Reviewerly/exHarmony huggingface dataset:
A Chinese Business License Text Detection Algorithm Based On Multi-Scale Features

In practice, text detection is needed for document image recognition, where the images have long text, large text, as well as dense small text areas. Connection Text Proposal Network (CTPN) is a classical model for text detection, but it is challenging for CTPN to detect dense small text areas. To overcome the challenge, a text detection model is proposed based on CTPN in this paper. The proposed model includes the following components: the residual network (ResNet50) and Feature Pyramid Network (FPN) are used to extract the feature layers with both high-level semantic information and shallow detail information; A Bi-directional Long Short-Term Memory (BiLSTM) network is applied to augment the representation of context information by the multi-scale feature layers; The text boxes on each scale are predicted by the feature layer, by which effectively detecting the text areas on various of scales; The ground-truth bounding box of each text box can be matched to the most appropriate anchors using a centralized approach, and the bounding box of text line is obtained by the post-processing method for text line construction. In particular, our experiment focuses on the text detection for Chinese business license. The experimental results show that the proposed model is more effective than the CTPN in terms of generating higher F -score and using less training data, which is only one third of that for the CTPN. Furthermore, the proposed model works well for the images with long text, large text and dense small text areas simultaneously, for which the CTPN fails.
Achieving Privacy-preserving Data Sharing for Dual Clouds

With the advent of the era of Internet of Things (IoT), the increasing data volume leads to storage outsourcing as a new trend for enterprises and individuals. However, data breaches frequently occur, bringing significant challenges to the privacy protection of the outsourced data management system. There is an urgent need for efficient and secure data sharing schemes for the outsourced data management infrastructure, such as the cloud. Therefore, this paper designs a dual-server-based data sharing scheme with data privacy and high efficiency for the cloud, enabling the internal members to exchange their data efficiently and securely. Dual servers guarantee that none of the servers can get complete data independently by adopting secure two-party computation. In our proposed scheme, if the data is destroyed when sending it to the user, the data will not be restored. To prevent the malicious deletion, the data owner adds a random number to verify the identity during the uploading procedure. To ensure data security, the data is transmitted in ciphertext throughout the process by using searchable encryption. Finally, the black-box leakage analysis and theoretical performance evaluation demonstrate that our proposed data sharing scheme provides solid security and high efficiency in practice.
Mitigating EM Side-Channel Attacks with Dynamic Delay Insertion and Data Bus Inversion

Cryptographic circuits are sensitive to electromagnetic (EM) side-channel attacks (SCAs), which aim to detect the EM emissions of these circuits. A novel technique is proposed to mitigate such attacks, by reducing the correlation between the processed data and EM emissions. This objective is achieved by combining energy-efficient data inversion with dynamic delay insertion. The added delay enhances the immunity against EM attacks for the cryptographic circuit without performance degradation and, in specific scenarios, even improves performance. Simulation results on a set of EM traces, captured from an 8-bit interposer-based off-chip memory bus, demonstrate the efficiency of the proposed technique by decreasing SNR below 1 and improving the worst-case bus latency by 9.5%.
Network Link Existence Prediction Method Based on Graph Autoencoder

With the development of computer technology, people have higher and higher requirements for network link quality, which brings new challenges to network management and analysis. By applying link prediction to computer network scenarios, link existence information in the network can be predicted, which facilitates network management and analysis. At present, graph autoencoder is one of the most important models for link prediction. However, the existing model does not take node centrality into account and may have the problem of over-fitting. Therefore, for the scenario of network link state prediction, we design a network link prediction model based on node centrality and noise injection in this paper. In this model, noise is injected before training, and then the decoder is improved based on the variational graph autoencoder model. Node importance index and the weights of the trained nodes are used to add coefficients to the decoder. Finally, the model is proved to be effective in link connectivity prediction.
Protecting Alexa from Remote Voice Command and Inaudible Voice Command Attacks

Alexa (voice assistant) is an IoT device that provides various services based on user’s voice command. It provides various services from providing information about weather and time, can play music, make a phone call, send messages, order online to control other IoT devices. Along with large number of services, it is vulnerable to different attacks such as home burglary, fake orders, invocation confusion, DoS attacks, voice squatting, inaudible voice command attack, remote voice command attacks, etc. These attacks are feasible mainly because of weak or no voice authentication mechanism for commands fired to the voice assistant device. Specifically, the device cannot identify the source of command, i.e. from where the command came and who gave it. Also, this means that the attacker can use recorded voice or a voice inaudible to human to fire any command and Alexa will execute it without any hesitation. In this paper, we proposed an approach that works based on challenge response mechanism. These challenges are kept dynamic, so the attacker cannot have response for the given challenge in hand during attack. Thus, this approach contributes an efficient solution that can protect voice assistant from remote voice commands and inaudible voice commands.
Considerations for the Effective National CDO Policy

