Reviewerly/exHarmony huggingface dataset:
Analysis of the Pattern Recognition Efficiency On Non-Optical Images

The article examines algorithms for processing non optical images using convolutional neural networks (CNN). Particular attention is paid to the recognition of objects on radar images of the Earth's surface and on X-ray images of baggage inspection. A brief overview of research in this topic is presented. The indicators of recognition efficiency on limited datasets using data augmentation are considered. In addition, the article describes in detail the processes of building neural networks and their training. A comparative analysis of CNNs is carried out for different training parameters and different network architectures. It has been shown that CNNs provide satisfactory results when processing non-standard types of images, such as Xray images of baggage or radar images from Synthetic Aperture Radar (SAR) systems. Furthermore, the text deals with various metrics such as accuracy, precision and recall. The suggested algorithms provides about 90-100% for recognition of SAR images and about 90% for X-ray images.
A Novel Network Covert Channel Model Based on Blockchain Transaction Parity

As information security is constantly challenged, it is very necessary to construct a practical and secure new model of information hiding. The existing cover channel models often has the risk of being easily disturbed and destroyed, and the different characteristics of shared resources are needs to be utilized to guarantee its concealment. Due to the security and reliability, decentralization, robustness and other characteristics, Blockchain has been applied to information hiding field (steganography), however, its concealment and practicality are difficult to meet the actual demand. In order to overcome the defects of the covert storage channel and blockchain-based information hiding scheme, we propose a novel network covert channel model based on Blockchain transaction addresses parity and formalize this network covert channel under the blockchain environment. By modulating the parity of the transaction address, the sender can transmit the secret message to the receiver through adding addresses when processing the business without occupying additional block chain space. Meanwhile, the relevant technologies of Blockchain (cryptography, P2P, etc.) ensure that the scheme has good tamper-resistant, multi-line communication and receiver anonymity, guarantying the concealment of communication and the security of information.
Machine Biometrics - Towards Identifying Machines in a Smart City Environment

This paper deals with the identification of machines in a smart city environment. The concept of machine biometrics is proposed in this work for the first time, as a way to authenticate machine identities interacting with humans in everyday life. This definition is imposed in modern years where autonomous vehicles, social robots, etc. are considered active members of contemporary societies. In this context, the case of car identification from the engine behavioral biometrics is examined. For this purpose, 22 sound features were extracted and their discrimination capabilities were tested in combination with 9 different machine learning classifiers, towards identifying 5 car manufacturers. The experimental results revealed the ability of the proposed biometrics to identify cars with high accuracy up to 98% for the case of the Multilayer Perceptron (MLP) neural network model.
Key Generation Based Fuzzy Logic and Elliptic Curve Cryptography for Internet of Things (IoT) Authentication

The security and privacy of the network in the Internet of Things is playing an important challenge for researchers and engineers. Considering that packets are exchanged between the end user and the sensing devices, it is then important to ensure the security, privacy and integrity of the transmitted data by designing a secure and a lightweight authentication protocol for the IoT environment. In this paper, we present a novel method of authentication and encryption based on Elliptic Curve Cryptography (ECC) and on random numbers generated by fuzzy logic for the improvement of the authentication and the encryption in IoT systems. We evaluate our novel key generation method using standard randomness tests such as: Frequency test, Frequency test with mono block, run test, discrete Fourier transform test and advanced discrete Fourier transform test. Our results show superior performance.
Reinforced NMT for Sentiment and Content Preservation in Low-resource Scenario

The preservation of domain knowledge from source to the target is crucial in any translation workflows. Hence, translation service providers that use machine translation (MT) in production could reasonably expect that the translation process should transfer both the underlying pragmatics and the semantics of the source-side sentences into the target language. However, recent studies suggest that the MT systems often fail to preserve such crucial information (e.g., sentiment, emotion, gender traits) embedded in the source text in the target. In this context, the raw automatic translations are often directly fed to other natural language processing (NLP) applications (e.g., sentiment classifier) in a cross-lingual platform. Hence, the loss of such crucial information during the translation could negatively affect the performance of such downstream NLP tasks that heavily rely on the output of the MT systems. In our current research, we carefully balance both the sides (i.e., sentiment and semantics) during translation, by controlling a global-attention-based neural MT (NMT), to generate translations that encode the underlying sentiment of a source sentence while preserving its non-opinionated semantic content. Toward this, we use a state-of-the-art reinforcement learning method, namely, actor-critic , that includes a novel reward combination module, to fine-tune the NMT system so that it learns to generate translations that are best suited for a downstream task, viz. sentiment classification while ensuring the source-side semantics is intact in the process. Experimental results for Hindi–English language pair show that our proposed method significantly improves the performance of the sentiment classifier and alongside results in an improved NMT system.
Chinese grammatical error diagnosis based on sequence tagging methods

