Reviewerly/exHarmony huggingface dataset:
The Role Of Blockchain Technology In Decentralized Real Estate Marketplace: Recent Findings

The share of international Real Estate (RE) equities is twice as high as financial markets. Despite this, due to the liquidity and lack of worldwide accessibility of RE, the number of investors is substantially fewer. In the existing structure, tenants, landlords, and investors are barely content. In recent days Blockchain technology is playing a vital role in the building environment where dependability of transaction attainment is predominant. Blockchain facilitates decentralized, cryptographically secured, trustworthy, and immutable record-keeping of transactions. However, more research exploration is much required to understand the process and complications in implementing blockchain solutions in the built environment. The proposed research in this article is aimed to evaluate the existing use of Blockchain platforms in the case of the Real Estate sector and recognize the benefits, feasibility, and scope for future work. The contribution to the knowledge of this paper is in the demonstration of the process to follow to implement a blockchain solution for a Real Estate marketplace. The findings provide the significance and the vital role of the potential applications of blockchain in the built environment.
Image-Goal Navigation in Complex Environments via Modular Learning

We present a novel approach for image-goal navigation, where an agent navigates with a goal image rather than accurate target information, which is more challenging. Our goal is to decouple the learning of navigation goal planning, collision avoidance, and navigation ending prediction, which enables more concentrated learning of each part. This is realized by four different modules. The first module maintains an obstacle map during robot navigation. The second predicts a long-term goal on the real-time map periodically, which can thus convert an image-goal navigation task to several point-goal navigation tasks. To achieve these point-goal navigation tasks, the third module plans collision-free command sets for navigating to these long-term goals. The final module stops the robot properly near the goal image. The four modules are designed or maintained separately, which helps cut down the search time during navigation and improve the generalization to previously unseen real scenes. We evaluate the method in both a simulator and in the real world with a mobile robot. The results in real complex environments show that our method attains at least a 17% increase in navigation success rate and a 23% decrease in navigation collision rate over some state-of-the-art models.
A Methodology for Mapping Perceived Spatial Qualities

This manuscript proposes a five-step methodology that enables the mapping of perceived spatial qualities. To achieve such a goal, crowdsourcing and neural network methods were used. Crowdsourcing enables gathering data from people, and neural networks facilitate extending that knowledge to perform automatic classifications. The proposed method is then applied in the implementation of two use cases: perceived safety and perceived atmosphere of urban spaces. The use cases were conducted in the frame of the project Streetwise with a project partner that had the goal of creating the first maps of perceived spatial quality in Switzerland. The results obtained from the use cases showed that the application of the proposed methodology grants capturing the perceptions of a collective accurately.
Latent Semantic Based Fuzzy Kernel Support Vector Machine for Automatic Content Summarization

Recently, the bounteous amount of data/information has been available on the Internet which makes it very complicated to the customers to calculate the preferred data. Because the huge amount of data in a system is mandated to discover the most proper data from the corpus. Content summarization selects and extracts the related sentence depends upon the calculation of the score and rank of the corpus. Automatic content summarization technique translates from the higher corpus into smaller concise description. This chooses the very important level of the texts and implements the complete statistics summary. This paper proposes the novel technique that employs the latent semantic analysis (LSA) method where the LSA is derived from natural language processing. Also, it depends upon the particular threshold provided with the device. Statistical feature based model used to compact with inaccurate and ambiguity of the feature weights. Redundancy is removed with cosine similarity and it was presented an enhancement to the proposed method. Finally, fuzzy kernel support vector machine approach of machine learning technique is applied, so this novel model trains the classifier and predicts the statistics summary. This paper focuses to compare together with the another summarization dataset DUC (Document Understanding Conference) like ItemSum, Baseline, Summarizer, Recall Oriented Understudy for Gisting Evaluation (ROUGE) S and ROUGE L on DUC2007. The experiments and result section displays that our proposed model obtains an important performance improvement over the other classifier text summarizes.
Achieving Conversational Goals with Unsupervised Post-hoc Knowledge Injection

A limitation of current neural dialog models is that they tend to suffer from a lack of specificity and informativeness in generated responses, primarily due to dependence on training data that covers a limited variety of scenarios and conveys limited knowledge.One way to alleviate this issue is to extract relevant knowledge from external sources at decoding time and incorporate it into the dialog response.In this paper, we propose a posthoc knowledge-injection technique where we first retrieve a diverse set of relevant knowledge snippets conditioned on both the dialog history and an initial response from an existing dialog model.We construct multiple candidate responses, individually injecting each retrieved snippet into the initial response using a gradient-based decoding method, and then select the final response with an unsupervised ranking step.Our experiments in goal-oriented and knowledge-grounded dialog settings demonstrate that human annotators judge the outputs from the proposed method to be more engaging and informative compared to responses from prior dialog systems.We further show that knowledge-augmentation promotes success in achieving conversational goals in both experimental settings.
Failure-aware Elastic Cloud Workflow Scheduling

