Reviewerly/exHarmony huggingface dataset:
High Accuracy Farsi Language Character Segmentation and Recognition

Despite many advances in optical character recognition in general, there are still serious challenges remaining in recognizing Farsi text. The main reason is the cursive nature of the letters in written Farsi, i.e., depending on the position of a letter within a word, it might join to its neighboring letters, which consequently changes the shape of the character. As a result, each letter can have up to four different character shapes. In addition to the problem of segmenting the characters, the increased number of characters makes the recognition task even more challenging. This paper introduces a complete framework for character recognition, including a method for segmenting the characters and one for classifying the resulting separated characters. Character segmentation is performed using a new sliding-window algorithm with a high accuracy rate of 98.23%. With a total of 32 Farsi letters resulting in 114 character shapes, an almost perfect character recognition rate of 99.94% is achieved using the proposed Fisher characters method. The final system, including segmentation and recognition modules, achieves a recognition rate of 98.17% and is robust against the scale and rotation of the image, and the font size of the written text.
Trajectory Planning for Autonomous Vehicle Using Digital Map

In this paper, an algorithm for autonomous vehicle trajectory planning is proposed. The proposed algorithm consists of two parts. In the first part, transformation of given coordinates from Cartesian system to Frenet-Serret system is applied, along with interpolation of points in trajectory. In the second part, most efficient trajectory is being chosen and optimized. The algorithm was developed in Udacity's simulator, where it was tested and compared with other similar algorithms. The algorithm shows promising performance through testing.
Payment Authorization in Smart Environments: Security-Convenience Balance

One of the major roadblocks to mass adoption of smart environments and IoT services (and IoE in future) is the lack of ubiquitous solutions for passive and at the same time secure payment authorization at physical locations where services are provided. The main research goal of this work is to comprehensively evaluate such system proposed by the authors. When customers approach a point of sale it identifies them using face biometrics. After the order is completed, the system takes advantage of multimodal context-aware payment authorization to make a multi-criteria selection of the authorization method, optimally to make the whole process fully passive. All this enables a controlled balance between payment security and convenience for the client and for the seller. Empirical tests at an existing point of sale have been performed, the usage data have been collected, statistically analyzed and confronted with formulated research hypotheses.
ISO 25010 Support in Test Point Analysis for Testing Effort Estimation

Estimation of the testing effort prepared on the early stage of software development process (e.g. in the design phase) could be a significant help for both project management and resources distribution optimization. We designed and implemented a tool, called IoTEAM, estimating time necessary to test the system, on the basis of its UML model (class and sequence diagrams). That tool is a unique combination of two methods: transformation from UML class and sequence diagrams to the results of Function Point Analysis and Test Point Analysis (TPA). The resulting approachMalanowska, Agnieszka can be used for estimation of time of the whole testing process – from unit tests to acceptance tests. Unfortunately, TPA was proposed almost twenty years ago, so is not consistent with contemporaryBluemke, Ilona quality standards. In this paper, we present how to incorporate ISO 25010 standard in TPA method.
Mixed Reality to Enable Construction Design Comprehension for Digital Natives

The median age of a construction professional has been increasing over the past decade, recently approaching 41 years old. Current reports indicate that more workers are retiring than those entering the industry, leading to an imminent and severe labor shortage. One way of countering the effects of those problems is through attracting younger talent into the industry. One key descriptive of the current young generation is being “digital natives”, individuals who have lived their entire life during times of technological advancements. BIM, building information modeling, defined as the development and use of a 3D virtual model containing both physical and functional information, is increasingly adopted for its numerous benefits during design and construction. Researchers have suggested numerous emerging technologies to complement the use of BIM, especially on the construction site. One technology is mixed reality, MR, which enables the viewing of virtual content in the user’s field of view. Prior research theorized that viewing designs through MR may enable design communication to trained professionals. This paper explores whether MR can communicate BIM content in a simple enough manner that individuals, without any prior construction experience, could successfully construct the intended building elements. An industry generated BIM is viewed by middle school students through an MR device, and their behavior and design understanding is presented. Specific aspects of design comprehension and participants perceptions are also studied. This paper contributes to the body of knowledge by empirically showing that digital natives can comprehend and build designs using only MR without need for prior training, supplementing the shortage of labor faced in the industry by accelerating their integration into the workforce.
Deep Geometry Embedding Networks for Robust Facial Landmark Detection

