Reviewerly/exHarmony huggingface dataset:
A longitudinal study of topic classification on Twitter

Twitter represents a massively distributed information source over topics ranging from social and political events to entertainment and sports news. While recent work has suggested this content can be narrowed down to the personalized interests of individual users by training topic filters using standard classifiers, there remain many open questions about the efficacy of such classification-based filtering approaches. For example, over a year or more after training, how well do such classifiers generalize to future novel topical content, and are such results stable across a range of topics? In addition, how robust is a topic classifier over the time horizon, e.g., can a model trained in 1 year be used for making predictions in the subsequent year? Furthermore, what features, feature classes, and feature attributes are most critical for long-term classifier performance? To answer these questions, we collected a corpus of over 800 million English Tweets via the Twitter streaming API during 2013 and 2014 and learned topic classifiers for 10 diverse themes ranging from social issues to celebrity deaths to the "Iran nuclear deal". The results of this long-term study of topic classifier performance provide a number of important insights, among them that: (i) such classifiers can indeed generalize to novel topical content with high precision over a year or more after training though performance degrades with time, (ii) the classes of hashtags and simple terms contain the most informative feature instances, (iii) removing tweets containing training hashtags from the validation set allows better generalization, and (iv) the simple volume of tweets by a user correlates more with their informativeness than their follower or friend count. In summary, this work provides a long-term study of topic classifiers on Twitter that further justifies classification-based topical filtering approaches while providing detailed insight into the feature properties most critical for topic classifier performance.
Financial development and monetary policy effectiveness in Africa

As African countries await the birth of her monetary union, the link between economic policies and the real economy will continue to dominate policy debate. This paper investigates whether financial development influences the effectiveness of monetary policy on output and inflation in Africa. We apply standard panel data techniques to annual data from 1990–2015 for a panel of 39 African countries, and find a weak relationship between financial development and monetary policy effectiveness in Africa. The results show no statistical evidence of the relationship for output growth, whereas a negative relationship exists in the case of inflation, but only at their contemporaneous levels. Thus, there is need to strengthen the monetary transmission mechanism in African countries through deliberate efforts to deepen financial sector development.
Constructions and the problem of discovery: A case for the paradigmatic

Abstract Much of the patterned use of language occupies a poorly charted middle ground of usages that are neither frozen, one-off items listable in dictionaries nor products of maximally general rules found in grammars. Similarly, these usages fly below the radar of modular theories of language that make a strict distinction between items in a lexicon and the rules of syntax for combining them. Early constructionist approaches showed this neglected territory to be teeming with conventional form-meaning pairings, i.e., lexico-grammatical constructions. While corpora have been seen as a source for investigating these constructions, they entail a fundamental but seldom-noted limitation: constructions are constituted by both syntagmatic and paradigmatic relations, but corpora lack the paradigmatic dimension. Thus corpora can reveal multiword items but not the relations among them that constitute constructions. We elaborate on an alternative, illustrating how the design of an existing machine-readable language model affords discovery of lexico-grammatical constructions by capturing bottom up the relations they contract with other patterns. The network is noise-ridden, but we exploit the noise as the necessary background against which constructions can be set into relief and thus made discoverable.
Towards Declarative and Data-Centric Virtual Machine Image Management in IaaS Clouds

Virtual machine image (VMI) management has become one of the key infrastructure components in Infrastructure as a Service (IaaS) cloud systems. Any “good” VMI management system should support flexible and efficient VMI services to cloud users, and offer scalable, easy-to-maintain and efficient VMI management for cloud providers. While there have been a number of systems and optimizations for VMI management, this paper investigates a declarative and data-centric approach to VMI management for both cloud users and providers. Specifically, by viewing VMI management as a data-intensive application, we propose Hemera, a novel VMI management system prototype based on relational database systems. Hemera adopts a data-centric approach to VMI management system design, where a VMI is modeled as structured data. With the data-centric approach, the key operations of VMI management can be recast naturally as programs based on SQL language. Moreover, Hemera embraces a series of automatic optimization opportunities with their root from databases. We have developed a system prototype based on MySQL. Our experimental results show the efficiency and feasibility of our declarative and data-centric approach to VMI Management.
An effective High Recall Retrieval method