With an increasing complexity and functionality in cloud data centers, fault tolerance becomes an essential requirement for tasks executed in clouds, especially for workflows with task precedences. Hosts and network devices are the main physical components in a cloud data center. The PB (Primary-Backup) model is a desirable approach to fault tolerance. Many PB-based workflow scheduling algorithms have been proposed for host faults. However, only a few studies focus on cloud workflow scheduling considering network device faults. This paper analyzes the fault-tolerant properties for scheduling dependent tasks and migrating VMs based on the PB model, considering both host and network device faults in a cloud data center. A failure-aware elastic cloud workflow scheduling algorithm is designed for both host and network device fault tolerance. Additionally, an elastic resource provisioning mechanism is proposed and incorporated into the proposed algorithm to improve resource utilization. Performance evaluations on both randomly generated and real-world workflows show that the proposal effectively improves resource utilization while guaranteeing fault tolerance.
A Modified Whale Optimization Algorithm as Filter-Based Feature Selection for High Dimensional Datasets

Convergence speed is a common issue in most meta-heuristic algorithms including Whale Optimization Algorithm (WOA). In WOA, the control parameter, $$a$$ , that decreases linearly could affect its convergence speed which resulted in low performance in finding better solutions for feature selection. Thus, a modified WOA (mWOA) is proposed by linearly increasing the control parameter value to solve the issue. mWOA is implemented as a filter-based feature selection method on 4 benchmark high-dimensional datasets (HDD). The performance of the selected features by mWOA was evaluated and compared against original WOA and without feature selection (No FS) using Naïve Bayes, C4.5, K-Nearest Neighbors, and Support Vector Machine classifiers. The features selected by mWOA produced promising classification accuracy as compared to WOA and No FS in 11 out of 20 test cases. Interestingly, mWOA managed to achieve the highest classification accuracy with 90.75% in the dataset that has the largest features. In the future, further improvement of mWOA is vital to overcome the convergence speed issue and decrease the computational time in HDD.
Toward Personalization of User Preferences in Partially Observable Smart Home Environments

The technologies used in smart homes have recently improved to learn the user preferences from feedback in order to enhance the user convenience and quality of experience. Most smart homes learn a uniform model to represent the thermal preferences of users, which generally fails when the pool of occupants includes people with different sensitivities to temperature, for instance, due to age and physiological factors. Thus, a smart home with a single optimal policy may fail to provide comfort when a new user with a different preference is integrated into the home. In this article, we propose a Bayesian reinforcement learning framework that can approximate the current occupant state in a partially observable smart home environment using its thermal preference and, then, identify the occupant as a new user or someone who is already known to the system. Our proposed framework can be used to identify users based on the temperature and humidity preferences of the occupant when performing different activities to enable personalization and improve comfort. We then compare the proposed framework with a baseline long short-term memory learner that learns the thermal preference of the user from the sequence of actions that it takes. We perform these experiments with up to five simulated human models each based on hierarchical reinforcement learning. The results show that our framework can approximate the belief state of the current user just by its temperature and humidity preferences across different activities with a high degree of accuracy.
Secure Key Management Based Mobile Authentication in Cloud

Authentication is important to the security of user data in a mobile cloud environment.Because of the server's user credentials, it is subject to attacks.To maintain data authentication, a novel authentication mechanism is proposed.It consists of three independent phases: Registration, login, and authentication and key agreement.The user registers with the Registration Center (RC) by producing a secret number that isn't stored in the phone, which protects against privileged insider attacks.The user and server generate a nonce for dynamic user identity and agree on a session secret key for safe communication.The passwords are not stored on the computer or provided in plain text, they are resistant to replay, guessing, and stolen verification attacks.The suggested protocol uses a one-way hash function and XOR operations, with the client having remote access to a large number of servers over a secure communication channel.Concentrates on HMAC and SHA3 for Collision Free Hashing and to overcome length extension attacks.HMACs are substantially less affected by collisions than their underlying hashing algorithms alone.So adding an HMAC to an MD5 or SHA hash would make it substantially more difficult to break via a rainbow table.
Future of Work: How Artificial Intelligence Will Change the Dynamics of Work Culture and Influence Employees Work Satisfaction Post-covid-19

