Reviewerly/exHarmony huggingface dataset:
Transfer of a high-level knowledge in HoughNet neural network

In this paper, we study the recently introduced neural network architecture HoughNet for the ability to accumulate transferable high-level features. The main idea of that neural network is to use convolutional layers separated with Fast Hough Transform layers to enable an analysis of complex non-linear statistics along different lines. We show that different convolutional blocks in this neural network have essentially different purposes. While initial features extracting is task-specific, the main part of the neural network operates with high-level features and do not require re-training in order to be applied to data from a different domain. To prove our statement, we two sets of the images with different origins and demonstrate Transfer Learning presence in the neural network except for the first layers which are highly task-specific.
Depth-map inpainting using learned patch-based propagation

This paper proposed a patch-based inpainting algorithm for depth map reconstruction using a stereo pair image. The proposed approach is based on a geometric model for patch synthesis. The lost pixels recovered by copying pixel values from the source based on a similarity criterion. We used a trained neural network to choose “best similar” patch. Experimental results show that the proposed method provides better results than the state-of-the-art methods in both subjective and objective measurements for depth map reconstruction.
Training Effective Model for Real-Time Detection of NSFW Photos and Drawings

Convolutional Neural Networks (CNN) show state of the art results on variety of tasks. The paper presents the scheme how to prepare highly accurate (97% on the test set) and fast CNN for detection not suitable or safe for work (NSFW) images. The present research focuses on investigating questions concerning identifying NSFW pictures with nudity by neural networks. One of the main features of the present work is considering the NSFW class of images not only in terms of natural human nudity but also include cartoons and other drawn pictures containing obscene images of the primary sexual characteristics. Another important considered issue is collecting representative dataset for the problem. The research includes the review of existing nudity detection methods, which are provided by traditional machine learning techniques and quite new neural networks based approaches. In addition, several important problems in NSFW pictures filtering are considered in the study.
A method of fluorescent fibers detection on identity documents under ultraviolet light

In this work we consider the problem of the fluorescent security fibers detection on the images of identity documents captured under ultraviolet light. As an example we use images of the second and third pages of the Russian passport and show features that render known methods and approaches based on image binarization non applicable. We propose a solution based on ridge detection in the gray-scale image of the document with preliminary normalized background. The algorithm was tested on a private dataset consisting of both authentic and model passports. Abandonment of binarization allowed to provide reliable and stable functioning of the proposed detector on a target dataset.
OnlineStats.jl: A Julia package for statistics on data streams

The growing prevalence of big and streaming data requires a new generation of tools.Data often has infinite size in the sense that new observations are continually arriving daily, hourly, etc.In recent years, several new technologies such as Kafka (Apache Software Foundation, n.d.-a) and Spark Streaming (Apache Software Foundation, n.d.-b) have been introduced for processing streaming data.Statistical tools for data streams, however, are under-developed and offer only basic functionality.The majority of statistical software can only operate on finite batches and require re-loading possibly large datasets for seemingly simple tasks such as incorporating a few more observations into an analysis.OnlineStats is a Julia (Bezanson, Edelman, Karpinski, & Shah, 2017) package for highperformance online algorithms.The OnlineStats framework is easily extensible, includes a large catalog of algorithms, provides primitives for parallel computing, and offers a weighting mechanism that allows new observations have a higher relative influence over the value of the statistic/model/visualization.
Application of Design Thinking for Elicitation Requirements in Mobile Applications

Design Thinking has been used by organizations as a requirement elicitation technique for making use of the immersion procedure that, as a consequence, brings the team stakeholder closer to the software project and allows the creation of projects with a design that is closer to the end user and with higher quality, minimizing the problems in requirements elicitation.In this work, we evaluate the elicitation process of software requirements of mobile applications developed in a Brazilian Public Agency using Design Thinking.Furthermore, we presented a practical case study in which Design Thinking is used in eliciting requirements of developed applications.We also compared the phases of development process adopted by the Agency with the Design thinking recommendations.Our comparative study demonstrates that the traditional methodology adopted by the Agency is bureaucratic and hinders the creation process, as well as the interaction between stakeholders and members of the development team and the transfer of knowledge.
Distributed Trajectory Generation for Multi-Quadrotors Using Receding Horizon Control and Sequential Convex Programming

