Reviewerly/exHarmony huggingface dataset:
Decoupled Adaptive Convolutional Neural Networks for the Few-Shot Image Classification

Deep neural networks have achieved remarkable results in large-scale data domain. However, few-shot image classification is still a difficult and important problem. In this work, we analyze recently proposed deep neural networks of the meta-learning ability enable them to solve few-shot tasks, and try to find out where the ability comes from. Based on the analysis, we conclude a unified meta-learning framework for the few-shot tasks and propose a novel meta-learning network composed of two decoupled convolutional channels. One of them uses a typical convolutional neural network (CNN) to classify the query sample. The other channel is responsible for adaptively generating the parameters of the CNN with the support images. Unlike previous work in which the query and support input always share modules, our approach improves the flexibility and adaptability via decoupling the two channels. We demonstrate that our approach is able to achieve competitive performance with the existing state-of-the-art methods on two common few-shot image classification benchmarks while being substantially simpler and easier to train on.
K-means clustering algorithm based on bee colony strategy

Abstract The traditional swarm intelligence optimization K-means algorithm has some problems, such as poor global search ability and blind area selection of initial center points, which leads to the reduction of clustering availability. In order to avoid the above limitations, this paper proposed IABC k-means algorithm. Firstly, the employed bees stage in the traditional artificial bee colony ABC algorithm uses the current colony optimal solution information to guide its optimization search. Secondly, this paper wanted to solve the search ability of the employed bees and expand the information sharing range between various individuals, a random guidance mechanism is proposed in the onlookers bees stage. Finally, chaotic sequence is introduced in the scout bee stage to accelerate the convergence speed of the algorithm. IABC algorithm is proposed and applied to K-means clustering algorithm to improve the poor global search ability of K-means algorithm and the random selection of initial center points. Experiments show that the IABC and IABCK-means proposed in this paper effectively improves the clustering availability.
Analyzing the Extremist Reviewer Groups on Online Products

In today's e-commerce, online reviews play a critical part in decision-making. Customers, who make up a large portion of the population, read evaluations of items or businesses before deciding what to buy or where to get it from. Because there is a monetary reward in publishing false/fraudulent evaluations, there has been significant growth in misleading opinion spam on online review platforms. A fake review, often known as a fraudulent review or opinion spam, is a false review. Positive ratings of a specific object may attract more consumers and improve sales, while unfavourable evaluations may result in lower demand and sales. These fake/fraudulent reviews are produced with the intent of deceiving potential consumers to promote/hype or discredit their businesses. Our research aims to determine if a review is genuine or not.
Image segmentation application combined with DRLSE and moving mesh method

Abstract Aiming at the time-consuming problem of level set method in processing image segmentation, this paper presents an image segmentation algorithm combining DRLSE and moving mesh method. This method is applied to segment images into multiple blocks in different initial regions. When a large image needs to be segmented into multiple blocks, the level set method has to calculate various data at each pixel. So, there are problems such as low-efficiency and time-consuming processing. In order to solve this problem, we propose a moving mesh method to process image segmentation based on the level set method. By calculating the monitor function about the image gradient, the grid encryption is automatically realized with the change of image gradient. This makes the calculation of the image segmentation more accurate. In order to improve efficiency, our method will reduce the grid to 1/4. Then the level set method is applied to achieve the result of computational acceleration. In order to verify the performance and accuracy of the new method, our method segmented some images several times. Experiments show that our method can quickly and automatically segment some images into multiple blocks without affecting the results.
Intelligent Technologies in E-learning and Intelligent Tutoring Systems

This paper proposes results of the literature analysis of Intelligent Tutoring Systems (ITSs) from technological point of view. Exploring and classifying used architectures and technologies we outline important criteria for comparison of used intelligent technologies and it’s usefulness of for tutoring and learning and outline trends in the area of ITSs.
Secure Data Sharing in Images Using Cryptographic Approach

Visual cryptography is the most secure form for exchanging the information. The implementation is done through image files. Image data sharing is a way which is used to exchange the sensitive data in the form of pictures in a secured way. Few security problems are encountered if the image is confidential and is shared only with the authorized user. Image sharing cryptography comes into picture where the unauthorized user can’t view the actual image. the original picture is hidden with the multiple layers of images to provide the security and it can be viewed with the key for decrypting the image. This technique is useful to secure the confidential and the copyright images without any external risk. In the multimedia system, the message will be encrypted with the use of public key encryption algorithm, this encrypted data will be superimposed on the image file. The copyrights are protected besides preserving the privacy of user, whose identities are only revealed in case of illegal re-distribution. In our proposed system Blow fish algorithm is used. This algorithm is a block cipher. The text is divided into fixed length of blocks during encryption and decryption process.
Online State Estimation Method for Dynamical Systems Based-On OS-KELM

