nils-herrmann/clean_scirepeval huggingface dataset:
Deep Learning and Wavelets for High-Frequency Price Forecasting

This paper presents improvements in financial time series prediction using a Deep Neural Network (DNN) in conjunction with a Discrete Wavelet Transform (DWT). When comparing our model to other three alternatives, including ARIMA and other deep learning topologies, ours has a better performance. All of the experiments were conducted on High-Frequency Data (HFD). Given the fact that DWT decomposes signals in terms of frequency and time, we expect this transformation will make a better representation of the sequential behavior of high-frequency data. The input data for every experiment consists of 27 variables: The last 3 one-minute pseudo-log-returns and last 3 oneminute compressed tick-by-tick wavelet vectors, each vector is a product of compressing the tick-by-tick transactions inside a particular minute using a DWT with length 8. Furthermore, the DNN predicts the next one-minute pseudo-log-return that can be transformed into the next predicted one-minute average price. For testing purposes, we use tick-by-tick data of 19 companies in the Dow Jones Industrial Average Index (DJIA), from January 2015 to July 2017. The proposed DNN’s Directional Accuracy (DA) presents a remarkable forecasting performance ranging from 64% to 72%.
A simulation of priority based earliest deadline first scheduling for cloud computing system

Cloud is a new trend in the computing. It is the combination of distributed, parallel and grid computing. It is a pay on demand computing. Basically cloud is a shared pool of resources. The resources are shared among users who are geographically distributed. As the area of cloud is increasing, several issues are also increasing. These issues are related to security, resource allocation, performance and cost. The paper has its main focus on the resource allocation problem and to solve the problem we are discussing a task allocation algorithm. The algorithm that we will discuss in this paper uses the two task scheduling algorithm one is priority based and other is earliest deadline first scheduling algorithm. The tasks are allocated on the basis of their priority and the task having higher priority get scheduled first. The simulation is performed on the CloudSim toolkit. CloudSim is a JAVA based simulator. The proposed work will results in higher performance and also improves memory utilization.
Empirical Analysis of Denial-of-Service Attacks in the Bitcoin Ecosystem

We present an empirical investigation into the prevalence and impact of distributed denial-of-service (DDoS) attacks on operators in the Bitcoin economy. To that end, we gather and analyze posts mentioning “DDoS” on the popular Bitcoin forum bitcointalk.org. Starting from around 3 000 different posts made between May 2011 and October 2013, we document 142 unique DDoS attacks on 40 Bitcoin services. We find that 7% of all known operators have been attacked, but that currency exchanges, mining pools, gambling operators, eWallets, and financial services are much more likely to be attacked than other services. Not coincidentally, we find currency exchanges and mining pools are much more likely to have DDoS protection such as CloudFlare, Incapsula, or Amazon Cloud. We show that those services that have been attacked are more than three times as likely to buy anti-DDoS services than operators who have not been attacked. We find that big mining pools (those with historical hashrate shares of at least 5%) are much more likely to be DDoSed than small pools. We investigate Mt. Gox as a case study for DDoS attacks on currency exchanges and find a disproportionate amount of DDoS reports made during the large spike in trading volume and exchange rates in spring 2013. We conclude by outlining future opportunities for researching DDoS attacks on Bitcoin.
Observe locally, infer globally: A space-time MRF for detecting abnormal activities with incremental updates

We propose a space-time Markov random field (MRF) model to detect abnormal activities in video. The nodes in the MRF graph correspond to a grid of local regions in the video frames, and neighboring nodes in both space and time are associated with links. To learn normal patterns of activity at each local node, we capture the distribution of its typical optical flow with a mixture of probabilistic principal component analyzers. For any new optical flow patterns detected in incoming video clips, we use the learned model and MRF graph to compute a maximum a posteriori estimate of the degree of normality at each local node. Further, we show how to incrementally update the current model parameters as new video observations stream in, so that the model can efficiently adapt to visual context changes over a long period of time. Experimental results on surveillance videos show that our space-time MRF model robustly detects abnormal activities both in a local and global sense: not only does it accurately localize the atomic abnormal activities in a crowded video, but at the same time it captures the global-level abnormalities caused by irregular interactions between local activities.
Detect Rumors Using Time Series of Social Context Information on Microblogging Websites

Automatically identifying rumors from online social media especially microblogging websites is an important research issue. Most of existing work for rumor detection focuses on modeling features related to microblog contents, users and propagation patterns, but ignore the importance of the variation of these social context features during the message propagation over time. In this study, we propose a novel approach to capture the temporal characteristics of these features based on the time series of rumor's lifecycle, for which time series modeling technique is applied to incorporate various social context information. Our experiments using the events in two microblog datasets confirm that the method outperforms state-of-the-art rumor detection approaches by large margins. Moreover, our model demonstrates strong performance on detecting rumors at early stage after their initial broadcast.
Achieving near-optimal traffic engineering in hybrid Software Defined Networks

