Reviewerly/exHarmony huggingface dataset:
Secure Framework for Cloud based E-Education using Deep Neural Networks

E-Education is an important aspect that facilitates the online and virtual education. The advancements in the education institutes are increasing tremendously which requires high-end servers, software's, and applications that leads to the huge investment cost. The optimized solution is adoption of cloud services. Hosting the applications and documents over the cloud provides flexibility, performance, and quality of the education. Despite the advantages, the main challenge associated with the E-Education over the cloud is privacy and security of the outsourced data. In the existed solutions stores all the documents in the same storage area, similar type of authentication algorithm for all users, and single type of encryption algorithm is applied for all documents. In this paper, a secure framework of E-Education over the cloud environment is proposed that performs the authentication of the cloud users in the effective way through the multiple-factors based on the role and activities of a user. To extract the accurate features of the biometric face modality VGG2F Convolutional neural network is applied. Documents storage classification, secure question paper generation, and verification are proposed in the secure way. The proposed model reduces the overhead, processing time, and billing cost by adopting different deployment models, authentication mechanisms, and cryptographic ciphers. The biometric face authentication through the VGG2F model improves the accuracy, and hybrid combination of encryption algorithms with different key sizes applied on the documents resists various attacks. The evaluated results show that the proposed framework is suitable for E-education in the cloud.
Learning Factorized Cross-View Fusion for Multi-View Crowd Counting

Crowd counting has been a long-standing task in surveillance video analysis. Most of existing methods focus on a single-view setting. Crowd counting with multiple views can provide richer and complementary information across views. However, the task is still inadequately explored in the literature. Previous works have attempted either to project each camera view onto a common geometric 2-D ground-plane and estimate crowd density map through aggregation [1], or set up connections among all pixel pairs [2]. However, registering a local view to the global ground-plane is error-prone and fails to explicitly model the critical inter-view correlation. Full inter-pixel connections inevitably lead to explosion of parameters. To solve these problems, in this paper, we propose an efficient module that effectively does the job of cross-view fusion by directly modeling the correlation between each pair of views. More specifically, to distill and transfer all useful information from multiple sources views to a target camera view, we factorize the full transformation into a generic-fusion component that encodes all geometric / semantic information of this target view, and a view-specific affine-transform component that encodes the scene geometry / semantics cues of specific source view. This factorization significantly reduces the parameter redundancy and enables plug-and-play of new cameras. Extensive experiments on three multi-view counting datasets (PETS2009, DukeMTMC, and CityStreet) clearly and consistently demonstrate the superiority of the proposed method.
A Dichotomy for the Generalized Model Counting Problem for Unions of Conjunctive Queries

We study the \em generalized model counting problem, defined as follows: given a database, and a set of deterministic tuples, count the number of subsets of the database that include all deterministic tuples and satisfy the query. This problem is computationally equivalent to the evaluation of the query over a tuple-independent probabilistic database where all tuples have probabilities in $\set0,\frac1 2, 1 $. Previous work has established a dichotomy for Unions of Conjunctive Queries (UCQ) when the probabilities are arbitrary rational numbers, showing that, for each query, its complexity is either in polynomial time or \#P-hard. The query is called \em safe in the first case, and \em unsafe in the second case. Here, we strengthen the hardness proof, by proving that an unsafe UCQ query remains \#P-hard even if the probabilities are restricted to $\set0,\frac1 2, 1 $. This requires a complete redesign of the hardness proof, using new techniques. A related problem is the \em model counting problem, which asks for the probability of the query when the input probabilities are restricted to $\set0,\frac1 2 $. While our result does not extend to model counting for all unsafe UCQs, we prove that model counting is \#P-hard for a class of unsafe queries called Type-I forbidden queries.
Image-based Activity Recognition from IMU Data

In this paper we propose an approach to improve the performance of human activity recognition (HAR) from inertial sensors data, based on image processing techniques. Our approach creates image representations of the time-series data to take advantage of the strengths that convolutional neural networks (CNNs) have shown when dealing with image data. We have conducted an evaluation using benchmark datasets that are considered among the most relevant in HAR. Our results show that our approach is able to outperform the state of the art in all cases.
Accelerating Set Intersections over Graphs by Reducing-Merging

