Reviewerly/exHarmony huggingface dataset:
Comparison of Authentication Schemes on IoT

The smart home environment provides various devices that can analyze and create decisions without human interaction by utilizing IoT technology. Limited resources and requirements in a smart home environment present a number of challenges, such as the connectivity of devices to communicate with each other. Some researchers have compared smart home environment schemes, which are authentication solutions using specific methods. This paper presents a literature review of several smart home schemes which are proposed by some re-searchers utilize multi-criteria classification. From the literature review obtained, the khan et al. scheme is the most applicable scheme to apply to an IoT-based smart home. This scheme requires a smaller energy package during the transmission and reception process by a node, which is 284 bits.
Characterizing the Weight Space for Different Learning Models

Deep Learning has become one of the primary research areas in developing intelligent machines. Most of the well-known applications (such as Speech Recognition, Image Processing and NLP) of AI are driven by Deep Learning. Deep Learning algorithms mimic human brain using artificial neural networks and progressively learn to accurately solve a given problem. But there are significant challenges in Deep Learning systems. There have been many attempts to make deep learning models imitate the biological neural network. However, many deep learning models have performed poorly in the presence of adversarial examples. Poor performance in adversarial examples leads to adversarial attacks and in turn leads to safety and security in most of the applications. In this paper we make an attempt to characterize the solution space of a deep neural network in terms of three different subsets viz. weights belonging to exact trained patterns, weights belonging to generalized pattern set and weights belonging to adversarial pattern sets. We attempt to characterize the solution space with two seemingly different learning paradigms viz. the Deep Neural Networks and the Dense Associative Memory Model, which try to achieve learning via quite different mechanisms. We also show that adversarial attacks are generally less successful against Associative Memory Models than Deep Neural Networks.
Face detection authentication analysis on smartphones

Abstract Smartphones are the absolute very most widely known in addition to significant personal systems. Along with their traditional make use of, that is, referring to as and also texting, they have also been used to do multiple security-sensitive activities, including electronic banking in addition to purchasing, social networking, taking pictures, and also e-mailing. On a positive side, smartphones have boosted the quality of life by offering multiple services that customers need, for example, anytime-anywhere processing. Nonetheless, on the other side, they also pose safety as well as private privacy hazards to the customers’ saved data. Consumer authentication is the preliminary series ofdefense to avoid unwarrantable accessibility to the mobile phone. New smartphones using Apple and additionally Samsung have utilized face features to understand their individuals. These smartphone distributors assert that this present-day technology is actually among the most risk-free and also safe and secure in addition to trusted biometrics methods. This paper checks out the functional components of the face identity method installed in these smartphones. Completion results of this particular questionnaire have presented that the majority of cell phone individuals are fulfilled with the face detection strategy while opening their phones. Nevertheless, 59 per-cent of smartphones carry out not utilize face detection approach while doing getting in the app store, offering much a lot less trust fund on this function where economic packages are consisted of.
GCI fusion based multi-detection multitarget tracking

Multi-detection (MD) observation systems are characterized by multiple observation modes (OMs) and thus simultaneously generate multiple measurements for each target. The main difficulty of exploiting MD systems for multitarget tracking (MTT), in contrast to single-detection (SD) systems, is the great amount of extra computational resources required in order to solve the resulting multidimensional assignment problem. This paper proposes a novel computationally efficient MTT approach for MD systems, wherein a bank of OM-dependent MTT filters with SD model are employed and the OM-dependent posteriors are then fused based on the well-known generalized covariance intersection (GCI) rule. In this way, the computational complexity is significantly reduced compared to existing MTT algorithms with MD model. The effectiveness of the proposed algorithm is assessed by simulation experiments.
Forming university students healthy lifestyle through the mastering the discipline “Pedagogy”

The article deals with the problem of students (future teachers) health. To solve the problem of preserving and strengthening the health of university students, the authors propose a group of methods for the formation of healthy lifestyle (HLS) culture through the mastering the educational discipline of the pedagogical cycle. In the formation of a student`s healthy lifestyle culture the main components are distinguished: cognitive-semantic, emotional-volitional, reflexive-evaluative. The assessment of the levels of healthy lifestyle formation among university students is proposed. Within the framework of the discipline “Pedagogy” in its first module “Introduction to the teaching profession” a developed program “Health as a value” was proposed to second-year students of the Kaluga State University named after K.E. Tsiolkovsky. This program was implemented in the experimental group.
KETERLIBATAN MASYARAKAT TERHADAP PENCEGAHAN DAN PENANGGULANGAN KEBAKARAN LAHAN GAMBUT TELUK PAKU KECAMATAN BATI-BATI KABUPATEN TANAH LAUT KALIMANTAN SELATAN