Facial landmark detection has witnessed substantial progress due to introducing convolutional neural networks. Nonetheless, current convolutional neural networks-based approaches ignore the useful geometric relationship between different facial locations. To address this issue, we propose a new module to model the facial geometric relationship. The module can be integrated into the convolutional neural networks architecture to obtain the geometric representation, whereafter we leverage bilinear pooling operation to embed it into high-level feature maps of original face image so as to produce the more discriminative face representation. Extensive evaluation experiments on multiple challenging benchmark datasets demonstrate that our captured geometric information is robust against occlusion and head pose variation and our proposed method outperforms state-of-the-art methods.
Semantic Core Parsing in Search Engine Optimization Process

This article describes the methodology of competitor's benchmarking regarding internal search engine optimization activities, primary, focusing on competitor's semantic core parsing and its editing as well as aims on disclosing approximate calculations of resources spent and effectiveness reached. Observation includes small and medium enterprises in business-to-consumers market and business-to-business one. All of the investigated firms were not the market leaders, however almost half of them could be considered as market challengers. The research proves that applying specific software and manual algorithms of extracted keywords processing is more effective than using standard keywords builders, including Google AdWords™ keyword planner. The analysis has shown that main goal that was set in all the cases - incoming web traffic (both from the context ads and organic search results) increase on a target web-page - has been reached within all the requirements. All the additional spending on mentioned services as well as extra working hours were justified.
Autonomous Learning Recommendation Algorithm Based on K-Means and K-Nearest Neighbor

Based on the network autonomous learning platform of Anhui university of traditional Chinese medicine, big data technology is employed to analyze the course resources, learners’ interests and so on. Through the recommendation system model, learning resources are recommended according to learners’ interest and curriculum similarity. In this system, K-means algorithm and KNN (k-nearest neighbor) collaborative filtering algorithm are effectively combined to recommend TOP-N course in the platform to learners. The experimental data indicate that the performance of the recommendation system is directly affected by the similarity threshold and the value of the recommended item TOP-N.
Detection of static objects in an image using texture analysis

The article deals with the design of a method for the automatic detection of static objects in the image captured by an optical microscope. The search algorithm for static objects in the image - non-moving cilia is based on texture description methods. The texture of the image is described by statistical values, where it can be noticed that background texture, cells and cilia have different mathematical statistical parameters. Just based on the different statistical parameters of the textures, the classification for each texture parameter was done separately. As a result, the resulting classification takes into account the most predominant group to which the pixel has been assigned. The output from the algorithm is a mask, where the original image is overlayed by the obtained mask and cilia area is contoured.
Part-Based Convolutional Network for Imbalanced Age Estimation

Age estimation based on unconstrained face images remains a challenging problem in computer vision and pattern recognition. We address this by ensembling part-based features and designing a cost sensitive loss to overcome the general imbalance data in age estimation. Specifically, we treat age estimation as a multi-class classification problem and mainly make two contributions: (i) We present a Part-based Convolutional Network(PCN) for age estimation to extract regional features instead of the holistic ones, which can preserve more discriminative features in facial regions like forehead, canthus, cheek, jaw, etc; (ii) A balanced loss for multi-class classification is designed to handle the extremely imbalance age distribution. The loss pays more attention to the hard examples, and can automatically adjust the weights of samples according to their contribution. State-of-the-art performance was achieved on FG-NET, MORPH and CACD databases, validating the effectiveness of the proposed approach.
A Comprehensive Evaluation Method of the Learning Situation of the Students of the Quality Course Website

Aiming at the problem that the results of the students in the excellent course website are not vivid and intuitive enough, this paper put forward a comprehensive evaluation method for the students of the excellent course website by improving the radar graphics method. The flow of this method is as follows: First, the index system of comprehensive evaluation for the students in the excellent course website is established, and then the comprehensive evaluation model is constructed by using the radar graphics method to make a systematic analysis of the evaluation for the students in the excellent course website, and through the experiment, the comprehensive evaluation of the learning situation of the students on the website shows that the experimental results have good effect, comprehensive angle, high reliability of data, and have practical reference significance.
DevID: Blockchain-Based Portfolios for Software Developers

