Miracle-dz/UniFAR-FTUs huggingface dataset:
Heterogeneous Cash-Flow Risk and the Cross Section of Return Correlations

Heterogeneous cash-flow risk affects the cross section of stock returns. Assets with a low covariance of cash-flow and consumption growth can serve as natural hedges against negative shocks in the economy. I find that stocks with high cash-flow risk have higher excess returns and a higher degree of asymmetric dependence, i.e. a higher correlation of stock returns with market returns during downturns relative to upturns. I use a consumption-based asset-pricing model with heterogeneous habits to make predictions about the effects of the firm-level cash-flow risk on the cross section of returns and return correlations. These predictions are largely confirmed by data.
The Use of Edmodo: Its Impact on Learning and Students' Attitudes Toward It

Aim/Purpose: Edmodo is a free and secure social learning network for teachers, students, and parents. This research aims to investigate the impact of using the Edmodo network among the students at Imam Abdulrahman bin Faisal University and students’ attitudes toward it. Background: The creators of e-learning systems have recently taken remarkable strides, including the development of a full range of techniques and means of communication. Social networks encourage collaborative work and thus have huge potential to increase information sharing among users, which makes these networks especially useful in academic and higher-education systems. Edmodo network is one of the main choices to be adopted in education process. Methodology: This research has an experimental design based on a set of online tests. It also includes the development of a scale to determine students’ attitudes toward Edmodo Contribution: There is a lack of studies on the adoption of Edmodo within higher education. This research is an investigation of the impact that using Edmodo had on students at Imam Abdulrahman bin Faisal University. Findings: The findings of this research show that using Edmodo leads to a statistically significant improvement in learning skills among higher-education students. The results also illustrate that students have positive attitudes toward the use of Edmodo in their courses. Recommendations for Practitioners: Adopting Edmodo within their teaching model. Recommendation for Researchers: More investigation on this topic with a larger number of participants is recommended. Impact on Society: This investigation provides society with better understanding of adopting Edmodo within higher education. Future Research: Further work should include investigations of this topic that include a larger number of participants with more diverse backgrounds. The challenges of using Edmodo also need to be investigated in further studies.
Series Multi-criterion Shortest Paths for Unmanned Aircraft Routing over Urban Terrains

This paper describes a new approach for finding an approximate Euclidean shortest path with additional constraints, which we call a multi-criterion shortest path, between two given points that avoids vertical obstacles in a three-dimensional space. Currently, there does not exist any polynomial-time algorithm that solves this problem exactly, even without additional constraints. Furthermore, existing approximation algorithms mostly focus on reducing the running time at the expense of the accuracy. Here, we aim at increasing the accuracy of an approximate shortest path. To this end, our new algorithm is based on geometric principles to determine all obstacle segments and partial obstacle segments that are visible to each other. Using this information, we employ Mixed Integer Linear Programming to compute an approximate shortest path between two given points. We test our algorithm on a number of test cases and find that our method returns approximate shortest paths that are shorter than those returned by other currently available algorithms. Moreover, our method is more flexible since it allows users to define additional constraints or criteria in exploring different types of optimal paths. keywords: 3D shortest paths, visibility graph, vertical obstacles and Mixed Integer Linear Programming
Hyper Dimension Shuffle: Efficient Data Repartition at Petabyte Scale in Scope

In distributed query processing, data shuffle is one of the most costly operations. We examined scaling limitations to data shuffle that current systems and the research literature do not solve. As the number of input and output partitions increases, naïve shuffling will result in high fan-out and fan-in. There are practical limits to fan-out, as a consequence of limits on memory buffers, network ports and I/O handles. There are practical limits to fan-in because it multiplies the communication errors due to faults in commodity clusters impeding progress. Existing solutions that limit fan-out and fan-in do so at the cost of scaling quadratically in the number of nodes in the data flow graph. This dominates the costs of shuffling large datasets. We propose a novel algorithm called Hyper Dimension Shuffle that we have introduced in production in SCOPE, Microsoft's internal big data analytics system. Hyper Dimension Shuffle is inspired by the divide and conquer concept, and utilizes a recursive partitioner with intermediate aggregations. It yields quasilinear complexity of the shuffling graph with tight guarantees on fan-out and fan-in. We demonstrate how it avoids the shuffling graph blow-up of previous algorithms to shuffle at petabyte-scale efficiently on both synthetic benchmarks and real applications.
Molecular identification of arbuscular mycorrhizal fungi colonizing Athyrium yokoscense of the Ikuno mine site, Japan.