Given two sets of vertices Sa and Sb of a graph, computing their common vertices, namely set intersection, is one primitive operation in many graph algorithms such as triangle counting, maximal clique enumeration, and subgraph matching. Thus, accelerating set intersections is beneficial to these algorithms. In the paper, we propose a novel reducing-merging framework for set intersections over graphs rather than intersecting the two sets directly. In the reducing phase, the vertices that cannot fall into the intersection are screened out by applying the range reduction. Based on the truncated subsets, the intersection can be easily obtained using the classic merging algorithm. To optimize the range codes that sketch the vertices, we formulate the problem of range code optimization and prove its NP-hardness. We develop efficient yet effective algorithms for two typical scenarios global intersection and local intersection. Moreover, we present a novel two-level merging algorithm to enhance the performance. The results of extensive experiments over real graphs show that our approach can achieve significant speedups compared to the merge-based algorithm.
Classifying virtual reality-based collaboration environments: practical insights for application in fashion design

Virtual reality (VR) technology offers great potential for design collaboration by enabling designers to enhance the collaborative exploration process. However, few studies have provided practical insights into the use of VR-based collaboration environment (VRCE) in the field of fashion design. Therefore, this study explored the main features of VRCE solutions currently available and classified their major types in order to develop a deeper contextual understanding of the use of VRCE for fashion design collaboration. The VRCE solutions were collected from various media channels and finally classified into three representative types: virtual meeting and design planning, three-dimensional virtual prototyping, and design review. Consequently, the findings emphasised that the VRCE is useful for general design collaboration. Yet, to more efficiently process the use of domain-specific information and design process required for collaborative exploration of the fashion design concept development, additional technology functions that integrate with the fashion design process should be developed.
Forecasting Interaction Order on Temporal Graphs

Link prediction is a fundamental task for graph analysis and the topic has been studied extensively for static or dynamic graphs. Essentially, the link prediction is formulated as a binary classification problem about two nodes. However, for temporal graphs, links (or interactions) among node sets appear in sequential orders. And the orders may lead to interesting applications. While a binary link prediction formulation fails to handle such an order-sensitive case. In this paper, we focus on such an interaction order prediction problem among a given node set on temporal graphs. For the technical aspect, we develop a graph neural network model named Temporal ATtention network (TAT), which utilizes the fine-grained time information on temporal graphs by encoding continuous real-valued timestamps as vectors. For each transformation layer of the model, we devise an attention mechanism to aggregate neighborhoods' information based on their representations and time encodings attached to their specific edges. We also propose a novel training scheme to address the permutation-sensitive property of the problem. Experiments on several real-world temporal graphs reveal that TAT outperforms some state-of-the-art graph neural networks by 55% on average under the AUC metric.
Model order selection for approximate Boolean matrix factorization problem

A key step in applying Boolean matrix factorization (BMF) is establishing the correct model order for the data, i.e., decide where the knowledge stops and the noise starts, or simply, decide the proper number of factors that describe the data well. There are two main approaches to BMF, namely, Discrete Basis Problem (DBP) and Approximation Factorization Problem (AFP). Although the model order selection technique for DBP exists, there is no technique tailored for AFP. We show that the number of factors for DBP cannot be used in AFP, and we present a novel way, reflecting the nature of AFP, how to establish the proper number of factors. Moreover, we show that the number of factors established for AFP is – from a knowledge-representation viewpoint – better than that for DBP.
Dynamic Syntax

Abstract Dynamic Syntax (DS: Kempson et al. 2001; Cann et al. 2005) is an action-based grammar formalism which models the process of natural language understanding as monotonic tree growth. This paper presents an introduction to the notions of incrementality and underspecification and update, drawing on the assumptions made by DS. It lays out the tools of the theoretical framework that are necessary to understand the accounts developed in the other contributions to the Special Issue. It also represents an up-to-date account of the framework, combining the developments that have previously remained distributed in a diverse body of literature.
A Novel Bio-inspired Optimization Framework for Effective Crop Land Allocation and Utilization

Land availability for agriculture has become scarce in the present technological world. Effective usage of the cultivable land is constrained due to less availability of water and cost-effective land allocation for cultivation has turned out more challenging. Crop planning optimization is termed as a combinatorial optimization problem, in which effective utilization of land plays a major role. The primary objective of this paper is to perform optimal land allocation to improve agricultural income. Ant colony optimization algorithm (ACO), Social Spider Algorithm (SSA), and LINGO global solver are applied for optimizing the agricultural land in this work. The study area chosen for this research is agricultural areas in Coimbatore, situated in Tamilnadu state, India. This research outcome has shown that planned land allocation through optimization algorithms could significantly increase the profit in agriculture to a considerable extent.
Searching Frame-Recurrent Attentive Deformable Network for Real-Time Video Deraining

