Reviewerly/exHarmony huggingface dataset:
An Overview on CryptDb and Word2vec Approaches

Big data is a vast data set that was used in many areas.Online applications are subject to theft of confidential information because opponents can exploit software errors to access private data, and because curious or malicious officials can capture and lose data.CryptDB is a functional system that provides security and confidentiality through a set of operations.The obvious confidentiality of these attacks is for applications supported by SQL databases.It works by executing SQL queries on encrypted data using robust coding systems that support SQL. Word2Vec outputs word vectors that can be displayed as a large piece of text, or even we first train data.Word2Vec forms and word similarity assessment.Without a doubt, this article calls for proper research that sheds light on the security features using CryptDB to prevent data theft and privacy breaches in the server.The motivation of this research is to have an overview of CryptDB and Word2Vec implementation on the existing research approaches.
Modeling Event Salience in Narratives via Barthes' Cardinal Functions

Events in a narrative differ in salience: some are more important to the story than others. Estimating event salience is useful for tasks such as story generation, and as a tool for text analysis in narratology and folkloristics. To compute event salience without any annotations, we adopt Barthes' definition of event salience and propose several unsupervised methods that require only a pre-trained language model. Evaluating the proposed methods on folktales with event salience annotation, we show that the proposed methods outperform baseline methods and find fine-tuning a language model on narrative texts is a key factor in improving the proposed methods.
Fusion of virtual and real scenes for the data preparation of holographic stereogram

Different light-fields information collection methods will cause different light-fields information cannot be fused and recorded into the holographic stereogram. In order to print the holographic stereogram recording information of virtualreal fusion, the depth-image-based rendering (DIBR) technology is applied to the holographic stereogram fabricated by effective perspective images’ segmentation and mosaicking method (EPISM). This method firstly obtains matching real scene sampling parameters according to the virtual scene, and combines with the improved DIBR technology to draw the initial perspective images without holes or crack problems. After EPISM processing, the synthetic perspective images of real and virtual scenes with the same parameters are fused. Finally, the fused perspective images are recorded into holographic stereogram. The experimental results demonstrate this proposed method obtain high quality virtual-real fusion display.
Efficient Hearing-Dog-Robot Searching for User Using Life Pattern Clustering

Hearing dogs can search for deaf people to alert important life sounds. The dogs use their own body and touch the people to inform them of the sound. Meanwhile, few of the dogs work actually. To solve the problem, a hearing-dog robot has been proposed and developed so far. Furuta et al. proposed a method to estimate a user’s life pattern. The life pattern is estimated using past searching data acquired from what the robot had searched for the user. The proposal aims to more quickly detect the user. But, lack and bias of the past searching data make the method weak. This paper proposes a method to estimate life pattern of the user. The life pattern is described as proper probabilistic distribution. To achieve the probabilistic distribution, the method applies a clustering algorithm for the past data. The method hires Dirichlet process mixture model for the clustering. We conducted a simulation experiment to evaluate the method. The experiment prepared a user model that has a certain life pattern on the experiment environment. We evaluated time cost for the robot to search for the user model. The experimental result showed efficient searching in comparison with the method of Furuta et al.
Improvement of center-correction reset between guide-model switching for shipborne radar

Abstract Antenna servo system of shipborne radar has added digital-guide model and mutual-guide model with the support of center correction in order to capture and track targets conveniently. However, the switching between these two models would create larger step change resulting from the non-reset of center-correction. To this point, this paper represents the working principle of digital guidance, mutual guidance and center correction, and analyses the reason of step change. Then an novel approach is presented to improve the reset of center-correction. The results of experiments indicate that it can avoid the step change caused by the existence of center-correction value, and it enhances the reliability of facility effectively.
Deep Learning Classification and Recognition Model Construction of Face Living Image Based on Multi-feature Fusion

Convolutional neural network, as a common method of deep learning, is excellent in image recognition and classification performance. As one of the key technologies of biometrics recognition, face recognition is a research hotspot in the field of pattern recognition. Based on the basic model, a multi-layer feature fusion face recognition model structure is proposed. This model performs feature extraction on each convolution pooled feature map in the basic model and fuses the features obtained from each layer as the final face representation features. PCA (Principle Component Analysis), LDA (Linear Discriminate Analysis) and LPP (Locality Preserving Projection) are effective feature extraction methods. We fused them into the basic model to build a multi-layer feature fusion face recognition based on the basic model. Experimental results show that the feature fusion model method is significantly higher in recognition rate than the basic model method. Compared with the basic model, the feature fusion model can extract face features more effectively, thereby improving the face recognition rate.
Artificial Bee Colony Algorithm Optimization for Video Summarization on VSUMM Dataset

