motivated by a real world application, we study the multiple knapsack problem with assignment restrictions (MKAR). We are given a set of items, each with a positive real weight, and a set of knapsacks, each with a positive real capacity. In addition, for each item a set of knapsacks that can hold that item is specified. In a feasible assignment of items to knapsacks, each item is assigned to at most one knapsack, assignment restrictions are satisfied, and knapsack capacities are not exceeded. We consider the objectives of maximizing assigned weight and minimizing utilized capacity. We focus on obtaining approximate solutions in polynomial computational time. We show that simple greedy approaches yield 1/3-approximation algorithms for the objective of maximizing assigned weight. We give two different 1/2-approximation algorithms: the first one solves single knapsack problems successively and the second one is based on rounding the LP relaxation solution. For the bicriteria problem of minimizing utilized capacity subject to a minimum requirement on assigned weight, we give an (1/3,2)-approximation algorithm.
A context-based model for Sentiment Analysis in Twitter

Most of the recent literature on Sentiment Analysis over Twitter is tied to the idea that the sentiment is a function of an incoming tweet. However, tweets are filtered through streams of posts, so that a wider context, e.g. a topic, is always available. In this work, the contribution of this contextual information is investigated. We modeled the polarity detection problem as a sequential classification task over streams of tweets. A Markovian formulation of the Support Vector Machine discriminative model as embodied by the SVMhmm algorithm has been here employed to assign the sentiment polarity to entire sequences. The experimental evaluation proves that sequential tagging effectively embodies evidence about the contexts and is able to reach a relative increment in detection accuracy of around 20% in F1 measure. These results are particularly interesting as the approach is flexible and does not require manually coded resources.
Toward Fast and Accurate Vehicle Detection in Aerial Images Using Coupled Region-Based Convolutional Neural Networks

Vehicle detection in aerial images, being an interesting but challenging problem, plays an important role for a wide range of applications. Traditional methods are based on sliding-window search and handcrafted or shallow-learning-based features with heavy computational costs and limited representation power. Recently, deep learning algorithms, especially region-based convolutional neural networks (R-CNNs), have achieved state-of-the-art detection performance in computer vision. However, several challenges limit the applications of R-CNNs in vehicle detection from aerial images: 1) vehicles in large-scale aerial images are relatively small in size, and R-CNNs have poor localization performance with small objects; 2) R-CNNs are particularly designed for detecting the bounding box of the targets without extracting attributes; 3) manual annotation is generally expensive and the available manual annotation of vehicles for training R-CNNs are not sufficient in number. To address these problems, this paper proposes a fast and accurate vehicle detection framework. On one hand, to accurately extract vehicle-like targets, we developed an accurate-vehicle-proposal-network (AVPN) based on hyper feature map which combines hierarchical feature maps that are more accurate for small object detection. On the other hand, we propose a coupled R-CNN method, which combines an AVPN and a vehicle attribute learning network to extract the vehicle's location and attributes simultaneously. For original large-scale aerial images with limited manual annotations, we use cropped image blocks for training with data augmentation to avoid overfitting. Comprehensive evaluations on the public Munich vehicle dataset and the collected vehicle dataset demonstrate the accuracy and effectiveness of the proposed method.
Helpful Answers To Modal And Hypothetical Questions

This paper describes a computational pragmatic model which is geared towards providing helpful answers to modal and hypothetical questions. The work brings together elements from fonna l . semantic theories on modality m~d question answering, defines a wkler, pragmatically flavoured, notion of answerhood based on non-monotonic inference aod develops a notion of context, within which aspects of more cognitively oriented theories, such as Relevance Theory, can be accommodated. The model has been inlplemented. The research was fundexl by ESRC grant number R000231279.
Crowd Monitoring Using Image Processing

The understanding of crowd behaviour in semi-confined spaces is an important part of the design of new pedestrian facilities, for major layout modifications to existing areas and for the daily management of sites subject to crowd traffic. Conventional manual measurement techniques are not suitable for comprehensive data collection of patterns of site occupation and movement. Real-time monitoring is tedious and tiring, but safety-critical. This article presents some image processing techniques which, using existing closed-circuit television systems, can support both data collection and on-line monitoring of crowds. The application of these methods could lead to a better understanding of crowd behaviour, improved design of the built environment and increased pedestrian safety.
Multi-Class Twitter Emotion Classification: A New Approach

Micro blogging today has become a very popular communication tool among Internet users. Millions of users share opinions on different aspects of life every day. Therefore micro blogging web-sites are rich sources of data for opinion mining and sentiment analysis. Because micro blogging has appeared relatively recently, there are a few research works that are devoted to this topic. In this paper, we are focusing on using Twitter, the most popular micro blogging platform, for the task of Emotion analysis. We will show how to automatically collect a corpus for Emotion analysis and opinion mining purposes and then perform linguistic analysis of the collected corpus and explain discovered phenomena. Using the corpus, we will build a Emotion classifier that will be able to determine the emotion class of the person writing.
The resolution of ruptures in the therapeutic alliance.

