Reviewerly/exHarmony huggingface dataset:
Discovery of Potential High Utility Itemset from Uncertain Database using Multi Objective Discrete Differential Evolutionary Algorithm

In this research, we use the Multi-Objective Discrete Differential Evolutionary Algorithm (MODDEA) method to efficiently mine the high utility itemset mining (HUIM) in a limited period based on the mining of potential high utility itemset from uncertain dataset. MODDE-based HUI miners use five key processes and two objective functions. Both fitness value and uncertainty probability of each item is given equal importance while mining Potential High Utility Itemset (PHUI) from uncertain dataset. The suggested approach is tested on the Retail Utility dataset, which is a real-world example. Utility threshold is not required to mine the PHUIs.
Digital risk distribution and COVID-19: How contact tracing is promoted as a solution to equilibrate public health and economic prosperity during pandemics

Digital contact tracing appears as an ideal solution to tackle long-term economic damage due to necessary lockdown measures during a pandemic. This essay shows that the challenge of balancing citizen's health and a functioning society is not just coming up today. Commercial centres were already in the Middle Ages worried about their economic prosperity and adopted isolation measures. Although there are much more data available today, pandemic preparedness remains constrained by temporal and spatial realities, thus limiting public health management to the national state. Based on the examples of China and Switzerland, we elaborate on how individual and collective needs can be balanced differently regarding the implementation of a digital contact tracing system. While China's Health Code App is close to social surveillance, Switzerland has turned away from Europe to develop its own Swiss solution due to disagreement about data protection. It becomes clear that the attempts to properly balance public health and economic prosperity during a pandemic must be constantly readjusted and cannot simply be delegated to a digital technology.
Reading-Assistance Tools Among Deaf and Hard-of-Hearing Computing Professionals in the U.S.: Their Reading Experiences, Interests and Perceptions of Social Accessibility

Automatic Text Simplification (ATS) software aims at automatically rewrite complex text to make it simpler to read. Prior research has explored the use of ATS as a reading assistance technology, identifying benefits from providing these technologies to different groups of users, including Deaf and Hard-of-hearing (DHH) adults. However, little work has investigated the interests and requirements of specific groups of potential users of this technology. Considering prior work establishing that computing professionals often need to read about new technologies in order to stay current in their profession, in this study, we investigated the reading experiences and interests of DHH individuals with work experience in the computing industry in ATS-based reading assistance tools, as well as their perspective on the social accessibility of those tools. Through a survey and two sets of interviews, we found that these users read relatively often, especially in support of their work, and were interested in tools to assist them with complicated texts; but misperceptions arising from public use of these tools may conflict with participants’ desired image in a professional context. This empirical contribution motivates further research into ATS-based reading assistance tools for these users, prioritizing which reading activities users are most interested in seeing the application of this technology, and highlighting design considerations for creating ATS tools for DHH adults, including considerations for social accessibility.
Augmented Reality in the Learning Sciences

Augmented reality (AR) combines digitally generated 3D content with real-world objects that users are looking at. The “virtual” computer-generated 3D content is overlaid on a view of the real world through a specialized display. All augmented reality technologies involve some form of display technology that combines real and virtual content – including headset devices, camera-enabled smartphones and tablets, computer-based webcams, and projectors displaying interactive images on a physical surface. These technologies support real-time tracking of hands, 3D objects, and bodies as they push on or touch virtual objects. This enables a more-natural interaction between the learner and the virtual content. AR technologies support learning by allowing learners to interact with 3D representations; they enable embedded assessments; they support groups of learners engaging with shared virtual objects; and they tap into a child’s natural inclination to play and experiment by moving around and touching and manipulating objects.
A roadmap for present focus and future trends of blockchain technology in the built environment

Blockchain technology has gained attention across the globe within the last 10 years and has taken centre stage in the financial technology sector. Even though innovation that applies to the construction industry is sometimes different from what is obtainable in other sectors, they share the same unique features. Therefore, this makes blockchain technology relevant to the built environment. This study aims to assess the research studies carried out on blockchain technology in the built environment domain in a bid to draw knowledge from the present focus of these studies while identifying research gaps for future studies. To achieve this, a scientometric review was carried out, and SCOPUS database was searched for studies related to blockchain technology in the built environment. The retrieved documents showed the first publication on this study area was made in 2017. Hence, this study covered research publications from 2017 to 2021. The findings revealed that most of these studies are not experimental as they only rely on literature from other sectors to draw conclusions for the built environment. The study further revealed that most of the publications in this domain are from the USA, Australia, India and the United Kingdom with only two publications from the African continent. This shows a research gap that can be explored from the African perspective. From the cluster analysis, research in this domain has focused primarily on blockchain technology components and only a few practical fields of its application. It was thus recommended that experimental and case study research should be carried out on its application in asset management and transfer, smart city development, land use, data/information management, cyber-physical systems, among others. Practically, the study highlighted blockchain technology usefulness in promoting social and economic sustainability of the built environment through its different applicabilities.
Refractive Index Finding using Multiple Light Reflections with Modified Sagnac Interferometer

