Reviewerly/exHarmony huggingface dataset:
Copy-Move Forgery Detection Using SWT-DCT and Four Square Mean Features

Image forgery detection is challenging to solve, especially in blind image forgery detection. Copy-move forgery is a common and popular method in image forgery. Copy-move forgery become more challenging to detect with addition of post-processing operation in the image. In this paper, the proposed methodology is copy-move detection with Stationary Wavelet Transform (SWT), Discrete Cosine Transform (DCT), and Four-Square Mean Features. SWT is used because it enables to handle various kind of post-processing operation. Likewise, DCT has many advantages such as the lower dimension and robust to various attacks. The proposed methodology is evaluated using CoMoFoD dataset and the performance are measured using precision, recall, and F1-scores. The result reveal that the methodology is successful in detecting duplicated region and robust against brightness change, contrast adjustment, image blurring, and color reduction.
Intensity-Based 2D-3D Registration Using Normalized Gradient Fields

2D-3D registration is central to image guided minimal invasive endovascular therapies such as the treatment of aneurysms. We propose a novel intensity-based 2D-3D registration method based on digitally reconstructed radiographs and the so-called Normalized Gradient Fields (NGF) as a distance measure. We evaluate our method on publicly available clinical data and compare it to five other state-of-the-art 2D-3D registration methods. The results show that our method achieves better accuracy with comparable results in terms of the number of successful registrations and robustness.
Exploring Language Learning Strategies of Saudi EFL learners at Shaqra University, Saudi Arabia

Among the effective learning tools that enhance the L2 learning process, learning strategies are considered the most vital and essential techniques could be used by the learners for improving their L2 achievement. The current study explored the preferred language learning strategies for group of Saudi EFL learners in Shaqra University, Saudi Arabia. The sample of the study included 60 male undergraduate students enrolled in English department in 2019 academic session. The sample responded to the Strategy Inventory for Language Learning (SILL) questionnaire by Oxford. Results were analyzed using SPSS program. Findings showed that the sample used almost all strategies in an average level. The findings showed that social strategy was used most frequently by this group of students followed by metacogntive strategy. Both strategy categories used with high range according to Oxford scale. While the rest of the strategies fell in the average range, compensation, cognitive, affective, except for the memory strategy which was used with low range. Based on the findings of the study, it is implied that training programs are suggested in this case to explain to the students the importance of using all LLSs as much as possible without focusing on one particular strategies and neglecting the others.
International Applications of Automated Scoring

When Page (1966b) prophesied the advent of “automated essay grading” (i.e., machine scoring of writing), he viewed the technology as a “teacher’s helper” based on his belief that the evaluation of writing was a major impediment for teachers assigning more writing in the classroom. For the last 10 years, researchers have been developing automated essay scoring (AES) engines that can be used for languages other than English. In this chapter we present a survey of AES engines developed in both English and other languages in order to provide a benchmark of developments in machines. By having knowledge of work in other languages, the field may be able to address some of the challenges facing machine scoring in English, both for essays and for responses other than essays.
FPGA Implementation of Speech Recognizer for Isolated Words

This paper investigates the implementation of feature extraction of pre-recorded speech signal on FPGA board. The technique used is Mel Frequency Cepstral feature extraction. This is followed by the HLS implementation of comparison of feature vectors with the existing data. Comparison of vectors is done by Dynamic Time Warping (DTW) method. The aim of this paper is to implement the above techniques on FPGA and compare the results of the output with that of outputs from software. Also utilization of resources on FPGA board are investigated to ensure feasibility of implementation. In near future, the speaking with machines would become most common in everyday lives. This paper would serve a major part in speech recognition on hardware which is most preferred over the software implementation due to the computational speed and security.
Budget Active Learning for Deep Networks

In the digital world unlabeled data is relatively easy to acquire but expensive to label even with use of domain experts. On the other hand, state-of-the-art Deep Learning methods are dependent on large labeled datasets for training. Recent works on Deep Learning focus on use of Active Learning (AL) with uncertainty for model training. Although most uncertainty AL selection strategies are very effective, they fail to take informativeness of the unlabeled instances into account and are prone to querying outliers. In order to address these challenges, we propose a Budget Active Learning (BAL) algorithm for Deep Networks that advances active learning methods in three ways. First, we exploit both the uncertainty and diversity of instance using uncertainty and correlation evaluation metrics. Second, we use a budget annotator to label high confidence instances, and simultaneously update the selection strategy. Third, we incorporate AL in Deep Networks and perform classifications on untrained and pretrained models with two classical and a plant-seedling sets of data while minimizing the prediction loss. Experimental results on the three datasets of varying sizes demonstrate the efficacy of the proposed BAL method over other state-of-the-art Deep AL methods.
Social and Communication Competences of Students – Future Teachers