The High Recall Retrieval (HRR) problem is one of the fundamental tasks for many applications such as patent retrieval, legal search, medical search, marketing research, charging and collecting tax, and literature review, etc. Given the data set obtained by the user's query, the HRR problem is defined as finding the full set of relevant documents while less review effort will be required. It is very expensive to review a lot of documents since most of the reviewers are experts in the specific fields such as patent attorneys, lawyers, marketing, and medical professionals. However, the existing HRR methods have been far from satisfactory to make them enumerate all relevant documents. This is due to the fact that not only the sheer volume of documents inevitably including noises (non-relevant documents) but also the threshold measurements have been inadequately adopted. To deal with these problems, we propose a novel solution to efficiently find all the relevant documents among a large set of results. It consists of two steps: (a) to effectively classify the entire documents and (b) to select the representative documents in each class. We formalized the problem and theoretically verified the upper-bound of our method. In the experiments, our method is more efficient than the state-of-the-art query expansion methods.
A general notion of equivalence for abstract argumentation

We introduce a parametrized equivalence notion for abstract argumentation that subsumes standard and strong equivalence as corner cases. Under this notion, two argumentation frameworks are equivalent if they deliver the same extensions under any addition of arguments and attacks that do not affect a given set of core arguments. We also provide exact characterizations and complexity results. The proposed notion of equivalence is motivated by its capability to capture the concept of local simplifications. In fact, our equivalence notion allows to decide whether a sub-framework can be replaced by another one without changing the extensions in the framework which undergoes this change. Moreover, as our characterizations demonstrate deciding this form of equivalence does not require an analysis of the entire framework. This makes it an appealing formal underpinning for establishing general replacement patterns in argumentation frameworks.
Adaptive weights clustering of research papers

Abstract The JEL classification system is a standard way of assigning key topics to economic articles to make them more easily retrievable in the bulk of nowadays massive literature. Usually the JEL (Journal of Economic Literature) is picked by the author(s) bearing the risk of suboptimal assignment. Using the database of the Collaborative Research Center from Humboldt-Universität zu Berlin we employ a new adaptive clustering technique to identify interpretable JEL (sub)clusters. The proposed Adaptive Weights Clustering (AWC) is available on http://www.quantlet.de/ and is based on the idea of locally weighting each point (document, abstract) in terms of cluster membership. Comparison with $$k$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>k</mml:mi> </mml:math> -means or CLUTO reveals excellent performance of AWC.
Rational Mining Of Bitcoin

Bitcoin is a decentralized cryptocurrency. It is secure as long as majority of the computational resources are with honest miners who follow the Bitcoin protocol. There has been several attacks on Bitcoin mining process in recent years. Selfish mining has been proposed by which miners can get more reward than their fair share. If 33% of the miners are follow the selfish mining strategy, then the Bitcoin system will no longer remain decentralized. We propose a new mining strategy called the Rational Mining, following which only 28% of miners are enough to make Bitcoin decentralized.We analyze the different strategies and show how a miner can choose a strategy to maximize its gain under different parameter selection.
Personal characters to bits mapping using Dot Pattern Character Encoding Scheme (DPCES)

In this paper, a flexible dot-pattern based mapping technique for steganographic encoding of messages is presented, with the aim that statistical analysis would not reveal the secret message even if the steganographic system is under attack and the hidden bits are revealed from the cover media used in the steganography technique like text, image etc. To retrieve the message the attacker needs to know the specific mapping scheme used to encode the characters with dot pattern. Thus the method presented is highly secured compared to traditional steganography schemes where standard characters to bits mapping techniques are used. The corresponding binary data is generated from a specific and personal Dot Pattern Character Encoding Scheme (DPCES), which is embedded onto the cover media and sent through the public channel to the intending receiver having prior knowledge of the communication-specific encoding scheme. We have implemented the proposed DPCES using Visual Studio 2015, and tested with image covers which show that the stego media can maintain desired secrecy.
A Tool to Design Interactive Characters Based on Embodied Cognition

Creating interactive characters is one of the most challenging tasks of videogame design. In order to facilitate such an endeavor, we introduce a decisional and behavior synthesis architecture integrated in the game engine Unity3D. A distinguishing feature of this architecture is that it embraces embodied cognition principles and uses them as implementation requirements. From these, we derive an architecture, which is based on a novel combination of previously proposed systems, together with some simplifications. We also argue that the architecture proposed has properties-modularity, scalability, and stability-which can be beneficial for its practical industrial adoption, particularly in the context of the recent improvement in machine learning techniques. Artificial intelligence is a quite technical domain, and we believe such a tool can facilitate interactive character creation by creative minds in industrial applications.
On-body wearable device localization with a fast and memory efficient SVM-kNN using GPUs