Refractive index finding using multiple light reflections with modified Sagnac interferometer is proposed here. The thickness of the material and fringes pattern are fundamental parameters in such measurement. In this proposed technique, multiple light reflections, by the two parallel mirrors attached to a piezo-electric transducer (PZT) vibrating at a frequency of 420Hz, introduced the phase retardation. The phase retardation and reflected angles could be found and employed for refractive index determination. Four identical interferograms were obtained by a half-wave plate (HWP) oriented at 0, π/2, π and 3π/2 rad with respect to the incident plane. The intensities at their respective patterns were evaluated and the phase shift could be computed. Using numerous reflections with a Sagnac interferometer, this technique is presented to obtain multiple fringes with a sample fixed in position at certain carrier frequencies.
Comparative Study and Analysis of SddNet Model for Real Time Object Detection

The quantity of outwardly Visually impaired people in India would increment to 24.1 million of every 2010 and to 31.6 million out of 2020 around 90 percent of the world&#x0027;s outwardly disabled live in non-industrial nations. They can confront many difficulties when they approach their day by day exercis es themselves in their home climate. Sight is the vital human sense and assumes a significant part in human view of the climate. In this manner, there is a wide assortment of PC vision items and administrations that are utilized in the advancement of new electronic gadgets for the visually impaired. This framework depends on vision enhancer module explicitly blind casualties and outwardly weakened individual. It planned in a manner by which the outwardly weakened individual can take help of outsider application which sends ongoing edges to the PC based remote organized framework. It chips away at Real-Time object discovery utilizing SSD_MOBILENET calculation and TENSORFLOW APIs
A service recommendation system based on rough multidimensional matrix in cloud-based environment

In order to efficiently and accurately realize the service recommend to cloud-based environment, a collaborative filtering algorithm using rough decision mechanism in handling the uncertainty is established. We proposed a service recommendation system in cloud-based environment that helps a user to select the best services from different cloud providers that match user requirements. The new system combines items' attributes information (decision tags) and user's attribute information (decision times), achieves high performance and alleviates the cold-start problems of recommendation systems. A rough multidimensional matrix model based on cooperative filtering algorithm is proposed. Specifically, we design useful selection criteria based on items' attributes and users' attributes, and combine the criteria in an optimization framework of cloud-based environment. Experiments show that this method can effectively solve the cold start problem of new projects, and is more accurate than the traditional collaborative filtering algorithm.
Sequence recommendations for groups: A dynamic approach to balance preferences

We are living in the age of recommendations: it has been estimated that two-thirds of the films viewed on Netflix come from recommendations while the 35% of Amazon sales regard goods suggested to users. There are many factors to consider when providing a new suggestion: in addition to being useful, it should also be relevant and serendipitous, starting from historical data previously collected. In particular, the notion of context has to be considered since it induces some dynamic aspects in the definition of user preferences. The role of context becomes particularly important when we shift from single (myopic) suggestions to be provided to an individual user, to sequences of recommendations for groups of users. When the preferences of individual users are combined to define the preference of a new ephemeral group, dynamic contextual concerns have to be considered in order to provide the best possible experience and extend the group life, preventing the defection of some members because their preferences are not balanced. In this paper we introduce our proposal for producing sequences of recommendations for groups of users which is based on the Multi-Objective Simulated Annealing optimization technique and takes into account dynamic aspects. Moreover, we propose some strategies for extracting the required dynamic information from log data typically available and present the experimental results of the application of our approach in some real-world case studies.
Image Forgery Detection Using Metadata Analysis and ELA Processor

Nowadays, many fake pictures are spreading through computerized media these days. Recognition of such phony pictures is inescapable for the uncovering of picture-based cybercrimes. Producing pictures and distinguishing them are capable investigation regions in this computerized time. The forged pictures are recognized by using neural networks that furthermore distinguishes the areas of the image that have been controlled and reveals the segments of the first picture. It inclines to be executed on desktop application on Java stage and consequently made available to regular clients. The compression percentage of the unfamiliar substance in a fake picture is unique to that of the first image and is distinguished using error level analysis. Another component used along with compression percentage is image metadata. Regardless of the way that it is possible to change the metadata content making it unreliable isolated, here, it is used as a secondary limit for blunder level examination decisions. Image fabrication has become a significant issue as many individuals utilize these controlled pictures and use them to misdirect others in numerous circumstances. To conquer this sort of issue, we concocted a utilization of "image forgery detection" where this application will distinguish the picture is genuine or fake using an ELA processor.
A Novel Memetic Algorithm Based on Multiparent Evolution and Adaptive Local Search for Large-Scale Global Optimization