Software Defined Networking (SDN) is an emerging networking paradigm which intends to merge networks into the age of the cloud, providing fine-grained control, simplified configurations, unprecedented flexibility and seamless scalability. However, due to the large set of unresolved challenges as well as the deployment cost, network evolution to fully SDN systems will take a long time. In fact, SDN elements are incrementally deployed in enterprise networks, producing a transitional network form of hybrid SDN (H-SDN). An H-SDN system consists of traditional networking elements and SDN elements, accommodating both conventional traffic and SDN traffic. In this paper, we investigate traffic engineering (TE) in H-SDN, where the SDN controller strategically routes SDN traffic so as to optimize the TE performance over all network links shared with uncontrollable conventional traffic. Two hybrid modes are studied: (1) the barrier mode, where the two forms of traffic are routed in separated capacity spaces; and (2) the hybrid mode, where each link can be fully occupied by either form of traffic. We propose fast algorithms for the TE problems in both scenarios with provable approximation guarantees. Theoretical analysis and computer simulations validate the efficacy of our algorithms.
Five-factor model of personality and transformational leadership.

This study linked traits from the 5-factor model of personality (the Big 5) to transformational leadership behavior. Neuroticism, Extraversion, Openness to Experience, and Agreeableness were hypothesized to predict transformational leadership. Results based on 14 samples of leaders from over 200 organizations revealed that Extraversion and Agreeableness positively predicted transformational leadership; Openness to Experience was positively correlated with transformational leadership, but its effect disappeared once the influence of the other traits was controlled. Neuroticism and Conscientiousness were unrelated to transformational leadership. Results further indicated that specific facets of the Big 5 traits predicted transformational leadership less well than the general constructs. Finally, transformational leadership behavior predicted a number of outcomes reflecting leader effectiveness, controlling for the effect of transactional leadership.
ReAct: Online Multimodal Embedding for Recency-Aware Spatiotemporal Activity Modeling

Spatiotemporalactivity modeling is an important task for applications like tour recommendation and place search. The recently developed geographical topic models have demonstrated compelling results in using geo-tagged social media (GTSM) for spatiotemporal activity modeling. Nevertheless, they all operate in batch and cannot dynamically accommodate the latest information in the GTSM stream to reveal up-to-date spatiotemporal activities. We propose ReAct, a method that processes continuous GTSM streams and obtains recency-aware spatiotemporal activity models on the fly. Distinguished from existing topic-based methods, ReAct embeds all the regions, hours, and keywords into the same latent space to capture their correlations. To generate high-quality embeddings, it adopts a novel semi-supervised multimodal embedding paradigm that leverages the activity category information to guide the embedding process. Furthermore, as new records arrive continuously, it employs strategies to effectively incorporate the new information while preserving the knowledge encoded in previous embeddings. Our experiments on the geo-tagged tweet streams in two major cities have shown that ReAct significantly outperforms existing methods for location and activity retrieval tasks.
CONDENSATION—Conditional Density Propagation for Visual Tracking

The problem of tracking curves in dense visual clutter is challenging. Kalman filtering is inadequate because it is based on Gaussian densities which, being unimo dal, cannot represent simultaneous alternative hypotheses. The Condensation algorithm uses “factored sampling”, previously applied to the interpretation of static images, in which the probability distribution of possible interpretations is represented by a randomly generated set. Condensation uses learned dynamical models, together with visual observations, to propagate the random set over time. The result is highly robust tracking of agile motion. Notwithstanding the use of stochastic methods, the algorithm runs in near real-time.
Mining Frequent Patterns without Candidate Generation

Mining frequent patterns in transaction databases, time-series databases, and many other kinds of databases has been studied popularly in data mining research. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still costly, especially when there exist prolific patterns and/or long patterns.
In this study, we propose a novel frequent pattern tree (FP-tree) structure, which is an extended prefix-tree structure for storing compressed, crucial information about frequent patterns, and develop an efficient FP-tree-based mining method, FP-growth, for mining the complete set of frequent patterns by pattern fragment growth. Efficiency of mining is achieved with three techniques: (1) a large database is compressed into a highly condensed, much smaller data structure, which avoids costly, repeated database scans, (2) our FP-tree-based mining adopts a pattern fragment growth method to avoid the costly generation of a large number of candidate sets, and (3) a partitioning-based, divide-and-conquer method is used to decompose the mining task into a set of smaller tasks for mining confined patterns in conditional databases, which dramatically reduces the search space. Our performance study shows that the FP-growth method is efficient and scalable for mining both long and short frequent patterns, and is about an order of magnitude faster than the Apriori algorithm and also faster than some recently reported new frequent pattern mining methods.
Evolino: Hybrid Neuroevolution/Optimal Linear Search for Sequence Learning

