Reviewerly/exHarmony huggingface dataset:
Poster: Development of Communication Skills for Future Engineers

The analysis of the researches and the activity of modern engineer showed that his communication skills are a vital component of his professional competence nowadays. Having analyzed modern approaches to the development of communication skills we have designed organizational and pedagogical conditions for future engineers’ communication skills development at foreign language classes. They include the organization of teaching process in cooperative groups, the use of portfolio method and mind maps, individual and cooperative, as cross-functional training devices and the use of interdisciplinary case-studies in professional field with the use of discussion moderation methods, according to the recommendations, developed to adapt a designed case to the knowledge and motivation level of each student’s group. The ascertaining stage of our experiment showed that control and experimental groups have the same level of communication skills development which is insufficient.
On Fully Homomorphic Encryption for Privacy-Preserving Deep Learning

Given the rise of Machine Learning (ML) applications using sensitive private data, we present an implementation of Fully Homomorphic Encryption (FHE) with Convolutional Neural Networks (CNNs) for privacy-preserving Deep Learning (DL). This permits to utilize DL image recognition algorithms without compromising personal data of served customers, e.g medical images. The combination of FHE and CNN is accomplished by using logic circuits, which enable to conduct deep learning algorithms on ciphertext instead of plaintext. We provide a set of practical measurements from an implementation on the number of logic circuit operations required to carry out privacy-preserving ML. The results are function of the numerical representation and security parameters. Our results indicate that the protection of customer data is a trade-off between the required level of security, prediction capability and computational complexity. Thus, we reach full correct classification with 6 bits in the fractional part, and evaluate the time costs associated with our circuits as a function of its security parameters.
Are Quantum Radar Arrays Possible?

Recently, a quantum-enhanced radar transmitter operating at microwave frequencies, called a quantum two-mode squeezing radar (QTMS radar), was demonstrated in the laboratory. Inspired by this, we discuss the possibility of building an array of quantum radars. In order for quantum radars to be practically relevant, it is important that an array can be built. We find that it is indeed possible to build such an array, but that the details matter. A quantum illumination radar array may not be very effective, but a QTMS radar array is quite amenable to array signal processing. We also briefly discuss the practical aspects of building a quantum radar array.
Active Vision for Extraction of Physically Plausible Support Relations

Robots manipulating objects in cluttered scenes require a semantic scene understanding, which describes objects and their relations. Knowledge about physically plausible support relations among objects in such scenes is key for action execution. Due to occlusions, however, support relations often cannot be reliably inferred from a single view only. In this work, we present an active vision system that mitigates occlusion, and explores the scene for object support relations. We extend our previous work in which physically plausible support relations are extracted based on geometric primitives. The active vision system generates view candidates based on existing support relations among the objects, and selects the next best view. We evaluate our approach in simulation, as well as on the humanoid robot ARMAR-6, and show that the active vision system improves the semantic scene model by extracting physically plausible support relations from multiple views.
Online Product Recommendation System by Using Eye Gaze Data

Recommendation system takes information related to the users' habits or interest or profile to suggest users with more convenient or similar materials that the users might be interested in. In general, these systems mostly rely on explicit feedback techniques (rating, search history etc.) to recommend products. In this case, users need to interact directly with the system. We seek implicit methods (indirect interaction) to relate users' preferences and recommend desired products automatically on the interface in order to minimize the meddling interaction and workload. In this paper, we present a recommendation system that will use users' eye gaze data to apprehend their interest to recommend products as an implicit feedback technique. Eye gaze data can provide information about the products that the users are interested in, without any direct interaction with the system. Eye gaze features is very effective as an implicit interaction technique. So, integrating eye gaze features with recommendation system will generate more user-oriented results. This system will collect users' eye gaze data during an e-commerce website navigation through a web-cam based eye tracker. Other features of a product (ratings, number of orders) were also included in generating the results in order to get more convenient results. Finally, a clustering-based machine learning algorithm was used to group the similar product based on the input data and recommend similar products to the users, implicitly expressed greater interest. In this study, we concluded that users can find their desired products with less physical assertion and more satisfaction.
E-voting system using Blockchain technology