In many fields, including management, computer, and communication, Large-Scale Global Optimization (LSGO) plays a critical role. It has been applied to various applications and domains. At the same time, it is one of the most challenging optimization problems. This paper proposes a novel memetic algorithm (called MPCE & SSALS) based on multiparent evolution and adaptive local search to address the LSGO problems. In MPCE & SSALS, a multiparent crossover operation is used for global exploration, while a step-size adaptive local search is utilized for local exploitation. A new offspring is generated by recombining four parents. In the early stage of the algorithm execution, global search and local search are performed alternately, and the population size gradually decreases to 1. In the later stage, only local searches are performed for the last individual. Experiments were conducted on 15 benchmark functions of the CEC'2013 benchmark suite for LSGO. The results were compared with four state-of-the-art algorithms, demonstrating that the proposed MPCE & SSALS algorithm is more effective.
Self-Supervised Depth Enhancement

Most of the existing learning-based depth enhancement techniques train in a supervised fashion. These techniques extensively rely either on synthetic datasets for noise and artefact free depth maps or, on pre-processing of the real-world depth maps to mimic artefact-free depth maps. The results from these techniques although promising might not optimally generalize to the vagaries of real-world settings. In this paper, we propose a technique to enhance depth maps in a self-supervised way, which enables us to work directly on the real-world depth maps captured by commercial RGB-D cameras which inherently contains artefacts. To achieve self-supervision, captured depth maps are initially degraded by adding more hole-artefacts based on edge-information obtained from the corresponding RGB image, the training is then done by allowing the model to learn to estimate depth in the regions of holes by a masking process. Through generalization across a large training set, we are able to predict accurate depth values in the regions of hole-artefacts without even observing a single artefact-free depth map during training. The proposed method is able to circumvent the dependence on synthetic datasets and/or complicated setups to capture accurate depth maps by working only on real-world, incomplete data in a self-supervised manner.
Profiling digital nativeness of pre-service teachers: validity evidence for the Vietnamese Digital Natives Assessment Scale (V-DNAS)

With the increasing significance of technology use in both daily life and education, the digital native assessment scale (DNAS) [developed by Teo, T. (2013). An initial development and validation of a Digital Natives Assessment Scale (DNAS). Computers & Education, 67, 51–57.] has been widely used as a tool to investigate the digital nativeness of different subjects/participants and in different languages worldwide. The current study aimed to continue exploring the cross-cultural invariance of DNAS in the Vietnamese version (V-DNAS) and examine the digital nativeness of Vietnamese pre-service teachers. Using two sources of validity evidence from cognitive response processes and the internal structure of validity of DNAS, the study findings showed that the DNAS, in general, had an adequate model fit in the Vietnamese version, which contributed to confirm the invariance of the scale in different languages. The study also showed that Vietnamese pre-service teachers endorsed all elements of digital nativeness in general, with a significant difference by students' geographic locations. The study provided contributions for scale validation research and teacher education, alongside suggestions for future research to avoid its limitations.
Cryptanalysis of SIMON (32/64) Cipher Using Satisfiability Modulo Theories

SMT stands for Satisfiability Modulo Theories, and it is an efficient framework for solving constraint satisfaction challenges expressed in first-order logic. It uses for software and hardware verification. We show the power of SMT solvers in cryptanalysis specific block ciphers. We represent a block cipher in Boolean equations and translate them into a suitable format that is solved by existing SMT solver and recover the key. The fact that our approach requires a plaintext–ciphertext pair to extract the potential key is a significant feature. We demonstrate the cryptanalysis of the lightweight SIMON block cipher using the proposed procedure. We compare the results of known plaintext attacks on SIMON using the z3py tool up to 8 rounds.
The fundamental role of density functions in the binary classification problem

