Reviewerly/exHarmony huggingface dataset:
Photovoltaic Power Production Estimation Based on Numerical Weather Predictions

A photovoltaic (PV) power generation system relying on meteorological forecast provided by the European Centre for Medium-Range Weather Forecast (ECMWF) (ERA5 database) is proposed. Three years of data collected from photovoltaic panels deployed in Milan, Italy, have been analyzed, both in clear-sky and cloudy conditions. The Ineichen-Perez model has been used as a reference for clear-sky conditions. The power measurements were compared with the power calculated using ECMWF, based on solar theory and technical characteristics of the PV plant in place. cumulative complementary distribution function (CCDF) and errors, have been calculated to determine the accuracy of the model. Results indicate a good agreement in terms of generated power statistics, showing that ERA5 data can be reliably used to design solar plants as well as to forecast their performance and energy production.
Clustering and evolutionary approach for longitudinal web traffic analysis

In recent years, data-driven approaches have attracted the interest of the research community. Considering network monitoring, unsupervised machine learning solutions such as clustering are particularly appealing to let the network analysts observe patterns, and track the evolution of traffic over time. In this paper, we present a novel unsupervised methodology to automatically process and analyze batches of HTTP traffic, looking just at the URL structure. First, we describe IDBSCAN, Iterative-DBSCAN. We design it to obtain well-shaped clusters, and to simplify the choice of parameters — often a cumbersome step for the network analyst. Second, we show LENTA, Longitudinal Exploration for Network Traffic Analysis, which allows to automatically observe the evolution over time of traffic, naturally highlighting trends and pinpointing anomalies. We first evaluate IDBSCAN and LENTA on synthetic data to compare their performance against well-known algorithms. Then we apply them on a real case, facing the analysis of hundred thousands of URLs collected from a live network. Results show both the goodness of clusters produced by IDBSCAN and LENTA ability to highlight changes in traffic, facilitating the analyst job.
Integrating Bayesian and Discriminative Sparse Kernel Machines for Multi-class Active Learning

We propose a novel active learning (AL) model that integrates Bayesian and discriminative kernel machines for fast and accurate multi-class data sampling. By joining a sparse Bayesian model and a maximum margin machine under a unified kernel machine committee (KMC), the proposed model is able to identify a small number of data samples that best represent the overall data space while accurately capturing the decision boundaries. The integration is conducted using the maximum entropy discrimination framework, resulting in a joint objective function that contains generalized entropy as a regularizer. Such a property allows the proposed AL model to choose data samples that more effectively handle non-separable classification problems. Parameter learning is achieved through a principled optimization framework that leverages convex duality and sparse structure of KMC to efficiently optimize the joint objective function. Key model parameters are used to design a novel sampling function to choose data samples that can simultaneously improve multiple decision boundaries, making it an effective sampler for problems with a large number of classes. Experiments conducted over both synthetic and real data and comparison with competitive AL methods demonstrate the effectiveness of the proposed model.
Efficiently Learning Fourier Sparse Set Functions

Learning set functions is a key challenge arising in many domains, ranging from sketching graphs to black-box optimization with discrete parameters. In this paper we consider the problem of efficiently learning set functions that are defined over a ground set of size $n$ and that are sparse (say $k$-sparse) in the Fourier domain. This is a wide class, that includes graph and hypergraph cut functions, decision trees and more. Our central contribution is the first algorithm that allows learning functions whose Fourier support only contains low degree (say degree $d=o(n)$) polynomials using $O(k d \log n)$ sample complexity and runtime $O( kn \log^2 k \log n \log d)$. This implies that sparse graphs with $k$ edges can, for the first time, be learned from $O(k \log n)$ observations of cut values and in linear time in the number of vertices. Our algorithm can also efficiently learn (sums of) decision trees of small depth. The algorithm exploits techniques from the sparse Fourier transform literature and is easily implementable. Lastly, we also develop an efficient robust version of our algorithm and prove $\ell_2/\ell_2$ approximation guarantees without any statistical assumptions on the noise.
Cascade Attention: Multiple Feature Based Learning for Image Captioning

Most recent researches in image captioning adopt attention mechanism based on encoder-decoder framework, where the attention module aligns input features for the decoder and boosts performance consequently. A common defect of traditional attention methods is that the inequality among different types of inputs is ignored, resulting in under-exploitation of certain informative features. In this paper, we propose a novel cascade attention module, which processes different types of input in a sequential manner. The cascade attention module enables inputs of higher priorities to affect the attention of other inputs so as to emphasize such inequality. We implement our model by introducing global feature of the image to the captioning process of R-CNN based frameworks, where such feature is rich of context information but takes few effects via traditional attention module. Experimental results demonstrate that our proposed method is able to exploit feature of different types, acquiring improvements on multiple automatic measurements.
Cross-Layer Optimization of Big Data Transfer Throughput and Energy Consumption

