Reviewerly/exHarmony huggingface dataset:
Chatty Bot: An Alternative to Implement a Memory Training Option for the Upcoming Older Adults in Indonesia

Indonesia, the world’s fourth most populous nation, is an aging country. The significant demographic changes force this middle-income developing country to prepare for the new society. We proposed chatbot usage as a new medium for the baby-boomers to interact. This cohort will or already enter their retirement age soon. We argue this technology suitable due to familiarity with the usage. This research uses quasi-experimental research in the form of within-subject measurement implemented to the people older than 55 years old and user of cross-platform messaging service with simple memory training. A chatbot prototype called Chatty Bot was developed to learn the participants’ ability to detect the features given. Chatty Bot has proven acceptable for its system’s usability and predicted to still be used by the majority of the participants for at least one month. The simple chatbot technology recommended enhances the effective communication channel, related but not limited only to healthy aging.
CCA-security from adaptive all-but-one lossy trapdoor functions

In this paper, we propose the notion of adaptive all-but-one lossy trapdoor functions (aABO-LTFs), a variant of all-but-one lossy trapdoor functions. An aABO-LTF is parameterised by a set of branches. Given the lossy branch, the function statistically loses the information of its inputs. Given injective branches, the function is injective, and there is a trapdoor that enables efficient function inversion. What differentiates an aABO-LTF and an ABO-LTF is that for an aABO-LTF, the lossy branch is indistinguishable from the other branches even if the adversary gets to ask for function inversions on any injective branches apart from the lossy branch. We demonstrate the usefulness of the adaptivity of aABO-LTFs by providing generic and efficient constructions of an adaptively chosen-ciphertext secure (CCA-secure) public-key encapsulation mechanism (KEM) and an adaptive deterministic public-key encryption (DPKE) without random oracles using aABO-LTFs in a very simple black-box way. Our constructions are direct in the sense of that it avoids generic transformations using one-time signatures or message authentication codes typically found in standard model CCA-secure constructions. Moreover, we show that aABO-LTFs can be instantiated generically by lossy trapdoor primitives, including lossy trapdoor functions (LTFs) and identity-based (lossy) trapdoor functions (IB-LTFs). We also demonstrate that the lattice-based ABO-LTFs proposed by Alwen et al. (CRYPTO'13) are aABO-LTFs. Several existing CCA-secure KEM and DPKE schemes can be described by our generic constrictions. Therefore, our work unifies these seemingly unrelated schemes and explains the design principles behind these schemes.
Hardware-friendly model compression technique of DNN for edge computing

The research proposes a design methodology to compress the existing DNN models for low-cost edge devices. To reduce the computation complexity and memory cost, several novel model compression techniques are proposed. (1) A DNN model used to conduct image classification tasks is quantized into integer-based model for both the inference and training. 8-bit quantization is chosen in this work to balance the model training accuracy and cost. (2) A stochastic rounding scheme is implemented during the gradient backpropagation process to relieve the gradient diminishing risk. (3) To further reduce the training error caused by the gradient diminishing problem, a dynamic backpropagation algorithm is implemented. By dynamically scaling the magnitudes of gradient during the backpropagation, e.g. enlarging the magnitude of the gradient when it's too small to be quantized, it can effectively overcome the information loss due to the quantization error. As a result, such a DNN model for image classification is quantized into 8-bit model including training, which reduces the computation complexity by 8X and decreases the memory size by 6X Owing to the proposed dynamic backpropagation and stochastic training algorithms, the gradient diminishing issue during backpropagation is relieved. The training speed is reduced by 3X while classification error rates of state-of-art databases, e.g. ImageNet and CIFAR-10, are maintained similarly compared to the original model without quantization.
Peculiarities of Interaction Among Professional Educational Institutions, Employers and Social Partners

The social order posed by the society to the training of highly qualified personnel for the strategic needs of the labor market is associated with the changes in the economy, including the processes of its computerization and digitalization. Transformations in the digital economy determine new requirements for specialists’ training, their competences and qualification. The rapid changes in socio-economic conditions cause the need to transform the system of vocational training in order to meet the demands for competencies that correspond to the current technologic trends and methods of production. The reforms of vocational education system can be significant in resolving contradictions between the quality of training, on the one hand, and public and employers’ demands, on the other hand. Close cooperation of professional educational institutions, employers and social partners through their joint design and development of teaching technologies and methods for advanced vocational training of qualified specialists can be considered as one of the most efficient factors and conditions for resolving the above mentioned contradictions. These new conditions have already been created in the Centers for Advanced Vocational Training (CAVT), which can be characterized as a new type of infrastructural solution to the problem of aggregation of advanced vocational training programs and material and technical resources owned by science, education, production. The article focuses on some features of interaction and cooperation among vocational educational institutions, employers and social partners (social and public-private partnerships, networking cooperation, educational and technological cluster), which are taken into account in the performance of the Center for Advanced Vocational Training of the Kemerovo region.
A Robust Zero Watermarking Algorithm for Medical Images Based on Tetrolet-DCT