Data is a strategic asset for digital transformation. National innovation based on data has become a matter of global competition and survival. For pursuing a national innovation, it is important that the governance clearly defines the role and responsibility to lead innovation at the national level. In this regard, a national chief data officer (CDO) system has emerged recently as a new paradigm for national data innovation, mainly in the United States, the United Kingdom, and South Korea. This study employs a comparative approach to explaining the trends and common features of the national CDO system. The focal point of analysis is the legal base of CDO system, organization and governance, the required capability and authority of CDO, and its hiring process. Summing up, the study shows that an organization-wide awareness of the benefits of data innovation, a powerful authority to lead and coordinate regarding agencies, and a competent supporting organization are crucial to the successful operation of a CDO system.
Privacy and Security Enabling for Healthcare Data using Lightweight Deep learning with Cryptography

These days, smart devices are essential in dislodging the traditional healthcare management system. In this case, the Internet of Things (IoT) propels the healthcare sector's growth by enabling connections between objects at any time and from any location, even in a diverse setting. However, two main issues with IoT-enabled medical infrastructure are the security of patient data and protecting customer privacy. In order to safeguard healthcare data in IoT-enabled healthcare infrastructure, this study provides a unique encryption strategy using elliptic curve cryptography, Advanced Encryption Standard (AES), and Serpent. By integrating both symmetric and asymmetric-based encryption approaches, the proposed hybrid encryption technique enhances security measures for healthcare data. Additionally, the proposed scheme uses an elliptic curve-based digital signature to guarantee data integrity. This study presents formal security analysis as well as performance comparisons to demonstrate the effectiveness of the proposed approach. Results and analysis demonstrate the success of the presented methodology.
COVID-19 Face Mask Detection and Identification using FaceNet

All over the world, the COVID-19 pandemic has alarmed humans for the past two years. The spread of this virus continues to mutate mush and more. It makes preventing the spread of the epidemic a challenge. Therefore, we need to plan to deal with more mutations in the future. We consider the current time attendance record. Many organizations still use the old-time attendance method, namely fingerprint scanning, offering a method for detecting faces and identifying the faces of mask wearers. This study proposes constructing a mask detection system using a single shot multi-box detector as a pre-train model to distinguish masks and compare faces to identify personnel with FaceNet trained using facial data from school personnel. The system is practical in face detection and face comparison, accurate at 98%.
Conclusions and Outlook

Chemistry is not only of interest for chemists, but rather for the society at large, as the output of this discipline generates both welfare and hazard for our civilisation. Therefore, understanding chemical knowledge and its evolution is of central importance. In this book we introduced a formal setting for chemical knowledge as a complex dynamical system resulting from the mutual interaction of the social, semiotic and material systems of chemistry.
Video conferencing as a new way of organization educational process in music professional education

The epidemiological situation in the world with the arrival of the pandemic did not serve as a reason to interrupt the educational process in higher educational institutions. In March 2020, all universities, including music, were forced to adapt to new conditions by organizing the educational process online. Scientists wrote that “The COVID-19 pandemic has prompted educators to rethink educational practices, especially with regard to technology”. In a review of the practices of the European Union, the Organization for Economic Cooperation and Development, it was noted that: "The spread of COVID-19 fell on the active phase of the digitalization process, unwittingly becoming a trigger to accelerate the implementation of the accumulated potential of using digital technologies". In accordance with state strategic development programs, e-education, e-government and e-health should be improved and actively implemented after the end of the pandemic. The authors of the article consider the use of videoconferencing for organizing the educational process in professional music education not only as a forced measure, to make the educational process continuous during an exacerbation of the epidemiological situation, but also to assess the prospects for using this technology in the future, when the world will overcome the pandemic crisis.
End-to-End Event Factuality Identification with Cross-Lingual Information