Abstract In view of the problems such as exploding gradient or vanishing gradient or inefficiency caused by parallel problems when traditional neural networks deal with long text grammar error correction. In this paper, Chinese grammatical error detection is proposed as a sequence tagging problem for named entity recognition, and a Chinese grammatical error detection model based on BERT BILSTM CRF is designed and implemented by using Bert model to construct word vectors. Meanwhile, three groups of comparative tests were designed: CRF, BILSTM CRF and BERT CRF. In the end, BERT BILSTM CRF model achieves the error detection accuracy of 66.67% and 52.94% in M (missing error) and W (ordering error), respectively. BERT CRF and CRF models have also achieved good results in S (selection error) and R (redundant words). The research shows that it is feasible to solve grammatical error detection as a sequence labeling problem. On the other hand, a Chinese grammatical error detection model based on BERT BILSTM CRF is proposed for the first time.
The International Comparable Corpus: Challenges in building multilingual spoken and written comparable corpora

This paper reports on the efforts of twelve national teams in building the International Comparable Corpus (ICC; https://korpus.cz/icc) that will contain highly comparable datasets of spoken, written and electronic registers. The languages currently covered are Czech, Finnish, French, German, Irish, Italian, Norwegian, Polish, Slovak, Swedish and, more recently, Chinese, as well as English, which is considered to be the pivot language. The goal of the project is to provide much-needed data for contrastive corpus-based linguistics. The ICC corpus is committed to the idea of re-using existing multilingual resources as much as possible and the design is modelled, with various adjustments, on the International Corpus of English (ICE). As such, ICC will contain approximately the same balance of forty percent of written language and 60 percent of spoken language distributed across 27 different text types and contexts. A number of issues encountered by the project teams are discussed, ranging from copyright and data sustainability to technical advances in data distribution.
Data Authentication for Wireless Sensor Networks with High Detection Efficiency Based on Reversible Watermarking

Data authentication is an important part of wireless sensor networks (WSNs). Aiming at the problems of high false positive rate and poor robustness in group verification of existing reversible watermarking schemes in WSNs, this paper proposes a scheme using reversible watermarking technology to achieve data integrity authentication with high detection efficiency (DAHDE). The core of DAHDE is dynamic grouping and double verification algorithm. Under the condition of satisfying the requirement of the group length, the synchronization point is used for dynamic grouping, and the double verification ensures that the grouping will not be confused. According to the closely related characteristics of adjacent data in WSNs, a new data item prediction method is designed based on the prediction-error expansion formula, and a flag check bit is added to the data with embedded watermarking during data transmission to ensure the stability of grouping, by which the fake synchronization point can be accurately identified. Moreover, the embedded data can be recovered accurately through the reversible algorithm of digital watermarking. Analysis and experimental results show that compared with the previously known schemes, the proposed scheme can avoid false positive rate, reduce computation cost, and own stronger grouping robustness.
Capturing and Considering Idea Development in School Pupils’ Design Activities

There are many ways to look at the efficacy and value of the inclusion of children in design activities within HCI. Whilst the experience of participation can be highly beneficial for children, there is value, even if it is only to confirm the merit of the children's participation, in looking at the process of ideation and in considering to what extent children can ideate. Whilst design is by nature untethered, being able to capture and consider the diversity of design ideas across multiple design teams and to look at how such ideas are developed, can facilitate reflection and potentially improve practice. This work explores the diversity of ideas and the iterative development of ideas during the incremental design of a mobile game with 26 teen informants working in small groups over four design sessions. A method to visualize the evolution of ideas across design sessions is presented. Groups’ behaviors are mapped to three positions according to how conservative and how innovative the participants were. We explore the implications of our work for practitioners and researchers.
Fine-Grained Texture Identification For Reliable Product Traceability

