nils-herrmann/clean_scirepeval huggingface dataset:
The new statistics: why and how.

We need to make substantial changes to how we conduct research. First, in response to heightened concern that our published research literature is incomplete and untrustworthy, we need new requirements to ensure research integrity. These include prespecification of studies whenever possible, avoidance of selection and other inappropriate data-analytic practices, complete reporting, and encouragement of replication. Second, in response to renewed recognition of the severe flaws of null-hypothesis significance testing (NHST), we need to shift from reliance on NHST to estimation and other preferred techniques. The new statistics refers to recommended practices, including estimation based on effect sizes, confidence intervals, and meta-analysis. The techniques are not new, but adopting them widely would be new for many researchers, as well as highly beneficial. This article explains why the new statistics are important and offers guidance for their use. It describes an eight-step new-statistics strategy for research with integrity, which starts with formulation of research questions in estimation terms, has no place for NHST, and is aimed at building a cumulative quantitative discipline.
Islamic Microfinance : An Emerging Market Niche

Muslim-majority countries do not use formal financial services (Honohon 2007).1 Even when financial services are available, some people view conventional products as incompatible with the financial principles set forth in Islamic law. In recent years, some microfinance institutions (MFIs) have stepped in to service low-income Muslim clients who demand products consistent with Islamic financial principles—leading to the emergence of Islamic microfinance as a new market niche.
Let there be color!: joint end-to-end learning of global and local image priors for automatic image colorization with simultaneous classification

We present a novel technique to automatically colorize grayscale images that combines both global priors and local image features. Based on Convolutional Neural Networks, our deep network features a fusion layer that allows us to elegantly merge local information dependent on small image patches with global priors computed using the entire image. The entire framework, including the global and local priors as well as the colorization model, is trained in an end-to-end fashion. Furthermore, our architecture can process images of any resolution, unlike most existing approaches based on CNN. We leverage an existing large-scale scene classification database to train our model, exploiting the class labels of the dataset to more efficiently and discriminatively learn the global priors. We validate our approach with a user study and compare against the state of the art, where we show significant improvements. Furthermore, we demonstrate our method extensively on many different types of images, including black-and-white photography from over a hundred years ago, and show realistic colorizations.
Using Word Embedding to Enable Semantic Queries in Relational Databases

We investigate opportunities for exploiting Artificial Intelligence (AI) techniques for enhancing capabilities of relational databases. In particular, we explore applications of Natural Language Processing (NLP) techniques to endow relational databases with capabilities that were very hard to realize in practice. We apply an unsupervised neural-network based NLP idea, Distributed Representation via Word Embedding, to extract latent information from a relational table. The word embedding model is based on meaningful textual view of a relational database and captures inter-/intra-attribute relationships between database tokens. For each database token, the model includes a vector that encodes these contextual semantic relationships. These vectors enable processing a new class of SQL-based business intelligence queries called cognitive intelligence (CI) queries that use the generated vectors to analyze contextual semantic relationships between database tokens. The cognitive capabilities enable complex queries such as semantic matching, reasoning queries such as analogies, predictive queries using entities not present in a database, and using knowledge from external sources.
Characterizing Multi-Agent Team Behavior from Partial Team Tracings: Evidence from the English Premier League

Real-world AI systems have been recently deployed which can automatically analyze the plan and tactics of tennis players. As the game-state is updated regularly at short intervals (i.e. point-level), a library of successful and unsuccessful plans of a player can be learnt over time. Given the relative strengths and weaknesses of a player’s plans, a set of proven plans or tactics from the library that characterize a player can be identified. For low-scoring, continuous team sports like soccer, such analysis for multi-agent teams does not exist as the game is not segmented into “discretized” plays (i.e. plans), making it difficult to obtain a library that characterizes a team’s behavior. Additionally, as player tracking data is costly and difficult to obtain, we only have partial team tracings in the form of ball actions which makes this problem even more difficult. In this paper, we propose a method to overcome these issues by representing team behavior via play-segments, which are spatio-temporal descriptions of ball movement over fixed windows of time. Using these representations we can characterize team behavior from entropy maps, which give a measure of predictability of team behaviors across the field. We show the efficacy and applicability of our method on the 2010-2011 English Premier League soccer data.
The use of environmental, health and safety research in nanotechnology research.