This article outlines some of the main features of a research program on ruptures in the therapeutic alliance and reports some of the major findings to date. A rupture in the therapeutic alliance is a deterioration in the quality of the relationship between patient and therapist; it is an interpersonal marker that indicates a critical opportunity for exploring and understanding the processes that maintain a maladaptive interpersonal schema. Following the task-analytic research paradigin, a preliminary model of the resolution process was developed and then tested and revised with 2 different data sets. A series of lag 1 sequential analyses were used to confirm the hypothesized sequences of events within resolution sessions and to demonstrate a difference between resolution and nonresolution sessions. This article describes the evolution of a model of rupture resolution and then discusses its implications for treatment development and evaluation.
Dimensional synthesis of a lightweight shoulder exoskeleton

Powered exoskeletons can provide motion enhancement for both healthy and physically challenged people. Compared with lower limb exoskeletons, upper limb exoskeletons are required to have multiple degrees-of-freedom and can still produce sufficient augmentation force. Designs using serial mechanisms usually result in complicated and bulky exoskeletons that prevent themselves from being wearable. This paper presents a new exoskeleton aimed to achieve compactness and wearability. We consider a shoulder exoskeleton that consists of two spherical mechanisms with two slider crank mechanisms. The actuators can be made stationary and attached side-by-side, close to a human body. Thus better inertia properties can be obtained while maintaining lightweight. The dimensions of the exoskeleton are synthesized to achieve maximum output force. Through illustrations of a prototype, the exoskeleton is shown to be wearable and can provide adequate motion enhancement of a human's upper limb.
Quad-Mesh Generation and Processing: A Survey

Triangle meshes have been nearly ubiquitous in computer graphics, and a large body of data structures and geometry processing algorithms based on them has been developed in the literature. At the same time, quadrilateral meshes, especially semi-regular ones, have advantages for many applications, and significant progress was made in quadrilateral mesh generation and processing during the last several years. In this survey we discuss the advantages and problems of techniques operating on quadrilateral meshes, including surface analysis and mesh quality, simplification, adaptive refinement, alignment with features, parametrization, and remeshing.
Blending Scrum practices and CMMI project management process areas

Software development organizations that have been employing capability maturity models, such as SW-CMM or CMMI for improving their processes are now increasingly interested in the possibility of adopting agile development methods. In the context of project management, what can we say about Scrum’s alignment with CMMI? The aim of our paper is to present the mapping between CMMI and the agile method Scrum, showing major gaps between them and identifying how organizations are adopting complementary practices in their projects to make these two approaches more compliant. This is useful for organizations that have a plan-driven process based on the CMMI model and are planning to improve the agility of processes or to help organizations to define a new project management framework based on both CMMI and Scrum practices.
Developing Vehicular Data Cloud Services in the IoT Environment

The advances in cloud computing and internet of things (IoT) have provided a promising opportunity to resolve the challenges caused by the increasing transportation issues. We present a novel multilayered vehicular data cloud platform by using cloud computing and IoT technologies. Two innovative vehicular data cloud services, an intelligent parking cloud service and a vehicular data mining cloud service, for vehicle warranty analysis in the IoT environment are also presented. Two modified data mining models for the vehicular data mining cloud service, a Naïve Bayes model and a Logistic Regression model, are presented in detail. Challenges and directions for future work are also provided.
Removal of Accessory Breasts: A Novel Tumescent Liposuction Approach