Texture exists in lots of the products, such as wood, beef and compression tea. These abundant and stochastic texture patterns are significantly different between any two products. Unlike the traditional digital ID tracking, in this paper, we propose a novel approach for product traceability, which directly uses the natural texture of the product itself as the unique identifier. A texture identification based traceability system for Pu’er compression tea is developed to demonstrate the feasibility of the proposed solution. With tea-brick images collected from manufactures and individual users, a largescale dataset has been formed to evaluate the performance of tea-brick texture verification and searching algorithm. The texture similarity approach with local feature extraction and matching achieves the verification accuracy of 99.6% and the top-1 searching accuracy of 98.9%, respectively.
Performance evaluation of man-portable devices based on the cloud center of gravity

Abstract In order to accurately assess the performance of man-portable devices, a method of evaluating the performance of man-portable devices based on the cloud center of gravity evaluation method is proposed. Firstly, a performance evaluation index system was constructed by analyzing the factors affecting the performance of man-portable devices. Then, using the cloud center of gravity judgment method, a man-portable device performance evaluation model is established and the weighted deviation is applied to determine the evaluation results. Finally, using examples, we verify the feasibility and effectiveness of the method. The research work provides ideas and references for scientific assessment of the performance of man-portable equipment, and further provides a theoretical basis for the modification and development of this type of equipment.
Defending Adversarial Attacks via Semantic Feature Manipulation

Machine learning models have demonstrated vulnerability to adversarial attacks, more specifically misclassification of adversarial examples. In this article, we propose a one-off and attack-agnostic Feature Manipulation (FM)-Defense to detect and purify adversarial examples in an interpretable and efficient manner. The intuition is that the classification result of a normal image is generally resistant to non-significant intrinsic feature changes, e.g., varying the thickness of handwritten digits. In contrast, adversarial examples are sensitive to such changes since the perturbation lacks transferability. To enable manipulation of features, a Combo-variational autoencoder is applied to learn disentangled latent codes that reveal semantic features. The resistance to classification change over the morphs, derived by varying and reconstructing latent codes, is used to detect suspicious inputs. Furthermore, Combo-VAE is enhanced to purify the adversarial examples with good quality by considering class-shared and class-unique features. We empirically demonstrate the effectiveness of detection and quality of purified instances. Our experiments on three datasets show that FM-Defense can detect nearly 100 percent of adversarial examples produced by different state-of-the-art adversarial attacks. It achieves more than 99 percent overall purification accuracy on the suspicious instances that close the manifold of clean examples.
Fusing Information by Knowledge-Guidance Based Clustering in Cross-Domain Collaborative Filtering

In the cross-domain memory-based collaborative filtering (CF) Recommendation algorithms, how to fuse the information from domains to give the overall similarity of users is the key step. Two manners have been proposed for this topic. One is to calculate the overall similarity of two users from the concatenated rating of the given ratings of different domains, the other is to calculate the overall similarity of two users from the similarities each of which is calculated from one of the given domains. Different from these two manners, this paper presents a distinguished manner where the information fusion of different domains is realized by a knowledge-guidance based clustering and the overall similarity of two users is calculated from the fused rating. Base on this manner of calculating overall similarity, we design a novel cross-domain memory-based Collaborative Filtering Recommendation algorithm. Experiment on a real dataset shows that the proposed algorithm outperforms other manner based approaches.
Application of the intra cluster, characteristic of k-means clustering method in English score analysis in Colleges

Abstract As an important data mining method, clustering algorithm can find valuable information hidden in data objects through unsupervised classification method. It is widely used in data analysis, image segmentation, feature learning and other fields. It is one of the common methods in traditional machine learning algorithm. K-means clustering algorithm is a partition-based algorithm in clustering analysis, which is efficient and accurate, easy to implement, and is generally implemented in machine learning and other fields. In this paper, based on the characteristics of K-means clustering method, the scores of each part of 30 college students' English scores are used as the input data, and the cluster center is used as the influencing factor to participate in the clustering process. The clustering effect is evaluated under the average distance of the characteristics within the cluster after several iterations.
Research on Classroom Evaluation Algorithm Based on CNN Text Preprocessing

