Reviewerly/exHarmony huggingface dataset:
An Ensemble Model For Human Posture Recognition

Human Body Pose Estimation (HBPE) and HumanBody Posture Recognition(HBPR) have improved significantly in the past decade. Gaining access to huge amounts of data, Kinect camera, neural networks and specifically deep convolutional neural networks (deep convnets) have led to fascinating success in these fields. In this paper we propose an ensemble model for human body posture recognition. Deep convnets are the main building block and fundamental aspect of our proposed model. We leverage deep convnets in two variations to classify postures. First, we use them for an end-to-end training scenario. We perform transfer learning with Imagenet weights on deep convnets with our gathered dataset of RGB images to classify five different postures. Second, we use a pre-trained deep convnet[1] (pose estimator) for estimating human body joints in RGB images. The pre-trained pose estimator has been trained to calculate a total of 17 2D joints coordinates and we utilize these coordinates to train a decision tree-based classifier for classification among five classes. Both variations are examined with different settings. The best settings for both variations are combined together to create our proposed model. More specifically, the classification layers of both variations are stacked together and fed to a logistic regression unit for a better classification result. Transfer learning, training and experiments in this paper are based on only RGB images from our gathered dataset and human body joints coordinates extracted from these images, which conveys that our proposed model does not require depth images or any sensor. Eventually, experimental results on the images show that the proposed model has higher performance than fundamental variations. Specifically, our model is able to correctly recognize the human posture in the majority of the images that one of the two fundamental variations fails to classify. The code for the proposed model and our gathered dataset are available on github <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Facial Action Unit Recognition in the Wild with Multi-Task CNN Self-Training for the EmotioNet Challenge

Automatic understanding of facial behavior is hampered by factors such as occlusion, illumination, non-frontal head pose, low image resolution, or limitations in labeled training data. The EmotioNet 2020 Challenge addresses these issues through a competition on recognizing facial action units on in-the-wild data. We propose to combine multi-task and self-training to make best use of the small manually / fully labeled and the large weakly / partially labeled training datasets provided by the challenge organizers. With our approach (and without using additional data) we achieve the second place in the 2020 challenge - with a performance gap of only 0.05% to the challenge winner and of 5.9% to the third place. On the 2018 challenge evaluation data our method outperforms all other known results.
Step into the Cloud or Stop with Virtualization – The Project Manager’s Dialectic Dilemma

Virtualization and Cloud Computing are terminologies which are often used interchangeably within organizations largely due to the fact that, modern IT trends justify that the ‘Cloud’ is an inevitable and extremely lucrative technology which will have to be adopted for uninterrupted business continuity and better manageability. This paper attempts to draw the boundaries, whereby infrastructure project managers should consider staying with a virtualized infrastructure instead of stepping into the cloud. Reduced capital Expenditures (CapEx), Operational expenditures (OpEx), Total Cost of Ownership (TCO) and Infrastructure availability are some of the major factors that have been considered, from a project manager’s perspective, while discussing the content presented in this paper.
Pruning Deficiency of Big Data Analytics using Cognitive Computing

Since past few years the size of the data is growing extremely at fast rates 10 times faster in growth. This will include all the responsibilities to make smart decisions streaming from the browsing patterns and produce extra supplements which aids in the decision-making progress. As the size of the data is recorded from a variety of devices like mobile sensors, remote sensing and data is recorded from everywhere; huge amount of data gets stored which is sometimes even never analysed. Big data is a very “big” thing which is getting stored and increasing the volume of raw data sometimes 90% of the raw data sets are never analysed and are just discarded from the memory. Out of it just 10% gets analysed sometimes and are converted into information from those raw data sets. So, analysis of data by human beings could be time consuming but processing enormous amount of data at a large scale using cognitive can be done. In this paper, we tried to focus on the areas where the cognitive computing can be used in order to lessen the shortcomings of the big data analytics, principles from which cognitive computing came. We also focused on the urgent need on how the language processing of data is done to understand the meaning of the rough data and process it to useful information.
Application of Hamming Code Based Error Correction Algorithm in Quantum Key Distribution System

In the quantum key distribution system, one party needs to share the quantum state to the other through a quantum channel. However, due to external influences, the quantum state may change during transmission, resulting in a certain error in the original key acquired by the receiver. In this paper, an error correction algorithm based on Hamming code is proposed and verified in the quantum key distribution system. The algorithm can adapt to different bit error rate scenarios by variable length coding. Besides, the algorithm can correct the random error as well as burst error in the original key after combining the key interleaving algorithm.
Hardware Acceleration of CNN with One-Hot Quantization of Weights and Activations