The estimation for state variables of systems with non-linear or unknown signals has great significance. In comparison with traditional NNs algorithm, the ELM algorithm has much shorter training time. Adopting kernel function and on-line update method, the OS-KELM algorithm can obtain better performance than ELM algorithm. In this paper, an on-line state estimation method has been proposed based on OS-KELM algorithm and Lyapunov stability analysis. Compared to estimators based on NNs algorithm or ELM algorithm, the proposed method takes less computing resource and faster computing speed. Simulations show that, the estimators designed by this method can approximate state variables of systems with non-linear or unknown signals in satisfying accuracy. At the same time, the approximations keep stability and robustness.
Single-shot integrated multi-photon split state tomography

We propose a segmented coupled waveguide array as a new form of compact optical quantum circuit and apply it for the on-chip multi-photon split state tomography with optimized performance and no need of reconfigurability.
The Effect of WHATSAPP as a Social Network on Improving Intermediate EFL Learners’ Collocation’s Common Verb

The effectiveness of social networks on improving EFL learners’ language skills is demonstrated in many empirical studies. Unfortunately, a few studies have been conducted concerning sub-skills and components. According to the researches that the researcher has done, no empirical study has been done to empirically document the link between teaching collocation common verb through “WHATSAPP’ messenger and its role in reinforcing EFL learners’ common verb collocation. Thus, this study was conducted to fill this gap in the literature. At first, the researcher selected 90 intermediate students. Then, the researcher administered the Preliminary English Test (PET) for homogenizing the participants. Given the results of this test, 60 students were accepted as the research sample for this study through convenience sampling and randomly assigned to two groups consisting of the experimental (n=30) and control group (n=30). The learners were members of eight intact classes. The Teacher Made Collocation’ Common Verb Test (TMCVT) was administered as a pretest to evaluate the participants' possible tacit knowledge of knowing the meaning of the verbs before the actual intervention. Finally, 40 words were selected to teach to learners of both groups. Both groups received similar training; for example, the number of sessions, the number of verbs, and the objective of common verb training received equal attention. However, the main variable was kept away from the control group and no “WHATSAPP” application was used in this class in all accounts. Finally, at the end of the 12th session, when the intended numbers of verbs (n=40) were taught, the Teacher Made Collocation’s Common Verb Test was administered as a posttest. The comparison of the scores of the Test showed that the scores of the experimental group were higher than those of the control group. This provided a statistically significant relationship between the independent variable, teaching common verbs through the “WHATSAPP” application, and EFL learners’ collocation common verb which is the dependent variable. The results of this study point to a probability that using “WHATSAPP” for teaching verbs and the results of this study showed that using “WHATSAPP” for teaching can have a significant impact on EFL learners’ collocation’s common verb. The results of this study can help teachers, syllabus designers, and material developers.
PixelPyramids: Exact Inference Models from Lossless Image Pyramids

Autoregressive models are a class of exact inference approaches with highly flexible functional forms, yielding state-of-the-art density estimates for natural images. Yet, the sequential ordering on the dimensions makes these models computationally expensive and limits their applicability to low-resolution imagery. In this work, we propose PixelPyramids, <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> a block-autoregressive approach employing a lossless pyramid decomposition with scale-specific representations to encode the joint distribution of image pixels. Crucially, it affords a sparser dependency structure compared to fully autoregressive approaches. Our PixelPyramids yield state-of-the-art results for density estimation on various image datasets, especially for high-resolution data. For CelebA-HQ 1024 × 1024, we observe that the density estimates (in terms of bits/dim) are improved to ~44% of the baseline despite sampling speeds superior even to easily parallelizable flow-based models.
New approach to the chunk recoginition in Polish

This paper discusses the problem of shallow parsing of Polish, most specifically — chunking. We describe the linguistic work on annotation guidelines development, manual corpus annotation, and preparing the neural models used for chunking - the first one for the Polish language, and conducted the evaluation of these models. Finally, we describe conclusions and plans for future work.
Gsdnet: Gated Self-Supervised Denoising Speech Control Network

