Reviewerly/exHarmony huggingface dataset:
New Benchmarks for Barcode Detection Using Both Synthetic and Real Data

Given the wide use of barcodes, there is a growing demand for their efficient detection and recognition. However, the existing publicly available datasets are insufficient and of poor quality. Moreover, recently proposed approaches were trained on different private datasets, which makes the comparison of proposed methods even more unfair. In this paper, we propose a simple yet efficient technique to generate realistic datasets for barcode detection problem. Using the proposed method, we synthesized a dataset of $$\sim $$ 30,000 barcodes that closely resembles real barcode data distribution in terms of size, location, and number of barcodes on an image. The dataset contains a large number of different barcode types (Code128, EAN13, DataMatrix, Aztec, QR, and many more). We also provide a new real test dataset of 921 images, containing both document scans and in-the-wild photos, which is much more challenging and diverse compared to existing benchmarks. These new datasets allow a fairer comparison of existing barcode detection approaches. We benchmarked several deep learning techniques on our datasets and discuss the results. Our code and datasets are available at https://github.com/abbyy/barcode_detection_benchmark .
Breast Cancer Recognition by Computer Aided Based on Improved Fuzzy c-Mean and ANN

Breast cancer is the most common reason of women’s disease and also the major reason of cancer deaths around the world. The best test used for examination and early diagnosis is X-ray mammography. The malignant and benign tissues usually appear with weak contrast and often not very clear in mammography image. The proposed method is computer-aided diagnosis to support radiologists in their diagnosis. The proposed algorithm focus initially on detecting tumor in mammograms image based on improved fuzzy c-mean algorithm (FCM). Discrete wavelet transformation (DWT) and principle component analysis (PCA) are implemented on the binary image resulted from detection process prior to extract two types of features which uses as input to the neural network for classification the tumor to benign or malignant. The proposed algorithm was tested over several images taken from the Mammographic Image Analysis Society (MIAS) database. The accuracy of detection the tumor in image is 100%, while the accuracy of classification of tumor was 92.9%.
Quantum key distribution with multiphoton pulses: an advantage

In this article, we introduce a quantum key distribution protocol for the line of sight channels based on coincidence measurements. We present a proof-of-concept implementation of our protocol. We show that using coincidence measurements to monitor multi-photon pulses results in a higher secure key rate over longer distances for such channels. This key rate is higher than popular implementations of quantum key distribution protocol based on BB84, for example, the GLLP analysis [ Quant. Info. Comput. 4 , 325 ( 2004 ) 10.26421/QIC4.5-1 ]. In the experiment, we could generate around 74% more key bits per signal pulse as compared to the GLLP analysis of BB84 protocol with similar parameters and equal value of mean photon number.
Tsukamoto's Fuzzy Logic Development Analysis to Predict Caesarean or Normal Delivery

Fuzzy logic is a commonly used method to build a system whose reasoning resembles human intuition or reasoning. Tsukamoto fuzzy logic and certainty factor are two methods which have in common that contain uncertainty. Today, the well known process of childbirth is normal childbirth and cesarean where the choice of this is dubious. Inappropriate choice of delivery will endanger the safety of both mother and baby. This study was conducted to determine the type of cesarean or normal delivery with the development of Tsukamoto fuzzy logic with certainty factor to prevent the problem of doubt about the choice of delivery. This study uses age, blood pressure, pelvic size, fetal weight, breech presentation, and cesarean history as input. The research output is a type of normal or cesarean delivery. The test was conducted on 30 people who had given birth whose medical records were processed using a web application. The results obtained are the type of delivery that was tested using those 30 medical record data which had 100% similarity results with the development of Tsukamoto fuzzy.
Template-Based Multi-solution Approach for Data-to-Text Generation

Data-to-text generation is usually defined into two parts: planning how to order and structure the information, and generating a text grammatically correct and fluent, that is faithful to the facts described in the input knowledge base source. A typically knowledge base consists of Resource Description Framework (RDF) triples which describe the entities and their relations. There are plenty of end-to-end solutions proposed to generate natural language descriptions from RDF, however, they require large and noise-free training datasets, lack control over how the text will be generated and there is no guarantee that the generated text verbalizes all and only the input. We address these problems by proposing a modular solution that uses templates and generates multiple texts over the data-to-text generation phases, returning the best one. Our experiments on a real-world dataset demonstrate that our approach generates higher quality texts and outperforms some baseline models regarding BLEU, METEOR, and TER.
Comparative Study of LUC, ElGamal and RSA Algorithms in Encoding Texts

