Reviewerly/exHarmony huggingface dataset:
Using Channel Concatenation and Lightweight Atrous Convolution in U-net for Accurate Rectal Cancer Segmentation

Accurate segmentation of rectal cancer from CT images of rectal cancer is the basis of rectal cancer diagnosis. In this article, we propose a new segmentation method based on fully convolutional neural networks. The encoding block consists of a channel concatenation block and a residual block (CesBlock) and a lightweight atrous convolution block (LacBlock) for skip connection. By combining dice loss and cross entropy loss as the new loss function, the obtained model can realize automatic and accurate segmentation of CT images of rectal cancer. The contraction path of the symmetrical convolutional neural network ensures enough feature extraction, and the expansion path ensures the recovery of pixels. In the experiments, the recognized evaluation indicators are utilized to verify the proposed method, which has an improvement of 2.7% on the dice similarity coefficient (DSC).
The DibiChain Protocol: Privacy-Preserving Discovery and Exchange of Supply Chain Information

Connecting and exchanging information across organizations becomes increasingly important as supply chains become more complex and expectations with regard to sustainability, transparency and resilience increase. At the same time, organizations are adamant about protecting any competitive advantage which derives from private information about, for example, supplier networks, available inventory or production processes. Technology aimed at enabling information exchange within and across supply chains must therefore ensure high degrees of privacy and control over private information. In light of this, we specify the DibiChain protocol for the discovery and exchange of supply chain information. The protocol prioritizes data minimization in shared data stores, avoidance of persistent user identifiers and anonymous communication with minimal intermediation. We further outline how the DibiChain protocol can serve as the foundation for privacy-preserving supply chain applications, including an anonymous discovery service for GS1 EPCIS event data.
CycleGAN -based Cloud2painting Translation

Image2image translation is one of the most popular image processing tasks. In this work, we use the powerful CycleGAN model and some traditional image processing technology to transform images of cloud into sketch portraits of specific objects. Precisely, this work extract the outer contour of the cloud and use the trained CycleGAN model to transform the outer contour into a specific image (taking the sketch of fish as an example) and the output of the model shows its good translation effect. Moreover, this work set the images of cloud without contour extraction as the control group, which proves the necessity of our preprocessing technology.
A Theoretical Approach to Reinforcement Learning Algorithm and Nash Equilibrium to Maximize the Score in Imperfect Information Game

Computers have just overshadowed the degree of human play in imperfect information games like Scrabble, yet there remains an opportunity to get better. Specifically, there is a lot to be acquired about the rival's tiles and moves. Reinforcement learning is one of the parts of Machine Learning. It is tied in with making an appropriate move to increase reward in a specific circumstance. The Nash Equilibrium is a hypothesis in game theory that communicates that a player can achieve the ideal outcome by not wandering from their initial strategy. Reinforcement learning algorithm and game theory would be best technique for training the agent. It will learn from as much as it plays, such that our agent can play and win the game with different persons with their different playing strategy they use for Scrabble game. To do this, our agents need to find out about other players' strategies and win every time they play against the human.
ES-CycleGAN: An Improved CycleGAN for VI-to-IR Translation

Infrared image has been used in many fields due to its strong environmental adaptability. But it has little data volume because of its prohibitive cost. In order to compensate for the deficiency of the infrared image dataset, CycleGAN is improved to be named ES-CycleGAN, which is used for translation of VI (visible images) to IR (infrared images). Converting asymmetric translation task to symmetric translation task by using visible single channel information instead of VI. At the same time, the image edge loss is calculated by the Canny operator, and the style loss is calculated by perceptual loss to ensure the content and style of the image. To enhance the translation of infrared images, extended cyclic consistency loss is used to replace the original cyclic consistency loss. In the Flir dataset, we implemented it and evaluated the results using PSNR and SSIM methods. The implementation results show that it effectively increases the quality of infrared image generation and better retains the image structure of the original data.
NkhukuProbe: Using a Sensor-Based Technology Probe to Support Poultry Farming Activities in Malawi

