Reviewerly/exHarmony huggingface dataset:
A decentralised approach for link inference in large signed graphs

Social networks as large graphs have interesting information embedded within. The presence of links between nodes characterises the underlying relationships between nodes. Link inference is an interesting problem and has been studied for unsigned and signed graphs. Whilst the signed links give more insight into the node relationships, the class imbalance and the limited availability of signed graph datasets obstructs the studies in this domain. Furthermore, the studies in literature usually consider a single large graph and ignore the underlying potentially different sub-graphs in the original graph. In this work, we consider signed graphs for link inference with a focus on negative links and adopt a decentralised approach to learn the graph and sub-graph embeddings, i.e., we consider sub-graphs of the original signed graph for link inference. As we focus on negative links, the problem becomes more challenging due to the class-imbalance and sparsity of the sub-graphs. For the input graph, we employ a decentralised approach to learn the latent factors in the sub-graphs using probabilistic matrix factorisation. We perform an extensive experimental study using real datasets to assess the applicability and effectiveness of the approach. The results show that the decentralised approach is a promising consideration and gives encouraging results for the performance and scalability of the solution.
Metamorphic data sources: A user-centric paradigm to consume linked data in interactive workspaces

In the last years, the debate about the success or failure of Linked Data (LD) has been growing. Despite the ever-increasing number of available ontologies and LD datasets, there is still a limited number of applications to let people benefit from using this huge amount of data. Some evident problems relate to the limited opportunities offered to the end users, i.e., people without skills in computer programming, to access, navigate and visualize LD. Tools supporting such tasks typically do not consider the end users’ needs; even when they provide abstraction mechanisms to avoid programming, they do not properly hide the complexity of getting oriented into the plethora of available resources. Thus, they end up to be inadequate to real daily scenarios. In this paper, we propose an approach that enables end users to create visually entry points, which we call Metamorphic Data-Sources (MDSs), to query and visualize the LD without requiring any prior knowledge of semantic Web or visualization technologies. Through the MDS visual paradigm, end users can tailor ad-hoc data sources to retrieve information on topics they are interested in. The MDS creation process is also driven by a quality model that further helps users select LD elements potentially free of data quality problems. The paper also reports on the results of a user study that we conducted to assess the validity of the MDS paradigm with respect to the user needs.
Comparison of solar power measurements in alpine areas using a mobile dual-axis tracking system

Abstract The rising demand for sustainable energy requires to identify the sites for photovoltaic systems with the best performance. This paper tackles the question of feasibility of photovoltaic power plants at high altitude. A direct comparison between an alpine and an urban area site is conducted in the south of Austria. Two low-cost automatic photovoltaic power measurement devices with dual-axis sun tracking and maximum power point tracking are deployed at two test sites. The system periodically performs a scan over the southern semihemisphere and executes maximum power point adjustment in order to assess the performance for a given direction. The gathered data shows a higher photovoltaic power yield in the higher altitude test site. Furthermore, the high altitude photovoltaic power as a function of azimuth and elevation angle appears to be not only higher but also more flat than in lower altitudes. This indicates a lower power loss in case of deviation from the optimal solar angles. The results show that even on low-cost hardware a difference in photovoltaic power can be observed, even though in this experiment it amounts to less than 5% increase of peak power in higher altitudes. However, the measured peak powers on the mountain are more stable and therefore closer to a constant level than the heavily fluctuating peak power values at the low altitude site. Additionally, a slight shift in optimal elevation angles between altitudes can be observed, as the optimum angle turns out to be lower on the high altitude site. This angle shift could be caused by snow reflections on the mountainous test site.
Application of Retrograde Analysis on Fighting Games

With the advent of the fighting game AI competition, there has been recent interest in two-player fighting games. Monte-Carlo Tree-Search approaches currently dominate the competition, but it is unclear if this is the best approach for all fighting games. In this paper we study the design of two-player fighting games and the consequences of the game design on the types of AI that should be used for playing the game, as well as formally define the state space that fighting games are based on. Additionally, we also characterize how AI can solve the game given a simultaneous action game model, to understand the characteristics of the solved AI and the impact it has on game design.
One-day ahead PV power forecasts using 3D Wavelet Decomposition

