Reviewerly/exHarmony huggingface dataset:
Challenges for Automation in Adaptive Abstraction

Models are well-defined abstractions that provide cost-effective representations of the real-world for a precise purpose. When dealing with complex problems, there usually exist multiple abstractions, typically describing partially overlapping details of the system under study, and resulting in a hierarchy of abstractions. Adaptive abstraction leverages these levels with the aim of dynamically adapting the abstractions used during system execution. In this paper, we describe such process in terms of a MAPE-K (Monitor-Analyze-Plan-Execute over a shared Knowledge) control loop to discuss the challenges towards adaptive abstraction automation. In particular, we elaborate on adaptively selecting a candidate over multiple abstractions, an unaddressed issue in the literature. The discussion is supported by a running example in an agent-based simulation scenario.
A Comparative Assessment of Machine Learning Algorithms for Events Detection

Nowadays, one can observe massive amount of data production by numerous devices interacting with their environment and end users. [1] Such data can be the subject of advanced processing usually through machine learning algorithms. Hence, we are able to provide intelligent applications and analytics in many research domains like health informatics, information technology, environmental sciences, and so on so forth. However, choosing the appropriate machine learning model for data processing can be one of the most difficult tasks. In this paper, we try to facilitate researchers providing a ‘benchmarking’ of multiple machine learning algorithms to reveal their advantages and drawbacks. This effort mostly focuses on the accuracy of the studied algorithms and adopts various datasets found in the respective literature. We provide a short description of the adopted models, the datasets and extensive experimental evaluation accompanied by numerical results and our qualitative review on the outcomes.
A topic‐based term frequency normalization framework to enhance probabilistic information retrieval

Abstract Many well‐known probabilistic information retrieval models have shown promise for use in document ranking, especially BM25. Nevertheless, it is observed that the control parameters in BM25 usually need to be adjusted to achieve improved performance on different data sets; additionally, the assumption in BM25 on the bag‐of‐words model prevents its direct utilization of rich information that lies at the sentence or document level. Inspired by the above challenges with respect to BM25, we first propose a new normalization method on the term frequency in BM25 (called BM25 QL in this paper); in addition, the method is incorporated into CRTER 2 , a recent BM25‐based model, to construct CRTER 2 QL . Then, we incorporate topic modeling and word embedding into BM25 to relax the assumption of the bag‐of‐words model. In this direction, we propose a topic‐based retrieval model, TopTF, for BM25, which is then further incorporated into the language model (LM) and the multiple aspect term frequency (MATF) model. Furthermore, an enhanced topic‐based term frequency normalization framework, ETopTF, based on embedding is presented. Experimental studies demonstrate the great effectiveness and performance of these methods. Specifically, on all tested data sets and in terms of the mean average precision (MAP), our proposed models, BM25 QL and CRTER 2 QL , are comparable to BM25 and CRTER 2 with the best b parameter value; the TopTF models significantly outperform the baselines, and the ETopTF models could further improve the TopTF in terms of the MAP.
Understanding pointing for workspace tasks on large high-resolution displays

Navigating on large high-resolution displays (LHRDs) using devices built for traditional desktop computers can be strenuous and negatively impact user experience. As LHRDs transition to everyday use, new user-friendly interaction techniques need to be designed to capitalise on the potential offered by the abundant screen space on LHRDs. We conducted a study which compared mouse pointing and eye-tracker assisted pointing (MAGIC pointing) on LHRDs. In a controlled experiment with 35 participants, we investigated user performance in a one-dimensional pointing task and a map-based search task. We determined that MAGIC pointing had a lower throughput, but participants had the perception of higher performance. Our work contributes insights for the design of pointing techniques for LHRDs. The results indicate that the choice of technique is scenario-dependent which contrasts with desktop computers.
Big Data Aplication for Selecting Theses Topics

The chairs of thesis research in Computer Sciences at the University of Mumbai, in India, have different tools that provide academic support to those who begin the course. Students present difficulty in selecting the topic to study and, in some cases, this is a reason to neglect or delay in the career. Having a registry of thesis of the graduates proved to be useful for obtaining patterns on the relationship between the research area addressed in this thesis and the characteristics that define the researcher. For this study, the knowledge discovery process in Database was used. Finally, the results of the use of algorithms J4.8 in WEKA, ID3 tags in RapidMiner and CART IN Knime and the various models generated, with the data collected were presented.
Non-transitive and collusion resistant quorum controlled proxy re-encryption scheme for resource constrained networks

