Reviewerly/exHarmony huggingface dataset:
Feature-filter: Detecting adversarial examples by filtering out recessive features

Deep neural networks (DNNs) have achieved state-of-the-art performance in numerous tasks involving complex analysis of raw data, such as self-driving systems and biometric recognition systems. However, recent works have shown that DNNs are under threat from adversarial example attacks. The adversary can easily change the outputs of DNNs by adding small well-designed perturbations to inputs. Adversarial example detection is fundamental for robust DNN-based services. From a human-centric perspective, this paper divides image features into dominant features comprehensible to humans and recessive features incomprehensible to humans yet exploited by DNNs. Based on this perspective, the paper proposes a new viewpoint that imperceptible adversarial examples are the product of recessive features misleading neural networks, and that the adversarial attack enriches these recessive features. The imperceptibility of the adversarial examples indicates that the perturbations enrich recessive features but hardly affect dominant features. Therefore, adversarial examples are sensitive to filtering out recessive features, while benign examples are immune to such operations. Inspired by this idea, we propose a label-only adversarial detector that is referred to as a feature-filter. The feature-filter utilizes the discrete cosine transform (DCT) to approximately separate recessive features from dominant features and obtain a filtered image. A comprehensive user study demonstrates that the DCT-based filter can reliably filter out recessive features from the test image. By comparing only the DNN’s prediction labels on the input and its filtered version, the feature-filter can detect imperceptible adversarial examples in real time with high accuracy and few false-positives.
Exploring Data Integrity of Dual-Channel Supply Chain Using Blockchain Technology

The study intends to solve the problems of complex product circulation caused by information asymmetry and the untimely communication of production and sales information in the process of product sales to reduce the cost in the process of product circulation. Based on blockchain technology, the data integrity of the dual-channel supply chain is studied. First, the data of the supply chain conduct coordinated management to achieve the integrity of the supply chain data. Then, under the background that both retailers and suppliers are risk-neutral individuals, the benchmark model of the dual-channel supply chain is constructed, and the online and offline sales prices of products under different decision-making modes are analyzed. Finally, taking fresh agricultural products as an example, the sales strategies of the online and offline sales channels of fresh agricultural products are studied, and a dual-channel supply chain model is constructed. The profit of each member in the supply chain system under this model is obtained by the inverse method. The simulation results demonstrate that the retailer’s revenue and the total revenue of the system increase obviously with the growth of the price discount coefficient after the price discount strategy is applied. When the compensation cost is between 1,000 and 3,000, the profit of retailers in the supply chain system is improved by using the price coordination mechanism, while the profit of suppliers decreases to some extent. When the value of compensation cost is 7,000–9,000, the application of the price coordination mechanism increases the profit of suppliers in the supply chain system, while the profit of retailers declines to a certain extent. The research content reported here effectively alleviates the profit conflict and the double marginal effect between the two channels and enriches the theoretical system knowledge of the coordination of the two channels’ supply chain of agricultural products.
CNN Based Character Recognition and Classification in Tamil Palm Leaf Manuscripts

Palm leaf manuscripts are extremely important as they have a rich source of information. As a result, simple access to ancient manuscripts must be provided to share this information with the rest of the world and to promote future study into ancient literature. This study deals with Convolutional Neural Network (CNN)-based Optical Character Recognition (OCR) system for accurately digitizing and identifying the characters for Tamil palm leaf manuscripts. The convolution layer, pooling layer, activation layer, fully connected layer, and classifier of the convolutional neural network is employed in this article. Palm-leaf manuscripts were scanned and the scanned images are used to generate the character set database. The database is divided into 67 separate classes, each of which contains roughly 100 individual samples. OCR recognition of the palm leaf manuscripts and problems associated with this are illustrated. A working example of the character recognition method for Tamil palm-leaf manuscript was implemented using the CNN model. The CNN model was found to have a better recognition rate. The prediction rate and accuracy are great because of the large number of features retrieved for each layer of CNN.
Programmable Hardware BBC Micro:Bit as a Tool for Developing Teacher Competencies

The teacher&#x2019;s portfolio presenting the mastery of competencies focused on the student, the educational process and professional development is an important part of a teacher&#x2019;s career growth in Slovakia. The teacher must demonstrate that they are able to innovate learning practices, considering the educational needs of each pupil. Programmable hardware that is easy and fast to use allows teachers to make computer science lessons more attractive to students on the one hand and increase teacher self-confidence on the other. We decided to be the first working group in Slovakia that create a project to implement the innovation at national level education by using physical computing principles to develop student&#x2019;s digital skills. In cooperation with Slovak mobile company, we have realized the project, focused on education with the BBC micro:bit in Slovak schools. Thanks to the project teachers could have not only hardware but also access to information they needed to start teaching with BBC micro:bit. It helped to eliminate the problems with how to start and teachers could focus on the students. After teaching with micro:bit teachers were more motivated and confident. Based on the post-questionnaire, we found that computer science teachers perceive their competencies higher than before the start of the project.
Relational Agents for the Homeless with Tuberculosis Experience: Providing Social Support Through Human–agent Relationships

