Reviewerly/exHarmony huggingface dataset:
vrSensory: Designing Inclusive Virtual Games with Neurodiverse Children

We explore virtual environments and accompanying interaction styles to enable inclusive play. In designing games for three neurodiverse children, we explore how designing for sensory diversity can be understood through a formal game design framework. Our process reveals that by using sensory processing needs as requirements we can make sensory and social accessible play spaces. We contribute empirical findings for accommodating sensory differences for neurodiverse children in a way that supports inclusive play. Specifically, we detail the sensory driven design choices that not only support the enjoyability of the leisure activities, but that also support the social inclusion of sensory-diverse participants. The participants displayed behaviors in the multi-user version consistent with their behaviors in the single user version with the addition of social behavior. We tie these techniques to game design mechanics to iterate on our efforts to support inclusive game development. Preliminary results are discussed.
Reversible contrast enhancement for medical images with background segmentation

Contrast enhancement (CE) of medical images is helpful to bring out the unclear content in the interested regions. Recently, reversible CE has been proposed so that the original version of a contrast-changed image can be exactly recovered. This property can be used to save storage space or facilitate the archiving system. To enhance the regions of interest (ROI) without introducing visual distortions, the technique of image segmentation (e.g. using Otsu's method) has been used to obtain the background before conducting the CE process. To segment the ROI more accurately, an interactive algorithm called GrabCut is employed in the proposed scheme. In addition, a new preprocessing strategy is adopted to preserve the image quality through the CE process. Consequently, the content in the selected regions can be better brought out while the reversibility of the CE process is achieved. The experimental results on 30 chest radiograph images and 20 magnetic resonance images have demonstrated the efficacy of the proposed scheme for reversible CE. The evaluation results are provided to show the better performances of the proposed method in achieving CE effects and preserving image quality.
Chatbot and bullyfree Chat

In this technology world, a recent technology called chatbot which have been in demand and usage for every business purpose and have hit the market.Chatbots is an interaction between person and bot which gives us a efficient service and it also gives the way to develop customer engagement and efficiency by reduction of cost by using these service.Chatbots can be accessible at anytime,which can handle capacity that is chatbot can chat with thousands of people at a time,It has a flexible attribute as well as customer satisfaction. A chatbot is constructed using natural language processing with the help of machine learning algorithm for training the bot and to make up the bot to perform in a right way and so training and testing is done using ML.This paper gives an overview of chatbot and challenges we faced behind the chatbot with extra features of images.
Similarity of medical concepts in question and answering of health communities

The ability to automatically categorize submitted questions based on topics and suggest similar question and answer to the users reduces the number of redundant questions. Our objective was to compare intra-topic and inter-topic similarity between question and answers by using concept-based similarity computing analysis. We gathered existing question and answers from several popular online health communities. Then, Unified Medical Language System concepts related to selected questions and experts in different topics were extracted and weighted by term frequency -inverse document frequency values. Finally, the similarity between weighted vectors of Unified Medical Language System concepts was computed. Our result showed a considerable gap between intra-topic and inter-topic similarities in such a way that the average of intra-topic similarity (0.095, 0.192, and 0.110, respectively) was higher than the average of inter-topic similarity (0.012, 0.025, and 0.018, respectively) for questions of the top 3 popular online communities including NetWellness, WebMD, and Yahoo Answers. Similarity scores between the content of questions answered by experts in the same and different topics were calculated as 0.51 and 0.11, respectively. Concept-based similarity computing methods can be used in developing intelligent question and answering retrieval systems that contain auto recommendation functionality for similar questions and experts.
A model with length-variable attention for spoken language understanding

Intent detection (ID) and slot filling (SF) are important components in spoken language understanding (SLU) of a dialogue system. The most widely used method is pipeline manner which detects the user's intent at first, then labels the slots. For the purpose of addressing error propagate, some researchers combine these two tasks together by ID and SF joint model. However, the joint models usually perform well only on one of these tasks due to the different values of the trade-off parameter. We therefore propose an encoder-decoder model with a new tag scheme which unifies these two tasks into one sequence labeling task. In our model, the process of slot filling can receive an intent information and the performance about multiple tags of a word has been improved. Moreover, we show a length-variable attention which can selectively look at a subset of source sentence in the sequence labeling model. Experimental results on two datasets display that the proposed model with length-variable attention outperforms over other joint models. Besides, our method will automatically find the balance between two tasks and achieve better overall performances.
Predicting ore content throughout a machine learning procedure – An Sn-W enrichment case study

