Reviewerly/exHarmony huggingface dataset:
PTCP: A Priority-Driven Congestion Control Algorithm to Tame TCP Incast in Data Centers

Data centers have become a prevalent infrastructure to host a large number of services, such as social networking and Web search, which generally have rigorous delay requirements. Even a slight increase in delay can seriously affect the performance of applications. Therefore, there have been several efforts focusing on minimizing the flow completion time (FCT) to meet the delay requirements, such as ICTCP, which adjusts the receive window according to the available bandwidth and DCTCP that uses ECN to provide the feedback to end hosts in mixing workloads. However, both approaches are only for a specific scene and cannot effectively solve the problem that packets losses lead to timeouts due to TCP incast. Moreover, they all use the fair-share mechanism, which causes the increasing of the FCT for small flows, when background flows pour into the network. For example, background flows cause the increasing(≈ 5ms) of the FCT for the ICTCP in an incast experiment. To solve these problems, we propose a priority-driven congestion control algorithm, PTCP, which can effectively avoid the incast problem and improve the FCT in mixing workloads. The PTCP leverages the priority to adjust the receive window and control the interval time of ACK. To evaluate the PTCP, we conduct extensive experiments in NS-2. The simulation results show that the PTCP outperforms greatly previous schemes both in the incast scenario and mixing workloads.
InTool Explorer: An Interactive Exploratory Analysis Tool for Versatile Visualizations of Neuroscientific Data

The bottleneck for progress in many research areas within neuroscience has shifted from the data acquisition to the data analysis stages. In the present article, we propose a method named InTool Explorer that we have developed to perform interactive exploratory data analysis, focusing on neuroanatomy as an example of its utility. This tool is freely-available software that has been designed to facilitate the study of complex neuroscience data. InTool Explorer requires no more than an internet connection and a web browser. The main contribution of this tool is to provide a user-designed canvas for data visualization and interaction, to perform specific exploratory tasks according to the user needs. Moreover, InTool Explorer permits visualization of the datasets in a very dynamic and versatile way using a linked-card approach. For this purpose, the tool allows the user to select among different predefined card types. Each card type offers an abstract data representation, a filtering tool or a set of statistical analysis methods. Additionally, InTool Explorer makes it possible linking raw images to the data. These images can be used by InTool Explorer to define new customized filtering cards. Another significant contribution of this tool is that it allows fast visualization of the data, error finding, and re-evaluation to establish new hypotheses or new lines of research. Thus, regarding its practical application in the laboratory, InTool Explorer provides a new opportunity to study and analyze neuroscience data prior to any statistical analysis being carried out.
Guest Retention Through Automation

The technology development in hospitality is continuing at a relentless pace which is challenging for the hospitality professional for both present and the future generations. The hotel front office is moving towards automation with less human interface. Reservations are mostly being made with help of booking engines and guest interaction with hotels are by apps and chatbots. Artificial intelligence (AI) also occupies a major role to facilitate and enhance guest experience. The trends now include use of augmented reality, predictive analysis, beacons, robotics, block chain technology, and biophilic designs in the hotel. The research is to study about how various hotel chains are adopting new technology and incorporating it in their establishment. The research is based upon data collected from hotel websites and other secondary sources to determine the acceptance of new trends by the hotel chains. The result suggests that though some international hotel chains have started accepting the new trends, the major Indian chains specially are lacking behind.
GeCoLan: A Constraint Language for Reasoning About Ecological Networks in the Semantic Web

Ecological Networks (ENs) describe the structure of existing real ecosystems and help planning their expansion, conservation and improvement. While various mathematical models of ENs have been defined, to our knowledge they focus on simulating ecosystems, but none of them deals with verifying whether any transformation proposals, as those collected in participatory decision-making processes for public policy making, are consistent with land usage restrictions. As an attempt to fill this gap, we developed a model to represent the specifications for the local planning of ENs in a way that can support both the detection of constraint violations within new proposals of expansion, and the reasoning about improvements of the networks. In line with the GeoSpatial Semantic WEB, our model is based on an OWL ontology for the representation of ENs. Moreover, we define a language, GeCoLan, supporting constraint-based reasoning on semantic data. Even though this paper focuses on EN validation, our language can be employed to enable more complex tasks, such as the generation of proposals for improving ENs. The present paper describes our ontological specification of ENs, the GeCoLan language for reasoning about specifications, and the tools we developed to support data acquisition and constraint verification on ENs.
Breast Tumor Detection Via Fuzzy Morphological Operations

