Reviewerly/exHarmony huggingface dataset:
On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator

Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise.
A Critical Heuristics Approach for Approximating Fairness in Method Engineering

Information system (IS) development projects often fail because of unclear wishes and needs of concerned parties, or because the developed IS or the used system development method (SDM) is not fully supported by concerned parties. In this study it is investigated how stakeholders that are concerned with the SDM are identified and involved in the engineering of such methods. The Critical Systems Heuristics (CSH) method can be used to identify stakeholders in method engineering, along with their concerns. CSH is meta-modelled and reviewed in twelve interviews with practitioners in software development, system engineering, and consultancy in order to evaluate its applicability in an organizational context. Subsequent modifications made to the contemporary CSH method are validated in an expert validation session. The resulting evolved CSH method enables method engineers to take into account their challenges and contexts, and the method can be instantiated for organizations that engineer methods for internal or external use.
Chinese Named Entity Recognition Incorporating Multi-scale Features

Deep learning technology has been widely used in the field of natural language processing, making the deep learning-based Chinese Social Media Named Entity Recognition (NER) method is becoming more and more important. However, the public datasets in the field of Chinese Social Media are small in size, which cannot allow deep learning models to fully learn, resulting in low accuracy of model recognition. At the same time, Chinese Social Media has more new words and abbreviations, which contains a lot of noise. To solve the above problems, we propose a Chinese Social Media NER model based on multi-scale features BiLSTM-CRF, which combines character features, word segmentation features, radical features and pinyin features at the embedding layer to obtain multi-scale semantics information. Then, the acquired features are transferred to the BiLSTM network (CIFG Cell) for encoding, and finally the encoded tags are decoded through the CRF layer to obtain the prediction results. Experimental results prove that the BiLSTM-CRF model based on multi-scale features can improve the accuracy of the model when performing Chinese social media named entity recognition. At the same time, we use CIFG Cell to have higher recognition efficiency than Basic LSTM Cell in network coding. When the model is trained, we use the gradient truncation method and the Dropout layer to further avoid problems such as gradient explosion and model over-fitting.
Self-Adaptive Security for SLA Based Smart Contract

The current smart contracts development practices do not follow a standard security development process to understand and explore the security vulnerabilities and attacks in smart contracts. We propose a self-adaptive security modeling approach for modeling and analyzing Service Level Agreement (SLA) based smart contracts using attack scenarios and goal models. There are many security vulnerabilities and bugs in the smart contract due to inconsistencies in the design of smart contracts. The objective of the proposed research is to help the smart contract security requirements designers and developers to understand the security vulnerabilities, concerns, and to propose a self-adaptive security approach to provide countermeasures to detect and mitigate these attacks in the SLA smart contract in the domain of Blockchain-Based Cloud (BBC).
Competitive Pokémon Usage Tier Classification

This chapter investigates competitive Pokémon usage tier classification given a Pokémon’s stats and typing. Pokémon were classified into the usage tiers defined by the competitive battling website Pokémon Showdown based on their individual base stats, the sum of all their base stats (BST), and their number of type weaknesses and type resists. Classifications were done using Weka’s J48 decision tree, lazy IBk 1-nearest neighbor, and logistic regression algorithm. The algorithms were evaluated by the metrics of accuracy and precision. The results of this study give insight into what factors most impact a Pokémon’s use on competitive teams, and could give future insights on how Pokémon may perform as other Pokémon fall in and out of popularity, and as more Pokémon are added in future games.
Improved Content Based Image Retrieval Process Based on Deep Convolutional Neural Network and Salp Swarm Algorithm

