nils-herrmann/clean_scirepeval huggingface dataset:
Activity Recognition in the Home Using Simple and Ubiquitous Sensors

In this work, a system for recognizing activities in the home setting using a set of small and simple state-change sensors is introduced. The sensors are designed to be “tape on and forget” devices that can be quickly and ubiquitously installed in home environments. The proposed sensing system presents an alternative to sensors that are sometimes perceived as invasive, such as cameras and microphones. Unlike prior work, the system has been deployed in multiple residential environments with non-researcher occupants. Preliminary results on a small dataset show that it is possible to recognize activities of interest to medical professionals such as toileting, bathing, and grooming with detection accuracies ranging from 25% to 89% depending on the evaluation criteria used .
Direction matters: hand pose estimation from local surface normals

We present a hierarchical regression framework for estimating hand joint positions from single depth images based on local surface normals. The hierarchical regression follows the tree structured topology of hand from wrist to finger tips. We propose a conditional regression forest, i.e. the Frame Conditioned Regression Forest (FCRF) which uses a new normal difference feature. At each stage of the regression, the frame of reference is established from either the local surface normal or previously estimated hand joints. By making the regression with respect to the local frame, the pose estimation is more robust to rigid transformations. We also introduce a new efficient approximation to estimate surface normals. We verify the effectiveness of our method by conducting experiments on two challenging real-world datasets and show consistent improvements over previous discriminative pose estimation methods.
Generating test data from state-based specifications

*** DRAFT ***   Although the majority of software testing in industry is conducted at the system level, most formal research has focused on the unit level. As a result, most system level testing techniques are only described informally. This paper presents formal testing criteria for system level testing that are based on formal speci cations of the software. Software testing can only be formalized and quanti ed when a solid basis for test generation can be de ned. Formal speci cations represent a signi cant opportunity for testing because they precisely describe what functions the software is supposed to provide in a form that can be easily manipulated. This paper presents general criteria for generating test inputs from state-based speci cations. The criteria include techniques for generating tests at several levels of abstraction for speci cations. These techniques provide coverage criteria that are based on the speci cations, and are made up of several parts, including test pre xes that contain inputs necessary to put the software into the appropriate state for the test values. The test generation process includes several steps for transforming specications to tests. Empirical results from a comparative case study application of these criteria are presented.
Increasing ATM Efficiency withAssistant Based Speech Recognition

Initiatives to integrate Automatic Speech Recognition into Air Traffic Management (ATM) exists at least since the late 90s. Some success to replace pseudo pilots have been reported, but its integration into controller assistant tools is missing. German Aerospace Center (DLR) and Saarland University developed Assistant Based Speech Recognition (ABSR) enabling command recognition rates better than 95%. However, good recognition rates are no convincing argument for decision makers. Therefore, we conducted an ABSR validation study with eight air traffic controllers to quantify the benefits with respect to workload and efficiency. The study validates that ABSR does not just reduce controllers’ workload, which would already be a lot, but this paper presents that ABSR significantly increases ATM efficiency. Fuel reductions of 60 liters (16 gallons) per flight and a throughput increase by two arrivals per hour are possible. AcListant®, Automatic Speech Recognition (ASR), Assistant Based Speech Recognition (ABSR), ATM Efficiency, Electronic Flight Strips, Aircraft Label
Beyond the Last Touch: Attribution in Online Advertising

Online advertisers often utilize multiple publishers to deliver ads to multi-homing consumers. These ads often generate externalities and their exposure is uncertain, which impacts advertising effectiveness across publishers. We analytically analyze the inefficiencies created by externalities and uncertainty when information is symmetric between advertisers and publishers, in contrast to most previous research that assumes information asymmetry. Although these inefficiencies cannot be resolved through publisher side actions, attribution methods that measure the campaign uncertainty can serve as an alternative solution to help advertisers adjust their strategies. Attribution creates a virtual competition between publishers, resulting in a team compensation problem. The equilibrium may potentially increase the aggressiveness of advertiser bidding leading to increased advertiser profits. The popular last-touch method is shown to over-incentivize ad exposures, often resulting in lowering advertiser profits. The Shapley value achieves an increase in profits compared to last-touch. Popular publishers and those that appear early in the conversion funnel benefit the most from advertisers using last-touch attribution. The increase in advertiser profits come at the expense of total publisher profits and often results in decreased ad allocation efficiency. We also find that the prices paid in the market will decrease when more sophisticated attribution methods are adopted.
Pattern Recognition

