Reviewerly/exHarmony huggingface dataset:
Global Solar Radiation Forecasting Using Square Root Regularization-Based Ensemble

In recent decades, the integration of solar energy sources has gradually become the main challenge for global energy consumption. Therefore, it is essential to predict global solar radiation in an accurate and efficient way when estimating outputs of the solar system. Inaccurate predictions either cause load overestimation that results in increased cost or failure to gather adequate supplies. However, accurate forecasting is a challenging task because solar resources are intermittent and uncontrollable. To tackle this difficulty, several machine learning models have been established; however, the forecasting outcomes of these models are not sufficiently accurate. Therefore, in this study, we investigate ensemble learning with square root regularization and intelligent optimization to forecast hourly global solar radiation. The main structure of the proposed method is constructed based on ensemble learning with a random subspace (RS) method that divides the original data into several covariate subspaces. A novel covariate-selection method called square root smoothly clipped absolute deviation (SRSCAD) is proposed and is applied to each subspace with efficient extraction of relevant covariates. To combine the forecasts obtained using RS and SRSCAD, a firefly algorithm (FA) is used to estimate the weights assigned to individual forecasts. To handle the complexity of the proposed ensemble system, a simple and efficient algorithm is derived based on a thresholding rule and accelerated gradient method. To illustrate the validity and effectiveness of the proposed method, global solar radiation datasets of eight locations of Xinjiang province in China are considered. The experimental results show that the proposed RS-SRSCAD-FA achieves the best performances with a mean absolute percentage error, root-mean-square error, Theil inequality coefficient, and correlation coefficient of 0.066, 20.21 W/<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:msup><mml:mrow><mml:mi mathvariant="normal">m</mml:mi></mml:mrow><mml:mrow><mml:mn mathvariant="normal">2</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math>, 0.016, 3.40 s, and 0.98 in site 1, respectively. For the other seven datasets, RS-SRSCAD-FA still outperforms other approaches. Finally, a nonparametric Friedman test is applied to perform statistical comparisons of results over eight datasets.
Data Management: Relational vs Blockchain Databases

This paper presents an initial exploitative study of how the relational and blockchain databases compare in defining and deploying data structures, populating these with new entries, and retrieving the relevant data for further use. The aim of this study is to better inform the software developers in general and the distributed application developers in particular.
2500-Channel single-shot areal profilometer using hyperspectral interferometry with a pinhole array

Surface profilometry techniques such as coherent scanning interferometry or focus variation require long scan times and are thus vulnerable to environmental disturbance. Hyperspectral interferometry (HSI) overcomes the problem by recording all the spatial and spectral information necessary to reconstruct a 2D surface height map in a single shot. In this paper, we present a new HSI system that uses a pinhole array to provide the necessary gaps for the spectral information. It is capable of measuring 2500 independent points, twice the previous maximum number, with a maximum unambiguous depth range of ∼825 µm and a larger maximum surface tilt angle of 33.3 mrad. The use of phase information allows height to be measured to a precision of ∼6 nm, an order of magnitude improvement on previous HSI systems.
Locality-Preserving Oblivious RAM

Oblivious RAMs, introduced by Goldreich and Ostrovsky [JACM'96], compile any RAM program into one that is "memory oblivious", i.e., the access pattern to the memory is independent of the input. All previous ORAM schemes, however, completely break the locality of data accesses (for instance, by shuffling the data to pseudorandom positions in memory). In this work, we initiate the study of locality-preserving ORAMs—ORAMs that preserve locality of the accessed memory regions, while leaking only the lengths of contiguous memory regions accessed. Our main results demonstrate the existence of a locality-preserving ORAM with poly-logarithmic overhead both in terms of bandwidth and locality. We also study the tradeoff between locality, bandwidth and leakage, and show that any scheme that preserves locality and does not leak the lengths of the contiguous memory regions accessed, suffers from prohibitive bandwidth. To the best of our knowledge, before our work, the only works combining locality and obliviousness were for symmetric searchable encryption [e.g., Cash and Tessaro (EUROCRYPT'14), Asharov et al. (STOC'16)]. Symmetric search encryption ensures obliviousness if each keyword is searched only once, whereas ORAM provides obliviousness to any input program. Thus, our work generalizes that line of work to the much more challenging task of preserving locality in ORAMs.
Simple Schemes in the Bounded Storage Model