Breast cancer is one of most dangerous diseases and more common in women. The early detection of cancer is one of the most key factors for possible cure. There are numerous methods of diagnosis amongst which: clinical examination, sonar and mammography, which is the best and more effective in detecting breast cancer. Detection of breast tumors is difficult because of the weak illumination in the image and the overlap between regions. Segmentation is one the crucial steps in locating the tumors, which is an important method of diagnosis of the computer. In this study, segmentation techniques are proposed based on; classic morphology and fuzzy morphology, and a comparison between them. The proposed methods were tested using the database of mini -MIAS, which contains 322 images. After the comparison the statistical results, it shows, the detection of tumor boundary with fuzzy morphology give the higher accuracy than the results in classic morphology. The accuracy is 60.69%, 58.61% respectively due to the high flexibility of foggy logic in dealing with the low lighting in the medical images.
Amalgamation of General and Domain Specific Word Embeddings for Improved Hierarchical Aspect Aggregation

Aspect aggregation or grouping similar aspects is a vital phase in review summarization. Hierarchical aspect aggregation is needed due to the multi-granular nature of aspects. This paper presents a novel approach for hierarchical aspect aggregation employing an amalgamation of domain specific and domain independent word embeddings along with agglomerative clustering to output a multi-granular structure of aspects. We evaluate our output using both internal and external measures. Our results outperform the state of the art approach in multi-granular aspect aggregation.
NEW TECHNOLOGIES OF EDUCATION ON THE BASIS OF SMALL ANTHROPOMORPHIC ROBOT ROMA

The article describes the use of the anthropomorphic robot ROMA in the educational process. The trends and the main threats associated with the avalanche-like development of robotics and total robotization of society, and the tasks that the teachers face are considered. A small robot ROMA developed at the Kazan Federal University is presented, and its characteristics are described. A comparison is made with the characteristics of foreign analogues, whence it is clear that the presented robot occupies a worthy place in its niche. Various variants for using the ROMA robot in the educational process — from primary school classes to senior university students, are considered. There are educational disciplines and specific topics on which the use of the robot can give a significant impetus to understanding and mastering the subject. On the one hand, this is connected with the students’ interest in the robot as such, and on the other hand, the robot allows to physically demonstrate certain abstract concepts, the effect of algorithm execution, etc. The robot can be used at school for technology lessons. It may be indispensable when studying programming at various levels, 3D modeling, the theory of automatic control, elements of artificial intelligence, pattern recognition, and in a number of other courses. Currently, the process of developing methodological support for various disciplines is underway.
Work-in-Progress: Development of a Framework to Foster Collaborative Learning Among Engineering Students Using Moodle Mobile App

Students’ learning patterns vary from person to person. Some students prefer to learn by seeing, hearing, reflecting, acting or reasoning logically while others prefer to learn by visualizing, drawing analogies and building mathematical models. Traditionally, Engineering education has focused on content-based or design-based approaches that allowed students to develop problem solving skills. Use of technology for instructional purposes or communication has not been very effective in Sri Lankan universities in the past. This research is to find out how we can improve Sri Lankan Engineering students’ engagement in studies via collaborative learning by using mobile technology. In addition, research will compare the effectiveness of different online collaborative learning methodologies that use mobile based and web-based learning and teaching platforms in Sri Lanka.
Quality metric of 3D models using Weber's Law

According to its causes and effects, it becomes essential to judge the quality of the model after a treatment, compared to the initial image. In fact, the quality of 3D objects becomes a paramount criterion for any treatment. Many authors proposed descriptors to evaluate the quality or the natural appearance of images. However, it is obvious that the best correlation between the results obtained and human visual perception. We stand out against current trends by avoiding purely developing and mathematical measures, or completely inspired by the HVS (Human Visual System). Indeed our new metric 3DrwPSNR based on the use of Weber's law that takes into account the human visual system. This law translates a logarithmic eye’s perception of light. This property led us to develop a metric that takes into consideration the relative difference of models and not the absolute difference. These measurements prove there are much correlated with the human visual appreciation of the processed 3D objects.
Robust video-frame classification for bronchoscopy

During bronchoscopy, a physician uses the endobronchial video to help navigate and observe the inner airways of a patient's lungs for lung cancer assessment. After the procedure is completed, the video typically contains a significant number of uninformative frames. A video frame is uninformative when it is too dark, too blurry, or indistinguishable due to a build-up of mucus, blood, or water within the airways. We develop a robust and automatic system, consisting of two distinct approaches, to classify each frame in an endobronchial video sequence as informative or uninformative. Our first approach, referred as the Classifier Approach, focuses on using image-processing techniques and a support vector machine, while our second approach, the Deep-Learning Approach, draws upon a convolutional neural network for video frame classification. Using the Classifier Approach, we achieved an accuracy of 78.8%, a sensitivity of 93.9%, and a specificity of 62.8%. The Deep-Learning Approach, gave slightly improved performance, with an accuracy of 87.3%, a sensitivity of 87.1%, and a specificity of 87.6%.
Reuse (or Lack Thereof) in Travis CI Specifications: An Empirical Study of CI Phases and Commands