With the emergence of data deluge, the energy footprint of global data movement has surpassed 100 terawatt hours, costing more than 20 billion US dollars to the world economy. During an active data transfer, depending on the number of hops between the source and destination, the networking infrastructure consumes between 10% - 75% of the total energy, and the rest is consumed by the end systems. Even though there has been extensive research on reducing the power consumption at the networking infrastructure, the work focusing on saving energy at the end systems has been limited to the tuning of a few application-level parameters. In this paper, we introduce a novel cross-layer optimization framework which jointly considers application-level and kernel-level parameters to minimize the energy consumption without sacrificing from the transfer throughput. We present three different algorithms which can dynamically tune the CPU frequency level, number of active CPU cores, number of active transfer threads, number of parallel TCP streams, and the level of transfer command pipelining to achieve different user-set goals. Experimental results show that our proposed algorithms outperform the state-of-the-art solutions, achieving up to 80% higher throughput while consuming 48% less energy.
Multi-objective Bayesian optimisation with preferences over objectives

We present a multi-objective Bayesian optimisation algorithm that allows the user to express preference-order constraints on the objectives of the type objective A is more important than objective B. These preferences are defined based on the stability of the obtained solutions with respect to preferred objective functions. Rather than attempting to find a representative subset of the complete Pareto front, our algorithm selects those Pareto-optimal points that satisfy these constraints. We formulate a new acquisition function based on expected improvement in dominated hypervolume (EHI) to ensure that the subset of Pareto front satisfying the constraints is thoroughly explored. The hypervolume calculation is weighted by the probability of a point satisfying the constraints from a gradient Gaussian Process model. We demonstrate our algorithm on both synthetic and real-world problems.
Learning from Bad Data via Generation

Bad training data would challenge the learning model from understanding the underlying data-generating scheme, which then increases the difficulty in achieving satisfactory performance on unseen test data. We suppose the real data distribution lies in a distribution set supported by the empirical distribution of bad data. A worst-case formulation can be developed over this distribution set, and then be interpreted as a generation task in an adversarial manner. The connections and differences between GANs and our framework have been thoroughly discussed. We further theoretically show the influence of this generation task on learning from bad data and reveal its connection with a data-dependent regularization. Given different distance measures (\eg, Wasserstein distance or JS divergence) of distributions, we can derive different objective functions for the problem. Experimental results on different kinds of bad training data demonstrate the necessity and effectiveness of the proposed method.
Evaluating Active Learning Sampling Strategies for Opinion Mining in Brazilian Politics Corpora

Politics is a commonly used domain in Opinion Mining applications, in which opinions may change over time. Nevertheless, the usual approaches for Opinion Mining are not able to deal with the characteristics and the challenges brought by continuous data streams; so, an alternative is the use of techniques such as Active Learning, which labels selected data rather than the entire data set. The Active Learning approach requires the choice of a sampling strategy to select the most valuable instances. However, no study has performed an analysis in order to identify the best strategies for Opinion Mining. In this sense, we evaluated eight Active Learning sampling strategies, from which Entropy achieved the best results. In addition, due to the lack of publicly available stream data sets written in Portuguese, we created and evaluated corpora from Twitter and Facebook about the 2018 Brazilian presidential elections.
CSL: FPGA Implementation of Lightweight Block Cipher for Power-constrained devices

The exploration of interconnected devices, embedded devices, sensors, and various network-connected devices helps to communicate each other and exchange communications. These devices overcome with security threats related to privacy and data exchange over billions of devices are interconnected. Lightweight block ciphers aim to provide a feasible solution for power-constrained devices which includes Radio-frequency identification (RFID) tags, ubiquitous computing, wireless sensor network, aggregation network and IoT. In this paper, we have implemented a lightweight block cipher compact, secure, and lightweight (CSL). It operates on 64-bit block size, and key size varies from 64-bit to 128-bit key for encryption and decryption. The hardware implementation of CSL algorithm was thrived using field programmable gate array (FPGA) architecture. A pipelined design of compact S-boxes implemented on Digilent Nexys 4 DDR Artix™-7 board. Our experimental results of CSL consumes 1145 LUTs (Lookup Tables) and has fewer memory requirements. CSL shows resistant against various cryptanalytic attacks.
Identifying phishing attacks in communication networks using URL consistency features