In this paper a novel reconstruction method is presented that uses the topological relationship of detected image features to create a highly abstract but semantically rich 3D model of the reconstructed scenes. In the first step, a combined image-based reconstruction of points and lines is performed based on the current state of art structure from motion methods. Subsequently, connected planar threedimensional structures are reconstructed by a novel method that uses the topological relationships between the detected image features. The reconstructed 3D models enable a simple extraction of geometric shapes, such as rectangles, in the scene.
On-line Active Reward Learning for Policy Optimisation in Spoken Dialogue Systems

The ability to compute an accurate reward function is essential for optimising a dialogue policy via reinforcement learning. In real-world applications, using explicit user feedback as the reward signal is often unreliable and costly to collect. This problem can be mitigated if the user’s intent is known in advance or data is available to pre-train a task success predictor off-line. In practice neither of these apply for most real world applications. Here we propose an on-line learning framework whereby the dialogue policy is jointly trained alongside the reward model via active learning with a Gaussian process model. This Gaussian process operates on a continuous space dialogue representation generated in an unsupervised fashion using a recurrent neural network encoder-decoder. The experimental results demonstrate that the proposed framework is able to significantly reduce data annotation costs and mitigate noisy user feedback in dialogue policy learning.
Evaluation of nutritional assessment techniques in elderly people newly admitted to municipal care

To evaluate the Subjective Global Assessment (SGA) and the Mini Nutritional Assessment (MNA) with regard to validity using a combination of anthropometric and serum-protein measurements as standard criteria to assess protein-energy malnutrition ( A municipality in the south of Sweden. During a year, starting in October 1996, 148 females and 113 males, aged ≥65–104 y of age, newly admitted to special types of housing for the elderly, were included in the study. According to SGA, 53% were assessed as malnourished or moderately malnourished on admission. The corresponding figure from MNA was 79% malnourished or at risk of malnutrition. Both tools indicated that anthropometric values and serum proteins were significantly lower in residents classified as being malnourished (P<0.05). Sensitivity in detecting PEM was in SGA 0.93 and in MNA 0.96 and specificity was 0.61 and 0.26, respectively. Using regression analysis, weight index and serum albumin were the best objective nutritional parameters in predicting the SGA- and MNA classifications. Item ‘muscle wasting’ in SGA and ‘self-experienced health status’ in MNA showed most predictive power concerning the odds of being assessed as malnourished. SGA was shown to be the more useful tool in detecting residents with established malnutrition and MNA in detecting residents who need preventive nutritional measures.
Morsel-driven parallelism: a NUMA-aware query evaluation framework for the many-core age

With modern computer architecture evolving, two problems conspire against the state-of-the-art approaches in parallel query execution: (i) to take advantage of many-cores, all query work must be distributed evenly among (soon) hundreds of threads in order to achieve good speedup, yet (ii) dividing the work evenly is difficult even with accurate data statistics due to the complexity of modern out-of-order cores. As a result, the existing approaches for plan-driven parallelism run into load balancing and context-switching bottlenecks, and therefore no longer scale. A third problem faced by many-core architectures is the decentralization of memory controllers, which leads to Non-Uniform Memory Access (NUMA). In response, we present the morsel-driven query execution framework, where scheduling becomes a fine-grained run-time task that is NUMA-aware. Morsel-driven query processing takes small fragments of input data (morsels) and schedules these to worker threads that run entire operator pipelines until the next pipeline breaker. The degree of parallelism is not baked into the plan but can elastically change during query execution, so the dispatcher can react to execution speed of different morsels but also adjust resources dynamically in response to newly arriving queries in the workload. Further, the dispatcher is aware of data locality of the NUMA-local morsels and operator state, such that the great majority of executions takes place on NUMA-local memory. Our evaluation on the TPC-H and SSB benchmarks shows extremely high absolute performance and an average speedup of over 30 with 32 cores.
FEATURE EXTRACTION USING SURF ALGORITHM FOR OBJECT RECOGNITION

Video surveillance is active research topic in computer vision research area for humans & vehicles, so it is used over a great extent. Multiple images generated using a fixed camera contains various objects, which are taken under different variations, illumination changes after that the object’s identity and orientation are provided to the user. This scheme is used to represent individual images as well as various objects classes in a single, scale and rotation invariant model.The objective is to improve object recognition accuracy for surveillance purposes & to detect multiple objects with sufficient level of scale invariance.Multiple objects detection& recognition is important in the analysis of video data and higher level security system. This method can efficiently detect the objects from query images as well as videos by extracting frames one by one. When given a query image at runtime, by generating the set of query features and it will find best match it to other sets within the database. Using SURF algorithm find the database object with the best feature matching, then object is present in the query image.  Image recognition, Query image, Local feature, Surveillance system, SURF algorithm.
Trusted Design in FPGAs