Continuous Integration (CI) is a widely used practice where code changes are automatically built and tested to check for regression as they appear in the Version Control System (VCS). CI services allow users to customize phases, which define the sequential steps of build jobs that are triggered by changes to the project. While past work has made important observations about the adoption and usage of CI, little is known about patterns of reuse in CI specifications. Should reuse be common in CI specifications, we envision that a tool could guide developers through the generation of CI specifications by offering suggestions based on popular sequences of phases and commands. To assess the feasibility of such a tool, we perform an empirical analysis of the use of different phases and commands in a curated sample of 913 CI specifications for Java-based projects that use Travis CI-one of the most popular public CI service providers. First, we observe that five of nine phases are used in 18%-75% of the projects. Second, for the five most popular phases, we apply association rule mining to discover frequent phase, command, and command category usage patterns. Unfortunately, we observe that the association rules lack sufficient support, confidence, or lift values to be considered statistically significantly interesting. Our findings suggest that the usage of phases and commands in Travis CI specifications are broad and diverse. Hence, we cannot provide suggestions for Java-based projects as we had envisioned.
Research of image deblurring based on Tikhonov regularization

To deal with the shortcoming of the Tikhonov regularization in which the blurred image is extended with a zeros extension matrix. With careful analysis of the algorithms with Tikhonov regularization under zero boundary condition, a new extension matrix for the blurred image was proposed. The improved algorithm is in accord with the real image blurring process. Experimental results show that compared with other popular algorithms, the improved method performs favorably in solving the problem of the Average degradation and Gaussian degradation.
Preserving SLA Parameters for Trusted IaaS Cloud: An Intelligent Monitoring Approach

Background: Cloud Computing (CC) provides an open computing feature where a large number of customers can interact with the cloud services hosted by the cloud service providers (CSP) for their business requirements. Objective: Service Level Agreements (SLAs) are legal agreements between an enterprise and the CSP. To increase the confidence of the customer towards their CSP(s) will be determined by SLA fulfillment and possible actions should be adopted to minimize the violation of the SLAs. Which in turn will also lead to the revenue generation towards CSP. When SLA is breached by the customer, penalties are imposed; whereas SLA breach by the CSP will affect the trust of its valuable customers. Methods: Identify the metrics with respect to the resources and its Service level agreements (SLAs) and measure (monitoring) this to maintain its value and if the value is less as per the SLAs (threshold values) then take precautionary actions to maintain this. Results: The results indicate the present status of the resource utilization of the CPU and categories into three levels and based upon this take necessary actions to maintain the SLAs. Conclusion: The methodology discussed herein this paper can be used for monitoring the SLA parameter and to avoid the circumstances that can lead to breaching of the SLA(s).
Utility Token Offerings and Crypto Exchange Listings: How Regulation Can Help?

The majority of utility token fundraising models (Initial Coin Offerings, ICOs) de facto favor speculation over consumption. The public interest is hence to protect investors’ rights by application of a new or traditional regulatory regime to such offerings. However, given that ICOs and crypto markets are a globally unregulated phenomenon, direct regulation threatens to stifle innovation and be unenforceable when startups and investors move supply and demand to unregulated markets. This article advocates for a balanced principle-based approach to direct regulation of utility token offerings. Further, the majorly unregulated market of crypto exchanges fuels crypto economy but provides for no gatekeeper’s function: no standardized token listing practices which would cater for commercial viability and innovativeness of the listed startup. Building upon the results of an empirical and case study this article argues that the new inclusive governance models applied by crypto exchanges in startup pre-vetting may fill this void.
Load balancing strategy for cloud computing based on dynamic replica technology

Dynamic Replica Technology can solve the problem of load balancing in cloud computing. Multiple replicas are distributed in different nodes and the load of a node can be dispersed to other nodes that have the replicas. This paper presents a replica manage strategy based on file access frequency to solve the problem of load balancing, including the time for creating the replicas, the number of the replicas, the location of the replicas, the time for deleting the replicas and deleting the replicas. The strategy determines the number and the location of replicas according to the file access frequency, node’s storage space, network bandwidth, replica consistency maintenance cost and other factors. The replica will be deleted when the file access frequency is low or it has not been accessed for a long time. Experimental results reveal that the algorithm can not only achieve load balancing but also improve the performance of the system.
HeteroYARN: A Heterogeneous FPGA-Accelerated Architecture Based on YARN