The cost of labeling data remains high, even with the effective implementation of deep neural networks in speech recognition. At the same time, noise still hampers the performance of speech-recognition methods. Thus, it is still challenging to make full use of data sets to enhance the robustness of recognition systems. In this letter, we construct GSDNet, a gated self-supervised denoising speech control network that consists of three parts (a denoising feature-extraction frontend, a speech recognition encoder, and a decoder based on gated convolutionary neural networks with self-supervised regression), to provide a low-cost method for training a robust speech recognition system, and we apply it to equipment-control tasks. Finally, the experimental results with the THCH30 and AISHELL data sets for equipment control show that the word error rate is less than 0.2 without a language model.
Fibonacci and modulo principle for hiding information using hill cipher algorithm

This digital era makes everyone feel insecure about their privacy, such as information. This is a nowadays problem, but it can be solved with the security system. Cryptography is one of the security systems, which scrambles message using a key. The key is important to process both encrypt and decrypt in cryptography. One of the cryptography algorithms is hill cipher. It is an algorithm which uses symmetric key. In this research, Fibonacci and modulo principle will be used to create the key, and then the key will be used to scramble the message using hill cipher algorithm. Finally, the information can only be opened by people who know the key correctly.
MIRAM: Mitigating Rowhammer attack in a DRAM memory using Time window counter

A hardware attack is a vulnerable criterion that creates a weakness in a computer system enabling attack through remote or physical access to system hardware. This type of vulnerability may be due to random flaws in operation allowing attacks in the system. Rowhammer attackers access confidential data, harming the entire system and gaining user credentials. In this paper, a counter window was used to mitigate error-causing row hammers in the memory system and validated attacks. The experiment conducted by injecting the row hammer attack in DRAM memory causes bit flipping, which leads to information loss. Using counters to measure the continuous access of memories. Activation in a row and refreshes row before it reaches the threshold. This approach to memory security enables us to better anticipate and prevent such vulnerabilities.
Real Time Controllable Multi Channel HD Digital Video Processing System Based on FPGA and MicroBlaze

In order to realize the real-time processing and display control of multi-channel high-definition digital video, this paper proposes an embedded solution for real-time processing of multi-channel high-definition digital video based on FPGA (Field Programmable Gate Array). The overall system design includes three parts: hardware platform design with MicroBlaze as the core controller, software application design and OSD (On Screen Display) display control design. The system proposed in this paper implements the encoding, decoding, scaling and overlay of multiple high-definition video streams in a single FPGA embedded with MicroBlaze, which improves the system integration. Through the design of the OSD control layered architecture, the information interaction between the keystrokes and the OSD image interface is realized, and four video display parameters can be configured on the screen. Experiments show that the system proposed in this paper can process and display four high-definition video streams in real time, with an output frame rate of 60 frames/s. Compared with the current mainstream video fusion system solutions, the system integration level has been improved.
Analysis of the possibilities for using machine learning algorithms in the Unity environment

Reinforcement learning algorithms are gaining popularity, and their advancement is made possible by the presence of tools to evaluate them. This paper concerns the applicability of machine learning algorithms on the Unity platform using the Unity ML-Agents Toolkit library. The purpose of the study was to compare two algorithms: Proximal Policy Optimization and Soft Actor-Critic. The possibility of improving the learning results by combining these algorithms with Generative Adversarial Imitation Learning was also verified. The results of the study showed that the PPO algorithm can perform better in uncomplicated environments with non-immediate rewards, while the additional use of GAIL can improve learning performance.
Information multiple distillation super resolution network based on feedback mechanism

There are lots of image data in the field of remote sensing, most of which have low-resolution due to the limited image sensor. The super-resolution method can effectively restore the low-resolution image to the high-resolution image. However, the existing super-resolution method has both heavy computing burden and number of parameters, which greatly limits the super resolution method in the mobile terminal. For saving costs, we propose the information multiple distillation network based on feedback mechanism (Feedback-IMDN), which considers the feedback mechanism as the framework to attain lower features through high-level refining. Further, for high-level feature extraction, we use the information multiple distillation blocks (IMDBs) to carry out hierarchical feature extraction with the method of course learning in the case of a small number of parameters. Compared to other state-of-the-art lightweight algorithms, our proposed algorithm can reach convergences more rapidly with fewer parameters, and the performance of the network can be markedly enhanced on the image texture and object contour reconstruction with better peak signal-to-noise ratio (PSNR) and structural similarity (SSIM).
Straggler Mitigation Through Unequal Error Protection for Distributed Approximate Matrix Multiplication

