Reviewerly/exHarmony huggingface dataset:
Energy Management for Smart Homes—State of the Art

Smart home is a concept that aims to enhance the comfort of residents and facilitate household activities. The smart home is an application of ubiquitous computing which can provide the user with context-aware automated or assistive services in the form of ambient intelligence, remote control of home appliances, or automation. Smart homes attempt to integrate smartness into homes to guarantee the residents’ convenience, safety, and security, while conserving the energy. The capabilities of a smart home in the context of different applications, have been scrutinized for this investigation. Different proposed architectures, protocols, and infrastructures have been taken into consideration. As the data management process is a vital part of a smart home system, many procedures of data collection, storage, and analysis have been surveyed. Methods of data acquisition has also been discussed. Existing challenges, pros, and cons of proposed schemes along with future perspectives of smart homes are identified in this report, which is intended to promote future research directions.
Enhancing reliability and efficiency for real-time robust adaptive steganography using cyclic redundancy check codes

The development of multimedia and deep learning technology bring new challenges to steganography and steganalysis techniques. Meanwhile, robust steganography, as a class of new techniques aiming to solve the problem of covert communication under lossy channels, has become a new research hotspot in the field of information hiding. To improve the communication reliability and efficiency for current real-time robust steganography methods, a concatenated code, composed of Syndrome–Trellis codes (STC) and cyclic redundancy check (CRC) codes, is proposed in this paper. The enhanced robust adaptive steganography framework proposed is this paper is characterized by a strong error detection capability, high coding efficiency, and low embedding costs. On this basis, three adaptive steganographic methods resisting JPEG compression and detection are proposed. Then, the fault tolerance of the proposed steganography methods is analyzed using the residual model of JPEG compression, thus obtaining the appropriate coding parameters. Experimental results show that the proposed methods have a significantly stronger robustness against compression, and are more difficult to be detected by statistical based steganalytic methods.
The seasonal variability and trends for the surface solar irradiation in northeastern region of Brazil

Earlier studies have demonstrated that the Northeastern Brazilian region (NEB) has the most abundant solar energy resource of the Brazilian territory due to its geographical location, and its environmental characteristics. However, the spatial and seasonal variability was little evaluated. This work investigated the surface solar irradiation variability and trends based on a statistical analysis of the ground data acquired in automated weather stations operated by the Brazilian Institute of Meteorology. A data quality control procedure was accomplished according to WMO criteria to avoid suspicious data. The results pointed out a remarkable variability in seasonal and annual scales. The cluster analysis provided five regional patterns presenting quite interesting temporal complementary regimes for the incoming solar irradiation. The surface solar irradiation achieves the highest values in the Southeastern area of NEB and the lowest in the Western area during the austral summer. The annual and seasonal trends of the surface solar irradiation were investigated using the Mann-Kendall method and the Sen technique. The surface solar irradiation in the Southeastern area of NEB is decreasing by 50 Wh/m2/year since 2008. On the other hand, it is increasing around 40 Wh/m2/year in the semi-arid area at the same timeframe.
Retrieval of Cloud Optical Thickness from Sky-View Camera Images using a Deep Convolutional Neural Network based on Three-Dimensional Radiative Transfer

Observation of the spatial distribution of cloud optical thickness (COT) is useful for the prediction and diagnosis of photovoltaic power generation. However, there is not a one-to-one relationship between transmitted radiance and COT (so-called COT ambiguity), and it is difficult to estimate COT because of three-dimensional (3D) radiative transfer effects. We propose a method to train a convolutional neural network (CNN) based on a 3D radiative transfer model, which enables the quick estimation of the slant-column COT (SCOT) distribution from the image of a ground-mounted radiometrically calibrated digital camera. The CNN retrieves the SCOT spatial distribution using spectral features and spatial contexts. An evaluation of the method using synthetic data shows a high accuracy with a mean absolute percentage error of 18% in the SCOT range of 1–100, greatly reducing the influence of the 3D radiative effect. As an initial analysis result, COT is estimated from a sky image taken by a digital camera, and a high correlation is shown with the effective COT estimated using a pyranometer. The discrepancy between the two is reasonable, considering the difference in the size of the field of view, the space–time averaging method, and the 3D radiative effect.
Evolutionary Computation, Optimization, and Learning Algorithms for Data Science

