Miracle-dz/UniFAR-FTUs huggingface dataset:
Buzzwords Build Momentum: Global Financial Twitter Sentiment and the Aggregate Stock Market

Abstract We examine the long-term relationship between signals derived from nine years of unstructured social media microblog text data and financial market developments in five major economic regions. Employing statistical language modeling techniques we construct directional sentiment metrics and link these to aggregate stock index returns. To address the noise in finance-related Twitter messages we identify expert users whose tweets predominantly focus on finance topics. We document that expert users are the main drivers behind an interdependence between Twitter sentiment and financial markets. The direct prediction value of expert sentiment metrics for stock index returns, however, is found to be elusive and short-lived. Yet, we detect significant predictive gains over benchmark models in times of negative market returns. In consequence, the relation between expert sentiment metrics and stock indices is sufficient to devise hypothetically profitable cross-sectional as well as time series momentum investment strategies for futures based on Twitter signals that survive basic transaction cost assumptions. In this context, our results show that expert sentiment signals can yield higher risk-adjusted returns than classical price-based signals.
Spatial information extraction from travel narratives: Analysing the notion of co-occurrence indicating closeness of tourist places

Recent advancements in social media have generated a myriad of unstructured geospatial data. Travel narratives are among the richest sources of such spatial clues. They are also a reflection of writers’ interaction with places. One of the prevalent ways to model this interaction is a points of interest (POIs) graph depicting popular POIs and routes. A relevant notion is that frequent pairwise occurrences of POIs indicate their geographic proximity. This work presents an empirical interpretation of this theory and constructs spatially enriched POI graphs, a clear augmentation to popularity-based POI graphs. A triplet pattern, rule-based spatial relation extraction technique SpatRE is proposed and compared with standard relation extraction systems Ollie and Stanford OpenIE. A travel blogs data set is also contributed containing labelled spatial relations. The performance is further evaluated on SemEval 2013 benchmark data sets. Finally, spatially enriched POI graphs are qualitatively compared with TripAdvisor and Google Maps to visualise information accuracy.
Finding optimal reactive power dispatch solutions by using a novel improved stochastic fractal search optimization algorithm

In this paper, a novel improved Stochastic Fractal Search optimization algorithm (ISFSOA) is proposed for finding effective solutions of a complex optimal reactive power dispatch (ORPD) problem with consideration of all constraints in transmission power network. Three different objectives consisting of total power loss (TPL), total voltage deviation (TVD) and voltage stabilization enhancement index are independently optimized by running the proposed ISFSOA and standard Stochastic Fractal Search optimization algorithm (SFSOA). The potential search of the proposed ISFSOA can be highly improved since diffusion process of SFSOA is modified. Compared to SFSOA, the proposed method can explore large search zones and exploit local search zones effectively based on the comparison of solution quality. One standard IEEE 30-bus system with three study cases is employed for testing the proposed method and compared to other so far applied methods. For each study case, the proposed method together with SFSOA are run fifty run and three main results consisting of the best, mean and standard deviation fitness function are compared. The indication is that the proposed method can find more promising solutions for the three cases and its search ability is always more stable than those of SFSOA. The comparison with other methods also give the same evaluation that the proposed method can be superior to almost all compared methods. As a result, it can conclude that the proposed modification is really appropriate for SFSOA in dealing with ORPD problem and the method can be used for other engineering optimization problems.
SEMIPARAMETRIC INFERENCE FOR INTEGRATED VOLATILITY FUNCTIONALS USING HIGH-FREQUENCY FINANCIAL DATA