The distribution patterns of trace elements are very useful for predicting mineral deposits occurrence. Machine learning techniques were used for the computation of adequate models in trace elements' prediction. The main subject of this research is the definition of an adequate model to predict the amounts of Sn and W in the abandoned mine area of Lardosa (Central Portugal). Stream sediment samples (333) were collected within the study area and their geochemical composition - As, B, Be, Cd, Co, Cr, Cu, Fe, Ni, P, Sn, U, V, W, Y, and Zn - used as input attributes. Different machine learning techniques were tested: Decision Trees (CART), Multilayer Perceptron (MLP) and Support Vector Machines (SVM). For regression and clustering, CART, MLP approaches were tested and for the classification, problem SVM was used. These algorithms used six different inputs – N1 to N6 – aiming to pick out the best-performing model. The results show that CART is the optimized predictor for Sn and W. Concerning the regression approach, correlation coefficients of 0.67 for Sn (with Input N1) and 0.70 for W (with Input N3) were obtained. Regarding the classification problem, an error rate of 0.10 was reached for both Sn (Input N1) and W (Input N2). The classification process is the best methodology to predict Sn and W, using as input the trace element concentrations in the collected stream sediment samples, Lardosa area, Portugal.
COPA: Highly Cost-Effective Power Back-Up for Green Datacenters

Traditional datacenters employ costly diesel generators (DG) and uninterrupted power supplies (UPS) to back up power. However, some or even all racks of a green datacenter can still be powered by renewable energy during grid power outages. This makes the utilization of the DGs and UPSs in green datacenters significantly lower than in traditional datacenters. In this paper, we propose a highly cost-effective power back-up (COPA) approach for green datacenters by leveraging the availability characteristics of renewable energy as well as grid power outages. COPA contributes three new techniques. The first technique, called least UPS capacity planning, determines the least rated power capability and runtime of the UPSs to guarantee the normal operations of a green datacenter during grid power outages. The second technique, named cooperative UPS/renewable power supply, employs UPS and renewable energy at the same time to supply power to each rack when grid power fails. The last one, dubbed renewable-energy-aware dynamic power management, controls the power consumption dynamically based on the available capacity of renewable energy and UPS. We build an experimental cluster consisting of 10 servers, and use four representative benchmarks as well as verified data about the availability characteristics of solar and wind energy to evaluate COPA. The results show that COPA reduces 47 percent and 70 percent of the power back-up cost for a solar energy powered datacenter and a wind energy powered datacenter, respectively. Moreover, COPA guarantees the application's Service Level Agreement (SLA) for at least 20 minutes (over 79 percent outages) and 56 minutes on average while enabling the back-up power to last for at least 2 hours and for 3 hours on average, which cannot be achieved by other under-provisioning power back-up approaches.
Matting-Based Residual Optimization for Structurally Consistent Image Color Correction

Image color correction aims to eliminate color differences between images, especially for color consistency in panoramic or stereoscopic images. Nowadays, the global color correction methods cannot correct local color differences, while the local color correction methods usually lead to structural inconsistency between local regions and image clarity reduction. To address these problems, we propose a matting-based residual optimization for structurally consistent image color correction (MROC). Inspired by the residual image, we formulate the image color correction problem as the optimization of a residual image between the input target image and the resulting image. The residual image is initialized and improved by the soft matting method with a closed-form solution. Besides, a data term is introduced to identify those pixels with higher color and structural consistencies and preserve these pixels during optimization. The whole computational infrastructure operates at the pixel level to correct local color differences while maintaining image clarity. Experimental results demonstrate that the performance of the proposed MROC method is superior to the state-of-the-art image color correction methods. Furthermore, the proposed matting-based residual optimization can also be incorporated in a variety of color correction methods, with enhanced outcomes justified by a group of image quality assessment metrics.
Themis: Efficient and Adaptive Resource Partitioning for Reducing Response Delay in Cloud Gaming

