Reviewerly/exHarmony huggingface dataset:
Augmented Reality Assistance for Aging People

The development of Augmented Reality is becoming robust and matured by the combination of real-time performance with virtual information. With the proliferating geriatric population, the benefits of AR systems can be suitably harnessed for supporting the elderly in home activities, mobility and entertainment, among others. Hence, the development of AR to assist aging population is now feasible, but an overview of the existing research is not yet attempted. The objective of this systematic literature review is to provide the detail technical progress on the current situation of AR application and reviews the different domains using AR for supporting the ageing population. Search was conducted on five databases including Scopus, IEEE Xplore, Science Direct, ACM Digital Library and Springer Link.350 papers were initially searched and after detail study 71 were selected for the review. To conclude, the review proposed six components to be noted when developing new AR applications for the elderly.
Image Features Correlation with the Impression Curve for Automatic Evaluation of the Computer Game Level Design

In this study, we present the confirmation of existence of the correlation of the image features with the computer game level Impression Curve. Even a single image feature can describe the impression value with good precision (significant strong relationship, Pearson $$r>$$ 0,5). Best results were obtained using by combining several image features using multiple regression (significant very strong positive relationship, Pearson r = 0,82 at best). We also analyze the different set of image features at different level design stages (from blockout to final design) where significant correlation (strong to very strong) was observed regardless of the level design variant. Thanks to the study results, the user impression of virtual 3D space, can be estimated with a high degree of certainty by automatic evaluation using image analysis.
The Socio-Technical Approach to Cybersecurity Awareness

This chapter gives an overview of socio-technical systems, and socio-technical systems analysis. It describes the approach taken in conducting the Systems Dependency Analysis (SDA), and the part of the Soft Systems Methodology that was used to enable the participants in the workshops to describe and analyse their organisation’s systems, applications and networks.
Fast Channel-Dependent Transmission Map Estimation for Haze Removal with Localized Light Sources

The presence of haze, dust, suspensions of various kinds on images significantly degrades visibility, which makes it difficult to analyze the scenes. Obtaining an accurate transmission map is crucial for eliminating the effect of such particles. In this work, the transmission map was refined considering the dependence of scattering on the wavelength and using the soft lightening mask. According to this assumption, each of the RGB light components corresponds to separate coefficients characterizing the model. The coefficients were obtained by maximizing the total structural similarity index (SSIM) when comparing the reconstructed images with the haze-free images from the I-HAZE dataset. Experiments were carried out on datasets with natural (O-HAZE and I-HAZE) and synthesized (SOTS outdoor and indoor) haze; the PSNR and SSIM metrics were calculated. The results obtained demonstrate an improvement in the quality of haze removal by the proposed method compared to the basic methods.
The Use of Quantum Computing with 3D Modeling in the Industrial Sector

This chapter represents the results obtained through the investigation about the computer problem solving of 3D tridimensional figures in the industrial sector and defines the quantum computer paradigm, which defines the capacity storage of information in amplitude values based in cubits and not in bits as commonly used and known. The present research establishes a state-of-the-art knowledge in quantum computing and modeling whose development platforms are open-source type. A methodology is established based on the project to be developed: problem approach, justification, objectives, state of the art, research methodology, result and analysis, conclusions.
Anomaly Detection Using Edge Computing AI on Low Powered Devices

In the industrial environment, maintaining a permanent good state of functioning for every piece of equipment has a substantial importance. This, however, is very difficult to attain, due to the mechanical wear, the environment of operation, or improper usage. Predictive maintenance is a practice that is performed to determine the condition of the machinery in service and estimate the time when the maintenance should occur. The challenge of detecting a possible fault in a piece of equipment before it occurs is one of the main tasks of the predictive maintenance process. Reading data from sensors and creating firmware that monitors the equipment can be time and resource-consuming, and not practical if the equipment is changed frequently. Nowadays, the computational power of Artificial Intelligence exceeds that of a computer. As the industrial equipment and the hardware components of a conventional computer are getting increasingly expensive and demanded, more and more entities are running Machine Learning algorithms, which make the data exchange with a server that runs this service a more feasible process. This approach poses several challenges due to latency, privacy, bandwidth, and network connectivity. To solve these limitations, computation should be moved as much as possible towards the Edge, directly on the devices that gather the data. In this article, we propose a compact and low-powered solution that is accurate and small enough to be fitted on a microcontroller or a device that runs on the Edge. This approach ensures that a minimum amount of resources are used. The solution consists of an Unsupervised learning algorithm that can detect anomalies in the vibration patterns of the bearings or the casing of industrial motors. It uses an Autoencoder that takes as input the median absolute deviation of each measurement set provided by an accelerometer, then with the help of a classifier compares the values provided by the output to values that are known to be normal vibration patterns and decides if it deals with an anomaly or not. The low-powered Edge device is an ESP32 board that consumes only 160 mAh on full load but also being powerful enough to maintain WiFi and Bluetooth capabilities when needed. On a more economical operating mode, without WiFi and Bluetooth capabilities it can consume as low as 3 mAh [1]. This feature and the fact that the board is connected directly to the data-gathering sensor makes it preferable to an algorithm hosted on a remote server or a local machine due to low resource consumption and easy maintainability. The Autoencoder is fitted on this board and runs continuously until it encounters an anomaly, which in turn provokes an alert to the user.
Field Instruction Multiple Data