Phytoremediation is one of most innovative technologies that utilize the natural properties of plants to remediate hazardous waste sites (Salt et al. 1998, Krämer and Chardonnens 2001, McGrath and Zhao 2003). For more costeffective phytoremediation, hyperaccumulators play an important role of phytoremediation of heavy metals and it is an attractive option to utilize a hyperaccumulating plant after phytoremediation such as the recovery of valuable metals and the production of useful materials (Baker et al. 2000). In Europe and North America, many studies have been conducted to find more effective plants for phytoremediation of various pollutants (e.g., Tarnau et al. 2001). In Japan, on the other hand, studies have been published only relatively recently (e.g., Saito et al. 2004). The fern Athyrium yokoscense, which is native to the Far East including Japan, often flourishes in mine sites and areas that are highly polluted with heavy metals. Moreover, it is well known that the fern accumulates a large amount of metals in the tissues, particularly in the roots (Nishizono et al. 1987). Nishizono et al. (1988, 1989) studied the mechanisms involved in metal detoxification and accumulation of this fern, and suggested that a 9.5-kDa cysteine-rich peptide, which was induced by the exposure of the fern to copper, may contribute to the copper-tolerance of the fern growing in copper-contaminated soil. In addition, Xian (1990) reported that the compartmentalization of excess metals in the root cell wall has been believed to be important for the metal tolerance and accumulation of At. yokoscense. Molecular Identification of Arbuscular Mycorrhizal Fungi Colonizing Athyrium yokoscense of the Ikuno Mine Site, Japan
COMPARISON BETWEEN DIFFERENT NEURAL NETWORKS AS EEG PATTERN CLASSIFIERS

2. Methods In this paper the improvements in a system for a concrete human-machine interface especially adapted for physically disable are studied. This system is based on EEG signal analysis and it will try to discriminate mental states in order to translate these mental states into actions of a computer. Distinguishing EEG patterns related to different mental states could allow a paralyzed person to control devices such as wheelchairs or computers. 2.1. Data Collection EEG signals were collected for the purpose of developing and testing the system mentioned above. The subject of the experiments was a healthy, thirty-five-year-old, righthanded woman. The data were collected using the following procedure: The subject was asked to relax and try to think of nothing in particular during the first part of the experiment. The beginning of the experiments was indicated with the command "we are starting". Six seconds later, the subject was told to start thinking about a motor action with the indication "now". Six more seconds of recording were taken, after which the subject was told to relax once more, this then serving as the start phase for the next attempt. This process is illustrated in Figure 1
Online Data Stream Mining on Interactive Trajectories in Soccer Games

In sports, especially soccer and other ball-sports, the interaction between trajectories of players take a major role in purposes of game analysis. Existing approaches follow th e way of mining spatio-temporal, sequential patterns or spat ial patterns, but do not incorporate interactions between mult iple, interactive trajectories. This paper proposes an online da ta mining method that is able to recognize repeating patterns o f trajectory movements regardless of how they are scaled, rot a ed or translated. The result is an efficient data stream mining application, which provides meaningful information within the current game. The framework is designed in a modular manner in order to enhance the capabilities easily. The evaluationof the algorithms proves the efficiency of the provided concepts.
The Size Distribution of ‘ Cities ’ Delineated with a Network Theory-based Method and Smartphone GPS Data ∗

We apply a network theory-based method to delineate ‘cities’ independent of administrative boundaries in Japan. We divide the country into cells of 1×1 km over which a random walker moves. Its switching probabilities are specified by GPS-based people flow data. We find the groups of the cells that describe the long-run behavior of the random walker in the most effective manner. The combination of two lognormal distributions yields a better fit of the city size distribution than the distribution with a Pareto upper tail. A jump diffusion process is a stochastic process of the city population underlying such a distribution.
Generative Dependency Language Modeling Using Recurrent Neural Networks

This thesis proposes an approach to incorporating syntactical data to the task of generative language modeling. We modify the logic of a transition-based dependency parser to generate new words to the buffer using the top items in the stack as input. We hypothesize that the approach provides benefits in modeling long-term dependencies. We implement our system along with a baseline language model and observe that our approach provides an improvement in perplexity scores and that this improvement is more significant in modeling sentences that contain longer dependencies. Additionally, the qualitative analysis of the generated sentences demonstrates that our model is able to generate more cohesive sentences.
“ Identification of Diseases in Cotton Plant Leaf using Support Vector Machine ”

