nils-herrmann/clean_scirepeval huggingface dataset:
A Collision-Mitigation Cuckoo Hashing Scheme for Large-Scale Storage Systems

With the rapid growth of the amount of information, cloud computing servers need to process and analyze large amounts of high-dimensional and unstructured data timely and accurately. This usually requires many query operations. Due to simplicity and ease of use, cuckoo hashing schemes have been widely used in real-world cloud-related applications. However, due to the potential hash collisions, the cuckoo hashing suffers from endless loops and high insertion latency, even high risks of re-construction of entire hash table. In order to address these problems, we propose a cost-efficient cuckoo hashing scheme, called MinCounter. The idea behind MinCounter is to alleviate the occurrence of endless loops in the data insertion by selecting unbusy kicking-out routes. MinCounter selects the “cold” (infrequently accessed), rather than random, buckets to handle hash collisions. We further improve the concurrency of the MinCounter scheme to pursue higher performance and adapt to concurrent applications. MinCounter has the salient features of offering efficient insertion and query services and delivering high performance of cloud servers, as well as enhancing the experiences for cloud users. We have implemented MinCounter in a large-scale cloud testbed and examined the performance by using three real-world traces. Extensive experimental results demonstrate the efficacy and efficiency of MinCounter.
Similarity of Color Images

We describe two new color indexing techniques. The rst one is a more robust version of the commonly used color histogram indexing. In the index we store the cumulative color histograms. The L 1-, L 2-, or L 1-distance between two cumulative color histograms can be used to deene a similarity measure of these two color distributions. We show that while this method produces only slightly better results than color histogram methods, it is more robust with respect to the quantization parameter of the histograms. The second technique is an example of a new approach to color indexing. Instead of storing the complete color distributions, the index contains only their dominant features. We implement this approach by storing the rst three moments of each color channel of an image in the index, i.e., for a HSV image we store only 9 oating point numbers per image. The similarity function which is used for the retrieval is a weighted sum of the absolute diierences between corresponding moments. Our tests clearly demonstrate that a retrieval based on this technique produces better results and runs faster than the histogram-based methods.
Integrated monitoring and information systems for managing aquatic invasive species in a changing climate.

Changes in temperature, precipitation, and other climatic drivers and sea-level rise will affect populations of existing native and non-native aquatic species and the vulnerability of aquatic environments to new invasions. Monitoring surveys provide the foundation for assessing the combined effects of climate change and invasions by providing baseline biotic and environmental conditions, although the utility of a survey depends on whether the results are quantitative or qualitative, and other design considerations. The results from a variety of monitoring programs in the United States are available in integrated biological information systems, although many include only non-native species, not native species. Besides including natives, we suggest these systems could be improved through the development of standardized methods that capture habitat and physiological requirements and link regional and national biological databases into distributed Web portals that allow drawing information from multiple sources. Combining the outputs from these biological information systems with environmental data would allow the development of ecological-niche models that predict the potential distribution or abundance of native and non-native species on the basis of current environmental conditions. Environmental projections from climate models can be used in these niche models to project changes in species distributions or abundances under altered climatic conditions and to identify potential high-risk invaders. There are, however, a number of challenges, such as uncertainties associated with projections from climate and niche models and difficulty in integrating data with different temporal and spatial granularity. Even with these uncertainties, integration of biological and environmental information systems, niche models, and climate projections would improve management of aquatic ecosystems under the dual threats of biotic invasions and climate change.
Bottom-up and top-down reasoning with convolutional latent-variable models

Convolutional neural nets (CNNs) have demonstrated remarkable performance in recent history. Such approaches tend to work in a “unidirectional” bottom-up feed-forward fashion. However, biological evidence suggests that feedback plays a crucial role, particularly for detailed spatial understanding tasks. This work introduces “bidirectional” architectures that also reason with top-down feedback: neural units are influenced by both lower and higher-level units. We do so by treating units as latent variables in a global energy function. We call our models convolutional latentvariable models (CLVMs). From a theoretical perspective, CLVMs unify several approaches for recognition, including CNNs, generative deep models (e.g., Boltzmann machines), and discriminative latent-variable models (e.g., DPMs). From a practical perspective, CLVMs are particularly well-suited for multi-task learning. We describe a single architecture that simultaneously achieves state-of-the-art accuracy for tasks spanning both high-level recognition (part detection/localization) and low-level grouping (pixel segmentation). Bidirectional reasoning is particularly helpful for detailed low-level tasks, since they can take advantage of top-down feedback. Our architectures are quite efficient, capable of processing an image in milliseconds. We present results on benchmark datasets with both part/keypoint labels and segmentation masks (such as PASCAL and LFW) that demonstrate a significant improvement over prior art, in both speed and accuracy.
Extractive Summary as Discrete Latent Variables

