Reviewerly/exHarmony huggingface dataset:
DoAR: An Augmented Reality Based Door Security Prototype Application

The advent of massive technological paradigm shift to subsequent interest in augmented reality (AR) paved the way for designers to design, develop and deploy new use cases of AR in the service industry. In this paper, we investigate the use of augmented reality as an option for providing entry into secured buildings by comparing it against the traditional radio frequency identification devices cards. 51 participants from various backgrounds were recruited to enter a secured building using an AR-based app and an RFID card. The results stipulate that AR-based app had a greater acceptance among participants in terms of security and ease of use. 40 participants thought AR-based app is more secure than RFID cards and 34 participants felt the application was easier to use compared to RFID cards. 32 participants indicated that they would prefer to use the AR-based on daily basis. The result only came short in favor of RFID-based systems in terms of faster access with a margin of 3 persons where RFID systems outperformed AR-Based systems. The results indicate that the system is a suitable option but further research is needed to deploy the research in real-world settings.
Globally Assisted Instance Normalization for Bandwidth-Efficient Neural Style Transfer

Instance normalization (IN) has been widely considered as a key technique in fast neural style transfer algorithms to generate high-quality stylized images. However, because of the calculations of channel-wise means and standard deviations, instance normalization requires layer-by-layer inference flow for CNN accelerators. This kind of dataflow results in huge DRAM bandwidth which is unaffordable for mobile devices or embedding applications. We propose a novel normalization method named globally assisted instance normalization (GAIN) which receives generated statistics from a global branch without actually calculating channel-wise means and standard deviations. Our method generates comparable stylized results and incorporates block-based inference flows to avoid intermediate data transmission. For fast neural style transfer at Full HD 30 fps and 4K UHD 60 fps, we only need 2.52 GB/s and 15.40 GB/s of DRAM bandwidth respectively, which are 90.22% and 92.53% lower than IN with the layer-by-layer inference flow method.
Customized UHF RFID Sensor Tags to Feed Biomechanical Models

In this work, an UHF RFID tag integrated with an Inertial Measurement Unit (IMU) sensor has been designed, realized, and tested. It leverages on a specific RFID chip provided with both wireless and wired memory access. High robustness, effectiveness, and working range, are assured through the use of an on-board battery to boost the chip sensitivity and to supply an ultra-low power microcontroller and the sensors. In such a context, specific electromagnetic aspects have been faced to insulate the battery from the radiofrequency (RF) circuitry and a Planar Inverted-F Antenna (PIFA) has been designed to match the RFID chip input impedance. Different devices have been realized, tested, and then applied on specific human body segments to feed a virtual human-body biomechanical model and validate the RFID-based approach.
Impact of human and environmental interactions on flooding incident in the Jeneberang River South Sulawesi

Abstract Human interaction with the environment due to negative impacts. This can be seen, among others, in the interaction of the community with the environment in the Jeneberang watershed area, South Sulawesi Province, which has an impact in the form of flood events. This paper aims to determine the extent of the impact of human and environmental interactions on flooding in the area. The study was carried out through several studies and studies on human interactions and the natural environment in the Jeneberang watershed, literature studies, reviewing and concluding various journals, as well as collecting data through analysis of maps and secondary data from relevant agencies and primary data from the community as the main actors. The results of the study indicate that human interaction with the environment in the Jeneberang watershed has an impact in the form of flooding because the Jeneberang watershed management activities have not been optimally integrated, which are indicated by: 1. Watershed characteristics are not taken into account in infrastructure development. 2. There is still limited understanding of land-use communities about the characteristics of rainfall and surface runoff, as well as their relation to landslides and sedimentation, 3. The influence of mining activities on river flow narrowing and dam silting is not taken into account, and 4. The occurrence of vegetation degradation in downstream. In order to optimize flood control efforts in the Jeneberang river, it is necessary to carry out integrated management of the Jeneberang watershed by integrating all activities in all sectors. Planning for flood control and environmental conservation in general needs to really consider physical factors in the form of climate, hydrology, geology, tectonics, in addition to vegetation, management, technology, and socio-economic and cultural factors. Communities need to be motivated to continue trying to increase their income and welfare, accompanied by efforts to increase understanding and awareness of the importance of maintaining and preserving the environment, through diversifying livelihoods and utilizing natural resources and land that always prioritizes conservation aspects.
Denoising Signals on the Graph for Distributed Systems by Secure Outsourced Computation