Large-scale machine learning and data mining methods routinely distribute computations across multiple agents to parallelize processing. The time required for the computations at the agents is affected by the availability of local resources and/or poor channel conditions, thus giving rise to the &#x201C;straggler problem.&#x201D; In this paper, we address this problem for distributed approximate matrix multiplication. In particular, we employ Unequal Error Protection (UEP) codes to obtain an approximation of the matrix product to provide higher protection for the blocks with a higher effect on the multiplication outcome. We characterize the performance of the proposed approach from a theoretical perspective by bounding the expected reconstruction error for matrices with uncorrelated entries. We also apply the proposed coding strategy to the computation of the back-propagation step in the training of a Deep Neural Network (DNN) for an image classification task in the evaluation of the gradients. Our numerical experiments show that it is indeed possible to obtain significant improvements in the overall time required to achieve DNN training convergence by producing approximation of matrix products using UEP codes in the presence of stragglers.
Integrated Transfer Learning Method for Image Recognition Based on Neural Network

As recognition technology advances in various fields, the problem of requiring a large number of correct image recognition is also accompanied. So as to promote the correctness of image recognition, we adopted two Convolutional Neural Network (CNN) integrated migration learning network models VGG-16 CNN and LeNet-5 CNN. Experimental results prove that the performance of image recognition in various models are improved. Also, the migration learning and convolutional neural network are used to construct a K-selection migration learning algorithm and a migration framework model whose parameters are migrated to the image data set. The use of a label data set with a large sample size, the selection of a pre-trained convolutional neural network model with a deep layer of complex network structure and the selection of an appropriate network fine-tuning depth can optimize the migration adaptation effect of the migration network. The image recognition precision of the algorithm is improved by about 3%, which further improves the migration adaptation effect.
Image Classification Using Convolutional Neural Network Based on Feature Selection for Edge Computing

In the field of image classification, convolutional neural networks are widely used because of its efficient feature extraction ability. However, some of the features extracted by convolutional neural networks have insignificant difference among classes, which not only have little contribution to classification, but also cause the increasing of complexity of classifier. And this will cause some difficulties in embedded deployment and application in many fields such as edge computing. Feature selection methods have the ability to select features that are helpful to classification, reduce the dimensions of features and improve the classification accuracy. In view of the existence of labels in training of image classification, and considering the potential role of labels information in feature selection, we propose an image classification method using convolutional neural network based on fuzzy clustering to select features. On the basis of image features extracted by convolution neural network, this method reconstructs the classifier according to the feature selection algorithm by means of fuzzy clustering, so as to select the valuable features to participate in the image classification. Specifically, we divide the features extracted by convolutional neural network from the image of training sets according to labels, and use Fuzzy C-Means to obtain the distribution of each feature on each class. Then we design a measure of the distribution difference between classes according to the cluster centers and membership degree matrices, to select the features with obvious inter-class difference to participate in classification. The experimental results on several image datasets verify the effectiveness of the proposed method.
Optimal Sensor Gain Control for Minimum-Information Estimation of Continuous-Time Gauss-Markov Processes

We consider the scenario in which a continuous-time Gauss-Markov process is estimated by the Kalman-Bucy filter over a Gaussian channel (sensor) with a variable sensor gain. The problem of scheduling the sensor gain over a finite time interval to minimize the weighted sum of the data rate (the mutual information between the sensor output and the underlying Gauss-Markov process) and the distortion (the mean-square estimation error) is formulated as an optimal control problem. A necessary optimality condition for a scheduled sensor gain is derived based on Pontryagin’s minimum principle. For a scalar problem, we show that an optimal sensor gain control is of bang-bang type, except the possibility of taking an intermediate value when there exists a stationary point on the switching surface in the phase space of canonical dynamics. Furthermore, we show that the number of switches is at most two and the time instants at which the optimal gain must be switched can be computed from the analytical solutions to the canonical equations.
Metric Learning with Feature Embedding for Segmentation Quality Evaluation

Segmentation quality evaluation is an essential step to quantity the performance of segmentation algorithms. It can be used as a feedback for correcting segmentation errors or selecting appropriate algorithm parameters. We propose a novel evaluation framework where a convolutional neural network is designed for distinguishing the segmentation quality instead of using ground truth images. Our work has three primary contributions: First, we evaluate the quality of object segmentation by learning region features. A novel feature embedding model is proposed to integrate meta evaluation principles in a metric learning process. Second, it exempts the requirement of ground truths in the test stage, where object features of trained classes are used for the discrepancy calculation. Third, a large-scale object segmentation evaluation dataset is constructed, which contains various segmentation qualities under different assumptions. The experimental results on PASCAL VOC2012 dataset demonstrate that our method improves the evaluation accuracy and outperforms the popular supervised evaluation measures.