Reviewerly/exHarmony huggingface dataset:
Multi-camera Marker-based Real-time Head Pose Estimation System

Estimating human head pose is a common task in many applications such as human attention modeling, fitting model to video, and visual reality... In this work, we implement a complete fiducial marker based human head pose estimation system. Our system uses fiducial markers as the key points to do Perspective n-Point, solves the 2D to 3D correspondence problem, and 3D points to 3D points registration. We also apply the depth data from RGB-D cameras as extra information to find the associated rotation and translation of the object head in the camera coordinate. Besides that, to overcome occlusion situation and also increase the tracking accuracy, we surround the user by multiple cameras. Our system will then fuse all pose results and introduce the final output as the most reliable and stable pose of the current object's head. At the end, our system could quickly detect the movement of object's head, and achieve high output frame rate that usually required in the real-time controlling applications.
Energy Cost and Performance-Sensitive Bi-objective Scheduling of Tasks in Clouds

Cloud computing attracts a growing number of organizations to deploy their applications in distributed data centers for low latency and cost-effectiveness. The growth of arriving instructions makes it challenging to minimize their energy cost and improve Quality of Service (QoS) of applications by optimizing resource provisioning and instruction scheduling. This work formulates a bi-objective constrained optimization problem, and solves it with a Simulated-annealing-based Adaptive Differential Evolution (SADE) algorithm to jointly minimize both energy cost and instruction response time. The minimal Manhattan distance method is adopted to obtain a knee for good tradeoff between energy cost minimization and QoS maximization. Real-life data-based experiments demonstrate SADE achieves lower instruction response time, and smaller energy cost than several state-of-the-art peers.
K-spin Hamiltonian for quantum-resolvable Markov decision processes

The Markov decision process is the mathematical formalization underlying the modern field of reinforcement learning when transition and reward functions are unknown. We derive a pseudo-Boolean cost function that is equivalent to a K-spin Hamiltonian representation of the discrete, finite, discounted Markov decision process with infinite horizon. This K-spin Hamiltonian furnishes a starting point from which to solve for an optimal policy using heuristic quantum algorithms such as adiabatic quantum annealing and the quantum approximate optimization algorithm on near-term quantum hardware. In arguing that the variational minimization of our Hamiltonian is approximately equivalent to the Bellman optimality condition for a prevalent class of environments we establish an interesting analogy with classical field theory. Along with proof-of-concept calculations to corroborate our formulation by simulated and quantum annealing against classical Q-Learning, we analyze the scaling of physical resources required to solve our Hamiltonian on quantum hardware.
A Differential Evolution Algorithm for Contrast Optimization

Image Enhancement is one of the most important phases of the image processing system. Contrast Enhancement plays a key role in this step. Histogram Equalization (HE) is one of the main tools used to improve the contrast of an image. However, the use of HE causes an increase in the natural brightness of the image, which is not desirable in many types of applications such as consumer electronics products. To solve these limitations, it is proposed in this paper a variation of the Differential Evolution metaheuristic algorithm for Contrast Optimization called DECO. The results obtained were statistically compared with other techniques and metaheuristic algorithms. The results showed that DECO is competitive compared with other techniques.
Unsupervised Deep Hashing with Structured Similarity Learning

Hashing technology, one of the most efficient approximate nearest neighbor searching methods due to its fast query speed and low storage cost, has been widely used in image retrieval. Recently, unsupervised deep hashing methods have attracted more and more attention due to the lack of labels in real applications. Most unsupervised hashing methods usually construct a similarity matrix with the features extracted from the images, and then guide the hash code learning with this similarity matrix. However, in unsupervised scenario, such similarity matrix may be unreliable due to the affect of noise and irrelevant objects in images. In this paper, we propose a novel unsupervised deep hashing method called Deep Structured Hashing (DSH). In the new method, we first learn both continuous and binary structured similarity matrices with explicit cluster structure to better preserve the semantic structure, where the binary one preserves the coarse-grained semantic structure while the continuous one preserves the fine-grained semantic structure. And then jointly optimize three kinds of losses to learn high quality hash codes. Extensive experiments on three benchmark datasets show the superior retrieval performance of our proposed method.
Some Novelties in Map Reducing Techniques to Retrieve and Analyze Big Data for Effective Processing