The burgeoning networked computing devices create many distributed systems and generate new signals on a large scale. Many Internet of Things (IoT) applications, such as peer-to-peer streaming of multimedia data, crowdsourcing, and measurement by sensor networks, can be modeled as a form of big data. Processing massive data calls for new data structures and algorithms different from traditional ones designed for small-scale problems. For measurement from networked distributed systems, we consider an essential data format: signals on graphs. Due to limited computing resources, the sensor nodes in the distributed systems may outsource the computing tasks to third parties, such as cloud platforms, arising a severe concern on data privacy. A de-facto solution is to have third parties only process encrypted data. We propose a novel and efficient privacy-preserving secure outsourced computation protocol for denoising signals on the graph based on the information-theoretic secure multi-party computation (ITS-MPC). Denoising the data makes paths for further meaningful data processing. From experimenting with our algorithms in a testbed, the results indicate a better efficiency of our approach than a counterpart approach with computational security.
Asymmetric Convolution View Adaptation Networks for Skeleton-Based Human Action Recognition

Benefiting from the extensive use of depth sensors, human action recognition plays an important role in a great number of works, including human-computer interaction, security monitoring, motion-sensing games, medical care. In most recent works, depth cameras have been used to capture required action data for action identification. Especially, feature extraction based on skeleton information has made satisfactory achievements in action recognition and has been gradually extended to various algorithms. However, the research of view invariance is not deep enough. To improve the performance of skeleton recognition, the paper proposes an improved asymmetric convolution adaptive network, achieving desirable results on the public benchmark dataset of NTU RGB + D 60. The model combines an advanced adaptive module and asymmetric convolution blocks, which can effectively extract features from the original skeleton. Ablation studies and comparative experiments indicate that the performance of the model is better than many of the most state-of-the-art algorithms.
Research on the support system of strengthening and rebuilding project in a university

This is a major strengthening and rebuilding project in a university, during the construction process we found the condition of the original structure is unsatisfactory. For security reasons, it must take a support system during the construction. After the program selection and calculation, finally determine to use the scaffolding support scheme. Compare to other schemes, this scheme first ensures the construction safe, then can save about 240,000 yuan. It provides an economical and safe choice for the internal support of the renovation project in the future.
Central Bank Digital Currency: Motivations and Implications

The rise of digital currencies such as Bitcoin, as well as the blockchain distribution technology, has sparked a lot of interest. These developments increase the chances of having a major impact on the financial system and perhaps the whole of the economy. This paper addresses the question of whether the central bank should extract digital currency that can be used by the general public. It starts by discussing potential reasons for the central bank to withdraw digital money. The effects of this new age digital currency are being tested, focusing on the central bank ledgers, monetary policy, banking system and financial stability, and payments. Finally, CBDC differs from the digital currency in a significant way considered.
An Interactive Tool for Designing End-To-End Secure Workflows

Kotha, Ravi Kanth Narendra Kumar, N. V. Ramakrishnudu, T. Purohit, Shruti Nalam, HarikaDesigning workflows is a challenging task, in particular workflows having end-to-end security requirements. There were several approaches suggested to design a secure workflow. However, several well-known systems are being successfully attacked, years after their usage. A new perspective on protocol specifications was provided in Kumar and Shyamasundar (Analyzing protocol security through information-ow control. Springer, pp. 159–171, 2017 [1]), by interpreting them as information flows and their impact on the state of the stakeholders. Based on this approach, an interactive tool for designing and developing end-to-end secure workflows is discussed in this paper. A basic working prototype of the tool is developed and initial experimental results are discussed.
Enterprise information security risk assessment model

Nowadays, it is important for enterprises to protect commercial information from theft and unauthorized access. An insufficient level of protection can lead to a loss of competitive advantage, the outflow of customers, and the deterioration of relations with partners. The authors use Unified Modeling Language for modeling the information security process. This modeling language is a convenient tool that allows you to form a representation of the specific possibilities of interaction and functioning of objects. Unified Modeling Language activity diagrams provide a visual representation of a sequence of actions that are stages risk assessment and management. The proposed methodology makes it possible to simulate the sequence of stages that make up the essence of the risk assessment process, which consists of assessing the potential damage in monetary terms and the level of risk of threats.
Structural Stress and Strain Analysis Using a 3D Scanner

The paper shows a way how a 3D scanner can be used in a combination with numerical simulations and corresponding analytical results to estimate deformations and stresses in a loaded structural element, such as beams, pipes, shafts, etc. To compare measurement results, a series of numerical simulations were performed for a test object, which was a short pipe that was loaded externally by a force obtained via a manual clamp, which was continuously measured via a force sensor. A general approach to measure strain via a 3D scanner at the object’s surface is explained. However, the surface strain is not sufficient without an analytical consideration to obtain the information what is really occurring inside a loaded structural element, regarding deformations and stresses. Finally, the simulation and the analytical results are compared with the real measurements that were used to calculate strain tensor components, which are used to calculate stress tensor components using the generalized Hooke’s law.
The parallel multi-objective genetic algorithm in fuzzy clustering for discrete elements