In recent years, the heterogeneous distributed platform integrating with FPGAs to accelerate computation tasks has been widely studied to deal with the deluge of data. However, most of current works suffer from poor universality and low resource utilization that run specific algorithms with the highly customized structure. Moreover, there are still many challenges, such as data curation, task scheduling, and resource management, which further limit the scalability of a CPU-FPGA distributed platform. In this paper, we present HeteroYARN, an FPGA-accelerated heterogeneous architecture based on YARN platform, which provides resource management and programming support for computing-intensive applications using FPGAs. In particular, the HeteroYARN abstracts FPGA accelerators as general resources and provides programming APIs to utilize those accelerators easily. Our HeteroYARN simplifies the request and usage of FPGA resources to enhance the efficiency of the heterogeneous framework while maintaining previous workflow unchanged. Experimental results using two representative algorithms, K-means and Naive Bayes classifier, which are accelerated by FPGAs, demonstrate the usability of the HeteroYARN framework and show performance speedup improvement by 7.5x (K-means) and 2.3x (Naive Bayes) respectively compared to conventional CPU-only applications provided by Mahout.
Large-scale parcellation of the ventricular system using convolutional neural networks

Enlarged ventricles are a marker of several brain diseases; however, they are also associated with normal aging. Better understanding of the distribution of ventricular sizes in a large population would be of great clinical value to robustly define imaging markers that distinguish health and disease. The AGES-Reykjavik study includes magnetic resonance imaging scans of 4811 individuals from an elderly Icelandic population. Automated brain segmentation algorithms are necessary to analyze such a large data set but state-of-the-art algorithms often require long processing times or depend on large manually annotated data sets when based on deep learning approaches. In an effort to increase robustness, decrease processing time, and avoid tedious manual delineations, we selected 60 subjects with a large range of ventricle sizes and generated training labels using an automated whole brain segmentation algorithm designed for brains with ventriculomegaly. Lesion labels were added to the training labels, which were subsequently used to train a patch-based three-dimensional U-net Convolutional Neural Network for very fast and robust labeling of the remaining subjects. Comparisons with ground truth manual labels demonstrate that the proposed method yields robust segmentation and labeling of the four main sub-compartments of the ventricular system.
Fuzzy rules-based system for description spatial relations among handwriting strokes

Spatial relations are important ingredients for the interpretation of global meaning of structured objects as well as resolving the uncertainty caused by the ambiguities in the feature extraction stage. In this paper, we present a fuzzy rulebased system that accomplished the task of automated linguistic of spatial relationships between each neighboring pair of on-line handwritten stroke characters. We introduced the fuzzy logic in order to evaluate the possible interpretation and precision of the relation itself. The multiclass SVM classifiers are used for ourexperiment to classify the obtained spatial relations. Experiments usingMAYASTROUN database showed that the proposed method produces more intuitive results with a recognition rate of 94.82%. In fact, the experimental results highlighted that our approach outperforms other approaches that are reported in literature.
An efficient virtual machine allocation algorithm for parallel and distributed simulation applications

Summary Allocating appropriate resource for parallel and distributed simulation (PADS) applications in clouds is an intuitive way to improve their execution efficiency. However, the heterogeneity of virtual machine (VMs) in clouds with respect to both their computing power and network latency influences the execution efficiency of PADS applications on different combinations of VMs. Besides, frequent synchronization is one of the characteristics during the execution of PADS applications, which seriously challenges the prediction of the influence of VMs' computing power and network latency on their execution efficiency, and makes allocating appropriate VMs difficult as a result. This paper first proposes a revivification‐based prediction model (ERP), which revives the execution based on statistical data from actual execution of PADS applications to predict the running time of PADS applications on different combinations of VMs. Then, an ERP‐based Allocation algorithm, namely, ERPA, is raised to optimize VMs allocation to minimize the running time of PADS applications in clouds. A series of experiments are conducted to compare the proposed ERPA with three resource allocation algorithms, ie, Gang‐scheduling‐based, Makespan‐based, and Max‐Min‐based algorithms, and the experimental results demonstrate the advantage of ERPA in improving execution efficiency of PADS applications in clouds. In particular, for communication‐sensitive PADS applications, the advantage of ERPA is more significant.