This project presents a technique used image processing techniques for fast and accurate detection of plant diseases. The steps followed by these researchers in detection of leaf spot diseases are: image acquisition, image preprocessing, disease spot segmentation, feature extraction and disease classification. The accuracy of result depends on method used for disease spot detection. The main obstacle in disease spot detection is noise, which is introduced by camera flash, change in illumination, noisy background and presence of vein in the plant leaf. Therefore a method which wipes out the noise and provides better disease spot segmentation is needed.
A Novel Approach in Analyzing Traffic Flow by Extreme Learning Machine Method

The objective of this study is to detect abnormal behaviours of moving objects captured in highway traffic flow footages, classify them by using artificial learning methods, and lastly to predict the future thereof (regression). To this end, the system being the object of the design and application consists of three stages. In the first stage, to detect the moving object in the video, background/foreground segmentation method of Mixture of Gaussian (MOG), and to track the moving object, Kalman Filter-Hungarian algorithm method have been used. In the second stage, by using the coordinates of the object, such details as location, distance in terms of time, and speed of the object are obtained, and by using total pixel count data relating to the shape of the object are obtained. The software based on the specifically elaborated algorithm compares these data with the data in the table of rules set down for the road under surveillance, and generates an attribute table comprising anomalies of the objects in the video. In the last stage, however, the data included in the attribute table have been classified and predictions by the artificial learning method, Extreme Learning Machine (ELM) made.
The Role of Information in Visual Word Recognition: A Perceptually-Constrained Connectionist Account

Proficient readers typically fixate near the center of a word, with a slight bias towards word onset. We explore a novel account of this phenomenon based on combining informationtheory with perceptual constraints in a connectionist model of visual word recognition. This account posits that the amount of information-content available for word identification varies across fixation locations and across languages. These differences contribute to the overall fixation location bias in different languages, make the novel prediction that certain words are more readily identified when fixating at an atypical fixation location, and predict specific cross-linguistic differences. We tested these predictions across several simulations in English and Hebrew, and in a behavioral experiment. The results confirmed that the bias to fixate closer to word onset aligns with reducing uncertainty in the visual signal, that some words are more readily identified at atypical fixation locations, and that these effects vary across languages.
Metamorphic Detection Using Function Call Graph Analysis

Metamorphic Detection Using Function Call Graph Analysis by Prasad Deshpande Well-designed metamorphic malware can evade many commonly used malware detection techniques including signature scanning. In this research, we consider a score based on function call graph analysis. We test this score on several challenging classes of metamorphic malware and we show that the resulting detection rates yield an improvement over previous research.
Supporting Hybrid Workloads for In-Memory Database Management Systems via a Universal Columnar Storage Format

The proliferation of modern data processing ecosystems has given rise to open-source columnar data formats. The key advantage of these formats is that they allow organizations to load data from database management systems (DBMSs) once instead of having to convert it to a new format for each usage. These formats, however, are read-only. This means that organizations must still use a heavy-weight transformation process to load data from their original format into the desired columnar format. We aim to reduce or even eliminate this process by developing an in-memory storage management architecture for transactional DBMSs that is aware of the eventual usage of its data and operates directly on columnar storage blocks. We introduce relaxations to common analytical format requirements to efficiently update data, and rely on a lightweight in-memory transformation process to convert blocks back to analytical forms when they are cold. We also describe how to directly access data from third-party analytical tools with minimal serialization overhead. To evaluate our work, we implemented our storage engine based on the Apache Arrow format and integrated it into the CMDB DBMS. Our experiments show that our approach achieves comparable performance with dedicated OLTP DBMSs while also enabling orders of magnitude faster data exports to external data science and machine learning libraries than existing approaches.
Reach and content of Dutch junk news on Facebook

[25] contains a recent and extensive review on scientific research on “fake news” on social media. In their review, they list shortcomings in current scientific research on this subject. The most prominent shortcomings they feature are that most work focusses on the effect of “fake news” as opposed to determining its reach and that it is done on the more research-friendly Twitter instead of on Facebook, the world’s biggest social network. Additionally, according to [16] “fake news” has become a politicised umbrella term for false content and disinformation. In this work we address these shortcomings by researching what the reach of junk news on Dutch Facebook is, compared to mainstream news. We define reach as the collective user engagement on all posts of a set Facebook pages in terms of its number of reactions, comments and shares, our engagement metrics. Junk news is content that lacks professional journalism traits such as transparency and accountability, has misleading, exaggerating and emotionally driven language and is sourced from untrustworthy media. We research Facebook, the world’s biggest social network. More specifically, we analyse Dutch Facebook. Addressing the additional shortcoming of [25] that most research is currently done on the US. From a seed list of junk and mainstream news domains provided by Nieuwscheckers; two experts on journalism and new media, we collect all posts published by their associated Facebook pages between Jan 2013 and Dec 2017 with their engagement metrics using the Facebook API. On our 58,986 junk news Facebook posts we got 25,314,481 reactions, 9,725,792 comments and 9,335,548 shares. Comparing these numbers with mainstream news, junk news published 1.36% more posts, while getting 16.6% more reactions, 51.65% more comments and 54.8% more shares; indicating larger user engagement on junk news than on mainstream news. An in-depth comparison between the distributions of junk and mainstream news has shown that the greater user engagement on junk news is a trend. Thus, junk news on Dutch Facebook is a phenomenon to behold. We find its reach to be even greater than mainstream news. Due to its absolute and relative popularity junk news on Facebook warrants further scientific research.
Hardware and Systems Strategies for Neural Acceleration