Current Neural Network learning algorithms are limited in their ability to model non-linear dynamical systems. Most supervised gradient-based recurrent neural networks (RNNs) suffer from a vanishing error signal that prevents learning from inputs far in the past. Those that do not, still have problems when there are numerous local minima. We introduce a general framework for sequence learning, EVOlution of recurrent systems with LINear outputs (Evolino). Evolino uses evolution to discover good RNN hidden node weights, while using methods such as linear regression or quadratic programming to compute optimal linear mappings from hidden state to output. Using the Long ShortTerm Memory RNN Architecture, the method is tested in three very different problem domains: 1) context-sensitive languages, 2) multiple superimposed sine waves, and 3) the Mackey-Glass system. Evolino performs exceptionally well across all tasks, where other methods show notable deficiencies in some.
A Computational Model of Graded Cueing: Robots Encouraging Behavior Change

Our work is motivated by the evidence that children with ASD are often behind in their development of imitative behavior, and that practicing through repeated interactions with a therapist can improve imitation abilities. Our model approximates those effects with a socially assistive robot in order to use technology to broaden access to ASD therapy. Our model of graded cueing informs how specific of feedback each participant needs based on their performance in previous rounds of imitation.  Using Probabilistic Models to Make Up for Limited
A longitudinal study of the social and emotional predictors and consequences of cyber and traditional bullying victimisation

Few longitudinal studies have investigated how cyberbullying interacts with traditional bullying among young people, who are increasingly using online environments to seek information, entertainment and to socialise. This study aimed to identify the associations between the relative contribution of cyberbullying victimisation and traditional bullying victimisation on social and emotional antecedents and outcomes among adolescents. Participants were a cohort of 1,504 adolescents from 16 Australian schools followed from age 13 to 15 years. Adolescents experiencing social and emotional difficulties were more likely to be cyberbullied and traditionally bullied, than traditionally bullied only. Those targeted in both ways experienced more harm and stayed away from school more often than those traditionally bullied only. These findings suggest a high coexistence of cyber and traditional bullying behaviours and their antecedents, and higher levels of harm from a combination of these behaviours for adolescents over time. Future research should engage students as co-researchers to enhance school and parent strategies to support adolescents experiencing difficulties, and to reduce the likelihood of both cyber and traditional bullying.
Additive Quantization for Extreme Vector Compression

We introduce a new compression scheme for high-dimensional vectors that approximates the vectors using sums of M codewords coming from M different codebooks. We show that the proposed scheme permits efficient distance and scalar product computations between compressed and uncompressed vectors. We further suggest vector encoding and codebook learning algorithms that can minimize the coding error within the proposed scheme. In the experiments, we demonstrate that the proposed compression can be used instead of or together with product quantization. Compared to product quantization and its optimized versions, the proposed compression approach leads to lower coding approximation errors, higher accuracy of approximate nearest neighbor search in the datasets of visual descriptors, and lower image classification error, whenever the classifiers are learned on or applied to compressed vectors.
Semantic Sensor Data Search in a Large-Scale Federated Sensor Network

Sensor network deployments are a primary source of massive amounts of data about the real world that surrounds us, measuring a wide range of physical properties in real time. However, in large-scale deployments it becomes hard to effectively exploit the data captured by the sensors, since there is no precise information about what devices are available and what properties they measure. Even when metadata is available, users need to know low-level details such as database schemas or names of properties that are specific to a device or platform. Therefore the task of coherently searching, correlating and combining sensor data becomes very challenging. We propose an ontology-based approach, that consists in exposing sensor observations in terms of ontologies enriched with semantic metadata, providing information such as: which sensor recorded what, where, when, and in which conditions. For this, we allow defining virtual semantic streams, whose ontological terms are related to the underlying sensor data schemas through declarative mappings, and can be queried in terms of a high level sensor network ontology.
Arrays in Blitz++

Numeric arrays in Blitz++ rival the eeciency of Fortran, but without any extensions to the C++ language. Blitz++ has features unavailable in Fortran 90/95, such as arbitrary transpose operations, array renaming, tensor notation, partial reductions, multicomponent arrays and stencil operators. The library handles parsing and analysis of array expressions on its own using the expression templates technique, and performs optimizations (such as loop transformations) which have until now been the responsibility of compilers.
Elastic Distributed Bayesian Collaborative Filtering