Decentralized applications, also known as dApps, are the new paradigm for writing business-critical software. Recruiting developers with appropriate qualifications and skills for this activity is key, yet challenging. The main problem is that the portfolio of developers is usually scattered across centralized platforms like GitHub and LinkedIn, and vendor locked. This can result in an incomplete impression of their capabilities. We address this problem and introduce DevID, a blockchain-based portfolio for developers. Over time, this portfolio enables developers to build up a trustworthy collection of records that showcase their capabilities and expertise. They can import data assets from third parties into a unified DevID portfolio, add projects and skills, and receive endorsements. All portfolio records are stored on a scalable distributed ledger and owned by developers themselves. The essential idea is to exploit the tamper-proof property of the blockchain while providing durable storage. To demonstrate the practical value of DevID, we build the competition-based platform, dAppCoder, for the development of decentralized applications. On dAppCoder clients are able to submit their ideas and developers can find work. dAppCoder utilizes DevID portfolios to match these clients and developers. We fully implement our ideas and conduct a deployment trial. Our trial demonstrates that DevID is efficient at storing portfolio records.
Application of fuzzy cognitive map-based TRIZ inventive principles for sustainable sediment management in dam reservoirs

Abstract The present paper contributes to the development and discussion on fuzzy cognitive map (FCM)-based theory for inventive problem solving (TRIZ) for sustainable sediment management in reservoirs. FCM combines aspects of fuzzy logic, neural networks, semantic networks, expert systems, and nonlinear dynamical systems. TRIZ is a constructive methodology that includes practically reproducible models and methods that allow the development of new inventions as well as the teaching of the process, the models, and the methods of creating inventions. A proposed approach in this paper is an improvement methodology that is designed to bring about rapid improvements/changes to processes by defining and implementing the changes that can be quickly identified and easily implemented, thereby reducing the cost and time to bring about improvement and change in reservoirs. Results of this study provide a road map for how to introduce FCM and TRIZ into local sustainable sediment management with consideration of technical and executive requirements, economic factors, social welfare, and environmental impacts.
Unsupervised Cross-Domain Person Re-identification Based on Style Transfer

Person re-identification (person Re-ID) is mostly viewed as an image retrieval problem across different cameras. Most of existing models have achieved significant improvement on single-domain setting but failed to generalize to new domain due to the image style variations between domains. In this work, we aim to adapt a model trained on source domain to unlabeled target domain without significant performance drop. Thus, a novel style transfer framework that allows us to separate image style from content is proposed in Re-ID. Firstly, the source domain image is transferred to the target style while retaining the ID information. Then the source domain images and transferred images are combined to train a style-independent Re-ID model. Experiments show that we achieved higher performance of unsupervised cross-domain person Re-ID on the Market-1501 and DukeMTMC-reID datasets.
Demo: counterpoint by construction

We present Music Tools, an Agda library for analyzing and synthesizing music. The library uses dependent types to simplify encoding of music rules, thus improving existing approaches based on simply typed languages. As an application of the library, we demonstrate an implementation of first-species counterpoint, where we use dependent types to constrain the motion of two parallel voices.
Improved threshold function image denoising method

In this paper, an improved threshold function is proposed for the discontinuity problem of hard threshold function and the constant deviation of original wavelet coefficients and estimated coefficients in soft threshold function. This function improves the deficiency of traditional threshold function by introducing control coefficients, and the function have certain flexibility. The experimental results show that the improved wavelet threshold function has a better effect in removing image gaussian noise than traditional threshold function. Moreover, the PSNR(peak signal to noise ratio ) and EPI (edge retention index) values of the image are both improved.
Research on Network Security Risk Assessment Method Based on Bayesian Reasoning

To accurately assess the vulnerability of computer networks, the paper proposes a BNAG model and vulnerability assessment algorithm. Firstly, a new attack graph model is proposed, as well as the E-Loop algorithm, which is used for solving the loop problem in the attack graph. Secondly, to disentangle the confused node relationship during the attack graph conversion process, an algorithm is designed to generate a Bayesian network attack graph model BNAG. Finally, to obtain the reachability of the path, the metrics such as node attack difficulty and node state transition is introduced, and the corresponding algorithm is given. Based on this, the posterior probability is calculated.
The impact of visual media on social cognitive concept: The case of cultural object Ru (Korea)

