Reviewerly/exHarmony huggingface dataset:
Generative Adversarial Optimization

Inspired by the adversarial learning in generative adversarial network, a novel optimization framework named Generative Adversarial Optimization (GAO) is proposed in this paper. This GAO framework sets up generative models to generate candidate solutions via an adversarial process, in which two models are trained alternatively and simultaneously, i.e., a generative model for generating candidate solutions and a discriminative model for estimating the probability that a generated solution is better than a current solution. The training procedure of the generative model is to maximize the probability of the discriminative model. Specifically, the generative model and the discriminative model are in this paper implemented by multi-layer perceptrons that can be trained by the back-propagation approach. As of an implementation of the proposed GAO, for the purpose of increasing the diversity of generated solutions, a guiding vector ever introduced in guided fireworks algorithm (GFWA) has been employed here to help constructing generated solutions for the generative model. Experiments on CEC2013 benchmark suite show that the proposed GAO framework achieves better than the state-of-art performance on multi-modal functions.
Blockchain-based micropayment systems

The inception of blockchain catapulted the development of innovative use cases utilizing the trustless, decentralized environment, empowered by cryptocurrencies. The envisaged benefits of the technology includes the divisible nature of a cryptocurrency, that can facilitate payments in fractions of a cent, enabling micropayments through the blockchain. Micropayments are a critical tool to enable financial inclusion and to aid in global poverty alleviation. The paper conducts a study on the economic impact of blockchain-based micropayment systems, emphasizing their significance for socioeconomic benefit and financial inclusion. The paper also highlights the contribution of blockchain-based micropayments to the cybercrime economy, indicating the critical need of economic regulations to curtail the growing threat posed by the digital payment mechanism.
Abusiveness is Non-Binary: Five Shades of Gray in German Online News-Comments

Online news comment sections face a surge in uncivil, abusive and even straightforwardly hateful and threatening posts. In Germany especially the refugee crisis beginning in 2015 has sparked a lot of controversial and even unacceptable user comments. Overwhelmed by the amount of content and facing the risk of fines and a churn of readers as well as advertisers, many platforms shut down their comment sections as a last resort. To reduce their moderation effort, academics started applying machine learning to classify comments automatically. However, these efforts so far have been mostly focused on English texts. To provide similar systems for German, this paper implements and evaluates six different machine learning classifiers and five different strategies to convert textual comments into machine-compatible vectors. Contrary to common belief in the domain, comments often evade binary classification: Often comments are not only hateful, or insulting or threatening but fall within multiple of these categories. Hence, we will go beyond traditional multi-class classification models and prototypically evaluate the use of multi-label techniques. The first evaluations indicate that systems for abusive language detection are transferable to the German language and that supporting multi-labels might not only help to improve the detection of rare abusiveness types but also lead to a more realistic representation of actual online commentary.
Ant Colony Optimization Algorithm and Artificial Immune System Applied to a Robot Route

This Article aims to introduce two meta-heuristics techniques: Ant Colony Optimization (ACO) and Artificial Immune System (AIS) to find the best route for a robot. The ACO is an algorithm based on the ant food search process, and the AIS is inspired by the defending mechanism of the human organism. In order to illustrate and compare the potential of these techniques, this paper applies both techniques in a problem of determining the shortest possible route for a robot without hitting any obstacles in three different maps. According to the tests, the ACO shows better results regarding the number of iterations to reach the global optimum, while the AIS shows better results when it comes to the processing time. From the result, it can be seen that the ACO found a solution to all maps demonstrating it is an excellent choice for this problem type.
Incept-N: A Convolutional Neural Network Based Classification Approach for Predicting Nationality from Facial Features

Nationality of a human being is a well-known identifying characteristic used for every major authentication purpose in every country. Albeit advances in application of Artificial Intelligence and Computer Vision in different aspects, its’ contribution to this specific security procedure is yet to be cultivated. With a goal to successfully applying computer vision techniques to predict a human’s nationality based on his facial features, we have proposed this novel method and have achieved an average of 93.6% accuracy with very low mis-classification rate.
Deep Semantic Instance Segmentation of Tree-Like Structures Using Synthetic Data

