Reviewerly/exHarmony huggingface dataset:
Classification of Colorectal Cancer Based on Histological Image Using a Combination of Color Histogram, Haralick and k-NN

Colorectal cancer is the progression of malignancy of cancer that occurs in the colon or rectum, which is a small part of the large intestine before the anus. Colorectal cancer is the third most common type of malignant cancer in the world, and it is estimated that by 2030 it will increase by 60%, from 2.2 million new cases with a prevalence of 1.1 million deaths from this cancer. Colorectal cancer can occur due to several factors such as a history of colon polyps, a history of chronic diseases that occur in the intestines and lifestyle and often men are more at risk of developing the disease than women. In the early stages of cancer often cannot be known and do not cause symptoms and will only be realized after developing quite severe, therefore an early examination is needed in order to determine the condition of the cancer and as an anticipation or prevention so as not to worsen the situation. Examination of colorectal cancer can be done with a biopsy technique, where tissue or histopathological images can be observed. can be done automatically by utilizing computer vision technology using machine learning methods. This study aims to propose a method that can classify colorectal cancer 5000 histology images. The method proposed in this study uses the Random Forest machine learning method with a combination of Color Histogram and Haralick feature extraction. The results obtained in this study provide a good accuracy value of 93,53%.
An improved method for measuring small-angle variation based on phase-enhanced total-internal-reflection heterodyne interferometry

This research presents an improved method to measure small-angle variation using the phase-enhanced total-internal-reflection (TIR) heterodyne interferometric approach. In the method, a small angular variation can be estimated with measuring a change of the difference in phase between two orthogonal polarizations of a totally-reflected ray from a phase-enhanced TIR apparatus. The method yielded the measurement sensitivity and errors exceeding to $130(0^{\circ}/\circ)$ and $ 2\times 10^{-4}\circ$ within the extent of 0.3°.
Human Posture Classification Using a Dual Deep Convolutional NN With Silhouette Images

This paper proposes a dual deep convolutional neural network (DDCNN) model with two-view images to classify four human body postures, including standing, bending, sitting, and lying. The proposed image-based classification system consists of three parts. The first part segments a moving human body from the images captured from left and right cameras. A minimum enclosing rectangle (MER) of each body silhouette is then found. In the second part, the two silhouette images from the MERs are sent to the two convolutional networks in the DDCNN with AlexNet as the backbone network. The third part concatenates the flattened features extracted from the convolutional networks and performs classification using a fully connected network. Experimental results and comparisons with different deep learning models show the advantage of the proposed classification system.
An Environmental Sensing and APP Display System Based on IoT

This research develops a cheap and multi-functional environment sensing and APP display system. The sensing end of this system is composed of CO2 sensor, PM2.5 sensor, GPS module, memory module, microprocessor, Bluetooth,,,,,etc.. The sensed information can be stored in the memory module, and then use computer and mobile phone to observe the curve of data changes, and proceed further analyze, discuss, and decision-making. This research mainly writes a mobile APP program, uses Bluetooth to control the sensor memory module data, and then displays them on the mobile phone. The values sensed by various sensors can be observed from the mobile phone screen, and the changing curve can be present. In addition, it can also display the sensing time and location of a sensing value in map mode, so that the complete sensing data information can be clearly understood from the mobile phone.
Designing a Roll Call System with Facial Recognition on Kubeflow

This study is based on the Kubeflow machine learning development platform in order to deploy a real-time roll call system. Kubeflow is based on Kubernetes, which is convenient for container management and portability. Face recognition is done in three steps. First, MTCNN detects a face in the image. Then, FaceNet extracts the features from the face. Finally, SVM finds out the identity of the face closest to the detected face. The average accuracy of the 30 classes in this study is approximately 94.2%, and the execution speed is about 35fps, with Intel Core i7-10700 CPU and NVIDIA GeForce RTX 3060 GPU.
Design of a High-Resolution Video Reconstruction and Transcoding System based on Super-Resolution GAN

With the popularization of high-definition video devices, more and more users and video streaming service providers desire to restore early classic videos and deliver classic video contents with improved resolution. However, existing video transcoding systems do not support super-resolution video reconstruction. A high-resolution video reconstruction and transcoding system design based on super-resolution image generative adversarial network (GAN) is proposed to perform super-resolution video reconstruction for low resolution videos and then transcode videos with different resolutions to meet the playback requirements of heterogeneous mobile devices in order to provide users with good video viewing quality. Users can have good video viewing quality with the proposed system even in the absence of the original high-resolution videos.
Data Synchronization in Non-Uniform Latency Custom DSP Designs