Cloud gaming has been increasing in popularity recently, but issues relating to maintaining low interaction delay for users to guarantee satisfactory gaming experience is still prevalent. Interaction delays caused by server-side processing are heavily influenced by how the processes partition the resources. However, finding the optimal partitioning policy that minimizes the response delay is complicated by several critical challenges. In this paper, we propose Themis, a system that enables efficient and adaptive online resource partitioning for reducing response delay in cloud gaming. Briefly, Themis employs machine learning technology to build a performance model which is able to capture the complex relationships between resource partition and system performance. With this model, Themis divides the processes into disjoint groups and partitions resources among process groups, which greatly simplifies the resource partition problem while ensuring high partitioning effectiveness. To tackle dynamic workload changes, Themis leverages reinforcement learning to learn how different partitioning actions affect system performance in an online manner, and adaptively choose the best actions for minimizing response delay in real time. We evaluate Themis in a real cloud gaming environment using several real games. The experimental results show that Themis can reduce the response delay by 17% to 36% compared to a system without resource partitioning, and outperforms other resource partitioning policies significantly. To the best of our knowledge, this is the first work to optimize response delay in cloud gaming through resource partitioning.
Critic-based Attention Network for Event-based Video Captioning

In this paper, we investigate utilizing an actor-critic architecture for an event-based video captioning. In this captioning task, the video can contain multiple overlapping events. We consider the words as the actions that our model takes sequentially. The architecture of our model consists of an actor network to predict the captions given temporal segments in a video and a critic network to measure the quality of the generated captions. Our model, first, localizes events in the video, then by using the localized events, it locates temporal segments in the video. We adopt a global network to generate a caption for each temporal segment. We propose an attention mechanism to account for the importance of each localized event in captioning a temporal segment in the video. We provide a set of experiments on utilizing our method in the task of event-based video captioning on ActivityNet Captions and TACoS-MultiLevel datasets. Experimental results show that our method outperforms state-of-the-art video captioning methods.
An Efficient Workload Clustering Framework for Large-Scale Data Centers

The cloud workload refers to charge made by a huge diversity of independent services and applications located on cloud infrastructures. Workload characteristics can be defined by tasks or Virtual Machines (VMs). Then again, tasks/VMs scheduling, allocation, workload predictions are important topics, which are gaining steadily increasing attention particularly in the past few years. In all these fields, clustering methods are often used to recognize groups of workload components characterized by comparable behaviors. In order to attain an effective clustering, the appropriate clustering technique needs to be selected. This choice is vital especially when there are vast choices that provide different results. To address such issue, this paper presents a comprehensive review of clustering categories application in cloud workload clustering. This paper also proposes a novel systematic framework to select the suitable tasks/VMs clustering method in large-scale data centers based on clustering purpose, validation indices and comparison of results.
Image conditions for machine-based face recognition of juvenile faces

Machine-based facial recognition could help law enforcement and other organisations to match juvenile faces more efficiently. It is especially important when dealing with indecent images of children to minimise the workload, and deal with moral and stamina challenges related to human recognition. With growth related changes, juvenile face recognition is challenging. The challenge not only relates to the growth of the child’s face, but also to face recognition in the wild with unconstrained images. The aim of the study was to evaluate how different conditions (i.e. black and white, cropped, blur and resolution reduction) can affect machine-based facial recognition of juvenile age progression. The study used three off-the-shelf facial recognition algorithms (Microsoft Face API, Amazon Rekognition, and Face++) and compared the original images and the age progression images under the four image conditions against an older image of the child. The results showed a decrease in facial similarity with an increased age gap, in comparison to Microsoft; Amazon and Face++ showed higher confidence scores and are more resilient to a change in image condition. The image condition ‘black and white’ and ‘cropped’ had a negative effect across all three APIs. The relationship between age progression images and the younger original image was explored. The results suggest manual age progression images are no more useful than the original image for facial identification of missing children, and Amazon and Face++ performed better with the original image.
Anonymous Transactions with Revocation and Auditing in Hyperledger Fabric

In permissioned blockchain systems, participants are admitted to the network by receiving a credential from a certification authority. Each transaction processed by the network is required to be authorized by a valid participant who authenticates via her credential. Use case settings where privacy is a concern thus require proper privacy-preserving authentication and authorization mechanisms.Anonymous credential schemes allow a user to authenticate while showing only those attributes necessary in a given setting. This makes them a great tool for authorizing transactions in permissioned blockchain systems based on the user’s attributes. In most setups, there is one distinct certification authority for each organization in the network. Consequently, the use of plain anonymous credential schemes still leaks the association of a user to the organization that issued her credentials. Camenisch, Drijvers and Dubovitskaya (CCS 2017) therefore suggest the use of a delegatable anonymous credential scheme to also hide that remaining piece of information.In this paper, we propose the revocation and auditability—two functionalities that are necessary for real-world adoption—and integrate them into the scheme. We present a complete protocol, its security definition and the proof, and provide its open-source implementation. Our distributed-setting performance measurements show that the integration of the scheme with Hyperledger Fabric, while incurring an overhead in comparison to the less privacy-preserving solutions, is practical for settings with stringent privacy requirements.
Light weight Cryptographic solutions for Fog Based Blockchain