Digital image and medical image retrieval from several repositories are improving gradually, so the capacity of repositories increases rapidly. The semantic space is the main issue on content-based image retrieval (CBIR), which exists among the semantic level as well as increases the data recognized through human and low level visible data obtained through the image. The CBIR system utilizes the deep convolutional neural network (DCNN), which is trained to medical image characterization and the digital image by salp swarm optimization algorithm (SSA). The average classification accuracy for medical image is 86.805%, a mean average precision is 79%, Average Recall Rate (ARR) is 91.7% and [Formula: see text]-measure is 84.9%, are achieved during retrieval task. For image retrieval, the Average Precision Rate (APR) improved from 39%, 40%, 36% and 42.5% to 86.8% and the ARR enhanced from 39.5%, 40.5%, 35.5% and 42.5% to 86.8%. The [Formula: see text]-measure is improved from 39.5%, 40.5%, 35.5% and 42.5% to 86.8% as different with Local tetra patterns (LTrP), LOOP, local derivative pattern (LDP) and local mean differential excitation pattern (LMDeP) separately on Corel-1K dataset. The presented method is most suitable for multimodal digital images and medical image retrieval for various parts of the body.
The Study of Traveling Wave Tube Large Signal Model Based on Machine Learning

Driven by success in areas such as computer vision and natural language processing, attempts have been made in this work to combine deep learning with the large signal of the Traveling Wave Tube (TWT) to assist in predicting the output performance of the tube and advancing the pre-design of TWT. By feeding the trained artificial neural network with several feature parameters, the output power with the tube length can be predicted.
Universal photonic processors in a glass-based femtosecond laser writing platform

Femtosecond laser writing (FLW) can open new perspectives on universal photonic processors (UPPs). We propose here two building blocks for the realization of FLW-UPPs and we show the preliminary results obtained on a 6-mode device.
Classification of Digital Chess Pieces and Board Position using SIFT

Assistive technology has been given more attention in recent years to help people with disabilities to perform common tasks. Rather than designing a specialised tool for the task, it is more cost-effective and less inhibitory to make use of existing hardware integrated with a smart interface. Towards this end goal, we present our work on assisting a visually impaired person playing an online chess game. We evaluated an invariant feature descriptor, i.e., SIFT, for the task of classifying individual chess pieces across multiple visual themes. We compared two strategies for building the visual codebook, i.e., k-means clustering vs. image blending. The proposed pipeline receives live screen feeds from the browser at a fixed interval and produces an output in the form of chess pieces’ label and board position. Our proposed pipeline, paired with a visual codebook built using k-means clustering, managed an average accuracy rate of 6/10.
A Survey Paper on Acceleration of Convolutional Neural Network using Field Programmable Gate Arrays

Lately, Deep Learning has indicated its capacity by adequately tackling complex learning issues which were not possible previously. Specifically, Convolutional Neural Networks are most generally utilized and have indicated their viability in image detection and recognition problems. Convolutional Neural Networks(CNN) can be used to perform different kinds of tasks. However, to recognize even a single image, billions of calculations are required. For example, ResNet 50 takes 8 billion calculations just to figure out what's in a single image. So we need to find out ways to be able to run that superfast and that's why we need hardware accelerators. In this paper the basic information about Convolutional Neural Networks is provided along with the key operations involved and the brief idea about Field Programmable Gate Arrays(FPGA) is given which enable them to be used for accelerating the inference process of Convolutional Neural Networks. Various techniques which were employed previously for accelerating the Convolutional Neural Networks are discussed. We are focussing on designing a energy efficient Field Programmable Gate Arrays accelerator for accelerating the inference process of Convolutional Neural Networks with reduced latency and increased throughput.
The spatial data sharing security model based on hybrid encryption algorithm

Hybrid encryption algorithms are flexible tools for modeling the correlation of random variables. They cover the scope from completely negative correlation to positive correlation, including independent cases and contain asymmetric correlation and broadly employed Gaussian correlation structure. The pair-encryption algorithm of the hybrid encryption algorithm takes advantage of the ease of use of the two-variable encryption algorithm, and it is recommended to decompose the hybrid encryption algorithm into a set of two-variable encryption algorithms. We have successfully applied this method to spatial data and established a powerful interpolation method on basis of spatial logarithm.
The Effect of Nutrition Education Using Pocketbook Media on Iron and Protein Intake