Many DSP algorithms for streams of constant-sample-rate data work by processing the signal by pieces, splitting the input signal into fixed-length blocks in order to calculate some parameter related to that chunk of signal. In custom multiblock DSP architectures, this kind of operations are laid out to be carried out in parallel. This poses a challenge since these operations may have greatly different latency values, so the data needs to be synchronized somehow for the following DSP block to acknowledge the results of the operations and keep on with the computation. In our proposal, we monitor the operations in order to synchronize the output data by storing the results until all operations are done. Every output is queued up in a FIFO structure so that when all the operations are complete the results can then be transferred to the next module simultaneously. The control structure is built so that the architecture also works when the signal blocks have variable length, or when the operations have variable latency. This approach allows for a reduction in the number of registers needed for a delay chain implementation, it is more flexible if the latency of the operations change, and can be easily updated if new operations are introduced. This architecture has been used and extensively tested in an Automatic Modulation Classifier implementation on an FPGA as part of a signal processing chain with parallel calculations of linear regressions and central moments of a constant-rate signal divided into blocks of different lengths.
Pixelated polarizing system for dynamic interferometry events employing a temporal phase unwrapping algorithm

We describe a robust system to retrieve the phase using a temporal phase unwrapping approach. Combining the polarization Michelson interferometer with a polarizing sensor and temporal phase unwrapping algorithms the phase unwrapping procedure is not only simplified, but also the computing time is reduced one order of magnitude compared with other algorithms and we can analyze dynamic events in real-time, i.e., 7.4fps. In the approach taken, each pixel is processed independently obtaining the phase difference for a specific time opening the possibility of real-time analysis and the masking process is avoided limiting the noise propagation in spatial manner. Validation experiments are presented.
Design and implementation of indoor smokers detection and alarm system based YOLOv5

In order to solve the automatic detection and alarm of indoor smokers, this paper designs and implements an indoor smoker detection and alarm system based on YOLOv5 model. This paper uses YOLOv5 as the target recognition model to complete the recognition of cigarettes and human objects, uses Deepsort target tracking algorithm to achieve the determination of smoking action, and combines Facenet face recognition algorithm and sklearn framework and other technologies to achieve face recognition and alarm function. The test experiment proves that the accuracy rate of the smoking person detection and alarm system based on the method of this paper is high and has certain practical and promotion prospects.
Research on electromagnetic attack of advanced encryption standard based on long short-term memory and sparse autoencoder

Deep learning techniques have been widely used in the field of Side Channel Attack (SCA), which poses a serious threat to the security of cryptographic algorithms. However, deep learning-based side channel attack also has problems such as inefficient models, poor robustness, and longtime consumption. To address these problems, this paper focuses on the performance of Long Short-term Memory(LSTM) combining with the dimensional compression technique of Sparse Auto Encoder (SAE), and validates it on fully synchronized and unsynchronized EM traces captured under first-order bool mask protection. The experimental results show that compared with multilayer perceptron (MLP) and convolutional neural network (CNN), LSTM achieves more than 90% training accuracy and test accuracy, with higher robustness, lower parameters and faster convergence speed, even when the jitter in the dataset increases from 0 to 50 and 100.
Information security issues analysis and solution

As companies grow, the company's talent is growing, its projects are increasing, and its information assets are growing, it becomes essential to protect information security. This paper explains and analyses the security issues by using qualitative analysis approach and gives information security management solutions to protect information assets. By redesigning the company structure, assessing security risks, generating information system security management plan and establishing information security management system.
Laser-driven white illumination system based on diffuse reflection

In this paper, a scheme of laser-driven white light illumination based on diffuse reflection is proposed, which can effectively avoid the blue spot phenomenon. The white light with color temperature of 4717 K, lumen of 9.705 lm and color rendering index of 62 can be obtained by using the illumination module with 0.05 W blue laser excitation, which is consistent with the theoretical calculation results. We propose two methods to improve the white light uniformity, namely hook face and concentration gradient.
A novel pedestrian re-identification algorithm framework based on deep learning