Video deraining has become an issue of great interest since rain streaks inevitably affect video quality. Most of the existing works focus on heuristically designing the network architecture to integrate available information derived from the temporal dimension. However, their inferences take a long time, so that the practicability is somewhat ignored. To solve this problem, we develop a real-time video deraining network in a frame-recurrent manner. It includes a fast attentive deformable alignment module and an automatically-discovered spatial-temporal reconstruction module. In which, the alignment is composed of a single newly-built deformable convolution under the channel attention mechanism to keep the accurate motion consistency and reduce time-consuming by a wide margin. The reconstruction part for the first time introduces the architecture search technique for video deraining to automatically discover a high-effective architecture by designing an effective and compact search space. Experimental results demonstrate remarkable superiority both in computational efficiency and actual performance compared to other state-of-the-art approaches.
Rethinking Noise Modeling in Extreme Low-Light Environments

Recent research has shown Convolutional Neural Networks (CNNs) trained in a fully-supervised fashion achieve promising performance on extreme low-light image denoising task. However, a large amount of "noisy-clean" image pairs are required to train a network, which are difficult to obtain. In this paper, we propose a compact yet effective noise model to generate synthetic noisy images for training. Especially, we address the severe color distortion problem in low-light images by identifying a novel noise component, black calibration error, as its physical origin. We prove that a small error at the sensing stage will strongly affect the following in-camera signal processing (ISP) pipeline and eventually lead to color bias. Experiment results demonstrate that the proposed model is superior in preserving perceptual quality and achieves state-of-the-art performance among existing noise synthesis methods.
Host-Based Virtual Machine Workload Characterization Using Hypervisor Trace Mining

Cloud computing is a fast-growing technology that provides on-demand access to a pool of shared resources. This type of distributed and complex environment requires advanced resource management solutions that could model virtual machine (VM) behavior. Different workload measurements, such as CPU, memory, disk, and network usage, are usually derived from each VM to model resource utilization and group similar VMs. However, these course workload metrics require internal access to each VM with the available performance analysis toolkit, which is not feasible with many cloud environments privacy policies. In this article, we propose a non-intrusive host-based virtual machine workload characterization using hypervisor tracing. VM blockings duration, along with virtual interrupt injection rates, are derived as features to reveal multiple levels of resource intensiveness. In addition, the VM exit reason is considered, as well as the resource contention rate due to the host and other VMs. Moreover, the processes and threads preemption rates in each VM are extracted using the collected tracing logs. Our proposed approach further improves the selected features by exploiting a page ranking based algorithm to filter non-important processes running on each VM. Once the metric features are defined, a two-stage VM clustering technique is employed to perform both coarse- and fine-grain workload characterization. The inter-cluster and intra-cluster similarity metrics of the silhouette score is used to reveal distinct VM workload groups, as well as the ones with significant overlap. The proposed framework can provide a detailed vision of the underlying behavior of the running VMs. This can assist infrastructure administrators in efficient resource management, as well as root cause analysis.
Implementing Virtual Reality in K-12 Classrooms: Lessons Learned from Early Adopters

There is very little research on K-12 education outside designated research studies on how teachers use VR for learning. This study addresses gap in research by asking: How did teachers start with VR and how do they use it? We interviewed nine teachers from different primary, lower secondary, and upper secondary schools—who had one to five years of experience with VR—among the first to include VR in their teaching, making them early adopters. All schools used HDMs, but the number, brand, and model varied. VR was used to teach various subjects and topics, but mostly for a limited part of the lesson. Due to the limited number of HMDs, the teaching was often organized as stations, where the students took turns using the HMDs and working on other tasks related to the theme. This requires good planning from the teacher and a certain degree of self-regulation among the students, as the teacher’s eyes and ears are on the student(s) wearing the HMDs. The early adoption of VR sometimes encountered challenges related to technical (e.g., firewalls) or ethical issues (e.g., privacy), but some argued that for innovation to happen, it was necessary to “act first and ask later”.
A parallel memory efficient outlier detection algorithm for large unstructured point clouds

The removal of erroneous points is an important pre-processing step in the analysis of point clouds, to ensure a high quality evaluation of the measured objects. However, modern optical 3D scanning technologies generate point clouds that can contain hundreds of millions of points. Existing algorithms for the removal of erroneous points can face difficulties, due to the amount of memory that is required to process these point clouds. We present a new method that is based on the well known Local Outlier Factor algorithm. It adapts the calculation of the factor in minor ways to reduce its runtime. More importantly, we employ a new processing strategy that significantly reduces the overall memory consumption of the algorithm. This enables the detection and removal of outliers even in very large point clouds. In order to show the effectiveness of our new method, we evaluate the processing on multiple, differently sized point sets and demonstrate the configurable memory consumption of our new technique.
The KR-Elliptic Curve Public Key Cryptosystem

