nils-herrmann/clean_scirepeval huggingface dataset:
Bootstrapping Graph Convolutional Neural Networks for Autism Spectrum Disorder Classification

Using predictive models to identify patterns that can act as biomarkers for different neuropathoglogical conditions is becoming highly prevalent. In this paper, we consider the problem of Autism Spectrum Disorder (ASD) classification where previous work has shown that it can be beneficial to incorporate a wide variety of meta features, such as socio-cultural traits, into predictive modeling. A graph-based approach naturally suits these scenarios, where a contextual graph captures traits that characterize a population, while the specific brain activity patterns are utilized as a multivariate signal at the nodes. Graph neural networks have shown improvements in inferencing with graph-structured data. Though the underlying graph strongly dictates the overall performance, there exists no systematic way of choosing an appropriate graph in practice, thus making predictive models non-robust. To address this, we propose a bootstrapped version of graph convolutional neural networks (G-CNNs) that utilizes an ensemble of weakly trained G-CNNs, and reduce the sensitivity of models on the choice of graph construction. We demonstrate its effectiveness on the challenging Autism Brain Imaging Data Exchange (ABIDE) dataset and show that our approach improves upon recently proposed graph-based neural networks. We also show that our method remains more robust to noisy graphs.
Aoristic Signatures and the Spatio-Temporal Analysis of High Volume Crime Patterns

The spatial analysis of crime and the current focus on hotspots has pushed the area of crime mapping to the fore, especially in regard to high volume offences such as vehicle theft and burglary. Hotspots also have a temporal component, yet police recorded crime databases rarely record the actual time of offence as this is seldom known. Police crime data tends, more often than not, to reflect the routine activities of the victims rather than the offence patterns of the offenders. This paper demonstrates a technique that uses police START and END crime times to generate a crime occurrence probability at any given time that can be mapped or visualized graphically. A study in the eastern suburbs of Sydney, Australia, demonstrates that crime hotspots with a geographical proximity can have distinctly different temporal patterns.
Fake News: A Technological Approach to Proving the Origins of Content, Using Blockchains.

In this article, we introduce a prototype of an innovative technology for proving the origins of captured digital media. In an era of fake news, when someone shows us a video or picture of some event, how can we trust its authenticity? It seems that the public no longer believe that traditional media is a reliable reference of fact, perhaps due, in part, to the onset of many diverse sources of conflicting information, via social media. Indeed, the issue of "fake" reached a crescendo during the 2016 U.S. Presidential Election, when the winner, Donald Trump, claimed that The New York Times was trying to discredit him by pushing disinformation. Current research into overcoming the problem of fake news does not focus on establishing the ownership of media resources used in such stories-the blockchain-based application introduced in this article is technology that is capable of indicating the authenticity of digital media. Put simply, using the trust mechanisms of blockchain technology, the tool can show, beyond doubt, the provenance of any source of digital media, including images used out of context in attempts to mislead. Although the application is an early prototype and its capability to find fake resources is somewhat limited, we outline future improvements that would overcome such limitations. Furthermore, we believe that our application (and its use of blockchain technology and standardized metadata) introduces a novel approach to overcoming falsities in news reporting and the provenance of media resources used therein. However, while our application has the potential to be able to verify the originality of media resources, we believe that technology is only capable of providing a partial solution to fake news. That is because it is incapable of proving the authenticity of a news story as a whole. We believe that takes human skills.
An agent-oriented, trust-aware approach to improve the QoS in dynamic grid federations

In this paper a distributed approach aimed at improving the Quality of Service in dynamic grid federations is presented. Virtual Organizations are grouped into large-scale Federations on which the original goals and scheduling mechanisms are left unchanged, while Grid nodes can be quickly instructed to join or leave any Grid at any time. Moreover, an agent-oriented framework is designed to observe and characterize past behaviors of nodes in terms of resource sharing and consumption, as well as to determine the trust relationships occurring between each pair of nodes. By combining trust and historical behaviors into a unified convenience measure, software agents are able to evaluate the i) advantages of node’s membership with specific any Grids, and ii) whether the actual set of Grid nodes is able to meet the original goal – in terms of resource sharing and consumption – of the Virtual Organizations. The convenience measure has been exploited to design a fully decentralized, greedy procedure, aimed at controlling the Grid Formation process. Extensive simulations have shown that the coordinated and decentralized process of Grid Formation provides a powerful mean to improve the overall Quality of Service of the Grid Federation.
WHAT CAN MACHINEs LEARN AND WHAT dOES IT MEAN FOR OCCUPATIONS AND THE ECONOMY ?

