Reviewerly/exHarmony huggingface dataset:
Ordinal Regression Using Noisy Pairwise Comparisons for Body Mass Index Range Estimation

Ordinal regression aims to classify instances into ordinal categories. In this paper, body mass index (BMI) category estimation from facial images is cast as an ordinal regression problem. In particular, noisy binary search algorithms based on pairwise comparisons are employed to exploit the ordinal relationship among BMI categories. Comparisons are performed with Siamese architectures, one of which uses the Bradley-Terry model probabilities as target. The Bradley-Terry model describes probabilities of the possible outcomes when elements of a set are repeatedly compared with one another in pairs. Experimental results show that our approach outperforms classification and regression-based methods at estimating BMI categories.
Blending under deconstruction

Abstract The cognitive-linguistic theory of conceptual blending was introduced by Fauconnier and Turner in the late 90s to provide a descriptive model and foundational approach for the (almost uniquely) human ability to invent new concepts. Whilst blending is often described as ‘fluid’ and ‘effortless’ when ascribed to humans, it becomes a highly complex, multi-paradigm problem in Artificial Intelligence. This paper aims at presenting a coherent computational narrative, focusing on how one may derive a formal reconstruction of conceptual blending from a deconstruction of the human ability of concept invention into some of its core components. It thus focuses on presenting the key facets that a computational framework for concept invention should possess. A central theme in our narrative is the notion of refinement, understood as ways of specialising or generalising concepts, an idea that can be seen as providing conceptual uniformity to a number of theoretical constructs as well as implementation efforts underlying computational versions of conceptual blending. Particular elements underlying our reconstruction effort include ontologies and ontology-based reasoning, image schema theory, spatio-temporal reasoning, abstract specification, social choice theory, and axiom pinpointing. We overview and analyse adopted solutions and then focus on open perspectives that address two core problems in computational approaches to conceptual blending: searching for the shared semantic structure between concepts—the so-called generic space in conceptual blending—and concept evaluation, i.e., to determine the value of newly found blends.
Semi-Dense Stereo Matching Using Dual CNNs

A robust solution for semi-dense stereo matching is presented. It utilizes two CNN models for computing stereo matching cost and performing confidence-based filtering, respectively. Compared to existing CNNs-based matching cost generation approaches, our method feeds additional global information into the network so that the learned model can better handle challenging cases, such as lighting changes and lack of textures. Through utilizing non-parametric transforms, our method is also more self-reliant than most existing semi-dense stereo approaches, which rely highly on the adjustment of parameters. The experimental results based on Middlebury Stereo dataset demonstrate that the proposed approach outperforms the state-of-the-art semi-dense stereo approaches.
AFARTICA

Frequent item-set mining has been exhaustively studied in the last decade. Several successful approaches have been made to identify the maximal frequent item-sets from a set of typical item-sets. The present work has introduced a novel pruning mechanism which has proved itself to be significant time efficient. The novel technique is based on the Artificial Cell Division (ACD) algorithm which has been found to be highly successful in solving tasks that involve a multi-way search of the search space. The necessity conditions of the ACD process have been modified accordingly to tackle the pruning procedure. The proposed algorithm has been compared with the apriori algorithm implemented in WEKA. Accurate experimental evaluation has been conducted and the experimental results have proved the superiority of AFARTICA over apriori algorithm. The results have also indicated that the proposed algorithm can lead to better performance when the support threshold value is more for the same set of item-sets.
Game theory for cyber deception

Deceptive and anti-deceptive technologies have been developed for various specific applications. But there is a significant need for a general, holistic, and quantitative framework of deception. Game theory provides an ideal set of tools to develop such a framework of deception. In particular, game theory captures the strategic and self-interested nature of attackers and defenders in cybersecurity. Additionally, control theory can be used to quantify the physical impact of attack and defense strategies. In this tutorial, we present an overview of game-theoretic models and design mechanisms for deception and counter-deception. The tutorial aims to provide a taxonomy of deception and counter-deception and understand how they can be conceptualized, quantified, and designed or mitigated. This tutorial gives an overview of diverse methodologies from game theory that includes games of incomplete information, dynamic games, mechanism design theory to offer a modern theoretic underpinning of cyberdeception. The tutorial will also discuss open problems and research challenges that the HoTSoS community can address and contribute with an objective to build a multidisciplinary bridge between cybersecurity, economics, game and decision theory.
On approximate nesting of multiple social network graphs

