Reviewerly/exHarmony huggingface dataset:
Green Behavior for Energy Efficiency as a Sustainable Campus Support

In order to make the environment safe and comfortable, humans need to have ethics. With good environmental ethics will make life more meaningful. Ethical towards the environment, among others, by protecting the environment from damage or loss. This study aims to analyze student behavior towards the use of electrical energy in providing sustainable campus carrying capacity. This field research is qualitative with a phenomenological approach to find out the daily, intersubjective world (world of life) of IAIN Salatiga students. This study reveals the meaning of concepts or phenomena of experience based on the awareness that occurs in IAIN Salatiga students in utilizing electrical energy on campus in lighting systems, air systems, and supporting equipment. Data collection techniques through observation, interviews, and documentation. The data that has been obtained is analyzed by reducing, presenting, and drawing conclusions or verification. Checking the validity of the data using triangulation techniques and sources. The results show that the behavior of IAIN Salatiga students is in the form of concern for the use of electrical energy, which includes lighting systems, air systems, and supporting equipment.
IsoqurPEKS: An Isogeny-Based Quantum-Resistant Public-Key Encryption Scheme with Keyword Search

Since the convenience and advancement of cloud applications, many users (e.g., companies or individuals) adopt remote cloud services to reduce the local storage overload and computing consumption. However, before transferring them to the cloud server, users always encrypt outsourced data for the privacy of important data, which deprives flexible usage of these data. Public key encryption with keyword search (PEKS) undoubtedly offers a precise resolution to this issue. Unfortunately, most PEKS schemes cannot fight against quantum computing attackers, which is increasingly a research hotspot. To achieve postquantum security and privacy-preserving search function, we propose a quantum-resistant PEKS scheme named IsoqurPEKS. Our proposed instantiation satisfies basic semantic security indistinguishable against chosen keyword attack (IND-CKA), and IsoqurPEKS is proved to be secure under the security model. Furthermore, we compare IsoqurPEKS with the other eight current PEKS schemes with respect to security properties, communication, and computation costs. The comparison results indicate that the proposed scheme has the best security and performance among the nine PEKS schemes.
An Efficient Face Detection and Recognition System Using RVJA and SCNN

The basic process for an extensive range of security systems functioning in real-time applications is facial recognition. Considering several factors like lower resolution, occlusion, illumination, noise, along with pose variation, a satisfactory outcome was not achieved by various models developed for face recognition (FR). Therefore, by utilizing reconstruction scheme-centric Viola–Jones algorithm (RVJA) and shallowest sketch-centered convolution neural network (SCNN) methodologies, an effectual face detection and recognition (FDR) system has been proposed here by considering the aforementioned factors. Specifically, first, the algorithm identifies faces in a provided image by determining its global facial model in various positions along with poses; then, it sequentially enhanced the recognition outcome by utilizing SCNN. Initially, by employing the RVJA, face detection (FD) has been performed. The unconstrained face images are handled by the proposed RVJA having efficient properties such as boundedness and invariance, together with the ability to rebuild the actual image. After that, for FR, the SCNN methodology is utilized, thus learning the complicated features of the face-detected images. Next, regarding metrics like area under curve (AUC), recognition accuracy (RA), and average precision (AP), the proposed methodology’s experiential outcome is analogized with other prevailing methodologies. The experimental outcome displayed that the facial images are recognized by the proposed model with higher accuracy than that of the other conventional methodologies.
Automated Sanitizing Machine for Recording Thermal Detection and Hygiene Application

Abstract A multipurpose automatic disinfection booth is an automated, non-contact, alcohol-based disinfection station, which finds its use in hospitals, work, premises, offices, schools, and other relevant places. It serves multiple purposes of sanitizing not only the human body but all of the components they are carrying. By using UV lights, it sanitizes the whole object or accessories carried by a person. Along with this functioning, it also provides a fully automated hand sanitizing mechanism which is followed by temperature detection of the person entering the machine, and it is loaded with smart sensors to take appropriate decisions. It has a security mechanism in form of a barricade to restrict the potentially unwanted person from entering. Also, it stores data of the person in form of an image for security purposes along with providing free energy.
A high dimensional functional time series approach to evolution outlier detection for grouped smart meters

Smart metering infrastructures collect data almost continuously in the form of fine-grained long time series.These massive data series often have common daily patterns that are repeated between similar days or seasons and shared among grouped meters.Within this context, we propose an unsupervised method to highlight individuals with abnormal daily dependency patterns, which we term evolution outliers.To this end, we approach the problem from the standpoint of High Dimensional Functional Time Series and we use the concept of functional depth to exploit the dynamic group structure and isolate individual meters with a different evolution.The performance of the proposal is first evaluated empirically through a simulation exercise under different evolution scenarios.Subsequently, the importance and need for an evolution outlier detection method are shown by using actual smart-metering data corresponding to photo-voltaic energy generation and circuit voltage records.Here, our proposal detects outliers that might go unnoticed by other approaches of the literature that have demonstrated to be effective capturing magnitude and shape abnormalities.
Challenges of COVID-19 Lockdown on Indian Universities