&lt;div data-canvas-width="266.11469461802045"&gt;There have been very few studies explaining the theoretica l basis on the importance of visible cultural heritage. This study provides the philosophical background of this topic based on a phenomenological point of view and&lt;/div&gt;&lt;div data-canvas-width="408.0393972989316"&gt;explains the significant impact on social members’ cognition. The case of Ru, one of the traditional Korean building types, is introduced as a representative example; how its concept has been defined, changed, and forgotten in Korean culture. The importance of having a correct understanding of how cognition is composed of different types of experiences of cultural heritage is further explained. In this context, the importance of semantic mode and pictorial mode classified by Husserl is argued as the most powerful medium in human cognition based on phenomenological analysis. In this respect, the important role of Virtual Reality (VR) was highlighted. Considering the pace of recent technology and researches, breaking the barrier between experiencing the physical object and the VR may be a matter of time. Phenomenological classification of cultural heritage, which was designed for explaining all the types of cultural heritage, is introduced. The importance of developing a valid VR model and its role in cultural studies is emphasized via the phenomenological classification of cultural heritage. Finally, the balance of the inductive and deductive approach in a cultural study is suggested for more prolific and balanced achievements.&lt;/div&gt;&lt;div data-canvas-width="408.0393972989316"&gt; &lt;/div&gt;&lt;div data-canvas-width="408.0393972989316"&gt;&lt;p&gt;Highlights:&lt;/p&gt;&lt;ul&gt;&lt;li&gt;This article provides the philosophical background of the importance of visible cultural heritage based on the phenomenological point of view.&lt;/li&gt;&lt;li&gt;Significant impact on social members’ cognition of the visible cultural heritage is discussed in the case of the traditional Korean building.&lt;/li&gt;&lt;li&gt;In this respect, the important role of Virtual Reality technology is highlighted.&lt;/li&gt;&lt;/ul&gt;&lt;p&gt; &lt;/p&gt;&lt;/div&gt;
Performance Trade-offs in Weight Quantization for Memory-Efficient Inference

Over the past decade, Deep Neural Networks (DNNs) trained using Deep Learning (DL) frameworks have become the workhorse to solve a wide variety of computational tasks in big data environments. To date, DL DNNs have relied on large amounts of computational power to reach peak performance, typically relying on the high computational bandwidth of GPUs, while straining available memory bandwidth and capacity. With ever increasing data complexity and more stringent energy constraints in Internet-of-Things (IoT) application environments, there has been a growing interest in the development of more efficient DNN inference methods that economize on random-access memory usage in weight access. Herein, we present a systematic analysis of the performance trade-offs of quantized weight representations at variable bit length for memory-efficient inference in pre-trained DNN models. In this work, we vary the mantissa and exponent bit lengths in the representation of the network parameters and examine the effect of DropOut regularization during pre-training and the impact of two different weight truncation mechanisms: stochastic and deterministic rounding. We show drastic reduction in the memory need, down to 4 bits per weight, while maintaining near-optimal test performance of low-complexity DNNs pre-trained on the MNIST and CIFAR-10 datasets. These results offer a simple methodology to achieve high memory and computation efficiency of inference in DNN dedicated low-power hardware for IoT, directly from pre-trained, high-resolution DNNs using standard DL algorithms.
Automatic Keyphrase Extraction from Persian Scientific Documents Using Semantic Relations

With the rapid growth of Persian scientific documents in online databases, the need for quick access to these documents has increased in recent years. As a common way to provide a succinct summarization of a document, Automatic Keyphrase Extraction (AKE) plays an important role in various applications, e.g. indexing, summarization and information retrieval. Although lots of methods have been proposed for AKE, they have not resulted in satisfactory performance for Persian texts. The main reasons include dependency on linguistic resources, training datasets, and special NLP tools that are not properly available for Persian language. Considering the special need for an effective and efficient method to carry out the task of AKE on Persian documents, we have proposed a novel Thesaurus-based method. The method leverages semantic relations between words of a given document to construct graphs of related words and then extract candidate keyphrases which are semantically and grammatically correct using a scientific and technical thesaurus. Final keyphrases will be selected based on a weighting method in which the position of the phrase in the text, i.e. abstract, body, etc., would affect its weight. The experimental results show that our method outperforms the similar works on Persian texts under three evaluation metrics.
Cultural Event Extraction for Chinese Public Libraries

Event extraction is an important research topic for information extraction. The event extraction technique extracts events of interest from the unstructured information and presents them to the user in a structured manner. Digital Library Promotion Project launched by National Library of China collects public cultural event news released by Chinese public libraries, and this paper presents the method of collecting events from cultural domain news text and propose an approach to discover cultural event automatically. To distinguish cultural news from noisy data and identify the event types, we established a cultural keywords dictionary. For event argument recognition, we adopted pattern matching method. Experimental result valid the method, and a F1 score of 86.43% on average is achieved with cultural event news from 319 Chinese public libraries.