Our research suggests that ML technologies will indeed grow more pervasive, but within job categories, what we define as the “suitability for machine learning” (SML) of work tasks varies greatly. We further propose that our SML rubric, illustrating the variability in task-level SML, can serve as an indicator for the potential reorganization of a job or an occupation because the set of tasks that form a job can be separated and re-bundled to redefine the job. Evaluating worker activities using our rubric, in fact, has the benefit of focusing on what ML can do instead of grouping all forms of automation together.
Plant growth-promoting rhizobacteria (PGPR): emergence in agriculture.

Plant growth-promoting rhizobacteria (PGPR) are the rhizosphere bacteria that can enhance plant growth by a wide variety of mechanisms like phosphate solubilization, siderophore production, biological nitrogen fixation, rhizosphere engineering, production of 1-Aminocyclopropane-1-carboxylate deaminase (ACC), quorum sensing (QS) signal interference and inhibition of biofilm formation, phytohormone production, exhibiting antifungal activity, production of volatile organic compounds (VOCs), induction of systemic resistance, promoting beneficial plant-microbe symbioses, interference with pathogen toxin production etc. The potentiality of PGPR in agriculture is steadily increased as it offers an attractive way to replace the use of chemical fertilizers, pesticides and other supplements. Growth promoting substances are likely to be produced in large quantities by these rhizosphere microorganisms that influence indirectly on the overall morphology of the plants. Recent progress in our understanding on the diversity of PGPR in the rhizosphere along with their colonization ability and mechanism of action should facilitate their application as a reliable component in the management of sustainable agricultural system. The progress to date in using the rhizosphere bacteria in a variety of applications related to agricultural improvement along with their mechanism of action with special reference to plant growth-promoting traits are summarized and discussed in this review.
Robust Direct Visual Odometry Estimation for a Monocular Camera Under Rotations

The objective of this letter is to enhance the accuracy of the monocular visual odometry estimation. The proposed approach estimates pose of the camera via direct approach, by minimizing a novel photometric residue between the current image and warped version of the consecutive image. The novel photometric residue incorporates orientation information accessible from complementary sensor to overcome the inalienable nonlinearity of pose estimation, accordingly lessening the susceptibility to trajectory noise. In addition, this letter introduces a homography-based rotation motion estimation embedded into the photometric residue to reduce tracking failure amid rotations, which could be attributed to lesser number of features with parallax. The proposed methodology permits to integrate Gaussian mixture based prior into visual odometry estimation to cater for changes in motion type, from general to rotation and other way round. Comprehensive experiments have been conducted to exhibit the efficacy of the proposed solution over the best in class visual odometry and visual-inertial systems.
Multi-Pointer Co-Attention Networks for Recommendation

Many recent state-of-the-art recommender systems such as D-ATT, TransNet and DeepCoNN exploit reviews for representation learning. This paper proposes a new neural architecture for recommendation with reviews. Our model operates on a multi-hierarchical paradigm and is based on the intuition that not all reviews are created equal, i.e., only a selected few are important. The importance, however, should be dynamically inferred depending on the current target. To this end, we propose a review-by-review pointer-based learning scheme that extracts important reviews from user and item reviews and subsequently matches them in a word-by-word fashion. This enables not only the most informative reviews to be utilized for prediction but also a deeper word-level interaction. Our pointer-based method operates with a gumbel-softmax based pointer mechanism that enables the incorporation of discrete vectors within differentiable neural architectures. Our pointer mechanism is co-attentive in nature, learning pointers which are co-dependent on user-item relationships. Finally, we propose a multi-pointer learning scheme that learns to combine multiple views of user-item interactions. We demonstrate the effectiveness of our proposed model via extensive experiments on 24 benchmark datasets from Amazon and Yelp. Empirical results show that our approach significantly outperforms existing state-of-the-art models, with up to 19% and 71% relative improvement when compared to TransNet and DeepCoNN respectively. We study the behavior of our multi-pointer learning mechanism, shedding light on 'evidence aggregation' patterns in review-based recommender systems.
Global ranking of knowledge management and intellectual capital academic journals: 2013 update

The purpose of this study is to update a global ranking of knowledge management and intellectual capital (KM/IC) academic journals.  Two different approaches were utilized: a survey of 379 active KM/IC researchers; and the journal citation impact method. Scores produced by the application of these methods were combined to develop the final ranking.  Twenty-five KM/IC-centric journals were identified and ranked. The top six journals are: Journal of Knowledge Management, Journal of Intellectual Capital, The Learning Organization, Knowledge Management Research & Practice, Knowledge and Process Management and International Journal of Knowledge Management. Knowledge Management Research & Practice has substantially improved its reputation. The Learning Organization and Journal of Intellectual Capital retained their previous positions due to their strong citation impact. The number of KM/IC-centric and KM/IC-relevant journals has been growing at the pace of one new journal launch per year. This demonstrates that KM/IC is not a scientific fad; instead, the discipline is progressing towards academic maturity and recognition.  The developed ranking may be used by various stakeholders, including journal editors, publishers, reviewers, researchers, new scholars, students, policymakers, university administrators, librarians and practitioners. It is a useful tool to further promote the KM/IC discipline and develop its unique identity. It is important for all KM/IC journals to become included in Thomson Reuters’ Journal Citation Reports.  This is the most up-to-date ranking of KM/IC journals.
On Tighter Generalization Bound for Deep Neural Networks: CNNs, ResNets, and Beyond