In human–computer interaction (HCI) research, relational agents (RAs) are increasingly used to improve social support for vulnerable groups including people exposed to stigmas, alienation, and isolation. However, technical support for tuberculosis (TB) patients, one such vulnerable group, remains insufficient due to the nature of the infectious disease and difficulties in accessing the homeless community. To derive design considerations for developing RAs targeting homeless TB patients, we conducted an empirical study on the patients. Data were collected through participatory observations and interviews and were processed using deductive thematic analysis. The patients’ environmental and behavioral characteristics were classified, which showed that understanding these factors in the design of an RA is important because the patients’ perception, attitudes, and expectations towards the agent are shaped by (and also shape) their environmental and behavioral characteristics, which consequently affect the nature of relationships formed between them. Therefore, we drew the following design considerations: (1) protection of privacy is a prerequisite to the use of an RA for homeless TB patients and can be addressed from both short-term (technical) and long-term (sociotechnical) perspectives; (2) the homeless group emphasized affective support from the agent, suggesting that relationships per se are already valuable to people who have been socially isolated and stigmatized; (3) consideration of the past memories in selecting social cues can facilitate the exchange of affective expressions in user–agent interaction; and (4) an RA should clarify to its interlocuters its identity as a machine to avoid confusing people with low technological literacy.
Twitter Mining based Forecasting of Cryptocurrency using Sentimental Analysis of Tweets

The decentralization of cryptocurrency has decreased the level of central control, which has impacted international trade and ties. There is also an urgent need for a credible way of projecting the price of cryptocurrencies, which is currently unavailable. A novel method to predict cryptocurrency price is proposed in this paper, which makes use of deep learning techniques such as the recurrent neural network (RNN), gated recurrent unit (GRU), convolution 1D, and the long short-term memory (LSTM). This method considers a variety of factors such as market capitalization, volume, circulating supply, and maximum supply. It is more accurate at recognizing long-term relationships than the LSTM. Developed in Python, the proposed approach was tested on a range of real-world data sets. The findings demonstrate that the proposed method may be used to properly predict the price of cryptocurrencies.
Support-Set Based Multi-Modal Representation Enhancement for Video Captioning

Video captioning is a challenging task that necessitates a thorough comprehension of visual scenes. Existing methods follow a typical one-to-one mapping, which concentrates on a limited sample space while ignoring the intrinsic semantic associations between samples, resulting in rigid and uninformative expressions. To address this issue, we propose a novel and flexible framework, namely Support-set based Multi-modal Representation Enhancement (SMRE) model, to mine rich information in a semantic subspace shared between samples. Specifically, we propose a Support-set Construction (SC) module to construct a support-set to learn underlying connections between samples and obtain semantic-related visual elements. During this process, we design a Semantic Space Transformation (SST) module to constrain relative distance and administrate multi-modal inter-actions in a self-supervised way. Extensive experiments on MSVD and MSR-VTT datasets demonstrate that our SMRE achieves state-of-the-art performance. Our code is released at https://github.com/SMRE-CV/SMRE.
Improved swarm-wavelet based extreme learning machine for myoelectric pattern recognition

Myoelectric signal generated by muscles is one of the bio-signals which is used by humans to control equipments. To achieve this purpose, a good myoelectric pattern recognition (M-PR) is required. The applied classifier and extracted feature sets greatly affect the success of M-PR. This paper proposes a hybrid and fast classifier, extreme learning machine (ELM) which is enhanced by improved hybrid particle swarm optimization with wavelet mutation (improved swarm-wavelet). ELM is, in essence, a single-hidden layer feed-forward neural network that keeps off iterative learning to save the training time. In addition to improving the actual performance of M-PR, we also evaluate the optimization of ELM using improved swarm-wavelet in this paper. The swarm-wavelet is improved by using the particle refresh and applying velocity improvement to avoid trapped in local minima. In ELM, the improved swarm-wavelet is used to find the most suitable parameters to increase the classification accuracy. Furthermore, this paper provides comparisons of improved swarm-wavelet-ELM, swarm-wavelet-ELM and standard swarm-ELM. The experimental results show that the improved swarm-wavelet-ELM, our proposed method, is the most accurate classifier with mean accuracy of 99.6%.
Stop the Spread: A Contextual Integrity Perspective on the Appropriateness of COVID-19 Vaccination Certificates

We present an empirical study exploring how privacy influences the acceptance of vaccination certificate (VC) deployments across different realistic usage scenarios. The study employed the privacy framework of Contextual Integrity, which has been shown to be particularly effective in capturing people's privacy expectations across different contexts. We use a vignette methodology, where we selectively manipulate salient contextual parameters to learn whether and how they affect people's attitudes towards VCs. We surveyed 890 participants from a demographically-stratified sample of the US population to gauge the acceptance and overall attitudes towards possible VC deployments to enforce vaccination mandates and the different information flows VCs might entail. Analysis of results collected as part of this study is used to derive general normative observations about different possible VC practices and to provide guidance for the possible deployments of VCs in different contexts.
Detecting and Responding to Concept Drift in Business Processes