A large number of engineering, science, and computational problems have yet to be solved in a computationally efficient way. One of the emerging challenges is how evolving technologies grow towards autonomy and intelligent decision making. This leads to collection of large amounts of data from various sensing and measurement technologies, e.g., cameras, smart phones, health sensors, smart electricity meters, and environment sensors. Hence, it is imperative to develop efficient algorithms for generation, analysis, classification, and illustration of data. Meanwhile, data is structured purposefully through different representations, such as large-scale networks and graphs. Therefore, data plays a pivotal role in technologies by introducing several challenges: how to present, what to present, why to present. Researchers explored various approaches to implement a comprehensive solution to express their results in every particular domain, such that the solution enhances the performance and minimizes cost, especially time complexity. In this chapter, we focus on data science as a crucial area, specifically focusing on a curse of dimensionality (CoD) which is due to the large amount of generated/sensed/collected data, especially large sets of extracted features for a particular purpose. This motivates researchers to think about optimization and apply nature-inspired algorithms, such as meta-heuristic and evolutionary algorithms (EAs) to solve large-scale optimization problems. Building on the strategies of these algorithms, researchers solve large-scale engineering and computational problems with innovative solutions. Although these algorithms look un-deterministic, they are robust enough to reach an optimal solution. To that end, researchers try to run their algorithms more than usually suggested, around 20 or 30 times, then they compute the mean of result and report only the average of 20/30 runs’ result. This high number of runs becomes necessary because EAs, based on their randomness initialization, converge the best result, which would not be correct if only relying on one specific run. Certainly, researchers do not adopt evolutionary algorithms unless they face a problem which is suffering from placement in local optimal solution, rather than global optimal solution. In this chapter, we first develop a clear and formal definition of the CoD problem, next we focus on feature extraction techniques and categories, then we provide a general overview of meta-heuristic algorithms, its terminology, and desirable properties of evolutionary algorithms.
Unsupervised Domain Adaptation for Multispectral Pedestrian Detection

Multimodal information (e.g., visible and thermal) can generate robust pedestrian detections to facilitate around-the-clock computer vision applications, such as autonomous driving and video surveillance. However, it still remains a crucial challenge to train a reliable detector working well in different multispectral pedestrian datasets without manual annotations. In this paper, we propose a novel unsupervised multimodal domain adaptation framework for multispectral pedestrian detection, by iteratively generating pseudo annotations and updating the parameters of our designed multispectral pedestrian detector on target domain. Pseudo annotations are generated using the detector trained on source domain, and then updated by fixing the parameters of detector and minimizing the cross entropy loss without back-propagation. Training labels are generated using the pseudo annotations by considering the characteristics of similarity and complementarity between well-aligned visible and infrared image pairs. The parameters of detector are updated using the generated training labels by minimizing our defined multi-detection loss function with back-propagation. The optimal parameters of detector can be obtained after iteratively updating the pseudo annotations and parameters. Experimental results show that our proposed unsupervised multimodal domain adaptation method achieves significantly higher detection performance than the approach without domain adaptation, and is competitive with the supervised multispectral pedestrian detectors.
Challenges in Designing Exploit Mitigations for Deeply Embedded Systems

Memory corruption vulnerabilities have been around for decades and rank among the most prevalent vulnerabilities in embedded systems. Yet this constrained environment poses unique design and implementation challenges that significantly complicate the adoption of common hardening techniques. Combined with the irregular and involved nature of embedded patch management, this results in prolonged vulnerability exposure windows and vulnerabilities that are relatively easy to exploit. Considering the sensitive and critical nature of many embedded systems, this situation merits significant improvement. In this work, we present the first quantitative study of exploit mitigation adoption in 42 embedded operating systems, showing the embedded world to significantly lag behind the general-purpose world. To improve the security of deeply embedded systems, we subsequently present μArmor, an approach to address some of the key gaps identified in our quantitative analysis. μArmor raises the bar for exploitation of embedded memory corruption vulnerabilities, while being adoptable on the short term without incurring prohibitive extra performance or storage costs.
Experimental investigation of Markovian and non-Markovian channel addition

The study of memory effects in quantum channels helps in developing characterization methods for open quantum systems and strategies for quantum error correction. Two main sets of channels exist, corresponding to system dynamics with no memory (Markovian) and with memory (non-Markovian). Interestingly, these sets have a nonconvex geometry, allowing one to form a channel with memory from the addition of memoryless channels and vice versa. Here, we experimentally investigate this nonconvexity in a photonic setup by subjecting a single qubit to a convex combination of Markovian and non-Markovian channels. We use both divisibility and distinguishability as criteria for the classification of memory effects, with associated measures. Our results highlight some practical considerations that may need to be taken into account when using memory criteria to study system dynamics given by the addition of Markovian and non-Markovian channels in experiments.
Engineering complex data integration, harmonization and visualization systems