To further promote the improvement of pedestrian re-identification performance, this paper studies the reid framework based on "reid-strong-baseline", and uses different optimization schemes to improve the network performance. Firstly, the study tests three kinds of loss: Softmax, triplet hard, and Softmax + triplet hard, to verify the Rank-1 performance obtained and which can achieve the best performance. Secondly, based on the prototype network obtained by applying Softmax + triplet hard loss, we utilize several optimization methods including data enhancement, learning rate optimization, sampling method, and Label smoothing. Then we study the effectiveness of these optimizations on the performance of the Baseline model and the degree of improvement. Finally, this paper studies the efficiency of different Backbone and network depths on the performance of pedestrian re-identification.
CAPTCHA characters recognition based on image processing and support vector machine algorithms

CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) is widely used in major mainstream websites as a security mechanism to classify human and computer. In recent years, the design and breaking technology of CAPTCHA have become an important research issue in order to verify security and reliability, which involves image processing, pattern recognition, artificial intelligence, computer vision and etc. This paper outlines some typical CAPTCHAs and presents an approach to recognize the text-based CAPTCHAs based on image processing and support vector machine algorithms. By testing the simple test-based CAPTCHAs, the experimental results show that the approach has an efficient recognition.
Research on vulnerability of classification method of a complex information system

With the development of science and technology, the demand for automation and intelligence has nearly penetrated every corner of society. Single software and specific needs of information systems can no longer meet the growing needs of people. A complex information system composed of various systems, smart devices, and software emerged. The security of such complex information systems is becoming increasingly important. Attacks on complex information systems have become an important factor in harming national security, political stability, economic lifeline, and citizen security. Risk factors are weak links in the information system that may be threatened to cause damage, and the risk factors are transformed into damage to assets under certain conditions. Although the existing vulnerability management specification standards contain relevant content of risk assessment, the scope is not enough to support and cover the assessment of risk factors in information systems. In this paper, we comprehensively investigate and analyze the vulnerability standards of various vulnerability classification for information systems, and propose a classification standard for the analysis and grading of risk factors of complex information systems, which can provide a reference for the classification of information system risk factors in finance, public communications, and energy industries.
Fast recognition of cat and dog images based on network compression

Cat and dog recognition is a classic problem in the field of image recognition. This paper proposes a fast detection algorithm FAST-CD-Classification-Net for this problem. The algorithm improves F1 from 0.7299 to 0.9875 with the help of residual structure; The product acceleration network reduces the running time from 0.025s to 0.008s without significantly reducing the accuracy. Experiments on the data set CD-KAGGLE show that the accuracy and robustness of the recognition algorithm designed in this paper are better than other algorithms.
Distortion-aware adversarial attack against steganalysis detectors

This paper analyzes the similarity between steganography and adversarial attack, and then analyzes the attack and defense stage of adversarial samples from the perspective of steganography, which provides a broader idea for researchers in this field. This paper validates the efficiency of steganography on help resisting adversarial attack and proposes a steganalysis based method to enhance the adversarial attack against steganalysis detectors with the help of a distortion function. We combine the distortion map with gradient information to select the disturbed pixels in each attack iteration to generate adversarial samples that can bypass steganalysis detectors. The results show that steganography technology can help detecting normal adversarial samples, and our enhanced adversarial samples achieve a much higher transparency rate against our steganography based method than other normal adversarial samples.
Denglu1-extension: solve password vulnerabilities and optimize user experience

In this paper, a browser extension called “denglu1-extension” is presented, which helps users generate strong passwords and save them to the trusted user agent. The user agent can send the stored passwords to the browser extension to complete automatic login. Furthermore, in order to enhance the user experience, we also developed a captcha recognition module. According to experimental evaluation, denglu1-extension can efficiently avoid password vulnerabilities.
An occlusion detection algorithm for small targets on the surface of photovoltaic modules based on deep learning