Biomedical and inertial sensors can be used to generate a complete view of complicated physiological changes in a continuous, real-time, and non-invasive manner. As inappropriate on-body attachments may result in errors, the localization information requires calibration. In most previous studies of on-body wearable device localization, the run-time performance was not evaluated in terms of execution time and memory usage. Recently, smartphones have been used as data processing centers and recognition systems for wearable devices. However, the hardware disadvantages of phone devices have a negative effect on both real-time response and system accuracy. In this study, we focus on a high-performance localization system for the mobile environment and propose a memory efficient algorithm that is accelerated by using graphics processing units (GPUs). The algorithm combines a k-nearest neighbor classifier with a support vector machine (SVM)-based decision-maker to refine the classification outcome. This contributes to a faster and more accurate on-body localization for wearable devices. Experiment results demonstrated that the proposed method achieves an accuracy of 92.94%% and is faster than Serial-SVM and GPU-SVM by a factor of 6.81×and 3.87×, respectively. Therefore, the proposed technique can offer a comparable accuracy to SVM-based methods with faster recognition in the mobile environment.
Improving Performance of Breast Cancer Risk Prediction by Incorporating Optical Density Image Feature Analysis

The purpose of this study is to improve accuracy of near-term breast cancer risk prediction by applying a new mammographic image conversion method combined with a two-stage artificial neural network (ANN)-based classification scheme.The dataset included 168 negative mammography screening cases. In developing and testing our new risk model, we first converted the original grayscale value (GV)-based mammographic images into optical density (OD)-based images. For each case, our computer-aided scheme then computed two types of image features representing bilateral asymmetry and the maximum of the image features computed from GV and OD images, respectively. A two-stage classification scheme consisting of three ANNs was developed. The first stage included two ANNs trained using features computed separately from GV and OD images of 138 cases. The second stage included another ANN to fuse the prediction scores produced by two ANNs in the first stage. The risk prediction performance was tested using the rest 30 cases.With the two-stage classification scheme, the computed area under the receiver operating characteristic curve (AUC) was 0.816 ± 0.071, which was significantly higher than the AUC values of 0.669 ± 0.099 and 0.646 ± 0.099 achieved using two ANNs trained using GV features and OD features, respectively (P < .05).This study demonstrated that applying an OD image conversion method can acquire new complimentary information to those acquired from the original images. As a result, fusion image features computed from these two types of images yielded significantly higher performance in near-term breast cancer risk prediction.
Causal Inference on Multivariate and Mixed-Type Data

How can we discover whether X causes Y, or vice versa, that Y causes X, when we are only given a sample over their joint distribution? How can we do this such that X and Y can be univariate, multivariate, or of different cardinalities? And, how can we do so regardless of whether X and Y are of the same, or of different data type, be it discrete, numeric, or mixed? These are exactly the questions we answer. We take an information theoretic approach, based on the Minimum Description Length principle, from which it follows that first describing the data over cause and then that of effect given cause is shorter than the reverse direction. Simply put, if Y can be explained more succinctly by a set of classification or regression trees conditioned on X, than in the opposite direction, we conclude that X causes Y. Empirical evaluation on a wide range of data shows that our method, Crack, infers the correct causal direction reliably and with high accuracy on a wide range of settings, outperforming the state of the art by a wide margin. Code related to this paper is available at: http://eda.mmci.uni-saarland.de/crack .
Image Boundary Extension With Mean Value for Cosine–Sine Modulated Lapped/Block Transforms

We present a novel image boundary extension and mean value extension (MVE) for directional lapped transforms, particularly cosine-sine modulated lapped transforms (CSMLTs). Lapped transforms are usually used with an extension technique, such as periodic extension (PE) or symmetric extension (SE), for nonexpansive convolution at signal boundaries. When directional textures (oblique lines and curves) appear at the 2D signal (image) boundaries, both PE and SE produce directional discontinuities, which degrade the sparsity of the transformed coefficients, especially in the case of directional lapped transforms. MVE reduces the discontinuities for directional textures better than PE or SE do; it thus improves the efficiency of the sparse representation based on directional lapped transforms. Moreover, to reduce computational costs compared with those of directional lapped transforms, we introduce new directional block transforms called cosine-sine modulated block transforms. These new transforms are derived from a minimum tile processing (tiling) of M-band CSMLTs with 2M filter lengths and nonexpansive convolutions. The resulting directional block transforms, particularly in the case of the MVE, have richer directional selectivity and have better performance compared with a discrete Fourier transform as shown in experiments.
Discovering critical intersections in a road network

