nils-herrmann/clean_scirepeval huggingface dataset:
A REVIEW OF VISION BASED HAND GESTURES RECOGNITION

With the ever-increasing diffusion of computers into the society, it is widely believed that present popular mode of interactions with computers (mouse and keyboard) will become a bottleneck in the effective utilization of information flow between the computers and the human. Vision based Gesture recognition has the potential to be a natural and powerful tool supporting efficient and intuitive interaction between the human and the computer. Visual interpretation of hand gestures can help in achieving the ease and naturalness desired for Human Computer Interaction (HCI). This has motivated many researchers in computer vision-based analysis and interpretation of hand gestures as a very active research area. We surveyed the literature on visual interpretation of hand gestures in the context of its role in HCI and various seminal works of researchers are emphasized. The purpose of this review is to introduce the field of gesture recognition as a mechanism for interaction with computers.
Freeway speed limits and traffic fatalities in Washington State.

In 1987 individual states in the USA were allowed to raise speed limits on rural freeways from 55 to 65 mph. Analyses of the impact of the increased speed limits on highway safety have produced conflicting results.



To determine if the 1987 speed limit increase on Washington State's rural freeways affected the incidence of fatal crashes or all crashes on rural freeways, or affected average vehicle speeds or speed variance.



An ecological study of crashes and vehicle speeds on Washington State freeways from 1974 through 1994.



The incidence of fatal crashes more than doubled after 1987, compared with what would have been expected if there had been no speed limit increase, rate ratio 2.1 (95% confidence interval (CI), 1.6-2.7). This resulted in an excess of 26.4 deaths per year on rural freeways in Washington State. The total crash rate did not change substantially, rate ratio 1.1 (95% CI, 1.0-1.3). Average vehicle speed increased by 5.5 mph. Speed variance was not affected by the speed limit increase.



The speed limit increase was associated with a higher fatal crash rate and more deaths on freeways in Washington State.
Peek-a-Boo: I see your smart home activities, even encrypted!

A myriad of IoT devices such as bulbs, switches, speakers in a smart home environment allow users to easily control the physical world around them and facilitate their living styles. However, an attacker inside or near a smart home environment can potentially exploit the innate wireless medium used by these devices to exfiltrate sensitive information about the users and their activities, invading user privacy. With this in mind, in this work, we introduce a novel multi-stage privacy attack against user privacy in a smart environment. It is realized utilizing state-of-the-art machine-learning approaches for detecting and identifying particular types of IoT devices, their actions, states, and ongoing user activities in a cascading style by only observing passively the wireless traffic from smart home devices. The attack effectively works on both encrypted and unencrypted communications. We evaluate the efficiency of the attack with real measurements from an extensive set of popular off-the-shelf smart home IoT devices utilizing a set of diverse network protocols like WiFi, ZigBee, and BLE. Our results show that an adversary passively sniffing the network traffic can achieve very high accuracy (above 90%) in identifying the state and actions of targeted smart home devices and their users. In contrast to earlier straightforward approaches, our multi-stage privacy attack can perform activity detection and identification automatically, without extensive background knowledge or specifications of analyzed protocols. This allows an adversary to efficiently aggregate extensive behavior profiles of targeted users. To protect against this privacy leakage, we also propose a countermeasure based on generating spoofed network traffic to hide the real activities of the devices. We also demonstrate that the provided solution provides better protection than existing solutions.
Perception Differences between the Depressed and Non-Depressed Users in Twitter

One’s state of mind will influence her perception of the world and people within it. In this paper, we explore attitudes and behaviors toward online social media based on whether one is depressed or not. We conducted semistructured face-to-face interviews with 14 active Twitter users, half of whom were depressed and the other half non-depressed. Our results highlight key differences between the two groups in terms of perception towards online social media and behaviors within such systems. Non-depressed individuals perceived Twitter as an information consuming and sharing tool, while depressed individuals perceived it as a tool for social awareness and emotional interaction. We discuss several design implications for future social networks that could better accommodate users with depression and provide insights towards helping depressed users meet their needs through online social media.
WHY ‘OPEN INNOVATION’ IS OLD WINE IN NEW BOTTLES