In this paper, we consider learning a Bayesian collaborative filtering model on a shared cluster of commodity machines. Two main challenges arise: (1) How can we parallelize and distribute Bayesian collaborative filtering? (2) How can our distributed inference system handle elasticity events common in a shared, resource managed cluster, including resource ramp-up, preemption, and stragglers? To parallelize Bayesian inference, we adapt ideas from both matrix factorization partitioning schemes used with stochastic gradient descent and stale synchronous programming used with parameter servers. To handle elasticity events we offer a generalization of previous partitioning schemes that gives increased flexibility during system disruptions. We additionally describe two new scheduling algorithms to dynamically route work at runtime. In our experiments, we compare the effectiveness of both scheduling algorithms and demonstrate their robustness to system failure.
The big data ecosystem at LinkedIn

The use of large-scale data mining and machine learning has proliferated through the adoption of technologies such as Hadoop, with its simple programming semantics and rich and active ecosystem. This paper presents LinkedIn's Hadoop-based analytics stack, which allows data scientists and machine learning researchers to extract insights and build product features from massive amounts of data. In particular, we present our solutions to the ``last mile'' issues in providing a rich developer ecosystem. This includes easy ingress from and egress to online systems, and managing workflows as production processes. A key characteristic of our solution is that these distributed system concerns are completely abstracted away from researchers. For example, deploying data back into the online system is simply a 1-line Pig command that a data scientist can add to the end of their script. We also present case studies on how this ecosystem is used to solve problems ranging from recommendations to news feed updates to email digesting to descriptive analytical dashboards for our members.
Loop antenna for the LTE/WWAN metal-casing laptop computer

A coupled-fed loop antenna with a small occupied volume (35 mm × 8 mm × 3 mm) can be installed in the hinge slot region of laptop computers to cover the long term evolution (LTE) and wireless wide area network (WWAN) operation in the 698–960 MHz, and 1710–2690 MHz is presented. The coupled-fed loop antenna can be installed in the hinge slot region between the display ground and keyboard ground of laptop computers, without additional clearance region occupied by the antenna. With the integration of the hinge slot region in laptop computers, several resonant modes of the hinge slot can be excited to combine the resonant modes of the coupled-fed loop to cover the LTE/WWAN operation without increasing the antenna size. This makes the proposed antenna attractive for achieving full metal-casing laptop computer applications. The measured antenna efficiency is about 53–65% in the lower band and is about 58–72% in the upper band. Working principle of the proposed antenna on achieving LTE/WWAN dual-wideband operation is described in this study.
Rumination and overgeneral memory in depression: effects of self-focus and analytic thinking.

Previous research has shown that, compared with a rumination induction, a brief distraction procedure reduces overgeneral autobiographical memory in depression. The authors investigated whether this effect depends on reductions in analytic thinking or reductions in self-focus. Focus of attention (high vs. low self-focus) and thinking style (high vs. low analytical thinking) were independently manipulated in depressed patients in a 2 x 2 design. Autobiographical recall was measured pre- and postmanipulation. Thinking style significantly affected overgeneral memory, whereas focus of attention significantly affected despondent mood. Reducing analytical self-focus reduced overgeneral memory, suggesting that high levels of naturally occurring ruminative analytic thinking may be important in the maintenance of overgeneral memory. Overgeneral memory in depression may be associated with chronic ruminative attempts to make sense of current or past difficulties.
Diagnose brain tumor through MRI using image processing clustering algorithms such as Fuzzy C Means along with intelligent optimization techniques

Magnetic Resonance Imaging (MRI) is one of the best technologies currently being used for diagnosing brain tumor. Brain tumor is diagnosed at advanced stages with the help of the MRI image. Segmentation is an important process to extract suspicious region from complex medical images. Automatic detection of brain tumor through MRI can provide the valuable outlook and accuracy of earlier brain tumor detection. In this paper an intelligent system is designed to diagnose brain tumor through MRI using image processing clustering algorithms such as Fuzzy C Means along with intelligent optimization tools, such as Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). The detection of tumor is performed in two phases: Preprocessing and Enhancement in the first phase and segmentation and classification in the second phase
Neural Network Recognizer for Hand-Written Zip Code Digits

This paper describes the construction of a system that recognizes hand-printed digits, using a combination of classical techniques and neural-net methods. The system has been trained and tested on real-world data, derived from zip codes seen on actual U.S. Mail. The system rejects a small percentage of the examples as unclassifiable, and achieves a very low error rate on the remaining examples. The system compares favorably with other state-of-the art recognizers. While some of the methods are specific to this task, it is hoped that many of the techniques will be applicable to a wide range of recognition tasks.