Reviewerly/exHarmony huggingface dataset:
A Modular Distributed IoT Service Discovery

The Internet of Things (IoT) consists of distributed entities that federate to implement use cases. Driven by its applications, distributed IoT services federate dynamically to deliver complex functionality.A fundamental requirement for service composition is discovery. The IoT requires a semantically-rich, dynamically extensible, low complexity service discovery mechanism. Existing mechanisms fail in delivering this functionality.We present a distributed modular directory of service properties. It can be extended at runtime. Combined our so-called search provider directories form an IoT ontology. With the novel modularization, our simple directories mash-up to a complex ontology. We introduce a query federation mechanism. Based on first-order logic it enables mapping complex semantic queries on the simple search providers. We evaluate our prototype regarding latency and usability.
Randomness of Shares Versus Quality of Secret Reconstruction in Black-and-White Visual Cryptography

Standard Naor-Shamir visual cryptography scheme is modified in a way that allows sampling from all possible 16 two-by-two tiles for generating shares. Such a procedure makes shares not only looking random but simply being random in the statistical sense. Of course there is a price to pay in the form of slightly deteriorated quality of the reconstructed secret. In this paper we try to find a reasonable compromise between the randomness of shares and the quality of the secret reconstruction.
Profile Reconciliation Through Dynamic Activities Across Social Networks

Since today’s online social media serve diverse purposes such as social and professional networking, photo and blog sharing, it is not uncommon for people to have multiple profiles across different social networks. Finding or reconciling these profiles would allow the creation of a holistic view of different facets of a person’s life that can be used by recommender systems, human resource management, marketing activities and also raise awareness about the potential threats to one person’s privacy. In this paper, we propose a new approach for reconciling profiles based on their temporal activity (i.e., timestamped posts) shared across similar-scope social networks. The timestamped posts are compared by considering different dynamic attributes originating from what the user shares (geographical data, text, tags, and photos) and static attributes (username and real name). Our evaluation on Flickr and Twitter social networks datasets shows that the temporal activity is a good predictor of two profiles referring or not to the same user.
Adaptive DBSCAN Algorithm Based on Sample Density Gradient

DBSCAN is a classic and commonly applied density-based clustering algorithm, but its clustering accuracy depends on the choice of two input parameters. This paper presents a new algorithm for adaptative parameter determination in DBSCAN. The assumption of this new algorithm is that regions with larger sample density gradient usually corresponds to the edge areas of clusters. The main idea is to generate some pre-clusters and determine the values of parameters by their statistics information. We first randomly pick pairs of points in the sample space to form circles, which are called "disks". Then we estimate the density information of a disk by random sampling, and define the criteria of disk quality to select disks with larger sample density gradient. Finally, we obtain the suitable parameters of DBSCAN in terms of the distributions of radius and points number of these extracted disks by Gaussian kernel density estimation. Experimental results show that the new algorithm improves the accuracy of DBSCAN and performs better than classic algorithms like k-means and Birch in some cases.
Revisiting Rowhammer Attacks in Embedded Systems

As the cell density of DRAM modules keeps increasing to guarantee the growing demand for memory capacity in modern computer and embedded system, the electromagnetic interference between memory cells increase significantly, leading to security vulnerabilities that can be exploited. In particular, the widespread Rowhammer vulnerability has been proven to exist in the most common DRAM modules manufactured after 2012. In this paper we are revisiting the exploitation approaches using the Rowhammer bug and we are providing an overall study of their security implications on Embedded Systems such as mobiles or tablets based on ARM architecture. Furthermore, we present our implementation approach on the Phys Feng Shui methodological attack for Android on a LG Nexus 5 device and we highlight the practical issues that arise when trying to trigger the Rowhammer attack on a real system.
vABS: Towards Verifiable Attribute-Based Search Over Shared Cloud Data

With the proliferation of cloud computing and data-as-a-service (DaaS), more and more organizations and individuals outsource their data to a third-party service provider. While enjoying the benefits of cloud-based data outsourcing, the data owners are at the risk of losing control of data integrity and access management. In this demonstration, we present a system called vABS, which enables verifiable Attribute-Based Search over shared cloud data. The vABS system adopts the common DaaS architecture, in which the server provides search services to users on behalf of data owners. By employing a novel zero-knowledge approach proposed in our prior work [1], vABS not only provides users with good search experiences, but also supports authenticated query processing with fine-grained access control, which is crucial to many high-security applications.
Accelerated near-field algorithm of sparse apertures by non-uniform fast Fourier transform