In this paper, we compare various methods to compress a text using a neural model. We find that extracting tokens as latent variables significantly outperforms the state-of-the-art discrete latent variable models such as VQ-VAE. Furthermore, we compare various extractive compression schemes. There are two best-performing methods that perform equally. One method is to simply choose the tokens with the highest tf-idf scores. Another is to train a bidirectional language model similar to ELMo and choose the tokens with the highest loss. If we consider any subsequence of a text to be a text in a broader sense, we conclude that language is a strong compression code of itself. Our finding justifies the high quality of generation achieved with hierarchical method, as their latent variables are nothing but natural language summary. We also conclude that there is a hierarchy in language such that an entire text can be predicted much more easily based on a sequence of a small number of keywords, which can be easily found by classical methods as tf-idf. We speculate that this extraction process may be useful for unsupervised hierarchical text generation.
Efficiency and Cost of Economical Brain Functional Networks

Brain anatomical networks are sparse, complex, and have economical small-world properties. We investigated the efficiency and cost of human brain functional networks measured using functional magnetic resonance imaging (fMRI) in a factorial design: two groups of healthy old (N = 11; mean age = 66.5 years) and healthy young (N = 15; mean age = 24.7 years) volunteers were each scanned twice in a no-task or "resting" state following placebo or a single dose of a dopamine receptor antagonist (sulpiride 400 mg). Functional connectivity between 90 cortical and subcortical regions was estimated by wavelet correlation analysis, in the frequency interval 0.06-0.11 Hz, and thresholded to construct undirected graphs. These brain functional networks were small-world and economical in the sense of providing high global and local efficiency of parallel information processing for low connection cost. Efficiency was reduced disproportionately to cost in older people, and the detrimental effects of age on efficiency were localised to frontal and temporal cortical and subcortical regions. Dopamine antagonism also impaired global and local efficiency of the network, but this effect was differentially localised and did not interact with the effect of age. Brain functional networks have economical small-world properties-supporting efficient parallel information transfer at relatively low cost-which are differently impaired by normal aging and pharmacological blockade of dopamine transmission.
Community detection, link prediction, and layer interdependence in multilayer networks

Complex systems are often characterized by distinct types of interactions between the same entities. These can be described as a multilayer network where each layer represents one type of interaction. These layers may be interdependent in complicated ways, revealing different kinds of structure in the network. In this work we present a generative model, and an efficient expectation-maximization algorithm, which allows us to perform inference tasks such as community detection and link prediction in this setting. Our model assumes overlapping communities that are common between the layers, while allowing these communities to affect each layer in a different way, including arbitrary mixtures of assortative, disassortative, or directed structure. It also gives us a mathematically principled way to define the interdependence between layers, by measuring how much information about one layer helps us predict links in another layer. In particular, this allows us to bundle layers together to compress redundant information and identify small groups of layers which suffice to predict the remaining layers accurately. We illustrate these findings by analyzing synthetic data and two real multilayer networks, one representing social support relationships among villagers in South India and the other representing shared genetic substring material between genes of the malaria parasite.
Personalized Response Generation via Domain adaptation

In this paper, we propose a novel personalized response generation model via domain adaptation (PRG-DM). First, we learn the human responding style from large general data (without user-specific information). Second, we fine tune the model on a small size of personalized data to generate personalized responses with a dual learning mechanism. Moreover, we propose three new rewards to characterize good conversations that are personalized, informative and grammatical. We employ the policy gradient method to generate highly rewarded responses. Experimental results show that our model can generate better personalized responses for different users.
What 'gaps'? Reply to Grush and Churchland