Using FPGAs, a designer can separate the design process from the manufacturing flow. Therefore, the owner of a sensitive design need not expose the design to possible theft and tampering during its manufacture, dramatically simplifying the process of assuring trust in that design. Modern FPGAs include bitstream security features that turn the fielded design trust problem into an information security problem, with well-known cryptographic information security solutions. The generic nature of the FPGA base array allows the validation expense to be amortized over all designs targeted to that base array. Even the task of checking design tools is simplified by using non-destructive checks of the FPGA design.
Analytical inversion-mode varactor modeling based on the EKV model and its application to RF VCO design

An analytical modeling approach for the CV-characteristics of inversion-mode MOS varactors that is based on the continuous EKV model equations is presented. Based on this approach it is possible to obtain an analytical expression for the effective large signal capacitance of varactors incorporated into a VCO and to calculate and optimize the resulting VCO tuning sensitivity KVCO. We present a simple but quite accurate hyperbolic tangent approximation for the CV-characteristic of inversion-mode MOS varactors that depends on selected design variables and is therefore well suited to be used in a systematic VCO design flow. In order to verify the validity and accuracy of the modeling approach the CV-characteristics obtained by using the EKV based simulation model are compared with Spectre (Cadence) simulations using a BSIM 3.3 transistor model. As reference semiconductor technology we use a 0.35 µm CMOS process (C35) from austriamicrosytems (AMS).
HoloClean: Holistic Data Repairs with Probabilistic Inference

We introduce HoloClean, a framework for holistic data repairing driven by probabilistic inference. HoloClean unifies qualitative data repairing, which relies on integrity constraints or external data sources, with quantitative data repairing methods, which leverage statistical properties of the input data. Given an inconsistent dataset as input, HoloClean automatically generates a probabilistic program that performs data repairing. Inspired by recent theoretical advances in probabilistic inference, we introduce a series of optimizations which ensure that inference over HoloClean’s probabilistic model scales to instances with millions of tuples. We show that HoloClean finds data repairs with an average precision of ∼ 90% and an average recall of above ∼ 76% across a diverse array of datasets exhibiting different types of errors. This yields an average F1 improvement of more than 2× against state-of-the-art methods.
Measuring personality in one minute or less : A 10-item short version of the Big Five Inventory in English and German

To provide a measure of the Big Five for contexts in which participant time is severely limited, we abbreviated the Big Five Inventory (BFI-44) to a 10-item version, the BFI-10. To permit its use in cross-cultural research, the BFI-10 was developed simultaneously in several samples in both English and German. Results focus on the psychometric characteristics of the 2-item scales on the BFI-10, including their part-whole correlations with the BFI-44 scales, retest reliability, structural validity, convergent validity with the NEO-PI-R and its facets, and external validity using peer ratings. Overall, results indicate that the BFI-10 scales retain signiWcant levels of reliability and validity. Thus, reducing the items of the BFI-44 to less than a fourth yielded eVect sizes that were lower than those for the full BFI-44 but still suYcient for research settings with truly limited time constraints.
Robust stock trading using fuzzy decision trees

Stock market analysis has traditionally been proven to be difficult due to the large amount of noise present in the data. Different approaches have been proposed to predict stock prices including the use of computational intelligence and data mining techniques. Many of these methods operate on closing stock prices or on known technical indicators. Limited studies have shown that Japanese candlestick analysis serve as rich information sources for the market. In this paper decision trees based on the ID3 algorithm are used to derive short-term trading decisions from candlesticks. To handle the large amount of uncertainty in the data, both inputs and output classifications are fuzzified using well-defined membership functions. Testing results of the derived decision trees show significant gains compared to ideal mid and long-term trading simulations both in frictionless and realistic markets.
3D Texture Recognition Using Bidirectional Feature Histograms

Textured surfaces are an inherent constituent of the natural surroundings, therefore efficient real-world applications of computer vision algorithms require precise surface descriptors. Often textured surfaces present not only variations of color or reflectance, but also local height variations. This type of surface is referred to as a 3D texture. As the lighting and viewing conditions are varied, effects such as shadowing, foreshortening and occlusions, give rise to significant changes in texture appearance. Accounting for the variation of texture appearance due to changes in imaging parameters is a key issue in developing accurate 3D texture models. The bidirectional texture function (BTF) is observed image texture as a function of viewing and illumination directions. In this work, we construct a BTF-based surface model which captures the variation of the underlying statistical distribution of local structural image features, as the viewing and illumination conditions are changed. This 3D texture representation is called the bidirectional feature histogram (BFH). Based on the BFH, we design a 3D texture recognition method which employs the BFH as the surface model, and classifies surfaces based on a single novel texture image of unknown imaging parameters. Also, we develop a computational method for quantitatively evaluating the relative significance of texture images within the BTF. The performance of our methods is evaluated by employing over 6200 texture images corresponding to 40 real-world surface samples from the CUReT (Columbia-Utrecht reflectance and texture) database. Our experiments produce excellent classification results, which validate the strong descriptive properties of the BFH as a 3D texture representation.
A real-time monocular vision-based frontal obstacle detection and avoidance for low cost UAVs in GPS denied environment