Numerous studies have shown that teachers significantly shape student's learning context. Socially and emotionally competent teachers tend to develop supportive and encouraging relationship with their students by promoting intrinsic motivation, focusing on student's strengths and abilities, mediating through conflict situations and encouraging appropriate communication and prosocial behaviour. Teachers with good communication skills will create a more successful teaching and learning ambience for the students, and without communication, the teaching and learning process will not take place. Educators, parents and public today recognize the need for an educational agenda to improve academic performance, but also to enhance students' social and communicational competence. The aim of the present study was to examine social and communication competence of the students of educational sciences and teacher education at the University of Zagreb Department Of Croatian Studies. As part of the most important aspects of the future educational work, the participants pointed out independence at work, the awareness that they would contribute to the future of the society and the awareness that they play a useful role in society. Participants also expressed optimism about their communication skills with students, planning and organizing teaching process and maintaining discipline, and to some less extent their competences for successful cooperation with students’ parents. Results were discussed in terms of possibilities to contribute to future teachers’ education.
Block encryption of quantum messages

Block encryption is a fundamental cryptographic primitive in modern cryptography. However, it is impossible for block encryption to achieve the same security as one-time pad. Quantum mechanics has changed the modern cryptography, and lots of researches have shown that quantum cryptography can outperform the limitation of traditional cryptography. This article proposes a new constructive mode for private quantum encryption, named $$\mathcal {EHE}$$, which is a very simple method to construct quantum encryption from classical primitive. Based on $$\mathcal {EHE}$$ mode, we construct a quantum block encryption scheme from pseudorandom functions. If the pseudorandom functions are standard secure, our scheme is indistinguishable encryption under chosen plaintext attack. If the pseudorandom functions are permutation on the key space, our scheme can achieve the same security as quantum one-time pad, and the secret key can be securely reused for exponential times if the receiver sends a confirmation after every round of “encryption–decryption.” Thus, our scheme can be viewed as a positive answer to the open problem in quantum cryptography “how to unconditionally reuse or recycle the whole key of private-key quantum encryption.”
Machine Learning on the Move

This workshop will teach instructors how to incorporate Machine Learning into their mobile course using ML Kit for Firebase. ML Kit provides powerful machine learning functionality to your app running either iOS or Android and is for both experienced and novice machine learning developers. With just a few lines of code, you can use these powerful and easy to use machine learning packages. This workshop will focus on using machine learning in mobile applications to solve real-world problems.
Fast and accurate tilt-shift-immune phase-shifting algorithm based on self-adaptive selection of interferogram subblocks and principal component analysis

To eliminate the effect of tilt-shift error on the accuracy of phase-shifting interferometry (PSI), a fast and accurate tilt-shift-immune phase-shifting algorithm based on the self-adaptive selection of interferogram subblocks and principal component analysis (SSPCA) is proposed. First, each interferogram is divided into several subblocks, and principal component analysis and the least-squares method (LSM) are applied to obtain the phase-shift value of each subblock. Next, according to the correlation coefficients between each phase-shift curve, valid and invalid subblocks can be distinguished. Finally, all phase-shift values of the valid subblocks are used to fit the tilt phase-shift plane, and phase results can be obtained using the LSM. Simulations indicate that the accuracy of SSPCA can reach 0.03 rad both for small (1 rad) and large ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>2</mml:mn> </mml:mrow> <mml:mi>π<!-- π --></mml:mi> </mml:math> rad) tilt amplitudes, and it takes only one-tenth or less of the processing time of iterative algorithms. Experiments proved that SSPCA can be applied even without a precision phase shifter and thus provides a low-cost approach for PSI with both high precision and speed.
Vulnerability Exploitations Using Steganography in PDF Files

This article analyses the ways malicious executable files hides with Steganography on the most used files of our daily basis such as PDF, Word, Text, and Image.It demonstrates how data is hidden and gathers innovative ways of identifying potential attacks to prevent them by engaging the safety and exploitation of files distributed online.It is concerned with infected files that can have malicious executable applications embedded, executing itself upon the opening of the original file.Several experiments are detailed exploiting gaps in PDF, email and image files in order to draw awareness to security professionals and Ethical hackers' trainees.
Collaborative Recommendation for Scenic Spots based on Degree Centrality