The rapid development of the information age of big data has promoted the development of traditional medicine and online diagnosis, as well as aroused concerns about the privacy of patient information. Aiming at the hidden dangers of copying, cutting, tampering, etc. in the dissemination of medical images, combined with the characteristics of medical images, this paper proposed a novel zero-watermarking algorithm based on Tetrolet-DCT, focusing on how to improve its security. Logistic map chaotic encryption is performed on the watermark, the Tetrolet-DCT transform is used to extract the visual feature of medical image, and the watermark information is embedded and extracted by combining the zero-watermarking technology. Experimental data show that the algorithm proposed in this paper can effectively extract the watermark without any changing of the medical image, and has a higher NC value under conventional attacks and geometric attacks. it has better invisibility and robustness.
Modulation Classification of Active Attack Signals for Internet of Things Using GP-CNN Network

The traditional modulation classification method is difficult to cope with the changing wireless electromagnetic environment and the complex signal model. On this basis, this paper proposes a data-driven automatic modulation classification (AMC) method using a global pooling-based convolutional neural network (GP-CNN). Stepping convolution is used to replace the pooling layer to avoid loss of signal details and global pooling (GP) is utilized to replace the fully-connected for a lower computational complexity. Simulations verify the superiority of the proposed method, which outperforms other deep neural network methods and approaches the optimal bound of the maximum likelihood method. Moreover, the influence of the network parameters on performance is also explored.
Population network structure impacts genetic algorithm optimisation performance

A genetic algorithm (GA) is a search method that optimises a population of solutions by simulating natural evolution. Good solutions reproduce together to create better candidates. The standard GA assumes that any two solutions can mate. However, in nature and social contexts, social networks can condition the likelihood that two individuals mate. This impact of population network structure over GAs performance is unknown. Here we introduce the Networked Genetic Algorithm (NGA) to evaluate how various random and scale-free population networks influence the optimisation performance of GAs on benchmark functions. We show evidence of significant variations in performance of the NGA as the network varies. In addition, we find that the best-performing population networks, characterised by intermediate density and low average shortest path length, significantly outperform the standard complete network GA. These results may constitute a starting point for network tuning and network control: seeing the network structure of the population as a parameter that can be tuned to improve the performance of evolutionary algorithms, and offer more realistic modelling of social learning. 1
Data Protection and Data Privacy Act for BIG DATA Governance

Usual data will reveal the basic information of the data provider, whereas big data can be used to scrutinize for understandings and to provide tips for enhanced strategic decision making. As a result, implementing data protection and data privacy acts on big data becomes essential to safeguard this complex information from corruption, compromise, or loss. The significance of data protection upsurge as the volume of data increases and stored continues to grow at exceptional rates. In this chapter, the author discusses the governance of big data using data protection and privacy acts and proposes some ideas how these acts can be deployed in the future. In addition, the author also presented the current General Data Protection Regulation (GDPR) which is commonly used to protect and safeguard individual and organizational data. Moreover, the author also presented some latest data security technologies that are being deployed in digital era and how it can be useful for big data analytics.
IoT solution information security certification conceptual framework

The rapid growth of Internet of Things (IoT) solutions development and the rise of agile development utilization, combined with the so-called "low touch economy" and the recent discussions on privacy and data protection brought several demands related to Information Security. Despite the existence of several efforts – either academic or not – focused on the definition and implementation strategies for certification of Information Security models designed for Information Technology and Communications (ICT) solutions, these aren't widely adopted. In addition, there are significant differences between typical IoT solutions and ICT solutions as traditionally presented, which ends up demanding different certification strategies. Continuous and more dynamic certification models (using cutting edge technologies such as blockchain, self-regulation, analytics, and artificial intelligence) are demanded in this context. This work discusses more effective forms of certification, using innovative edge concepts and technologies, at first aiming to identify a set of inhibiting factors, offenders, challenges or issues that need to be addressed correctly when developing an effective large-scale security certification model.
Use of big data for estimation of impacts of meteorological variables on environmental radiation dose on Ulleung Island, Republic of Korea