Cryptography has an important role in the process of data security. One of the benefits of applying cryptography is creating a sense of security in hiding data without having to doubt how much data you have. LUC, El Gamal and RSA have different characters, like formatting key processes, encryption and decryption process. So we need an algorithm performance comparison. Steps taken include determining the public key and private key, encryption process, decryption process, and testing in terms of program implementation time using several different texts. Based on tests of the encryption process, the results show that the fastest algorithm is the RSA algorithm. Other than that, the fastest decryption process is the LUC algorithm.
The Tools and Tactics Used in Intimate Partner Surveillance: An Analysis of Online Infidelity Forums

Abusers increasingly use spyware apps, account compromise, and social engineering to surveil their intimate partners, causing substantial harms that can culminate in violence. This form of privacy violation, termed intimate partner surveillance (IPS), is a profoundly challenging problem to address due to the physical access and trust present in the relationship between the target and attacker. While previous research has examined IPS from the perspectives of survivors, we present the first measurement study of online forums in which (potential) attackers discuss IPS strategies and techniques. In domains such as cybercrime, child abuse, and human trafficking, studying the online behaviors of perpetrators has led to better threat intelligence and techniques to combat attacks. We aim to provide similar insights in the context of IPS. We identified five online forums containing discussion of monitoring cellphones and other means of surveilling an intimate partner, including three within the context of investigating relationship infidelity. We perform a mixed-methods analysis of these forums, surfacing the tools and tactics that attackers use to perform surveillance. Via qualitative analysis of forum content, we present a taxonomy of IPS strategies used and recommended by attackers, and synthesize lessons for technologists seeking to curb the spread of IPS.
Gender Recognition from Face Images Using SIFT Descriptors and Trainable Features

Nowadays, recognition of gender from facial image became an important problem in computer vision, security, verbal–nonverbal communication and human–computer interaction application. Recognition of gender is a challenging research problem because facial image contains many information such as gender, facial expression, age, ethnic origin in computer-aided applications, based on the facial image quality gender recognition depends. In this paper, a new gender recognition method is proposed that combines both scale-invariant feature transform (SIFT) as domain-specific approach and combination of shifted filter responses (COSFIRE) as trainable features. The proposed method will give better performance of variation in poses, different expressions and changes in illumination condition. This method tested by taking gender face recognition technology (GENDER-FERET) results shows that GENDER-FERET dataset will give better result in various illumination conditions. COSFIRE algorithm which is used for visual recognition problem will work better in given trainable character. The advantage of using SIFT is it will not get affected by changes in scale, blur, rotation, change in illumination and affine transformation.
Research and Implementation of Campus Network Search Engine Based on Scrapy Framework and Elasticsearch

To solve the problem of general search engine's inability to timely include information in campus network and time-lag, this paper proposes a perfect solution of campus network search engine based on Elasticsearch full-text search engine and Scrapy framework. This paper mainly analyzes the operation process of Elasticsearch, studies the search mechanism of Elasticsearch, and designs the system architecture of campus network search engine on this basis, and presents the key implementation technologies of web crawling module, index and query module and Front-end display module of the system. Finally, through experimental verification, compared with the traditional general search engine, the campus network search engine designed in this paper has better relevance and quantity of search results, and the inclusion of new information in the campus network is better.
Post-Quantum Cryptography

For the last few chapters, we have kept a focus on quantum key distribution, or QKD. This maturing technology is still going to be relevant for a long time. What's going to come with it is known as post-quantum cryptography, which will be the main topic for this chapter. We'll explore an overview of this topic, going through the basics of the most relevant approaches to encryption schemes in the post-quantum realm.
Prediction Model for Breast Cancer Detection Using Machine Learning Algorithms

Cancer is termed as a fatal genetic disease that can change the behavior and growth of human cells. Breast cancer is the most prevalent cancer occurring among women in today’s world. According to an Indian survey, the death rate caused by cancer has increased by approximately 25% from the past few years. Breast cancer proves out to be deadly in majority of the cases and is the major reason for death among women. Tumor is a term used for the abnormal growth of cell tissues in the human body. Benign and Malignant are the two types of cancer tumors. The main objective of this research work is to prepare a report on the percentage of people suffering with cancer tumors using machine learning algorithms. The authors have taken advantage of the most efficient machine learning algorithms to develop models for prediction which will detect breast cancer occurring rate. Authors have used a breast cancer data set which consists of 567 rows of 30 different attributes of cancer characteristics out of which benign and malignant data has been taken as the target attribute. A prediction has been done to calculate the percentage of people affected with the benign and malignant tumors and pictorial results have been displayed as results. In this proposed approach, the authors have used supervised learning algorithms like K-Nearest-Neighbors, Naïve Bayes, Decision tree, and support vector machine prediction models for an unbiased estimate. The results indicated that K-Nearest-Neighbors is the best predictor with 91.6% accuracy. This model will be capable of providing a higher degree of accuracy compared to already existing literature work. The authors have compared their experimental results with the existing approaches and found satisfactory.
Blockchain and Other Emerging Technologies