We focus on proposing a threshold proxy re-encryption scheme for networks with resource constrained devices. We show that the existing quorum controlled proxy re-encryption scheme by Jakobsson does not meet the basic definition of proxy re-encryption scheme. Also, we show that the existing scheme is transitive, not collusion safe, and is transferable. We propose a gradual and simultaneous, collusion-safe, non-transitive quorum controlled proxy re-encryption scheme for delegating the ciphertext encrypted for one user to another user. The proposed scheme can be used in resource constrained networks such as hierarchical wireless sensor networks. As the proposed scheme is gradual and simultaneous, each node in the network needs to carry out the minimal computations. The proposed scheme is proved to be correct and is IND-CPA secure.
Recommender System Based on User Evaluations and Cosmetic Ingredients

This study considers the compatibility between users and basic skin care products based primarily on the products' ingredients. We have developed a product recommender system that is expected to recommend products that provide the desired cosmetic effect for different user groups depending on age and skin type. From the cosmetics review site, based on an analysis of user evaluations, we extracted the names of ingredients that are thought to have the best effect and developed a method to recommend products that include these ingredients as their main ingredients. We propose the ingredient frequency-inverse product frequency (IF-IPF) method to derive ingredients characterizing strong-effect product group. We have defined the scale “the recommended product satisfaction level” to evaluate the effectiveness of our recommendation service. As a result, our system can recommend products with a high degree of serendipity and hidden attraction, among others.
Cost-Optimized Parallel Computations Using Volatile Cloud Resources

In recent years, the parallel computing community has shown increasing interest in leveraging cloud resources for executing parallel applications. Clouds exhibit several fundamental features of economic value, like on-demand resource provisioning and a pay-per-use model. Additionally, several cloud providers offer their resources with significant discounts; however, possessing limited availability. Such volatile resources are an auspicious opportunity to reduce the costs arising from computations, thus achieving higher cost efficiency. In this paper, we propose a cost model for quantifying the monetary costs of executing parallel applications in cloud environments, leveraging volatile resources. Using this cost model, one is able to determine a configuration of a cloud-based parallel system that minimizes the total costs of executing an application.
Security Issues and Solutions in Cloud Robotics: A Survey

Reliability comes with security and security comes with latest technology and any technology is best suited for any application area until it possess a high security features. Security is considered to be one of the prominent feature which makes it more acceptable and deployable to diversified and multidisciplinary applications. The paper try to put forth an in depth review on various security aspects confining more on the security details related to cloud robotics. Cloud robotics is a term involved and emerged with the fusion of two dipole concepts namely cloud computing and robotics. Security flaws in these two technologies are the major concerns and can affect the cloud robotics. Cloud computing suffers from various type of vulnerabilities like Network based attacks, Data storage based attacks, Virtualization based attacks is also prone to operating system level attacks. The same way robotics also suffers from attacks namely denial of service attack, and dictionary attack etc. Thus it can be termed as that cloud robotics need to be safeguard from all such threats mentioned above. The draft in particular thus throws light on the different cryptographic algorithms: RSA (Rivest Shamir Andelmen), AES (Advanced Encryption Standard), ECC (Elliptic Curve Cryptography). Exclusive to the algorithms cited above, the article discusses few more methods and techniques concerning to security which can be aptly applied to cloud robotics to make it more safe and secure.
Quantum Fuzzy Inference Based on Quantum Genetic Algorithm: Quantum Simulator in Intelligent Robotics

Successful sophisticated search solutions of intractable robotic task’s as global robust intelligent and cognitive smart control in unpredicted (unconventional)/hazard control situations or multi-criteria imperfect control goal is based on quantum control principles (as quantum neural network for deep machine learning or quantum genetic optimization algorithm). It is important in these cases to choose types and kind of quantum correlations, as example, between PID-controller in coefficient gain schedule. Extracted from classical states (as example, from modeling of control coefficient gain’s laws) quantum hidden correlations (that physically rigor and mathematically strong correctness, and corresponds to main qualitative properties in general of ill-defined control object) are considered as an additional physical computing and hidden quantum information resources. These information resources changes the time-dependent laws of the coefficient gains schedule of the traditional controllers as PID-controllers with guarantees the achievement of control goal in hazard situations. This article discusses the application of quantum genetic algorithm to automatically choice the optimal type and kind of correlations in the quantum fuzzy inference. Efficiency of quantum search algorithm in imperfect KB self-organization on the Benchmark system “cart – pole” demonstrated.
Micro-burst Analysis and Mitigation for 5G Low-latency Communication Services