In the collaborative filtering recommendation algorithm, similarity calculation shows a significant influence in recommended quality. Only by looking at the scores of all users and aiming to calculate the similarity of the normal recommendation algorithm, it is impossible to accurately reflect the user demand for the product. In order to eliminate these interferences, this research puts forward an algorithm based on degree centrality. The algorithm introduces the influence of the user in the similarity calculation, judges the influence according to the degree Centrality of the user's degree, and calculates the user's rating records on the target item for recommendation. Experimental validation: Compared to the original user-based recommendation method, the proposed recommendation algorithm based on degree centrality of this paper has certain improvements in Mean Absolute Error, Root Mean Square Error, F-rneasure, Coverage and Precision.
Enhancing named entity recognition from military news with bert

Abstract Information extraction from news pages is often used in various fields. Named entity recognition is a key step in information extraction. News data is difficult to process because of it has unstructured characteristics. So the author proposes a method to perceive the relationship between words and sentences. The method is integrated with BERT(Bidirectional Encoder Representations from Transformers)-BiLSTM(Bi-directional LongShortTerm Memory)-CRF(Conditional Random Field), referred to as the Bertbc model. The model was tested on the People’s Daily dataset and text data from military news. The results show that this method improves the recognition accuracy, recall rate, f value and recognition effect.
Manifold Regularization for Adversarial Robustness

Manifold regularization is a technique that penalizes the complexity of learned functions over the intrinsic geometry of input data. We develop a connection to learning functions which are locally stable, and propose new regularization terms for training deep neural networks that are stable against a class of local perturbations. These regularizers enable us to train a network to state-of-the-art robust accuracy of 70% on CIFAR-10 against a PGD adversary using $\ell_\infty$ perturbations of size $\epsilon = 8/255$. Furthermore, our techniques do not rely on the construction of any adversarial examples, thus running orders of magnitude faster than standard algorithms for adversarial training.
Generative Adversarial Network-Based Neural Audio Caption Model for Oral Evaluation

Oral evaluation is one of the most critical processes in children’s language learning. Traditionally, the Scoring Rubric is widely used in oral evaluation for providing a ranking score by assessing word accuracy, phoneme accuracy, fluency, and accent position of a tester. In recent years, by the emerging demands of the market, oral evaluation requires not only providing a single score from pronunciation but also in-depth, meaning comments based on content, context, logic, and understanding. However, the Scoring Rubric requires massive human work (oral evaluation experts) to provide such deep meaning comments. It is considered uneconomical and inefficient in the current market. Therefore, this paper proposes an automated expert comment generation approach for oral evaluation. The approach first extracts the oral features from the children’s audio as well as the text features from the corresponding expert comments. Then, a Gated Recurrent Unit (GRU) is applied to encode the oral features into the model. Afterwards, a Long Short-Term Memory (LSTM) model is applied to train the mappings between oral features and text features and generate expert comments for the new coming oral audio. Finally, a Generative Adversarial Network (GAN) is combined to improve the quality of the generated comments. It generates pseudo-comments to train the discriminator to recognize the human-like comments. The proposed approach is evaluated in a real-world audio dataset (children oral audio) collected by our collaborative company. The proposed approach is also integrated into a commercial application to generate expert comments for children’s oral evaluation. The experimental results and the lessons learned from real-world applications show that the proposed approach is effective for providing meaningful comments for oral evaluation.
Öğrenciler Arasında Blokzincir Farkındalığı Üzerine Bir Araştırma

to blockchain technology and its applications.Specifically, existing programs use traditional methods regardless of contemporary technological infrastructure.However, our research reveals that current university students prefer interactive education programs that contain more visual materials and less writing.The results of this survey are expected to be used to create such programs.We particularly expect that the results of our research will become useful for blockchain technology programs designed especially for university level.
Detection and Tracking of Moving UAVs

Detection and recognition of moving objects can provide an effective defense mechanism for critical infrastructure, airports, government, power stations, and other locations. It is used to increase the security of an internal perimeter with respect to the implementation of defense against the moving UAV (Unmanned Aerial Vehicle). With the rapid growth of the UAV, a very often discussed topic has become defense against unmanned vehicles. Their early detection and destruction can prevent the occurrence of undesirable situations. A camera located on another unmanned vehicle that protects a space will allow detecting and recognizing unwanted targets, and then tracking movements at the best time eliminates further movement of the detected device by release the net. Object detection and recognition are used to find, identify, and categorize moving objects in images and videos. This paper presents comprehensive set of algorithms and tools for detecting and recognizing moving objects. We use the System Toolkit. It is a set of several machine learning, functional and motion techniques for detecting and recognizing objects.
Multiplicative Controller Fusion: A Hybrid Navigation Strategy For Deployment in Unknown Environments