Grush and Churchland (1995) attempt to address aspects of the proposal that we have been making concerning a possible physical mechanism underlying the phenomenon of consciousness. Unfortunately, they employ arguments that are highly misleading and, in some important respects, factually incorrect. Their article 'Gaps in Penrose's Toilings' is addressed specifically at the writings of one of us (Penrose), but since the particular model they attack is one put forward by both of us (Hameroff and Penrose, 1995; 1996), it is appropriate that we both reply; but since our individual remarks refer to different aspects of their criticism we are commenting on their article separately. The logical arguments discussed by Grush and Churchland, and the related physics are answered in Part l by Penrose, largely by pointing out precisely where these arguments have already been treated in detail in Shadows of the Mind (Penrose, 1994). In Part 2, Hameroff replies to various points on the biological side, showing for example how they have seriously misunderstood what they refer to as 'physiological evidence' regarding to effects of the drug colchicine. The reply serves also to discuss aspects of our model 'orchestrated objective reduction in brain microtubules – Orch OR' which attempts to deal with the serious problems of consciousness more directly and completely than any previous theory. Logical arguments It has been argued in the books by one of us, The Emperor's New Mind (Penrose, 1989 – henceforth Emperor) and Shadows of the Mind (Penrose, 1994 – henceforth Shadows) that Gödel's theorem shows that there must be something non–computational involved in mathematical thinking. The Grush and Churchland (1995 – henceforth G&C) discussion attempts to dismiss this argument from Gödel's theorem on certain grounds. However, the main points that they put forward are ones which have been amply addressed in Shadows. It is very hard to understand how G&C can make the claims that they do without giving any indication that virtually all their points are explicitly taken into account in Shadows. It might be the case that the
Machine learning in space: extending our reach

We introduce the challenge of using machine learning effectively in space applications and motivate the domain for future researchers. Machine learning can be used to enable greater autonomy to improve the duration, reliability, cost-effectiveness, and science return of space missions. In addition to the challenges provided by the nature of space itself, the requirements of a space mission severely limit the use of many current machine learning approaches, and we encourage researchers to explore new ways to address these challenges.
Image quality quantification for fingerprints using quality-impairment assessment

A quality impairment assessment along with a quality score would enable Automatic Fingerprint Identification Systems (AFIS) to make appropriate decisions to a) reject the fingerprint and recapture another sample, b) use other fingers or biometric features for recognition, c) use image enhancement techniques. Our approach provides a quality score in addition to a quality impairment assessment into dry, wet or small contact area fingerprints, using which the fingerprint could be rejected to re-capture another sample after wiping the finger/using additional pressure. A manual labeling of dry, wet and normal fingerprint regions in the FVC2002 DB1 database is used to create classifiers for the quality impairment assessment. A block based quality impairment classification approach is used to compute an overall image quality score. The block classification into dry, wet or normal blocks has 96.07% accuracy. The overall quality score is between -1(poor quality) and 1(excellent quality), which is found to be satisfactory through a visual inspection.
Augmented Reality Meets Deep Learning for Car Instance Segmentation in Urban Scenes

The success of deep learning in computer vision is based on the availability of large annotated datasets. To lower the need for hand labeled images, virtually rendered 3D worlds have recently gained popularity. Unfortunately, creating realistic 3D content is challenging on its own and requires significant human effort. In this work, we propose an alternative paradigm which combines real and synthetic data for learning semantic instance segmentation models. Exploiting the fact that not all aspects of the scene are equally important for this task, we propose to augment real-world imagery with virtual objects of the target category. Capturing real-world images at large scale is easy and cheap, and directly provides real background appearances without the need for creating complex 3D models of the environment. We present an efficient procedure to augment these images with virtual objects. This allows us to create realistic composite images which exhibit both realistic background appearance as well as a large number of complex object arrangements. In contrast to modeling complete 3D environments, our data augmentation approach requires only a few user interactions in combination with 3D shapes of the target object category. We demonstrate the utility of the proposed approach for training a state-of-the-art high-capacity deep model for semantic instance segmentation. In particular, we consider the task of segmenting car instances on the KITTI dataset which we have annotated with pixel-accurate ground truth. Our experiments demonstrate that models trained on augmented imagery generalize better than those trained on synthetic data or models trained on limited amounts of annotated real data.
Motivation and cognitive control: from behavior to neural mechanism.