The paper presents a spatio-temporal forecasting for the photovoltaic (PV) power generation by combining the three-dimensional wavelet transform (3D-DWT) and the Least Square Support Vector Machines (LS-SVM) to deal with historical time series data of distributed PV plants in both spatial and temporal domain. The proposed forecasting method applies the wavelet decomposition to the PV power data collected from several PV installations in a three-dimensional space taking into account the spatial distribution of the PV locations and the related PV output power in a defined time framework. The wavelet decomposition output is then used as input for a forecasting model based on the LS-SVM to predict the solar PV power of each plant. A case study is presented using hourly time series of 9 PV installations located in the Greek island of Rhodes in order to predict the power generation of each individual PV plant at 24 hours-ahead time horizon. The forecast performance of the proposed approach is investigated by error metrics and compared with a prediction model based on simple LS-SVM to quantify the improvements achieved by the proposed method.
Dynamic Human Behavior Pattern Detection and Classification

Human dynamic behavior detection and classification based on video plays an important role in understanding human motion, and it has many interesting applications in crime watch, security surveillance monitoring, sports, and so on. This paper reports our initial research work on dynamic human behavior pattern detection and classification based on real-time video data. We focus on four types of detailed dynamic human behaviors: walking, standing, running, and sitting. A Convolutional Long Short-Term Networks (CLSTM) model is proposed and used for dynamic human behavior pattern detection and classification based on videos. This model combines the CNN model and LSTM to support learning, detection, and classification of dynamic human behavior patterns. AlexNet, one of CNN traditional architecture, is firstly used to learn visual representation of human behaviors based on time-based images. Later, the results will be used as inputs for the LSTM model to learn time-based sequence features to support detailed behavior pattern classification. A prototype of a Human Behavior Detection System is reported based on the CLSTM model, and some earlier case study results are presented.
An Exploration of the Interaction Between capsules with ResNetCaps models

Image recognition is an open challenge in computer vision since its early stages. The application of deep neural networks yielded significant improvements towards its solution. Despite their classification abilities, deep networks need datasets with thousands of labelled images and prohibitive computational capabilities to achieve good performance. To address some of these challenges, the CapsNet neural architecture has been recently proposed as a promising machine learning model for image classification based on the idea of capsules. A capsule is a group of neurons whose output represents the presence of features of the same entity. In this paper, we start from the CapsNet architecture to explore and analyse the interaction between the presence of features within certain, similar classes. This is achieved by means of techniques for the features interaction, working on the outputs of two independent capsule-based models. To understand the importance of the interaction between capsules, extensive experiments have been carried out on four challenging dataset. Results show that the exploitation of capsules interaction yields to performance improvements.
Newton’s second law based PSO for feature selection: Newtonian PSO

High dimensional data have brobdingnagian number of features, but not all features are useful. Irrelevant and redundant features may even reduce the classification accuracy. Feature selection is a process of selecting a subset of relevant features to
The readiness model of information technology implementation among universities in Indonesia

This study reports on how to combine several models in the context to assess the readiness of IT implementation in Higher Education Institution. Many studies are generated by researchers about the readiness model, that most Readiness models are developed by adopting, combining, and adapting models of some existing models. Researchers develop models based on input-process-output logic and processional models and causal relationships using the E-Readiness model and the ZEN Framework adoption model. The developed model is organized into seven variables and 50 indicators. Relationships between variables are linked with 12 links as hypotheses. For this model research to the break down the level of the assessment instrument. However, this study is limited to the model to be used for research.
Relevance Modeling with Multiple Query Variations

The generative theory for relevance and its operational manifestation --- the relevance model --- are based on the premise that a single query is used to represent an information need for retrieval. In this work, we extend the theory and devise novel techniques for relevance modeling using as set of query variations representing the same information need. Our new approach is based on fusion at the term level, the model level, or the document-list level. We theoretically analyze the connections between these methods and provide empirical support of their equivalence using TREC datasets. Specifically, our new approach of inducing relevance models from multiple query variations substantially outperforms relevance model induction from a single query which is the standard practice. Our approach also outperforms fusion over multiple query variations, which is currently one of the best known baselines for several commonly used test collections.
A regime-based approach for integrating wind information in spatio-temporal solar forecasting models

Spatio-temporal solar forecasting based on statistical models seldom integrates wind information. An AutoRegressive with eXogenous input (ARX) model was tested using global horizontal irradiation records from a set of pyranometers deployed in Oahu, Hawaii, USA, where northeasterly winds are predominant. When irradiance is forecasted 10-s ahead, interesting forecast skills (up to 30.8%) can be achieved when a site has upwind neighbors available. However, when forecast skill is mapped as a function of wind direction at 850 hPa (from an ERA 5 reanalysis), negative skill values can be observed when nondominant winds occur. A wind regime-based approach is proposed, where different ARX models are built for different wind direction intervals, which substantially improves the forecasting accuracy for the underperforming wind directions. When the regime definition also takes into account wind speed, the ARX model detects spatial patterns for faster winds, with several nondominant directions achieving skill scores higher than 20%. Replacing the wind reanalysis by historical forecasts from ERA 5 reduced the overall skill by less than 0.1%.
DT@IT Toolbox: Design Thinking Tools to Support Everyday Software Development