We present an accelerated algorithm for calculating the near-field of non-uniform sparse apertures with non-uniform fast Fourier transform (NUFFT). The distances of the adjacent units in non-uniform sparse apertures are unequal and larger than half a wavelength. The near-field of apertures can be calculated by the angular spectrum method and the convolution methods, and according to the different convolution kernels, the convolution methods can be divided as the Fresnel kernel convolution and the Rayleigh-Sommerfeld kernel convolution. The Fresnel kernel is the approximation of the Rayleigh-Sommerfeld kernel in the far regions of the near-field zone. In uniform apertures, the three methods can be accelerated by fast Fourier transform (FFT). However, FFT should be replaced by NUFFT for non-uniform sparse apertures. The simulation results reveal that the Rayleigh-Sommerfeld convolution with NUFFT (RS-NUFFT) can be applied to all aperture sizes, distributions and near-field distances. After investigating the error sources in RS-NUFFT, the techniques (padding zeros for apertures, increasing sampling rate for the convolution kernel) are developed for increasing the calculation accuracy of RS-NUFFT.
Transforming the Air Force Mission Planning Process with Virtual and Augmented Reality

The U.S. Air Force (USAF) mission planning process, briefing, and debriefing are critical for tactical planning based on operational objectives and team performance. Our research focuses on modernizing these processes through the use of new technology, which is tailored to the current and developing operational capabilities of the Department of Defense. The current state of mission planning in the USAF revolves around traditional methods, such as briefing missions with white boards, topographic maps, and toy planes. At the same time, missions and technologies are becoming increasingly complex. Although the methods in which operations are being conducted are advancing, methods to prepare for these operations are not seeing the same progress and it is vital to leverage new techniques to facilitate the efficient planning of complex missions. Our goal is to revolutionize the mission planning process by integrating modern technology into the process. To accomplish this, we looked to enhance mission planning, briefing, and debriefing with a more visual and immersive experience. We created a system in which “mission critical” intelligence data can be placed into an Excel spreadsheet and transformed into a virtual representation of the mission that can be viewed through virtual reality and augmented reality platforms. This implementation of present-day technology into mission planning operations has the potential to enhance mission performance and therefore reduce potential equipment damage and loss, as well as casualties.
Angular stroke requirements for solar tracking azimuthal mechanism at any latitude

To be installed at any latitude from Northern and Southern Hemisphere, a solar tracking mechanism must perform the necessary angular strokes according to the latitude. For a dual-axis solar tracking system azimuthal type, the angular stroke limits depend on the latitude φ and Earth declination angle δ. In the paper, the analytical and graphical dependences for altitude (α) and azimuth (ψ) angles are detailed. Daily variations, maximum daily values during a year for different latitudes in the Northern and Southern Hemisphere and the yearly limits by max/min values at all latitudes for altitude and azimuth angles are calculated and presented in the paper. Based on these variations and limits, the mechanism type (linkage, gears, cams etc.) and the appropriate control algorithms can be selected.
Classification of chronic myeloid leukemia cell subtypes based on microscopic image analysis.

This paper presents a simple and efficient computer-aided diagnosis method to classify Chronic Myeloid Leukemia (CML) cells based on microscopic image processing. In the proposed method, a novel combination of both typical and new features is introduced for classification of CML cells. Next, an effective decision tree classifier is proposed to classify CML cells into eight groups. The proposed method was evaluated on 1730 CML cell images containing 714 cells of non-cancerous bone marrow aspiration and 1016 cells of cancerous peripheral blood smears. The performance of the proposed classification method was compared to manual labels made by two experts. The average values of accuracy, specificity and sensitivity were 99.0 %, 99.4 % and 98.3 %, respectively for all groups of CML. In addition, Cohen's kappa coefficient demonstrated high conformity, 0.99, between joint diagnostic results of two experts and the obtained results of the proposed approach. According to the obtained results, the suggested method has a high capability to classify effective cells of CML and can be applied as a simple, affordable and reliable computer-aided diagnosis tool to help pathologists to diagnose CML.
Optimizing Cross-Platform Data Movement

Data analytics are moving beyond the limits of a single data processing platform. A cross-platform query optimizer is necessary to enable applications to run their tasks over multiple platforms efficiently and in a platform-agnostic manner. For the optimizer to be effective, it must consider data movement costs across different data processing platforms. In this paper, we present the graph-based data movement strategy used by Rheem, our open-source cross-platform system. In particular, we (i) model the data movement problem as a new graph problem, which we prove to be NP-hard, and (ii) propose a novel graph exploration algorithm, which allows Rheem to discover multiple hidden opportunities for cross-platform data processing.
The Educational Effects of Practical Manufacturing Activities in Graduation Research

