Reviewerly/exHarmony huggingface dataset:
Reliable State Estimation of an Unmanned Aerial Vehicle Over a Distributed Wireless IoT Network

Unmanned aerial vehicles (UAVs) have attracted a lot of attention due to their enormous potentiality in civil and military applications over the past years. In order to allow accurate control action of UAV, a robust and real-time state estimation technique is required. In this paper, we propose a Kalman filter based UAV state estimation technique when the communication takes place over wireless links in an Internet of Things (IoT) network. We consider that a set of sensors observes the state of the UAV and transmits the observation to a control center (central server) over a distributed wireless IoT network. To deal with the communication impairments due to wireless communication links between the UAV's sensors and the IoT system components, e.g., IoT gateways, a Bose-Chaudhuri-Hocquenghem coded communication system is presented. Based on the received signals at the IoT gateways, a global state estimation technique is proposed. Performance of the proposed communication and estimation scheme is demonstrated through numerical results for different conditions. From the comparison with a conventional estimation scheme, it is observed that the proposed scheme significantly outperforms the conventional scheme in terms of state estimation and error performance.
Augmentation not Duplication

Digital-augmentation of print-media can provide contextually relevant audio, visual, or haptic content to supplement the static text and images. The design of such augmentation--its medium, quantity, frequency, content, and access technique--can have a significant impact on the reading experience. In the worst case, such as where children are learning to read, the print medium can become a proxy for accessing digital content only, and the textual content is avoided. In this work, we examine how augmented content can change the reader's behaviour with a comic book. We first report on the usage of a commercially available augmented comic for children, providing evidence that a third of all readers converted to simply viewing the digital media when printed content is duplicated. Second, we explore the design space for digital content augmentation in print media. Third, we report a user study with 136 children that examined the impact of both content length and presentation in a digitally-augmented comic book. From this, we report a series of design guidelines to assist designers and editors in the development of digitally-augmented print media.
Group Signatures with Selective Linkability

Group signatures allow members of a group to anonymously produce signatures on behalf of the group. They are an important building block for privacy-enhancing applications, e.g., enabling user data to be collected in authenticated form while preserving the user’s privacy. The linkability between the signatures thereby plays a crucial role for balancing utility and privacy: knowing the correlation of events significantly increases the utility of the data but also severely harms the user’s privacy. Therefore group signatures are unlinkable per default, but either support linking or identity escrow through a dedicated central party or offer user-controlled linkability. However, both approaches have significant limitations. The former relies on a fully trusted entity and reveals too much information, and the latter requires exact knowledge of the needed linkability at the moment when the signatures are created. However, often the exact purpose of the data might not be clear at the point of data collection. In fact, data collectors tend to gather large amounts of data at first, but will need linkability only for selected, small subsets of the data. We introduce a new type of group signature that provides a more flexible and privacy-friendly access to such selective linkability. When created, all signatures are fully unlinkable. Only when strictly needed or desired, should the required pieces be made linkable with the help of a central entity. For privacy, this linkability is established in an oblivious and non-transitive manner. We formally define the requirements for this new type of group signatures and provide an efficient instantiation that provably satisfies these requirements under discrete-logarithm based assumptions.
On the Selection of Polynomials for the DLP Quasi-Polynomial Time Algorithm for Finite Fields of Small Characteristic

In this paper we characterize the polynomials $f$ over a finite field $F$ satisfying the following property: there exists an extension field $L$ of $F$ such that for any positive integer $\ell$ less than or equal to the degree of $f$, there exists $t_0$ in $L$ with the property that the polynomial $f-t_0$ has an irreducible factor in $L[x]$ of degree $\ell$. This result is then used to progress to the last step which is needed to remove the heuristic from one of the quasi-polynomial time algorithms for discrete logarithm problems (DLPs) in small characteristic. Our method is general and can be used to tackle similar problems which involve factorization patterns of polynomials over finite fields.
FiberWire