This paper presents a distributed trajectory planning method supporting parallel computation based on receding horizon control (RHC) and sequential convex programming (SCP) for quadrotor swarms in known environments with obstacles. The proposed method, denoted as distributed RHC-SCP (dRHC-SCP), divides the swarm trajectory planning problem into a series of short-horizon planning problems to reduce the computation burden. In each planning horizon, dRHC-SCP solves the swarm trajectory planning problem in an iterative framework via efficient SCP algorithm. In the iterative process of SCP, dRHC-SCP uses the trajectories generated in the last iteration as the nominal trajectories for next iteration to achieve distributed planning and decoupling of the inter-quadrotor collision avoidance constraints. Simulation studies on several scenarios verify the efficiency merit of dRHC-SCP. Comparative results with decoupled SCP (dSCP) demonstrate that dRHC-SCP has higher computational efficiency and better scalability for quadrotor swarm trajectory planning.
Image Based Identification and Localizing of Drones

As commercial drones have been widely used, concerns for collision accidents with people and invading secured properties are emerging. The identification and localization drone is a challenging problem. DroneTracker, commercially available SW, for two dones with Inspire2, Phantom4Pro is tested but the performances of identification and localization are very low. We have suggest a image processing based identification and localization method for drones using the relationship between drone image size and real distance. It is tested for same experimental environments. We have obtained not only clear identification of each drone, but much superior performances of identification and localization. Furthermore the exactness of tracking is very close to the original tracking path.
High Throughput and Low Latency LZ4 Compressor on FPGA

This paper presents an FPGA design implementing a single LZ4 lossless compression IP block, providing a throughput of 6 Gbps combined with extremely low latency, while still retaining full binary compatibility with the original LZ4 format. The best-known competitor is capable of processing up to 2 Gbps per block/engine with unknown latency. The presented design uses two key features: A low-latency 8-way match search unit and consequently a match buffer which allows encoding LZ4 sequences independently to reduce stalls in the data processing pipeline. The design was evaluated on several compression corpora with an average compression ratio of 1.7.
Visible Spectrum and Infra-Red Image Matching: A New Method

Textural and intensity changes between Visible Spectrum (VS) and Infra-Red (IR) images degrade the performance of feature points. We propose a new method based on a regression technique to overcome this problem. The proposed method consists of three main steps. In the first step, feature points are detected from VS-IR images and Modified Normalized (MN)-Scale Invariant Feature Transform (SIFT) descriptors are computed. In the second step, correct MN-SIFT descriptor matches are identified between VS-IR images with projection error. A regression model is trained on correct MN-SIFT descriptors. In the third step, the regression model is used to process the MN-SIFT descriptors of test VS images in order to remove misalignment with the MN-SIFT descriptors of test IR images and to overcome textural and intensity changes. Experiments are performed on two different VS-IR image datasets. The experimental results show that the proposed method works really well and demonstrates on average 14% and 15% better precision and matching scores compared to recently proposed Histograms of Directional Maps (HoDM) descriptor.
An Efficient Semantic Segmentation Method using Pyramid ShuffleNet V2 with Vortex Pooling

Efficient and accurate semantic segmentation is particularly important especially for applications like autonomous driving which requires real-time inference speed and high performance.Many works try to compromise spatial resolution to achieve real-time inference speed, which leads to poor performance.As a result, real-time segmentation task for embedded devices is still an open problem.In this paper, we focus on building a network with better performance possible while still achieve real-time inference speed.We first use a pyramid kernel size to capture more spatial information instead of using just a 3×3 kernel size for DWConvolution in ShuffleNet v2.Meanwhile, an efficient Vortex Pooling module is employed to aggregate the contextual information and generate highresolution features.Compared with other state-of-the-art realtime semantic segmentation networks, the proposed network achieves similar inference speed and better performance on embedded device.Specifically, we achieve state-of-the-art 73.46% mean IoU on Cityscapes test dataset, for a 768×1024 input, a speed of 46.1 frames per second on NVIDIA Jetson AGX Xavier embedded development board is achieved.
New quantum constacyclic codes with length n=2(qm+1)

By studying the properties of [Formula: see text]-cyclotomic cosets, the maximum designed distances of Hermitian dual-containing constacyclic Bose–Chaudhuri–Hocquenghem (BCH) codes with length [Formula: see text] are determined, where [Formula: see text] is an odd prime power and [Formula: see text] is an integer. Further, their dimensions are calculated precisely for the given designed distance. Consequently, via Hermitian Construction, many new quantum codes could be obtained from these codes, which are not covered in the literature.
Application of Data Mining Approach for Profiling Fire Incidents Reports of Bureau of Fire and Protection