Fully homomorphic encryption (FHE) has flourished since it was first constructed by Gentry (STOC 2009). Single instruction multiple data (SIMD) gave rise to efficient homomorphic operations on vectors in $$(\mathbb {F}_{t^d})^\ell $$ , for prime $$t$$ . RLWE instantiated with cyclotomic polynomials of the form $$X^{2^N}+1$$ dominate implementations of FHE due to highly efficient fast Fourier transformations. However, this choice yields very short SIMD plaintext vectors and high degree extension fields, e.g. $$\ell < 100, d > 100$$ for small primes ( $$t = 3, 5, \dots $$ ). In this work, we describe a method to encode more data on top of SIMD, Field Instruction Multiple Data, applying reverse multiplication friendly embedding (RMFE) to FHE. With RMFE, length- $$k$$ $$\mathbb {F}_{t}$$ vectors can be encoded into $$\mathbb {F}_{t^d}$$ and multiplied once. The results have to be recoded (decoded and then re-encoded) before further multiplications can be done. We introduce an FHE-specific technique to additionally evaluate arbitrary linear transformations on encoded vectors for free during the FHE recode operation. On top of that, we present two optimizations to unlock high degree extension fields with small $$t$$ for homomorphic computation: $$r$$ -fold RMFE, which allows products of up to $$2^r$$ encoded vectors before recoding, and a three-stage recode process for RMFEs obtained by composing two smaller RMFEs. Experiments were performed to evaluate the effectiveness of FIMD from various RMFEs compared to standard SIMD operations. Overall, we found that FIMD generally had $$>2{\times }$$ better (amortized) multiplication times compared to FHE for the same amount of data, while using almost $$k/2{\times }$$ fewer ciphertexts required.
Situational Awareness for Law Enforcement and Public Safety Agencies Operating in Smart Cities – Part 1: Technologies

This chapter and the one that follows discuss the use of Internet of Things (IoT) concepts, technologies, and processes, in support of situational awareness applications for law enforcement and public safety agencies operating in smart city environments. Situational awareness facilitates efficiently discerning the physical environment, knowing what is going on in the ecosystem under consideration. Typically, there are many actors, many events, many “moving parts,” many input sensors, and many stakeholders. Advanced analytics are needed to parse, analyze, and boil down, often in real time, the large dataset generated by the sensors, which often consists of extensive visual information of various types. Artificial intelligence methods are increasingly needed to enhance the cloud analytics’ ability to extract actionable information for law enforcement stakeholders within the timeframe of interest. Part 1 of this two-chapter set highlights a number of artificial intelligence methods that may be utilized by the situational awareness platforms that are being deployed at this time in the field. Part 2 of this two-chapter set discusses specific SA tool used by law enforcement and some practical challenges affecting the actual rollout of these platforms in urban and municipal police departments. Some ethics issues are also highlighted, along with a brief assessment of ongoing research in this fast-evolving field.
Fall Detector

The population in general is undergoing an aging process, which makes, even more alarming for statistics, where about 30% to 60% of the population over 65 years suffer from falls annually and, of these, 40% to 60% lead to serious injuries. It aims to achieve universal health coverage, including financial risk protection, access to quality essential health services and access to safe, effective, quality and affordable essential medicines and vaccines for all. This paper presents the study for the development of an IoT-based fall detection alarm system for the elderly using ESP8266 microcontroller and GY-521 accelerometer. In the elderly, fall induced injuries, can become fatal. The aim is to design a system to detect the fall and alert a guardian. In the proposed system, the fall is detected through the movement of the accelerometer, sends the information to the IFTTT platform (IF This Than That) which in turn sends the SMS to the person responsible for monitoring.
Generating Recommendations via Trust-Aware Recommendation System by the Topological Impact of Users in Social Trust Networks