A fundamental problem in Social Network Analysis is how to move from single-layer to multi-layer, which provide a holistic view. User profiles resolution has received considerable attention since it allows to match users on different online social networks (OSNs). However, to the best of our knowledge, no study has focused on nesting operation for merging OSNs graphs. This work is a first step in the direction of defining the data model and the algorithm to perform approximate nesting of multiple OSNs graphs, based on user features. We provide initial experimental evidence based on synthetic data.
Explanatory and Actionable Debugging for Machine Learning

Question answering from tables (TableQA) extracting answers from tables from the question given in natural language, has been actively studied. Existing models have been trained and evaluated mostly with respect to answer accuracy using public benchmark datasets such as WikiSQL. The goal of this demonstration is to show a debugging tool for such models, explaining answers to humans, known as explanatory debugging. Our key distinction is making it "actionable" to allow users to directly correct models upon explanation. Specifically, our tool surfaces annotation and models errors for users to correct, and provides actionable insights.
Sentiment analysis with genetically evolved gaussian kernels

Sentiment analysis consists of evaluating opinions or statements based on text analysis. Among the methods used to estimate the degree to which a text expresses a certain sentiment are those based on Gaussian Processes. However, traditional Gaussian Processes methods use a predefined kernels with hyperparameters that can be tuned but whose structure can not be adapted. In this paper, we propose the application of Genetic Programming for the evolution of Gaussian Process kernels that are more precise for sentiment analysis. We use use a very flexible representation of kernels combined with a multi-objective approach that considers simultaneously two quality metrics and the computational time required to evaluate those kernels. Our results show that the algorithm can outperform Gaussian Processes with traditional kernels for some of the sentiment analysis tasks considered.
Minimum distance computation of linear codes via genetic algorithms with permutation encoding

We design a heuristic method, a genetic algorithm, for the computation of an upper bound of the minimum distance of a linear code over a finite field. By the use of the row reduced echelon form, we obtain a permutation encoding of the problem, so that its space of solutions does not depend on the size of the base field or the dimension of the code. Actually, the efficiency of our method only grows non-polynomially with respect to the length of the code.
How Many Subpopulations Is Too Many? Exponential Lower Bounds for Inferring Population Histories

Reconstruction of population histories is a central problem in population genetics. Existing coalescent-based methods, like the seminal work of Li and Durbin (Nature, 2011), attempt to solve this problem using sequence data but have no rigorous guarantees. Determining the amount of data needed to correctly reconstruct population histories is a major challenge. Using a variety of tools from information theory, the theory of extremal polynomials, and approximation theory, we prove new sharp information-theoretic lower bounds on the problem of reconstructing population structure—the history of multiple subpopulations that merge, split and change sizes over time. Our lower bounds are exponential in the number of subpopulations, even when reconstructing recent histories. We demonstrate the sharpness of our lower bounds by providing algorithms for distinguishing and learning population histories with matching dependence on the number of subpopulations.
Multi-lingual geoparsing based on machine translation

Our method for multi-lingual geoparsing uses monolingual tools and resources along with machine translation and alignment to return location words in many languages. Not only does our method save the time and cost of developing geoparsers for each language separately, but also it allows the possibility of a wide range of having a wide range of language capabilities within a single interface. We evaluated our method in our LanguageBridge prototype on location named entities using newswire, broadcast news and telephone conversations in English, Arabic and Chinese data from the Linguistic Data Consortium (LDC). Our results for geoparsing Chinese and Arabic text using our multi-lingual geoparsing method are comparable to our results for geoparsing English text with our English tools. Furthermore, our experiments using our tools on machine translation approach in accuracy results on results from the same data that was translated manually, further showing the robustness of locations to machine translation.
Investigating Software Development Methodologies and Practices in Software Industry in Tanzania

The last few years have seen a growing development of software industry in East Africa and Tanzania in particular. The software industry has contributed up to 40% of the economic growth in the past 10 years with the number of registered firms increasing yearly. Despite these developments, few studies have investigated the software development methodologies and practices that have been applied in the software industry and how they affect the success or failure of the software projects. This study investigated software development activities in 18 firms to determine to what extent the software development methods are being adopted and the factors influencing the selection of such methodologies. The study used questionnaire, semi-structured interviews, and documentary review involving 54 developers and project managers from 18 software firms in Dar es Salaam, Tanzania. The study found that the majority of software firms have been adopting traditional software development methodologies accounting to 72% of 79 studied software products. Specifically, amongst 51 software products developed using traditional software methodology, nearly half of the software products adopted waterfall model. Moreover, agile methodologies and other iterative methods are rarely adopted in the software industry. The implications of the results are discussed.
OCAPIS: R package for Ordinal Classification and Preprocessing in Scala