Solar energy is more and more widely used, but photovoltaic is also easily affected by environmental factors. Because of the remote location of the photovoltaic power station, it is very difficult to clean the surface cover manually. However, the use of robot cleaning depends on the accurate identification of the shielding objects. Due to the wide variety of shielding objects, the current technology is difficult to identify accurately. In view of the fact that the types of fallen leaves of photovoltaic panels are complex and difficult to clean, An occlusion detection algorithm for small targets on the surface of photovoltaic modules based on deep learning is proposed, and the model network method for quickly detecting leaf occlusion and determining the occlusion position of photovoltaic panels is discussed. In this paper, an improved YOLO-PX algorithm is proposed to identify and classify the occlusion of photovoltaic modules. Target detection experiments are carried out on the field data set of photovoltaic power station by using the original YOLO algorithm and the improved YOLO-PX algorithm. The experimental results show that the effect of the improved algorithm is good, and the detection accuracy and recall rate are 96.3% and 94.2% respectively. This method can provide technical support for the intelligent operation and maintenance of photovoltaic power station.
A novel convolutional neural network for gesture recognition

Gesture recognition, as an important means of human-computer interaction, can achieve more natural and flexible human-computer interaction, so it has been widely concerned by researchers in the field of computer vision. At present, most gesture recognition algorithms are based on monocular visual images and recognize the apparent features of hands. Most gesture image segmentation methods are carried out in color space according to skin color information. These methods are highly susceptible to interference from the external environment, such as lighting, background, etc. Convolutional neural network has the advantages of strong anti-interference and outstanding self-organization and self-learning ability. Therefore, based on the principle of convolutional neural network, a novel deep convolutional neural network dedicated to gesture recognition was designed in this paper. This network combines skin color information with finger position information for gesture recognition. Experimental results showed that the algorithm based on fingertip position information has better performance than the algorithm based solely on skin color information. Moreover, the network has simple structure and few parameters. Compared with VGG16 and other classical networks, the recognition accuracy is basically the same under the premise of fewer parameters and structural layers, and the recognition effect is better than other classical networks.
Enhancing sarcasm detection with external knowledge

Sarcasm detection aims to identify whether a text is sarcastic or not. In this paper, we propose a knowledge- and sentiment-enriched framework. Instead of modeling users' features or searching word pairs and snippets with sentiment conflicts in text, our framework integrates dialogue-related external knowledge and leverages inter-sentence sentiment to aid understanding sarcasm with the discussion context. Experiments on two discussion datasets show that our proposed framework yields better performance with enriched knowledge and sentiment information.
Backdoor attack based on feature in federated learning

Federated learning enables participants to construct a better model without sharing their private local data with each other. In the context of the continuous introduction of laws and regulations aimed at protecting data and privacy security, such as the "Data Security Law", federated learning has been more valued and more widely used. However, federated learning is vulnerable to attacks, one is backdoor attack. Here, we propose a backdoor attack method based on feature, used the CIFAR-10 data set and the ResNet18 model to research in the two different scenarios which one used the data that malicious participant participate in normal training as a backdoor and another used the data that implanting during the training as a backdoor. Especially, when we used the data that malicious participant participate in normal training as a backdoor, the attack success rate is about 50% while the attack does not affect the training process.
Design and research on safety management and control system of fort machine technology operation and maintenance

At present, people attach great importance to information security issues, and the implementation of different security level protection systems continues to deepen. However, some internal networks still need to face various forms of information security issues in the actual trial process. Based on the above content, this paper studies the design of the technical operation and maintenance safety management and control system of fort-and-fort machine, analyzes the main contents of the system design. At the same time, this paper also summarizes the relevant experience, hoping to provide a reasonable reference for workers in the same field.
Construction of "one body four wings" application-oriented talent training mode for accounting specialty based on OBE concept in the digital intelligence era

The development of digital intelligence technology has subverted the organizational management and business process of accounting, which makes the training mode of accounting talents in colleges and universities face challenges. Facing the needs of the digital intelligence era and guided by the outcome-based education concept, this paper has constructed a "One Body Four Wings" training mode for accounting professionals supported by the curriculum system, teaching methods, teaching staff and academic evaluation system, which has effectively solved the common problems in the training of accounting professionals in applied undergraduate colleges under the background of digital intelligence.
Using LARA to create annotated manuscripts and inscriptions for museums: an initial feasibility study

We argue that museums presenting exhibits of ancient texts may be able to benefit from accompanying them with annotated electronic versions. We present a short study using two sample annotated texts built using the Learning And Reading Assistant (LARA) platform for a fragment of an Old Norse manuscript and an inscription in Ancient Egyptian hieroglyphs respectively. The results of an online questionnaire suggested that people already interested in ancient languages would view the idea very positively.