Blockchain is not a standalone technology. In fact, it is often used with different technologies. The fact that there are many other emerging technologies doesn’t mean you have to choose between blockchain, AI, IoT, or any other type of application; they can enhance each other and create even better use cases for your organization. This chapter describes how you could integrate several technologies to create a better overall solution. Of course, this is only a limited list, as you could pick any set of possible applications and come to a better outcome. You should take into account that this is not always the case, and simply throwing together applications could also easily lead to disaster. When creating a new process or application, or when you want to work with several emerging technologies, you should know that a certain level of risk is involved.
Right-of-Stake: Deterministic and Fair Blockchain Leader Election with Hidden Leader

Mainstream applications typically require a trusted authority where application clients will connect to obtain a service. In the blockchain decentralized environment, this trusted authority or the leader changes frequently and is selected randomly depending on the protocol. Such procedures may take an unacceptable amount of time or resources to establish the leader causing overheads, latency or processing issues if mainstream applications are to adopt the blockchain. Focusing on these issues, we present Right-of-Stake (RoS), a novel approach in a synchronous blockchain network to deterministically elect a leader or block proposer out of a group of participants, each with different stakes. This procedure is completed in a guaranteed equitable manner while removing the need for Proof-of-Work's (PoW) aggressive-resource computations or Proof-of-Stake's (PoS) inter-node negotiations. Besides, through the use of zero-knowledge range proofs, RoS has a distinct advantage of being able to hide the identity of future leaders until the point when the leader surfaces to propose the block. We also simulate RoS and show that it can recover from Denial-of-Service attacks which have been a point of contention in arguments against deterministic leader election protocols. We view RoS as a possible consensus replacement for blockchains that require an improved leader election process when deployed for mainstream applications.
An Integrated Online Learning Approach Using a Smartphone-Based Active Learning System and a Web Video On-Demand System

In general, at the universities the lecturers use the grades to evaluate the students’ performance. However, the grades do not show the accumulated knowledge. Thus, the students can’t understand whether they have studied enough to understand all the topics of the lecture. In our previous work, we implemented a smartphone-based Active Learning System (ALS), which uses 360-degrees cameras for searching the students’ location. By using the ALS, we can judge the students’ learning style. However, in the case of remote or online lectures, we need to improve our system. Recently, because of COVID-19, most of the universities are using online learning systems. However, the used online learning systems are not yet stable and we cannot get students’ learning status as in the case of ALS. Therefore, in this paper, we present an integrated approach using ALS and a Web video on-demand system for online remote lectures.
Pedestrian Tracking Combined with Deep Learning and Camera Network Topology in Non-overlapping Multi-camera Surveillance

This paper proposes a method of pedestrian tracking combining deep learning and camera network topology in non-overlapping multi-camera surveillance. The cross-camera pedestrian tracking task is divided into three parts: firstly, extracting structured information from single camera through deep learning; secondly, establishing the camera network topology; finally, extracting the candidate pedestrian in adjacent cameras by structured information and camera network topology, getting trajectory in next camera by using person re-identification method until there is no trajectory. Meanwhile, the experimental results show that our method can obtain pedestrian trajectory successfully and effectively.
Evaluation of usage of E resources and INMAS Library Services Through Users Perspective

The study has been an attempted to find out about the usage pattern of electronic &amp; print journals, books and other services among users, delivered by the Institute of Nuclear Medicine &amp; Allied Sciences (INMAS) TIRC Library. A questionnaire survey was conducted in INMAS to study the pattern of usage of library services. A total number of 150 questionnaires were distributed randomly among Scientists &amp; Service Officers, Defence Research Technical Cadre (DRTC) and Research scholars of INMAS and 121 users responded back. The outcome of the study shows that users were quite aware about the services of the library, actively participated in collection development of books and journals, more comfortable and preferring in usage of electronic information resources. With the introduction of electronic journals/resources in library, the usage is increased manifold among users. Printed resources are also referred in good numbers by the users.
End-Edge-Cloud Collaborative System: A Video Big Data Processing and Analysis Architecture