Poultry farming is a significant income-generating activity in sub-Saharan African (SSA) households. Poultry farmers frequently have to overcome extreme environmental conditions to maintain their chickens’ wellbeing. Prior research has proposed automating poultry farming activities to control environmental conditions (e.g., temperature and humidity). However, these interventions have never been implemented, in this context, to understand how they would work and participants’ perceptions. Further, chicken coops in SSA have different configurations that would make technology automation difficult. To explore how technology can be used to address this problem, we worked with local collaborators to design and deploy “NkhukuProbe”—a low-cost sensor-based technology that poultry farmers can interact with via USSD (Unstructured Supplementary Service Data) to monitor and adjust chicken coop conditions. First, we conducted a review of related work on poultry farming in SSA and a pilot study with poultry farming experts. Findings from this work guided the design of NkhukuProbe. Then, we deployed NkhukuProbe in 15 Malawian households for one month. The goals of our deployment were to understand participants’ experiences using NkhukuProbe and to learn about other ways of using sensors in this context. To achieve these goals, we used interview, diary, observation and data logging to collect data throughout the deployment. Our findings suggest that a technology probe's approach unveiled different opportunities for using sensors to support poultry farming in SSA. Further, NkhukuProbe motivated participants to think of other ways of using sensors. We present design implications based on these findings and offer new perspectives on the role of technology in supporting poultry farming activities.
Dual knowledge distillation for bidirectional neural machine translation

Building strong and robust neural machine translation systems needs large amount of high-quality parallel corpora. However, most of language pairs are limited in quantity, coverage and quality. In order to make full use of parallel sentences, we proposed dual knowledge distillation and bidirectional neural machine translation, leveraging one side information to boost the performance of another side. Our approach which only takes advantage of parallel corpora is supplementary to semi-supervised technology e.g. back-translation. Experiments on several low resource dataset show our approach achieves significant improvement over strong baseline. Our method combined with back-translation achieves state-of-the-art scores of 31.88 and 39.01 on IWSLT14 English-German and German-English translation tasks. Experiments on several low resource dataset show our approach achieves significant improvement over strong baseline. Our method combined with back-translation achieves state-of-the-art scores of 31.88 and 39.01 on IWSLT14 English-German and German-English translation tasks.
Application of sequential hierarchical signal method in online monitoring of partial discharge in GIS

After analyzing the various interferences when monitoring the Gas Insulated Substation (GIS) partial discharge, this paper puts forward a hierarchical sequential model, which can reduce the disturbance of partial discharge signal. The hierarchical sequential method, based on the Fourier series and wavelet transform method, can effectively and accurately suppress the white noise and periodic narrowband interference, so as to extract the useful partial discharge signal from the strong interference background. The method is simple, flexible and easy to apply to the other occasions. In the meanwhile, the subsequent data and simulation both verify the accuracy of the method, which will have wide range of application.
3D Human Pose Estimation: Using Context Information in Monocular Video

We propose a context-based two-stage 3D human pose estimation network structure. The first stage is to obtain the 2D human pose and 2D key-points in the video stream data, this stage is crucial to the subsequent work and the entire process. By analyzing the limitations and shortcomings of existing models, we proposed a context-based human pose estimation network structure, and incorporate the BILSTM structure into the pose machine method. In our model, Invisible key-points can be jointly predicted by human pose in current frame and context information. Through quantification and visualization experiments, we have proved that it has a good mitigating effect on the invisible key points caused by occlusion and the wrong linking of human key-points. In the second stage, the 3D human pose is obtained through sparse representation and 3D reconstruction. The experimental results show that the method we designed has higher accuracy than the existing human body pose estimation method of video streaming, and has better performance in the occlusion problem.
Design and Implementation of Real-time Semantic Segmentation Network Based on FPGA

With the rapid development of deep learning, the neural network of semantic segmentation has been developed towards the miniaturization of the network structure and the lightweight development of the network model. At the same time, FPGA-based neural network hardware accelerators have been proposed. The situation that the network module is too complex and computationally intensive to implement and apply on edge platforms is gradually being solved. However, the implementation of real-time processing network on the edge platform is still of great significance in many areas, such as robots, UAVs, driverless, etc. In this paper, a lightweight semantically segmented neural network Efficient neural network (E-Net) is designed and implemented on the image acquisition board with Zynq 7035 FPGA as processing unit, which achieves the frame rate of 32.9 FPS and meets the requirements of real-time processing.
Session Based Recommendations Using Char-Level Recurrent Neural Networks

The use of long short-term memory (LSTM) for session-based recommendations is described in this research. This study uses char-level LSTM as a real-time recommendation service to test and offer the optimal solution. Our strategy can be used to any situation. Two LSTM layers and a thick layer make up our model. To evaluate the prediction results, we use the mean of squared errors. We also put our recall and precision metrics prediction to the test. The best-performing network had roughly 2000 classes and was a trainer for the last year of likes on an image-based social platform. On twenty objects, our best model had a recall value of 0.182 and a precision value of 0.061.
Towards Boosting Channel Attention for Real Image Denoising: Sub-band Pyramid Attention