In recent years, in many educational institutions, PBL leaning has been introduced and there have been many cases of robots being used as learning material. The educational attempts using robot contests and the effects have been reported. One robot contest, the NHK Student Robocon, tests the skills of university, technical college, and polytechnic students. The contest challenges of the NHK Student Robocon require an extremely high level of skill. A research group of authors has worked at the NHK Student Robocon since 2009. In this article, our ten years of activity at the NHK Robocon is summarized, and we will give a description of the technical background of the mechanism and the control methods of manufactured robots. In addition, the contest’s educational effects during both a fiscal year of entry and a fiscal year of non-entry are verified through a comparison of the scores of a Robocon-related subject group before and after participating in the NHK Student Robocon. Consequently, we will report the results (knowledge).
An adaptable task-oriented dialog system for stand-alone embedded devices

Long Duong, Vu Cong Duy Hoang, Tuyen Quang Pham, Yu-Heng Hong, Vladislavs Dovgalecs, Guy Bashkansky, Jason Black, Andrew Bleeker, Serge Le Huitouze, Mark Johnson. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations. 2019.
A Hybrid Scheme for Feature Selection of High Dimensional Educational Data

Now a days, in educational data sets, the gigantic amounts of records can be the problem in generating good quality data. Lately, a lot of educational researchers are using the data mining methodology to analyze the data. But, several research studies focus on the selection of right learning algorithm rather than carrying out the feature selection on data. Therefore, the dataset has problem like high computational complexity and performing classification on such data requires a lot of computational time. This paper will give a summary of the feature selection methods which are being used for analysis of features of data. The proposed hybrid methodology is a combination of both feature selection and wrapper based technique, which helps to enhance the quality of students' data set.
Remote Credential Management with Mutual Attestation for Trusted Execution Environments

Trusted Execution Environments (TEEs) are rapidly emerging as a root-of-trust for protecting sensitive applications and data using hardware-backed isolated worlds of execution. TEEs provide robust assurances regarding critical algorithm execution, tamper-resistant credential storage, and platform integrity using remote attestation. However, the challenge of remotely managing credentials between TEEs remains largely unaddressed in existing literature. In this work, we present novel protocols using mutual attestation for supporting four aspects of secure remote credential management with TEEs: backups, updates, migration, and revocation. The proposed protocols are agnostic to the underlying TEE implementation and subjected to formal verification using Scyther, which found no attacks.
Opinion Mining with Deep Contextualized Embeddings

Detecting opinion expression is a potential and essential task in opinion mining that can be extended to advanced tasks. In this paper, we considered opinion expression detection as a sequence labeling task and exploited different deep contextualized embedders into the state-of-the-art architecture, composed of bidirectional long short-term memory (BiLSTM) and conditional random field (CRF). Our experimental results show that using different word embeddings can cause contrasting results, and the model can achieve remarkable scores with deep contextualized embeddings. Especially, using BERT embedder can significantly exceed using ELMo embedder.
Prediction Poisoning: Utility-Constrained Defenses Against Model Stealing Attacks

With the advances of ML models in recent years, we are seeing an increasing number of real-world commercial applications and services e.g., autonomous vehicles, medical equipment, web APIs emerge. Recent advances in model functionality stealing attacks via black-box access (i.e., inputs in, predictions out) threaten the business model of such ML applications, which require a lot of time, money, and effort to develop. In this paper, we address the issue by studying defenses for model stealing attacks, largely motivated by a lack of effective defenses in literature. We work towards the first defense which introduces targeted perturbations to the model predictions under a utility constraint. Our approach introduces the perturbations targeted towards manipulating the training procedure of the attacker. We evaluate our approach on multiple datasets and attack scenarios across a range of utility constrains. Our results show that it is indeed possible to trade-off utility (e.g., deviation from original prediction, test accuracy) to significantly reduce effectiveness of model stealing attacks.
Privacy Implication of Location-Based Service: Multi-Class Stochastic User Equilibrium and Incentive Mechanism

As a tool to assist traffic guidance and improve service quality, location-based service (LBS) platforms such as route navigation apps rely heavily on the collection and analysis of users’ location/trajectory information, which may evoke privacy concerns. Because of such privacy concerns, users may choose not to provide their information. In certain cases, this may lead to the problem of insufficient data for LBS applications (e.g., travel time estimation). To address this issue, the paper develops a modeling framework to quantify the levels of privacy for mixed user groups and proposes an incentive mechanism to encourage users to provide their location/trajectory information. It is assumed that LBS users have smaller travel time perception error but experience some extra privacy costs compared with the non-LBS users. A bi-level optimal incentive model with stochastic user equilibrium and elastic demand is developed to capture the mixed behavior of multi-class network users. The problem is solved using a meta-heuristic approach combined of genetic algorithm, successive average algorithm, and multiple behavior equilibrium assignment algorithm. The results reveal that the modeling framework can capture the mixed behavior of groups with different privacy levels. The proposed incentive mechanism is able to ensure sufficient data, and simultaneously minimize the required incentive.
Deep Convolutional Neural Network with Edge Feature for Image Denoising