Recent evidence suggests that design thinking can contribute to agile software development by increasing attention towards user needs as well as collaboration in interdisciplinary teams. However, there is a lack of understanding and support on how to facilitate the integration of design thinking activities into the daily work of agile development teams. Our work extends existing research on integrating design thinking with agile software development methodologies by developing and validating a toolbox that software teams can use in their everyday work. We present the DT@IT Toolbox, a collection of design thinking methods targeted at design thinking novices that aims to support everyday software development activities. The toolbox was evaluated with a team from an SME based in Germany over a period of 12 weeks. As a result, participants reported that using the DT@IT Toolbox led to a better communication within the team, enhanced problem-solving skills, increased empathy towards users and led to a better understanding of the users needs.
Integrated image processing and GIS-based techniques using knowledge-driven approaches to produce potential radioactivity map for the uraniferous granite of Egypt

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Unsupervised continuous camera network pose estimation through human mesh recovery

Camera resectioning is essential in computer vision and 3D reconstruction to estimate the position of matching pinhole cameras in 3D worlds. While the internal camera parameters are usually known or can be easily computed offline, in camera networks extrinsic parameters need to be computed each time a camera changes position, thus not allowing for smooth and dynamic network reconfiguration. In this work we propose a fully markerless, unsupervised, and automatic tool for the estimation of the extrinsic parameters of a camera network, based on 3D human mesh recovery from RGB videos. We show how it is possible to retrieve, from monocular images and with just a weak prior knowledge of the intrinsic parameters, the real-world position of the cameras in the network, together with the floor plane. Our solution also works with a single RGB camera and allows the user to dynamically add, re-position, or remove cameras from the network.
A Multi-granularity Neural Network for Answer Sentence Selection

In open-domain question answering system, the granularities of the answers vary with different types of questions. For example, for the questions asking about locations (Location type questions), their answers are usually short phrases. While for the questions asking about reasons (Description type questions), their answers are usually long clauses or sentences. This insight can be used to improve the performance of answer sentence selection, which is a crucial component of the open-domain QA system. In this paper, we propose a novel Multi-Granularity Neural Network (MGNN) model to better evaluate the semantic matching of questions and answers. First, MGNN has three classes of channels with each computing the similarity of question and answer pairs from one of the three granularity levels: clause level, phrase level and ngram level. Then, MGNN uses a parametrization weighting scheme which considers question types to combine these different granularity channels. We carry out experiments on a public available benchmark dataset for question answering. Empirical results show that our method outperforms state-of-the-art methods.
Multisensor Multi-Target Tracking Based on GM-PHD Using Out-Of-Sequence Measurements

In this paper, we study the issue of out-of-sequence measurement (OOSM) in a multi-target scenario to improve tracking performance. The OOSM is very common in tracking systems, and it would result in performance degradation if we used it inappropriately. Thus, OOSM should be fully utilized as far as possible. To improve the performance of the tracking system and use OOSM sufficiently, firstly, the problem of OOSM is formulated. Then the classical B1 algorithm for OOSM problem of single target tracking is given. Next, the random finite set (RFS)-based Gaussian mixture probability hypothesis density (GM-PHD) is introduced. Consequently, we derived the equation for re-updating of posterior intensity with OOSM. Implementation of GM-PHD using OOSM is also given. Finally, several simulations are given, and results show that tracking performance of GM-PHD using OOSM is better than GM-PHD using in-sequence measurement (ISM), which can strongly demonstrate the effectiveness of our proposed algorithm.
KG-to-Text Generation with Slot-Attention and Link-Attention

Knowledge Graph (KG)-to-Text generation task aims to generate a text description for a structured knowledge which can be viewed as a series of slot-value records. The previous seq2seq models for this task fail to capture the connections between the slot type and its slot value and the connections among multiple slots, and fail to deal with the out-of-vocabulary (OOV) words. To overcome these problems, this paper proposes a novel KG-to-text generation model with hybrid of slot-attention and link-attention. To evaluate the proposed model, we conduct experiments on the real-world dataset, and the experimental results demonstrate that our model could achieve significantly higher performance than previous models in terms of BLEU and ROUGE scores.
Semantically Coherent 4D Scene Flow of Dynamic Scenes