The concept of ‘open innovation’ has received a considerable amount of coverage within the academic literature and beyond. Much of this seems to have been without much critical analysis of the evidence. In this paper, we show how Chesbrough creates a false dichotomy by arguing that open innovation is the only alternative to a closed innovation model. We systematically examine the six principles of the open innovation concept and show how the Open Innovation paradigm has created a partial perception by describing something which is undoubtedly true in itself (the limitations of closed innovation principles), but false in conveying the wrong impression that firms today follow these principles. We hope that our examination and scrutiny of the ‘open innovation’ concept contributes to the debate on innovation management and helps enrich our understanding.
IoT based Heart Attack Detection, Heart Rate and Temperature Monitor

The Internet of Things (IoT) is inter communication of embedded devices using networking technologies. The IoT will be one of the important trends in future, can affect the networking, business and communication. In this paper, proposing a remote sensing parameter of the human body which consists of pulse and temperature. The parameters that are used for sensing and monitoring will send the data through wireless sensors. Adding a web based observing helps to keep track of the regular health status of a patient. The sensing data will be continuously collected in a database and will be used to inform patient to any unseen problems to undergo possible diagnosis. Experimental results prove the proposed system is user friendly, reliable, economical.
Constructing Grounded Theory: A practical guide through qualitative analysis Kathy Charmaz Constructing Grounded Theory: A practical guide through qualitative analysis Sage 224 £19.99 0761973532 0761973532 [Formula: see text].

The grounded theory approach to doing qualitative research in nursing has become very popular in recent years. I confess to never really having understood Glaser and Strauss' original book: The Discovery of Grounded Theory. Since they wrote it, they have fallen out over what grounded theory might be and both produced their own versions of it. I welcomed, then, Kathy Charmaz's excellent and practical guide.
The story of phosphorus : Global food security and food for thought

Food production requires application of fertilizers containing phosphorus, nitrogen and potassium on agricultural fields in order to sustain crop yields. However modern agriculture is dependent on phosphorus derived from phosphate rock, which is a non-renewable resource and current global reserves may be depleted in 50–100 years. While phosphorus demand is projected to increase, the expected global peak in phosphorus production is predicted to occur around 2030. The exact timing of peak phosphorus production might be disputed, however it is widely acknowledged within the fertilizer industry that the quality of remaining phosphate rock is decreasing and production costs are increasing. Yet future access to phosphorus receives little or no international attention. This paper puts forward the case for including long-term phosphorus scarcity on the priority agenda for global food security. Opportunities for recovering phosphorus and reducing demand are also addressed together with institutional challenges.
How urban structure can affect city sustainability from an air quality perspective

Despite the progress made in controlling local air pollution, urban areas show increasing signs of environmental stress and air quality is one of the major concerns. The findings of several studies provide evidence that the shape of a city and the land use distribution determine the location of emission sources and the pattern of urban traffic, affecting urban air quality. This work focuses on the potential impacts of different land use patterns on urban air quality. Three imaginary cities were created, based on alternative urban planning strategies, considering different land use patterns: from the scenario of urban sprawl to the opposite scenario of a compact city with mixed land use. The mesoscale photochemical system MEMO/MARS was applied to evaluate the urban air quality in each of the idealized city structures. Simulation results indicate that more compact cities with mixed land use provide better urban air quality compared to disperse and network cities.
Occupancy Networks: Learning 3D Reconstruction in Function Space

With the advent of deep neural networks, learning-based approaches for 3D reconstruction have gained popularity. However, unlike for images, in 3D there is no canonical representation which is both computationally and memory efficient yet allows for representing high-resolution geometry of arbitrary topology. Many of the state-of-the-art learningbased 3D reconstruction approaches can hence only represent very coarse 3D geometry or are limited to a restricted domain. In this paper, we propose occupancy networks, a new representation for learning-based 3D reconstruction methods. Occupancy networks implicitly represent the 3D surface as the continuous decision boundary of a deep neural network classifier. In contrast to existing approaches, our representation encodes a description of the 3D output at infinite resolution without excessive memory footprint. We validate that our representation can efficiently encode 3D structure and can be inferred from various kinds of input. Our experiments demonstrate competitive results, both qualitatively and quantitatively, for the challenging tasks of 3D reconstruction from single images, noisy point clouds and coarse discrete voxel grids. We believe that occupancy networks will become a useful tool in a wide variety of learning-based 3D tasks.
Complex-YOLO: An Euler-Region-Proposal for Real-Time 3D Object Detection on Point Clouds