Environmental, health, and safety (EHS) concerns are receiving considerable attention in nanoscience and nanotechnology (nano) research and development (R&D). Policymakers and others have urged that research on nano's EHS implications be developed alongside scientific research in the nano domain rather than subsequent to applications. This concurrent perspective suggests the importance of early understanding and measurement of the diffusion of nano EHS research. The paper examines the diffusion of nano EHS publications, defined through a set of search terms, into the broader nano domain using a global nanotechnology R&D database developed at Georgia Tech. The results indicate that nano EHS research is growing rapidly although it is orders of magnitude smaller than the broader nano S&T domain.  EHS work is moderately multidisciplinary, but gaps in biomedical nano EHS's connections with environmental nano EHS are apparent. The paper discusses the implications of these results for the continued monitoring and development of the cross-disciplinary utilization of nano EHS research.
The Role of Self-Compassion in Goal Pursuit and Well-Being Among University Freshmen

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Energy Harvesting Electronics for Vibratory Devices in Self-Powered Sensors

Recent advances in energy harvesting have been intensified due to urgent needs of portable, wireless electronics with extensive life span. The idea of energy harvesting is applicable to sensors that are placed and operated on some entities for a long time, or embedded into structures or human bodies, in which it is troublesome or detrimental to replace the sensor module batteries. Such sensors are commonly called “self-powered sensors.” The energy harvester devices are capable of capturing environmental energy and supplanting the battery in a standalone module, or working along with the battery to extend substantially its life. Vibration is considered one of the most high power and efficient among other ambient energy sources, such as solar energy and temperature difference. Piezoelectric and electromagnetic devices are mostly used to convert vibration to ac electric power. For vibratory harvesting, a delicately designed power conditioning circuit is required to store as much as possible of the device-output power into a battery. The design for this power conditioning needs to be consistent with the electric characteristics of the device and battery to achieve maximum power transfer and efficiency. This study offers an overview on various power conditioning electronic circuits designed for vibratory harvester devices and their applications to self-powered sensors. Comparative comments are provided in terms of circuit topology differences, conversion efficiencies and applicability to a sensor module.
ENGAGING IMAGE CAPTIONING

Standard image captioning tasks such as COCO and Flickr30k are factual, neutral in tone and (to a human) state the obvious (e.g., “a man playing a guitar”). While such tasks are useful to verify that a machine understands the content of an image, they are not engaging to humans as captions. With this in mind we define a new task, PERSONALITY-CAPTIONS, where the goal is to be as engaging to humans as possible by incorporating controllable style and personality traits. We collect and release a large dataset of 201,858 of such captions conditioned over 215 possible traits. We build models that combine existing work from (i) sentence representations (Mazaré et al., 2018) with Transformers trained on 1.7 billion dialogue examples; and (ii) image representations (Mahajan et al., 2018) with ResNets trained on 3.5 billion social media images. We obtain state-of-theart performance on Flickr30k and COCO, and strong performance on our new task. Finally, online evaluations validate that our task and models are engaging to humans, with our best model close to human performance.
Towards secure e-voting using ethereum blockchain

There is no doubt that the revolutionary concept of the blockchain, which is the underlying technology behind the famous cryptocurrency Bitcoin and its successors, is triggering the start of a new era in the Internet and the online services. While most people focus only at cryptocurrencies; in fact, many administrative operations, fintech procedures, and everyday services that can only be done offline and/or in person, can now safely be moved to the Internet as online services. What makes it a powerful tool for digitalizing everyday services is the introduction of smart contracts, as in the Ethereum platform. Smart contracts are meaningful pieces of codes, to be integrated in the blockchain and executed as scheduled in every step of blockchain updates. E-voting on the other hand, is another trending, yet critical, topic related to the online services. The blockchain with the smart contracts, emerges as a good candidate to use in developments of safer, cheaper, more secure, more transparent, and easier-to-use e-voting systems. Ethereum and its network is one of the most suitable ones, due to its consistency, widespread use, and provision of smart contracts logic. An e-voting system must be secure, as it should not allow duplicated votes and be fully transparent, while protecting the privacy of the attendees. In this work, we have implemented and tested a sample e-voting application as a smart contract for the Ethereum network using the Ethereum wallets and the Solidity language. Android platform is also considered to allow voting for people who do not have an Ethereum wallet. After an election is held, eventually, the Ethereum blockchain will hold the records of ballots and votes. Users can submit their votes via an Android device or directly from their Ethereum wallets, and these transaction requests are handled with the consensus of every single Ethereum node. This consensus creates a transparent environment for e-voting. In addition to a broad discussion about reliability and efficiency of the blockchain-based e-voting systems, our application and its test results are presented in this paper, too.
Assessing broadband vegetation indices and QuickBird data in estimating leaf area index of corn and potato canopies