. Research assumed the title of “Community Involvement On The Prevention And Handling Peat Fire Teluk Paku In Bati-Bati Village Bati Bati Distric Tanah Laut Regency, South Borneo” Having objective of knowing the perception and participation of the community towards prevention and fire tackling peat on the gulf of spikes. Benefits from the study can inform us to the rural and become additional information in a bid to fire prevention peat that will involve society especially in the area of research locations. The motive of fire in peatlands is caused by human activities due to economic considerations. The reason for the burning of land because it is considered as the easiest way to open the land, effective and efficient.Sample selection with the census method of focus on the respondents who often actually interact with and dwell and sit among the remote and sparsely populated peat, the number of respondents in get as many as 80 people. The methodology used technique score scale 1-3-5 value a score based on set sums consisting of 4 the kinds of questions that each consisting of 5 to 6 at the present with the question. A conclusion that in get the perception of the community towards the fire prevention peat the gulf of a nail are categorized was in the middle of (1118). The perception of the community towards fire tackling peat included in a category high (1580). Level of participation of the community towards fire prevention peat categorized high (1770). Participation of the community fire tackling peat included in a category was (1348). Keywords : Peat Fire; Parception; Participation
Rectificatory Semantic Information Supplement Network(RSIS-net) For Dynamic Scene Deblurring

In this paper, we construct a Generative Adversarial Networks(GANs) to handle the problem of dynamic deblurring. The generator is based on Feature Pyramid Network (FPN-net), and channel-level correction is used in the encoding process of feature extraction to correct the deep features of blurred image. Since FPN-net exists semantic degradation Phenomenon, we repeatedly supplement the corrected semantic information in the decoding path of the generator. We named our algorithm as Rectificatory Semantic Information Supplement Network (RSIS-net), which contains the Semantic Information Rectification mechanism (SIR), the Semantic Information Supplement mechanism (SIS), and a more suitable low-level perception loss function(Ploss) of a deblurring task. Finally, the experiment results show that our RSIS-net outperforms the many state-of-the-art methods. In addition, the processing time of our lightweight network on a single frame is less than 0.5 seconds, which means that the algorithm supports real-time deblurring.
A Semantic Aware Meta-Path Model for Heterogeneous Network Representation Learning

Heterogeneous graph representation learning is to learn effective representations for nodes or (sub)graphs, which preserve node attributes and structural information. However, it is challenging to design a representation learning method for heterogeneous information networks (HINs) due to their diversity. Most of the existing HIN-oriented learning methods define a series of meta-paths. Then, they aggregate the representations learned from different meta-paths in the same hidden space. These methods do not consider semantic differences of different meta-paths, which leads to semantic confusion. And further affects the effectiveness of the learned representation. Given these issues, we introduce a Semantic Aware HIN Representation learning Network (SAHRN), which takes into account the semantics of different meta-paths. We mitigate the problem of semantic confusion by projecting nodes’ features into different hidden spaces separately according to different meta-paths. To further expand the scope of aggregation and enrich the aggregated information, we also design various variants of our model by adding layer aggregation. Extensive experiments on three standard HIN datasets show that SAHRN achieves consistent improvements compared to state-of-the-art graph representation learning methods. The experiments and analyses on each component of the model show the effectiveness of the proposed method. The source code is available on <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/pingpingand/SAHRN</uri> .
Pixel-Based Attack on ODENet Classifiers

With rapid improvements in deep learning, one of the challenging open issues is the exposure of neural systems to adversarial attack. Many competitions explored various attacks and prevention strategies against these attacks. Another worldview of profound neural systems with persistent inward activations was presented at NeurIPS 2018 that likewise acquired the best paper grant. With the neural ODE block, all discrete transformations can be parametrized as a continuous transformation that can be solved using a ODE solver. We break down picture/content classifiers with ODE Nets, upon subject to ill-disposed assaults and contrast it with standard profound models. Neural ODEs are more robust towards black-box attacks. Some of their intrinsic properties, such as accuracy–efficiency trade-off and versatile calculation cost, allow us to build powerful models further.
Blockchain, the Digital State, and the New World Order

Governments across the world are grappling with the emergence and integration of new technologies. Front runner Estonia provides the model for how a country might completely transform their government operations, economy, and society through a purposeful, strategic program of digitization. This chapter considers how such countries are approaching digital transformation, outlining considerations for governments and submitting the new paradigm outlined in the BS4SC model of a citizen-centric, data-driven, and decentralised economy.
Towards a Semantic Edge Processing of Sensor Data. An Incipient Experiment