research-article Share on E-voting system using Blockchain technology Authors: Syeda Sumbul Hossain Daffodil International University Daffodil International UniversityView Profile , Samen Anjum Arani NorthWestern Polytechnical University NorthWestern Polytechnical UniversityView Profile , Md. Tanvir Rahman NorthWestern Polytechnical University NorthWestern Polytechnical UniversityView Profile , Touhid Bhuiyan Daffodil International University Daffodil International UniversityView Profile , Delwar Alam Daffodil International University Daffodil International UniversityView Profile , Moniruz Zaman Daffodil International University Daffodil International UniversityView Profile Authors Info & Claims ICBTA 2019: Proceedings of the 2019 2nd International Conference on Blockchain Technology and ApplicationsDecember 2019 Pages 113–117https://doi.org/10.1145/3376044.3376062Published:20 March 2020Publication History 0citation317DownloadsMetricsTotal Citations0Total Downloads317Last 12 Months50Last 6 weeks6 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
Deep residual network for halftone image steganalysis with stego-signal diffusion

More and more convolution neural network (CNN) models are used for image steganalysis, which show superior performances than traditional steganalytic methods. However, no researches on halftone image steganalysis by CNN have yet been carried. In this paper, a novel residual CNN model with stego-signal diffusion for halftone image steganalysis is proposed and achieves state-of-the-art detection accuracy. Considering inverse halftoning can reconstruct the gray-scale image from the halftone image, inverse halftoning is used to preprocess the halftone image, which can diffuse the stego-signal to neighboring pixels. As a result, the difference between the cover and stego image is magnified on the texture. Then, the residual block is utilized to construct the CNN model, since it could preserve the stego-signal better than plain network, and the magnified difference allows the network to better identify cover and stego images. A series of experiments are conducted on a large-scale dataset. The detection accuracy is improved by the magnified difference, and our proposed model outperforms the previous methods.
Key protected classification for collaborative learning

Large-scale datasets play a fundamental role in training deep learning models. However, dataset collection is difficult in domains that involve sensitive information. Collaborative learning techniques provide a privacy-preserving solution, by enabling training over a number of private datasets that are not shared by their owners. However, recently, it has been shown that the existing collaborative learning frameworks are vulnerable to an active adversary that runs a generative adversarial network (GAN) attack. In this work, we propose a novel classification model that is resilient against such attacks by design. More specifically, we introduce a key-based classification model and a principled training scheme that protects class scores by using class-specific private keys, which effectively hide the information necessary for a GAN attack. We additionally show how to utilize high dimensional keys to improve the robustness against attacks without increasing the model complexity. Our detailed experiments demonstrate the effectiveness of the proposed technique. Source code will be made available at https://github.com/mbsariyildiz/key-protected-classification.
VanQver: the variational and adiabatically navigated quantum eigensolver

The accelerated progress in manufacturing noisy intermediate-scale quantum (NISQ) computing hardware has opened the possibility of exploring its application in transforming approaches to solving computationally challenging problems. The important limitations common among all NISQ computing technologies are the absence of error correction and the short coherence time, which limit the computational power of these systems. Shortening the required time of a single run of a quantum algorithm is essential for reducing environment-induced errors and for the efficiency of the computation. We have investigated the ability of a variational version of adiabatic quantum computation (AQC) to generate an accurate state more efficiently compared to existing adiabatic methods. The standard AQC method uses a time-dependent Hamiltonian, connecting the initial Hamiltonian with the final Hamiltonian. In the current approach, a navigator Hamiltonian is introduced which has a non-zero amplitude only in the middle of the annealing process. Both the initial and navigator Hamiltonians are determined using variational methods. A hermitian cluster operator, inspired by coupled-cluster theory and truncated to single and double excitations/de-excitations, is used as a navigator Hamiltonian. A comparative study of our variational algorithm (VanQver) with that of standard AQC, starting with a Hartree--Fock Hamiltonian, is presented. The results indicate that the introduction of the navigator Hamiltonian significantly improves the annealing time required to achieve chemical accuracy by two to three orders of magnitude. The efficiency of the method is demonstrated in the ground-state energy estimation of molecular systems, namely, H$_2$, P4, and LiH.
SCERM—A novel framework for automated management of cyber threat response activities