YUNXIAO LIU: Essays in High-frequency Financial Econometrics (Under the direction of George Tauchen and Chuanshu Ji) With the advent of intraday high-frequency data of financial assets since the late 1990s, the research of financial econometrics has entered into a “big data” era. New theoretical techniques using the theory of continuous time stochastic processes has been extensively developed, and new empirical evidence has been documented. In particular, due to its far-reaching applications in various fields such as risk management and option pricing, the study of volatility, which quantitatively measures the uncertainty of prices of financial assets, has drawn substantial attention from researchers and there has been a large amount of literature devoted to this topic, including both modelling and prediction. In this dissertation, we are firstly concerned with the statistical inference of the so-called integrated volatility functionals, which is a general class of quantities that are computed from volatility. Secondly, we also devise a simulation method to recover the probability distribution of prices of financial assets by taking advantage of the information contained in sampled price data. Accordingly, the dissertation consists of two parts. In the first part, we focus on the estimation of integrated volatility functionals, where the volatility process is assumed to be a long memory Itô semimartingale (LMIS), which is defined as the sum of an Itô semimartingale and a process satisfying certain regularity assumptions that in particular is able to capture the long memory property of financial volatility that has been vastly documented in literature. We provide central limit theorem (CLT) in such context. Furthermore, under the such LMIS assumption, we consider both parametric and nonparametric bootstrap inference methods of integrated volatility functionals, and we show the validity of both bootstrap methods by providing CLTs. Furthermore, with the usual assumption of volatility being Itô semimartingale, we consider an empirical-process form of integrated volatility iii functionals, and offer functional CLTs when the indexing parameter is of arbitrary finite dimensions. We also consider bootstrap inference in this empirical-process setting. In the second part, we consider Euler method with estimated spot volatility, from which we are able to regenerate and realize the stochastic dynamics of price of financial asset by taking advantage of the information contained in the observed prices. We provide both theoretical foundation and empirical application of this method.
Tensorized Determinantal Point Processes for Recommendation

Interest in determinantal point processes (DPPs) is increasing in machine learning due to their ability to provide an elegant parametric model over combinatorial sets. In particular, the number of required parameters in a DPP grows only quadratically with the size of the ground set (e.g., item catalog), while the number of possible sets of items grows exponentially. Recent work has shown that DPPs can be effective models for product recommendation and basket completion tasks, since they are able to account for both the diversity and quality of items within a set. We present an enhanced DPP model that is specialized for the task of basket completion, the tensorized DPP. We leverage ideas from tensor factorization in order to customize the model for the next-item basket completion task, where the next item is captured in an extra dimension of the model. We evaluate our model on several real-world datasets, and find that the tensorized DPP provides significantly better predictive quality in several settings than a number of state-of-the art models.
Electrification of a City Bus Network: An Optimization Model for Cost-Effective Placing of Charging Infrastructure and Battery Sizing of Fast Charging Electric Bus Systems

The deployment of battery-powered electric bus systems within the public transportation sector plays an important role to increase energy efficiency and to abate emissions. Rising attention is given to bus systems using fast charging technology. This concept requires a comprehensive infrastructure to equip bus routes with charging stations. The combination of charging infrastructure and bus batteries needs a reliable energy supply to maintain a stable bus operation even under demanding conditions. An efficient layout of the charging infrastructure and an appropriate dimensioning of battery capacity are crucial to minimize the total cost of ownership and to enable an energetically feasible bus operation. In this work, the central issue of jointly optimizing the charging infrastructure and battery capacity is described by a capacitated set covering problem. A mixed-integer linear optimization model is developed to determine the minimum number and location of required charging stations for a bus network as well as the adequate battery capacity for each bus line of the network. The bus energy consumption for each route segments is determined based on individual route, bus type, traffic and other information. Different scenarios are examined in order to assess the influence of charging power, climate and changing operating conditions. The findings reveal significant differences in terms of needed infrastructure depending on the scenarios considered. Moreover, the results highlight a trade-off between battery size and charging infrastructure under different operational and infrastructure conditions. The paper addresses upcoming challenges for transport authorities during the electrification process of the bus fleets and sharpens the focus on infrastructural issues related to the fast charging concept.
Transfer Learning Based Free-Form Speech Command Classification for Low-Resource Languages

Current state-of-the-art speech-based user interfaces use data intense methodologies to recognize free-form speech commands. However, this is not viable for low-resource languages, which lack speech data. This restricts the usability of such interfaces to a limited number of languages. In this paper, we propose a methodology to develop a robust domain-specific speech command classification system for low-resource languages using speech data of a high-resource language. In this transfer learning-based approach, we used a Convolution Neural Network (CNN) to identify a fixed set of intents using an ASR-based character probability map. We were able to achieve significant results for Sinhala and Tamil datasets using an English based ASR, which attests the robustness of the proposed approach.
The FB-RED algorithm for TCP over ATM

We present the FB-RED (fair-buffering random early detection) algorithm, a new congestion control scheme for TCP over ATM connections which takes advantage of ATM to improve the fairness characteristics of the RED gateway algorithm. Utilizing the fact that ATM is connection-oriented and the ABR explicit rate control mechanism, the FB-RED algorithm calculates the bandwidth-delay product of each connection and based on this value it ensures fair drop probabilities. Two variants of the FB-RED algorithm are presented. The first one weighs the drop probabilities of the original RED algorithm relative to each connection's bandwidth-delay product, while the second one changes the original RED algorithm to ensure that the drop probabilities of each connection roughly equals the optimal drop probability. Both variants are shown to markedly improve the fairness as compared to the original RED algorithm in various simulation topologies.
Adaptive context-aware correlation filter target tracking