In this study, the relationship between the environmental radiation dose rate and meteorological variables was investigated with multiple regression analysis and big data of those variables. The environmental radiation dose rate and 36 different meteorological variables were measured on Ulleung Island, Republic of Korea, from 2011 to 2015. Not all meteorological variables were used in the regression analysis because the different meteorological variables significantly affect the environmental radiation dose rate during different periods, and the degree of influence changes with time. By applying the Pearson correlation analysis and stepwise selection methods to the big dataset, the major meteorological variables influencing the environmental radiation dose rate were identified, which were then used as the independent variables for the regression model. Subsequently, multiple regression models for the monthly datasets and dataset of the entire period were developed.
Joint workshop on Games-Human Interaction (GHItaly21) and Multi-party Interaction in eXtended Reality (MIXR)

This paper shortly summarizes the research lines underlining the joint workshop held at CHItaly 2021 with title “Joint workshop on Games-Human Interaction (GHItaly21) and Multi-party Interaction in eXtended Reality (MIXR)”, and sketched the expected contributions.
Time-Delay Temperature Control System Design based on Recurrent Neural Network

A Recurrent Neural Network (RNN) is a special neural network sequence model that is very suitable for dealing with time series tasks. In various industrial processing systems, it has achieved good performances. In this paper, a RNN model which is driven by an ideal reference model is proposed for the single-input single-output(SISO) temperature control system with time-delay. An ideal reference model is introduced to provides a more valuable teaching signal for helping RNN controller to obtain higher learning efficiency and providing suitable control input to the temperature control system. Meanwhile, Adam optimization algorithm which can get adaptive learning rates is used to update parameters and improve the control performance of the RNN. Further, a classical integral proportional derivative (I-PD) controller is designed to reduce the effects caused by the temperature setting value kick during the RNN learning period. Simulations were developed under the MATLAB environment to evaluate the proposed control system performance. In order to demonstrate the efficiency and application of the proposed RNN control method, the simulation results based on the actual temperature model are compared quantitatively.
COMPARATIVE ANALYSIS OF PERFORMANCE BETWEEN ECMP AND NTH METHODS IN IMPLEMENTATION OF MICROTIC-BASED DUAL LINK LOAD BALANCING TECHNIQUES

Load balancing is a technique within the network that functions to divide the network into two or more links with the aim that internet access needed by clients on the network does not experience problems. However, there are various methods that can be used in the load balancing technique, so it is necessary to test the performance of these methods in order to choose which method has better performance. In this study, the methods implemented in the load balancing technique are ECMP and NTH. The research methodology used is qualitative and comparative, and PPDIOO as a network development model. By applying the comparative method, there is a comparison of the results of testing load balancing techniques using the ECMP and NTH methods. Testing is done by downloading files in two different sizes, with criteria less than 100 MB and more than 100 MB. Performance analysis of Quality of Service (QoS) is based on four parameters. There are throughput, delay, jitter and packet loss. The results of the Quality of Service (QoS) load balancing technique are the best after being tested and compared based on predetermined parameters. It concludes that the NTH method is slightly better than ECMP because it has a slightly larger throughput value.
An operation to promote diversity in evolutionary algorithms in a dynamic hybrid island model

Currently, there is a considerable variety of Evolutionary Algorithms (EAs) and due to their performances some of them become more popular. EAs can be implemented in different ways, such as the Island Model (IM). However, despite the good performance of some EAs and the possibilities of varying their implementations, they can converge to a local optimum mainly because of the loss of diversity in the population. This work proposes an operation for a dynamic hybrid IM (D-IM), aiming to promote diversity to the population if it is converging to a certain portion of the search space. Thus, the D-IM reacts to the possible local convergence of its population, in addition to adjust the topology according to the EAs in the islands. The results demonstrated that the proposed operation can improve the efficiency of the D-IM search process and be competitive for solving bounded constrained optimization problems.
iDeal Mobile Food and Beverage Platform

