nils-herrmann/clean_scirepeval huggingface dataset:
Federated Machine Learning: Concept and Applications

Today’s artificial intelligence still faces two major challenges. One is that, in most industries, data exists in the form of isolated islands. The other is the strengthening of data privacy and security. We propose a possible solution to these challenges: secure federated learning. Beyond the federated-learning framework first proposed by Google in 2016, we introduce a comprehensive secure federated-learning framework, which includes horizontal federated learning, vertical federated learning, and federated transfer learning. We provide definitions, architectures, and applications for the federated-learning framework, and provide a comprehensive survey of existing works on this subject. In addition, we propose building data networks among organizations based on federated mechanisms as an effective solution to allowing knowledge to be shared without compromising user privacy.
Effects of Violent Video Games on Aggressive Behavior : Potential Sex Differences

Evidence of the effects of playing violent video games on subsequent aggression has been mixed. This study examined how playing a violent video game affected levels of aggression displayed in a laboratory. A total of 43 undergraduate students (22 men and 21 women) were randomly assigned to play either a violent (Mortal Kombat) or nonviolent (PGA Tournament Golf) video game for 10 min. Then they competed with a confederate in a reaction time task that allowed for provocation and retaliation. Punishment levels set by participants for their opponents served as the measure of aggression. The results confirmed our hypothesis that playing the violent game would result in more aggression than would playing the nonviolent game. In addition, a Game Sex interaction showed that ssed
Stock Market Volatility and Macroeconomic Fundamentals

We revisit the relation between stock market volatility and macroeconomic activity using a new class of component models that distinguish short-run from long-run movements. We formulate models with the long-term component driven by inflation and industrial production growth that are in terms of pseudo out-of-sample prediction for horizons of one quarter at par or outperform more traditional time series volatility models at longer horizons. Hence, imputing economic fundamentals into volatility models pays off in terms of long-horizon forecasting. We also find that macroeconomic fundamentals play a significant role even at short horizons.
Recurrent Neural Networks for Semantic Instance Segmentation

We present a recurrent model for semantic instance segmentation that sequentially generates binary masks and their associated class probabilities for every object in an image. Our proposed system is trainable end-to-end from an input image to a sequence of labeled masks and, compared to methods relying on object proposals, does not require postprocessing steps on its output. We study the suitability of our recurrent model on three different instance segmentation benchmarks, namely Pascal VOC 2012, CVPPP Plant Leaf Segmentation and Cityscapes. Further, we analyze the object sorting patterns generated by our model and observe that it learns to follow a consistent pattern, which correlates with the activations learned in the encoder part of our network.
Disaster Hashtags in Social Media

Social media is a rich data source for analyzing the social impact of hazard processes and human behavior in disaster situations; it is used by rescue agencies for coordination and by local governments for the distribution of official information. In this paper, we propose a method for data mining in Twitter to retrieve messages related to an event. We describe an automated process for the collection of hashtags highly related to the event and specific only to it. We compare our method with existing keyword-based methods and prove that hashtags are good markers for the separation of similar, simultaneous incidents; therefore, the retrieved messages have higher relevancy. The method uses disaster databases to find the location of an event and to estimate the impact area. The proposed method can also be adapted to retrieve messages about other types of events with a known location, such as riots, festivals and exhibitions.
Identifying and Targeting Sporadic Oncogenic Genetic Aberrations in Mouse Models of Triple-Negative Breast Cancer.

Triple-negative breast cancers (TNBC) are genetically characterized by aberrations in TP53 and a low rate of activating point mutations in common oncogenes, rendering it challenging in applying targeted therapies. We performed whole-exome sequencing (WES) and RNA sequencing (RNA-seq) to identify somatic genetic alterations in mouse models of TNBCs driven by loss of Trp53 alone or in combination with Brca1 Amplifications or translocations that resulted in elevated oncoprotein expression or oncoprotein-containing fusions, respectively, as well as frameshift mutations of tumor suppressors were identified in approximately 50% of the tumors evaluated. Although the spectrum of sporadic genetic alterations was diverse, the majority had in common the ability to activate the MAPK/PI3K pathways. Importantly, we demonstrated that approved or experimental drugs efficiently induce tumor regression specifically in tumors harboring somatic aberrations of the drug target. Our study suggests that the combination of WES and RNA-seq on human TNBC will lead to the identification of actionable therapeutic targets for precision medicine-guided TNBC treatment. Using combined WES and RNA-seq analyses, we identified sporadic oncogenic events in TNBC mouse models that share the capacity to activate the MAPK and/or PI3K pathways. Our data support a treatment tailored to the genetics of individual tumors that parallels the approaches being investigated in the ongoing NCI-MATCH, My Pathway Trial, and ESMART clinical trials. Cancer Discov; 8(3); 354-69.
Natural Pragmatics and Natural Codes *