Micro-bursts are a common problem in data center networks, and many studies have analyzed ways to mitigate micro-burst traffic. However, little attention has been paid to micro-burst impacts in mobile networks even though fifth generation (5G) mobile networks have stringent performance requirements such as low-latency communications. For example, third generation partnership project (3GPP) defines the minimum requirement of end-to-end latency as 5 milliseconds. We need to consider how to avoid micro-bursts in a data center of 5G networks because the end-to-end definition includes not only radio access networks and mobile backhaul but also data center networks deploying user plane function (UPF). 5G networks have some differences from networks in past micro-burst studies. Applications requiring low-latency communications are not backend systems assumed in the previous studies. Augmented reality (AR) and vehicle to everything (V2X) messages are typical delay sensitive applications in 5G networks. Furthermore, the traditional approaches for absorbing micro-burst traffic such as a deep buffer and buffer allocation scheme cannot be used in 5G networks because packet buffering increases queueing latency. In this paper, we analyze micro-burst characteristics in 5G networks in order to clarify what types of applications and what network architecture cause micro-bursts. For mitigating micro-bursts, we evaluate quality of service (QoS) schedulers and window size control of transmission control protocol (TCP). The results show that the TCP window size control can reduce queuing latency to be sub-millisecond without any additional mechanisms or equipment while priority queueing (PQ) and weighted round robin (WRR) schedulers have some limitations.
Symmetry Detection and Classification in Drawings of Graphs

Symmetry is a key feature observed in nature (from flowers and leaves, to butterflies and birds) and in human-made objects (from paintings and sculptures, to manufactured objects and architectural design). Rotational, translational, and especially reflectional symmetries, are also important in drawings of graphs. Detecting and classifying symmetries can be very useful in algorithms that aim to create symmetric graph drawings and in this paper we present a machine learning approach for these tasks. Specifically, we show that deep neural networks can be used to detect reflectional symmetries with 92% accuracy. We also build a multi-class classifier to distinguish between reflectional horizontal, reflectional vertical, rotational, and translational symmetries. Finally, we make available a collection of images of graph drawings with specific symmetric features that can be used in machine learning systems for training, testing and validation purposes. Our datasets, best trained ML models, source code are available online.
Clustering green openspace using UAV (Unmanned Aerial Vehicle) with CNN (Convolutional Neural Network)

The latest in unmanned aerial vehicles (UAVs) and associated sensing systems make these increasingly attractive platforms to the remote sensing community. A large number of spatial details contained in these images opens the door for advanced monitoring applications. In this paper, we use this cost-effective and attractive technology for the automatic detection of green open spaces. Given a UAV image of trees acquired, then, we analyze these Convolutional Neural Networks (CNN) points of the prior classifier trained on a set of trees and no trees points. As output, CNN will mark each detected tree by super pixel. Then, in order to capture the shape of each tree, we propose to merge this pixel-level segmentation with a method based active contour on the Color threshold. Finally, we further analyze the texture of regions with pixel-level segmentation and use summing pixel to distinguish trees from other vegetation. Experimental results obtained in UAV images from extensive calculations using the program that has been made and the existing provisions get a result of error of 7.256% on the first trial, the second experiment is 5.156%, and the third experiment is 3.126%.
Output Compression, MPC, and iO for Turing Machines

In this work, we study the fascinating notion of output-compressing randomized encodings for Turing Machines, in a shared randomness model. In this model, the encoder and decoder have access to a shared random string, and the efficiency requirement is, the size of the encoding must be independent of the running time and output length of the Turing Machine on the given input, while the length of the shared random string is allowed to grow with the length of the output. We show how to construct output-compressing randomized encodings for Turing machines in the shared randomness model, assuming iO for circuits and any assumption in the set $$\{$$ LWE, DDH, N $$^{th}$$ Residuosity $$\}$$ . We then show interesting implications of the above result to basic feasibility questions in the areas of secure multiparty computation (MPC) and indistinguishability obfuscation (iO):
Gaussian process classification for variable fidelity data

In this paper we address a classification problem where two sources of labels with different levels of fidelity are available. Our approach is to combine data from both sources by applying a co-kriging schema on latent functions, which allows the model to account item-dependent labeling discrepancy. We provide an extension of Laplace inference for Gaussian process classification, that takes into account multi-fidelity data. We evaluate the proposed method on real and synthetic datasets and show that it is more resistant to different levels of discrepancy between sources than other approaches for data fusion. Our method can provide accuracy/cost trade-off for a number of practical tasks such as crowd-sourced data annotation and feasibility regions construction in engineering design.
Real-Time Vehicle Detection in Aerial Images Using Skip-Connected Convolution Network with Region Proposal Networks