Aiming at the problem that the traditional correlation filter target tracking algorithm has low tracking accuracy under the conditions of fast motion, occlusion and complex background, an adaptive context-aware correlation filter target tracking algorithm is proposed in this paper. On the basis of the relevant filtering algorithm, the boundary effect and fixed learning rate brought by cyclic displacement are improved as the main purpose. Firstly, an adaptive sampling strategy based on the extreme value of the response graph is added to the context information in the training stage of the classifier. Then, A piecewise learning rate adjustment strategy is utilized to make the algorithm better adapt to the target change. Finally, the performance of the algorithm is verified by the standard data set. The experimental results show that the proposed algorithm improves the tracking accuracy of DCF and SAMF algorithm respectively. It not only has good robustness in the case of fast motion, occlusion, complex background, etc., but also can be integrated into most relevant filtering algorithms as a framework.
Geometric Models of Similarity

This dissertation examines and discusses some phenomena related to the geometric representation of similarity. It takes its inspiration from the existing body of empirical research within the fields of perceptual and cognitive psychology, but also connects to certain areas of machine learning. The problems discussed concern the modelling of information integration behavior when concepts like asymmetry, integrality, separability and familiarity are taken into account. The dissertation investigates how these phenomena can be modelled using geometric representations in order to increase the descriptive power of the models. The main conclusions that can be drawn from this dissertation are that the descriptive powers of geometric models can be increased in a number of ways: a) By augmenting traditional geometric models with parameters of prominence they can reflect asymmetric similarity at least as well as previously known asymmetric models which include more parameters. b) Some specific metric, for instance, the Euclidean metric, should not be used merely by tradition. If there is reason to believe that there are groupings of dimensions such that the most descriptive metric differs between groups, the distance may be better described with a combination rule adding the contribution of each group/subspace together. c) Aspects of familiarity with stimuli should be taken into consideration, even for familiarity built up during a short period of time. When taking familiarity into consideration it may be possible to describe information integration over time with a finer granularity. Furthermore, by focusing on the part of the phenomenological data that reflect a more stable behavior, which seems to occur first when subjects are sufficiently familiar with the stimuli, the more stable behavior can be more accurately described.
Lessons and Actions: What We Learned from 10K SSD-Related Storage System Failures

Modern datacenters increasingly use flash-based solid state drives (SSDs) for high performance and low energy cost. However, SSD introduces more complex failure modes compared to traditional hard disk. While great efforts have been made to understand the reliability of SSD itself, it remains unclear what types of system level failures are related to SSD, what are the root causes, and how the rest of the system interacts with SSD and contributes to failures. Answering these questions can help practitioners build and maintain highly reliable SSD-based storage systems. In this paper, we study the reliability of SSD-based storage systems deployed in Alibaba Cloud, which cover near half a million SSDs and span over three years of usage under representative cloud services. We take a holistic view to analyze both device errors and system failures to better understand the potential casual relations. Particularly, we focus on failures that are Reported As “SSD-Related” (RASR) by system status monitoring daemons. Through log analysis, field studies, and validation experiments, we identify the characteristics of RASR failures in terms of their distribution, symptoms, and correlations. Moreover, we derive a number of major lessons and a set of effective methods to address the issues observed. We believe that our study and experience would be beneficial to the community and could facilitate building highly-reliable SSD-based storage systems.
Interpolated Spectral NGram Language Models

Spectral models for learning weighted non-deterministic automata have nice theoretical and algorithmic properties. Despite this, it has been challenging to obtain competitive results in language modeling tasks, for two main reasons. First, in order to capture long-range dependencies of the data, the method must use statistics from long substrings, which results in very large matrices that are difficult to decompose. The second is that the loss function behind spectral learning, based on moment matching, differs from the probabilistic metrics used to evaluate language models. In this work we employ a technique for scaling up spectral learning, and use interpolated predictions that are optimized to maximize perplexity. Our experiments in character-based language modeling show that our method matches the performance of state-of-the-art ngram models, while being very fast to train.
Automated Chess Commentator Powered by Neural Chess Engine