Convolutional layers treat the Channel features equally with no prioritization. When Convolutional Neural Networks (CNNs) are used for image denoising in real-world applications with unknown noise distributions, particularly structured noise with learnable patterns, modeling informative features can substantially boost the denoising performance. Channel attentions in real-world image denoising tasks exploit dependencies between the feature channels; therefore, they can be viewed as a frequency-domain filtering mechanism. Existing channel attention modules typically use global statics as descriptors to learn inter-channel correlations. These methods deem inefficient in learning representative coefficients for re-scaling the channels at frequency level. This paper proposes a novel Sub-band Pyramid Attention (SPA) model based on wavelet transform to recalibrate the extracted features’ frequency components in a more fine-grained fashion. Our method, in one sense, integrates the conventional frequency-domain filtering methods with deep learning architectures to achieve higher performance records. Experimental results show that ANNs equipped with the proposed attention module substantially improves upon the benchmark naive channel attention blocks. More specifically, we obtained a 3.97 dB gain compared to the best traditional algorithm, BM3D and a 1.87 dB to 0.18 dB gain over the DL-based methods in terms of denoising performance. Furthermore, our results show how the pyramid level affects the performance of the SPA blocks and exhibits favorable generalization capability for the SPA blocks.
Application of Collaborative Filtering Recommendation Algorithm in Internet Online Courses

Aiming at the problem that the overload of online education platform course resources leads to the difficulty of user selection, this paper mainly studies the improvement and application of collaborative filtering algorithm based on online course recommendation system, which organically combines personalized recommendation technology and online course system to meet the needs of users and online education platform. In the process of recommendation, firstly, user preferences are collected to establish a data model, and user login information and learning behavior information are used as implicit characteristics of user preferences. The loss rate of users in the computing platform is defined, the popularity of each course is calculated, and the relationship between users and courses is constructed, and the correlation and comparative analysis are carried out, Then, the traditional collaborative filtering algorithm is improved by introducing the implicit features after analysis, and the cosine similarity method is used to calculate the course similarity. Finally, the topN recommendation list is generated to get the recommendation results. Based on the desensitization data of an education platform, the experimental results show that the improved recommendation model can improve the precision of recommendation by introducing implicit features.
A Contemporary Access of Improved BEE Colony Optimization for Scheduling Problems in Cloud Environment

Today's competitive environment improves interoperability across many devices, illuminating resource distribution for user demands and preferences. In cloud computing, task scheduling triggers the goal of increasing performance by leveraging resource distribution efficiently. Many approaches that lead to task optimization for resource utilization were carried out with an emphasis on static and dynamic models. Today's computing systems are dynamic, and meta-heuristic algorithms can be useful in solving problems. Evolutionary algorithms with swarm intelligence help provide ideal solutions to today's difficult problems. This research work has implemented an improvement of Bee colony optimization for scheduling the tasks in cloud environment.
Fraud Detection Using Fuzzy C Means

Businesses sometimes make fraudulent financial reporting by taking into account the interests of the business and applying misleading accounting practices. Fraudulent financial reporting harms investors, business owners, business employees, government, creditor financial institutions, and other businesses that have commercial relations with the business. In order to prevent such damages, enterprises are audited by independent audit institutions. However, this audit is a difficult, time-consuming and costly process that requires reviewing all business records. In particular, the process has a proportionately increasing difficulty with the increasing number of business movements subject to audit. In this process, the knowledge and experience of the audit firm and the audit team play a significant role in the detection of irregularities by the company. The audit firm directs the audit process and completes the report in line with the findings obtained from the sample by sampling the business records based on its own knowledge and experience. In fact, the audit firm also accepts the audit risk with the report it prepares. In this study, it is aimed to develop a fuzzy logic based application that will facilitate the audit process of the audit firm and assist the auditor in detecting fraudulent transactions, and the success rate was achieved as 92% using the Fuzzy C-Means (FCM) method in cases of an anomaly by analyzing past data.
Periodic Intra-ensemble Knowledge Distillation for Reinforcement Learning

Off-policy ensemble reinforcement learning (RL) methods have demonstrated impressive results across a range of RL benchmark tasks. Recent works suggest that directly imitating experts' policies in a supervised manner before or during the course of training enables faster policy improvement for an RL agent. Motivated by these recent insights, we propose Periodic Intra-Ensemble Knowledge Distillation (PIEKD). PIEKD is a learning framework that uses an ensemble of policies to act in the environment while periodically sharing knowledge amongst policies in the ensemble through knowledge distillation. Our experiments demonstrate that PIEKD improves upon a state-of-the-art RL method in sample efficiency on several challenging MuJoCo benchmark tasks. Additionally, we perform ablation studies to better understand PIEKD.
Chapter 26: Geology of the Hishikari Gold Deposit, Kagoshima, Japan