3D printing offers significant potential in creating highly customized interactive and functional objects. However, at present ability to manufacture functional objects is limited by available materials (e.g., various polymers) and their process properties. For instance, many functional objects need stronger materials which may be satisfied with metal printers. However, to create wholly interactive devices, we need both conductors and insulators to create wiring, and electronic components to complete circuits. Unfortunately, the single material nature of metal printing, and its inherent high temperatures, preclude this. Thus, in 3D printed devices, we have had a choice of strong materials, or embedded interactivity, but not both. In this paper, we introduce a set of techniques we call FiberWire, which leverages a new commercially available capability to 3D print carbon fiber composite objects. These objects are light weight and mechanically strong, and our techniques demonstrate a means to embed circuitry for interactive devices within them. With FiberWire, we describe a fabrication pipeline takes advantage of laser etching and fiber printing between layers of carbon-fiber composite to form low resistance conductors, thereby enabling the fabrication of electronics directly embedded into mechanically strong objects. Utilizing the fabrication pipeline, we show a range of sensor designs, their performance characterization on these new materials and finally three fully printed example object that are both interactive and mechanically strong -- a bicycle handle bar with interactive controls, a swing and impact sensing golf club and an interactive game controller (Figure 1).
Sharp optimal recovery in the two component Gaussian mixture model

In this paper, we study the problem of clustering in the Two component Gaussian mixture model when the centers are separated by some Δ>0. We present a nonasymptotic lower bound for the corresponding minimax Hamming risk improving on existing results. We also propose an optimal, efficient and adaptive procedure that is minimax rate optimal. The rate optimality is moreover sharp in the asymptotics when the sample size goes to infinity. Our procedure is based on a variant of Lloyd’s iterations initialized by a spectral method. As a consequence of nonasymptotic results, we find a sharp phase transition for the problem of exact recovery in the Gaussian mixture model. We prove that the phase transition occurs around the critical threshold Δ¯ given by Δ¯2=σ2(1+ 1+2p nlogn)logn.
Segmentation of optical-electronic images from on-board systems of remote sensing of the earth by the artificial bee colony method

It was established that it is not possible to apply the known methods of image segmentation directly to segmentation of optical-electronic images of on-board systems of remote sensing of the Earth. We have stated the mathematical problem on segmentation of such images. It was established that the result of segmentation of images of on-board systems of remote sensing of the Earth is separation of an image into artificial objects (objects of interest) and natural objects (a background). It has been proposed to use the artificial bee colony method for segmentation of images. We described the essence of the method, which provides for determination of agents positions, their migration, conditions for stopping of an iteration process by the criterion of a minimum of a fitness function and determination of the optimal value of a threshold level. The fitness function was introduced, which has the physical meaning of a sum of variance brightness of segments of a segmented image. We formulated the optimization problem of image segmentation of an on-board optical-electronic observation system. It consists in minimization of a fitness function under certain assumptions and constraints.The paper presents results from an experimental study on application of the artificial bee colony method to segmentation of an optical-electronic image. Experimental studies on segmentation of an optical-electronic image confirmed the efficiency of the artificial bee colony method. We identified possible objects of interest on the segmented image, such as tanks with oil or fuel for aircraft, airplanes, airfield facilities, etc.The visual assessment of the quality of segmentation was performed. We calculated errors of the first type and the second type. It was established that application of the artificial bee colony method would improve the quality of processing of optical-electronic images. We observed a decrease of segmentation errors of the first type and the second type by the magnitude from 7 % to 33 % on average
Full Database Reconstruction with Access and Search Pattern Leakage

The widespread use of cloud computing has enabled several database providers to store their data on servers in the cloud and answer queries from those servers. In order to protect the confidentiality of data in the cloud, a database can be stored in encrypted form and all queries can be executed on the encrypted database. Recent research results suggest that a curious cloud provider may be able to decrypt some of the items in the database after seeing a large number of queries and their (encrypted) results. In this paper, we focus on one-dimensional databases that support range queries and develop an attack that can achieve full database reconstruction, inferring the exact value of every element in the database. We consider an encrypted database whose records have values from a given universe of N consecutive integers. Our attack assumes access pattern and search pattern leakage. It succeeds after the attacker has seen each of the possible query results at least once, independent of their distribution. If we assume that the client issues queries uniformly at random, we can decrypt the entire database with high probability after observing $$O(N^2 \log N)$$ queries.
Blockchain in manufacturing revolution based on machine to machine transaction: a systematic review