Learning-based approaches often outperform hand-coded algorithmic solutions for many problems in robotics. However, learning long-horizon tasks on real robot hardware can be intractable, and transferring a learned policy from simulation to reality is still extremely challenging. We present a novel approach to model-free reinforcement learning that can leverage existing sub-optimal solutions as an algorithmic prior during training and deployment. During training, our gated fusion approach enables the prior to guide the initial stages of exploration, increasing sample-efficiency and enabling learning from sparse long-horizon reward signals. Importantly, the policy can learn to improve beyond the performance of the sub-optimal prior since the prior's influence is annealed gradually. During deployment, the policy's uncertainty provides a reliable strategy for transferring a simulation-trained policy to the real world by falling back to the prior controller in uncertain states. We show the efficacy of our Multiplicative Controller Fusion approach on the task of robot navigation and demonstrate safe transfer from simulation to the real world without any fine-tuning. The code for this project is made publicly available at this https URL
A Study on Korean Emotion Index Using F1_score

Digital companies are processing and managing their business relationships between their customers, suppliers and employees in digitalized way. These companies innovate the traditional social structure by applying digital technology to society. Enterprises build and utilize information and communication technology (ICT) as a platform, including cloud computing, artificial intelligence (AI), and big data solutions. Transformation into a digital company and using data analysis processes in an organization will create new values or services. In addition, analyzing the flow of data within an organization in an e-business environment is a necessary activity for corporate survival.<BR> The purpose of this study is to analyze the emotions of internal and external customers, which are diversified in atypical data analysis. Wheel of emotions(Plutchik, 1980) is used to classify emotions and prioritize emotion words within the categorized emotion sets. The Korean emotional word index process was studied by classifying and ranking the emotional words in the emotion set using the F1 score, a tool for measurement of effect. The emotion index was ranked in 200,000 comments using 190 generalized emotional words in the emotion set. Emotions such as anger and joy, fear and happiness can be identified through statistical values. In addition, it is confirmed that customer needs or product preference analysis can be applied to get more insights in the company"s point of view.
Rethinking the Implications of Transformative Economic Innovations: Mapping Challenges of Private Law

Abstract This article participates in mapping existing legal implications stemming from contemporary innovation. The article relies on a case analysis of artificial intelligence, drones and blockchain, to reflect a majority of the underlying legal issues to which many emerging innovations might contribute, and it attempts to map them into different categories of challenges – liability, privacy, and property. It concludes by pinpointing three main reasons behind the identified legal implications: the growing “consciousness” and autonomy of emerging technologies, the growing availability of transformative innovations to the broad public and the development of participatory models in economy and other social spheres, including law, and the tendency for transformative innovations to function in regulatory uncertainty. As a means to cope with challenges generated by technological progress, the article leans towards a process-focused approach that promotes embedding values in the early stages of technological development.
A Natural Language Sentence Analysis Algorithm Based on Word Order Modifier Syntax Rules

This thesis discusses the decomposition of natural language sentences based on the rules of order-modified syntax. By making a comprehensive use of the combination of part-of-speech tagging, syntactic structure and sentence organization information, we propose to apply the custom rules to achieve the decomposition of sentences. The visualization of the decomposition results under a certain condition of part-of-speech tagging and word order is realized, which provides a new idea for the development of syntactic analysis structure.
Ternary Weighted Networks with Equal Quantization Levels

Recent progress in deep convolutional neural networks has considerably changed the landscape of speech recognition, computer vision, natural language processing and so on. However, limited by the large number of parameters, the memory space and high computational complexity, it is a challenging task to deploy the deep neural network model in embedded system. To solve this problem, we propose Equal Trained Ternary Quantization (ETTQ), a ternary quantization method by improving Trained Ternary Quantization, which uses only a full-precision scaling coefficient for each layer, and quantize the weights to three levels. These positive and negative weights have same absolute values that are trainable parameters. Experiments show that the performance of ETTQ is only slightly worse than TTQ, but can converge faster and more stable during the training over CIFAR-10 and CIFAR-100 datasets.
Homogeneous metric and matrix product codes over finite commutative principal ideal rings

In this paper, a necessary and sufficient condition for the homogeneous distance on an arbitrary finite commutative principal ideal ring to be a metric is obtained. We completely characterize the lower bound of homogeneous distances of matrix product codes over any finite principal ideal ring where the homogeneous distance is a metric. Furthermore, the minimum homogeneous distances of the duals of such codes are also explicitly investigated.