In biomedicine, binary classification problems are involved in diagnostic but also, for instance, in personalized medicine. The objective is to use information for correctly allocating subjects in groups. Frequently, this information implies high-dimensional data. An adequate classification rule is a trade-off between the sensitivity and the specificity. The ROC curve helps to understand, evaluate and compare the accuracy of classification processes. We propose a procedure for estimating the optimal classification rules based on a penalized estimator of the underlying probability distribution functions. We study its asymptotic properties. Through Monte Carlo simulations, we compare our proposal with a support vector machine-based ROC curve. We illustrate its practical use in a real-world problem. Results suggest that, despite some techniques promise to improve the results provided by traditional methods, in the binary classification problem, the limit is the actual relationship among the density functions.
The effectiveness of a hybrid Diffie-Hellman-RSA-AES model

With the emerging of quantum PCs that have incredibly strong attributes, the Diffie-Hellman (DH) is an encryption algorithm set of rules used to guarantee communication between two users through a communication channel that isn&#x0027;t always secure, although this algorithm has sometimes been effective at some point, but it is more vulnerable, at the DH level there is no mechanism to guarantee key exchange security, that is why they can be intercepted by a man in the middle. Several studies have been conducted in this context in which our Diffie-Hellman&#x005F;RSA&#x005F;AES (DRA) model is a mixture model that joins DH, RSA, and AES encryption algorithm. This model means to further develop security whileexchanging DH keys. The outcomes acquired show the viability of the techniques executed by the proposed model. In this article, we perform a comparative study between DH and DRA to analyze the reliability stage of every version a stimulation that shows the advantage of our DRA model compared to DH in this case we have proposed DRA as an alternative model to replace DH.
Observing wave–particle behavior of entangled atoms in cavity assisted delayed-choice experiment

A scheme is proposed to study the wave–particle duality of two entangled atoms in a cavity-QED framework. Two atoms may exhibit a mutating trend between particle and wave behaviors. This mutating behavior can be post-selected after the atomic states have been recorded on the state selective detectors. In the present proposal, second Hadamard transformation is always present, whereas the probabilities are the same as that in the case, where the state of the second Hadamard can be controlled by an ancilla. The whole scheme revolves around resonant, off-resonant, and Ramsey interactions of two level atoms with the field. To observe the desired behavior, cavity assisted atom-field multiple swappings are employed for tagging as well as for the better control and good fidelity.
EchoIA: A Cloud-Based Implicit Authentication Leveraging User Feedback

Implicit authentication (IA) transparently authenticates users by utilizing their behavioral data sampled from various sensors. Identifying the illegitimate user through constantly analyzing current users’ behavior, IA adds another layer of protection to the smart device. Due to the diversity of human behavior, existing research tends to utilize multiple features to identify users, which is less efficient. Irrelevant features may increase the system delay and reduce the authentication accuracy. However, dynamically choosing the best suitable features for each user (personal features) requires a massive calculation, making it infeasible in the real environment. In this paper, we propose EchoIA to find personal features with a small amount of calculation by leveraging user feedback derived from the correct rate of inputted passwords. By analyzing the feedback, EchoIA can deduce the true identities of current users and achieve a human-centered implicit authentication. In the authentication phase, our approach maintains transparency, which is the major advantage of IA. In the past two years, we conducted a comprehensive experiment to evaluate EchoIA. We compared it with four state-of-the-art IA schemes in the aspect of authentication accuracy and efficiency. The experiment results show that EchoIA has better authentication accuracy (93%) and less energy consumption (23-h battery lifetimes) than other IA schemes.
An empirical study for density peak clustering

Density Peak Clustering (DPC) is one of the most effective density-based clustering algorithms due to its ability to detect arbitrary clusters while being robust to noise. Since the first introduction in 2014, it has been cited a thousand times. This paper presents a comprehensive analysis of the DPC algorithm’s performance on some UCI and Gaussian data sets. These used data sets have different properties such as intersecting clusters, unbalanced data, or different densities, such that not many clustering algorithms can perform well. From the obtained results, we aim to evaluate advantages and disadvantages of the algorithm and propose some open research directions.
Detecting and modeling changes in a time series of proportions

We propose a framework to detect and model shifts in a time series of continuous proportions, that is, a vector of proportions measuring the parts of a whole. By reparameterizing the shape of a Dirichlet distribution, we can model the location and scale separately through generalized linear models. A hidden Markov model allows the coefficients of the generalized linear models to change, thus allowing for the time series to undergo multiple regimes. This framework allows a practitioner to adequately model seasonality, trends, or include covariate information as well as detect change points. The model's behavior is studied via simulation and through the analysis of lake phytoplankton data from 1992 through 2012. Our analyses demonstrate that the model can be effective in detecting and modeling changes in a time series of proportions. Pertaining to the phytoplankton data, the overall biomass has grown with some changes to the community level dynamics occurring circa 2000. Specifically, the proportion of cyanobacteria appears to have increased to the detriment of diatoms.