Leaf area index (LAI) is a key biophysical variable that can be used to derive agronomic information for field management and yield prediction. In the context of applying broadband and high spatial resolution satellite sensor data to agricultural applications at the field scale, an improved method was developed to evaluate commonly used broadband vegetation indices (VIs) for the estimation of LAI with VI–LAI relationships. The evaluation was based on direct measurement of corn and potato canopies and on QuickBird multispectral images acquired in three growing seasons. The selected VIs were correlated strongly with LAI but with different efficiencies for LAI estimation as a result of the differences in the stabilities, the sensitivities, and the dynamic ranges. Analysis of error propagation showed that LAI noise inherent in each VI–LAI function generally increased with increasing LAI and the efficiency of most VIs was low at high LAI levels. Among selected VIs, the modified soil-adjusted vegetation index (MSAVI) was the best LAI estimator with the largest dynamic range and the highest sensitivity and overall efficiency for both crops. QuickBird image-estimated LAI with MSAVI–LAI relationships agreed well with ground-measured LAI with the root-mean-square-error of 0.63 and 0.79 for corn and potato canopies, respectively. LAI estimated from the high spatial resolution pixel data exhibited spatial variability similar to the ground plot measurements. For field scale agricultural applications, MSAVI–LAI relationships are easy-to-apply and reasonably accurate for estimating LAI.
A survey on trust based detection and isolation of malicious nodes in ad-hoc and sensor networks

Mobile ad-hoc networks (MANETs) and wireless sensor networks (WSNs) have gained remarkable appreciation and technological development over the last few years. Despite ease of deployment, tremendous applications and significant advantages, security has always been a challenging issue due to the nature of environments in which nodes operate. Nodes’ physical capture, malicious or selfish behavior cannot be detected by traditional security schemes. Trust and reputation based approaches have gained global recognition in providing additional means of security for decision making in sensor and ad-hoc networks. This paper provides an extensive literature review of trust and reputation based models both in sensor and ad-hoc networks. Based on the mechanism of trust establishment, we categorize the state-of-the-art into two groups namely node-centric trust models and system-centric trust models. Based on trust evidence, initialization, computation, propagation and weight assignments, we evaluate the efficacy of the existing schemes. Finally, we conclude our discussion with identification of some unresolved issues in pursuit of trust and reputation management.
Macroeconomics of Unbalanced Growth: The Anatomy of Urban Crisis

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Adversarial Training Versus Weight Decay

Performance-critical machine learning models should be robust to input perturbations not seen during training. Adversarial training is a method for improving a model’s robustness to some perturbations by including them in the training process, but this tends to exacerbate other vulnerabilities of the model. The adversarial training framework has the effect of translating the data with respect to the cost function, while weight decay has a scaling effect. Although weight decay could be considered a crude regularization technique, it appears superior to adversarial training as it remains stable over a broader range of regimes and reduces all generalization errors. Equipped with these abstractions, we provide key baseline results and methodology for characterizing robustness. The two approaches can be combined to yield one small model that demonstrates good robustness to several whitebox attacks associated with different
LOGAN: Problem Diagnosis in the Cloud Using Log-Based Reference Models

Problem diagnosis is one crucial aspect in the cloud operation that is becoming increasingly challenging. On the one hand, the volume of logs generated in today's cloud is overwhelmingly large. On the other hand, cloud architecture becomes more distributed and complex, which makes it more difficult to troubleshoot failures. In order to address these challenges, we have developed a tool, called LOGAN, that enables operators to quickly identify the log entries that potentially lead to the root cause of a problem. It constructs behavioral reference models from logs that represent the normal patterns. When problem occurs, our tool enables operators to inspect the divergence of current logs from the reference model and highlight logs likely to contain the hints to the root cause. To support these capabilities we have designed and developed several mechanisms. First, we developed log correlation algorithms using various IDs embedded in logs to help identify and isolate log entries that belong to the failed request. Second, we provide efficient log comparison to help understand the differences between different executions. Finally we designed mechanisms to highlight critical log entries that are likely to contain information pertaining to the root cause of the problem. We have implemented the proposed approach in a popular cloud management system, OpenStack, and through case studies, we demonstrate this tool can help operators perform problem diagnosis quickly and effectively.
Agency and communion from the perspective of self versus others.