The bounded storage model promises unconditional security proofs against computationally unbounded adversaries, so long as the adversary's space is bounded. In this work, we develop simple new constructions of two-party key agreement, bit commitment, and oblivious transfer in this model. In addition to simplicity, our constructions have several advantages over prior work, including an improved number of rounds and enhanced correctness. Our schemes are based on Raz's lower bound for learning parities.
Multi-view deep unsupervised transfer leaning via joint auto-encoder coupled with dictionary learning

Transfer learning empowers machine learning algorithms the ability to train a model on a given task, capture the existing relationship in the data and reuse it for another task in the same or similar domain. In this paper, we present a Multi-view deep unsupervised transfer leaning via joint auto-en coder coupled with dictionary learning (MVT-DAE). In the proposed approach knowledge transfer is done in two stages. First, we perform multi-view dictionary learning based on low-rank tensor regularization in the source domain to learn the common intrinsic relationship among views. Then we transfer the learned dictionaries to the target domain, and we construct a new representation of both domain via sparse coding. In the second stage, we use two deep auto-encoders (DAE) from both sources to perform parameters transfer. Each DAE consist of an embedding layer and a label layer. The embedding layer reconstructs the input while the label layer is used to encode the label information. To relax the distribution distance of the embedding and label layer between the source and the target domain a joint maximum mean discrepancy (JMMD) is employed. Learning can be done via Stochastic gradient descent by sharing the embedding and label layer weights with the target domain. We conduct extensive experiments in two real-world datasets which demonstrated the effectiveness of our approach compared with the different state of the art baseline.
A Submodular Feature-Aware Framework for Label Subset Selection in Extreme Classification Problems

Elham J. Barezi, Ian D. Wood, Pascale Fung, Hamid R. Rabiee. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Continuous wavelet transform of Schwartz tempered distributions

The continuous wavelet transform of Schwartz tempered distributions is investigated and derive the corresponding wavelet inversion formula (valid modulo a constant-tempered distribution) interpreting convergence in S′(R). But uniqueness theorem for the present wavelet inversion formula is valid for the space SF′(R) obtained by filtering (deleting) (i) all non-zero constant distributions from the space S′(R), (ii) all non-zero constants that appear with a distribution as a union. As an example, in considering the distribution x21+x2=1−11+x2 we would omit 1 and retain only −11+x2. The wavelet kernel under consideration for determining the wavelet transform are those wavelets whose all the moments are non-zero. As an example, (1+kx−2x2)e−x2 is such a wavelet. k is an arbitrary constant. There exist many other classes of such wavelets. In our analysis, we do not use a wavelet kernel having any of its moments zero.
A convolutional neural network approach for gender and language variety identification

We present a method for gender and language variety identification using a convolutional neural network (CNN). We compare the performance of this method with a traditional machine learning algorithm – support vector machines (SVM) trained on character n-grams (n = 3–8) and lexical features (unigram s and bigrams of words), and their combinations. We use a single multi-labeled corpus composed of news articles in different varieties of Spanish developed specifically for these tasks. We present a convolutional neural network trained on word- and sentence-level embeddings architecture that can be successfully applied to gender and language variety identification on a relatively small corpus (less than 10,000 documents). Our experiments show that the deep learning approach outperforms a traditional machine learning approach on both tasks, when named entities are present in the corpus. However, when evaluating the performance of these approaches reducing all named entities to a single symbol “NE” to avoid topic-dependent features, the drop in accuracy is higher for the deep learning approach.
Rebuffering Optimization for DASH via Pricing and EEG-Based QoE Modeling

