Reviewerly/exHarmony huggingface dataset:
Algorithm of local features fusion and modified covariance-matrix technique for hand motion position estimation and hand gesture trajectory tracking approach

Nowadays, visual recognition based dynamic hand gesture tracking has gained very considerable attention. Hand gestures can play an important role as a non-touchable communication tool between machines and humans based on using the affordable built-in webcam. The need for replacing touch-based computing devices interaction has been increasing in many fields like healthcare, security, and generally as interface-based devices controlling. Especially with COVID19 outbreak spreading around the world, where people take a risk and avoid dealing with electronic consumer machines that required hand touching. However, in any dynamic hand gesture recognition system, dynamic hand gesture tracking is a very hard task, where position estimation over video frames for freely moving hand in the air is quite challenging. This shortcoming is due to hand great scale changes, posture variations, and translation problems. Hence, to tackle these difficulties and extract gesture features for gesture recognition phase accurately, this paper proposed an algorithm of dynamic hand trajectory tracking for gesture recognition. The presented algorithm proposes local features fusion based on Gabor-Canny- Hog features embedded an updated compact covariance matrix technique as sophisticated feature-based tracking, utilizing video sequences of IBGHT dataset. As a result, the proposed approach has shown adorable optimization achieving an accuracy rate of 96.97%, overcoming the problems of hand appearance variation in the complicated environment.
Recent Trends on Security Issues in Multi-Cloud Computing: A Survey

Cloud computing (CC) is no doubt the most popular and sprightly emerging technology in the field of computation. The reasons for its popularity is known to be its cost-effectiveness, ease of troubleshooting problems and variable server capacity. Despite these advantages, the shift from local computing to CC has given rise to many security challenges and a threat to the customers as well as service providers. In this paper, the basic features of CC, Multi-Cloud Computing(MCC), multi-Cloud security issues and their possible solutions are discussed.
A Model for Signatories in Cyber-Physical Systems

Distributed Internet of Things and cyber-physical systems can potentially be used as agents to automatically sign events and transactions on behalf of users. To accomplish this, there is a need for a model that can represent the relationships, credentials and organizational hierarchies of people and devices, facilitating agents acting as signatories in a controlled way. This paper proposes such a model, where people in different positions are entitled to sign on behalf of organizations or departments therein and extend that to representing machines. Central in this model is the Power of Attorney (PoA), which is a self-contained and signed digital document that for a limited time and in a defined context, authorizes a particular agent (whether a person or device) to sign on behalf of a principal. Although such self-contained PoAs can be stored anywhere, we propose a conceptual architecture based on PoAs and include a signatory registry that keeps track of organizational hierarchies in terms of people and devices according to the defined model and stored PoAs in that context.
Learning Intrinsic Symbolic Rewards in Reinforcement Learning

Learning effective policies for sparse objectives is a key challenge in Deep Reinforcement Learning (RL). A common approach is to design task-related dense rewards to improve task learnability. While such rewards are easily interpreted, they rely on heuristics and domain expertise. Alternate approaches that train neural networks to discover dense surrogate rewards avoid heuristics, but are high-dimensional, black-box solutions offering little interpretability. In this paper, we present a method that discovers dense rewards in the form of low-dimensional symbolic trees - thus making them more tractable for analysis. The trees use simple functional operators to map an agent's observations to a scalar reward, which then supervises the policy gradient learning of a neural network policy. We test our method on continuous action spaces in Mujoco and discrete action spaces in Atari and Pygame environments. We show that the discovered dense rewards are an effective signal for an RL policy to solve the benchmark tasks. Notably, we significantly outperform a widely used, contemporary neural-network based reward-discovery algorithm in all environments considered.
Association Rules over Time

Decisions made nowadays by Artificial Intelligence-powered systems are usually hard for users to understand. One of the most important issues faced by developers is on how to create more explainable Machine Learning models. In line with this, more explainable techniques need to be developed, where visual explanation also plays a more important role. This technique could also be applied successfully for explaining the results of Association Rule Mining. This chapter focuses on two issues: (1) How to discover the relevant association rules, and (2) How to express relations between more attributes visually. For the solution of the first issue, the proposed method uses Differential Evolution, while Sankey diagrams are adopted to solve the second one. This method was applied to a transaction database containing data generated by an amateur cyclist in past seasons, using a mobile device worn during the realization of training sessions that is divided into four time periods. The results of visualization showed that a trend in improving performance of an athlete can be indicated by changing the attributes appearing in the selected association rules in different time periods.
Dynamic mosaicking: combining A* algorithm with fractional Brownian motion for an optimal seamline detection

