Reviewerly/exHarmony huggingface dataset:
Blockchain-based Local Energy Grids: Advanced Use Cases and Architectural Considerations

Energy markets worldwide are undergoing a transformative change from fossil energy sources and centralized setups to renewable energy sources and highly decentralized markets with peer-to-peer trading. Blockchain technology has been considered a fundamental enabler for building decentralized energy systems and services. However, existing blockchain-based energy solutions may not be transferable to new contexts, as regulatory, infrastructural, and economic criteria and constrains may not fit or have not been made explicit with existing work. To this end, we present a 3+1 layer model for use case identification and for architecting their realization to ensure coverage early-on of the different critical dimensions. We report on findings from a major project running in Germany by describing five advanced use cases and discussing important architectural considerations.
Nlkd: Using Coarse Annotations For Semantic Segmentation Based on Knowledge Distillation

Modern supervised learning relies on a large amount of training data, yet there are many noisy annotations in real datasets. For semantic segmentation tasks, pixel-level annotation noise is typically located at the edge of an object, while pixels within objects are fine-annotated. We argue the coarse annotations can provide instructive supervised information to guide model training rather than be discarded. This paper proposes a noise learning framework based on knowledge distillation NLKD, to improve segmentation performance on unclean data. It utilizes a teacher network to guide the student network that constitutes the knowledge distillation process. The teacher and student generate the pseudo-labels and jointly evaluate the quality of annotations to generate weights for each sample. Experiments demonstrate the effectiveness of NLKD, and we observe better performance with boundary-aware teacher networks and evaluation metrics. Furthermore, the proposed approach is model-independent and easy to implement, appropriate for integration with other tasks and models.
Privacy-Preserving Cloud-Based DNN Inference

Deep learning as a service (DLaaS) has been intensively studied to facilitate the wider deployment of the emerging deep learning applications. However, DLaaS may compromise the privacy of both clients and cloud servers. Although some privacy preserving deep neural network (DNN) techniques have been proposed by composing cryptographic primitives, the challenges on computational efficiency have not been fully addressed due to the complexity of DNN models and expensive cryptographic primitives. In this paper, we propose a novel privacy preserving cloud-based DNN inference framework ("PROUD"), which greatly improves the computational efficiency. Finally, we conduct experiments on two datasets to validate the effectiveness and efficiency for the PROUD while benchmarking with the state-of-the-art techniques.
Automated Classification of Criminal and Violent Activities in Thailand from Online News Articles

Criminal and violent activities are a universal concern that affects a society's nature of life and economic dynamics. With dramatically increasing crime rates, law enforcement agencies have begun to show attention in utilizing machine learning approaches to analyze crime patterns to protect their communities. However, there are only a few studies that carried out experiments to classify Thai crime news articles into their proper categories. Also, the comparison of various machine learning algorithms toward this task has still been under-investigated. Therefore, in this paper, we aim to develop a framework to automate the classification and visualization of criminal and violent activities from online Thai news articles. Six classifiers are employed to classify crime news articles into one of the five crime categories including Burglary, Drug, Murder, Accident, and Corruption. The results have shown that Support Vector Machine and Logistic Regression approaches outperform other classifiers in terms of Accuracy, Precision, Recall, and F-Measure metrics.
HP2IFS: Head Pose estimation exploiting Partitioned Iterated Function Systems

Estimating the actual head orientation from 2D images, with regard to its three degrees of freedom, is a well known problem that is highly significant for a large number of applications involving head pose knowledge. Consequently, this topic has been tackled by a plethora of methods and algorithms the most part of which exploits neural networks. Machine learning methods, indeed, achieve accurate head rotation values yet require an adequate training stage and, to that aim, a relevant number of positive and negative examples. In this paper we take a different approach to this topic by using fractal coding theory and particularly Partitioned Iterated Function Systems to extract the fractal code from the input head image and to compare this representation to the fractal code of a reference model through Hamming distance. According to experiments conducted on both the BIWI and the AFLW2000 databases, the proposed PIFS based head pose estimation method provides accurate yaw/pitch/roll angular values, with a performance approaching that of state of the art of machine-learning based algorithms and exceeding most of non-training based approaches.
South African universities in a time of increasing disruption