Cyber Threat Management (CTM) involves prevention, detection, and response to cyber-attacks by identifying and understanding threats, and applying appropriate actions. This is not practical for an organization to perform these activities within the time-frame of an impending attack. Organizations should swiftly accumulate and share Cyber Threat Intelligence (CTI) with peers to make effective use of shared threat information. Efforts are underway for standardizing the expression of threats into a machine-understandable format. Structured Threat Information eXpression (STIX) is a comprehensive effort that structures CTI, enables its sharing, visualization, and analysis. Although a large volume of STIX reports is available publicly, their state remains poor. Reports are not appropriately formatted, use incorrect vocabulary, and mislabel or omit key components, which curtail their usefulness for effective cyber threat management. For a meaningful analysis, an analyst needs a curated document list categorized according to cyber threat management phases for the under-investigation threat. We believe that methods for valuation of structured threat documents based on cyber threat management phases are limited or non-existent. We present a novel framework named SCERM—Structured threat data Cleansing, Evaluation, and Refinement. SCERM formally models the STIX architecture and valuates reports on the basis of the use case "managing cyber threat response activities". It uplifts CTI by remapping wrongly placed contents to the STIX data model. SCERM refines incomplete or missing components through a pre-prepared dataset of curated blog reports. This process is repeated until the reports improve to a threshold suitable for cyber threat management. A case study is presented to demonstrate the working of SCERM. The evaluation valuates publicly available STIXs for cyber threat management. It is observed that current STIX reports have limited information on prevention and almost none for the response phase of cyber threat management. The results demonstrate that SCERM significantly enriches STIX reports. The improvement in prevention is 73% and in the response is a 100%.
An Efficient Implementation of LED Block Cipher on FPGA

LED is an ultra-lightweight block cipher targeting resource-constrained devices. The current hardware architectures of this cipher utilize large logic area, operate in low frequencies and have low throughput. To improve the trade-offs between area utilization and performance, an iterative round-based architecture of LED block cipher is implemented in this paper. LED algorithm is available in 64-bit and 128-bit key sizes. In this paper, the focus is on the 64-bit key with 64-bit block size. This algorithm is implemented on various Field Programmable Gate Array (FPGA) devices. The design is verified on several Altera and Xilinx devices using Altera Quartus II, ModelSim and Xilinx ISE simulators. Both low-cost and high-end FPGA devices were targeted. Tradeoffs between area and performance were considered, with the optimization for performance. The throughput and maximum operating frequency are benchmarked with the existing literature and better performance is achieved. The results show large improvements in maximum operating frequency and throughput as well as reduction in area utilization compared to recent designs of round-based LED block cipher.
Small Object Detection with Multiple Receptive Fields

Abstract Small object detection has been a problem in deep learning convolutional neural network models. A multi-rate dilated convolution module is proposed to form a feature map to locate small objects. Benefiting from the fact that the dilated convolution does not add extra complexity while maintaining the characteristics of the high-resolution feature map, this paper replaces the traditional convolution network by setting the dilated convolution with the ratio of 1, 2, and 5, and fuses the different convolution rate convolutions. The feature map branch, extracting high-level features, using the FPN algorithm as the basic network framework, combining a variety of receptive field feature maps, enhancing the detection ability of the algorithm for small objects, and the convergence accuracy is greatly improved. Experiments show that the proposed method achieves 81.9% MAP in the PASCAL VOC2007 dataset, which exceeds the traditional object detection algorithm.
Hilbert transform-based envelope substitution method for non-uniform sampling signal correction in white-light interferometry

White-light scanning interferometry is an effective and widely used technology for measuring the microscopic three-dimensional morphology of an object. However, it is easily affected by external disturbances and appears to have a non-uniform sampling problem, which reduces the measurement accuracy. In this study, an effective correction algorithm is presented, in which a Hilbert transform and a correlation analysis of the white light interference envelope curves, as well as the simulated ideal interference signal envelope, are employed for a robust and high precision signal correction. In addition, the proposed method is at least 4 times as accurate as a traditional method and achieves a high repeatability, which is analyzed through a simulation and contrast experiments.
An Improved Pre-processing Machine Learning Approach for Cross-Sectional MR Imaging of Demented Older Adults