This paper addresses a semantic stream processing pipeline, including data collection, semantic annotation, RDF data storage and query processing. We investigate whether the semantic annotation step could be moved on the edge, by designing and evaluating two alternative processing architectures. Experiments show that the edge processing fulfills the low-latency requirement, facilitating the parallel processing of the semantic enrichment for the sensor data.
Widening access to language learning in the institutes of technology in the Republic of Ireland

Higher Education Institutions (HEIs) in the Republic of Ireland have been called to widen participation in language learning by the government’s Languages Connect Strategy. The challenge is to increase the level of participation from 4% to 20%. Moreover, a recent nationwide study has revealed a grassroots demand for language provision that is currently unmet, and that prevailing perceptions of a lack of curricular space for languages may be one of the overriding obstacles to change. Mobile Assisted Language Learning (MALL) provides a possible solution to this dilemma, as it enables various forms of content located on virtual learning platforms to be accessed from anywhere. There is certainly a case for widening participation in language learning by harnessing new technologies. Online platforms create virtual spaces for language learning that transcend the confines of the traditional language classroom. MALL provides unlimited access to learning content and offers the potential for synchronous interaction with native speakers. Existing market leaders of MALL certainly have a global reach; however, their underlying pedagogy has been called into question. Further research and innovation are needed to create pedagogically sound learning platforms that might motivate language acquisition. Third level language educators need to create online learning pathways for languages, thereby overcoming perceived obstacles to change and bringing the second language acquisition process into the 21st century.
An Optimized Neural Network with Inertia Weight Variation of PSO for the detection of Autism

Neural Networks is applied for solving diverse number of problems involving classification, clustering and prediction of outcomes that forms the basis for resolving Artificial Intelligence related problems. Particle Swarm Optimization is a widely used algorithm to enhance the effectiveness of neural networks. As a part of this article, a new proposition of varying the inertia weight component during run - time of PSO Iterations is being introduced so as to avoid premature convergence in hybrid - PSO algorithm. The experimental results are compared against the feedforward network with random weights and feedforward network with optimised weights using PSO. The results show a significant increase in the performance.
Vertical Retargeting for Stereoscopic Images via Stereo Seam Carving

Vertical retargeting for stereoscopic images using seam manipulation-based approaches has remained an open challenge over the years. Even though horizontal retargeting had attracted a huge amount of interest, its seam coupling strategies were not capable to construct valid seam pairs for vertical retargeting. In this article, we propose two seam coupling strategies for vertical retargeting, namely, real mapping and virtual mapping. Our proposed mapping strategies were implemented to address the problems of multiple assignments and missing assignments, which are able to occur in the straightforward generalization from horizontal retargeting to vertical retargeting. On the basis of our proposed method, stereo seams were allowed to lay across occluded regions and occluding regions in stereo images. We maintained the geometric consistency by removing occluded pixels and corresponding occluding pixels in both stereo images. As a result, our method guarantees valid and geometrically consistent stereo seam pairs to be found in the horizontal direction. We generate vertically retargeted stereo images by removing or adding horizontal seam pairs iteratively. We conducted experiments on a number of indoor and outdoor scenes. Experimental results demonstrated that our method overcomes the limitations of vertical retargeting and is effective in preserving the geometric consistency.
Future Factories

The IoT presents a possibility to create an innovative collaboration setup between human and robots within the Industry 4.0, leveraging concepts like Intelligence Augmentation (IA). This project aims to develop a safe human-robot collaboration (HRC) system. The system will feature a robotic arm and advanced sensors, inter-networked in an edge computing framework. It will also make use of computer vision techniques, enabling a seamless and safe operation. Finally, the system operation will be verified on a set of prespecified manufacturing tasks, demonstrating adequate safety measures for effective collaboration.
UHUOPM: High Utility Occupancy Pattern Mining in Uncertain Data