For years the development of new neural networks and better training algorithms has been largely orthogonal to what kind of hardware might drive their usage. While neural networks have been around since the 1980s they were considered far too computationally intensive and impractical compared to canonical computer vision techniques. In 2012 a neural net named AlexNet handily broke the world record for image recognition tasks using a deep convolution neural network trained on commodity GPUs. This showed for the first time that deep convolutional neural networks can be trained on a low cost system in a reasonable time, one week, while producing state-of-the-art accuracy. Since then the exponential growth of even deeper neural network research has been enabled through high performance CPU/GPU devices and an awareness of the underlying hardware. This has opened the door for an emerging class of neural systems that requires an understanding of the neural learning problem coupled with hardware expertise. This survey presents an overview of the bottlenecks in neural network algorithms and the hardware and software systems designed to overcome them. A new taxonomy for research in neural acceleration is also presented that represent the three angles most commonly taken to optimize this algorithm technology enabled acceleration (TEA), system enabled acceleration (SEA) and algorithm enabled acceleration (AEA).
Vehicle Detection in UAV Aerial Video a

In recent years, unmanned aerial vehicle (UAV) has been increasingly applied to traffic monitoring field, especially the vehicle detection. However, there are many challenges for vehicle detection in the UAV aerial video, such as camera shake, interferential targets and a wide range of change of scene. To solve these problems, a new vehicle detection system which is suitable for detection in UAV aerial video is proposed. We use the bit plane to extract the lane surface and use the extracted lane surface to limit the detection area. We improve the Vibe algorithm so that it can be used in rapidly changing scenes. In addition, the moving target screening strategy is proposed to screen moving vehicles. This paper is the first one to introduce bit plane into detection method. Our novel detection system is another major contribution. Experiments show that our system outperforms existing detection algorithms in terms of accuracy and computation time.
AI-NTPU Emotional Chatting Machine at the NTCIR-14 STC-3 CECG Task

This paper gives an overview of our work for the NII Testbeds and Community for Information access Research (NTCIR)-14 Short Text Conversation (STC)-3 Chinese Emotional Conversation Generation (CECG) subtask. In NTCIR-14 STC3, emotion in the post-comment pairs was considered in both retrieval-based and generation-based approaches. In this subtask, we developed an emotion classification and two approaches, generation-based and retrieval-based approaches, to create responses to post. In STC3 CECG subtask repository, each post and comment were label with one emotions tag: Like, sadness, disgust, anger, and happiness. The posts and comment with emotion that not mentioned above were label as “other”. We develop an emotion classification model to label the comment we created. The purpose of this subtask is to create comment that is coherence and fluency to the post. Besides, the created post should be emotion consistency. In the retrieval-based approach, we used Apache Solr to search an appropriate comment to the post. In generation-based approach, we use attention-based sequence to sequence (Seq2Seq) model to create new comment to each post. For emotion classification model, we used Multilayer Perceptron (MLP). In the paper, we provide our procedure in detail for creating new comment to post. We provide two submissions: retrieval-based approach with emotion and generation-based with emotion. However, due to the format issue, the evaluation results of submission for generation-based with emotion is not provided. For the purpose of self-improvement, we provide our selfevaluation results to both two submissions. Further improvement suggestions are also provided in the paper.
Autoencoding Video Frames

This report details the implementation of an autoencoder trained with a learned similarity metric - one that is capable of modelling a complex dis- tribution of natural images - training it on frames from selected films, and using it to reconstruct video sequences by passing each frame through the autoencoder and re-sequencing the output frames in-order. This is primarily an artistic exploration of the representational capacity of the current state of the art in generative models and is a novel application of autoencoders. This model is trained on, and used to reconstruct the films Blade Runner and A Scanner Darkly, producing new artworks in their own right. Experiments passing other videos through these models is carried out, demonstrating the potential of this method to become a new technique in the production of experimental image and video.