Data pre-processing is the foremost step employed in building any machine learning (ML) model. It has a significant effect on the generalization performance of the model. In the present study, we have attempted to present the data pre-processing techniques for analysis of cross-sectional Magnetic Resonance Imaging (MRI) data of demented and non-demented older adults. The MRI dataset consists of 434 MR sessions of 416 subjects, aged between 18 to 96 years. Before performing classification of MRI data and its pattern analysis, characteristics of the dataset, such as sampling, imbalanced dataset, missing values, outliers, incompleteness, and the presence of irrelevant features, had been addressed. We involved ten major steps to process dataset for the ML model building. Experimental results on the cross-sectional data indicated a significant relative improvement in the pattern analysis achieved by doing PCA (Principal Component Analysis). Pattern analysis employing PCA resulted in noteworthy advancement in pattern recognition with an explained variance of 0.97377.
Vertex nomination via seeded graph matching

Abstract Consider two networks on overlapping, nonidentical vertex sets. Given vertices of interest (VOIs) in the first network, we seek to identify the corresponding vertices, if any exist, in the second network. While in moderately sized networks graph matching methods can be applied directly to recover the missing correspondences, herein we present a principled methodology appropriate for situations in which the networks are too large/noisy for brute‐force graph matching. Our methodology identifies vertices in a local neighborhood of the VOIs in the first network that have verifiable corresponding vertices in the second network. Leveraging these known correspondences, referred to as seeds, we match the induced subgraphs in each network generated by the neighborhoods of these verified seeds, and rank the vertices of the second network in terms of the most likely matches to the original VOIs. We demonstrate the applicability of our methodology through simulations and real data examples.
From Bits of Data to Bits of Knowledge—An On-Board Classification Framework for Wearable Sensing Systems

Wearable systems constitute a promising solution to the emerging challenges of healthcare provision, feeding machine learning frameworks with necessary data. In practice, however, raw data collection is expensive in terms of energy, and therefore imposes a significant maintenance burden to the user, which in turn results in poor user experience, as well as significant data loss due to improper battery maintenance. In this paper, we propose a framework for on-board activity classification targeting severely energy-constrained wearable systems. The proposed framework leverages embedded classifiers to activate power-hungry sensing elements only when they are useful, and to distil the raw data into knowledge that is eventually transmitted over the air. We implement the proposed framework on a prototype wearable system and demonstrate that it can decrease the energy requirements by one order of magnitude, yielding high classification accuracy that is reduced by approximately 5%, as compared to a cloud-based reference system.
Planning Cities Development Directions with the Application of Sentiment Analysis

The aim of the article is to present a model of sentiment analysis tool application for planning directions of city development.The study presents a model of using sentiment analysis to build city development strategies as well as realization of projects related to it.The presented model is a part of a larger work on developing the concept of the Regional Spatial Business Community (RSBC).The RSBC is created on the basis of electronic communities built by stakeholders involved in the development of a given city.On the basis of surveys, a six-stage city development procedure is proposed.Using the aforementioned procedure, we describe potential data sources, the goals of sentiment analysis application and expected effects.Analyses of the applicability of sentiment analysis are made on the basis of observation of available tools.The obtained model is applicable; however, its implementation requires certain conditions to be met.
Fingerprint Based Secured Voting

In the current scenario, most of the countries of the world hold their elections using Electronic Voting Machines, where your vote gets registered electronically with the help of an Electronic Machine without using and wasting ballot paper to vote for elections. As security is a major concern nowadays, ensuring that no person exercises the right to vote twice is the main aspect. We can resolve this issue by introducing Finger Print Based Voting, where a person can be authorized based on his Finger Print. This will put an end to fake voting. The domain of the project is the Internet of Things where we are building Fingerprint Based Biometric Voting Machine using Arduino. We know that IoT is the system of interrelated computing devices, mechanical and digital machines, objects, and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction. Thus our Fingerprint online module is an application where the user is recognized by his finger pattern. As we know that the minutiae features on a finger of each human being are different, the voter can be easily authenticated. The online system allows the voter to vote through his fingerprint. The fingerprint of the voter is used to uniquely and distinctively identify him/her using the fingerprint module. Also, the system promises the right to vote a candidate only once, thus not allowing the voter to vote for the second time. An admin is assigned to add all the candidates standing for the election. Only the admin has the right to add a candidate name and photo who are nominated. The Admin will also register the voter's name by verifying voter's details. Admin will authenticate the voter by verifying the voter's identity and then admin will register the voter. Once the user has got the voters id and password from the admin the user can log in and vote for the candidates who were nominated. The system will allow the user to vote for only one candidate. In the end, the election result is published by using the election id. Even users can view the election result.