Event factuality is a description of the real situation of events in text. Event Factuality Identification (EFI) is the basic task of many related applications in the field of natural language processing. At present, most studies about EFI are carried out with the annotated event mentions, which is not applicable for practical application, and ignores the opinion of different event sources on event factuality. Moreover, previous work did not use cross-lingual information for EFI. We propose an end-to-end joint model JESF, which uses Bert to encode sentences and uses lingual feature to enrich the semantic representation of sentences, and then use BiLSTM to capture the serialized semantic features of sentences; Then, the multi-head attention is used to learn the event characteristics and identify the event mentions; After that, use multi-head attention to identify the event source; Finally, GCNs is used to capture the syntactic and semantic features, mult-head attention is used to capture the semantic features of sentences, event and event source features are integrated to identify event factuality. Especially, we use different cross-lingual related methods to learn supplementary sematic features from aligned Chinese sentences. The experimental results on FactBank show that JESF is effective and the Chinese information is helpful for English EFI, and the more effective method is to use Chinese cue as features for EFI.
Autonomous Mobile Robot Design with Behaviour-based Control Architecture using Adaptive Neuro-Fuzzy Inference System (ANFIS)

Autonomous Mobile Robot (AMR) is a robotics-based platform that requires a control system to function correctly. Several robotics architectures have been proposed. Unfortunately, designing a control architecture is difficult and time-consuming, resulting in inaccurate control actions. As a result, the behaviour-based approach has emerged as the primary alternative to traditional robot control. This paper's contribution is a design for an AMR based on a behaviour-based control architecture. This paper discusses three AMR behaviors: planned goal-seeking behavior, wall-following behavior, obstacle-avoidance behavior, and color-detecting behavior. The Adaptive Neuro-Fuzzy Inference System (ANFIS) approach will be used to control all behaviour, which combines Fuzzy Logic and Artificial Neural Network to produce a reliable control algorithm. Competitive coordination is used to design behaviour coordination, particularly for obstacle-avoidance behaviour. Several experiments have been carried out. Based on the AMR's movement, it can be stated that all behaviours work well, where AMR can avoid obstacles, detect colours, follow walls, and decide the direction of motion.
Learning Adaptive Progressive Representation for Group Re-identification

Group re-identification (re-id) aims to retrieve a group of people across different surveillance cameras. Due to the change of group layout and membership, group re-id is much more difficult than person re-id. How to address these problems for group re-id is under-explored. In this paper, we propose the Adaptive Progressive group representation Learning Network (APLN) which consists of three innovations: First, we propose a progressive group representation method which fuses individual features together with relation features. Second, we propose a member mask to ignore the impact of changes in the number of members. The member mask is beneficial to getting a more robust group representation from volatile group samples. Third, we propose to use group proxy node as the global representation of the group context graph to obtain precise group context graph information by focusing on more significant individuals. Experimental results demonstrate that our proposed method outperforms the state-of-the-art performance on several group re-id datasets. Compared with the previous methods, the parameters of our model are much fewer and the inference speed is faster.
Locative Authoring: Evaluating the StoryPlaces Authoring Tool

Locative Narrative is a form of Interactive Digital Narrative (IDN) where the readers' location and movement are the main form of interaction. The StoryPlaces platform provides a general toolset for the creation and delivery of these location aware stories. However, while there is existing research on the reader experience with this technology, comparatively little is known about the author experience. We recruited five interactive narrative design students to participate in a usability test of the StoryPlaces pattern-based authoring tool, using observations, interviews, and analysis of their stories to understand their experience. We show that while participants superficially liked the interface of the StoryPlaces authoring tool, they had difficulty understanding the aspects that were less clearly visualised and struggled to test their creations. The patterns enabled them to add complex functionality easily but became a barrier if they wanted to deviate from them. Our findings mostly support Green's five principles of IDN authoring (on the value of visual metaphors and fast track testing), but suggests they need refinement as in application it was important to distinguish between the visualisation of different aspects of the story (location vs. logical structures), and that failure to properly visualise sometimes led to avoidance or displacement of activity rather than a drop in its quality.
A Novel Approach for Computation of Morphological Operations Using the Number Theoretic Transform

The fundamental operations of mathematical morphology are dilation and erosion. They are often implemented using a sliding window with the purpose to compute maximum respectively minimum of pixel values within the corresponding mask. We reformulate the problem of morphological dilation respectively erosion of an image with a non-flat filter as a convolution of their umbras. To this end, we propose to make use of the number theoretic transform to compute the convolution in this setting. In contrast to other possible schemes, this transform represents a completely discrete computational approach. It allows exact convolution of sequences made up of integers. Therefore we propose by the combination of umbra framework and number theoretic transform a well-engineered combination. There is no restriction on size or shape of the structuring element, and also flat and non-flat filters can be realised.
SAR Image Ship Detection Algorithm based on Machine Vision YOLO Model