Background: The Fourth Industrial Revolution (4IR) and the Coronavirus Disease 2019 (COVID-19) have disrupted the higher education environment in unprecedented ways. Aim: This article identifies the impact of increasing disruption driven by the 4IR and COVID-19 on the content and curriculum design of degree programmes in economic and management sciences offered by South African universities. Setting: Six South African and five top-tier US and UK universities. Methods: The study used a non-positivist qualitative research design and specifically the case-study approach. A document analysis of the information in university yearbooks and prospectuses was conducted, using a purposive sampling design. Results: An online presence will become more important due to increased disruption, and will not only ensure an additional revenue stream, but also promote continuity in operations and mitigate threats from competitors. COVID-19 has accelerated the extent of this disruption and expedited the migration to online teaching and learning platforms. Conclusion: Since science, technology, engineering and mathematics are integral to the majority of 4IR-related modules, South African universities must not shy away from degree programmes that ignore inter- and multi-disciplinary curriculum designs. Coupled with the challenges facing the majority of South African students to access electronic devices, data and the internet, COVID-19 has thrust this challenge to the forefront in the South African higher education landscape. By comparing the developments in South African universities with those in trendsetting, top-tier, global universities, management can assess the extent to which they are internationally competitive and adapting to the demands of the 4IR.
Gate Trimming: One-Shot Channel Pruning for Efficient Convolutional Neural Networks

Channel pruning is a promising technique of model compression and acceleration because it reduces the space and time complexity of convolutional neural networks (CNNs) while maintaining their performance. In existing methods, channel pruning is performed by iterative optimization or training with sparsity-induced regularization, which all undermine the utility due to their inefficiency. In this work, we propose a one-shot global pruning approach called Gate Trimming (GT), which is more efficient to compress the CNNs. To achieve this, GT performs the pruning operation once, avoiding expensive retraining or re-evaluation of channel redundancy. In addition, GT globally estimates the effect of channels across all layers by information gain (IG). Based on the IG of channels, GT accurately prunes the redundant channels and makes little negative effect on CNNs. The experimental results show that the proposed GT is superior to the state-of-the-art methods.
Energy Gain between Automatic and Manual Solar Tracking Strategies in Large Scale Solar Photovoltaic System- 12 Cities Comparison

The electrical power generation from a solar photovoltaic (PV) system can be maximized by using automatic solar tracker via single-axis or dual-axis solar tracker system. However, such system is more expensive and complex than the fixed solar system. Manual tracker that changes the tilt angle of PV panel on periodical basis is another alternative that can be considered. This paper compares the energy output performance of a grid connected large scale solar (LSS) PV system with the following solar tracking strategies i) automatic single-axis ii) automatic dual-axis iii) manual monthly adjusted tilt angle and iv) manual seasonally adjusted tilt angle. 12 cities worldwide were chosen systematically for this study. The PV mathematical model was developed by using MATLAB software to simulate the energy output of 1 MW LSS PV system in terms of its annual average daily energy output (MWh/day). The results show that the energy output of a single-axis solar tracker PV system is close to the dual-axis tracker system for most cities, especially the one located at 40 degrees latitude and above. Thus, single-axis tracker system is preferred due to less expensive, lower cost operation & maintenance, less complex than the dual-axis tracker. Moreover, the results show that the energy output from manual tracker with monthly optimal tilt angle is greater than seasonally. With energy gain that can reach up to 8% depending on the location's latitude angle as well as the local climatic conditions of each city. Generally, manual trackers are much cheaper, easier, most reliable, and longer lifespan than automatic trackers. Thus, manual tracking with monthly optimum tilt angle can be considered as an alternative solution between the high energy gain, expensive automatic solar tracker system and the low energy gain (relatively), yet cheap fixed panel solar PV system.
Illustrating the Gaps and Needs in the Training Support of Community Health Workers in India

In India and other developing countries, Community Health Workers (CHWs) provide the first line of care in delivering necessary maternal and child health services. In this work, we assess the training and skill-building needs of CHWs, through a mobile-based training intervention deployed for six months to 500 CHWs for conducting 144 training sessions in rural India. We qualitatively probed 1178 questions, asked by CHWs, during training sessions, and conducted a content analysis of the learning material provided to CHWs. Further, we interviewed 48 CHWs to understand the rationale of information seeking and perceptions of training needs. We present our understanding of the knowledge gaps of CHWs and how the current learning material and training methods are ineffective in addressing it. Our study presents design implications for HCI4D researchers for mobile learning platforms targeted towards CHWs. We also provide policy-level suggestions to improve the training of CHWs in India or a similar context.
“I...Got my Nose-Print. But it Wasn’t Accurate”: How People with Upper Extremity Impairment Authenticate on their Personal Computing Devices