Grice (1957) drew a famous distinction between natural(N) and non-natural(NN) meaning, where what is meant(NN) is broadly equivalent to what is intentionally communicated. This paper argues that Grice’s dichotomy overlooks the fact that spontaneously occurring natural signs may be intentionally shown , and hence used in intentional communication. It also argues that some naturally occurring behaviours have a signalling function, and that the existence of such natural codes provides further evidence that Grice’s original distinction was not exhaustive. The question of what kind of information, in cognitive terms, these signals encode is also examined.
Smart Agriculture Based on Cloud Computing and IOT

Issues concerning agriculture, countryside and farmers have been always hindering China’s development. The only solution to these three problems is agricultural modernization. However, China's agriculture is far from modernized. The introduction of cloud computing and internet of things into agricultural modernization will probably solve the problem. Based on major features of cloud computing and key techniques of internet of things, cloud computing, visualization and SOA technologies can build massive data involved in agricultural production. Internet of things and RFID technologies can help build plant factory and realize automatic control production of agriculture. Cloud computing is closely related to internet of things. A perfect combination of them can promote fast development of agricultural modernization, realize smart agriculture and effectively solve the issues concerning agriculture, countryside and farmers.
Security of the Blockchain Against Long Delay Attack

The consensus protocol underlying Bitcoin (the blockchain) works remarkably well in practice. However proving its security in a formal setting has been an elusive goal. A recent analytical result by Pass, Seeman and shelat indicates that an idealized blockchain is indeed secure against attacks in an asynchronous network where messages are maliciously delayed by at most ∆ 1/np, with n being the number of miners and p the mining hardness. This paper improves upon the result by showing that if appropriate inconsistency tolerance is allowed the blockchain can withstand even more powerful external attacks in the honest miner setting. Specifically we prove that the blockchain is secure against long delay attacks with ∆ ≥ 1/np in an asynchronous network.
An Analysis Matrix for the Assessment of Smart City Technologies: Main Results of Its Application

The paper presents the main results of a previously developed methodology to better evaluate new technologies in Smart Cities, using a tool to evaluate different systems and technologies regarding their usefulness, considering each application and how technologies can impact the physical space and natural environment. Technologies have also been evaluated according to how they are used by citizens, who must be the main concern of all urban development. Through a survey conducted among the Smart City Spanish network (RECI) we found that the ICT’s that change our cities everyday must be reviewed, developing an innovative methodology in order to find an analysis matrix to assess and score all the technologies that affect a Smart City strategy. The paper provides the results of this methodology regarding the three main aspects to be considered in urban developments: mobility, energy efficiency, and quality of life after obtaining the final score for every analyzed technology. This methodology fulfills an identified need to study how new technologies could affect urban scenarios before being applied, developing an analysis system to be used by urban planners and policy-makers to decide how best to use them, and this paper tries to show, in a simple way, how they can appreciate the variances between different solutions.
SpotGarbage: smartphone app to detect garbage using deep learning

Maintaining a clean and hygienic civic environment is an indispensable yet formidable task, especially in developing countries. With the aim of engaging citizens to track and report on their neighborhoods, this paper presents a novel smartphone app, called SpotGarbage, which detects and coarsely segments garbage regions in a user-clicked geo-tagged image. The app utilizes the proposed deep architecture of fully convolutional networks for detecting garbage in images. The model has been trained on a newly introduced Garbage In Images (GINI) dataset, achieving a mean accuracy of 87.69%. The paper also proposes optimizations in the network architecture resulting in a reduction of 87.9% in memory usage and 96.8% in prediction time with no loss in accuracy, facilitating its usage in resource constrained smartphones.
Understanding user behavior in Spotify

Spotify is a peer-assisted music streaming service that has gained worldwide popularity in the past few years. Until now, little has been published about user behavior in such services. In this paper, we study the user behavior in Spotify by analyzing a massive dataset collected between 2010 and 2011. Firstly, we investigate the system dynamics including session arrival patterns, playback arrival patterns, and daily variation of session length. Secondly, we analyze individual user behavior on both multiple and single devices. Our analysis reveals the favorite times of day for Spotify users. We also show the correlations between both the length and the downtime of successive user sessions on single devices. In particular, we conduct the first analysis of the device-switching behavior of a massive user base.
DDoS attack detection under SDN context