This is evident that covid-19 has a devastating effect on businesses and employees worldwide. As we are slowly moving toward retrieval, it is very clear that the business and the way of doing work will change forever in the new normal phase. The experience of Covid-19 has drastically fastened the organization’s digital transformation. Thus, the purpose of our study is to explore the impact of AI-enabled work culture on employees’ satisfaction level post-pandemic. Companies now focus more on data-driven strategies and decision-making, which seems critical today for their business survival and success tomorrow. Few people may argue that technology has always been a part of an organization’s work culture then; what is new? Moreover, the answer will be the overnight shift of work on-site to work from home, leading to a drastic change in the work culture. The situation caused due to covid-19 has forced the employees and organizations to rely fully on technology, which had made the organizations adopt the use of technology as it was never. Organizations today are investing more in digitalization and automation to secure their business ventures in these shifting dynamics. However, how will AI change the dynamics of future work? As the digitalization and use of technology have evolved, AI development has increased the fear of losing their jobs in people’s minds influencing their work satisfaction. Therefore, our paper aims to study the impact of artificial intelligence on the work culture and its effect on employees’ work satisfaction. Moreover, the paper also discusses the related gap found in the skillset of employees and managers to work on AI and other related technologies during the pandemic and the probable solutions. The responses of 150 employees were collected, and the results have concluded that AI-enabled work culture will significantly impact the employees’ work satisfaction post-covid-19.
Privacy-Preserving Link Prediction in Multiple Private Networks

In many cases, a network may be dispersedly recorded by different participants and each participant is one part of the original network, and no one is willing to share its data due to commercial competition. Therefore, each participant forms an independent private network, and they form a “ multiple private networks” regarding the original network. Existing methods only use the structure of private network itself to predict missing links, leading to underutilized information and deteriorated prediction accuracy. One natural question arises: how to integrate the information of multiple private networks by formulating a security protocol, so as to help each private network to better predict missing links in its own network without disclosing its structure to others. To this end, we propose an SMPC-LP method based on secure multiparty computation (SMPC) to solve this problem. The method fuses the information of each private network without disclosing their inputs, and then, the similarity score of each node pair is jointly calculated, achieving enhanced link prediction performance in each private network. The experimental results show that the SMPC-LP method can better predict the missing links than the methods only using the information of the one private network, without violating data privacy agreement.
Eye Tracking Hardware: Past to Present, and Beyond

This chapter aims to present the past and current technologies that enable estimation of a person’s gaze point. The topic is quite broad, as many techniques may be taken into account. These techniques differ in accuracy and precision of measurement as well as in potential possible applications and—last but not least—vary substantially in the cost of usage. When considering any eye tracking experiment, it is essential to choose a sufficient tool for the given task, so this chapter includes advice on which parameters of eye trackers should be taken into account when planning eye tracking experiments.
Miniaturization of a coherent monocular structured illumination system for future combination with digital holography

Holographic and 3D-measurement processes are an often-used tool in industry, medicine, and scientific applications. While small deviations of objects can be visualized by holographic means with high accuracy, optical systems with active structured illumination are a reliable source of absolute 3D-information in these fields. The combination of digital holography with structured illumination allows to simultaneously measure deformations and absolute 3D coordinates but also requires coherent light and has already been demonstrated in principle with a stereo camera setup. Multi-camera systems are limited to certain setup sizes given by the volume and distance of the detectors. Reducing the system to a one-camera (monocular) setup reduces space and acquisition costs. By using a multi-aperture illumination source an extremely high projection rate could be realized and reduced to a monocular approach with a novel voxel-calibration technique, while the projection system itself still requires a large amount of space. In this paper we present a miniaturized, monocular 3D-measurement system that works with repeatable, coherent speckles, generated by a fiber-coupled laser whose light was distributed by a fiber-switch to a diffuser plate connected with a measurement-head, also including a camera. By addressing different fibers through the switch, varying but repeatable patterns are generated. The size of the device (diameter < 3 cm) is now mainly limited by the volume of the camera. A first 3D-reconstruction of an object and an outlook for a combination of this system with digital holography is given, allowing absolute 3D-coordinates and relative deviations of object points to be measured simultaneously.
Breast cancer invasive carcinoma detection using deep convolution neural networks