It is widely known that there is a lot of useful information hidden in big data, and it is prevalent for individuals to mine crucial information for utilization in many real-world applications. To find patterns that can represent the supporting transaction, a recent study was conducted to mine high-utility occupancy patterns whose contribution to the utility of the entire transaction is greater than a certain value. Moreover, in realistic applications, patterns may not exist in transactions but be connected to an existence probability. In this paper, a novel algorithm, called High Utility-Occupancy Pattern Mining in Uncertain databases (UHUOPM), is proposed. The patterns found by this algorithm are called Potential High Utility Occupancy Patterns (PHUOPs). To reduce memory cost and time consumption and to prune the search space in the algorithm as mentioned above, probability-utility-occupancy list (PUO-list) and probability-frequency-utility table (PFU-table) are used. Finally, substantial experiments were conducted to evaluate the performance of proposed UHUOPM algorithm on both real-life and synthetic datasets.
Face Recognition Based on Phase Only Correlation (POC)

Face recognition is a technology that is widely used in the field of security and biometrics. Every human face has unique features such as the shape of the jaw and the contours of the face. In this study, face detection was carried out using the Phase Only Correlation (POC) method. POC is a face detection method based on the highest correlation value from the calculation of the phase and magnitude of an image. The data used in this study is in the form of facial images in RGB format. Stages of the study were divided into two, namely preprocessing and processing. The pre-processing stage includes the Region of Interest (ROI), image segmentation, grayscale transformation, and Discrete Fourier Transform (DFT). The processing phase includes Cross Spectrum, Inverse Cross Spectrum values, and correlation calculation. There are three testing schemes carried out in this study, namely testing of variations in lighting levels, expressions, and facial positions. From the results of the study, different face positions produce incorrect detection results because changes in the position of the image being tested make changes in the frequency distribution of the phase and magnitude values.
The Mode of Dynamic Block Transfer Teaching Resource Base of Distance Education Digital Virtual Machine Based on Cloud Computing

In order to realize the sharing of learning resources in distance education and meet the needs of different learners, this paper studies the mode of dynamic transfer of teaching resource base of digital virtual machine in distance education. This paper discusses the mode construction of teaching resource base from three aspects of cloud computing technology, teaching resource base mode and key technology, expounds the logical framework and platform framework of resource base mode, and points out the broad prospect of cloud computing technology in the construction of distance education resource base.
Early Diagnosis of Dental Caries Using Fuzzy Inference The Sugeno Method System

Many are not aware of the dangers of oral health, especially dental caries. In 2018 the proportion of dental caries in Indonesia was 45.3%, according to the results of the Basic Health Research (RISKESDAS). Until now, dental health services have not reached the middle and lower class of society due to the very high cost. One way to make it easier for the public to recognize the disease's characteristics and diagnose dental caries as early as possible is to make an expert system application. The system in this application is made using the Sugeno fuzzy method. System testing yields an accuracy of 93%.
Interpretable and effective hashing via Bernoulli variational auto-encoders

Due to the rapid increase in the amount of data generated in many fields of science and engineering, information retrieval methods tailored to large-scale datasets have become increasingly important in the last years. Semantic hashing is an emerging technique for this purpose that works on the idea of representing complex data objects, like images and text, using similarity-preserving binary codes that are then used for indexing and search. In this paper, we investigate a hashing algorithm that uses a deep variational auto-encoder to learn and predict the codes. Unlike previous approaches of this type, that learn a continuous (Gaussian) representation and then project the embedding to obtain hash codes, our method employs Bernoulli latent variables in both the training and the prediction stage. Constraining the model to use a binary encoding allow us to obtain a more interpretable representation for hashing: each factor in the generative model represents a bit that should help to reconstruct and thus identify the input pattern. Interestingly, we found that the binary constraint does not lead to a loss but an increase of search accuracy. We argue that continuous formulations learn a representation that can significantly differ from the code used for search. Minding this gap in the design of the auto-encoder can translate into more accurate retrieval results. Extensive experiments on seven datasets involving image data and text data illustrate these findings and demonstrate the advantages of our approach.
A two-level sampling strategy for pruning methods applied to credit scoring

Multiple Classifiers Systems (MCS) are based on the idea that the combination of the opinion of several experts can generate better results than when only one expert is used. Several MCS techniques have been developed; each one has its strengths and weaknesses depending on the context in which they are applied. This work presents a two-level sampling strategy for pruning methods that are applied to the credit scoring task. The first step of the proposal is to generate a pool using two well-known sampling methods, bagging and random subspace, that work complementarity in order to produce a diverse pool. After, a pruning method reduces the generated pool maintaining only the most competent classifiers. So, the proposal improves the MCS regarding the accuracy and the computational effort, since only a small percentage of the original pool is stored. The proposed architecture is evaluated in a credit scoring application, and the results showed that the proposed architecture obtained better accuracy rates than the single best approach and literature methods. These results were also obtained with ensembles whose sizes were around 20% of the original pools generated in the training phase.