Authentication has become increasingly ubiquitous for controlling access to personal computing devices (e.g., laptops, tablets, and smartphones). In this paper, we aim to understand the authentication process used by people with upper extremity impairment (UEI). A person with UEI lacks range of motion, strength, endurance, speed, and/or accuracy associated with arms, hands, or fingers. To this end, we conducted semi-structured interviews with eight (8) adults with UEI about their use of authentication for their personal computing devices. We found that our participants primarily use passwords and PINs as a verification credential during authentication. We found the process of authentication to have several accessibility issues for our participants. Consequently, our participants implemented a variety of workarounds that prioritized usability over security throughout the authentication process. Based on these findings, we present six broad subareas of research that should be explored in order to create more accessible authentication for people with UEI.
Reduced-Complexity Modular Polynomial Multiplication for R-LWE Cryptosystems

The ring-learning with errors (R-LWR) problem is utilized to build many ciphers resisting quantum-computing attacks and fully homomorphic encryption that allows computations to be carried out on encrypted data. Modular multiplication of long polynomials with large coefficients is the most critical operation in these schemes. The polynomial multiplication complexity can be reduced by the Karatsuba formula. In this paper, a new method is proposed to integrate the modular reduction into the Karatsuba polynomial multiplication. Modular reduction is applied to intermediate segment products instead of the final product. As a result, additional sub-structure sharing is enabled and the number of coefficient additions needed for assembling the segment products to get the final result is substantially reduced. For polynomial multiplications with decomposition factors 2, 3, and 4, the proposed scheme reduces the number of additions by 13-17%.
Simulation of mallet percussion instruments by a coupled modal vibroacoustic finite element model

A three-dimensional coupled vibroacoustic finite element model for physics-based simulations of the sound generation by mallet percussion instruments in the time domain is discussed in the present paper. The mechanical model takes the orthotropic material properties of the wooden sound bars and the nonlinear nature of the interaction force between the mallet head and the sound bar into account while the acoustical model considers radiation into an unbounded domain. A direct coupling of the sound bars, acoustical cavity resonators, and the excitation by a mallet is considered with exploiting the modal basis to reduce the number of degrees of freedom of the system. Both the mechanical and acoustical models are validated by comparing them to measurements performed on an Orff xylophone. A case study shows the capabilities of the coupled model, including the analysis of the energy balance, the effect of tuning the resonator, and the excitation of the torsional modes of the sound bar.
Bayesian Monitoring of Seismo-Volcanic Dynamics

Methods for volcano monitoring that are based on analysis of geophysical data often rely on deterministic approaches without considering the complex and dynamic nature of volcanic systems. To detect subtle changes within seismic sequences associated with volcanic unrest, specialized workflows for data classification and analysis are required. Here, we present an inference framework based on Bayesian Deep Learning as a probabilistic proxy, which allows monitoring continuous changes in seismic activity at volcanoes. This architecture has been designed and trained to detect and to classify individual earthquake transients from continuous seismic data recorded in volcanic environments. We tested this new framework by analyzing seismic data associated with eruptions at Bezymianny Volcano (Russia) during 2007. Our results demonstrate efficient signal detection and classification accuracy, and effective detection of changes in the volcanic system in the hours preceding eruptive activity. This approach can be extended to other volcanoes and earthquake-prone areas, and demonstrates a new application of deep learning in the field of seismic monitoring.
Theoretical and experimental investigation of the sources of error in stochastic parallel gradient descent-based digital modal decomposition technique

A detailed systematic investigation of the accuracy of digital modal decomposition process that uses stochastic parallel gradient descent (SPGD) algorithm is presented in this paper. Composite beams of known weights and phases corresponding to the eigenmodes of a three-mode fiber are generated theoretically and through experiments using a spatial light modulator (SLM). The weights and phases of the constituent scalar modes are extracted from the intensity profile of the composite beam using the SPGD method, for both theoretical and experimental conditions. Detailed analysis of the sources of error in such SPGD based digital modal decomposition method is carried out by generating composite beams of various modal ratios and phase combinations theoretically. Impact of the experimental errors such as effect of background noise, nonlinearity, misalignment of the camera and that due to the cumulative propagation phase, on the extracted weights and relative phase values are quantified. We find that any ambiguity at phase angles closer to 90 deg among the constituent modes especially when the modal weights are non-uniform, cannot be corrected easily and hence is a fundamental limitation of the intensity-based modal decomposition technique. The methodology used in this manuscript to identify the systemic errors in modal decomposition can be potentially extended to any digital decomposition technique.
Low-Dimensional Representation Learning from Imbalanced Data Streams