Complex data transformation, aggregation and visualization problems are becoming increasingly common. These are needed in order to support improved business intelligence and end-user access to data. However, most such applications present very challenging software engineering problems including noisy data, diverse data formats and APIs, challenging data modeling and increasing demand for sophisticated visualization support. This paper describes a data integration, harmonization and visualization process and framework that we have been developing. We discuss our approach used to tackle complex data aggregation and harmonization problems and we demonstrate a set of information visualizations that can be developed from the harmonized data to make it usable for its target audience. We use a case study of Household Travel Survey data mapping, harmonization, aggregation and visualization to illustrate our approach. We summarize a set of lessons that we have learned from this industry-based software engineering experience. We hope these will be useful for others embarking on challenging data harmonization and integration problems. We also identify several key directions and needs for future research and practical support in this area.
µ2 : A Lightweight Block Cipher

This paper presents a 64-bit lightweight block cipher, µ2 with a key size of 80-bit. µ2 is designed based on well-established design paradigms, achieving comparable performance and security when compared against existing state-of-the-art lightweight block ciphers. µ2 is based on the Type-II generalized Feistel structure with a round function, F that is a 16-bit ultra-lightweight block cipher based on the substitution-permutation network. Security evaluation indicates that µ2 offers a large security margin against known attacks such as differential cryptanalysis, linear cryptanalysis, algebraic attack and others.
A Weakly Supervised learning technique for classifying facial expressions

The universal hypothesis suggests that the six basic emotions - anger, disgust, fear, happiness, sadness, and surprise - are being expressed by similar facial expressions by all humans. While existing datasets support the universal hypothesis and comprise of images and videos with discrete disjoint labels of profound emotions, real-life data contains jointly occurring emotions and expressions of different intensities. Models, which are trained using categorical one-hot vectors often over-fit and fail to recognize low or moderate expression intensities. Motivated by the above, as well as by the lack of sufficient annotated data, we here propose a weakly supervised learning technique for expression classification, which leverages the information of unannotated data. Crucial in our approach is that we first train a convolutional neural network (CNN) with label smoothing in a supervised manner and proceed to tune the CNN-weights with both labelled and unlabelled data simultaneously. Experiments on four datasets demonstrate large performance gains in cross-database performance, as well as show that the proposed method achieves to learn different expression intensities, even when trained with categorical samples.
Background-modelling techniques for foreground detection and Tracking using Gaussian Mixture Model

Background Modelling and Foreground detection in sports has been achieved by cleverly developing a model of a background from a video by deducing knowledge from frames and comparing this model to every subsequent frame and subtracting the background region from it, hence leaving the foreground detected. This output from GMM background subtraction is fed into the feature extraction algorithm, which segregates the players based on teams. By extracting information of primary colors from each frame, the design of the algorithm based on the color of preference is done. Tracking algorithms Kalman and extended Kalman Filters help to predict and correct the location of players and in correctly estimating their trajectory on the field. Challenges such as shadowing, occlusions and illumination changes are addressed. The designed algorithms are tested against a set of performance parameters for the following datasets (Norway and FIFA) using MATLAB (2017b) and the inferences are respectively made. Object detection, motion detection and Kalman filter algorithms are implemented and the observed results are 100%, 84% and 100% accuracy respectively. With the results quantification and performance analysis, it is observed that with the decrease in contrast between player jerseys a decrease in detection accuracy occurs and with players crowded regions on the field and occluded players a decrease in tracking accuracy was observed.
A hybrid approach for generating reputation based on opinions fusion and sentiment analysis

Amazon, eBay, IMDb as well as several websites provide a convenient platform where users share their opinions on any entities without hindrance. Though those opinions are too many to be examined one by one, this is why a general reputation value will help people make a decision toward a target entity (purchase, download, rent …). This fact makes reputation generation task very challenging because an inaccurate reputation system will directly damage the credibility and popularity of the target entity. This paper aims to improve a recent work that handles the task of generating reputation based on fuzing and mining opinions expressed in natural languages and user feedback ratings. Therefore, we have proposed a hybrid approach that, (i) separates reviews into positive and negative based on their sentiment polarity by applying the two classifiers Naïve Bayes and Linear Support Vector Machine (LSVM), (ii) groups positive and negative reviews into principal opinion sets based on their semantic relations, (iii) calculates a custom reputation value separately for positive and negative groups by considering some statistics of principal opinion sets and finally (iv) computes the final reputation value using Weighted Arithmetic Mean. Experimental results show a significant improvement with respect to recent work.
Dual-supervised attention network for deep cross-modal hashing