The study aims to create a fire model that can generate pattern to obtain profile of fire incidents in Manila that can help facilitate the assessment of fire incidents by providing a quick, thorough, and scientific analysis of fire data, the outputs of which in turn can be utilized as basis for the formulation of new programs and fire prevention activities. The researchers applied K-means clustering approach and Elbow method using Python scikit-learn tool to process the fire incidents dataset and produce the number of clusters with centroid and correlation heatmap which resulted to five clusters. The most noticeable result is the Day attribute, Wednesday and Thursday, which are similar across all five clusters. In addition, most fire incidents occurred between 12:00 NN to 4:00 PM, around lunchtime and afternoon siesta. The result also shows that the poor are the most vulnerable likewise, the middle class. It concludes that government can reconsider its services on fire protection issues and fire disaster management. Data mining techniques applied to fire incident reports was limited in the Philippines. With this. more work should be carried out using prediction which can help in predicting the presence of a fire incident so that firefighter could immediately respond.
Role of Soft Outlier Analysis in Database Intrusion Detection

With the rapid development of World Wide Web and E-commerce, concern of security is a very sensitive issue in this modern era of information and communication technology. A lot of financial and brain effort has been invested in this problem and still requires serious attention due to the increasing threats. Database centered Intrusion Detection is a prominent field in this research circumference. Concept of outlier analysis in data mining can automate this intrusion detection process with higher accuracy. In this research, we present the role of soft outlier analysis in Database-centered Intrusion Detection while comparing its performance with its counterpart hard outlier analysis which ultimately enhances its productivity by improving the accuracy and reducing the false positive costs.
Investigating the Use of Email Application in Illiterate and Semi-Illiterate Population

The use of electronic communication has been significantly increased over the last few decades. Email is one of the most well-known means of electronic communication. Traditional email applications are widely used by a large population; however, illiterate and semi-illiterate people face challenges in using them. A major population of Pakistan is illiterate that has little or no practice of computer usage. In this paper, we investigate the challenges of using email applications by illiterate and semiilliterate people. In addition, we also propose a solution by developing an application tailored to the needs of illiterate/semi-illiterate people. Research shows that illiterate people are good at learning the designs that convey information with pictures instead of text-only, and focus more on one object/action at a time. Our proposed solution is based on designing user interfaces that consist of icons and vocal/audio instructions instead of text. Further, we use background voice/audio which is more helpful than flooding a picture with a lot of information. We tested our application using a large number of users with various skill levels (from no computer knowledge to experts). Our results of the usability tests indicate that the application can be used by illiterate people without any training or third-party’s help.
Forcing Interpretability for Deep Neural Networks through Rule-Based Regularization

Remarkable progress in the field of machine learning strongly drives the research in many application domains. For some domains, it is mandatory that the output of machine learning algorithms needs to be interpretable. In this paper, we propose a rule-based regularization technique to enforce interpretability for neural networks (NN). For this purpose, we train a rule-based surrogate model simultaneously with the NN. From the surrogate, a metric quantifying its degree of explainability is derived and fed back to the training of the NN as a regularization term. We evaluate our model on four datasets and compare it to unregularized models as well as a decision tree (DT) based baseline. The rule-based regularization approach achieves interpretability and competitive accuracy.
Agile Understanding Analysis and Comparison Through Students Evaluation

The software engineering ideology Agile is becoming increasingly common in the field of software development in comparison with the traditional techniques. Graduates with an advanced understanding of software engineering methodologies are subject to software organizations. Agile has already introduced to the software engineering education through the university curriculum, because of its success. They have begun teaching Agile theoretically, in laboratories effectively and have also held workshops where qualitative experience is exchanged with learners. After having finalized the initiatives in the laboratory by pursuing agile values, this article seeks to fulfill the Agile commitment for witnessing the perception of students who are graduate of Software Engineering course. This paper intended to do a survey type analysis through students after completing their software engineering course, because when they will move to software organizations this will help them to cope up with Agile. They will not feel that they have fallen down to the ocean of Project Management.
An Approach Towards High-Precision Docking of the Mobile Robots for Industrial Purposes

The fourth industrial revolution caused a dynamic development of production capabilities. To manufacture faster and more efficiently, production lines are being equipped with numerous sensors based on IoT technology. Thus, the machines, and especially robots working in the factories, need to operate in an even more flexible way. This does not only apply to the interchangeability of their tasks but also requires their mobility between the different workstations. Not necessarily does it have to be realised with the linear tracks. The era of Industry 4.0 brings about a real need for the implementation of autonomous mobile robots in the manufacturing sector. Therefore, dedicated docking systems need to be developed to allow improved mobility of industrial manipulators and maintain a satisfactory level of their precision. The following paper constitutes a case study exploring the viability of the implementation of industrial robots, placed on a manual trolley and an autonomous mobile robot MiR 100, for automotive training purposes. A detailed description of the design-and-test iteration process is presented herein, followed by results obtained from a real-life validation.
Multi-task Learning for Attribute Extraction from Unstructured Electronic Medical Records