The development of mobile payment in the country is significant each year, has become a trend and lifestyle among the people with a user level reaching 70% by the younger population and 67% by rising digital usage. One of the mobile payment service users that is widely used after transportation is as a means of payment for food delivery and on site dining. From the 2018 Nielsen Survey results, 41% of consumers claimed to have used food delivery services. Based on the JakPat Survey that 36.9% of consumers use mobile payment services for on site dining. Seeing the great opportunities and problems that are often repeated experienced by food &amp; beverage businesses, with the Ideal payment platform, a new innovation business model that can overcome the problems that are often experienced by food &amp; beverage businesses. Ideal Payment is a mobile digital platform application engaged in food &amp; beverage that connects customers and merchants in making ordering and payment transactions using a mobile smartphone without going through the cashier, Ideal payment platform that is equipped with ordering features such as (pickup, pre order and pay at table ) and can help improve the productivity of queue management and customer engagement management. The ideal payment platform can be used in various business types such as (restaurants, fast food, food court, dessert &amp; cakes and coffee shops). The feasibility of the Ideal payment platform business model shows that this business model can be run based on the NPV results with a positive value of NPV&gt; 0 which is Rp. 9,162,356,129
Enhancement of Degraded Images via Fuzy Intensification Model

The poor conditions of weather dust substantially reduce the overall quality of both the images taken, thus preventing useful image data from is being detected. A simple membership function is used in the proposed technique to set the pixels of a given channel to the range of zero to one, fluctuating intensifying operators applied according to various threshold and a new adjustment method designed specifically for this technology. Fuzzy theory provides a major issue—solving method between classical mathematics accuracy and the real world ‘is inherent imprecision. Fuzzy logic addresses the study of potential logic or several valued logics; instead of specified and accurate rationale, it applies approximation. This research aims to check the processing capability of the method proposed, whereby the findings acquired are able to filter the numerous degraded images.
A Patent Application for NEXTGEN Flood Early Warning System

This design fiction re-imagines an important informational element of the flood early warning system in order to unpack some of the questionable assumptions that society makes about disaster. In presenting an updated, ironic, vision of an alternative system, we highlight some of the ways that received ideas about the root causes of disaster, who is responsible for public safety, and the role of private sector innovation, are so embedded in the design of technologies used in crisis management that they have become taken for granted. This work demonstrates the potential for design fiction to serve as a tool in the evaluation and critique of safety-critical information systems and as a communication tool for conveying the complex findings of disaster research. It also points to new avenues of exploration for crisis informatics work on public warning systems.
An Experimental Study of the Vibrational Characteristics of a Diamond Circular Blade Using Electronic Speckle-Pattern Interferometry and FEM

Abstract The compact installation and technology for determining vibration characteristics by the ESPI method has been created. The experimental determination of the dynamic characteristics of a diamond circular blade with a diameter of 203.4 mm and a thickness of 1.19 mm using real-time electronic speckle interferometry is presented. 15 mode shapes of vibration were detected in the range from 100 to 5000 Hz. The program calculation of the natural frequencies and mode shapes is carried out for three values of the clamping inner diameter (42 mm, 44 mm, 46 mm). The options for calculating a disk with a rim and without a rim are considered. It is shown that the minimum mean squared error of the calculation is achieved for the values of the diameter of the disk 46 mm, 42 mm and 44 mm for the number of nodal circles 0, 1 and 2, respectively. To verify the accuracy of the interferometer, experimental, computational and analytical studies of console steel rod 200 × 22.25 × 3.78 mm in size were carried out.
Immunity Estimation of Abandoned Object Detection Video System to Environmental Impact

This article is devoted to the analysis both the environmental condition influences and different live scenarios of scene on the algorithm robustness of typical video surveillance camera which implement the abandoned object detection feature. Present study judge algorithm robustness, which also can be related to system immunity, as a percentage of correctly detected events over all implemented events. The study was carried out with the use of typical IP camera and doubled with the same studies by simulated MATLAB model of such a system to compare the immunity of real IP camera accuracy over math model. The obtained results can be used during the technical maintenance and service implementations for IP video tracking, as well as during further research aimed at increasing the robustness of such a systems.
Customized Summarizations of Visual Data Collections

Abstract We propose a framework to generate customized summarizations of visual data collections, such as collections of images, materials, 3D shapes, and 3D scenes. We assume that the elements in the visual data collections can be mapped to a set of vectors in a feature space, in which a fitness score for each element can be defined, and we pose the problem of customized summarizations as selecting a subset of these elements. We first describe the design choices a user should be able to specify for modeling customized summarizations and propose a corresponding user interface. We then formulate the problem as a constrained optimization problem with binary variables and propose a practical and fast algorithm based on the alternating direction method of multipliers (ADMM). Our results show that our problem formulation enables a wide variety of customized summarizations, and that our solver is both significantly faster than state‐of‐the‐art commercial integer programming solvers and produces better solutions than fast relaxation‐based solvers.