Ordinal data are those where a natural order exists between the labels. The classification and preprocessing of this type of data is attracting more and more interest in the area of machine learning, due to its presence in many common problems. Traditionally, ordinal classification problems have been approached as nominal problems. However, that implies not taking into account their natural order constraints. In this paper, an innovative R package named ocapis (Ordinal Classification and Preprocessing in Scala) is introduced. Implemented mainly in Scala and available through Github, this library includes four learners and two preprocessing algorithms for ordinal and monotonic data. Main features of the package and examples of installation and use are explained throughout this manuscript.
A Framework for Image Dark Data Assessment

Blindly applying data mining techniques on image dark data whose content and value are not clear, is highly likely to bring undesired result. Therefore, we propose an assessment framework which includes offline and online stages for image dark data. In offline stage, we first transform images into hash codes by Deep Self-taught Hashing (DSTH) algorithm, then construct a semantic graph, and finally use our designed Semantic Hash Ranking (SHR) algorithm to calculate the importance score. During online stage, we first translate the user's query into hash codes, then match the suitable data contained in the dark data, and finally return the weighted average value of these matched data to help the user cognize the dark data. The results on real-world dataset show our framework can apply to large-scale datasets, help the user conduct subsequent data mining work.
Unsupervised Adversarial Visual Level Domain Adaptation for Learning Video Object Detectors From Images

Deep learning based object detectors require thousands of diversified bounding box and class annotated examples. Though image object detectors have shown rapid progress in recent years with release of multiple large scale static image datasets, object detection on videos still remains an open problem due to unavailability of annotated video frames. Having a robust video object detector is an essential component for video understanding and curating large scale automated annotations in videos. Domain difference between images and videos makes the transferability of image object detectors to videos sub-optimal. The most common solution is to use weakly supervised annotations where a video frame has to be tagged for presence/absence of object categories. This still takes up manual effort. In this paper we take a step forward to attain zero supervision on video domain by adapting the concept of unsupervised adversarial image-to-image translation to perturb static high quality images to be visually indistinguishable from set of video frmes. We assume the presence of a fully annotated static image dataset and an unannotated video frames. Object detector is trained on adversarially transformed image dataset using the annotations of original dataset. Experiments on Youtube-Objects and Youtube-Objects-Subset datasets with two contemporary baseline object detectors reveal that such unsupervised pixel level domain adaptation boosts the generalization performance on video frames compared to direct application of image object detector. Also we achieve competitive performance compared to recent baselines of weakly supervised methods. This paper can be seen as an application of image translation for cross domain object detection.
Red–black trees with constant update time

We show how a few modifications to the red–black trees allow for constant worst-case update time (once the position of the element to be inserted or deleted is known). The resulting structure is based on relaxing some of the properties of the red–black trees while guaranteeing that the height remains logarithmic with respect to the number of nodes. Compared to the other search trees with constant worst-case update time, our tree is the first to provide a tailored deletion procedure without using the global rebuilding technique. In addition, our procedures are simpler and allow for an easier proof of correctness than those alternative trees.
Analysis of Primary Care Computerized Medical Records (CMR) Data With Deep Autoencoders (DAE)

The use of deep learning is becoming increasingly important in the analysis of medical data for applications such as pattern recognition for classification. The use of primary healthcare computational medical records (CMR) data is vital in prediction of infection prevalence across a population and decision making at a national scale. However, to date, the application of machine learning algorithms to CMR data remains under-utilised despite the potential impact for use in diagnostics or prevention of epidemics such as outbreaks of influenza. A particular challenge in epidemiology is how to differentiate incident cases from those that are follow-ups for the same condition. Furthermore, the CMR data are typically heterogeneous, noisy, high dimensional and incomplete making automated analysis difficult. Here we introduce a methodology for converting heterogeneous data such that it is compatible with a deep autoencoder for reduction of CMR data. This approach provides a tool for real time visualisation of these high dimensional data, revealing previously unknown dependencies and clusters. Our unsupervised nonlinear reduction method can be used to identify the features driving the formation of these clusters that can aid decision making in healthcare applications. The results in this work demonstrate that our methods can cluster more than 97.84\% of the data (clusters $>$5 points) each of which is uniquely described by three attributes in the data, Clinical System (CMR system), Read Code (as recorded) and Read Term (standardised coding). Further, we propose the use of Shannon Entropy as a means to analyse the dispersion of clusters and the contribution from the underlying attributes to gain further insight from the data. Our results demonstrate that Shannon Entropy is a useful metric for analysing both the low dimensional clusters of CMR data and also the features in the original heterogeneous data. Finally, we find that the entropy of the low dimensional clusters are directly representative of the entropy of the input data (Pearson Correlation = 0.99, R$^2$ = 0.98) and therefore the reduced data from the deep autoencoder is reflective of the original CMR data variability.
Optimizing Algorithms for low CPU Usage in Different Scenarios