Learning from data streams is among the contemporary challenges in the machine learning domain, which is frequently plagued by the class imbalance problem. In non-stationary environments, ratios among classes, as well as their roles (majority and minority) may change over time. The class imbalance is usually alleviated by balancing classes with resampling. However, this suffers from limitations, such as a lack of adaptation to concept drift and the possibility of shifting the true class distributions. In this paper, we propose a novel ensemble approach, where each new base classifier is built using a low-dimensional embedding. We use class-dependent entropy linear manifold to find the most discriminative low-dimensional representation that is, at the same time, skew-insensitive. This allows us to address two challenging issues: (i) learning efficient classifiers from imbalanced and drifting streams without data resampling; and (ii) tackling simultaneously high-dimensional and imbalanced streams that pose extreme challenges to existing classifiers. Our proposed low-dimensional representation algorithm is a flexible plug-in that can work with any ensemble learning algorithm, making it a highly useful tool for difficult scenarios of learning from high-dimensional imbalanced and drifting data streams.
Heterogeneously integrated III–V-on-Si microring resonators: a building block for programmable photonic integrated circuits

Beyond various application-specific photonic integrated circuits (PICs), a programmable PIC comprising identically designed generic building blocks with multiple active functionalities under different operational conditions is an attractive design option. Here, we propose and demonstrate proof-of-concept experiments of a heterogeneously integrated III–V-on-Si microring resonator (MRR) as such a versatile building block. The MRR can function as an electro-optical channel add-drop switch with signal amplification, a directly modulated injection laser, or a microcavity photodetector under different electrical bias conditions. We discuss the fabrication process for our heterogeneous integration platform based on an atomic layer deposition-assisted molecular bonding technique.
How Convolutional Neural Networks Deal with Aliasing

The convolutional neural network (CNN) remains an essential tool in solving computer vision problems. Standard convolutional architectures consist of stacked layers of operations that progressively downscale the image. Aliasing is a well-known side-effect of downsampling that may take place: it causes high-frequency components of the original signal to become indistinguishable from its low-frequency components. While downsampling takes place in the max-pooling layers or in the strided-convolutions in these models, there is no explicit mechanism that prevents aliasing from taking place in these layers. Due to the impressive performance of these models, it is natural to suspect that they, somehow, implicitly deal with this distortion. The question we aim to answer in this paper is simply: "how and to what extent do CNNs counteract aliasing?" We explore the question by means of two examples: In the first, we assess the CNNs capability of distinguishing oscillations at the input, showing that the redundancies in the intermediate channels play an important role in succeeding at the task; In the second, we show that an image classifier CNN while, in principle, capable of implementing anti-aliasing filters, does not prevent aliasing from taking place in the intermediate layers.
Graphnet: Graph Clustering with Deep Neural Networks

Existing deep graph clustering methods usually rely on neural language models to learn graph embeddings. However, these methods either ignore node feature information or fail to learn cluster-oriented graph embeddings. In this paper, we propose a novel deep graph clustering framework to tackle these two issues. First, we construct a feature transformation module to effectively integrate node feature information with graph topologies. Second, we introduce a graph embedding module and a self-supervised learning strategy to constrain graph embeddings by leveraging the graph similarity and the self-learning loss to group similar graphs together, thus encouraging the obtained graph embeddings to be cluster-oriented. Extensive experimental results on eight real-world graph datasets validate the superiority of the proposed method over existing ones.
Structuring a Comprehensive Software Security Course Around the OWASP Application Security Verification Standard

Lack of security expertise among software practitioners is a problem with many implications. First, there is a deficit of security professionals to meet current needs. Additionally, even practitioners who do not plan to work in security may benefit from increased understanding of security. The goal of this paper is to aid software engineering educators in designing a comprehensive software security course by sharing an experience running a software security course for the eleventh time. Through all the eleven years of running the software security course, the course objectives have been comprehensive - ranging from security testing, to secure design and coding, to security requirements to security risk management. For the first time in this eleventh year, a theme of the course assignments was to map vulnerability discovery to the security controls of the Open Web Application Security Project (OWASP) Application Security Verification Standard (ASVS). Based upon student performance on a final exploratory penetration testing project, this mapping may have increased students' depth of understanding of a wider range of security topics. The students efficiently detected 191 unique and verified vulnerabilities of 28 different Common Weakness Enumeration (CWE) types during a three-hour period in the OpenMRS project, an electronic health record application in active use.