Abstract The connection between some fundamental mathematical problems in number theory with cryptography has been done in this work. A new version of the elliptic curve public key cryptosystem (ECPKC) has been proposed by Karrar Taher and Ruma Ajeena. Their main idea is to design this cryptosystem focusses on generating the elliptic curve group, its order divides the Euler phi function that is computed securely based on two large primes. Faster and more secure computations to generate the keys, encryption and decryption processes, on the KR-ECPK algorithm are discussed. In comparing with other algorithms like RSA and ElGamal public key cryptosystem (EEPKC), the KR-ECPKC considers as an efficient algorithm. So, the KR-ECPKC is more appropriate for elliptic curve cryptographic applications.
Multi-source Neural Topic Modeling in Multi-view Embedding Spaces

Though word embeddings and topics are complementary representations, several past works have only used pretrained word embeddings in (neural) topic modeling to address data sparsity in short-text or small collection of documents.This work presents a novel neural topic modeling framework using multi-view embedding spaces: (1) pretrained topic-embeddings, and (2) pretrained word-embeddings (contextinsensitive from Glove and context-sensitive from BERT models) jointly from one or many sources to improve topic quality and better deal with polysemy.In doing so, we first build respective pools of pretrained topic (i.e., TopicPool) and word embeddings (i.e., WordPool).We then identify one or more relevant source domain(s) and transfer knowledge to guide meaningful learning in the sparse target domain.Within neural topic modeling, we quantify the quality of topics and document representations via generalization (perplexity), interpretability (topic coherence) and information retrieval (IR) using short-text, long-text, small and large document collections from news and medical domains.Introducing the multi-source multi-view embedding spaces, we have shown state-of-the-art neural topic modeling using 6 source (highresource) and 5 target (low-resource) corpora.
Geochemistry of salts and the effect of trace elements on human health: Turkey salt resources

Salt plays a vital role in many body functions. Humans must consume a well-balanced amount of salt to remain healthy as deficiency or excess of it causes many health problems. The major oxide element’s values are only evaluated while processing for production; however, the type and amount of the trace elements in the salt are also important as they may entail risks for human health. This study concerns the mineralogy and geochemistry of salt samples which are taken from several salt mines and localities in Turkey. In addition, these local samples are compared with well-known samples from Himalayas and Asal Lake (Djibouti Southeast Africa) salt mines. The X-Ray Diffraction (XRD) and Confocal Raman Spectroscopy (CRS) analysis reveal that the most of Turkish salts are mainly composed of halite with minor amounts of thenardite and gypsum as well as the low concentrations of some potentially toxic elements (PTEs) such as As, Cd, Co, Hg, Mn, Ni, Pb and Zn. The samples from Tuz Lake have significant contents of As, Co, Cu, Mn, Ni, U and Zn. The mineralogy and geochemistry of the samples reveal a mixture of residual seawaters with the redissolving of the crystalized salt minerals in the region. Although the quality of the salts is mostly evaluated with the content of NaCl, the significant contents of the PTEs in the salt may be accumulated by time and danger human health.
Efficient Cooperation of Heterogeneous Robotic Agents: A Decentralized Framework

In social and industrial facilities of the future such as hospitals, hotels, and warehouses, teams of robots will be deployed to assist humans in accomplishing everyday tasks like object handling, transportation, or pickup and delivery operations. In such a context, different robots (e.g., mobile platforms, static manipulators, or mobile manipulators) with different actuation, manipulation, and perception capabilities must be coordinated to achieve various complex tasks (e.g., cooperative parts assembly in the automotive industry or loading and unloading of palettes in warehouses) that require collaborative actions with each other and with human operators. The efficient supervision and coordination of a heterogeneous system mandates a decentralized framework that integrates high-level task planning, low-level motion planning and control, and robust real-time sensing of the robot's dynamic environment. Decentralization in multiagent robotic systems is of utmost importance because it provides flexibility, scalability, and fault-tolerance capabilities. In this work, we present the architecture of the decentralized framework developed within the context of the European Union project Co4Robots and its application in a multitasking collaboration scenario involving various heterogeneous robots and humans.