This paper proposes a series of results over the image processing algorithms and for the shortest path algorithm that influence the percentage of CPU utilization. Starting with the fractal generation to image analysis, compression for big images and also any algorithm, the parallel programing is today one of the most important factor for low consumption and good power balanced for the processing unit. A good balance between the processing capacity, the algorithm and the time of processing is hard to obtain due to the number of calculus. In this work a series of results and comparison between sequential and parallel algorithms are presented. Also, there are presented test scenarios in different cases that confirm or infirm the parallel strategy chosen. This paper will integrate the results obtained with the proposed algorithms reports or comparisons of the proposed parallelization methods and will provide conclusions and ideas for future research in this field with extensive uses.
Improved Quantum Multicollision-Finding Algorithm

The current paper improves the number of queries of the previous quantum multi-collision finding algorithms presented by Hosoyamada et al. at Asiacrypt 2017. Let an l-collision be a tuple of l distinct inputs that result in the same output of a target function. In cryptology, it is important to study how many queries are required to find l-collisions for random functions of which domains are larger than ranges. The previous algorithm finds an l-collision for a random function by recursively calling the algorithm for finding $$(l-1)$$ -collisions, and it achieves the average quantum query complexity of $$O(N^{(3^{l-1}-1) / (2 \cdot 3^{l-1})})$$ , where N is the range size of target functions. The new algorithm removes the redundancy of the previous recursive algorithm so that different recursive calls can share a part of computations. The new algorithm finds an l-collision for random functions with the average quantum query complexity of $$O(N^{(2^{l-1}-1) / (2^{l}-1)})$$ , which improves the previous bound for all $$l\ge 3$$ (the new and previous algorithms achieve the optimal bound for $$l=2$$ ). More generally, the new algorithm achieves the average quantum query complexity of $$O\left( c^{3/2}_N N^{\frac{2^{l-1}-1}{ 2^{l}-1}}\right) $$ for a random function $$f:X\rightarrow Y$$ such that $$|X| \ge l \cdot |Y| / c_N$$ for any $$1\le c_N \in o(N^{\frac{1}{2^l - 1}})$$ . With the same query complexity, it also finds a multiclaw for random functions, which is harder to find than a multicollision.
Better Guider Predicts Future Better: Difference Guided Generative Adversarial Networks

Predicting the future is a fantasy but practicality work. It is the key component to intelligent agents, such as self-driving vehicles, medical monitoring devices and robotics. In this work, we consider generating unseen future frames from previous observations, which is notoriously hard due to the uncertainty in frame dynamics. While recent works based on generative adversarial networks (GANs) made remarkable progress, there is still an obstacle for making accurate and realistic predictions. In this paper, we propose a novel GAN based on inter-frame difference to circumvent the difficulties. More specifically, our model is a multi-stage generative network, which is named the Difference Guided Generative Adversarial Network (DGGAN). The DGGAN learns to explicitly enforce future-frame predictions that is guided by synthetic inter-frame difference. Given a sequence of frames, DGGAN first uses dual paths to generate meta information. One path, called Coarse Frame Generator, predicts the coarse details about future frames, and the other path, called Difference Guide Generator, generates the difference image which include complementary fine details. Then our coarse details will then be refined via guidance of difference image under the support of GANs. With this model and novel architecture, we achieve state-of-the-art performance for future video prediction on UCF-101, KITTI.
Social Network Mining: A discussion

Abstract Social network is model in order to structuring social. In knowledge management, social structures need to be revealed to see social behavior and social change based on the interactions that have taken place. In knowledge technology, the semantic social structure is extracted from information sources to interpret social behavior and its changes, and the meaning is supported by social network analysis. However, social networks do not only reveal social structures and manage them, but social recognition by exploring the potential of social members (social actors) and the community so as to present competitiveness. Competence is defined by the existence of social actors, while the existence of social actors is expressed from the relationship between them whereby the relationship also proves the meaning the competence. By involving artificial intelligence (AI), the meaning can be affirmed in a variety of different ways depending on the treatment given to the source of information so that it is possible to predict and express on the characteristics and behavior of the data. It based on forensic data and information retrieval, for trusty information is generated. Moreover, social network mining is present by considering social network data and the resultant of extraction method so that social networks not only contain information but become knowledge. This serves to bridge the social network analysis gap based on primary and secondary data, so social engineering is possible by exploring all the potential presented by social network extraction.