In the last few years, two technological events emerged. First, the rising of Industry 4.0 concepts and technologies such as IoT allowed the direct link between machines on the shop floor, establishing a machine-to-machine communication (M2M). Second, blockchain enables transactions such payments and contracts without a formal reliable or accreditation institutions, such as banks or government.&#x0D; Goal: The goal of this paper is to explore the state-of-art of the blockchain technology applied to the manufacture sector. In this sense, the option of the M2M transaction being intermediated by blockchain on the shop floor is a hypothesis to be tested. Moreover, it could be questioned if these subjects are being appreciated by the academia.&#x0D; Design / Methodology / Approach: a systematic review of the literature was conducted, considering blockchain, the industrial context, and the Industry 4.0 related technologies.&#x0D; Results: As a result, only 12 papers that were classified according to their application fields and practice stages were obtained.&#x0D; Limitations of the investigation: this field of application is brand new; thus, the available literature is scarce; only 12 papers were obtained.&#x0D; Practical implications: As the main result, it was identified that the possibilities of blockchain applications to Industry have been poorly explored by literature.&#x0D; Originality / Value: The paper addresses the possibility of application of blockchain in M2M-based manufacture, and detaches the research gap in the academic literature related to it.
Reading detection in real-time

Observable reading behavior, the act of moving the eyes over lines of text, is highly stereotyped among the users of a language, and this has led to the development of reading detectors-methods that input windows of sequential fixations and output predictions of the fixation behavior during those windows being reading or skimming. The present study introduces a new method for reading detection using Region Ranking SVM (RRSVM). An SVM-based classifier learns the local oculomotor features that are important for real-time reading detection while it is optimizing for the global reading/skimming classification, making it unnecessary to hand-label local fixation windows for model training. This RRSVM reading detector was trained and evaluated using eye movement data collected in a laboratory context, where participants viewed modified web news articles and had to either read them carefully for comprehension or skim them quickly for the selection of keywords (separate groups). Ground truth labels were known at the global level (the instructed reading or skimming task), and obtained at the local level in a separate rating task. The RRSVM reading detector accurately predicted 82.5% of the global (article-level) reading/skimming behavior, with accuracy in predicting local window labels ranging from 72-95%, depending on how tuned the RRSVM was for local and global weights. With this RRSVM reading detector, a method now exists for near real-time reading detection without the need for hand-labeling of local fixation windows. With real-time reading detection capability comes the potential for applications ranging from education and training to intelligent interfaces that learn what a user is likely to know based on previous detection of their reading behavior.
Hyper-parameter optimization for support vector machines using stochastic gradient descent and dual coordinate descent

We developed a gradient-based method to optimize the regularization hyper-parameter, C, for support vector machines in a bilevel optimization framework. On the upper level, we optimized the hyper-parameter C to minimize the prediction loss on validation data using stochastic gradient descent. On the lower level, we used dual coordinate descent to optimize the parameters of support vector machines to minimize the loss on training data. The gradient of the loss function on the upper level with respect to the hyper-parameter, C, was computed using the implicit function theorem combined with the optimality condition of the lower-level problem, i.e., the dual problem of support vector machines. We compared our method with the existing gradient-based method in the literature on several datasets. Numerical results showed that our method converges faster to the optimal solution and achieves better prediction accuracy for large-scale support vector machine problems.
A Study on the Effect of CNN-Based Transfer Learning on Handwritten Indic and Mixed Numeral Recognition

Filling up forms at post offices, railway counters, and for application of jobs has become a routine for modern people, especially in a developing country like India. Research on automation for the recognition of such handwritten forms has become mandatory. This applies more for a multilingual country like India. In the present work, we use readily available pre-trained Convolutional Neural Network (CNN) architectures on four different Indic scripts, viz. Bangla, Devanagari, Oriya, and Telugu to achieve a satisfactory recognition rate for handwritten Indic numerals. Furthermore, we have mixed Bangla and Oriya numerals and applied transfer learning for recognition. The main objective of this study is to realize how good a CNN model trained on an entire different dataset (of natural images) works for small and unrelated datasets. As a part of practical application, we have applied the proposed approach to recognize Bangla handwritten pin codes after their extraction from postal letters.
It's time to rediscover HCI models

column Share on It's time to rediscover HCI models Author: Antti Oulasvirta Aalto University Aalto UniversityView Profile Authors Info & Claims InteractionsVolume 26Issue 4July-August 2019 pp 52–56https://doi.org/10.1145/3330340Published:26 June 2019Publication History 11citation1,663DownloadsMetricsTotal Citations11Total Downloads1,663Last 12 Months455Last 6 weeks45 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Investigation of Cloud Scheduling Algorithms for Resource Utilization Using CloudSim