Cross-modal hashing has received intensive attention due to its low computation and storage efficiency in cross-modal retrieval task. Most previous cross-modal hashing methods mainly focus on extracting correlated binary codes from the pairwise label, but largely ignore the semantic categories of cross-modal data. On the other hand, human perception exploits category information to connect cross-modal samples. Inspired by this fact, we propose to embed category information into hash codes. More specifically, we introduce semantic prediction loss into our framework to enhance hash codes with category supervision. In addition, there always exists a large gap between features from different modalities (e.g. text and images), leading cross-modal hashing to link irrelevant features for retrieval task. To address this issue, this paper proposes Dual-Supervised Attention Network for Deep Hashing (DSADH) to learn the cross-modal relationship via an elaborately-designed attention mechanism. Our cross-modal network applies cross-modal attention block to efficiently encode rich and relevant features to learn compact hash codes. Extensive experiments on three challenging benchmarks demonstrate that our proposed method significantly improves the retrieval results.
Common and Special Knowledge-Driven TSK Fuzzy System and Its Modeling and Application for Epileptic EEG Signals Recognition

Takagi-Sugeno-Kang (TSK) fuzzy systems are well known for their good balances between approximation accuracy and interpretability.Among a wide variety of existing TSK fuzzy systems, most of them are driven by special knowledge since the learned parameters of each fuzzy rule are totally different.However, common knowledge is equally important and useful in practice and hence a TSK fuzzy system embedded with common knowledge should be more intuitive and interpretable when tackling with realworld problems.In this paper, we propose a common and special knowledge-driven TSK fuzzy system (CSK-TSK-FS), in which the parameters corresponding to each feature in then-parts of fuzzy rules always keep invariant and these parameters are viewed as common knowledge.As for its modeling, except the gradient descent techniques and other existing training algorithms, we can obtain a trained CSK-TSK-FS from a trained GMM or a trained FLNN because the proposed fuzzy system CSK-TSK-FS is mathematically equivalent to a special GMM and a FLNN.CSK-TSK-FS has three characteristics: (1) with the classical centroid defuzzification strategy, the involved common knowledge can be separated from fuzzy rules such that the interpretability of CSK-TSK-FS can be enhanced; ( 2) it can be trained quickly by the proposed LLM-based training algorithm; (3) the equivalence relationships among CSK-TSK-FS, GMM and FLNN allow them to share some commonality in training such that the proposed LLM-based training algorithm provides a novel fast training tool for training GMM and FLNN.Experimental results on UCI, KEEL and epileptic EEG datasets demonstrate the promising classification of CSK-TSK-FS.
QF-RNN: QI-Matrix Factorization Based RNN for Time-Aware Service Recommendation

Driven by the widespread application of Service Oriented Architecture (SOA), the quantity of Web services and their users keep increasing in the service ecosystem. If historical service invocation records can be gathered and accumulated, it is meaningful to recommend suitable services that users may invoke in the near future. However, most existing recommend algorithms bear major limitation of not taking consideration of dynamic characteristics of both users and Quality of Service (QoS). To address this concern, this paper proposes a time-aware recommendation algorithm for runtime service selection. Firstly, a QoS Observation Matrix is created integrated with Invocation Record Matrix. Afterwards, matrix factorization is applied to extract user-preferences and service-features, respectively. Due to their dynamic characteristics, the Long Short Term Memory (LSTM) model is leveraged to learn and predict preferences and features. Finally, a service recommendation list is generated for users based on LSTM predictions. Experimental results on a real-world dataset show that the proposed algorithm outperforms baseline methods in terms of accuracy and recall.
NIRK-based Cholesky-factorized square-root accurate continuous-discrete unscented Kalman filters for state estimation in nonlinear continuous-time stochastic models with discrete measurements

Abstract This paper further advances the idea of accurate Gaussian filtering towards efficient unscented-type Kalman methods for estimating continuous-time nonlinear stochastic systems with discrete measurements. It implies that the differential equations evolving sigma points utilized in computations of the predicted mean and covariance in time-propagations of the Gaussian distribution are solved accurately, i.e. with negligible error. The latter allows the total error of the unscented Kalman filtering technique to be reduced significantly and gives rise to the novel accurate continuous-discrete unscented Kalman filtering algorithm. At the same time, this algorithm is rather vulnerable to round-off and numerical integration errors committed in each state estimation run because of the need for the Cholesky decomposition of covariance matrices involved. Such a factorization will always fail when the covariance's positivity is lost. This positivity lost issue is commonly resolved with square-root filtering implementations, which propagate not the full covariance matrix but its square root (Cholesky factor) instead. Unfortunately, negative weights encountered in applications of the accurate continuous-discrete unscented Kalman filter to high-dimensional stochastic systems preclude from designing conventional square-root methods. In this paper, we address this problem with low-rank Cholesky factor update procedures or with hyperbolic QR transforms used for yielding J-orthogonal square roots. Our novel square-root algorithms are justified theoretically and examined numerically in an air traffic control scenario.