In the wake of the growing coronavirus crisis, 25th March 2020 saw a pan-India lockdown being strictly enforced across the country, initially for a three-week period—which was further extended several times, either uniformly nationwide or on a state-by-state basis. As was expected, this led to tremendous hardship for a large swathe of the country’s population. Amid all this, the education sector in India was unduly affected in several ways. Now, nearly a year on, there is still no clarity on when educationalEducational Life institutions will be able to open and function “normally”. This paper delves into various aspects related to the way COVID-19Covid 19 lockdowns affected the education sector in India, universities especially. Offering a multiplicity of perspectives, the objective of the paper is to provide a balanced viewpoint on the way several dynamics of the COVID-19 lockdown in India panned out, especially the challenges that it presented to Indian universities. Following the methodology of interpretivism research philosophy, the paper relies primarily on secondary—yet credible sources of data for providing its perspectives. These especially include a multiplicity of online news sites and other publications that have been gleaned over extensively before providing pertinent information. Additionally, social media sites have also been tapped widely for this paper. While perspectives on such sites are frequently opinionated and subjective, a plethora of them when taken in tandem work towards providing a holistic set of beliefs centred around the primary theme of this paper. For data analysis, a thematic analysis has been used for identifying consistent themes gleaned from secondary data that has been obtained as described above. The findings of the paper point towards significant challenges faced by all stakeholdersStakeholder Power in Indian universities, with no clear resolution in sight, in the immediate future. As the ongoing vaccination drive intensifies, the challenges faced by these universities are likely to resolve gradually, over a prolonged period.
Variational autoencoder densified graph attention for fusing synonymous entities: Model and protocol

The prediction of missing links of open knowledge graphs (OpenKGs) poses unique challenges compared with well-studied curated knowledge graphs (CuratedKGs). Unlike CuratedKGs whose entities are fully disambiguated against a fixed vocabulary, OpenKGs consist of entities represented by non-canonicalized free-form noun phrases and do not require an ontology specification, which drives the synonymity (multiple entities with different surface forms have the same meaning) and sparsity (a large portion of entities with few links). How to capture synonymous features in such sparse situations and how to evaluate the multiple answers pose challenges to existing models and evaluation protocols. In this paper, we propose VGAT, a variational autoencoder densified graph attention model to automatically mine synonymity features, and propose CR, a cluster ranking protocol to evaluate multiple answers in OpenKGs. For the model, VGAT investigates the following key ideas: (1) phrasal synonymity encoder attempts to capture phrasal features, which can automatically make entities with synonymous texts have closer representations; (2) neighbor synonymity encoder mines structural features with a graph attention network, which can recursively make entities with synonymous neighbors closer in representations. (3) densification attempts to densify the OpenKGs by generating similar embeddings and negative samples. For the protocol, CR is designed from the significance and compactness perspectives to comprehensively evaluate multiple answers. Extensive experiments and analysis show the effectiveness of the VGAT model and rationality of the CR protocol.
ELAT: Ensemble Learning with Adversarial Training in defending against evaded intrusions

Network intrusion detection systems (NIDSs) now adopt machine learning (ML) for detection of wide attack variants. However, ML is also known vulnerable to adversarial attacks, which can degrade the accuracy of ML. A number of defense strategies have been proposed but mostly in image classification areas. In this work, we propose Ensemble Learning with Adversarial Training (ELAT) to combine adversarial training and ensemble learning into a solution. We compare four approaches: single, ensemble, adversarial and ELAT. In the experiments, several models were developed and tested using different approaches to know which method is robust against adversarial attacks for ML-based NIDSs. The average F1 score for the single models was 0.93 within a wide range (0.82-0.99), but dropped to 0.29 when facing adversarial attacks, particularly dropped to 0.07 caused by the strongest attack, Projected Gradient Descent (PGD). With ensemble, adversarial and ELAT, the average scores were recovered to 0.80, 0.88 and 0.91, respectively. In addition, this work involves prediction of the models and approach implemented behind the system using cosine similarity with an accuracy of 99.9%.
Lions and contamination: Monotone clearings

We consider a special variant of a pursuit-evasion game called lions and contamination. In a graph whose vertices are originally contaminated, a set of lions walks around the graph and each lion clears the contamination from every vertex it visits. The contamination, however, simultaneously spreads to any adjacent vertex not occupied by a lion. We study the relationship between different types of clearings of graphs, such as clearings which do not allow recontamination, clearings where at most one lion moves at each time step and clearings where lions are forbidden to be stacked on the same vertex. We answer several questions raised by Adams et al. [1].
Quantum codes from constacyclic codes over a semi-local ring