Image mosaicking is a combination of algorithms that use two or several images to create a single image. The resulting mosaic is a representation of a scene of the used images with a larger field of vision. However, since dynamic objects can exist in the overlap regions of these images, ghosting and parallax effects appear, therefore poor results are obtained. To overcome these unwanted effects and to achieve better results, a new method is presented in this paper. This approach uses a new way to detect dynamic objects in the common areas by using a fractional Brownian motion with a predetermined similarity function instead of a noise function, the Zero Normalized Cross Correlation. Thus, it will ensure that a map is created with each pixel having a unique value based on their surroundings even in homogeneous areas. Furthermore, this new approach combines the previously computed map with the machine learning algorithm A* for a fast and efficient way to find an optimal seamline. Consequently, the obtained experimental results were compared with different methods and better results were obtained as can be seen by a better quality seamline measure, a result mosaic without any artifacts and a faster computation time.
The paleoposition of the Antimonio depositional system (Sonora, Mexico): New insights from nonparametric and multivariate analysis of detrital zircon data

The Antimonio depositional system ranges from the Upper Permian to the uppermost Lower Jurassic. It is the only locality of Upper Triassic shallow marine carbonates in Mexico and through its speculative basement; it provides a potential link between the terrane framework of Mexico and the North American Cordillera. Various paleotectonic models have been proposed to explain the mixed siliciclastic-carbonate successions of the Antimonio depositional system in Sonora, Mexico, including interpreting it as a distinct allochthonous terrane or having a position close to the Laurentian basement. Microfacies, paleontological and paleomagnetic studies have suggested a paleogeographic position in low latitudes close to Laurentia. However, the basement structures of the Antimonio depositional system are poorly known and have numerous uncertainties. Here, we use detrital zircon geochronology to identify the sources of terrestrial sediments of the Antimonio depositional system and to propose potential sedimentary distribution patterns. The results allow us to clarify its paleotectonic position and consolidate the geodynamic model of González-León et al. (2005), in which the Antimonio depositional system was attached to the Caborca block. Four major zircon age distributions of detrital zircon were determined in the Barra los Tanques and Sierra del Álamo sections of the Rio Asunción Formation (Antimonio depositional system: (1) a Permo-Triassic group (207–292 Ma); (2) the group/population of 1.0–1.2 Ga; (3) a group clustering between 1.3 and 1.5 Ga and (4) the group of 1.6–1.8 Ga. This new data set was compared with published detrital zircon data from localities along the North American Cordillera, using two-sample Kolmogorov-Smirnov tests and hierarchical clustering. The results allow us to identify potential sources located in the Cordilleran continental arc and the ancestral Rocky Mountains, as well as in sediments and volcanic deposits of their respective surrounding areas. We suggest that the transport for these sediments was likely provided through the Chinle river system, and long-shore oceanic currents. Combining published data from Magsat magnetic anomalies with stratigraphy based paleontology, and our new geochronological data analysis, we suggest that the Antimonio depositional system must have been part of Laurentia and potentially linked to the Caborca block during the Late Triassic.
Making future floating cities sustainable: a way forward

It is expected that by 2030, 5 billion of the earth's population will live in cities. This growth in urbanisation results in significant challenges pertaining to environmental and social sustainability. Most of the cities are located near coastal areas and thus under the threat of sea rise. This paper explores sustainable floating cities with the key sustainable factors as an alternative to meet the requirement of future urbanisation. The purpose of this paper was to find the key sustainability factors and then explore the solutions to achieve them in the sustainable floating cities. A mixed research strategy using qualitative and quantitative approaches was adopted to achieve the aims of this research. The key sustainability factors were derived from the existing literature using a systematic review approach and then ranked using a questionnaire administrated among selected respondents. The top-ranked factors that include energy, water, food, waste, mobility and habitat regeneration were considered and were further explored using a systematic review method. The review suggests that there are different arrangements and techniques available to meet the requirement of future sustainable floating cities. The discussion reveals that the concept of floating cities is at its initial stage and has the potential for multidisciplinary research.
Data Auditing in Cloud Storage using Smart Contract

In general, Cloud storage is considered as a distributed model. Here, the data is usually stored on remote servers to properly maintain, back up and make it accessible to clients over a network, whenever required. Cloud storage providers keep the data and processes to oversee it on capacity servers based on secure virtualization methods. A security framework is proposed for auditing the cloud data, which makes use of the proposed blockchain technology. This ensures to efficiently maintain the data integrity. The blockchain structure inspects the mutation of operational information and thereby ensures the data security. Usually, the data auditing scheme is widely used in a Third Party Auditor (TPA), which is a centralized entity that the client is forced to trust, even if the credibility is not guaranteed. To avoid the participation of TPA, a decentralised scheme is suggested, where it uses a smart contract for auditing the cloud data. The working of smart contracts is based on blockchain. Ethereum is used to deploy a smart contract thereby eliminating the need of a foreign source in the data auditing process.
Text Sentiment Classification Based on Attention Mechanism and Decomposition Convolutional Neural Network model