&#x0D; &#x0D; &#x0D; &#x0D; The provision of nutrition education for teenage girls is an alternative to increase protein and iron intake to prevent anemia. This study aims to determine nutrition education's effect using pocketbook media on protein and iron intake in teenage girls. The research design used was quasi-experimental with a control group pre and post-test design. Sampling was done by the purposive sampling method—statistical analysis using Paired Samples T-test, Wilcoxon, Independent Samples T-test, and Mann Whitney. This study indicates an influence of nutrition education using pocketbook media on protein intake (p = 0.000) and iron (p = 0.000) in the treatment group. There is no effect of nutrition education using PowerPoint on protein intake (p = 0.326) and substances iron (p = 0.52) in the control group. Statistically, there was a significant difference between protein intake (p = 0.000) and iron (p = 0.001) of the treatment and control groups. It is recommended that UKS and students work together to conduct nutrition education routinely so that the nutrition information delivered can be given to all students in the school to prevent nutritional anemia.&#x0D; &#x0D; &#x0D; &#x0D;
Improved coherent point drift for 3D point clouds registration

Coherent Point Drift (CPD) is one of the popular robust point cloud registration algorithms in recent years. However, the algorithm uses fast Gaussian transformation to calculate the matrix-vector product, resulting in slower overall registration efficiency. We propose an improved coherent point drift (ICPD) algorithm, which introduces faster Gaussian lattice filtering to calculate the above product and uses the global squared iterative method to reduce the number of iterations of the CPD algorithm. In addition, the outlier w is not accurately expressed in CPD. We propose an iterative outlier formula to solve this problem. Experiments show that the improved algorithm is about two orders of magnitude faster than the CPD algorithm, 1-2 times faster than the ICP algorithm, and shows superior performance in environments with different noise and outlier distributions.
Research on the Optimization Model of Blockchain Hierarchical Proxy

In the decentralized blockchain, each node stores the same data copy that costs a lot of repeated storage space to store valuable information. It makes storage efficiency insufficient. Meanwhile, each node needs to exchange the huge messages to create and verify the block. With rapid increasing in the number of communication nodes and communication frequency, it has caused a large network load. In order to further improve the storage efficiency and decrease the network load, a hierarchical proxy model is proposed. Firstly, designing a hierarchical proxy model which accurately divides normal nodes and proxy node groups, and it performs directional division of tasks for different nodes. Secondly, in a small-scale management cluster composed of proxy nodes, cluster failure priority classification can effectively deal with the failure of proxy nodes. Through the hierarchical storage mode, the block header and body data are separated, and it uses global or local method to store. The hierarchical operation mode makes use of idle resources to make transaction verification and block creation run in parallel. On this basis, the proxy model adopts delay correction strategy and concentrated multiple correction strategy to further reduce the communication loss. Through malicious node blacklist, the node with malicious behavior is added to the blacklist, and it prevents the node from further making evil. Finally, the simulation experiments show that the hierarchical proxy model effectively resists malicious behaviors, reduces network load and has great advantages in terms of data storage efficiency.
Criminalizing Cybercrimes: Italian and Polish Experiences

Abstract The rapidly advancing development of technology has both positive and negative eff ects on society and its members. Moreover, legislation can be slow to catch up with reality. This also applies to any reaction of society to new forms of social deviance. There is typically a delay in the introduction of legislation which tries to give a legal framework to new technological developments. The authors have taken an exploratory approach, analysing changes in Italian and Polish penal law relating to cybercrime that have occurred in Italy and Poland so far. The timeline, pace, and scope of the processes of criminalization are presented for each country. Even though both legislators had and have the same goal, diff erences in the approach to achieving it are visible. The conclusions may lead to changes in the penal policies of both countries.
AID-Purifier: A Light Auxiliary Network for Boosting Adversarial Defense

In this study, we propose AID-Purifier that can boost the robustness of adversarially-trained networks by purifying their inputs. AID-Purifier is an auxiliary network that works as an add-on to an already trained main classifier. To keep it computationally light, it is trained as a discriminator with a binary cross-entropy loss. To obtain additionally useful information from the adversarial examples, the architecture design is closely related to the information maximization principle where two layers of the main classification network are piped into the auxiliary network. To assist the iterative optimization procedure of purification, the auxiliary network is trained with AVmixup. AID-Purifier can be also used together with other purifiers such as PixelDefend for an extra enhancement. Because input purification has been studied relative less when compared to adversarial training or gradient masking, we conduct extensive attack experiments to validate AID-Purifier’s robustness. The overall results indicate that the best performing adversarially-trained networks can be enhanced further with AID-Purifier. The code is available in https://github.com/yelobean/AIDPurifier.
Research on Image Copyright Confirmation and Protection Model Based on Blockchain