Detection of objects in aerial images has gained significant attention in recent years, due to its extensive needs in civilian and military reconnaissance and surveillance applications. With the advent of Unmanned Aerial Vehicles (UAV), the scope of performing such surveillance task has increased. The small size of the objects in aerial images makes it very difficult to detect them. Two-stage Region based Convolutional Neural Network framework for object detection has been proved quite effective. The main problem with these frameworks is the low speed as compared to the one class object detectors due to the computation complexity in generating the region proposals. Region-based methods suffer from poor localization of the objects that leads to a significant number of false positives. This paper aims to provide a solution to the problem faced in real-time vehicle detection in aerial images and videos. The proposed approach used hyper maps generated by skip connected Convolutional network. The hyper feature maps are then passed through region proposal network to generate object like proposals accurately. The issue of detecting objects similar to background is addressed by modifying the loss function of the proposal network. The performance of the proposed network has been evaluated on the publicly available VEDAI dataset.
Autonomous Robot Navigation for Automotive Assembly Task: An Industry Use-Case

Automobile industry faces one of the most flexible productivity caused by the number of customized models variants due to the buyers needs. This fact requires the production system to introduce flexible, adaptable and cooperative with humans solutions. In the present work, a panel that should be mounted inside a van is addressed. For that purpose, a mobile manipulator is suggested that could share the same space with workers helping each other. This paper presents the navigation system for the robot that enters the van from the rear door after a ramp, operates and exits. The localization system is based on 3DOF methodologies that allow the robot to operate autonomously. Real tests scenarios prove the precision and repeatability of the navigation system outside, inside and during the ramp access of the van.
Emotion Analysis on Weblog of Lifestyle Diseases

In this paper, we investigate the relation between patient lifestyle and emotional state transition by analyzing weblog articles written by patients with diabetes. To conduct our analysis, we use blood sugar level and body weight to indicate the disease condition, together with meal keywords and sentences that express feelings in the selected weblogs. A corpus annotated with a tag to word units in the target articles is constructed and a method of keyword classification based on the constructed corpus is proposed and evaluated. Finally, we analyze whether the indicated emotion is positive or negative in sentences expressing emotion in the corpus and investigate the relation between the emotion and a change in blood sugar level.
High-Resolution Optical Remote Sensing Image Registration via Reweighted Random Walk based Hyper-Graph Matching

High-resolution optical remote sensing image registration is still a challenging task due to non-linearity in the intensity differences and geometric distortion. In this paper, an efficient method utilizing a hyper-graph matching algorithm is proposed, which can simultaneously use the high-order structure information and radiometric information, to obtain thousands of feature point pairs for accurate image registration. The method mainly consists of the following steps: firstly, initial matching by Uniform Robust Scale-Invariant Feature Transform (UR-SIFT) is carried out in the highest pyramid image level to derive the approximate geometric relationship between the images; secondly, two-stage point matching is performed to find the matches, that is, a rotation and scale invariant area-based matching method is used to derive matching candidates for each feature point and an efficient hyper-graph matching algorithm is applied to find the best match for each feature point; thirdly, a local quadratic polynomial constraint framework is used to eliminate match outliers; finally, the above process is iterated until finishing the matching in the original image. Then, the obtained correspondences are used to perform the image registration. The effectiveness of the proposed method is tested with six pairs of high-resolution optical images, covering different landscape types—such as mountain area, urban, suburb, and flat land—and registration accuracy of sub-pixel level is obtained. The experiments show that the proposed method outperforms the conventional matching algorithms such as SURF, AKAZE, ORB, BRISK, and FAST in terms of total number of correct matches and matching precision.
Edge Property Based Stream Order Reduce the Performance of Stream Edge Graph Partition

Abstract The graph data is found everywhere in various disciplines, such as in social networks, biological networks, chemical compounds, and computer vision, etc. Currently, the size of graph data has dramatically increased; all disciplines have extracted knowledge from a graph by partitioning and distributing large-scale graph into different clusters using the distributed graph processing system or graph database. However, the graph partitioning has impacted to speed up or slow down the performance of those systems. Even if the stream edge graph partition has shown better partition quality than vertex graph partition for skew degree distribution of a graph and supports big graph partitioning, stream graph edge partitioners are affected by stream orders. In this study, we propose two edge properties based stream order models, TFB(Tree edges First, then Backward edges follow) and BFT(Backward edges First, then Tree edges follow). And study the effect of stream order on stream graph edge partitioners. The results show that TFB and BFT models significantly affect the quality of stream edge partition, except hashing. All algorithms show the best partitioning quality by Random order than other orders, TFB, BFT, BFS(Breadth-First Search), and DFS(Depth-First Search).