Lidar based 3D object detection is inevitable for autonomous driving, because it directly links to environmental understanding and therefore builds the base for prediction and motion planning. The capacity of inferencing highly sparse 3D data in real-time is an ill-posed problem for lots of other application areas besides automated vehicles, e.g. augmented reality, personal robotics or industrial automation. We introduce Complex-YOLO, a state of the art real-time 3D object detection network on point clouds only. In this work, we describe a network that expands YOLOv2, a fast 2D standard object detector for RGB images, by a specific complex regression strategy to estimate multi-class 3D boxes in Cartesian space. Thus, we propose a specific Euler-RegionProposal Network (E-RPN) to estimate the pose of the object by adding an imaginary and a real fraction to the regression network. This ends up in a closed complex space and avoids singularities, which occur by single angle estimations. The E-RPN supports to generalize well during training. Our experiments on the KITTI benchmark suite show that we outperform current leading methods for 3D object detection specifically in terms of efficiency. We achieve state of the art results for cars, pedestrians and cyclists by being more than five times faster than the fastest competitor. Further, our model is capable of estimating all eight KITTIclasses, including Vans, Trucks or sitting pedestrians simultaneously with high accuracy.
Probiotic bacteria: safety, functional and technological properties.

During the past two decades probiotic (health promoting) micro-organisms have been increasingly included in various types of food products, especially in fermented milks. Several aspects, including safety, functional and technological characteristics, have to be taken into consideration in the selection process of probiotic micro-organisms. Safety aspects include specifications such as origin (healthy human GI-tract), non-pathogenicity and antibiotic resistance characteristics. Functional aspects include viability and persistence in the GI-tract, immunomodulation, antagonistic and antimutagenic properties. Before probiotic strains, chosen on the basis of their good safety and functional characteristics, can benefit the consumer, they must first be able to be manufactured under industrial conditions. Furthermore, they have to survive and retain their functionality during storage, and also in the foods into which they are incorporated without producing off-flavours. Factors related to the technological and sensory aspects of probiotic food production are of utmost importance since only by satisfying the demands of the consumer can the food industry succeed in promoting the consumption of functional probiotic products in the future.
What drives consumers to shop online ? A literature review

While a large number of consumers in the US and Europe frequently shop on the Internet, research on what drives consumers to shop online has typically been fragmented. This paper therefore proposes a framework to increase researchers’ understanding of consumers’ attitudes toward online shopping and their intention to shop on the Internet. The framework uses the constructs of the Technology Acceptance Model (TAM) as a basis, extended by exogenous factors and applies it to the online shopping context. The review shows that attitudes toward online shopping and intention to shop online are not only affected by ease of use, usefulness, and enjoyment, but also by exogenous factors like consumer traits, situational factors, product characteristics, previous online shopping experiences, and trust in online shopping.
AN EVENT STRUCTURE ACCOUNT OF ENGLISH RESULTATIVES

Current syntactic accounts of English resultatives are based on the assumption that result XPs are predicated of underlying direct objects. This assumption has helped to explain the presence of reflexive pronouns with some intransitive verbs but not others and the apparent lack of result XPs predicated of subjects of transitive verbs. We present problems for and counterexamples to some of the basic assumptions of the syntactic approach, which undermine its explanatory power. We develop an alternative account that appeals to principles governing the well-formedness of event structure and the event structure-to-syntax mapping. This account covers the data on intransitive verbs and predicts the distribution of subject-predicated result XPs with transitive verbs.
Type I Membranes, Phase Resetting Curves, and Synchrony

Type I membrane oscillators such as the Connor model (Connor et al. 1977) and the Morris-Lecar model (Morris and Lecar 1981) admit very low frequency oscillations near the critical applied current. Hansel et al. (1995) have numerically shown that synchrony is difficult to achieve with these models and that the phase resetting curve is strictly positive. We use singular perturbation methods and averaging to show that this is a general property of Type I membrane models. We show in a limited sense that so called Type II resetting occurs with models that obtain rhythmicity via a Hopf bifurcation. We also show the differences between synapses that act rapidly and those that act slowly and derive a canonical form for the phase interactions.
Regulating the sharing economy

In this introductory essay, we explore definitions of the ‘sharing economy’, a concept indicating both social (relational, communitarian) and economic (allocative, profit-seeking) aspects which appear to be in tension. We suggest combining the social and economic logics of the sharing economy to focus on the central features of network enabled, aggregated membership in a pool of offers and demands (for goods, services, creative expressions). This definition of the sharing economy distinguishes it from other related peer-to-peer and collaborative forms of production. Understanding the social and economic motivations for and implications of participating in the sharing economy is important to its regulation. Each of the papers in this special issue contributes to knowledge by linking the social and economic aspects of sharing economy practices to regulatory norms and mechanisms. We conclude this essay by suggesting future research to further clarify and render intelligible the sharing economy, not as a contradiction in terms but as an empirically observable realm of socio-economic activity.
Micro Underwater Vehicle Hydrobatics: A Submerged Furuta Pendulum