This paper attempts to prove that the Artificial Bee Colony algorithm can be used as an optimization algorithm in sparse-land setup to solve Video Summarization.The critical challenge in doing quasi(real-time) video summarization is still time-consuming with ANN-based methods, as these methods require training time.By doing video summarization in a quasi (real-time), we can solve other challenges like anomaly detection and Online Video Highlighting.A simple threshold function is tested to see the reconstruction error of the current frame given the previous 50 frames from the dictionary.The frames with higher threshold errors form the video summarization.In this work, we have used Image histogram, HOG, HOOF, and Canny edge features as features to the ABC algorithm.We have used Matlab 2014a for doing the feature extraction and ABC algorithm for VS.The results are compared to the existing methods.The evaluation scores are calculated on the VSUMM dataset for all the 50 videos against the two user summaries.This research answers how the ABC algorithm can be used in a sparse-land setup to solve video summarization.Further studies are required to understand the performance evaluation scores as we change the threshold function.
Word Frequency Does Not Predict Grammatical Knowledge in Language Models

Neural language models learn, to varying degrees of accuracy, the grammatical properties of natural languages. In this work, we investigate whether there are systematic sources of variation in the language models' accuracy. Focusing on subject-verb agreement and reflexive anaphora, we find that certain nouns are systematically understood better than others, an effect which is robust across grammatical tasks and different language models. Surprisingly, we find that across four orders of magnitude, corpus frequency is unrelated to a noun's performance on grammatical tasks. Finally, we find that a novel noun's grammatical properties can be few-shot learned from various types of training data. The results present a paradox: there should be less variation in grammatical performance than is actually observed.
Enhancing chaos in multistability regions of Duffing map for an asymmetric image encryption algorithm

We investigate the dynamics of a two-dimensional chaotic Duffing map which exhibits the occurrence of coexisting chaotic attractors as well as periodic orbits with a typical set of system parameters. Such unusual behaviors in low-dimensional maps is inadmissible especially in the applications of chaos based cryptography. To this end, the Sine-Cosine chaotification technique is used to propose a modified Duffing map in enhancing its chaos complexity in the multistable regions. Based on the enhanced Duffing map, a new asymmetric image encryption algorithm is developed with the principles of confusion and diffusion. While in the former, hyperchaotic sequences are generated for scrambling of plain-image pixels, the latter is accomplished by the elliptic curves, S-box and hyperchaotic sequences. Simulation results and security analysis reveal that the proposed encryption algorithm can effectively encrypt and decrypt various kinds of digital images with a high-level security.
Does Context Matter? Assessing the Current State of Quality Practice During Software Development in Small Software Companies

Software is increasingly becoming central to human activities leading to rapid growth and evolution in the industry but with a compromise in quality. Contextual complexity in the small software companies brings in challenges that make quality hard to achieve. Total quality management provides an opportunity for adaptive approaches to ensure quality through the four pillars we stem out of the TQM theory. This study explores to assess the state of current quality practice in software development in small software companies to propose mechanisms that will address quality in terms of the units of analysis and related attributes by interviewing 12 practitioners in Gaborone Botswana whose findings show a low understanding and use of quality measures in practice.
Agatha Christie’nin Have You Got Everything You Want? başlıklı kısa hikâyesi örneğinde mekanın çevirisi

Agatha Christie, a well-known English crime novelist, was not only a frequent visitor of Istanbul, but she also used it as a setting in her work. “Have You Got Everything You Want?” (1934) from her short story collection entitled Parker Pyne Investigates is one of her narratives mentioning Istanbul. Even though Christie did not focus on Istanbul in her text, its translator can be said to have rewritten it, creating a different meaning for the city in the translation entitled İstanbul Yolunda Bir Macera published as a free supplement to Hareket Newspaper in 1963. “Segmentation of the text” and “evaluation of the proper names in the text”, which are two of the operations of analysis used in the studies of the Paris School of Semiotics and compiled by Sündüz Öztürk Kasar within the framework of her approach to semiotics of translation (Öztürk Kasar, 2009), are used as the analysis steps in this study. In order to evaluate the translation, “Systematics of Designificative Tendencies” (Öztürk Kasar &amp;amp; Tuna, 2017, p. 172) propounded by Sündüz Öztürk Kasar is used. The “over-interpreted” (Öztürk Kasar &amp;amp; Tuna, 2017, p. 172) signs and discourses can be said to reflect Istanbul’s Turkish and Islamic identity, and the translation seems to have been influenced by nationalist sentiments. This translation of the city leads us to consider the translator’s visibility in this text, believing that the invisibility of the translator (Venuti, 1995) is also related to the reading activity. This study suggests that translation of crime fiction in Turkey needs further research especially to help visibility of crime fiction translators.
Pixel-wise Dense Detector for Image Inpainting