There are a large number of current recommendation methods that have issues with cold starts and sparsity. In this study, these issues are addressed by proposing a novel trust-based recommendation method, and the proposed method uses trust information along with rating values to deal with “cold-start” users and items. Because in most real-world applications, only a few items are given feedback by the users. Therefore, we were faced with a sparse user-item matrix. Here, similar users are grouped using a random-walk-based method that calculates the influence of users in social networks. Then cluster seeds are identified among the most influential users. Assign unique labels to cluster seeds and use a novel label propagation method to spread labels to unassigned users. Finally, the combinations identified in the prediction process are used to predict missing ratings. To assess the efficiency of the proposed approach, several experiments were performed on the well-known and widely used real-world dataset called FilmTrust. The results are compared based on several known evaluation metrics, which are F1-Measure, Precision, Recall, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The proposed method achieved the lowest values of MAE and RMSE and the highest values of F1, Precision, and Recall in comparison to the other recommended methods. Results showed that the proposed method is superior to the traditional and modern methods in terms of accuracy and efficiency in most cases. Therefore, it can be concluded that using trust information leads to more accurate rating predictions.
Comparative Study of Some Wavelet-Based Numerical Methods to Solve Initial Value Problems

Ordinary differential equations, in particular initial value problems, play a vital role in applied mathematics. There are many methods available to solve these initial value problems. The operational matrix method based on wavelet is a recent one. In this paper, we briefly review some operational matrix methods. The operational matrix method from the Haar wavelet, the frame, and the Legendre wavelet is considered. We give a comparison of the solution by providing several numerical examples.
Bento Packaging Activity Recognition from Motion Capture Data

Human activity recognition (HAR) has been an important research field for more than a decade due to its versatile applications in different area. It has gained significant attention in the health care domain. Although it has similarity with other form of activity recognition, it offers a unique set of challenges. Body movements in a food preparation environment are considerably less than many other activities of interest in real world. In this paper, a comprehensive solution has been demonstrated for the Bento Box Packaging Challenge activity recognition. In this case, we present a well-planned approach to recognize activities during packaging tasks from motion capture data. We use dataset obtained from motion capture system where subjects have 13 markers on their upper-body area and by special use of cameras and body suit. We obtain around 50,000 sample for each of the activities. We reduce the data dimensionality and make the data suitable for the classification purpose by extracting reliable and efficient features. After feature extraction process, three different classifiers, e.g., random forest classifier, extra trees classifier, and gradient boosting classifier are compared to check the result. We conclude that this challenging dataset has been observed to work most efficiently for random forest classifier using hyperparameter tuning.
Image Captioning with X-Gelu Activated Xgl Transformer

Image captioning aims to extract multiple semantic features from an image and integrates them into a sentence-level description. For efficient description of the captions, it is necessary to learn higher order interactions between detected objects and the relationship among them. Most of the existing systems take into account the first order interactions while ignoring the higher order ones. In this paper, an efficient higher order interaction learning framework is proposed using encoderdecoder based image captioning framework. An efficient XGL Transformer (XGLT) is defined that integrates four XGL attention modules in image encoder and one at the sentence decoder. The XGL attention module uses low-rank bilinear pooling, x-GELU activation function and Skip-Squeeze and Excitation (SSE) network to leverage higher order interactions among multiple objects by exploiting spatial and channel-wise attention. The proposed model is validated by conducting extensive experiments on publicly available MSCOCO dataset that exhibit the efficacy of the proposed XGL-T model by outperforming other state-of-the-art significantly, as reported by BLEU, METEOR, CIDEr, ROUGE-L and SPICE evaluation metrics. An ablation study is also carried out to support the experimental results.
Composite: A Model of Virtual Organization