Phishing is a fraudulent attempt by cybercriminals, where the target audience is addressed by a text message, phone call or e-mail, requesting classified and sensitive information after presenting himself/herself as a legitimate agent. Successful phishing attack may result into financial loss and identity theft. Identifying forensic characteristics of phishing attack can help to detect the attack and its perpetuators and as well as to enable defence against it. To shield internet users from phishing assaults, numerous anti-phishing models have been proposed. Currently employed techniques to handle these challenges are not sufficient and capable enough. We aim at identifying phishing sites in order to guard internet users from being vulnerable to any form of phishing attacks by verifying the conceptual and literal consistency between the uniform resource locator (URL) and the web content. The implementation of the proposed PhishDetect method achieves an accuracy of 99.1%; indicating that it is effective in detecting various forms of phishing attacks.
A New Descriptor for Keypoint-Based Background Modeling

Background modeling is a preliminary task for many computer vision applications, describing static elements of a scene and isolating foreground ones. Defining a robust background model of uncontrolled environments is a current challenge since the model must manage many issues, e.g., moving cameras, dynamic background, bootstrapping, shadows, and illumination changes. Recently, methods based on keypoint clustering have shown remarkable robustness especially in bootstrapping and camera movements, highlighting however limitations in the analysis of dynamic background (i.e., trees blowing in the wind or gushing fountains). In this paper, an innovative combination between the RootSIFT descriptor and an average pooling is proposed in a keypoint clustering method for real-time background modeling and foreground detection. Compared to renowned descriptors, such as A-KAZE, this combination is invariant to small local changes in the scene, thus resulting more robust in dynamic background cases. Results, obtained on experiments carried out on two benchmark datasets, demonstrate how the proposed solution improves the previous keypoint-based models and overcomes several works of the current state-of-the-art.
Probability Propagation in Selected Aristotelian Syllogisms

This paper continues our work on a coherence-based probability semantics for Aristotelian syllogisms (Gilio, Pfeifer, and Sanfilippo, 2016; Pfeifer and Sanfilippo, 2018) by studying Figure III under coherence. We interpret the syllogistic sentence types by suitable conditional probability assessments. Since the probabilistic inference of P|S from the premise set $$\{P|M, S|M\}$$ is not informative, we add as a probabilistic constraint (i.e., an “existential import assumption”) to obtain probabilistic informativeness. We show how to propagate the assigned premise probabilities to the conclusion. Thereby, we give a probabilistic meaning to all syllogisms of Figure III. We discuss applications like generalised quantifiers (like Most S are P) and (negated) defaults.
Forensic Based Cloud Computing Architecture – Exploration and Implementation

The technology of cloud computing has been growing at an enormous speed. It is considered as one of the most prominent area of research in computer engineering and information technology. Cloud computing provides platforms, software, virtual machine, security and analytical services on the basis of pay as per usage policy. Hence, it furnishes economic advantage to cloud computing users and service providers. This new paradigm of information technology is very useful, even then, the privacy, security and forensic issues are the primary obstacles in the growth of cloud computing systems. It is need of the hour that cloud architecture should support and compatible with tools or techniques of digital forensics. Therefore, in this research paper an architecture is being proposed to support forensic accession and investigation of cloud based system which is required for the sustainable growth of the cloud computing applications and services.
Revisiting Depth-Based Face Recognition From a Quality Perspective

Face recognition using depth data has attracted increasing attention from both academia and industry in the past five years. Despite the large number of depth-based face recognition methods in the literature, high quality data are usually required for high recognition accuracy. In this paper, we measure the quality of 3D face data in terms of resolution and precision, and evaluate how the accuracy of three deep face recognition models varies on several benchmark databases as the facial depth data resolution changes from dense to sparse and as the precision changes from high to low. From the experimental results, several observations are made. (i) Given a high precision, a low resolution of 3K is sufficient to represent a 3D face; when the precision decreases, using higher resolutions can benefit face recognition, but the recognition accuracy becomes saturated as the resolution reaches 10K. (ii) Depth precision is more critical than resolution in depth-based face recognition, and a precision of 1mm is generally preferred as a good balance between accuracy and cost. (iii) The deep models trained with low-quality data perform more stable across data of different quality levels. We believe that these observations are beneficial for both depth sensor manufacturers and depth-based face recognition system developers.
Optimizing of the Number and Placements of Wearable IMUs for Automatic Rehabilitation Recording