Image denoising is a classical challenge in computer vision and has attracted a large amount of research in the past few decades in attempts to find new approaches to denoise various types of images. The endeavors have been even more striking with the recent inception of deep learning. Deep learning has a well-known strength in its accuracy and effectiveness but with the main drawback of very long converging time during deep learning network training. Moreover, current state-of-the-art denoising techniques still lack the edge feature, one of the crucial attributes for sharp images. Therefore, in this paper, we propose a novel Deep Convolutional neural network with Edge Feature (DCEF) for denoising Additive White Gaussian Noise (AWGN). However, as the deep learning model training takes too much time to converge, we also propose an adaptive learning rate using a triangle technique that allows much faster converging time comparing to state-of-the-art approaches. Our DCEF demonstrates that it outperforms existing state-of-the-art approaches in terms of average PSNR scores in $$ \upsigma $$ = 15 and 25 by 0.2 and 0.3, respectively, while achieving high MS-SSIM scores and using much fewer iterations to converge.
UTFPR at SemEval-2019 Task 6: Relying on Compositionality to Find Offense

We present the UTFPR system for the Of-fensEval shared task of SemEval 2019: A character-to-word-to-sentence compositional RNN model trained exclusively over the training data provided by the organizers.We find that, although not very competitive for the task at hand, it offers a robust solution to the orthographic irregularity inherent to tweets.
Aplikasi Pembelajaran Kriptograpi berbasis Mobile menggunakan Computer Assisted Instruction

The source of science is still dominated by books, although with the internet students get information with diverse scientific fields, but actually the information comes from material in the book. To be able to master a material from a book, students must first be able to read and understand well. Nowadays software is growing very rapidly. The world of education has also used software to create interactive learning application methods with multimedia concepts. This can help overcome, the level of difficulty experienced by students, in order to be able to attract student learning interest, so that it can automatically improve the learning process to be more active and effective. Actually this method has been widely used and has good benefits in learning. As for the achievements of this study to assist students in learning and understanding the learning of data security techniques, this is a solution in several learning problems, namely an explanation that is too fast, a conducive atmosphere, time, learning media and student interest.
Post-focus compression is not automatically transferred from Korean to L2 English*

Korean and English are both known to show on-focus pitch range expansion and post-focus pitch range compression (PFC).But it is not clear if this prosodic similarity would make it easy for Korean speakers to learn English focus prosody.In the present study, we conducted a production experiment using phone number strings to examine whether Korean learners of English produce a native-like focus prosody.Korean learners of English were classified into three groups (advanced, intermediate and low) according to their English proficiency and were compared to native speakers.Results show that intermediate and low groups of speakers did not increase duration, intensity, and pitch in the focus positions, nor did they compress those cues in the post-focus positions.Advanced speakers noticeably increased the acoustic cues in the focus positions to a similar extent as native speakers.However, their performance in post-focus positions was quite far from that of native speakers in terms of pitch and excursion size.These results thus demonstrate a lack of positive transfer of focus prosody from Korean to English in L2 learning, and learners may have to relearn it from scratch, which is consistent with a previous finding.More importantly, the results provide further support for the view proposed in other works that acoustic properties of PFC were not easily transferred from one language to another.
Designing Multi-Agent Systems from Ontology Models

This chapter presents our proposal for the development of multi-agent systems designed as ontology models supporting code generation and reasoning. The foundation of such work takes into consideration ontologies for agent-oriented software engineering aligned with the JaCaMo framework. These techniques are implemented in a tool that supports multi-agent systems core code generation for JaCaMo. The underlying ontology also allows for reasoning about the multi-agent systems models under development. Such comprehensive approach, therefore, spans through the modelling, programming, and verification of agent-oriented software.
A probabilistic bitwise genetic algorithm for B-spline based image deformation estimation

We propose a novel genetic algorithm to solve the image deformation estimation problem by preserving the genetic diversity. As a classical problem, there is always a trade-off between the complexity of deformation models and the difficulty of parameters search in image deformation. 2D cubic B-spline surface is a highly free-form deformation model and is able to handle complex deformations such as fluid image distortions. However, it is challenging to estimate an apposite global solution.