Compute Cloud comprises a distributed set of High-Performance Computing (HPC) machines to stipulate on-demand computing services to remote users over the internet. Clouds are capable enough to provide an optimal solution to address the ever-increasing computation and storage demands of large scientific HPC applications. To attain good computing performances, mapping of Cloud jobs to the compute resources is a very crucial process. Currently we can say that several efficient Cloud scheduling heuristics are available, however, selecting an appropriate scheduler for the given environment (i.e., jobs and machines heterogeneity) and scheduling objectives (such as minimized makespan, higher throughput, increased resource utilization, load balanced mapping, etc.) is still a difficult task. In this paper, we consider ten important scheduling heuristics (i.e., opportunistic load balancing algorithm, proactive simulation-based scheduling and load balancing, proactive simulation-based scheduling and enhanced load balancing, minimum completion time, Min-Min, load balance improved Min-Min, Max-Min, resource-aware scheduling algorithm, task-aware scheduling algorithm, and Sufferage) to perform an extensive empirical study to insight the scheduling mechanisms and the attainment of the major scheduling objectives. This study assumes that the Cloud job pool consists of a collection of independent and compute-intensive tasks that are statically scheduled to minimize the total execution time of a workload. The experiments are performed using two synthetic and one benchmark GoCJ workloads on a renowned Cloud simulator CloudSim. This empirical study presents a detailed analysis and insights into the circumstances requiring a load balanced scheduling mechanism to improve overall execution performance in terms of makespan, throughput, and resource utilization. The outcomes have revealed that the Sufferage and task-aware scheduling algorithm produce minimum makespan for the Cloud jobs. However, these two scheduling heuristics are not efficient enough to exploit the full computing capabilities of Cloud virtual machines.
Fast Perceptual Image Enhancement

The vast majority of photos taken today are by mobile phones. While their quality is rapidly growing, due to physical limitations and cost constraints the mobile phones cameras struggle to compare in quality with DSLR cameras. This motivates us to computationally enhance these images. We extend upon the results of Ignatov et al., where they are able to translate images from compact mobile cameras into images with comparable quality to high-resolution photos taken by DSLR cameras. However, the neural models employed require large amounts of computational resources and are not lightweight enough to run on mobile devices. We build upon the prior work and explore different network architectures targeting an increase in image quality and speed. With an efficient network architecture which does most of its processing in a lower spatial resolution, we achieve a significantly higher mean opinion score (MOS) than the baseline while speeding up the computation by 6.3 $$\times $$ on a consumer-grade CPU. This suggests a promising direction for neural-network-based photo enhancement using the phone hardware of the future.
Sparse Logistic Regression Learns All Discrete Pairwise Graphical Models

We characterize the effectiveness of a classical algorithm for recovering the Markov graph of a general discrete pairwise graphical model from i.i.d. samples. The algorithm is (appropriately regularized) maximum conditional log-likelihood, which involves solving a convex program for each node; for Ising models this is $\ell_1$-constrained logistic regression, while for more general alphabets an $\ell_{2,1}$ group-norm constraint needs to be used. We show that this algorithm can recover any arbitrary discrete pairwise graphical model, and also characterize its sample complexity as a function of model width, alphabet size, edge parameter accuracy, and the number of variables. We show that along every one of these axes, it matches or improves on all existing results and algorithms for this problem. Our analysis applies a sharp generalization error bound for logistic regression when the weight vector has an $\ell_1$ (or $\ell_{2,1}$) constraint and the sample vector has an $\ell_{\infty}$ (or $\ell_{2, \infty}$) constraint. We also show that the proposed convex programs can be efficiently solved in $\tilde{O}(n^2)$ running time (where $n$ is the number of variables) under the same statistical guarantees. We provide experimental results to support our analysis.
Blockchain as E-Commerce Platform in Indonesia

Abstract The progress of technology and information system force digitalization process such as e-commerce to emerge. In Indonesia, the utilization of e-commerce has flourished because it has many benefits and advantages to broaden the market and stay competitive. However, it is faced with many challenges such as fraud, commission fees, limited contact between buyer and seller and misuse of personal data. Blockchain implementation has the potential to solve this problem with increased security and transparency through the implementation of cryptocurrency in payment and smart contracts. This paper explores the usage of the blockchain, cryptocurrency and smart contract to e-commerce for a secure and efficient transaction in Indonesia. The result will be used to propose blockchain technology as e-commerce platform architecture and systems in Indonesia.