In view of the simple structure of a single neural network model, the traditional convolutional neural network cannot fully extract deep text features. This paper proposes a text sentiment classification based on Attention Mechanism and Decomposition Convolutional Neural Network model. First, comprehensive text features are obtained with the help of parallel Decomposition Convolutional Neural Network (DCNN). Secondly, it combines the attention mechanism to extract important feature information and improve the optimized text sentiment classification effect. Finally, the text is sentimentally classified at the classification layer. After conducting multiple sets of comparative experiments on three sets of Chinese data sets, the accuracy of the model in this paper reached 92.06%, 91.08% and 92.71% respectively. It is proved that the proposed model performs better than the single-channel model, and the use of Decomposed Convolutional Neural Network is better than traditional Convolutional Neural Network.
Effective strategies for information literacy education: Combatting ‘fake news’ and empowering critical thinking

Unhelpful assumptions around 'digital natives' and assumptions of inherent digital literacies can create problematic scenarios for education providers in the higher education space. While we can show that students are comfortable engaging with digital platforms, and online learning environments are certainly to be welcomed, ad hoc and poorly defined critical digital literacy education in schools result in students frequently bringing poor critical thinking to the online environments, which can bring both pedagogic and institutional challenges for universities. Critical digital literacy, and associated risk mitigation, is not something that can be assumed with 21st-century students, and universities have a role to play in the development of these key skills in their student bodies if we are to produce digitally aware and responsible graduates.
Cardiac MR Images Segmentation For Identification Of Cardiac Diseases Using Fuzzy Based Approach

The proposed research work presents image segmentation of cardiac MRI Images of ventricle segmentation. Most important task in image analysis is Segmentation of Images. Data Mining and Machine Learning approaches are now a day very much used for Left Ventricle Segmentation on Cardiac System which efficiently uses different kind of algorithms. There are various measuring tools to evaluate the chest pain, cardiac function, neurologic deficits, by Cardiac System. Today the cardiac related diseases are increasing too much in our society. So, earlier identification of disease is crucial for urgent treatment of cardiac diseases. Doctors are providing their suggestion on the basis of manual inspection with MRI and CT scans. Here, the task is based on technical work with morphological, threshold based segmentation and fuzzy based edge detection approach is applied for better classification of diseases. The task is used to classify cardiac arrhythmia cases, abnormal cardiac cases, left ventricular cases problems. So specifically medical based image segmentation consists higher impotency which betterment the organs localizations for betterment of the quality of the diagnosis and crucial works and become crucial stages for evaluation of functionality of heart failure with pre existing approaches. Here, the the performance produces by this method is more than 90% of accuracy for detection and classification of cardiac diseases. Some of the statistical parameter are basically MSE, PSNR, MAE etc are significantly performs better for the fuzzy based approach with better quality performance of MR images.
AuthCTC: Defending Against Waveform Emulation Attack in Heterogeneous IoT Environments

Widely deployed IoT devices have raised serious concerns for the spectrum shortage and the cost of multi-protocol gateway deployment. Recent emerging Cross-Technology Communication (CTC) technique can alleviate this issue by enabling direct communication among heterogeneous wireless devices, such as WiFi, Bluetooth, and ZigBee on 2.4 GHz. However, this new paradigm also brings security risks, where an attacker can use CTC to launch wireless attacks against IoT devices. Due to limited computational capability and different wireless protocols being used, many IoT devices are unable to use computationally-intensive cryptographic approaches for security enhancement. Therefore, without proper detection methods, IoT devices cannot distinguish signal sources before executing command signals. In this paper, we first demonstrate a new defined physical layer attack in the CTC scenario, named as waveform emulation attack, where a WiFi device can overhear and emulate the ZigBee waveform to attack ZigBee IoT devices. Then, to defend against this new attack, we propose a physical layer defensive mechanism, named as AuthCTC, to verify the legitimacy of CTC signals. Specifically, at the sender side, an authorization code is embedded into the packet preamble by leveraging the dynamically changed cyclic prefix. A WiFi-based detector is used to verify the authorization code at the receiver side. Extensive simulations and experiments using off-the-shelf devices are conducted to demonstrate both the feasibility of the attack and the effectiveness of our defensive mechanism.
A precision-based diagnostic model ADOBE-accurate detection of breast cancer using logistic regression approach