This paper mainly studies the detection technology of tiny size ship targets in SAR images based on the deep learning method. Firstly, the feature extraction network structure is redesigned to solve the problem that the feature information of small and medium-sized targets in SAR images is easily lost. Then, the path enhancement method is used to enrich the geometric position information in the high-level feature graph, and the fusion of adjacent feature graphs is used to enrich the expression ability of the feature graph. Finally, the effectiveness of the proposed algorithm is verified by experiments, and the detection results in some typical scenarios are shown and compared with the classical target detection algorithm.
End-to-End Low-level Hovering Control of Quadrotor Based on Proximal Policy Optimization

In this paper, an end-to-end low-level quadrotor hover control method is proposed to achieve stable hovering control under the complex disturbance. In our approach, we design a set of reward functions that meet the hovering task of the quadortor, use three layers of neural network to perceptually characterize the position, pitch, yaw, and roll information of the quadrotor, and adopt the algorithm of proximal policy optimization (PPO) to output the rotational speed of the rotor to achieve stable hovering control through the end-to-end training side. In addition, we use a representation sharing network design, where the Actor network shares a set of representation networks with the critic network to reduce the number of neural network parameters. In order to verify the effectiveness of the algorithm, we design an experiment of autonomous quadrotor hovering control in virtual scenes, and the experimental results show that our method can achieve quadrotor hovering under the complex disturbance.
Virtual Voice Assistant In Python (Friday)

We are living the era of computers where nowadays everything can be done using computers i.e., Artificial Intelligence and Machine Learning, have made technology very easy that now humans can interact with machines in such a way that their task can be done only by interacting with Machines i.e., Computers. Yes, we are talking about Virtual Personal Assistants (VPAs). As of now there are many such VPAs like Alexa, Bixby, Echo, Siri, Google Assistant available on desktop, mobiles and in device also but as the technology becomes easier it becomes difficult to use for particular people such as senior citizens, blind people, also children below certain age. Also, this VPAs which are present nowadays do not provide as much facilities. To overcome this, we developed voice assistant in python on windows system which provide user to perform any task without using keyboard. Also, user can send email just by giving receiver’s email and the message user wants to send. Also, now user can book a cab just by saying where he wants to go and also book bus and train tickets. Our main aim towards developing this is to make certain things more efficient.
Preparing future teachers to work with parents in a modern school

The purpose of the study is to reveal the features of preparing future teachers to work with parents in a modern school. The article pays special attention to its practice-oriented orientation, taking into account the specifics of the full-time and remote format of interaction with the family. The scientific novelty consists in a system-integrated approach to the organization of professional training of future teachers to work with parents, which involves the study of traditional and innovative practical and diagnostic tools by future teachers; their inclusion in the work of educational organizations; involvement in active diagnostic, educational, organizational, methodological work with parents. As a result, the main aspects of preparing future teachers to work with parents are identified and characterized.
An automatic parallelization ubiquitous machine learning accelerator on FPGA

Convolutionary neural networks have been commonly used for object recognition because, by emulating the nervous system’s action in living organisms, they can achieve high precision. Recently, development and deployment have further boosted the exponential growth of advanced businesses focused on intelligent systems. In specific, different fuel cells have been suggested for deep CNN based on the FPGA platform since it has characteristics of good efficiency, reconfigurability, and rapid growth, respectively. The processor architecture room has not been well explored, although existing FPGA prototypes have shown better performance over standard computers. One crucial concern is that computing efficiency does not fit the memory capacity offered by an FPGA platform. Due to its excellent precision, a Convolutionary computer program sees uses in a range of image processing applications ranging from target identification and tracking to scene comprehension. Various algorithms exist for calculating CNNs. Mostly in CPU, three service systems are found: artificial neural, convolutionary artificial neural, genetic analysis, and collective processing. The multiplication uses an out-of-order optimization technique to facilitate simultaneous information flow processing to optimize coarse-grained instruction computing. Next, we suggest the fused structure in CNNs will naturally fuse several layers, repurposing the intermediary details. We explore diverse architectures to optimize the performance of a CNN centered on this working to create. To decide the fusion and algorithm technique for each sheet, we develop an optimum algorithm. We are also designing an integrated toolchain using Xilinx to ease the routing from the template to the FPGA bitstream. Studies using commonly used VGG and Alex Net illustrate that, relative to the previous fusion-based FPGA acceleration for CNNs, our architecture achieves up to 1.5X fine aggregate.