Breast cancer is among the most common causes of cancer death in women across the world.Invasive ductal carcinomas account for nearly Eighty percent of all breast cancers. Invasive ductal carcinoma may impact women of any age that becomes more likely as women get older. Early diagnosis improves the chances of getting the correct way therapy and surviving, but it's a time-consuming procedure that can lead to pathologist disputes. Computer-Aided Detection(CAD) systems have possibility for diagnosis of abnormalities and also improving accuracy. We devised a computational technique for classifying the histopathology cancer images using Deep CNN in this study. Eosin and Hematoxylin -Stained breast histology image dataset are used. Deep Neural Network Architectures and Random Forest classifiers are used.
Computing Stabilizing Feedback Gains via a Model-Free Policy Gradient Method

In spite of the lack of convexity, convergence and sample complexity properties were recently established for the random search method applied to the linear quadratic regulator (LQR) problem. Since policy gradient approaches require an initial stabilizing controller, we propose a model-free algorithm that searches over the set of state-feedback gains and returns a stabilizing controller in a finite number of iterations. Our algorithm involves a sequence of relaxed LQR problems for which the associated domains converge to the set of stabilizing controllers for the original continuous-time linear time-invariant system. Starting from a stabilizing controller for the relaxed problem, the proposed approach alternates between updating the controller via policy gradient iterations and decreasing relaxation parameter in the LQR cost while preserving stability at all iterations. By properly tuning the relaxation parameter updates we ensure that the cost values do not exceed a uniform threshold and establish computable bounds on the total number of iterations.
Anatomy of Virtual Machine Placement Techniques in Cloud

With consistent advancement in virtualization techniques, organizations are building up enhanced datacenters that are capable of maintaining impactful resource management, high-performance benchmarks, and controlled power consumption for eco-friendly computing. Virtual machine placement problem has a significant part in designing the datacenters. Placing virtual machines in the cloud can be very profitable and beneficial but it can be the cause of many new problems, if not performed properly. It comprises multiple complex relations and designing factors that directly affect the operating cost of datacenters. It bridges up the customers with cloud administrators to obtain preferences and SLA of both, to bring out an optimal solution. As optimization has given a boost to many businesses, an efficiently optimized VM placement technique has got a lot of potentials and can bring a drastic rise for many organizations running toward the cloud. A comprehensive study has been performed to bring out the important traits of different VM placement solutions by surveying cloud literature. By assessing the capabilities and objectives of different approaches and techniques, this paper presents an in-depth comparison, unveiling the drawbacks, and suggestions to improvise the methods in this direction.
A Novel Feature Descriptor: Color Texture Description with Diagonal Local Binary Patterns Using New Distance Metric for Image Retrieval

The growth of digital data exponentially accelerates with each passing day. A storage media database usually contains large amounts of images and information content, which must be located and retrieved with relative ease. A novel distance metric and a diagonal local binary pattern (DLBPC) are introduced in this work for finding high-accuracy data. The device-independent L*a*b* color space is used in the description. The system's effectiveness has been tested using the dataset Wang-1K. The findings show that the recommended method is as effective as other systems that have been studied.
MRI Breast Image Segmentation Using Artificial Bee Colony Optimization with Fuzzy Clustering and CNN Classifier

Breast cancer is the most leading disease for women and many lost their lives without proper treatment and early diagnosis helps to reduce the death rate. In clinical procedure identifying the tumor take much processing time and extracting the abnormal part leads error at some time. To overcome this problem and helps the radiologist an automated framework with the integration of artificial bee colony and fuzzy C means clustering for extracting the cancerous part and classify the tumor as benign and malignant with CNN classification. Various objective measures like entropy, eccentricity, contrast, correlation, homogeneity, mean, variance, kurtosis, dice similarity index, hands-off distance and subjective measures like precision, recall, accuracy, MSE, PSNR were evaluated to ensure the efficiency and accuracy of segmentation and classification of MRI breast images with T1, T2—contrast enhanced images. Our hybrid approach yield 98% segmentation accuracy and 98.6% classification accuracy and it take average of 5 s for processing. We utilized public dataset like DDSM, MIAS and INbreast dataset for validation.
Human Computer Interaction Proclivity Formed from the Analysis and Interpretation of Survey

Human Computer Interaction—HCI is an interactive technique to perform computing and communication on what user says and what computer cognizes. In current era, development of an intelligent system grows rapidly where providing interactive interface pertaining to the users also plays an important role. Major classes of HCI styles are Command Line Interface (CLI), Graphical User Interface (GUI), Natural Language Interface (NLI), and Perceptual User Interface (PUI). The authors have hypothesized that amalgamated HCI approach of GUI with NLI and PUI is recommended and convenient option for intelligent system interaction. To manifest and clearly evince the hypothesis, the authors have conducted a survey to the experienced researchers of information technology domain. Analysis and interpretation of the survey suggests amalgamated interface for HCI. Combined interaction between human and computer are as per the prospective user's preferences.