A sound understanding of the importance of intersections within a road network is a key factor for the efficient and effective management and control of transport networks when traffic operations are saturated. Conventional indices (e.g. level of service, degree of saturation, capacity, or delay) reflect only the states of an intersection itself, but lack the ability to describe the influences among the intersections within a road network. This study specifically presents a novel index, the intersection rank, to evaluate the importance of an intersection within a road network; this index refers to the view of the PageRank algorithm and further considers static connection structures and dynamic influences of traffic volumes between intersections. The experiment, conducted using a simulated road network with 30 intersections, shows that the presented method has superior capability in discovering critical intersections and reveals the interrelationships between intersections. These findings can be used to discover important intersections and adjust scarce spatiotemporal resources of the road transportation system during saturated traffic operations; moreover, they will be helpful for practitioners in undertaking efficient control and management of the road network.
Lifecycle coevolution framework for many evolutionary and swarm intelligence algorithms fusion in solving complex optimization problems

In last few decades, many different evolutionary and swarm intelligence (EA and SI) algorithms have been delicately developed for solving complex optimization problems. However, according to no free lunch theorem (NFL), a general-purpose universal EA or SI algorithm that is efficient for various types of optimization problems is theoretically impossible. The main focus in this research is to prove that there are potential free lunches in coevolutionary optimization mode. The significant improvements in the generality and effectiveness of EA and SI search ability can be achieved by fusing unlimited number of optimization algorithms based on the new concept of Lifecycle Coevolution Framework (LCF). The main innovation of LCF is to run the different SI algorithms simultaneously, in which individuals of each optimizer dynamically shift their states of birth, searching, learning, reproduction, and death throughout the whole colony life cycle. Based on LCF, we instantiate a novel coevolutionary optimization algorithm called a Lifecycle Framework of Multiple Evolution Algorithms (LCFMEAs). For purposes of examining the success of LCFMEAs in solving complex optimization problems, 50 benchmark functions with different specifications are employed. The results show that LCFMEAs provide extremely competitive performance when it is compared with six widely used evolutionary and swarm intelligence algorithms.
A short solution to the many-player silent duel with arbitrary consolation prize

The classical constant-sum 'silent duel' game had two antagonistic marksmen walking towards each other. A more friendly formulation has two equally skilled marksmen approaching targets at which they may silently fire at distances of their own choice. The winner, who gets a unit prize, is the marksman who hits his target at the greatest distance; if both miss, they share the prize (each gets a 'consolation prize' of one half). In another formulation, if they both miss they each get zero. More generally we can consider more than two marksmen and an arbitrary consolation prize. This non-constant sum game may be interpreted as a research tournament where the entrant who successfully solves the hardest problem wins the prize. We consider only the 'symmetric' case where all players are identical (having the same probability of missing at a given distance), and for this case we give the first complete solution to the many-player problem with arbitrary consolation prize. Moreover our theorem includes both the zero and non-zero-sum cases (by taking particular values for the consolation prize), and has a relatively simple proof.
Improving Auto-Detection of Phishing Websites using Fresh-Phish Framework

Denizens of the Internet are under a barrage of phishing attacks of increasing frequency and sophistication. Emails accompanied by authentic looking websites are ensnaring users who, unwittingly, hand over their credentials compromising both their privacy and security. Methods such as the blacklisting of these phishing websites become untenable and cannot keep pace with the explosion of fake sites. Detection of nefarious websites must become automated and be able to adapt to this ever-evolving form of social engineering. There is an improved framework that was previously implemented called “Fresh-Phish”, for creating current machine-learning data for phishing websites. The improved framework uses a total of 28 different website features that query using python, then a large labeled dataset is built and analyze over several machine learning classifiers against this dataset to determine which is the most accurate. This modified framework improves the accuracy of modeling those features by using integer rather than binary values where possible. This article analyzes not just the accuracy of the technique, but also how long it takes to train the model.
Discrete Flower Pollination Algorithm for Solving the Symmetric Travelling Salesman Problem

The Travelling Salesman Problem (TSP) is an important NP-hard combinatorial optimisation problem that forms the foundation of many modern-day, practical problems such as logistic or network route planning. The Flower Pollination Algorithm (FPA) is a continuous optimisation algorithm that demonstrated promising results in comparison to other wellknown algorithms. This paper proposes a novel algorithm, Discrete Flower Pollination Algorithm (DFPA) based on the FPA for solving the TSP. The algorithm is compared to the Ant Colony Optimisation's (ACO) MAX-MIN Ant System (MMAS) as well as the Genetic Algorithm (GA) since they are well studied and developed. The DFPA results are significantly better than the GA in all tested instances.