Electronic medical records have been widely used in hospitals to store patient information in a digital format, which is convenient to reuse the patient’s medical data and make it become the data of teaching and scientific research. It is also convenient to analyze and mine the patient’s data, so as to provide the basis for medical research. However, most of the existing methods are based on structured data of electronic medical records, and researches on unstructured texts are very rare, which would lose a lot of important information. In this paper, we focus on attribute extraction from the unstructured text of electronic medical records, and propose a multi-task learning model to jointly learn related tasks to help improve the generalization performance of all the tasks. Specifically, we use an end-to-end neural network model to extract different attribute values from the same unstructured text. We take each sentence/segment of the text as an instance. For each instance, we first use the pre-trained word embedding to better initialize our neural network models, then we fine-tune them by using our domain corpus to capture domain specific semantics/knowledge. Considering that the importance of different instances for attribute extractors is not equal, we also use an attention mechanism to select the most important instances for those attribute extractors. Finally, our model use multi-task learning by solving multiple multi-class classification problems simultaneously. Experimental results show the effectiveness of our method.
An SNN-DBSCAN Based Clustering Algorithm for Big Data

Clustering is a technique to partition data into different groups in such a way that data items in a group are more similar to each other than the data points in any other group. The assumption of infinite main memory is very usual while designing most of the clustering algorithms but this assumption fails when the size of data set starts increasing. In this scenario, data needs to be stored in the secondary memory and time spent in the input/outputs (I/O) dominates the actual computational time. Therefore by reducing the I/O, the efficiency of the clustering techniques can be improved. In this paper, one shared near neighbor based algorithm is devised by minimizing its I/O complexity to make it suitable for the Big Data in external memory model proposed by Aggarwal and Vitter. There is no change in the computational steps, hence cluster quality remains the same. We implement the algorithm in the STXXL library to show its efficacy for Big Data sets.
IRIS: interactive real-time feedback image segmentation with deep learning

Volumetric examinations of the aorta are nowadays of crucial importance for the management of critical pathologies such as aortic dissection, aortic aneurism, and other pathologies, which affect the morphology of the artery. These examinations usually begin with the acquisition of a Computed Tomography Angiography (CTA) scan from the patient, which is later on postprocessed to reconstruct the 3D geometry of the aorta. The first postprocessing step is referred to as segmentation. Different algorithms have been suggested for the segmentation of the aorta; including interactive methods, as well as fully automatic methods. Interactive methods need to be fine-tuned on each single CTA scan and result in longer duration of the process, whereas fully automatic methods require the possession of a large amount of labeled training data. In this work, we introduce a hybrid approach by combining a deep learning method with a consolidated interaction technique. In particular, we trained a 2D and a 3D U-Net on a limited number of patches extracted from 25 labeled CTA scans. Afterwards, we use an interactive approach, which consists in defining a region of interest (ROI) by just placing a seed point. This seed point is later used as the center of a 2D or 3D patch to be fed to the 2D or 3D U-Net, respectively. Due to the low content variation of these patches, this method allows to correctly segment the ROIs without the need for parameter tuning for each dataset and with a smaller training dataset, requiring the same minimal interaction as state-of-the-art interactive methods. Later on, the new segmented CTA scans can be further used to train a convolutional network for a fully automatic approach.
A Cloud Trusting Mechanism Based on Resource Ranking

The cloud-based computing paradigm helps organizations grow exponentially through means of employing an efficient resource management under the budgetary constraints. As an emerging field, cloud computing has a concept of amalgamation of database techniques, programming, network, and internet. The revolutionary advantages over conventional data computing, storage, and retrieval infrastructures result in an increase in the number of organizational services. Cloud services are feasible in all aspects such as cost, operation, infrastructure (software and hardware) and processing. The efficient resource management with cloud computing has great importance of higher scalability, significant energy saving, and cost reduction. Trustworthiness of the provider significantly influences the possible cloud user in his selection of cloud services. This chapter proposes a cloud service selection model (CSSM) for analyzing any cloud service in detail with multidimensional perspectives.