Abstract The bonanza-grade, low-sulfidation epithermal Hishikari gold deposit is located in the Plio-Pleistocene volcanic area of southern Kyushu, Japan. The concealed veins were discovered in 1981 and the mine has since produced 5.462 million metric tons (Mt) of ore averaging 44.3 g/t Au (242 t Au) from 1985 to the end of 2018, at which time reserves were 7.98 Mt at 20.9 g/t Au. The Hishikari deposit consists of the Honko, Sanjin, and Yamada ore zones, which occur in a NE-trending area 2.8 km long and 1.0 km wide. The veins are hosted by basement sedimentary rocks of the Cretaceous Shimanto Supergroup and by overlying Hishikari Lower Andesites of Pleistocene age. Sinter occurs about 100 m above the Yamada ore zone. Temperature-controlled hydrothermal alteration zones occupy an area of &amp;gt;5 km long and 2 km wide. The Honko and Sanjin veins occur within a chlorite-illite alteration zone (paleotemperature &amp;gt;230°C), whereas the Yamada veins occur within an interstratified clay mineral zone (150°–230°C). The marginal alteration comprises quartz-smectite (100°–150°C) and cristobalite-smectite (&amp;lt;100°C) zones. Ore-grade veins are located between –60- and 120-m elev, with the paleowater table over the Honko-Sanjim veins at ~300-m elev. Overall, the Ag/Au wt ratio is about 0.6. Vein-forming minerals consist of quartz, adularia, and clay minerals plus truscottite, with electrum and minor pyrite, chalcopyrite, naumannite, galena, and sphalerite. The major veins formed from repeated episodes of boiling and strong fluid flow inferred from bands of quartz, adularia, and smectite with bladed quartz, columnar adularia, and truscottite.
Data Encryption and Decryption Techniques Using Line Graphs

Secure data transfer has become a critical aspect of research in cryptography. Highly effective encryption techniques can be designed using graphs in order to ensure secure transmission of data. The proposed algorithm in this paper uses line graphs along with adjacency matrix and matrix properties to encrypt and decrypt data securely in order to arrive at a ciphertext using a shared-key.
Self-Supervised Monocular Depth Estimation with Multi-constraints

For the self-supervised image depth estimation algorithm based on deep learning, the characteristics of self-supervised methods cause some problems such as visual shadow and infinity estimation. This paper tackles these problems by adopting the spatial consistency loss and multiple loss constraints. In this paper, the depth estimated network and pose estimated network are trained by binocular image sequence. And the depth estimated problem is transformed into image reconstruction and scene reconstruction to realize self-supervised training. Based on the Deep Convolutional Neural Networks(DCNN), the depth estimated network introduces the Deep Atrous Convolution (DAC) and the Atrous Spatial Pyramid Pooling (ASPP) modules to extract the multi-scale feature and increase the receptive field of the network. The spatial consistency of scene reconstruction strategy greatly reduces the estimated error of pose network. The quantitative and qualitative evaluation on the KITTI dataset shows that the proposed algorithm outperforms the existing self-supervised depth estimation algorithms in accuracy and error, even exceeds most of the supervised algorithms.
Model Compression with NAS and Knowledge Distillation for Medical Image Segmentation

Medical image segmentation task has been a hot research topic in the field of computer vision and natural field for many years. With the rapid development and application of convolutional neural networks, more and more medical segmentation models based on deep learning have been proposed, and more successful results and applications have been achieved in many disease segmentation tasks.The effect of medical image segmentation tasks is getting better. However, the computational cost of the medical image segmentation model has not been improved well. In this work, we propose a medical image segmentation model compression scheme that is theoretically applicable to all convolutional neural networks. First, we choose to construct a search space based on the number of convolution kernels at each location where convolution is used in the model, and then use neural network search to find a sub-network in this search space with less computation and high segmentation accuracy. Aiming at the encoding-decoding structure of the segmentation network, we propose Symmetrical-NAS to ensure that the encoding structure and decoding structure of any sub-network in our search space are symmetrical. Since it is too expensive to traverse the entire search space for training to find the most suitable architecture, we use weight sharing for training. During training, a sub-network in the search space is randomly selected for activation each time. Second, we use the method of knowledge distillation for training. We use the basic model as the teacher model and the searched sub-network as the student model to realize the knowledge transfer between the teacher model and the student model. Third, we use separable convolution instead of convolutional layer. Our method can be applied to various medicial image segmentation models regardless of model architectures and learning algorithms. Our method can reduce the computation of medical image segmentation models by 90× regarding FLOPs with little loss of segmentation effect. The code and demo will be publicly available.