Pricing is an effective mechanism for network resource allocation that can be used to achieve a desirable balance between efficiency and fairness. However, sophisticated utility models are needed to guarantee the performance of price-based resource allocation, especially with regard to video transmission. Among various performance indices of video transmission, rebuffering is an important one that influences user quality of experience (QoE). Therefore, a price-based bandwidth allocation scheme for a dynamic adaptive streaming over hypertext transfer protocol (DASH) system is proposed for mitigating the effect of rebuffering on QoE. The utility model of the proposed scheme considers the relationship between the allocated bandwidth and the rebuffering length, as well as the effect of rebuffering length on QoE. Specifically, electroencephalography (EEG) experiments are conducted, and the distribution of the subjects' time limits at which rebuffering arouse negative emotions is used for calibration. Based on this model, the DASH server collects the buffer state information of all DASH clients periodically to adjust its bandwidth allocation. Assuming the server protects itself from congestion by pricing the clients' requested bandwidth, a Stackelberg game is formulated to study the joint utility maximization on the revenue of the server and the utility of the clients. The Stackelberg equilibrium of the game is characterized, and an efficient searching algorithm is proposed for its solution. EEG experiments are repeated on another group of subjects to verify the generalization ability of the results, and simulation results are presented to validate the effectiveness of the proposed algorithm. The proposed algorithm is shown to have low complexity and outperforms both traditional price-based scheme and QoE maximized scheme in terms of rebuffering.
Illumination animating and editing in a single picture using scene structure estimation

Editing the Illumination information in an image is a fundamental problem in image processing. In this paper, by estimating the scene structure, we propose a novel method to animate the illumination of a single input image. We first estimate the depth map of the input image by incorporating user interaction into the probabilistic inference model. Then we present a depth-aware intrinsic images decomposition method, which decomposes the image into specular shading, diffuse shading, and albedo. Combining the depth maps and shading maps, we develop a rendering-based optimization model to effectively estimate the positions and colors of multiple lights in a scene. With the estimated scene structure, including the depth map, reflectance, shading, as well as light positions and colors, we can animate and edit the illumination of the image via re-rendering the original image by changing the light configuration. Our method can effectively process images with complex shadow, multiple lights (including both point and area light sources) and specular spots. We show a variety of examples, including both indoor and outdoor environment to validate the effectiveness of the proposed method.
Co-design the future CAPTAIN system with older adults

Co-design supports the identification of useful features and insights for the design of technologies from populations such as older adults who are often neglected from the design process. This paper presents the results of the design thinking approach and the consoriums' insights within the CAPTAIN H2020 funded project CAPTAIN aims at developing an e-coaching, home assistant. Focus is given on the coaching dimensions of the system derived from the ideation session with the older adults and the consortium's expertise. The Ideate session which was conducted through plenary face-to-face open discussion meeting, 26 older adults participated. The goal was to envisage the CAPTAIN system functionality and how it can assist in everyday problems that older adults face.
Elect: An Inconsistency Handling Approach for Partially Preordered Lightweight Ontologies

We focus on the problem of handling inconsistency in light-weight ontologies. We assume terminological knowledge bases (TBoxes) are specified in DL-Lite and that assertional facts (ABoxes) are partially preordered and may be inconsistent with respect to TBoxes. One of the main contributions of this paper is the provision of an efficient and safe method, called Elect, to restore consistency of the ABox with respect to the TBox. In the case where the assertional bases are flat (no priorities are associated with the ABoxes) or totally preordered, our method collapses with the well-known IAR semantics and non-defeated semantics, respectively. The semantic justification of Elect is obtained by first viewing a partially preordered ABox as a family of totally preordered ABoxes, and then applying non-defeated inference to each of the totally preordered ABoxes. We introduce the concept of elected assertions which allows us to provide an equivalent characterization of Elect without explicitly generating all totally preordered ABoxes. Finally we show that the computation of Elect is done in polynomial time.
Transparency for Beyond-Accuracy Experiences

Facilitating serendipitous recommendations via a user interface for a recommender system is challenging. Recommender systems that facilitate serendipitous recommendations create what are considered to be beyond-accuracy experiences. Because serendipitous recommendations are unexpected, users may experience some trust issues with the recommender system. This paper reports on the use of transparency for connection-making (a process/stage of serendipitous experience) in serendipity recommendations generation and examines trust with respect to an interactive serendipitous recommender system. We developed a user interface for JabRef (related work tab). The experiment studied the responses of users, comprised of undergraduate students, in a university environment. Data was collected for both subjective and objective evaluations. The analysis was used to determine the significance and impact of transparency in recommender systems in which serendipitous recommendations are generated. The results showed that greater transparency has a positive impact on user experience, helps with connection-making, and enhances the trust of learners who use recommender systems for learning purposes. Such systems can be used by faculty members and students during research and supervision in order to facilitate finding new research topics and trust in such systems.
Recognition of printed Urdu ligatures using convolutional neural networks