Tree-like structures, such as blood vessels, often express complexity at very fine scales, requiring high-resolution grids to adequately describe their shape. Such sparse morphology can alternately be represented by locations of centreline points, but learning from this type of data with deep learning is challenging due to it being unordered, and permutation invariant. In this work, we propose a deep neural network that directly consumes unordered points along the centreline of a branching structure, to identify the topology of the represented structure in a single-shot. Key to our approach is the use of a novel multi-task loss function, enabling instance segmentation of arbitrarily complex branching structures. We train the network solely using synthetically generated data, utilizing domain randomization to facilitate the transfer to real 2D and 3D data. Results show that our network can reliably extract meaningful information about branch locations, bifurcations and endpoints, and sets a new benchmark for semantic instance segmentation in branching structures.
The Standard Coder: A Machine Learning Approach to Measuring the Effort Required to Produce Source Code Change

We apply machine learning to version control data to measure the quantity of effort required to produce source code changes. We construct a model of a 'standard coder' trained from examples of code changes produced by actual software developers together with the labor time they supplied. The effort of a code change is then defined as the labor hours supplied by the standard coder to produce that change. We therefore reduce heterogeneous, structured code changes to a scalar measure of effort derived from large quantities of empirical data on the coding behavior of software developers. The standard coder replaces traditional metrics, such as lines-of-code or function point analysis, and yields new insights into what code changes require more or less effort.
Occluded Pedestrian Detection Based on Depth Vision Significance in Biomimetic Binocular

Pedestrian detection and tracking has become an important field in the field of computer vision research. However, the existing pedestrian detection algorithms have some problems, such as low accuracy and poor stability due to the similar background and overlapped occlusion interference. Therefore, an occluded pedestrian detection method based on binocular vision is proposed in this paper. We simulate the recognition of human brain and use the deep learning network MobileNet to detect and locate the initial pedestrians. Then, binocular depth is introduced as visual salience prior information, which solves the problem of identifying pedestrians with similar background and occlusion. The experimental results show that our pedestrian detection framework greatly improves the pedestrian error detection under similar background and occlusion conditions.
Matroidal root structure of skew polynomials over finite fields

A skew polynomial ring R = K [x; σ, δ ] is a ring of polynomials with non-commutative multiplication. This creates a difference between left and right divisibility, evaluations, and roots. A polynomial in such a ring may have more roots than its degree, which leads to the concepts of closures and independent sets of roots. In , this leads to the matroids and of right independent and left independent sets, which are isomorphic via the extension of the map φ : [1] → [1] defined by φ (a) = am, where . Extending the field of coefficients of R results in a new ring S of which R is a subring, and if the extension is taken to include roots of an evaluation polynomial of f (x), then all roots of f (x) in S are in the same conjugacy class.
DOD-CNN: Doubly-injecting Object Information for Event Recognition

Recognizing an event in an image can be enhanced by detecting relevant objects in two ways: 1) indirectly utilizing object detection information within the unified architecture or 2) directly making use of the object detection output results. We introduce a novel approach, referred to as Doubly-injected Object Detection CNN (DOD-CNN), exploiting the object information in both ways for the task of event recognition. The structure of this network is inspired by the Integrated Object Detection CNN (IOD-CNN) where object information is indirectly exploited by the event recognition module through the shared portion of the network. In the DOD-CNN architecture, the intermediate object detection outputs are directly injected into the event recognition network while keeping the indirect sharing structure inherited from the IOD-CNN, thus being `doubly-injected'. We also introduce a batch pooling layer which constructs one representative feature map from multiple object hypotheses. We have demonstrated the effectiveness of injecting the object detection information in two different ways in the task of malicious event recognition.
Bootstrapping Public Blockchains Without a Trusted Setup

We propose a protocol that allows the participants of a permissionless decentralized system to agree on a set of identities in the presence of a computationally-bounded Byzantine adversary. Our protocol guarantees that the fraction of identities belonging to the adversary in the set of identities is at most equal to the total computational hash power of the adversary.
Usability in virtual reality: evaluating user experience with interactive archaeometry tools in digital simulations