In the current surveillance system, video streams are firstly captured and compressed at the cameras, and then transmitted to the backend severs or cloud for big data analysis. It is impractical to aggregate all video streams from hundreds of thousands of cameras for big data analysis. Transcoding the videos to low-bitrate ones is the conventional solution to solve the aggregation bottleneck. However, it is recognized that transcoding will inevitably affect visual feature extraction, consequently degrading the subsequent analysis performance. To address these challenges, we thus propose a new video big data analysis framework, called end-edge-cloud collaborative system. Under the end-edge-cloud collaborative framework, a camera can output two streams simultaneously, including a compressed video stream for viewing and data storage, and a compact feature stream extracted from the original video signals for visual analysis. Video stream and feature stream are synchronized by unified identification. We identify three key technologies to enable the end-edge-cloud collaborative system, including analysis-friendly video coding, visual feature compact descriptor, and user-defined neural network and parameter updating. By real-time feeding only the feature streams into the cloud center, these cameras thus form a large-scale brain-like vision system for the smart city. A prototype has been implemented to demonstrate its feasibility. Experiment results show that our system can achieve high efficient video compression and guarantee the analysis performance. Furthermore, our system makes the big data analysis feasible which only need aggregate low bit-rate compressed feature stream.
Using wavelet transform to evaluate single shot phase measuring deflectometry data

Phase measuring deflectometry (PMD) is a well-established method for determining the topography of specular freeform surfaces. A disadvantage of the classical PMD method, however, is the sequential measurement process - it requires at least six camera images of phase-shifted sinusoidal fringe patterns for one measurement. Therefore, for moving objects in industrial production, as well as for non-fixable objects such as the human cornea, the classical PMD evaluation is not suitable anymore. To overcome this problem, single-shot methods using single-side-band demodulation have been presented, which allow for a deflectometric measurement based on just one single image capture. However, this kind of evaluation does not work for complex surface geometries that result in broadband fringe patterns, since the phase is only considered globally in the Fourier space. A new single-shot evaluation method for the phase determination using the Continuous Wavelet Transform (CWT) is presented. The advantage of the wavelet transform is that the signal can be evaluated locally in both spatial and frequency space, making it possible to measure even complex reflective surfaces in motion. First measurement results are shown and compared to the classic phase-shifting evaluation for a non-moving object. Furthermore, limits and possible enhancements of this new method are discussed. Phase measuring deflectometry (PMD) is a well-established method for determining the topography of specular freeform surfaces. A disadvantage of the classical PMD method, however, is the sequential measurement process - it requires at least six camera images of phase-shifted sinusoidal fringe patterns for one measurement. Therefore, for moving objects in industrial production, as well as for non-fixable objects such as the human cornea, the classical PMD evaluation is not suitable anymore. To overcome this problem, single-shot methods using single-side-band demodulation have been presented, which allow for a deflectometric measurement based on just one single image capture. However, this kind of evaluation does not work for complex surface geometries that result in broadband fringe patterns, since the phase is only considered globally in the Fourier space. A new single-shot evaluation method for the phase determination using the Continuous Wavelet Transform (CWT) is presented. The advantage of the wavelet transform is that the signal can be evaluated locally in both spatial and frequency space, making it possible to measure even complex reflective surfaces in motion. First measurement results are shown and compared to the classic phase-shifting evaluation for a non-moving object. Furthermore, limits and possible enhancements of this new method are discussed.
Multi-level factors affecting immediate and delayed concept recall in concept mapping with adult learners

Multi-level factors affecting immediate and delayed concept recall in concept mapping were examined across 716 concepts nested within 57 concept maps from adult learners. At the concept level, we examined a concept’s serial positioning, autobiographical elaboration (AE), and elaborative encoding. At the map level, we examined the compositional effects of AE and elaborative encoding while controlling for the number of concepts in a map. For concepts, we found a curvilinear effect of serial positioning on immediate recall, and a linear effect of elaborative encoding on immediate and delayed recall. No AE effect was found. Concept level effects of elaborative encoding and AE did not accumulate at the map level. The number of concepts in a map, however, did affect the likelihood of immediate recall and, to a lesser extent, delayed recall. Limitations and implications for research and practice are discussed.
Amplitude edge response characterization of interferometer ITF and the effect of defocus

As the industry standard for interferometer frequency response specifications, ITF has historically been calculated using expensive phase targets to capture interference-based phase information. Recent work in transfer function characterization of interferometers has produced a more accessible technique with the ability to predict a system’s ITF utilizing a modified amplitude edge response. This technique does not require a phase measurement and demonstrates correlation between the ITF and an amplitude edge response for coherent systems, even in the presence of significant defocus. Defocused instruments exhibit modulation dropout in their transfer functions, now shown to be related to the Talbot Effect, exhibited here by imaging both phase and amplitude single frequency, square wave targets. This presentation outlines the modified amplitude edge response technique, utilization, and results for focused and defocused system characterization.