In this paper, we propose a novel one-hot representation for weights and activations in CNN model and demonstrate its benefits on hardware accelerator design. Specifically, rather than merely reducing the bitwidth, we quantize both weights and activations into n-bit integers that containing only one non-zero bit per value. In this way, the massive multiply and accumulates (MACs) are equivalent to additions of powers of two that can be efficiently calculated with histogram based computaitons. Experiments on the ImageNet classification task show that comparable accuracy can be obtained on our proposed One-Hot Networks (OHN) compared to conventional fixed-point networks. As case studies, we evaluate the efficacy of the one-hot data representation on two state-of-the-art CNN accelerators on FPGA, our preliminary results show that 50% and 68.5% resource saving can be achieved on DaDianNao and Laconic respectively. Besides, the one-hot optimized Laconic can further achieve an average speedup of 4.94× on AlexNet.
A Participatory Design Case Study in Environmental Design Education

This article provides an overview and insights of challenges, potentials, and recommendations of teaching and applying Participatory Design (PD) in design education including the students' point of view through studying a case - a Master of Landscape Architecture studio course in Arizona State University, U.S. Students collaborated with Mo'ili'ili community members in Honolulu, Hawaii, U.S. for two semesters, to co-design the renovations of Old Stadium Park through three phases of PD process – field study & listening; community engagement workshop (50+ people); and pop-up design conversations (100+ people). This research illustrates the challenges of integrating PD in design education, which includes uncertainty and flexibility, funding, trust-building, time management, access to diverse knowledge centers, research ability, and communication hurdles. As well as implications and recommendations such as encouraging students to be flexible; embracing uncertainty; making explicit learning goals; leaving comfort zones; improving communication and organizational facilitation skills; and utilizing research ability for better trust-building with communities.
Anomaly Detection for Images Using Auto-encoder Based Sparse Representation

Anomaly detection is a pattern recognition task that aims at distinguishing abnormal patterns from normal ones. In this paper, we propose a convolutional auto-encoder based model to detect anomaly images by producing a sparse representation in the latent space. The proposed approach is able to represent the normal images using sparse encoding and the encoding can be well reconstructed by the decoder. However, the learned convolutional filters are not able to represent the abnormal images in a sparse way. Therefore, the decoder can not reconstruct the abnormal images with high quality. By assessing the reconstruction performance, we can distinguish the abnormal images from the normal ones. The experimental results show the superiority of our proposed model over other variants of auto-encoder based anomaly detection models in terms of AUC. In addition, the results show that the sparse representation based anomaly detection method could apply to different scenarios.
Exploring Phrase Grounding without Training: Contextualisation and Extension to Text-Based Image Retrieval

Grounding phrases in images links the visual and the textual modalities and is useful for many image understanding and multimodal tasks. All known models heavily rely on annotated data and complex trainable systems to perform phrase grounding - except for a recent work [38] that proposes a system requiring no training nor aligned data, yet is able to compete with (weakly) supervised systems on popular phrase grounding datasets. We explore and expand the upper bound of such a system, by contextualising both the image and language representation with structured representations. We show that our extensions benefit the model and establish a harder, but fairer baseline for (weakly) supervised models. We also perform a stress test to assess the further applicability of such a system for creating a sentence retrieval system requiring no training nor annotated data. We show that such models have a difficult start and a long way to go and that more research is needed.
On-Premises Versus SECaaS Security Models

The overarching aim of our research project was to determine whether replacing the traditional on-premises IT security model with SECaaS is a sensible decision in the context of small to medium-sized organizations. To accomplish this goal, a comparative analysis of the two cybersecurity models was conducted whereby relevant evidence was extracted from existing information. Findings revealed that the IT security management function is too sensitive to be left in the hands of a third-party cloud services provider. Rather, the function ought to be managed internally to minimize the potential attack vector as much as possible. However, despite our primary finding, we note that when cloud-based apps, operations, and data are involved, the SECaaS model is considered to be the most suitable IT security management approach. In particular, SECaaS outperforms the premise-based security model in the following core dimensions: cost-savings, ease of management, scalability and agility, expertise, constant updates, and cost- effective compliance. In our current research, we document the differences between the on-premises IT security and SECaaS models, and justify the case for SECaaS as the most suitable approach primarily focusing on the suite of cloud-based cybersecurity tools packaged with Microsoft 365 Business.
Application of Cloud Computing in Geological Hazard Early Warning System