Let Rr = Fq + v1F Fq + ··· + vr Fq, where q is the power of prime, vi2 = vi, vivj = vjvi = 0 for 1 ≤ i, j ≤ r and r ≥ 1. In this paper, the structure of λ-constacyclic codes over the ring Rr is studied and a Gray map ϕ from Rrn to Fq(r+1)n is given. The necessary and sufficient conditions for these codes to contain their Euclidean duals are determined. As an application, we obtain many new better quantum codes from dual-containing λ-constacyclic codes over Rr, for r = 1, 2, 3, that improve the known existing quantum codes.
Projectional Editing of Software Product Lines Using Multi-variant Model Editors

Abstract Model-driven software engineering (MDSE) as well as software product line engineering (SPLE) achieve productivity gains by raising the level of abstraction and fostering organized reuse. Consequently, the integrating discipline model-driven software product line engineering (MDSPLE) aims at combining the best of both worlds by creating multi-variant models which are (automatically) configured into single-variant models which are in turn adapted further (if required). Inherently complex multi-variant models call for urgently needed tools providing support for editing multi-variant models. In this paper, we present a framework for projectional multi-variant editors which make complexity manageable using a user-friendly representation. At all times, a domain engineer is aware of editing a multi-variant model which is necessary to assess the impact of changes on all model variants. Supporting a clear separation of product space (domain model) and variant space (variability annotations), our projectional multi-variant editors provide a novel approach to representing variability information which is displayed non-intrusively. Furthermore, the domain engineer may employ a projectional multi-variant editor to adapt the representation of the multi-variant domain model in a flexible way, according to the current focus of interest.
Italian EVALITA Benchmark Linguistic Resources, NLP Services and Tools

Abstract Starting from the first edition held in 2007, EVALITA is the initiative for the evaluation of Natural Language Processing tools for Italian. We describe the EVALITA4ELG project, whose main aim is to systematically collect the resources released as benchmarks for this evaluation campaign, and make them easily accessible through the European Language Grid platform. The collection is moreover integrated with systems and baselines as a pool of web services with a common interface, deployed on a dedicated hardware infrastructure.
CEFR Labelling and Assessment Services

Abstract Our pilot project aims to develop a set of text collections and annotation tools to facilitate the creation of datasets (corpora) for the development of AI classification models. These classification models can automatically assess a text’s reading difficulty on the levels described by the Common European Framework of Reference (CEFR). The ability to accurately and consistently assess the readability level of texts is crucial to authors and (language) teachers. It allows them to more easily create and discover content that meets the needs of students with different backgrounds and skill levels. Also, in the public sector using plain language in written communication is becoming increasingly important to ensure citizens can easily access and comprehend government information. EDIA already provides automated readability assessment services (available as APIs and an online authoring tool) for the CEFR in English. Support for Dutch, German and Spanish are added as part of this project. Using the infrastructure developed in this project the effort for creating high quality datasets for additional languages is lowered significantly. The tools and datasets are deployed through the European Language Grid. The project is scheduled to be completed in the second quarter of 2022.
IA-NGM: A bidirectional learning method for neural graph matching with feature fusion

Most existing deep learning methods for graph matching tasks tend to focus on affinity learning in a feedforward fashion to assist the neural network solver. However, the potential benefits of a direct feedback from the neural network solver to the affinity learning are usually underestimated and overlooked. In this paper, we propose a bidirectional learning method to tackle the above issues. Our method leverages the output of a neural network solver to perform feature fusion on the input of affinity learning. Such direct feedback helps augment the input feature maps of the raw images according to the current solution. A feature fusion procedure is proposed to enhance the raw features with pseudo features that contain deviation information of the current solution from the ground-truth one. As a result, the bidrectional alternation enables the learning component to benefit from the feedback, while keeping the strengths of learning affinity models. According to the results of experiments conducted on five benchmark datasets, our methods outperform the corresponding state-of-the-art feedforward methods.
Explorative Study on Asymmetric Sketch Interactions for Object Retrieval in Virtual Reality