Cryptography is the art of converting readable data into indecipherable format using keys and vice versa. Strength of the algorithm is mainly measured by the complexity and CPU cycles required for cryptanalysis in the algorithm. But in an energy constrained environment like IoT, CPU intense complex algorithms will degrade the efficiency. Light-weight or cut down versions of symmetric and asymmetric cryptographies are found best suited for constrained environments. So accordingly, we in this paper have surveyed a lot of Hardware and software based Light-weight cryptographic algorithms in a quest to find the best suited cryptographic algorithm for signing and hashing blockchain in a constrained environment.
A Continuous Role-Based Authentication Scheme and Data Transmission Protocol for Implantable Medical Devices

Over the last decade, new technologies have been developed to give support to efficient medical treatments. Implantable Medical Device (IMD) is one such supporting device whose usage has been increasing remarkably in extending and improving the quality of a patient's life. With the remotely monitored IMDs being prevalent today, all of the patient's data is readily available on these devices and is accessible over the internet. But, unauthorized access to IMDs could endanger the patient's life. To prevent such dangers, this paper proposes a continuous role-based authentication technique for user and IMD monitoring computer interaction. Also, while the numerous benefits of wireless connectivity are well understood, the recent research has shown that such connectivity can be exploited to compromise the security of IMDs and bring serious risk to the patient's life when an adversary tampers with the data that is being transferred over the channel. For this purpose, this paper proposes a definite protocol for secure data transmission during Computer-IMD interaction, by taking into account the constrained resources of an IMD, in particular, the memory and powered battery.
Wellness determination of the elderly using spatio-temporal correlation analysis of daily activities

With the advent of smart computing, Internet of Things (IoT) and sensor technology, it is now possible to determine the wellness of the elderly living alone in a home equipped with miniaturized sensors. The technology has the potential to seamlessly monitor daily activities performed by humans livi ng under free conditions. Such automatic activity monitoring systems depend on classification techniques, which can effectively interpret activities as normal or abnormal. Although a significant progress has been made in this area, the application of advanced classification techniques are still required to capture various aspects of activities to predict the wellness with high accuracy. In this paper, we propose a wellness classification model called Temporal Weighted Associative Classification (TWAC) by integrating the spatio-temporal contextual information for predicting wellness of the elderly by monitoring their daily usage patterns of household appliances equipped with sensors. The two key components of the model are Data Preparation Module (DPM) and Dynamic Classification Module (DCM). The DPM collects data from sensors and represents it into a format suitable to apply classification by capturing spatio-temporal information of each activity such as time, location, duration and sub-activities, if any. DCM dynamically generates classification rules by identifying correlations among frequent and less frequent activities recorded over a predefined time window. A classification model is then based on these rules to predict normal or abnormal activities. Moreover, the classification model learns from new data and dynamically updates the rules to accommodate the change in daily activities’ pattern, hence, over time the prediction become more accurate. TWAC is tested for its accuracy by comparing it with well-known classifiers such as C4.5, HMM, NB, SVM, CPAR and CBAR. Improved analysis results have been observed as documented in the experimental analysis. Based on a high accuracy, the proposed model will be suitable to develop systems used to forecast the behavior and wellness of the elderly living alone in smart homes.
Two-factor authentication framework for private cloud

Authorizing access to the public cloud has evolved over the last few years, from simple user authentication and password authentication to two-factor authentication (TOTP), with the addition of an additional field for entering a unique code. Today it is used by almost all major websites such as Facebook, Microsoft, Apple and is a frequently used solution for banking websites. On the other side, the private cloud solutions like OpenStack, CloudStack or Eucalyptus doesn't offer this security improvement. This article is presenting the advantages of this new type of authentication and synthetizes the TOTP authentication forms used by major cloud providers. Furthermore, the article is proposing to solve this challenge by presenting a practical solution for adding two-factor authentication for OpenStack cloud. For this purpose, the web authentication form has been modified and a new authentication module has been developed. The present document covers as well the entire process of adding a TOTP user, generating and sending the secret code in QR form to the user. The study concludes with OpenStack tools used for simplifying the entire process presented above.