As we are living in the era of digital data, it has become necessary to make use of it in intelligent ways. The data generation is not only increasing, but the rate with which data generates is also increasing. Huge data obtained from many sources can be processed and analyzed so as to get useful information. But, the problem is with volume of data, velocity with which data increases, and also different variety and complex structure of data. Storing such large amount of data and the process of retrieving huge data when required are time consuming. One of the solutions for effective processing of Big Data is parallel processing. The software solutions like Hadoop provides way to store and also to implement parallel processing of Big Data. In most of the situations, Big Data cannot be stored in a single system. Distributed File System that can run on different clusters can be used to process Big Data. By using MapReduce model, large dataset can be computed on commodity hardware clusters. This paper presents a novel work of implementing MapReduce technique to analyze and retrieve data. An attempt is made to retrieve data by adopting MapReduce technique. A task is divided into number of sub-tasks, and these sub-tasks can be processed simultaneously by different processors or number of commodity hardware. A novel and effective way of implementing MapReduce is represented in this paper. In other words, this work examines the method and the outcome of MapReduce technique, which is a solution to the problem of processing huge amount of data.
Comparison of backpropagation artificial neural network and SARIMA in predicting the number of railway passengers

Abstract Trains are one of the most popular public transportations in Indonesia. The data from the Indonesian Central Bureau of Statistics show an increasing trend in the number of train passengers in Indonesia. However, the improvement of the railway network and service is needed. This study aims to forecast the number of railway passengers in Indonesia to help the government make appropriate improvements in the railway system for the future and evaluate the potential loss due to COVID-19. We use the data from the Indonesian Central Bureau of Statistics, from January 2006 to February 2020 and assume that there is no pandemic of COVID-19. The two models we use are Backpropagation Artificial Neural Network (BPANN) and Seasonal ARIMA (SARIMA). To find the best model, we observe BPANN with various parameters and the potential SARIMA models in MATLAB and R software, respectively. Our finding is that Backpropagation Artificial Neural Network of 12-5-1 with a learning rate of 0.001 has a smaller root mean squared error (RMSE) compared to SARIMA (2,1,0)(0,1,2) 12 . Hence, it yields a more accurate forecast of the number of train passengers, which helps the railway company to improve and understand the loss due to COVID-19 accurately.
Planning for Aerial Robot Teams for Wide-Area Biometric and Phenotypic Data Collection

This work presents an efficient and implementable solution to the problem of joint task allocation and path planning in a multi-UAV platform. The sensing requirement associated with the task gives rise to an uncanny variant of the traditional vehicle routing problem with coverage/sensing constraints. As is the case in several multi-robot path-planning problems, our problem reduces to an mTSP problem. In order to tame the computational challenges associated with the problem, we propose a hierarchical solution that decouples the vehicle routing problem from the target allocation problem. As a tangible solution to the allocation problem, we use a clustering-based technique that incorporates temporal uncertainty in the cardinality and position of the robots. Finally, we implement the proposed techniques on our multi-quadcopter platforms.
Engineering Privacy-Preserving Machine Learning Protocols

Privacy-preserving machine learning (PPML) protocols allow to privately evaluate or even train machine learning (ML) models on sensitive data while simultaneously protecting the data and the model. So far, most of these protocols were built and optimized by hand, which requires expert knowledge in cryptography and also a thorough understanding of the ML models. Moreover, the design space is very large as there are many technologies that can even be combined with several trade-offs. Examples for the underlying cryptographic building blocks include homomorphic encryption (HE) where computation typically is the bottleneck, and secure multi-party computation protocols (MPC) that rely mostly on symmetric key cryptography where communication is often the~bottleneck.
Developing an Automatic Cooperating Neural Networks and Image Standardization Approach for Segmentation of X-Ray Computed Tomography Images

Medical image segmentation is a common image analysis task that can impact diagnostic accuracy. Image quality varies depending on application, acquisition parameters and patient preparation. This requires developers’ data preparation efforts prior training. Training on an explicit data nature imposes limitations on generalization. This requires further data pre/post processing efforts for each dataset of different nature. Hence, it’s essential to embed a core component in the segmentation algorithm to automatically handle data processing tasks. In this work, we developed a fully-automated system that transforms computed tomography (CT) images into a standard format prior to segmentation learning and then inverse-transform segments to the original image format to preserve co-registration. Furthermore, we have developed a multi neural network segmentation module that incorporates three convolutional task-specific neural networks that utilize global (two networks) and local (one network) information, while making the segmentation based on agreement of contextual information using probability heatmaps. For baseline non-standard training and testing on a diverse liver dataset, there was no learning convergence and testing results performed relatively poorly. In contrast, after standardized training on the same dataset, all networks converged and yielded dice of +90%. Using the same standardized training, standard and non-standard testing were compared after applying single variable variation. Standardization consistently improved performance by 2.5%, 34.7% and 1.5% for field-of-view, slice-thickness and anatomy, respectively. We also found that global-based segmentation outperformed local-based, and network cooperation significantly outperformed individual networks with dice 95.20%. This offers a segmentation system with better generalization capabilities to serve variable clinical environments.
Visualization of Deep Models on Nursing Notes and Physiological Data for Predicting Health Outcomes Through Temporal Sliding Windows