The paper proposes a multi-objective genetic algorithm in fuzzy clustering for discrete elements (PGC). The proposed algorithm is built for the fuzzy clustering problem using two objective functions. The first objective function is based on the DB index which used to evaluate separation of inside and outside of clusters. The second function utilized to determine the fuzzy relationship between each element and central clusters. In addition, author also proposes some improvement for the crossover, mutation and selection operators, and apply the parallel technique for the genetic algorithm. These improvements decreased the computing time and cost of PGC. In addition, PGC is also illustrated step by step via simulated data set, it also shows advantageous while compare to other algorithms including k-means, fuzzy c-means and Chen's algorithm. This algorithm is built based on the Matlab procedures.
Performance Monitoring of MongoDB on Varied Cluster Configuration: An Experimental Approach

Data has become one of the most valuable assets in today's digital world. The nature of the generated data varies from structured to semi-structured and even completely unstructured data items. Like many other verticals, the educational domain is also generating a huge volume of data with high variation. The main objective is to store and retrieve data in optimal time, efficiently utilizing recourses, and maintaining security. The evolution of different “Not only SQL (NoSQL)” data models to manage this huge volume and variety of data has helped to facilitate many powerful applications like Facebook, Instagram, WhatsApp, etc. NoSQL data models are provided by many vendors as installation-based services and cloud-based services as well. MongoDB is one of the popular document-oriented NoSQL data models. In this paper, our objective is to analyze the performance in different aspects of MongoDB on installation-based clustering system and on cloud-based clustering system. We have deployed the methodology for the stated semi-structured big-data handling with a case study. The performance of the execution of Mongo Query Language (MQL) on the two different platforms is analyzed.
A Time Dependent Epidemiology Model for Hospital Resource Management in Usual Scenarios and Pandemic

Medicine plays important role for patient's treatment and disease prevention in case of pandemic situations. A healthcare institutions such as pharmacies or hospitals must ensure the availability of medicines as well as other resources such as masks, beds, PPE kits, etc. Hospitals as a health service provider have to face several challenges. One of the challenges is unavailability of Medicines during crucial periods/outbreaks, also due to lack of centralized systems for inventory management results in poor treatment to patients in hospitals. Predictive analysis of the amount of medicine usage and resources can help in hospital asset management. But forecasting medicine usage based on only inventory data will not provide good results when an outbreak happens. As outbreaks cannot be time-series forecasted, it is not feasible to forecast medicine usage only using time-series forecasting models. We propose a forecasting model combination of time forecasting model and epidemiology SIR model to find different trends and seasonality during outbreaks.
Analyzing Correlation of the Relationship Between Technical Complexity Factors and Environmental Complexity Factors for Software Development Effort Estimation

In this paper, a new method called Correlation-based Feature Selection in Correction Factors is proposed. The method is based on the feature selection method used in software development effort estimation to reduce redundant correction factors. In this paper, the impact of correlation-based feature selection on the method’s estimation accuracy is investigated. Multiple linear regression was used as the basic technique for the correction factors preprocessed by the feature selection method. The results were evaluated using six unbiased accuracy measures through the 5-fold cross-validation over the historical dataset. The proposed method leads to a significant improvement in estimation accuracy by simplifying the evaluation of correction factor values in the use case points method, thus increasing the usefulness of the proposed method in practice.
Linear Gaussian Processes for Data-Efficient Robot Dynamics Learning

This paper proposes a linear Gaussian Process learning framework which can be used in the both of semi-parametric and non-parametric models. Inspired by the linear relationship in Euler–Lagrange equation, this approach reduces the dimension of the previous models, which in turn improve the learning efficiency. Besides, the linear relationship results in the better generalization. Simulational results verify the feasibility of the proposed method by predicting the status of a two degrees-of-freedom (DOF) manipulator.
Chapter 3. Swedish FrameNet++ – lexical samsara

One of the main goals of the Swedish FrameNet++ initiative is to recycle and include as many existing modern Swedish lexical resources as possible into one unified lexical macroresource useful for automatic language processing. In this chapter we describe the structure of Saldo, the central resource of Swedish FrameNet++, the design of the formal interlinking mechanism keeping the lexical macroresource together, and our work on Swesaurus, a Swedish wordnet, and a Swedish Roget-style thesaurus as components of Swedish FrameNet++.
Smart contracts and blockchain: legal issues and implications for Indian contract law