Abstract China’s geomorphology is complex and changeable. Geological hazards occur frequently. Landslides, mudslides and other geological hazards seriously threaten the lives and property of the people. Monitoring and early warning of geological hazards to reduce the impact of disasters is an urgent problem at this stage. In order to ensure the safety of people’s lives and property, the design and development of real-time monitoring and early warning systems are the most effective means of preventing and controlling disasters. This article is based on the Microsoft cloud computing platform. The geological data collected by the automatic monitoring station is transmitted to the cloud server through GPRS DTU and stored in SQL Server 2008. Generate Web Service through VS2010 platform, use ASP.NET technology to build dynamic website to display early warning details, complete Web Service call through Android smartphone platform, conduct real-time geological disaster warning information query, and provide users with cloud platform-based geological disaster warning system. After analyzing the causes of geological hazards, a model of geological hazard influencing factors, a model for calculating the probability of hazards in geological structures, and a process for processing geographic information data were designed. Using the data of the extracted GIS spatial database to predict geological disasters.
Bio Inspired Optimization Algorithms in Disaster

This paper mainly deals with Bio Inspired which means biological structures. Bio Inspired easily handle failure which determines the real-world complicated engineering issues. It continually finds the optimum resolution to unravel its drawback maintaining good balance among its parts. Widen the range and growth of Bio Inspired Algorithms analyze new fields of application. A disaster is a sudden, disastrous event that seriously disrupts the functioning of a community or society and causes human, material, and economic or environmental losses that exceed the community’s or society’s ability to cope using its own resources. This paper presents comparative study between ant colony optimization algorithm and fireflies algorithm to find shortest route during disaster. ACO is inspired by behavior of ants. The specialty of ACO algorithm is to find finest route with respect to each road’s length and damage degree. ACO provides reliable paths and also the decision is based on pheromone level released by ants. One of the recent developed swarm intelligence algorithm is firefly algorithm introduced by Yang. This algorithm inspired by behavior of insects. It works based on three rule: They are unisex, attractiveness and brightness. So these two algorithms helps in finding the shortest path between origin point and destination point.
Security and privacy challenges in the field of iOS device forensics

The purpose of this paper is to review existing research literature analyzing the existing challenges facing the forensics community during investigations involving iOS devices. The scope of this paper is to analyze the existing security and privacy challenges and review the research and techniques being developed to combat these challenges. Throughout this paper, examples, where techniques have been applied, are discussed and future topics of research are identified.
Zero-Shot Learning in the Presence of Hierarchically Coarsened Labels

Zero-shot image classification leverages side information including label attributes and semantic class hierarchies to transfer knowledge about fine-grained training classes to fine-grained zero-shot classes. In this paper, we consider the problem of zero-shot learning of fine-grained classes given a mixture of images with fine-grained and coarsened labels. We show how probabilistic hierarchical classification models can be used to simultaneously accommodate fine and coarse-grained labels in the zero-shot learning setting. We show that this approach is robust even to significant levels of coarsening.
Deep Learning Based Massive-MIMO Decoder

Massive Multiple Input Multiple Output (M-MIMO) is one of the key technology for 5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> generation (5G) communication systems. Designing of M-MIMO decoder is very complex and challenging task. Recently, deep neural network (DNN) emerges as an alternative way to perform many complex tasks. In this work we have presented a deep learning (DL) based architecture DLNet) for receive signal decoding in Massive-MIMO system. In this we have considered an uplink time-varying Gaussian random MIMO channel perfectly known to the receiver. Based on the knowledge of this MIMO channel and the received signal the proposed DLNet decoder is decoding the messages of all the users. The presented decoder is 50 layer deep neural network. Its architecture is based on the projected gradient descent algorithm. Our simulation result shows, the proposed DLNet decoder for M-MIMO system performs better than other MIMO decoding techniques, by 3 dB bit error rate (BER), atleast 124 times faster and 9 times less complex.
Call for a wake standard for artificial intelligence

Suggesting a Voice Name System (VNS) to talk to any object in the world.
Diffusion Collaborative Feedback Particle Filter