We present the new HippoCampus micro underwater vehicle, first introduced in . It is designed for monitoring confined fluid volumes. These tightly constrained settings demand agile vehicle dynamics. Moreover, we adapt a robust attitude control scheme for aerial drones to the underwater domain. We demonstrate the performance of the controller with a challenging maneuver. A submerged Furuta pendulum is stabilized by HippoCampus after a swing-up. The experimental results reveal the robustness of the control method, as the system quickly recovers from strong physical disturbances, which are applied to the system.
Beam search algorithms for multilabel learning

Multilabel learning is a machine learning task that is important for applications, but challenging. A recent method for multilabel learning called probabilistic classifier chains (PCCs) has several appealing properties. However, PCCs suffer from the computational issue that inference (i.e., predicting the label of an example) requires time exponential in the number of tags. Also, PCC accuracy is sensitive to the ordering of the tags while training. In this paper, we show how to use the classical technique of beam search to solve both these problems. Specifically, we show how to apply beam search to make inference tractable, and how to integrate beam search with training to determine a suitable tag ordering. Experimental results on a range of datasets show that the proposed improvements yield a state-of-the-art method for multilabel learning.
When are adaptive strategies most predictive of psychopathology?

In recent work, we showed that putatively adaptive emotion regulation strategies, such as reappraisal and acceptance, have a weaker association with psychopathology than putatively maladaptive strategies, such as rumination, suppression, and avoidance (e.g., Aldao & Nolen-Hoeksema, 2010; Aldao, Nolen-Hoeksema, & Schweizer, 2010). In this investigation, we examined the interaction between adaptive and maladaptive emotion regulation strategies in the prediction of psychopathology symptoms (depression, anxiety, and alcohol problems) concurrently and prospectively. We assessed trait emotion regulation and psychopathology symptoms in a sample of community residents at Time 1 (N = 1,317) and then reassessed psychopathology at Time 2 (N = 1,132). Cross-sectionally, we found that the relationship between adaptive strategies and psychopathology symptoms was moderated by levels of maladaptive strategies: adaptive strategies had a negative association with psychopathology symptoms only at high levels of maladaptive strategies. In contrast, adaptive strategies showed no prospective relationship to psychopathology symptoms either alone or in interaction with maladaptive strategies. We discuss the implications of this investigation for future work on the contextual factors surrounding the deployment of emotion regulation strategies.
Machine Learning-Based Method for Personalized and Cost-Effective Detection of Alzheimer's Disease

Diagnosis of Alzheimer's disease (AD) is often difficult, especially early in the disease process at the stage of mild cognitive impairment (MCI). Yet, it is at this stage that treatment is most likely to be effective, so there would be great advantages in improving the diagnosis process. We describe and test a machine learning approach for personalized and cost-effective diagnosis of AD. It uses locally weighted learning to tailor a classifier model to each patient and computes the sequence of biomarkers most informative or cost-effective to diagnose patients. Using ADNI data, we classified AD versus controls and MCI patients who progressed to AD within a year, against those who did not. The approach performed similarly to considering all data at once, while significantly reducing the number (and cost) of the biomarkers needed to achieve a confident diagnosis for each patient. Thus, it may contribute to a personalized and effective detection of AD, and may prove useful in clinical settings.
An automated region-of-interest segmentation for optic disc extraction

Optic disc segmentation in retinal fundus images plays a critical rule in diagnosing a variety of pathologies and abnormalities related to eye retina. Most of the abnormalities that are related to optic disc lead to structural changes in the inner and outer zones of optic disc. Optic disc segmentation on the level of whole retina image degrades the detection sensitivity for these zones. In this paper, we present an automated technique for the Region-Of-Interest segmentation of optic disc region in retinal images. Our segmentation technique reduces the processing area required for optic disc segmentation techniques leading to notable performance enhancement and reducing the amount of the required computational cost for each retinal image. DRIVE, DRISHTI-GS and DiaRetDB1 datasets were used to test and validate our proposed pre-processing technique.
Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.

Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.