Accessory breasts, usually with a protuberant appearance, are composed of both the central accessory breast tissue and adjacent fat tissue. They are a palpable convexity and cosmetically unsightly. Consequently, patients often desire cosmetic improvement. The traditional general surgical treatment for accessory breasts is removal of the accessory breast tissue, fat tissue, and covering skin as a whole unit. A rather long ugly scar often is left after this operation. A minimally invasive method frequently used by the plastic surgeon is to “dig out” the accessory breast tissue. A central depression appearance often is left due to the adjacent fat tissue remnant. From the cosmetic point of view, neither a long scar nor a bulge is acceptable. A minimal incision is made, and the tumescent liposuction technique is used to aspirate out both the central accessory breast tissue and adjacent fat tissue. If there is an areola or nipple in the accessory breast, either the areola or nipple is excised after liposuction during the same operation. For patients who have too much extra skin in the accessory breast area, a small fusiform incision is made to remove the extra skin after the accessory breast tissue and fat tissue have been aspirated out. From August 2003 to January 2008, 51 patients underwent surgery using the described technique. All were satisfied with their appearance after their initial surgery except for two patients with minimal associated morbidity. This report describes a new approach for treating accessory breasts that results in minimal scarring and a better appearance than can be achieved with traditional methods.
Spotting Suspicious Behaviors in Multimodal Data: A General Metric and Algorithms

Many commercial products and academic research activities are embracing behavior analysis as a technique for improving detection of attacks of many sorts-from retweet boosting, hashtag hijacking to link advertising. Traditional approaches focus on detecting dense blocks in the adjacency matrix of graph data, and recently, the tensors of multimodal data. No method gives a principled way to score the suspiciousness of dense blocks with different numbers of modes and rank them to draw human attention accordingly. In this paper, we first give a list of axioms that any metric of suspiciousness should satisfy; we propose an intuitive, principled metric that satisfies the axioms, and is fast to compute; moreover, we propose CrossSpot, an algorithm to spot dense blocks that are worth inspecting, typically indicating fraud or some other noteworthy deviation from the usual, and sort them in the order of importance (“suspiciousness”). Finally, we apply CrossSpot to the real data, where it improves the F1 score over previous techniques by 68 percent and finds suspicious behavioral patterns in social datasets spanning 0.3 billion posts.
Time-Contrastive Networks: Self-Supervised Learning from Multi-view Observation

We propose a self-supervised approach for learning representations of relationships between humans and their environment, including object interactions, attributes, and body pose, entirely from unlabeled videos recorded from multiple viewpoints . We train an embedding with a triplet loss that contrasts a pair of simultaneous frames from different viewpoints with temporally adjacent and visually similar frames . We call this model Time- Contrastive Networks (TCN). The contrastive signal encourages the model to discover meaningful dimensions and attributes that can explain the changing state of objects and the world from visually similar frames while learning invariance to viewpoint, occlusions, motion blur, lighting, background. The experimental evaluation of our multiviewpoint embedding technique examines its application to reasoning about object interactions, as well as human pose imitation with a real robot. We demonstrate that our model can correctly identify corresponding steps in complex object interactions, such as pouring (Table 1), between different videos and with different instances. We also show what is, to the best of our knowledge, the first self-supervised results for end-to-end imitation learning of human motions with a real robot (Table 2). Results are best visualized in videos available at 1 and the full paper is available at 2.
Path planning for a Parking Assistance System : Implementation and Experimentation

Manoeuvre assistance is currently receiving increasing attention from the car industry. In this article we focus on the implementation of a reverse parking assistance and more precisely, a reverse parking manoeuvre planner. This paper is based on a manoeuvre planning technique presented in previous work and specialised in planning reverse parking manoeuvre. Since a key part of the previous method was not explicited, our goal in this paper is to present a practical and reproducible way to implement a reverse parking manoeuvre planner. Our implementation uses a database engine to search for the elementary movements that will make the complete parking manoeuvre. Our results have been successfully tested on a real platform: the CSIRO Autonomous Tractor.
Cockroft-Walton voltage multiplier combined with switched-coupled-inductor boost converter

A new scheme of a step-up converter with very high voltage gain is analyzed in this paper. The scheme is based on the combination of the switched-coupled-inductor boost converter and the diode-capacitor Cockoft-Walton multiplier. The scheme provides a soft commutation of the switch and the diodes. The paper analyzes the main modes of operation and obtained the formulas for determining the DC voltage gain, the boundary between continuous and discontinuous modes of operation and formulas to calculate the maximal voltage stresses on the transistor and on the diodes. The experimental results proved the theoretical expectations.
A Low Cost Flyback CCM Inverter for AC Module Application

The unfolding-type flyback inverter operating in discontinuous conduction mode (DCM) is popular as a low-cost solution for a photovoltaic (PV) ac module application. This paper aims to improve the efficiency by using a scheme based on continuous conduction mode (CCM) for this application. Design issues, both for the power scheme and the control scheme, are identified and trade-offs investigated. An open-loop control of the secondary current, based on feedback control of the primary current, is proposed in order to bypass the difficulties posed by the moving right half plane zero in the duty cycle to secondary current transfer function. The results presented show an improvement of 8% in California efficiency compared to the benchmark DCM scheme for a 200-W PV module application. The output power quality at rated power level is capable of meeting IEC61727 requirements. The stability of the flyback inverter in CCM has been verified at selected working conditions.
Comparison of battery charging algorithms for stand alone photovoltaic systems