This is an experimental work presenting a study about usability experience of users in a cyber-archeology fully immersive 3D environment in Virtual Reality (VR). Particularly we research how users explore a realistic 3D environment with VR devices through archaeometry conventional techniques for archeology analysis and discoveries. Our main objective is to evaluate user’s experience with interactive archaeometry tools of a population called archeologists, not a VR expert, but expert on the context of the real experience of practicing archeology in remote site; and compare the results with another population called VR experts, in this case, not experts in archeology, but in VR technology and multimedia applications. Several standard metrics will be used to collect data about their interactions with the cyber system (efficacy, efficiency, satisfaction, level of presence and cyber-sickness). Two hypotheses will be tested with this experiment: a) it is possible to represent the virtual world as realistically as the real one, in such way that a person unfamiliar with this kind of technology, in this case the archaeologist, can develop analytical process of discoveries in the VR model; and b) if this VR model is passive of exploration, virtually it is possible to create analytical tools that will help the archaeologist to manipulate archaeometry tools. Both sample population had participated in usability tests and the results are promising.
The DART annotation scheme: form, appliability &amp; application

The Dialogue Annotation and Research Tool (DART) is a tool designed to facilitate large-scale corpus-based research into pragmatics-related aspects, including syntax, pragmatics (speech-acts), semantico-pragmatics (Searle’s ‘IFIDS’), as well as other interaction-relevant features. This chapter aims at providing a detailed description and discussion of the DART annotation scheme, which has already been successfully applied to a number of corpora from various domains, and provide examples of its application and appliability to various types of spoken data, illustrating e.g. the ability to create speaker profiles that allow the researcher to investigate features of (in)directness & politeness, initiative, etc. (cf. Weisser 2016d). In doing so, I shall draw on materials from task-oriented corpora (SPAADIA; SRI Amex), unconstrained dialogue (Switchboard), and ‘English as a lingua franca (ELF)’ data from ICE-HK, at the same time illustrating the advantages of the scheme over prior existing schemes.
A Regularization Approach to Combining Keywords and Training Data in Technology-Assisted Review

Manual keyword queries and supervised learning (technology-assisted review) have been viewed as conflicting approaches to high recall retrieval tasks (such as civil discovery and sunshine law requests) in the law. We propose a synthesis that uses a keyword list as a regularizer when learning a logistic regression model from labeled examples. Balancing keywords against training data requires knowing how the regularization penalty should scale with training set size. We show, however, that advice on scaling from theory is contradictory, software defaults are inconsistent, and standard practice (validation-based tuning) is impractical in many high-recall retrieval settings. Through experiments on simulated e-discovery data sets, we show that the penalization scheme suggested by a Bayesian interpretation is substantially safer than alternatives from stochastic optimization and computational learning theory. Combining keywords and training data provides better effectiveness on our datasets than using either alone, showing that both approaches bring value.
Building automated survey coders via interactive machine learning

Software systems trained via machine learning to automatically classify open-ended answers (a.k.a. verbatims) are by now a reality. Still, their adoption in the survey coding industry has been less widespread than it might have been. Among the factors that have hindered a more massive takeup of this technology are the effort involved in manually coding a sufficient amount of training data, the fact that small studies do not seem to justify this effort, and the fact that the process needs to be repeated anew when brand new coding tasks arise. In this article, we will argue for an approach to building verbatim classifiers that we will call “Interactive Learning,” and that addresses all the above problems. We will show that, for the same amount of training effort, interactive learning delivers much better coding accuracy than standard “non-interactive” learning. This is especially true when the amount of data we are willing to manually code is small, which makes this approach attractive also for small-scale studies. Interactive learning also lends itself to reusing previously trained classifiers for dealing with new (albeit related) coding tasks. Interactive learning also integrates better in the daily workflow of the survey specialist and delivers a better user experience overall.
IoT Resource Access utilizing Blockchains and Trusted Execution Environments

We consider IoT resources with a Trusted Execution Environment (TEE) and propose a model to provide trusted resource access that is linked to blockchain payments, ensuring the integrity and confidentiality of the IoT data. The model is built on the widely used OAuth 2.0 open authorization framework, which provides delegated authorization for IoT resources. We utilize hash-lock and time-lock mechanisms to cryptographically link trusted resource access, provided by the IoT resource's TEE, to authorization grants and blockchain payments. The model is implemented in the OP-TEE open source port for the Raspberry Pi that uses ARM's TrustZone and is evaluated on the Rinkeby public Ethereum testnet.
Some extensions of linear approximation and prediction problems for stationary processes

Let (B(t))t∈Θ with Θ=Z or Θ=R be a wide sense stationary process with discrete or continuous time. The classical linear prediction problem consists of finding an element in span¯{B(s),s≤t} providing the best possible mean square approximation to the variable B(τ) with τ>t. In this article we investigate this and some other similar problems where, in addition to prediction quality, optimization takes into account other features of the objects we search for. One of the most motivating examples of this kind is an approximation of a stationary process B by a stationary differentiable process X taking into account the kinetic energy that X spends in its approximation efforts.
On High-Resolution Adaptive Sampling of Deterministic Signals