Abstract Simultaneous semantically coherent object-based long-term 4D scene flow estimation, co-segmentation and reconstruction is proposed exploiting the coherence in semantic class labels both spatially, between views at a single time instant, and temporally, between widely spaced time instants of dynamic objects with similar shape and appearance. In this paper we propose a framework for spatially and temporally coherent semantic 4D scene flow of general dynamic scenes from multiple view videos captured with a network of static or moving cameras. Semantic coherence results in improved 4D scene flow estimation, segmentation and reconstruction for complex dynamic scenes. Semantic tracklets are introduced to robustly initialize the scene flow in the joint estimation and enforce temporal coherence in 4D flow, semantic labelling and reconstruction between widely spaced instances of dynamic objects. Tracklets of dynamic objects enable unsupervised learning of long-term flow, appearance and shape priors that are exploited in semantically coherent 4D scene flow estimation, co-segmentation and reconstruction. Comprehensive performance evaluation against state-of-the-art techniques on challenging indoor and outdoor sequences with hand-held moving cameras shows improved accuracy in 4D scene flow, segmentation, temporally coherent semantic labelling, and reconstruction of dynamic scenes.
Training Deep Neural Networks with Adversarially Augmented Features for Small-scale Training Datasets

In this paper, we propose a novel method to train deep neural networks for small-scale training dataset. Since we focus on the situation in which the training dataset is limited, increasing the number of training data by data augmentation is the most straight-forward solution. Instead of augmenting data at an input layer as in typical data augmentation, we adversarially augment features at hidden layer by adding small perturbations to the original features extracted from training data. We call the augmented features as adversarial features. To effectively avoid overfitting of the trained network, the perturbation is designed to be adversarial, which means that they are designed to significantly change the output of the network. Moreover, to induce the adversarial feature to be more reasonable as real data, we adopt a coefficient layer to constrain the adversarial feature to be represented by linear combination of the original features. Experimental results on several benchmark datasets show that our method substantially improves the performance of the deep neural network.
Understanding policing of cybe-rcrime in South Africa: The phenomena, challenges and effective responses

Cybercrime continues to be a detrimental problem in South Africa and continues to change in nature and sophistication. Innovations and technological advancements aimed at moving the world towards a digital age increase the risks of cybercrime. Concurrently, as the risk of cybercrime increases so does the challenge to police it. The policing of cybercrime is generally an afterthought for several organisations and individuals in South Africa. This type of crime has no regional, national or international boundaries, unlike “traditional crime” which has physical boundaries and limits in relation to jurisdiction. This contributes towards the challenge of detecting, investigating and combating it. Cyber criminals have intercepted vital and essential government, personal and business information online. Therefore, the primary objective of this paper is to explore the obstacles/challenges that hamper the effective and efficient policing of cybercrime in Durban, South Africa. A qualitative research approach was adopted, to explore the challenges of policing of cybercrime in the study area. The findings collected through semi-structured interviews with a total number of twenty (20) participants comprising of SAPS Directorate for Priority Crime Investigation (DPCI) officials, members of Bowline Security and members of the Durban community; suggest that there is a shortage of SAPS officials who are knowledgeable in handling cybercrime related cases. Policies and strategies to police cybercrime in Durban are insufficient because of the lack of resources, to adequately implement policy and promote cooperative strategic partnerships. Together, these findings suggest that all relevant stakeholder organisations should assist in minimising the challenge of policing of cybercrime.
Searching Runs in Streams

Runs, or maximal periodic substrings, capture the whole picture of periodic fragments in a string. Computing all runs of a string in the usual RAM model is a well-studied problem. We approach this problem in the streaming model, where input symbols arrive one by one and the available space is sublinear. We show that no streaming algorithm can identify all squares, and hence all runs, in a string. Our main contribution is a Monte Carlo algorithm which approximates the set of runs in the length-n input stream S. Given the error parameter $$\varepsilon =\varepsilon (n)\in (0,\frac{1}{2}]$$ , the algorithm reports a set of substrings such that for each run of exponent $$\beta \ge 2+\varepsilon $$ in S a single its substring is reported, with the same period and the exponent at least $$\beta -\varepsilon $$ ; for runs of smaller exponent, zero or one substring with the same period and exponent at least 2 is reported. The algorithm uses $$O(\frac{\log ^2 n}{\varepsilon })$$ words of memory and spends $$O(\log n)$$ operations, including dictionary operations, per symbol.