Software Defined Networking (SDN) has recently emerged as a new network management platform. The centralized control architecture presents many new opportunities. Among the network management tasks, measurement is one of the most important and challenging one. Researchers have proposed many solutions to better utilize SDN for network measurement. Among them, how to detect Distributed Denial-of-Services (DDoS) quickly and precisely is a very challenging problem. In this paper, we propose methods to detect DDoS attacks leveraging on SDN's flow monitoring capability. Our methods utilize measurement resources available in the whole SDN network to adaptively balance the coverage and granularity of attack detection. Through simulations we demonstrate that our methods can quickly locate potential DDoS victims and attackers by using a constrained number of flow monitoring rules.
Techniques for extraction and isolation of natural products: a comprehensive review

Natural medicines were the only option for the prevention and treatment of human diseases for thousands of years. Natural products are important sources for drug development. The amounts of bioactive natural products in natural medicines are always fairly low. Today, it is very crucial to develop effective and selective methods for the extraction and isolation of those bioactive natural products. This paper intends to provide a comprehensive view of a variety of methods used in the extraction and isolation of natural products. This paper also presents the advantage, disadvantage and practical examples of conventional and modern techniques involved in natural products research.
Inferring Semantic Layout for Hierarchical Text-to-Image Synthesis

We propose a novel hierarchical approach for text-to-image synthesis by inferring semantic layout. Instead of learning a direct mapping from text to image, our algorithm decomposes the generation process into multiple steps, in which it first constructs a semantic layout from the text by the layout generator and converts the layout to an image by the image generator. The proposed layout generator progressively constructs a semantic layout in a coarse-to-fine manner by generating object bounding boxes and refining each box by estimating object shapes inside the box. The image generator synthesizes an image conditioned on the inferred semantic layout, which provides a useful semantic structure of an image matching with the text description. Our model not only generates semantically more meaningful images, but also allows automatic annotation of generated images and user-controlled generation process by modifying the generated scene layout. We demonstrate the capability of the proposed model on challenging MS-COCO dataset and show that the model can substantially improve the image quality, interpretability of output and semantic alignment to input text over existing approaches.
Altered sleep–wake cycles and physical performance in athletes

Sleep-waking cycles are fundamental in human circadian rhythms and their disruption can have consequences for behaviour and performance. Such disturbances occur due to domestic or occupational schedules that do not permit normal sleep quotas, rapid travel across multiple meridians and extreme athletic and recreational endeavours where sleep is restricted or totally deprived. There are methodological issues in quantifying the physiological and performance consequences of alterations in the sleep-wake cycle if the effects on circadian rhythms are to be separated from the fatigue process. Individual requirements for sleep show large variations but chronic reduction in sleep can lead to immuno-suppression. There are still unanswered questions about the sleep needs of athletes, the role of 'power naps' and the potential for exercise in improving the quality of sleep.
Using Argumentative Structure to Interpret Debates in Online Deliberative Democracy and eRulemaking

Governments around the world are increasingly utilising online platforms and social media to engage with, and ascertain the opinions of, their citizens. Whilst policy makers could potentially benefit from such enormous feedback from society, they first face the challenge of making sense out of the large volumes of data produced. In this article, we show how the analysis of argumentative and dialogical structures allows for the principled identification of those issues that are central, controversial, or popular in an online corpus of debates. Although areas such as controversy mining work towards identifying issues that are a source of disagreement, by looking at the deeper argumentative structure, we show that a much richer understanding can be obtained. We provide results from using a pipeline of argument-mining techniques on the debate corpus, showing that the accuracy obtained is sufficient to automatically identify those issues that are key to the discussion, attracting proportionately more support than others, and those that are divisive, attracting proportionately more conflicting viewpoints.
Terms of empowerment/exemplars of prevention: toward a theory for community psychology.