The nature of contracts has seen transformation with advances in technology. The efficacy of contract law has been put to the test during this transition from paper-based traditional contracts to electronic contracts. A step forward in this changeover is the ‘smart contract’. Although the term ‘smart contract’ has been in use for two decades, there is as yet no consensus on its meaning. For this paper, we refer to smart contracts as those based on blockchain technology, which generates a decentralised tamper-evident ledger shared within a network of entities. The paper is a critical analysis of the efficacy and adequacy of the Indian Contract Act, 1872, and the Information Technology Act, 2000, in the context of smart contracts. It traces how accommodative Indian law has been to electronic contracts and extends this analysis to smart contracts. It probes smart contracts in the context of the general principles of contract formation, viz., meeting of the minds, offer, acceptance, consideration, capacity, performance, and enforceability. The legal and regulatory aspects of cryptocurrencies, which are often used as consideration, are examined in the Indian context. The paper also elucidates upon the synchronisation of law and technology.
Analysis of Machine Translation Tools for Translating Sentences from English to Malayalam and Vice Versa

Language is considered to be as an effective way to communicate each person's thoughts and expressions. In this multilingual globe, there has become a need for an automated system for converting creations into one language to another. Researches in Natural Language Processing (NLP) have familiarized the world with various machine translation tools. The existing range of machine translation tools differs in the way in which the translations are generated. Some of the commonly used approaches in machine translation are rules, example, statistical syntactic and hybrid based. Artificial neural network (ANN) is used by Google neural machine translation (GNMT), in the translation process. This paper presents the outcome of an analysis of the existing systems of machine translation tools based on the translations that has been conducted between two languages such as English and Malayalam. The paper also focuses on the different types of ambiguities such as lexical, structural and semantic that can occur during a translation. The comparative study which was carried out on eleven machine translation tools available on the web for carrying out the required translation is described in this paper with examples. Within the framework of this paper, we have carried out a study to identify current knowledge, the results and disadvantages in the outline of the automatic translation tools currently available for significant sentence translation between the languages English and Malayalam.
Block-Wise Intra-Prediction of Imaging Data Based on Overfitted Neural Networks with On-Line Learning

Block-wise intra-prediction is a key technique used by modern video codecs to reduce the amount of data to be compressed. Recently, machine learning (ML) has successfully improved block-wise intra-prediction by employing neural networks. Notwithstanding, the performance of such ML-based methods depends on the amount, quality, and relevance of the training data. Furthermore, they require signalling the learned parameters into the bitstream to be able to reconstruct the original data after decompression, thus increasing bitrates. This work proposes a novel block-wise intra-prediction strategy based on fully connected neural networks (FC-NNs) that avoids the two aforementioned shortcomings within the context of lossless compression. To do so, shallow FC-NNs are used, whose parameters are refined in an on-line manner using only the data being predicted. This allows to accurately fit the FC-NNs to the data of interest and replicate the optimization process, avoiding signaling the learned parameters. Experimental results indicate that the proposed ML-based intra-prediction strategy can outperform the intra-prediction used by modern video codecs with prediction accuracy gains of up to 7.01 dB PSNR.
SWANN: Small-World Architecture for Fast Convergence of Neural Networks

On-device intelligence has become increasingly widespread in the modern smart application landscape. A standing challenge for the applicability of on- device intelligence is the excessively high computation cost of training highly accurate Deep Learning (DL) models. These models require a large number of training iterations to reach a high convergence accuracy, hindering their applicability to resource-constrained embedded devices. This paper proposes a novel transformation which changes the topology of the DL architecture to reach an optimal cross-layer connectivity. This, in turn, significantly reduces the number of training iterations required for reaching a target accuracy. Our transformation leverages the important observation that for a set level of accuracy, convergence is fastest when network topology reaches the boundary of a Small-World Network. Small-world graphs are known to possess a specific connectivity structure that enables enhanced signal propagation among nodes. Our small-world models, called SWANNs, provide several intriguing benefits: they facilitate data (gradient) flow within the network, enable feature-map reuse by adding long-range connections and accommodate various network architectures/datasets. Compared to densely connected networks (e.g., DenseNets), SWANNs require a substantially fewer number of training parameters while maintaining a similar level of classification accuracy. We evaluate our networks on various DL model architectures and image classification datasets, namely, MNIST, CIFAR10, CIFAR100, and ImageNet. Our experiments demonstrate an average of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\approx 2.1\times $ </tex-math></inline-formula> improvement in convergence speed to the desired accuracy.