In this paper, we explore a new approach for automated chess commentary generation, which aims to generate chess commentary texts in different categories (e.g., description, comparison, planning, etc.). We introduce a neural chess engine into text generation models to help with encoding boards, predicting moves, and analyzing situations. By jointly training the neural chess engine and the generation models for different categories, the models become more effective. We conduct experiments on 5 categories in a benchmark Chess Commentary dataset and achieve inspiring results in both automatic and human evaluations.
Outsourcing Proofs of Retrievability

Proofs of Retrievability (POR) are cryptographic proofs that enable a cloud provider to prove that a user can retrieve his file in its entirety. POR need to be frequently executed by the user to ensure that their files stored in the cloud can be fully retrieved at any point in time. To conduct and verify POR, users need to be equipped with devices that have network access, and that can tolerate the (non-negligible) computational overhead incurred by the verification process. This clearly hinders the large-scale adoption of POR by cloud users, since many users increasingly rely on portable devices that have limited computational capacity, or might not always have network access. In this paper, we introduce the notion of <italic>outsourced proofs of retrievability</italic> (<inline-formula><tex-math notation="LaTeX">$\sf{ OPOR}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">OPOR</mml:mi></mml:math><inline-graphic xlink:href="karame-ieq1-2865554.gif"/></alternatives></inline-formula>), in which users can task an external auditor to perform and verify POR with the cloud provider. We argue that the <inline-formula><tex-math notation="LaTeX">$\sf{ OPOR}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">OPOR</mml:mi></mml:math><inline-graphic xlink:href="karame-ieq2-2865554.gif"/></alternatives></inline-formula> setting is subject to security risks that have not been covered by existing POR security models. To remedy that, we propose a formal framework and a security model for <inline-formula><tex-math notation="LaTeX">$\sf{ OPOR}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">OPOR</mml:mi></mml:math><inline-graphic xlink:href="karame-ieq3-2865554.gif"/></alternatives></inline-formula>. We then propose a generic procedure for transforming a public POR into an OPOR and we show the security of the resulting OPOR in our proposed security model. We demonstrate the transformation on two different instantiations of public POR schemes due to Shacham and Waters (Asiacrypt'08)—one based on BLS signatures and one using RSA signatures. A shortcoming of this transformation is that the generated OPOR inherits the high computational overhead from the underlying public key cryptography. Consequently, we propose afterwards an OPOR that is build from a private POR by Shacham and Waters. We implement a prototype based on our solutions, and evaluate their performance in a realistic cloud setting. Our evaluation results show that our proposals minimize user effort, and incur negligible overhead on the auditor.
Capacity Building in Cooperative Organization: Ground for Change

The concept of capacity building in cooperative society exist in all aspect of cooperatives activities and it is the important strategy that enable cooperative society and its members to perform effectively well in line with their goal and cooperative principles. For any cooperative society to perform very well and be able to compete favorably, it must invest reasonable amount of money to develop the ability of its members, employees and the society itself.
Finite-time consensus of multi-agent system via nonlinear event-triggered control strategy

This study investigates the finite-time consensus problem of leaderless and leader-following multi-agent systems. Two novel nonlinear consensus protocols are presented based on event-driven strategies, which can significantly reduce the communication consumption and the frequency of the controller updates. It is shown that the multi-agent system can achieve consensus in finite time under the proposed nonlinear consensus protocols. Moreover, the bound of the triggering interval is provided to illustrate that no Zeno behaviour exists. Finally, some simulations are employed to verify the effectiveness of the proposed consensus protocols.
Tunable Microwave Bandpass Filters With Complementary Split Ring Resonator and Liquid Crystal Materials

In this paper, a novel planar waveguide filter based on liquid crystal materials is devised, and a half-mode corrugated substrate integrated waveguide (HMCSIW) tunable bandpass filter with complementary split ring resonator (CSRR) structure is proposed. The HMCSIW uses an array of quarter-wave open-circuit stubs to replace conventional substrate integrated waveguide (SIW). While retaining the original cut-off frequency of corrugated substrate integrated waveguide (CSIW), we reduce the circuit size by splitting the central axis to achieve the miniaturization. By utilizing band-stop features of the CSRR in microstrip structure and combining with the electronically controlled dielectric constant of liquid crystal materials with high modulation efficiency of inverted microstrip line structure, a CSRR-loaded HMCSIW bandpass filter with liquid crystal materials is implemented. The experimental results demonstrate that the center frequency of the new filter can be adjusted in a range of 0.65 GHz, the pass-band insertion loss is less than 3.4 dB, the return loss is less than -10 dB, and the structure size is 32 mm $\times \,\, 76$ mm, which verify that the designed tunable band-pass filter achieves good frequency selection performance.