In the process of the rapid development of the information society, there are still many problems in the integrity, accuracy and security of image data. Blockchain technology stands out with its characteristics of decentralization, data immutability and data openness and transparency. Therefore, in view of the above problems, this paper proposes a block chain-based image copyright confirmation and protection model. The model includes five processes: consensus node joining, generation of registration data, consensus registration data, storage of registration data, and digital watermarking. Ensure that the image information cannot be tampered with after it is stored on the blockchain, and all nodes jointly maintain the storage; At the same time, the digital watermark is added to prevent the image information from leaking out of the chain to be embezzled by criminals for personal gain.
Robust Homomorphic Video Hashing

The Internet has been weaponized to carry out cybercriminal activities at an unprecedented pace. The rising concerns for preserving the privacy of personal data while availing modern tools and technologies is alarming. End-to-end encrypted solutions are in demand for almost all commercial platforms. On one side, it seems imperative to provide such solutions and give people trust to reliably use these platforms. On the other side, this creates a huge opportunity to carry out unchecked cybercrimes. This paper proposes a robust video hashing technique, scalable and efficient in chalking out matches from an enormous bulk of videos floating on these commercial platforms. The video hash is validated to be robust to common manipulations like scaling, corruptions by noise, compression, and contrast changes that are most probable to happen during transmission. It can also be transformed into the encrypted domain and work on top of encrypted videos without deciphering. Thus, it can serve as a potential forensic tool that can trace the illegal sharing of videos without knowing the underlying content. Hence, it can help preserve privacy and combat cybercrimes such as revenge porn, hateful content, child abuse, or illegal material propagated in a video.
An Integer Prefix-Based Methodology for Enhancing the Execution Performance of Any String Sorting Algorithm

Sorting has an important function in any software system that requires searching, uses SQL, or needs faster recovery after any system crash. Moreover, most data are stored as strings; thus faster string sorting is a blessing. Strings are accessed by reference and any string access requires two memory requests which would slow any string sorting algorithm. On the other hand, integers are accessed by value and any integer access requires one memory request, this means making that integer sorting should run faster than string sorting for the same number of elements and the same algorithm. Based on this hypothesis a methodology was designed which works on any sorting algorithm that could sort either string or integer arrays and makes string sorting runs faster. This methodology has five stages to accomplish the sorting process. First, for a given unsorted string array convert a prefix of every string in this array to an integer and place these integers in a parallel array of records of integers. The results for sequential execution for quicksort, merge, and heap sorts, or O (N log N) algorithms, showed an average speedup factor gain of eleven, eight, and four, respectively, on variable-length strings of alphabets only. Furthermore, after testing this methodology on slow, iterative algorithms with complexity O ( $${N}^{2}$$ ): bubble sort, selection sort, and insertion sort, the results showed an average speedup factor of 25, 45, and 70, respectively, on variable-length strings of alphabets only.
BJTU-Toshiba’s Submission to CCMT 2021 QE and APE Task

This paper presents the systems developed by Beijing Jiaotong University and Toshiba (China) Co., Ltd. for the CCMT 2021 quality estimation (QE) and automatic-post editing (APE) task. For QE task, we mainly rely on multiple pretrained language models, and propose a multi-phase pre-finetuning scheme, to adapt the pretrained models to the target domain and task. The pre-finetuning scheme consists of language-adaptative finetuning, domain-adaptative finetuning and task-adaptative finetuning. For APE task, we use BERT-initialized Transformer as the backbone model, and create different groups of synthetic data by different data augmentation methods, i.e. forward translation, round-trip translation and multi-source denoising autoencoder. Multi-model ensemble is adopted in both tasks. Experiment results on the development set show high accuracy on both QE and APE tasks, demonstrating the effectiveness of our proposed methods.