On the basis of previous research, the authors hypothesize that (a) person descriptive terms can be organized into the broad dimensions of agency and communion of which communion is the primary one; (b) the main distinction between these dimensions pertains to their profitability for the self (agency) vs. for other persons (communion); hence, agency is more desirable and important in the self-perspective, and communion is more desirable and important in the other-perspective; (c) self-other outcome dependency increases importance of another person's agency. Study 1 showed that a large number of trait names can be reduced to these broad dimensions, that communion comprises more item variance, and that agency is predicted by self-profitability and communion by other-profitability. Studies 2 and 3 showed that agency is more relevant and desired for self, and communion is more relevant and desired for others. Study 4 showed that agency is more important in a close friend than an unrelated peer, and this difference is completely mediated by the perceived outcome dependency.
Contextual moderation of racial bias: the impact of social roles on controlled and automatically activated attitudes.

Three experiments tested the hypothesis that the social roles implied by specific contexts can attenuate or reverse the typical pattern of racial bias obtained on both controlled and automatic evaluation measures. Study 1 assessed evaluations of Black and Asian faces in contexts related to athlete or student roles. Study 2 compared evaluations of Black and White faces in 3 role-related contexts (prisoner, churchgoer, and factory worker). Study 3 manipulated role cues (lawyer or prisoner) within the same prison context. All 3 studies produced significant reversals of racial bias as a function of implied role on measures of both controlled and automatic evaluation. These results support the interpretation that differential evaluations based on Race x Role interactions provide one way that context can moderate both controlled and automatic racial bias.
Two Decades of Research and Development in Transformational Leadership

The interests of the organization and its members need to be aligned. Such is a task for the transformational leader. In contrast to the transactional leader who practises contingent reinforcement of followers, the transformational leader inspires, intellectually stimulates, and is individually considerate of them. Transformational leadership may be directive or participative. Requiring higher moral development, transformational leadership is recognized universally as a concept. Furthermore, contrary to earlier expectations, women leaders tend to be more transformational than their male counterparts. Although a six-factor model of transformational/ transactional leadership best fits a diversity of samples according to confirmatory factor analyses, whether fewer factors are necessary remains an open question. Another important research question that has only been partially answered is why transformational leadership is more effective than transactional leadership in a wide variety of business, military, industrial, hospital, and educational circumstances.
Retro-λ: An Event-sourced Platform for Serverless Applications with Retroactive Computing Support

State changes over time are inherent characteristics of stateful applications. So far, there are almost no attempts to make the past application history programmatically accessible or even modifiable. This is primarily due to the complexity of temporal changes and a difficult alignment with prevalent programming primitives and persistence strategies. Retroactive computing enables powerful capabilities though, including computations and predictions of alternate application timelines, post-hoc bug fixes, or retroactive state explorations. We propose an event-driven programming model that is oriented towards serverless computing and applies retroaction to the event sourcing paradigm. Our model is deliberately restrictive, but therefore keeps the complexity of retroactive operations in check. We introduce retro-λ, a runtime platform that implements the model and provides retroactive capabilites to its applications. While retro-λ only shows negligible performance overheads compared to similar solutions for running regular applications, it enables its users to execute retroactive computations on the application histories as part of its programming model.
Topological insulators in three dimensions.

We study three-dimensional generalizations of the quantum spin Hall (QSH) effect. Unlike two dimensions, where a single Z2 topological invariant governs the effect, in three dimensions there are 4 invariants distinguishing 16 phases with two general classes: weak (WTI) and strong (STI) topological insulators. The WTI are like layered 2D QSH states, but are destroyed by disorder. The STI are robust and lead to novel "topological metal" surface states. We introduce a tight binding model which realizes the WTI and STI phases, and we discuss its relevance to real materials, including bismuth.