We establish a margin based data dependent generalization error bound for a general family of deep neural networks in terms of the depth and width of the networks, as well as the spectral norm of weight matrices. Through introducing a new characterization of the Lipschitz properties of neural network family, we achieve a tighter generalization error bound. Moreover, we show that the generalization bound can be further improved for bounded losses. In addition, we demonstrate that the margin scales with the product of norm, which eliminate the concern on the vacuity of the norm based bound. Aside from the general feedforward deep neural networks, our results can be applied to derive new bounds for several popular architectures, including convolutional neural networks (CNNs), residual networks (ResNets), and hyperspherical networks (SphereNets). When achieving same generalization errors with previous arts, our bounds allow for the choice of larger parameter spaces of weight matrices, inducing potentially stronger expressive ability for neural networks. Moreover, we discuss the limitation of existing generalization bounds for understanding deep neural networks with ReLU activations in classification.
A TOTP-Based Two Factor Authentication Scheme for Hyperledger Fabric Blockchain

In this paper, we propose a new authentication method to prevent authentication vulnerability of Claim Token method of Membership Service provide in Private BlockChain. We chose Hyperledger Fabric v1.0 using JWT authentication method of membership service. TOTP, which generate OTP tokens and user authentication codes that generate additional time-based password on existing authentication servers, has been applied to enforce security and two-factor authentication method to provide more secure services.
Trump typhoon: A rhetorical analysis of the Donald's Twitter feed

Tweets are Donald Trump's quickest and most frequently employed way to send shockwaves. Tweets allow the user to respond quickly to the Kairos of developing situations—an advantage to the medium, but perhaps also a disadvantage, as Trump's 3am and 4am tweets tend to show. In this paper, we apply the three classical modes of rhetoric—forensic/judicial, deliberative, and epideictic/ceremonial rhetoric—to see how the modes manifest in Donald Trump's tweets as a presidential candidate, as President-Elect, and as President. Does the use of these three modes shift as Trump's rhetorical situation and especially subject position shift? Besides looking for quantitative changes in Trump's favored modes over time, our qualitative analysis includes representative examples and interesting examples of Trump's use of each mode (and combinations of them) during each time period.
Internal Organization of the Alpha 21164, a 300-MHz 64-bit Quad-issue CMOS RISC Microprocessor

A new CMOS microprocessor, the Alpha 21164, reaches 1,200 mips/600 MFLOPS (peak performance). This new implementation of the Alpha architecture achieves SPECint92/SPECfp92 performance of 345/505 (estimated). At these performance levels, the Alpha 21164 has delivered the highest performance of any commercially available microprocessor in the world as of January 1995. It contains a quad-issue, superscalar instruction unit; two 64-bit integer execution pipelines; two 64-bit floating-point execution pipelines; and a high-performance memory subsystem with multiprocessor-coherent write-back caches. OVERVIEW OF THE ALPHA 21164 The Alpha 21164 microprocessor is now a product of Digital Semiconductor. The chip is the second completely new microprocessor to implement the Alpha instruction set architecture. It was designed in Digital's 0.5-micrometer (um) complementary metal-oxide semiconductor (CMOS) process. First silicon was powered on in February 1994; the part has been commercially available since January 1995. At SPECint92/SPECfp92 ratings of 345/505 (estimated), the Alpha 21164 achieved new heights of performance. The performance of this new implementation results from aggressive circuit design using the latest 0.5-um CMOS technology and significant architectural improvements over the first Alpha implementation.[1] The chip is designed to operate at 300 MHz, an operating frequency 10 percent faster than the previous implementation (the DECchip 21064 chip) would have if it were scaled into the new 0.5-um CMOS technology.[2] Relative to the previous implementation, the key improvements in machine organization are a doubling of the superscalar dimension to four-way superscalar instruction issue; reduction of many operational latencies, including the latency in the primary data cache; a memory subsystem that does not block other operations after a cache miss; and a large, on-chip, second-level, write-back cache. The 21164 microprocessor implements the Alpha instruction set architecture. It runs existing Alpha programs without modification. It supports a 43-bit virtual address and a 40-bit physical address. The page size is 8 kilobytes (KB). In the following sections, we describe the five functional units of the Alpha 21164 microprocessor and relate some of the design decisions that improved the performance of the microprocessor. First, we give an overview of the chip's internal organization and pipeline layout. Internal Organization Figure 1 shows a block diagram of the chip's five functional units: the instruction unit, the integer function unit, the floating-point unit, the memory unit, and the cache control and bus interface unit (called the C-box). The three on-chip caches are also shown. The instruction cache and data cache are primary, direct-mapped caches. They are backed by the second-level cache, which is a set-associative cache that holds instructions and data.
A framework for enriching lexical semantic resources with distributional semantics