We herein propose a distributed collaborative feedback particle filter (D-FPF) based on the diffusion strategy for nonlinear filtering problems. Each particle at each sensor is updated via a collaborative feedback structure incorporating the collaborative feedback gain and aggregate innovation. The localized intermediate state estimates are further shared among the neighbors. The importance sampling, resampling, or variance calculation, which are mandatory in the traditional distributed particle filters, are not requisite. Illustrative target tracking simulations validate that the proposed D-FPF could markedly outperform the distributed Gaussian particle filter in terms of both tracking and particle variance performance. The D-FPF could obtain favourable tracking performance even with an extremely small amount of particles.
Modeling and Simulation of a Solar Powered Golf Cart Charging Station in Sharjah

In this study, a performance analysis of golf cart charge station that utilizes the solar photovoltaic as power source to charge the carts involved in the University of Sharjah, (UOS) campus is conducted. Charging station designing consists of various stages. The initial step was to calculate the power needed to charge the load. The sizing of the PV array for the charging station requirements using Sharjah solar resources is determined. Finally, the design had been simulated using the MATLAB-Simulink software to check for the proposed system's feasibility to meet project objectives. A general view of the research is shown followed by experimental, sizing procedure, examination of the size and dynamic simulation. The results obtained showed that the output voltage from the solar system can reach up to 150 volts. Regarding the output from the voltage DC regulators it was stable and constant at 50 volts which is suitable to charge the load battery.
A Profit Maximization Scheme in Cloud Computing With Deadline Constraints

Cloud computing has attracting more and more attention for its flexibility and economic benefits. To maintain the supply-demand relationship among different participants in cloud computing environment, the exchange of value is the inner drive. From the perspective of cloud service provider, its primary concern is to earn the profit, which can be obtained by finishing the tasks published from customers. In this paper, we consider each task consists of numbers of sub-tasks in the logical order, each sub-task corresponds to a type of service requests, which can be served in unique multi-server system. On this basis, we propose a profit maximization problem in the multistage multi-server queue systems, in which customers are served at more than one stage, arranged in a series structure. Moreover, a deadline constraint is taken into consideration, which demonstrates the maximum tolerance degree that the customers can wait. Therefore, how to configure the parameters in multistage multi-server queue systems to maximize profit on the premise of reducing the waiting times of customers is a critical issue for cloud service provider. To address this problem, we first discuss the probability distribution function of the waiting time for single multi-server system and multistage multi-server queue systems respectively, and then propose a profit maximization model under the deadline constraint. Due to the complexity of this model, the analytical solution can hardly be obtained, we study a heuristic method to search for the optimal solution. At last, a series of numerical simulations are implemented to describe the performance of the proposed profit maximization scheme, the results show that not only the profit can be maximized, but also the waiting time of customers have been reduced effectively.
Microscopic Traffic Monitoring and Data Collection Cloud Platform Based on Aerial Video

Real-time traffic video streaming, such as roadside surveillance and aerial video, has been widely used in traffic monitoring nowadays. However, most of the traditional traffic data collection methods lack mobility that can only collect macroscopic data. In this paper, an intelligent traffic monitoring system based on an open source cooperative platform called SAGE2 was developed. Based on the integrated big screen TV wall of SAGE2, a map-based aerial traffic video streaming management interface was designed. In the image pre-processing section, it provides functions such as lens distortion removal, top view projection transforms, and video stabilization; simulate video streaming to provide instant and long-term micro-flow data collection. Micro-traffic flow data provides high-resolution information both in time and space which can be used to analyze the driving behavior of individuals and the public. Combined with the lane level map, it can provide a variety of visual vehicle flow presentations, such as intersection traffic distribution that can also be used to develop an innovative application in the future.
Evaluation Methodology for Distributed Data Usage Control Solutions

Data sharing offers a lot of benefits i n sectors like manufacturing, food, smart grids, healthcare, etc. not only for data providers, but also, for consumers. Despite this fact, in distributed systems if trust among participants and data sovereignty are not provided, data providers are reluctant to exchange their data, especially when referring to sensible information. To guarantee data sovereignty, data must be controlled and tracked not only at the time of the request, but also, when these are used afterwards in a distributed way to comply with legal regulations and the requirements of the providers.There is an increasing number of distributed data usage control solutions, and it is difficult to understand the benefits and flaws that each one can have regarding a specific application. This paper presents a methodology to evaluate the level of data sovereignty offered by existing solutions regarding distributed data usage control and data provenance. Firstly, the features that must be developed in every distributed data usage control solution to satisfy a minimum set of requirements are described. Second, from a use case of the RESPOND H2020 project, the applicability of the capacities that have been identified i s demonstrated. Third, a first approach is proposed to evaluate the completeness offered by every distributed data usage control solution in terms of data sovereignty.