We present a holistic technique for recognition of text in cursive scripts using printed Urdu ligatures as a case study. Convolutional neural networks (CNNs) are trained on high-frequency ligature clusters for feature extraction and classification. A query ligature presented to the system is first divided into primary and secondary ligatures that are separately recognized and later associated in a postprocessing step to recognize the complete ligature. Experiments are carried out using transfer learning on pretrained networks as well as by training a network from scratch. The technique is evaluated on ligatures extracted from two standard databases of printed Urdu text, Urdu printed text image (UPTI) and Center of Language Engineering (CLE), as well as by combining the ligatures of the two datasets. The system realizes high recognition rates of 97.81% and 89.20% on the UPTI and CLE databases, respectively.
Anomaly detection of event sequences using multiple temporal resolutions and Markov chains

Streaming data services, such as video-on-demand, are getting increasingly more popular, and they are expected to account for more than 80% of all Internet traffic in 2020. In this context, it is important for streaming service providers to detect deviations in service requests due to issues or changing end-user behaviors in order to ensure that end-users experience high quality in the provided service. Therefore, in this study we investigate to what extent sequence-based Markov models can be used for anomaly detection by means of the end-users’ control sequences in the video streams, i.e., event sequences such as play, pause, resume and stop. This anomaly detection approach is further investigated over three different temporal resolutions in the data, more specifically: 1 h, 1 day and 3 days. The proposed anomaly detection approach supports anomaly detection in ongoing streaming sessions as it recalculates the probability for a specific session to be anomalous for each new streaming control event that is received. Two experiments are used for measuring the potential of the approach, which gives promising results in terms of precision, recall, $$F_1$$-score and Jaccard index when compared to k-means clustering of the sessions.
Isolating Speculative Data to Prevent Transient Execution Attacks

Hardware security has recently re-surfaced as a first-order concern to the confidentiality protections of computing systems. Meltdown and Spectre introduced a new class of exploits that leverage transient state as an attack surface and have revealed fundamental security vulnerabilities of speculative execution in high-performance processors. These attacks derive benefit from the fact that programs may speculatively execute instructions outside their legal control flows. This insight is then utilized for gaining access to restricted data and exfiltrating it by means of a covert channel. This study presents a microarchitectural mitigation technique for shielding transient state from covert channels during speculative execution. Unlike prior work that has focused on closing individual covert channels used to leak sensitive information, this approach prevents the use of speculative data by downstream instructions until doing so is determined to be safe. This prevents transient execution attacks at a cost of 18 percent average performance degradation.
Towards Risk-Driven Security Requirements Management in Agile Software Development

The focus on user stories in agile means non-functional requirements, such as security, are not always explicit. This makes it hard for the development team to implement the required functionality in a reliable, secure way. Security checklists can help but they do not consider the application's context and are not part of the product backlog. In this paper we explore whether these issues can be addressed by a framework which uses a risk assessment process, a mapping of threats to security features, and a repository of operationalized security features to populate the product backlog with prioritized security requirements. The approach highlights the relevance of each security feature to product owners while ensuring the knowledge and time required to implement security requirements is made available to developers. We applied and evaluated the framework at a Dutch medium-sized software development company with promising results.
CIM: Camera in Motion for Smartphone Authentication

The photo response non-uniformity of a smartphone camera is a reliable hardware fingerprint that can be used to authenticate the smartphone owner. This type of camera-based authentication is convenient and of low cost since it requires only pictures taken by a smartphone. However, as shown in this paper, existing camera-based authentication systems are either impractical or subject to fingerprint forgery attacks, in which an adversary intentionally submits forged pictures with fake fingerprints. We propose Camera in Motion (CIM), a practical and reliable camera-based smartphone authentication system. In CIM, a user is asked to move his/her smartphone along a specific route, take pictures of QR codes displayed on the verifier's interface in burst mode, and submit particular burst pictures to the verifier for authentication. We find that, because burst images are captured in rapid succession, the random noise components of a captured image can be partially preserved across multiple images that are captured in a row. The preserved noise forms a forgery-sensitive noisechain embedded in burst images. We also find that there exists various correlations between the movement of the camera and the noise components of the captured images. The noisechain and these correlations are then explored for forgery detection. We performed extensive experiments with 22 smartphones of 5 different models. Our experiment results show that CIM can achieve 100% true acceptance rate at 0% false acceptance rate in both fingerprint matching and forgery detection.