The quality of classroom teaching is a key factor affecting the quality of higher education, and accurate classroom evaluation is an important means to improve the quality of classroom teaching. The data used for classroom evaluation generally include subjective evaluation unstructured text and objective structured data, but most classroom evaluations use objective structured data, and subjective evaluation of unstructured text has not been fully utilized. To solve this problem, a class evaluation back propagation neural network algorithm based on convolutional neural network text preprocessing is proposed. The algorithm uses CNN to convert the subjective data of the evaluation into structured data. Combined with the objective evaluation data and processed by the BP network, the emotional tendency of the overall evaluation quality of the classroom is obtained. Experiments show that the accuracy of sentiment classification with structured text data is 2.33% higher than that without adding text data. The addition of text data can more accurately evaluate the quality of classroom teaching.
Dual Mode Controlled Water Surface Garbage Collecting Robot by using Embedded Deep Learning

Plastic waste has become one of the most prevailing environmental concerns in modern society. Without proper environmental control policies and regulations in place, we have witnessed plastic garbage being irresponsibly disposed of into rivers and other water reservoirs, causing blockage of water passage as well as toxic contamination. Since plastic materials are unable to degrade in nature so quickly as biomaterials, human workers are typically required to collect plastic garbage, either manually or mechanically assisted. In some conditions, the maneuvers could cause personal injuries and infections. To resolve this issue, this paper proposes the design and development of a prototype water surface garbage collecting robot. The prototype was implemented on a Raspberry Pi board and controllable from an Android device via wireless (WiFi™) channel. Controlling robot movement, speed, and conveyor belt could be done either manually or automatically. The former was enabled by intuitive user interface, while the latter relied on deep learning of local scene acquired within active range. Experimental results of automatic operation, driven by deep learning models demonstrated garbage collecting performance at 83.25% accuracy.
Underwater image enhancement based on dual U-net

As land resources have continually decreased, ocean exploration by humans has steadily grown. Underwater imaging is one of the most intuitive means to reflect the internal conditions of the ocean. However, due to the complex imaging environment of the ocean and light scattering in the sea, underwater images exhibit severe degradation, making it difficult to distinguish effective information. Thus, underwater imaging must be enhanced. Compared with traditional methods (e.g. histogram equalization method) and modeling methods, deep learning has been well applied in the field of computer vision. The key points are the acquisition of the training set and the generalization ability of the convolution model. Because the model-based method often needs to measure prior data manually in advance, it will cause inevitable errors; and direct generalization of the neural network will also cause image blurring. In this paper, we design a double U-Net for underwater image enhancement with strong generalization ability, in combination with modeling and deep learning methods. The gray image of the input image is processed with the attention mechanism in advance, and the relevant transmittance information is obtained using a U-Net. Then, the input image is processed with the information output from each layer of the previous U-Net. The final result is obtained by dividing the two U-Net results by pixels. The proposed network is trained using a paired training set generated by CycleGAN. Through quantitative and qualitative analysis, our method is proved to be more effective than the methods in recent papers in the field of underwater image enhancement.
DeSAN: De-anonymization against Background Knowledge in Social Networks

Social network de-anonymization is a challenging research problem. Gigantic volumes of social network data get collected by third-party applications to mine knowledge for devising government policies, business decisions, health records, and many more. Social network data is vulnerable to privacy leakage due to the presence of sensitive information. Furthermore, attackers knowledge and their manipulation capabilities have also expanded in multi-folds. As a result, modelling the attacker's knowledge helps design a practical privacy model that could overcome attackers capabilities. Semantic knowledge has the potential to disclose privacy where the information is imprecise and inaccurate. This paper proposes a deanonymization technique, DeSAN, against imprecise and inaccurate attacker knowledge. The proposed technique assumes the attacker's knowledge, comprehensive and realistic. We have implemented the proposed DeSAN technique on a real social dataset, which exhibits encouraging result in terms of deanonymization accuracy.
Docker Security: Architecture, Threat Model, and Best Practices

With our ever-growing demands, virtualization is the technology to cater to our computing needs, thereby enabling users to leverage the full prowess of their computing resources. Virtualization, simply said, results in efficient usage of our resources, and containerization is the most prominent way to implement it. Docker is a container-based technology that helps to achieve virtualization. On the one hand, Docker helps us to provide a central point of control for various containers, however, on the other hand, it is a source of many security attacks too—if not configured properly. In this paper, we will be focusing on the Docker architecture, which is essential to understand how and from where attacks might originate. We will then dwell upon Docker threats along with various attack scenarios and steps we can take to eliminate such attacks. Further, we will shed light on some good practices to secure the Docker containers.