When it comes to assessing General Internal Medicine (GIM) patients’ state, physicians often rely on structured, time series physiological data because it’s more efficient and requires less effort to review than unstructured nursing notes. However, these text-based notes can have important information in predicting a patient’s outcome. Therefore, in this paper we train two convolutional neural networks (CNN) on in-house hospital nursing notes and physiological data with temporally segmented sliding windows to understand the differences. And we visualize the process in which deep models generate the outcome prediction through interpretable gradient-based visualization techniques. We find that the notes model provides overall better predictions results and it is capable of sending warnings for crashing patients in a more timely manner. Also, to illustrate the different focal points of the models, we identified the top contributing factors each deep model utilizes to make predictions.
A Smart Education Model for Future Learning and Teaching Using IoT

The Internet of things (IoT) is a giant network of heterogeneous things namely people, intelligent objects and devices, information and data all of which collect and share data about each other and the environment which they are a part of. IoT is a revolutionary technology that has already started to upgrade human lives in all aspects: Smart Cities, Smart Healthcare, Home Automation, Safety and Security, and Education. With the advent of the Internet of Things (IoT) in the education sector, the complete education model has changed enormously. It has created a platform for information sharing and communication between the people and the environment in any educational organization. This paper explains the basics of IoT and aims at highlighting all the practical methods for integrating IoT features in education. It classifies the impact of IoT on education into a three-dimensional model with Stakeholders, Applications, and Learning Modes as the dimensions. It classifies the applications into four areas: campus management, class management, disability accommodation, and improving students learning. The author has focused on how these three IoT dimensions will help in making a smarter education model.
PSPAB: Privacy-preserving average procurement bidding system with double-spending checking

Several organizations use auctions in a procurement bidding system to maintain a low procurement cost. Although several privacy-preserving auction solutions for different application scenarios have been proposed over the past few decades, none of them can perform efficient average procurement bidding while ensuring strong privacy protection for the bids of suppliers. To address this problem, we propose PSPAB, a lightweight, secure average procurement bidding system based on cryptographic tools, to provide full privacy for bids. In addition, this system allows the procurement manager to identify the users in the case of double spending. We formally prove the security of PSPAB under a semi-honest adversary model. Experimental results validate the theoretical analysis and practical application of PSPAB in real-world scenarios.
Model Development in Predicting Seaweed Production Using Data Mining Techniques

Production trends nowadays can be predicted by identifying hidden patterns in variables and factors underlying the production processes. In the industry sector, data mining is a valuable data analysis method that can help forecast variabilities in the different industrial processes, particularly, the production area. Useful knowledge can be analyzed from data in databases that can be very helpful in identifying specific factors that improve the quantity and quality of products. Thus, this study utilized data from an agency database that was analyzed using data mining techniques to develop a model on seaweed production. Seaweed production is continuously increasing its share in the market but is also faced with problems and constraints. This study determined significant variables that can predict high seaweed production and compared classification accuracies of Naïve Bayes, J48, CART, logistic regression and CHAID algorithms. A prediction model on seaweed production is generated that can benefit the seaweed industry sector in designing interventions to increase its production. Validation of the result through other data mining techniques is recommended by this study.
The effect experiential learning model based concrete-pictorial-abstract (EL-CPA) on mathematics attitude of deaf students

Abstract Mathematical attitude is a psychological response that arises when students interact in learning mathematics in school. For deaf students, attitudes towards mathematics have an important role in their success in learning mathematics. Deaf students are students who have hearing loss and verbal communication. The experts revealed that the visual potential of deaf students is a potential that can be utilized in mathematics learning activities. One learning model that utilizes the visual potential of deaf students is the EL-CPA model. The research aims to determine the effect of the application of the EL-CPA model on the mathematical attitudes of deaf students in Special Schools. This research is a quasi-experimental study with a sample of 20 deaf students. The sample was chosen randomly from 10 Special Schools in the city of Bogor. Data were analyzed using non-parametric statistics through binomial tests. The results of this study indicate that there is an effect of the EL-CPA model on the mathematical attitudes of deaf students with a Sig value of 0.024. Therefore, researchers recommend applying the EL-CPA learning model for deaf students in special schools.
Screen Magnification for Office Applications