Organization is, as it were, a temporally stable whirlpool appearing on flow of imperfect information processing. At that time, there are going on two processes concurrently. One is to make optimal organization to perform task. The other is to process imperfect information. If we describe this process as an inherent model based on discrete structure, many decentralized autonomous units continuously try to connect to others, from which some nested hierarchical system forms with optimal structure. Composite is a model of virtual organization. It is also a model of hierarchical system, based on distributed autonomous units and their relations. Each unit has own basic endowment sets and operators. It is trying to connect to each other, using its local scope under some local rules, and makes network topology. A composite is hierarchically composed of the nested sub-composites through this continuous process. It is spontaneously growing up by composition, and sometime later collapsing by decomposition. Composite and its composing process show that there arises characteristic geometry and structural complexity. At the base layer, Decentralized Autonomous Units (DCAUs) and their connecting form some topological geometry, which shows some kind of complexity. Also, spontaneous stratification with nested structure represents generating complexity. In the middle hierarchical layer, there will emerge measurable complexity. On the other hand, composite is an information processing system simultaneously with a self-organizing system. In particular, it deals with imperfect information. So, it makes bifurcation and a new branch for future forecasting. Higher stratified composite based on wider domain of DAUs could see the further future in the global scope. The top of composite is transformed, according to composing process. It can be optimal process with global scope. In simulation, there often happen many local optimal solutions by local scope. As it can procure the higher global scope, it will escape from a trap of local optimal structure. Composite holds nested stratification and composing process with repeating operation and recursive operation. This composing process generates complexity and computational complexity at the conceptual simulation. From the Paradise Lost, human beings have always developed the newer systems for imperfect information processing. However, there remains invariant imperfect information against the transformation of the systems. Thus, it is necessary for any information systems from CPU to AI, and to world market to predict the near future. While they are seeking for efficiency or advantages, surely imperfect information will appear in their small spaces. Composite model enables us to generate inherent information for predicting the near future against imperfect information. However, for acquiring genuine generator by means of which we can overcome the invariant part of imperfect information, we need to develop a conceptual model of Transcendental Genesis.
Explanation using examples

This chapter introduces methods for achieving an example-based explanation of interpreting the behaviors of black box machine learning models and identifying the characteristics of sample data. These methods help in building a better mental model of model behavior by selecting and showing other samples from a training set to explain the prediction results of a sample of interest. Moreover, practitioners and researchers can utilize methods to identify influential examples or detect mislabeled samples. To properly evaluate samples when selecting appropriate examples, this chapter introduces six methods categorized into similarity- and influence-based methods according to their applied measures. A simple binary classification and the fashion-MNIST problem are also covered to help better understand example-based explanation methods.
Alteration in Gold-Only Deposits

As metamorphic fluids depart from their source they move to areas of lower temperature, lower pressure and into country rocks with which they are no longer in equilibrium. Reaction between rocks and fluid changes the rocks and this produces an alteration zone or halo. The reaction also modifies the fluid with the potential to cause gold deposition.
Modeling Autonomic Systems

Today software systems are very complex and changing. Therefore, preserving their functionality and the quality of their service seems to be a difficult. Consequently, the concept of self-adaptation was introduced to handle the problem of the increasing complexity and dynamism of modern software systems. In this context, researchers have been studying self-adaptation for over a decade, which has resulted in a vast body of knowledge. Nevertheless, as technology progresses and software systems are increasingly integrated, new challenges emerge. Among these challenges are the need for new engineering approaches and methods to model and verify self-adaptive systems.In this paper, we provide a survey on modeling and verifying self-adaptive systems through presenting and classifying the main existing approaches that have made significant progress in this area.
A Semantic Fuzzy System to Intelligent Documents Summarization

Due to the information technology revolution, there are many and varied methods of document summarization to obtain specific information from documents. Automated summarization methods rely on identifying important points in all relevant documents to produce a concise summary. Therefore, this paper presents an intelligent classification-based automated summarization system using a semantic neuro-fuzzy approach. The proposed system consists of five integrated phases, which are the Document Pre-processing, the intermediate representation, the Index Matrices Weight Calculation, the Neuro fuzzy system, and the Summary Generation, respectively.Â Finally, a fast clustering algorithm is applied to define a number of fuzzy models with an increasing number of input variables determined by the user according to their ranks. Then, according to a user-defined confidence level, the summary is generated from the knowledge base by a better understanding of the fuzzy rules. The performance of the proposed model was evaluated using Recall-Oriented Understudy for Gisting Evaluation (ROUGE), which showed improved results compared to other techniques in terms of average accuracy, recall, and F-measure in the document understanding conference (DUC) dataset.
Construction of Intelligent Student Management Evaluation Information System Based on ANN Algorithm

With the rapid development of our country’s economy, the deepening of reform and opening up, the increasing globalization of the economy and the development of information networks, in this era of craftsmanship as a defense, the demand for applied human resources is increasing. The scale of colleges and universities is also expanding, the overall quality of college students is declining, their ideas are becoming more and more complex, the management of college students cannot adapt to the development of the new situation, and the existing problems are more obvious. In this paper, an investigation is carried out on the intelligent student management evaluation information system calculated by ANN, and on the basis of the literature, the relevant knowledge and theoretical basis of the school management evaluation information system is understood, and then the intelligent school management evaluation information system using ANN calculation is carried out. Finally, through the test conclusion, it is concluded that if the adopted ANN model reaches this level of tendency, more scientific and reasonable management evaluation indicators can be designed, and can become a useful supplement to existing indicators.