From the increase in the number of rehabilitation patients, precise and detailed records of rehabilitation have become difficult due to the busyness of physical therapists. This creates difficulty for quantitative analysis of rehabilitation. Therefore, we are constructing a system that automatically records rehabilitation using activity recognition techniques with wearable sensors. The system can offer the dual prospect of decreasing a practitioners’ load and enabling quantitative analysis of rehabilitation. In general, a large number of wearable sensors are demanded to be placed on each part of a patient’s body for accurate activity recognition. However, managing a larger number of wearable sensors incurs significant time and effort of therapists and patients to apply them and adds to a patients’ discomfort, consequently it significantly decreases practicality. Therefore, we investigate the suitable number and positions of wearable sensors for activity recognition for rehabilitation. Experiments were carried out with 16 healthy subjects wearing seven wearable inertial measurement units (IMUs). The subjects performed 10 different rehabilitation activities chosen by a qualified physical therapist as typical ones used in real rehabilitation therapy. The activities were recognized while reducing the number of sensors with all combinations of sensor placements using six classification algorithms. Then, the accuracy on each setting was examined. As a result, 0.833 of F-measure value was obtained when using three sensors on the waist, right thigh, and right lower leg.
Goal Model Integration: Advanced Relationships and Rationales Documentation

Integrating new technology in a business environment raises many challenges such as ensuring that this technology meets stakeholder requirements and contributes to organizational goals. However, before analyzing the impact of technology on requirements and goals, goal models of the current context and of the proposed technology should be merged to reflect the whole context. Existing merging approaches mainly focus on merging partial views of a goal model, which belong to one context. However, merging different goal models to reflect one holistic context, such as in technology integration, is not addressed. This paper presents a Goal Integration Method targeting different initial contexts, enabling completeness and consistency analysis of the integrated goal model, and providing traceability to rationales and decisions made at integration time. The method introduces advanced relationships and procedures to capture newly added elements or raised conflicts that may occur during the integration. The method is presented with the help of a conceptual model and an algorithm. It also exploits the User Requirements Notation with tool support (jUCMNav) for building and integrating goal models. The feasibility of the method is illustrated through a case study. The method formalizes the integration of multiple goal models belonging to different contexts, and the accommodation of new requirements, while providing comprehensive traceability and rationales.
Pneuxels

We often perceive other peoples' presence implicitly, through the traces of their interactions with physical objects. What if our urban environments could mediate these traces allowing remotely located people perceive each other's presence collectively? We developed Pneuxels, a network of programmable inflatables, placed at remote sites, that allow visitors in one site to perceive the presence of visitors in other sites, promoting thereby a sense of collective awareness and place-making. Pneuxels (Pneumatic Pixels) are pneumatically actuated pixels, connected through a web-socket platform, that change their physical state based on input from other Pneuxels, from the environment, or from users. We discuss our experiences in designing, prototyping, and testing Pneuxels, and we report our results from preliminary user studies.
The Same Size Dilated Attention Network for Keypoint Detection

In recent years, convolution neural networks have significant progress on keypoint detection. However, lots of approaches require multiple upsampling of small featuremaps to produce the final output. The huge information loss in the upsampling process is the key to restrict the accuracy. This work presents a new network architecture, which is designed to solve the problem of information loss in the process of upsampling and feature fusion compared with the previous approaches. We replace the normal convolution in the backbone network by using dilated convolution with stride of one to keep featuremaps’ size consistent, which avoids the multi-times upsampling during prediction. We also explore the feature fusion methods and propose a feature fusion block (AFB), which improves the accuracy and accelerates the convergence of model with the help of multi-scale resampling and attention mechanism. Excellent results are achieved on MsCOCO 2017 dataset and Ali apparel keypoints dataset. The code will be released for further research.
Collaborative Attention Network with Word and N-Gram Sequences Modeling for Sentiment Classification

Current state-of-the-art models for sentiment classification are CNN-RNN-based models. These models combine CNN and RNN in two ways: parallel models or serial models. Parallel models use CNN to capture n-grams and RNN to model word sequences, while serial models feed n-grams into RNN to model n-gram sequences. However, these models are different from the way humans read text. Intuitively, humans read text by capturing semantic elements that are made up of both words and n-grams. To tackle this problem, we propose a collaborative attention network with word and n-gram sequences modeling. Our model jointly processes sequences in both word granularity and n-gram granularity to form text embedding with collaborative attention. It utilizes a LSTM encoder to capture long-term dependencies among words and a CNN-LSTM component to capture long-term dependencies among n-grams on the same text. Next we incorporate these two parts via an attention mechanism to highlight keywords in sentences. Experimental results show our model effectively outperforms other state-of-the-art CNN-RNN-based models on several public datasets of sentiment classification.
Towards Serendipitous Research Paper Recommender Using Tweets and Diversification

In this paper, we examine whether a user’s tweets can help to a generate more serendipitous recommendations. In addition, we investigate whether the use of diversification applied on a list of recommended items further improves serendipity. To this end, we conduct an experiment with $$n=22$$ subjects. The result of our experiment shows that the subject’s tweets did not improve serendipity, but diversification results in more serendipitous recommendations.