In this work, we study the topic of high-resolution adaptive sampling of a given deterministic and differentiable signal and establish a connection with classic approaches to high-rate quantization. Specifically, we formulate solutions for the task of optimal high-resolution sampling, counterparts of well-known results for high-rate quantization. Our results reveal that the optimal high-resolution sampling structure is determined by the density of the signal-gradient energy, just as the probability density function defines the optimal high-rate quantization form. This paper has three main contributions: The first is establishing a fundamental paradigm bridging the topics of sampling and quantization. The second is a theoretical analysis of nonuniform sampling, for arbitrary signal dimension, relevant to the emerging field of high-resolution signal processing. The third is a new practical approach to nonuniform sampling of one-dimensional signals that enables reconstruction based only on the sampling time points and the signal extrema locations and values. Experiments for signal sampling and coding showed that our method outperforms an optimized tree-structured sampling technique.
A classroom concentration model based on computer vision

With the development of computer vision technology, automatic classroom behavior analysis has attracted more and more attention and the estimation of students' head pose is the most essential one of them. This paper proposed a classroom concentration model based on the head pose and position information collected by convolution neural networks. On the self-built dataset, the accuracy of the head pose classification model is 94.6%. In the real scene test, the results of our method are consistent with the manual analysis of the corresponding teaching videos. It means that the method can efficiently reflect real students' concentration situation during class and help teachers and experts carry out teaching analysis and rethink.
Axiomatic Systems and Topological Semantics for Intuitionistic Temporal Logic

The importance of intuitionistic temporal logics in Computer Science and Artificial Intelligence has become increasingly clear in the last few years. From the proof-theory point of view, intuitionistic temporal logics have made it possible to extend functional languages with new features via type theory, while from its semantical perspective several logics for reasoning about dynamical systems and several semantics for logic programming have their roots in this framework. In this paper we propose four axiomatic systems for intuitionistic linear temporal logic and show that each of these systems is sound for a class of structures based either on Kripke frames or on dynamic topological systems. Our topological semantics features a new interpretation for the ‘henceforth’ modality that is a natural intuitionistic variant of the classical one. Using the soundness results, we show that the four logics obtained from the axiomatic systems are distinct.
Structured group local sparse tracker

Sparse representation is considered as a viable solution to visual tracking. In this study, the authors proposed a structured group local sparse tracker (SGLST), which exploits the local patches inside target candidates in the particle filter framework. Unlike the conventional local sparse trackers, the proposed optimisation model in SGLST not only adopts local and spatial information of the target candidates but also attains the spatial layout structure among them by employing a group-sparsity regularisation term. To solve the optimisation model, the authors proposed an efficient numerical algorithm consisting of two subproblems with the closed-form solutions. Both qualitative and quantitative evaluations on the benchmarks of challenging image sequences demonstrate the superior performance of the proposed tracker against several state-of-the-art trackers.
Stacked autoencoders and extreme learning machine based hybrid model for electrical load prediction

Electrical load prediction plays an important role in power system management and economic development. However, because electrical load has non-linear relationships with several factors such as the political environment, the economic policy, the human activities, the irregular behaviors and the ot her factors, it is quite difficult to predict power load accurately. In order to further improve the electrical load forecasting performance, a hybrid model is proposed in this paper. The proposed hybrid model combines the Stacked AutoEncoders (SAE) and extreme learning machines (ELMs) to learn the characteristics of the time series data of electrical load. In this proposed method, in order to utilize the characteristics of the electrical load in different depths, the outputs of each layer of the SAE are taken as the inputs of one specific ELM. Then, the obtained results from the constructed different ELMs are integrated by the linear regression to obtain the final output. The linear regression part is trained by the least square estimation method. In addition, the hybrid model is applied to predict two real-world electrical load time series. And, detailed comparisons with the SAE, ELM, the back propagation neural network (BPNN), the multiple linear regression (MLR) and the support vector regression (SVR) are done to show the advantages of the proposed forecasting model. Experimental and comparison results demonstrate that the proposed hybrid model can achieve much better performance than the comparative methods in electrical load forecasting application.