Drawing tools for Virtual Reality (VR) enable users to model 3D designs from within the virtual environment itself. These tools employ sketching and sculpting techniques known from desktop-based interfaces and apply them to hand-based controller interaction. While these techniques allow for mid-air sketching of basic shapes, it remains difficult for users to create detailed and comprehensive 3D models. Our work focuses on supporting the user in designing the virtual environment around them by enhancing sketch-based interfaces with a supporting system for interactive model retrieval. An immersed user can query a database containing detailed 3D models and replace them with the virtual environment through sketching. To understand supportive sketching within a virtual environment, we made an explorative comparison between asymmetric methods of sketch interaction, i.e., 3D mid-air sketching, 2D sketching on a virtual tablet, 2D sketching on a fixed virtual whiteboard, and 2D sketching on a real tablet. Our work shows that different patterns emerge when users interact with 3D sketches rather than 2D sketches to compensate for different results from the retrieval system. In particular, the user adopts strategies when drawing on canvas of different sizes or using a physical device instead of a virtual canvas. While we pose our work as a retrieval problem for 3D models of chairs, our results can be extrapolated to other sketching tasks for virtual environments.
Cloud Native Development, Containers, and Open Source Licensing

Abstract This chapter deals with containers, a Linux operating system feature that enables isolation of a user-space instance from the rest of the system. Given the abundance of open source software in containers, issues of open source license interpretation and compliance naturally arise in the container setting. One of the characteristics of the rapid rise of container technology adoption has been a perceived inattention to open source license compliance by many container users. To the extent this is a fair assessment, it may reflect the ease with which a user of container technology can assemble and distribute complex, open source-based software collections without awareness of their contents or associated licensing terms. At the same time, because containers may be new and unfamiliar to lawyers, they may be overly concerned about the impact of open source license obligations arising out of the use of containers.
Regularizing Activation Distribution for Ultra Low-bit Quantization-Aware Training of MobileNets

MobileNets are a family of CNN architectures that are designed for parameter efficient model deployment. However, quantization aware training (QAT) of MobileNets for lowprecision model development has not been very successful partly due to the parameter efficient nature of the model. The activation signal in MobileNet shows fairly large magnitude diversity for each channel, which can lead to poor quantization results. We address this issue by including a new loss term for QAT, which regularizes the diversity of the activation signal in each channel. The proposed method improves the accuracy of the state-of-theart QAT when evaluated on MobileNet with CIFAR10/100, and ImageNet datasets.
Class Activation Map Refinement via Semantic Affinity Exploration for Weakly Supervised Object Detection

Weakly Supervised Object Detection (WSOD) aims to train a detector to specify the interesting targets in an image by only using image-level labels. An important trend of current WSOD methods is to integrate object detection with Weakly Supervised Semantic Segmentation (WSSS), so that more discriminative regions can be obtained and more accurate detection can be achieved. However, due to the unreliable segmentation supervision generated by WSOD, their performance is still very limited. To address this problem, in this paper, we propose a novel end-to-end framework termed Class activation map Guided Detection Network (CGDN), where the detection process is guided by Class Activation Map (CAM) rather than the segmentation results. The proposed CGDN is composed of a detection branch and a CAM refinement branch, where the CAM refinement branch critically refines the CAMs generated by the detection branch, and then the refined CAMs are deployed to provide more reliable fore-ground cues for the detection branch in turn. Therefore, the two branches interact which leads to progressively improved detection and CAM outputs. Extensive experiments on PASCAL VOC 2007 and 2012 datasets verify the effectiveness of our proposed network.
Optimal Noise-Aware Imaging with Switchable Prefilters

Most consumer digital cameras employ a single-chip image sensor with a color filter array (CFA), where the purpose of an in-camera imaging pipeline is to generate a noise-free and color-corrected standard RGB image from mosaic CFA RAW data. The joint design of camera spectral sensitivity (CSS) and the imaging pipeline has great potential to derive better imaging quality. However, since there is a trade-off between the robustness to noise and the accuracy of color reproduction, one fixed CSS cannot realize optimal imaging in terms of both aspects under various noise levels. Thus, in this paper, we propose noise-aware imaging using camera prefilters for each noise level, where we jointly design the spectral sensitivity of the prefilters, that of CFA, and imaging networks to realize optimal imaging in all noise levels. Experimental results under various noise levels demonstrate that our imaging method using the prefilters outperforms existing methods based on a fixed CSS.
A Noise Preserving Sharpening Filter for CT Image Enhancement

There is growing interest in employing deep neural networks (DNN) for image sharpening. However, sharpening medical computed tomography (CT) images is challenging because sharpening substantially amplifies high-frequency noise. Alternatively, sharpening algorithms that are also designed to denoise produce images lacking texture. Most importantly, radiologists strongly prefer reading images at a consistent level of noise. Hence it is preferable that a sharpening algorithm not substantially change the noise energy or texture.In this work, we propose a noise preserving sharpening filter (NPSF) to sharpen CT images while keeping the noise energy and texture in the result similar to that of the input. We achieve this by adding appropriately scaled noise while training. Furthermore, the NPSF is characterized by three user-adjustable parameters which give flexibility to achieve a desired level of sharpness and noise. Our experiments show that the NPSF can sharpen noisy images while producing desired noise level and texture.