Recent GAN-based image inpainting approaches adopt an average strategy to discriminate the generated image and output a scalar, which inevitably lose the position information of visual artifacts. Moreover, the adversarial loss and reconstruction loss (e.g., l1 loss) are combined with tradeoff weights, which are also difficult to tune. In this paper, we propose a novel detection-based generative framework for image inpainting, which adopts the min-max strategy in an adversarial process. The generator follows an encoder-decoder architecture to fill the missing regions, and the detector using weakly supervised learning localizes the position of artifacts in a pixel-wise manner. Such position information makes the generator pay attention to artifacts and further enhance them. More importantly, we explicitly insert the output of the detector into the reconstruction loss with a weighting criterion, which balances the weight of the adversarial loss and reconstruction loss automatically rather than manual operation. Experiments on multiple public datasets show the superior performance of the proposed framework. The source code is available at https://github.com/Evergrow/GDN_Inpainting.
BANKNOTE RECOGNITION SYSTEM FOR VISUALLY IMPAIRED PEOPLE

Paper currency recognition is one of the most important needs  for visually impaired people to deal with different life circumstances . In this paper a layered framework is developed in which an Egyptian banknote recognition system is given. The developed system can recognize banknote in all geometric transformation  (scaling, rotation, translation, shearing) and all lightning effects .The system is validated by using dataset which contains 1200 images. The underlying image dataset has been  divided into five main categories (clear , rotated, noisy, folded  and old images ).The system is tested and the recognition  accuracy is 93% in all categories. The first layer in the framework deals with watermark security. The second layer  of the framework, the segmentation has been applied, so that its value, special textures and  special  drawings are extracted . In the third layer of the framework, the local banknote features have been utilized. the fourth  layer is a classification layer where different techniques are applied. Moreover, the developed system recognizes multiple banknotes. The implemented framework paves the way for various mobile applications are used  effectively and commercially to assist visually impaired people. یعد التعرف على العملات الورقیة أحد أهم احتیاجات الأشخاص ضعاف البصر للتعامل مع ظروف الحیاة المختلفة. فی هذا البحث تم اقتراح إطار متعدد الطبقات تم فیه تقدیم نموذج التعرف على الأوراق النقدیة المصریة. یمکن أن یتعرف النظام  على الأوراق النقدیة فی جمیع عملیات التحویل الهندسی (القیاس ، والدوران ، وقرب او بعد الصورة ، والمیل) وجمیع تأثیرات الضوء. للتحقق من صحة النظام ، تم إنشاء مجموعة بیانات یدویة من 1200 صورة. تم تقسیم مجموعة بیانات الصورة الأساسیة إلى خمس فئات رئیسیة (صور واضحة ، مستدیرة ، مشوشة ، مطویة ، قدیمة) تم اختبار النموذج بشکل مکثف وتم تسجیل الدقة لتصل الى 93٪ فی المتوسط فی جمیع الفئات. تعنى طبقة النظام الأولى بالتحقق من  العلامة المائیة ، وفی الطبقة الثانیة یتم استخراج قیمتها ونص ممیز ورسومات خاصة , وفی الطبقة الثالثة من النظام ، تم استخدام خصائص الأوراق النقدیة المحلیة . الطبقة الرابعة هی طبقة تصنیف حیث یمکن تطبیق تقنیات مختلفة. علاوة على ذلک ، یتعرف النظام على العدید من الأوراق النقدیة المتواجدة فی نفس الصورة. یمهد هذا النظام الطریق لمختلف تطبیقات الهاتف المحمول التی یمکن استخدامها بشکل فعال وتجاری لمساعدة الأشخاص المعاقین بصریا
On the Application of Intervention Measures in English Teaching

In the process of English learning, students will always encounter some problems, resulting in low learning efficiency, weariness and even giving up learning. Based on this, the relevant departments of the school should take corresponding measures to intervene in order to help students solve problems and get out of difficulties. This paper analyzes the causes and characteristics of English learning failure, and puts forward several intervention measures, aiming to help students turn around the situation and get on the right track.
STUDENTS’ MOTIVATION ON ENGLISH NATIVE SPEAKER’S TEACHING

Abstract: This is a descriptive research of third grade students of English Department, Gorontalo state university. It was conducted to figure out the level of students’ motivation on Englih Native speaker’s teaching. In this study, there were 20 students participating in ths research. The data were derived from questionnaire and analyzed by using simple calculation analysis, while interview was conducted to gain more information. The result showed that 40% students has high motivation with score range 61%-80%. Therefore, i can conclude that the students’s motivation on english native speaker’s teaching was categorized as High Motivation. The result of interview also showed that the students have high motivation when taught by english native speaker. It concluded that the competence in teaching and pedagogy skill of native speaker gave good impact and contribution in education.
LibGuides: Drop-In Library Support Sessions: Home

Resources on search strategies, doing a literature review, getting the most out Google Scholar and APA 7 Referencing from library support drop-in sessions in November 2020.
Chemistry History Education Infiltrating Cultivation of Scientific Literacy