The battery is the most common method of energy storage in stand alone solar systems; the most popular being the valve regulated lead acid battery (VRLA) due to its low cost and ease of availability. Photovoltaics are not an ideal source for charging batteries as their output is heavily dependent on weather conditions. Therefore, when batteries are used in photovoltaic systems, the performance characteristics differ significantly from batteries used in more traditional applications and the battery life is usually shortened. In conditions of varying solar radiation and load profile the battery may experience a low state of charge (SOC). A low SOC for extended periods of time will cause increased sulphation, which severely reduces the life of the battery. Typically, steps are carried out to protect the battery and to charge the battery more effectively. Such methods include intermittent charging (IC), three stage charging (TSC) and interrupted charge control (ICC), among others. This paper quantifies the effectiveness of these three battery charging algorithms and evaluates their ability to maintain the battery at a high state of charge. The measurement setup is comprised of a solar simulator, which replicates the output of a large 50 W photovoltaic panel using a low power cell. Repeatable load and solar radiation profiles and temperature control are implemented using LabView so that identical operating conditions can be set up to compare the three battery charging systems.
Low-profile fully integrated 60 GHz 18 element phased array on multilayer liquid crystal polymer flip chip package

We present an ultra-thin, low cost, fully integrated 60GHz phased array antenna module supporting 18 TX/RX antenna elements for 60 GHz WiGig applications. The RFIC is assembled on a 15 mm × 12 mm liquid crystal polymer (LCP) package containing the antenna elements. All millimeter-wave signal routing was done on the same layer as the die pads to minimize the number of package substrate layers (4 total metal layers) and achieve very small total package thickness of 0.41 mm. The measurement results for the package showed good agreement with the simulation. The measured peak directivity of the steerable array was 17.4 dBi and routing losses were 0.5 - 1dB.
eTransQual : a transaction process-based approach for capturing service quality in online shopping

Existing e-service quality scales mainly focus on goal-oriented e-shopping behavior excluding hedonic quality aspects. As a consequence, these scales do not fully cover all aspects of consumer's quality evaluation. In order to integrate both utilitarian and hedonic e-service quality elements, we apply a transaction process model to electronic service encounters. Based on this general framework capturing all stages of the electronic service delivery process, we develop a transaction process-based scale for measuring service quality (eTransQual). After conducting exploratory and confirmatory factor analysis, we identify five discriminant quality dimensions: functionality/design, enjoyment, process, reliability and responsiveness. All extracted dimensions of eTransQual show a significant positive impact on important outcome variables like perceived value and customer satisfaction. Moreover, enjoyment is a dominant factor in influencing both relationship duration and repurchase intention as major drivers of customer lifetime value. As a result, we present conceptual and empirical evidence for the need to integrate both utilitarian and hedonic e-service quality elements into one measurement scale.
IkeaBot: An autonomous multi-robot coordinated furniture assembly system

We present an automated assembly system that directs the actions of a team of heterogeneous robots in the completion of an assembly task. From an initial user-supplied geometric specification, the system applies reasoning about the geometry of individual parts in order to deduce how they fit together. The task is then automatically transformed to a symbolic description of the assembly-a sort of blueprint. A symbolic planner generates an assembly sequence that can be executed by a team of collaborating robots. Each robot fulfills one of two roles: parts delivery or parts assembly. The latter are equipped with specialized tools to aid in the assembly process. Additionally, the robots engage in coordinated co-manipulation of large, heavy assemblies. We provide details of an example furniture kit assembled by the system.
Differentiable Scheduled Sampling for Credit Assignment

We demonstrate that a continuous relaxation of the argmax operation can be used to create a differentiable approximation to greedy decoding for sequence-tosequence (seq2seq) models. By incorporating this approximation into the scheduled sampling training procedure (Bengio et al., 2015)–a well-known technique for correcting exposure bias–we introduce a new training objective that is continuous and differentiable everywhere and that can provide informative gradients near points where previous decoding decisions change their value. In addition, by using a related approximation, we demonstrate a similar approach to sampled-based training. Finally, we show that our approach outperforms cross-entropy training and scheduled sampling procedures in two sequence prediction tasks: named entity recognition and machine translation.