Cancer is a prevalent disease which comes in several forms. The need of the hour in cancer research is to be able to diagnose cancer in its early stages. The furthermost common forms of cancer among women us breast cancer. In recent times, there has been a drastic increase in the number of breast cancer cases among women. As a wide range of medical data is available in electronic form and with easy access to Machine Learning(ML) techniques disease progression risk evaluation has been made easier. These ML tools can aid in giving us complex insights from the massive amounts of available data. Some of the techniques used for developing predictive models for perfect decision making in cancer research are Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Support Vector Machines (SVMs), and Decision Trees (DTs). Although it is acceptable that ML is used to predict cancer progression, we need some level of validation. In this paper, we have come up with a review of several ML methods in modelling cancer progression. We discuss several predictive models based on supervised ML techniques and the inputs given by users, along with the data available. The results that were obtained from Logistic Regression show us that this method gave a significantly higher accuracy than most other classifiers. The best accuracy is 98.2%, however, the best precision and recall is 100 and 98.60% correspondingly.
An Attention-based Deep Relevance Model for Few-shot Document Filtering

With the large quantity of textual information produced on the Internet, a critical necessity is to filter out the irrelevant information and organize the rest into categories of interest (e.g., an emerging event). However, supervised-learning document filtering methods heavily rely on a large number of labeled documents for model training. Manually identifying plenty of positive examples for each category is expensive and time-consuming. Also, it is unrealistic to cover all the categories from an evolving text source that covers diverse kinds of events, user opinions, and daily life activities. In this article, we propose a novel attention-based deep relevance model for few-shot document filtering (named ADRM), inspired by the relevance feedback methodology proposed for ad hoc retrieval. ADRM calculates the relevance score between a document and a category by taking a set of seed words and a few seed documents relevant to the category. It constructs the category-specific conceptual representation of the document based on the corresponding seed words and seed documents. Specifically, to filter irrelevant yet noisy information in the seed documents, ADRM employs two types of attention mechanisms (namely whole-match attention and max-match attention ) and generates category-specific representations for them. Then ADRM is devised to extract the relevance signals by modeling the hidden feature interactions in the word embedding space. The relevance signals are extracted through a gated convolutional process, a self-attention layer, and a relevance aggregation layer. Extensive experiments on three real-world datasets show that ADRM consistently outperforms the existing technical alternatives, including the conventional classification and retrieval baselines, and the state-of-the-art deep relevance ranking models for few-shot document filtering. We also perform an ablation study to demonstrate that each component in ADRM is effective for enhancing filtering performance. Further analysis shows that ADRM is robust under varying parameter settings.
PatrIoT: Policy Assisted Resilient Programmable IoT System

This paper presents PatrIoT, which efficiently monitors the behavior of a programmable IoT system at runtime and suppresses contemplated actions that violate a given declarative policy. Policies in PatrIoT are specified in effectively propositional, past metric temporal logic and capture the system's expected temporal invariants whose violation can break its desired security, privacy, and safety guarantees. PatrIoT has been instantiated for not only an industrial IoT system (EVA ICS) but also for two home representative automation platforms: one proprietary (SmartThings) and another open-source (OpenHAB). Our empirical evaluation shows that, while imposing only a moderate runtime overhead, PatrIoT can effectively detect policy violations.
DAMFA: Decentralized Anonymous Multi-Factor Authentication

Token-based authentication is usually applied to enable single-sign-on on the web. In current authentication schemes, users are required to interact with identity providers securely to set up authentication data during a registration phase and receive a token (credential) for future accesses to various services and applications. This type of interaction can make authentication schemes challenging in terms of security and usability. From a security point of view, one of the main threats is the compromisation of identity providers. An adversary who compromises the authentication data (password or biometric) stored with the identity provider can mount an offline dictionary attack. Furthermore, the identity provider might be able to track user activity and control sensitive user data. In terms of usability, users always need a trusted server to be online and available while authenticating to a service provider.
A C Code Generator for Fast Inference and Simple Deployment of Convolutional Neural Networks on Resource Constrained Systems

Inference of Convolutional Neural Networks in time critical applications usually requires a GPU. In robotics or embedded devices these are often not available due to energy, space and cost constraints. Furthermore, installation of a deep learning framework or even a native compiler on the target platform is not possible. This paper presents a neural network code generator (NNCG) that generates from a trained CNN a plain ANSI C code file that encapsulates the inference in single a function. It can easily be included in existing projects and due to lack of dependencies, cross compilation is usually possible. Additionally, the code generation is optimized based on the known trained CNN and target platform following four design principles. The system is evaluated utilizing small CNN designed for this application. Compared to TensorFlow XLA and Glow speed-ups of up to 11.81 can be shown and even GPUs are outperformed regarding latency.