Research on cognitive control and executive function has long recognized the relevance of motivational factors. Recently, however, the topic has come increasingly to center stage, with a surge of new studies examining the interface of motivation and cognitive control. In the present article we survey research situated at this interface, considering work from cognitive and social psychology and behavioral economics, but with a particular focus on neuroscience research. We organize existing findings into three core areas, considering them in the light of currently vying theoretical perspectives. Based on the accumulated evidence, we advocate for a view of control function that treats it as a domain of reward-based decision making. More broadly, we argue that neuroscientific evidence plays a critical role in understanding the mechanisms by which motivation and cognitive control interact. Opportunities for further cross-fertilization between behavioral and neuroscientific research are highlighted.
An Analytical Study of Routing Attacks in Vehicular Ad-hoc Networks ( VANETs )

Vehicular Ad-hoc Network (VANET) is a rising & most challenging research area to provide Intelligent Transportation System (ITS) services to the end users. The implementation of routing protocols in VANET is an exigent task as of its high mobility & frequent link disruption topology. VANET is basically used to provide various infotainment services to each and every end user; these services are further responsible to provide an efficient driving environment. At present, to provide efficient communication in vehicular networks several routing protocols have been designed, but the networks are vulnerable to several threats in the presence of malicious nodes. Today, security is the major concern for various VANET applications where a wrong message may directly or indirectly affect the human lives. In this paper, we investigate the several security issues on network layer in VANET. In this, we also examine routing attacks such as Sybil & Illusion attacks, as well as available solutions for such attacks in existing VANET protocols. KeywordsVehicular Ad-hoc Network (VANET), Intelligent Transportation System (ITS), On-board Unit (OBU), security attacks, privacy.
Social MatchUP -: a Memory-like Virtual Reality Game for the Enhancement of Social Skills in Children with Neurodevelopmental Disorders

This paper presents Social MatchUP, a multiplayer Virtual Reality game for children with Neurodevelopmental Disorders (NDD). Shared virtual reality environments (SVREs) allow NDD children to interact in the same virtual space, but without the possible discomfort or fear caused by having a real person in front of them. Social MatchUP is a simple Concentration-like game, run on smartphones, where players should communicate to match up all the pairs of images they are given. Because every player can only interact with half of the pictures, but can see what his companion is doing, the game improves social and communication skill, and can be used also as a learning tool. A simple and easy-to-use customization tool was also developed to let therapists and teachers adapt the game context to the needs of the children they take care of.
Object Detection and Tracking for Autonomous Navigation in Dynamic Environments

We address the problem of vision-based navigation in busy inner-city locations, using a stereo rig mounted on a mobile platform. In this scenario semantic information becomes important: rather than modelling moving objects as arbitrary obstacles, they should be categorised and tracked in order to predict their future behaviour. To this end, we combine classical geometric world mapping with object category detection and tracking. Object-category specific detectors serve to find instances of the most important object classes (in our case pedestrians and cars). Based on these detections, multi-object tracking recovers the objects’ trajectories, thereby making it possible to predict their future locations, and to employ dynamic path planning. The approach is evaluated on challenging, realistic video sequences recorded at busy inner-city locations.
Effect of Mindfulness-Based Stress Reduction Training on Health Care Worker Safety: A Randomized Waitlist Controlled Trial.

The study assessed the impact of mindfulness training on occupational safety of hospital health care workers.



The study used a randomized waitlist-controlled trial design to test the effect of an 8-week mindfulness-based stress reduction (MBSR) course on self-reported health care worker safety outcomes, measured at baseline, postintervention, and 6 months later.



Twenty-three hospital health care workers participated in the study (11 in immediate intervention group; 12 in waitlist control group). The MBSR training decreased workplace cognitive failures (F [1, 20] = 7.44, P = 0.013, ) and increased safety compliance behaviors (F [1, 20] = 7.79, P = 0.011, ) among hospital health care workers. Effects were stable 6 months following the training. The MBSR intervention did not significantly affect participants' promotion of safety in the workplace (F [1, 20] = 0.40, P = 0.54, ).



Mindfulness training may potentially decrease occupational injuries of health care workers.