The teaching of chemical history is of great educational significance for improving students' scientific literacy. It is not only conducive to the cultivation and improvement of students' scientific spirit and scientific thinking, but also enables students to develop their scientific thinking and scientific inquiry ability and establish a scientific essence view.
On the Application of Computer Archives Management System and the Path of Security Maintenance

Abstract The concept of archives management is closely related to the renewal of the concept of computer technology. Various technologies in the field of computer are changing with the times. At present, computer technology has been widely used in various fields of real life. In the field of file management, the computer has greatly improved its work efficiency, speed and quality. At present, in the file management work, the computer file management system has got good feedback effect and popularization rate. However, in the computer environment, the file management system in the actual application will also appear a lot of security problems [1] . It has become an important problem to do a good job in the security maintenance of computer file management system.
Log(Graph): A Near-Optimal High-Performance Graph Representation

Today's graphs used in domains such as machine learning or social network analysis may contain hundreds of billions of edges. Yet, they are not necessarily stored efficiently, and standard graph representations such as adjacency lists waste a significant number of bits while graph compression schemes such as WebGraph often require time-consuming decompression. To address this, we propose Log(Graph): a graph representation that combines high compression ratios with very low-overhead decompression to enable cheaper and faster graph processing. The key idea is to encode a graph so that the parts of the representation approach or match the respective storage lower bounds. We call our approach "graph logarithmization" because these bounds are usually logarithmic. Our high-performance Log(Graph) implementation based on modern bitwise operations and state-of-the-art succinct data structures achieves high compression ratios as well as performance. For example, compared to the tuned Graph Algorithm Processing Benchmark Suite (GAPBS), it reduces graph sizes by 20-35% while matching GAPBS' performance or even delivering speedups due to reducing amounts of transferred data. It approaches the compression ratio of the established WebGraph compression library while enabling speedups of up to more than 2x. Log(Graph) can improve the design of various graph processing engines or libraries on single NUMA nodes as well as distributed-memory systems.
A Content-Based Approach to Profile Expansion

Collaborative Filtering algorithms suffer from the so-called cold-start problem. In particular, when a user has rated few items, recommendations offered by these algorithms are not too accurate. Profile Expansion techniques have been described as a way to tackle this problem without bothering the user with additional information requests by increasing automatically the size of the user profile. Up to now, only collaborative approaches had been proposed for Profile Expansion. However, content-based techniques can also be used. We perform a manual analysis of a movie dataset to analyze how content features behave. According to this analysis, we propose a content-based approach, which is also combined with collaborative information. Concretely, we expose the advantages and disadvantages of the combination with a popularity feature. Moreover, a comparison to pure collaborative approaches is performed. Our approach is evaluated in a new system situation. That is, not only the active user has few ratings, but also most of the users. The results show that content-based information is useful for rating prediction. In addition, recommendations are less personalized as popularity feature acquires more relevance for item selection.
Digital Examination Techniques and Considerations

This chapter is designed to assist the Forensic Document Examiner (FDE) with examinations using digital image technology. The areas discussed are hardware and software considerations, imaging, direct examinations, comparative examinations, and demonstratives. In order to conduct digital examinations, FDEs need to ensure that they have the appropriate resources to adequately comply with the standards. All digital examinations start with capturing an image of the physical items to be examined. This is accomplished with either a scanner or camera. The transition to digital examinations brings with it some additional equipment requirements, particularly sufficient computing power to process large image files. Direct examinations involve the use of image editing software to obtain a result that may not be visible to the FDE without the aid of editing software or other imaging equipment. An effective method of visualizing machine printing or writing on charred paper is infrared photography.
Discussion on the Interactive Mode of College English Teaching under Internet Plus Perspective

under the background of “Internet plus”, new electronic technology is developing vigorously, and the application of Internet technology is more and more extensive, which has promoted the development of all fields of society. The Internet plus teaching mode is attracting more and more attention. It helps students to provide teaching resources by using the massive information database of the Internet, changing the traditional single teaching mode in the past English classroom, and making English teaching no longer limited by time and space. In practical teaching, teachers pay more attention to imparting basic theory and knowledge, which results in students' English ability remaining in the examination, and their oral expression and communication ability can not meet the needs of entering the society in the future. In view of this teaching phenomenon, the current requirements for students' English learning ability and English practice ability are gradually improving. In order to adapt to the new teaching requirements and cultivate high-quality English talents in line with the social needs, the College English interactive education mode emerges as the times require. The application of College English Interactive Teaching mode is conducive to the construction of a diversified network education ecological environment to meet the needs of students The needs of personalized development. This article will analyze and explore the advantages of interactive mode of College English education and how to apply the interactive education mode under the perspective of Internet plus.