People with low vision use screen magnifiers to interact with computers. They usually need to zoom and pan with the screen magnifier using predefined keyboard and mouse actions. When using office productivity applications (e.g., word processors and spreadsheet applications), the spatially distributed arrangement of UI elements makes interaction a challenging proposition for low vision users, as they can only view a fragment of the screen at any moment. They expend significant chunks of time panning back-and-forth between application ribbons containing various commands (e.g., formatting, design, review, references, etc.) and the main edit area containing user content. In this demo, we will demonstrate MagPro, an interface augmentation to office productivity tools, that not only reduces the interaction effort of low-vision screen-magnifier users by bringing the application commands as close as possible to the users’ current focus in the edit area, but also lets them easily explore these commands using simple mouse actions. Moreover, MagPro automatically synchronizes the magnifier viewport with the keyboard cursor, so that users can always see what they are typing, without having to manually adjust the magnifier focus every time the keyboard cursor goes off screen during text entry.
A Comparative Study of the Grey Wolf Optimizer and Firefly Algorithm in Mathematical Benchmark Functions of the CEC 15 Competition

The main goal of this paper is to study the performance of two metaheuristics, that are classified as belonging to Swarm Intelligence, when dealing with complex optimization problems. In this case, the Algorithms are the Grey Wolf Optimizer (GWO) algorithm and Firefly Algorithm (FA). In this case study, we are presenting in this paper the Mathematical Benchmark Functions that were given in the IEEE Congress on Evolutionary Computation 2015 (CEC’ 15) competition, in order to compare these popular algorithms.
Pre-Eating Play: Fabrication Experiences for Playful Human-Food Interaction

Recent progress of human--food interaction (HFI) has led to a growing interest in the merger of digital technologies for playful experiences in eating. Although these interactive technologies have been primarily integrated with eating experiences, we find that progress has also been made in digital food fabrication process in the human--computer interaction (HCI) field. Food fabrication techniques are used to expand the design space of food preparation, and it is accomplished before the eating stage. Many products have introduced playful experiences to this preparation stage. However, we have not yet explored food fabrication techniques in the context of play in depth. This paper discusses the food preparation stage and how it can be designed to be playful by introducing several product cases. Using this knowledge, we further discuss how such methods can be applied to previous digital food fabrication techniques with our own example of a playful food fabrication approach. We aim to enable various stages of human--food interaction to be more playful by understanding food as a toy-like object that can be independent from or related to the eating stage.
Log-Nets: Logarithmic Feature-Product Layers Yield More Compact Networks

We introduce Logarithm-Networks (Log-Nets), a novel bio-inspired type of network architecture based on logarithms of feature maps followed by convolutions. Log-Nets are capable of surpassing the performance of traditional convolutional neural networks (CNNs) while using fewer parameters. Performance is evaluated on the Cifar-10 and ImageNet benchmarks.
An NLP Approach to Estimating Effort in a Work Environment

Effort estimation in Software development is becoming increasingly hard to do correctly, this is in part due to the growing complexity of software projects, but also due to the higher amount of projects in total. Specialists in multiple fields are required to work together to obtain a realistic estimate, but even then, there is a good amount of risk involved when planning based on the estimate. This leads to cost increases both for the actual estimation process and the losses taken due to wrong estimations. There are already a few project estimation systems out there which take into account presumed system size, development cycles (design, develop, test) or other project related variables. We want to introduce a more granular estimation system, which uses the text descriptions of various tasks but also takes into account available metadata like seniority of the employee which will be working on said tasks, client and project/project type when estimating. The system is built on a deep neural network. The results we are getting are promising so far and we are working on establishing the human baseline accuracy while the tool is available for company employees to use.
MR Environments Constructed for a Large Indoor Physical Space

To resolve the problem that existing mixed reality (MR) apparatus are unable to scan and model a large and complex indoor scene at once, we present a powerful toolkit for constructing MR environment easily. Our toolkit establishes and maintains accurate mapping between the virtual and physical space, and sets the occlusion relationships of the walls. Additionally, we design spatial anchor deployment strategy supporting deviation correction between the real and virtual spaces, so that the spatial anchors can maintain a virtual object’s location and orientation in the real world. Our experiments and applications show that the toolkit is convenient for constructing MR apps targeting large physical spaces in which users can roam in real time.