This paper presents a novel monocular vision-based realtime obstacle detection and avoidance for a low cost unmanned aerial vehicle (UAV) in an unstructured, GPS denied environment. We propose a mathematical model to estimate the relative distance from the UAV's camera to an obstacle which will subsequently be used in a collision avoidance algorithm. We validate our model with some real time experiments under both stationary and dynamic motion of the UAV during its flight. The results show good agreement with the ground truth values with an acceptable percentage of error in estimation under 3% thus proving it can facilitate obstacle detection and avoidance for low cost and lightweight UAVs.
Reconceptualizing Users as Social Actors in Information Systems Research

A concept of the user is fundamental to much of the research and practice of information systems design, development, and evaluation. Usercentered information studies have relied on individualistic cognitive models to carefully examine the criteria that influence the selection of information and communication technologies (ICTs) that Robert W. Zmud was the accepting senior editor for this paper. people make. In many ways, these studies have improved our understanding of how a good information resource fits the people who use it. However, research approaches based on an individualistic user concept are limited. In this paper, we examine the theoretical constructs that shape this user concept and contrast these with alternative views that help to reconceptualize the user as a social actor. Despite pervasive ICT use, social actors are not primarily users of ICTs. Most people who use ICT applications utilize multiple applications, in various roles, and as part of their efforts to produce goods and services while interacting with a variety of other people, and often in multiple social contexts. Moreover, the socially thin user construct limits our understanding of information selection, manipulation, communication, and exchange within complex social contexts. Using analyses from a recent study of online information service use, we develop an institutionalist concept of a social actor whose everyday interactions are infused with ICT use. We then encourage a shift from the user concept to a concept of the social actor in IS research. We suggest that such a shift will sharpen perceptions of how organizational contexts shape ICT-related practices, and at the same time will help researchers more accurately portray the complex and multiple roles that people fulfill while adopting, adapting, and using information systems. Reconceptualizing Users as Social Actors 198 MIS Quarterly Vol. 27 No. 2/June 2003
Computational Sociolinguistics: A Survey

Language is a social phenomenon and variation is inherent to its social nature. Recently, there has been a surge of interest within the computational linguistics (CL) community in the social dimension of language. In this article we present a survey of the emerging field of “computational sociolinguistics” that reflects this increased interest. We aim to provide a comprehensive overview of CL research on sociolinguistic themes, featuring topics such as the relation between language and social identity, language use in social interaction, and multilingual communication. Moreover, we demonstrate the potential for synergy between the research communities involved, by showing how the large-scale data-driven methods that are widely used in CL can complement existing sociolinguistic studies, and how sociolinguistics can inform and challenge the methods and assumptions used in CL studies. We hope to convey the possible benefits of a closer collaboration between the two communities and conclude with a discussion of open challenges.
Personalized machine learning for robot perception of affect and engagement in autism therapy

Robots have the potential to facilitate future therapies for children on the autism spectrum. However, existing robots are limited in their ability to automatically perceive and respond to human affect, which is necessary for establishing and maintaining engaging interactions. Their inference challenge is made even harder by the fact that many individuals with autism have atypical and unusually diverse styles of expressing their affective-cognitive states. To tackle the heterogeneity in children with autism, we used the latest advances in deep learning to formulate a personalized machine learning (ML) framework for automatic perception of the children’s affective states and engagement during robot-assisted autism therapy. Instead of using the traditional one-size-fits-all ML approach, we personalized our framework to each child using their contextual information (demographics and behavioral assessment scores) and individual characteristics. We evaluated this framework on a multimodal (audio, video, and autonomic physiology) data set of 35 children (ages 3 to 13) with autism, from two cultures (Asia and Europe), and achieved an average agreement (intraclass correlation) of ~60% with human experts in the estimation of affect and engagement, also outperforming nonpersonalized ML solutions. These results demonstrate the feasibility of robot perception of affect and engagement in children with autism and have implications for the design of future autism therapies.