We present an approach to combining distributional semantic representations induced from text corpora with manually constructed lexical-semantic networks. While both kinds of semantic resources are available with high lexical coverage, our aligned resource combines the domain specificity and availability of contextual information from distributional models with the conciseness and high quality of manually crafted lexical networks. We start with a distributional representation of induced senses of vocabulary terms, which are accompanied with rich context information given by related lexical items. We then automatically disambiguate such representations to obtain a full-fledged proto-conceptualization, i.e. a typed graph of induced word senses. In a final step, this proto-conceptualization is aligned to a lexical ontology, resulting in a hybrid aligned resource. Moreover, unmapped induced senses are associated with a semantic type in order to connect them to the core resource. Manual evaluations against ground-truth judgments for different stages of our method as well as an extrinsic evaluation on a knowledge-based Word Sense Disambiguation benchmark all indicate the high quality of the new hybrid resource. Additionally, we show the benefits of enriching top-down lexical knowledge resources with bottom-up distributional information from text for addressing high-end knowledge acquisition tasks such as cleaning hypernym graphs and learning taxonomies from scratch.
Mood and the reliance on the ease of retrieval heuristic.

Four studies investigate the relationship between individuals' mood and their reliance on the ease retrieval heuristic. Happy participants were consistently more likely to rely on the ease of retrieval heuristic, whereas sad participants were more likely to rely on the activated content. Additional analyses indicate that this pattern is not due to a differential recall (Experiment 2) and that happy participants ceased to rely on the ease of retrieval when the diagnosticity of this information was called into question (Experiment 3). Experiment 4 shows that reliance on the ease of retrieval heuristic resulted in faster judgments than reliance on content, with the former but not the latter being a function of the amount of activated information.
Diagnosis and treatment of plantar fasciitis.

Plantar fasciitis, a self-limiting condition, is a common cause of heel pain in adults. It affects more than 1 million persons per year, and two-thirds of patients with plantar fasciitis will seek care from their family physician. Plantar fasciitis affects sedentary and athletic populations. Obesity, excessive foot pronation, excessive running, and prolonged standing are risk factors for developing plantar fasciitis. Diagnosis is primarily based on history and physical examination. Patients may present with heel pain with their first steps in the morning or after prolonged sitting, and sharp pain with palpation of the medial plantar calcaneal region. Discomfort in the proximal plantar fascia can be elicited by passive ankle/first toe dorsiflexion. Diagnostic imaging is rarely needed for the initial diagnosis of plantar fasciitis. Use of ultrasonography and magnetic resonance imaging is reserved for recalcitrant cases or to rule out other heel pathology; findings of increased plantar fascia thickness and abnormal tissue signal the diagnosis of plantar fasciitis. Conservative treatments help with the disabling pain. Initially, patient-directed treatments consisting of rest, activity modification, ice massage, oral analgesics, and stretching techniques can be tried for several weeks. If heel pain persists, then physician-prescribed treatments such as physical therapy modalities, foot orthotics, night splinting, and corticosteroid injections should be considered. Ninety percent of patients will improve with these conservative techniques. Patients with chronic recalcitrant plantar fasciitis lasting six months or longer can consider extracorporeal shock wave therapy or plantar fasciotomy.
Character Strengths Among Youth

Four hundred and fiftynine students from 20 different high school classrooms in Michigan participated in focus group discussions about the character strengths included in the Values in Action Classification. Students were interested in the subject of good character and able to discuss with candor and sophistication instances of each strength. They were especially drawn to the positive traits of leadership, practical intelligence, wisdom, social intelligence, love of learning, spirituality, and the capacity to love and be loved. Students believed that strengths were largely acquired rather than innate and that these strengths developed through ongoing life experience as opposed to formal instruction. They cited an almost complete lack of contemporary role models exemplifying different strengths of character. Implications of these findings for the quantitative assessment of positive traits were discussed, as were implications for designing character education programs for adolescents. We suggest that peers can be an especially important force in encouraging the development and display of good character among youth.