In order to develop theory any community of scientists must agree as to what constitutes its phenomena of interest. A distinction is made between phenomena of interest and exemplars. The concept "prevention" is viewed as an exemplar, whereas the concept "empowerment" is suggested as a leading candidate for the title "phenomena of interest" to Community Psychology. The ecological nature of empowerment theory is described, and some of the terms of empowerment (definitions, conditions, and periods of time) are explicated. Eleven assumptions, presuppositions, and hypotheses are offered as guidelines for theory development and empirical study.
Making a Point with Pointer Networks : Arranging Shuffled Stories

Sorting images in an album is an interesting task in computer vision and machine learning. It allows us to build systems that can generalize and exploit the temporal relationship between images. We thus, explore this task by implementing various models and reporting the performance on the Visual Storytelling (VIST) dataset, which also includes associated captions of the images. Previous implementation of pairwise comparison model fails to capture the overall context of the story. We hence, propose a more intuitive approach to this problem taking into account and using a latent representation of the input (shuffled) sequence, which uses Pointer Network (ptrNet) to utilize the whole contextual information to perform story ordering/sorting. We display the effectiveness of this model through various experiments. We make the source code available on Github 1 and a live demo of the best model here2.
Learning Metric-Topological Maps for Indoor Mobile Robot Navigation

Autonomous robots must be able to learn and maintain models of their environments. Research on mobile robot navigation has produced two major paradigms for mapping indoor environments: grid-based and topological. While grid-based methods produce accurate metric maps, their complexity often prohibits efficient planning and problem solving in large-scale indoor environments. Topological maps, on the other hand, can be used much more efficiently, yet accurate and consistent topological maps are often difficult to learn and maintain in large-scale environments, particularly if momentary sensor data is highly ambiguous. This paper describes an approach that integrates both paradigms: grid-based and topoIogica1. Grid-based maps are learned using artificial neural networks and naive Bayesian integration. Topological maps are generated on top of the grid-based maps, by partitioning the latter into coherent regions. By combining both paradigms, the approach presented here gains advantages from both worlds: accuracy/consistency and efficiency. The paper gives results for autonomous exploration, mapping and operation of a mobile robot in populated multi-room environments.
Literature derived reference models for the adoption of online shopping

The aim of the authors of this paper was to identify areas that would aid in developing a better understanding of the dynamics of a customer’s decision to shop online. To accomplish this, a review of the empirical studies on the antecedents of online shopping was performed. From an extensive literature search, a total of 45 relevant articles were identified. The factors that have been investigated in these studies were classified according to their similarity and patterns of their findings were identified and analyzed.
IndoTrack: Device-Free Indoor Human Tracking with Commodity Wi-Fi

Indoor human tracking is fundamental to many real-world applications such as security surveillance, behavioral analysis, and elderly care. Previous solutions usually require dedicated device being carried by the human target, which is inconvenient or even infeasible in scenarios such as elderly care and break-ins. However, compared with device-based tracking, device-free tracking is particularly challenging because the much weaker reflection signals are employed for tracking. The problem becomes even more difficult with commodity Wi-Fi devices, which have limited number of antennas, small bandwidth size, and severe hardware noise.
 In this work, we propose IndoTrack, a device-free indoor human tracking system that utilizes only commodity Wi-Fi devices. IndoTrack is composed of two innovative methods: (1) Doppler-MUSIC is able to extract accurate Doppler velocity information from noisy Wi-Fi Channel State Information (CSI) samples; and (2) Doppler-AoA is able to determine the absolute trajectory of the target by jointly estimating target velocity and location via probabilistic co-modeling of spatial-temporal Doppler and AoA information. Extensive experiments demonstrate that IndoTrack can achieve a 35cm median error in human trajectory estimation, outperforming the state-of-the-art systems and provide accurate location and velocity information for indoor human mobility and behavioral analysis.
Explainable Fashion Recommendation with Joint Outfit Matching and Comment Generation

Most previous work on fashion recommendation focuses on designing visual features to enhance recommendations. Existing work neglects user comments of fashion items, which have been proved effective in generating explanations along with better recommendation results. We propose a novel neural network framework, neural fashion recommendation (NFR), that simultaneously provides fashion recommendations and generates abstractive comments. NFR consists of two parts: outfit matching and comment generation. For outfit matching, we propose a convolutional neural network with a mutual attention mechanism to extract visual features of outfits. The visual features are then decoded into a rating score for the matching prediction. For abstractive comment generation, we propose a gated recurrent neural network with a cross-modality attention mechanism to transform visual features into a concise sentence. The two parts are jointly trained based on a multi-task learning framework in an end-to-end back-propagation paradigm. Extensive experiments conducted on an existing dataset and a collected real-world dataset show NFR achieves significant improvements over state-of-the-art baselines for fashion recommendation. Meanwhile, our generated comments achieve impressive ROUGE and BLEU scores in comparison to human-written comments. The generated comments can be regarded as explanations for the recommendation results. We release the dataset and code to facilitate future research.