Concept drift, which refers to changes in the underlying process structure or customer behaviour over time, is inevitable in business processes, causing challenges in ensuring that the learned model is a proper representation of the new data. Due to factors such as seasonal effects and policy updates, concept drifts can occur in customer transitions and time spent throughout the process, either suddenly or gradually. In a concept drift context, we can discard the old data and retrain the model using new observations (sudden drift) or combine the old data with the new data to update the model (gradual drift) or maintain the model as unchanged (no drift). In this paper, we model a response to concept drift as a sequential decision making problem by combing a hierarchical Markov model and a Markov decision process (MDP). The approach can detect concept drift, retrain the model and update customer profiles automatically. We validate the proposed approach on 68 artificial datasets and a real-world hospital billing dataset, with experimental results showing promising performance.
Application of Improved Machine Learning and Fuzzy Algorithm in Educational Information Technology

In order to improve the teaching effect of intelligent education, this paper combines machine learning and fuzzy algorithm to improve the application effect of educational information technology and the use of information platform. Moreover, this paper collects the user’s behavior information in the process of using the platform as the user’s interest description and selects personalized upgrade resources for the user. In addition, this paper introduces the similarity as a personalized recommendation evaluation standard and calculates the average similarity between the recommended related resources and the user’s interest. Finally, this paper constructs an educational information system based on improved machine learning and fuzzy algorithms. The experimental research results show that the intelligent system proposed in this paper can play an important role in educational information processing and teaching quality improvement.
XDMOM: A Real-Time Moving Object Detection System Based on a Dual-Spectrum Camera

A low-cost and power-efficient video surveillance system, named XDMOM, is developed for real-time moving object detection outdoors or in the wild. The novel system comprises four parts: imaging subsystem, video processing unit, power supply, and alarm device. The imaging subsystem, which consists of a dual-spectrum camera and rotary platform, can realize 360-degree and all-day monitoring. The video processing unit uses a power-efficient NVIDIA GeForce GT1030 chip as the processor, which ensures the power consumption of the whole system maintains a low level of 60~70 W during work. A portable lithium battery is employed to supply power so that the novel system can be used anywhere. The work principle is also studied in detail. Once videos are recorded, the single-stage neural network YOLOv4-tiny is employed to detect objects in a single frame, and an adaptive weighted moving pipeline filter is developed to remove pseudo-targets in the time domain, thereby reducing false alarms. Experimental results show that the overall correct alarm rate of the novel system could reach 85.17% in the daytime and 81.79% at night when humans are monitored in real outdoor environments. The good performance of the novel system is demonstrated by comparison with state-of-the-art video surveillance systems.
Simultaneous 3D dithering of multiple images by curves

We present a method to simultaneously dither multiple images by space curves, seen from multiple views. The space curves are a-priori designed to create varying coverages over a contrasting background, when projected in the different directions. By carefully placing the proper curves at specific pixel locations, the desired dithering effect of multiple images can be achieved, as one 3D model consisting of only space curves, and from multiple directions. The presented approach employs dark space curves of finite width over light background. The dithering of two or three different B&W images from two or three different directions is demonstrated, using computer simulations as well as 3D printed results.
Rampant Smoothing (RTS) Algorithm: an optimized consensus mechanism for private Blockchain enabled technologies

Abstract Blockchain is a distributed database method of storing electronic information in digital form that makes it challenging to transform, hack, or rogue the system. Each time a new operation transpires on the Blockchain, an irreversible information of that transaction is buffered in all connected ledgers. Each distributed ledger is programmable, immutable and timestamped. This paper follows an immutable policy to ensure chronological trust-administration, security and privacy to resolve anomalies within linearly stored blocks. To prove the validity of submitted data, a consensus mechanism is required, which was attained by implementing a novel ‘Rampant Smoothing Algorithm’ that was encoded using Solidity (i.e., aimed at developing smart contracts). Experimental policies were aligned with Good Clinical Data Management Practices that portrayed effective ‘smart contract modeling’ to demonstrate resistance against majority consensus attacks by harnessing the core feature of ‘privacy decentralization’. During implementation of Blockchain network, an interconnected system of six hundred nodes (i.e., data points) were gradually configured.
Spatially Formulated Connected Automated Vehicle Trajectory Optimization with Infrastructure Assistance

This paper presents a constrained connected automated vehicles (CAVs) trajectory optimization method on curved roads with infrastructure assistance. Specifically, this paper systematically formulates trajectory optimization problems in a spatial domain and a curvilinear coordinate. As an alternative of temporal domain and Cartesian coordinate formulation, our formulation provides the constrained trajectory optimization flexibility to describe complex road geometries, traffic regulations, and road obstacles, which are usually spatially varying rather than temporal varying, with assistances vehicle to infrastructure (V2I) communication. Based on the formulation, we first conducted a mathematical proof on the controllability of our system, to show that our system can be controlled in the spatial domain and curvilinear coordinate. Further, a multiobjective model predictive control (MPC) approach is designed to optimize the trajectories in a rolling horizon fashion and satisfy the collision avoidances, traffic regulations, and vehicle kinematics constraints simultaneously. To verify the control efficiency of our method, multiscenario numerical simulations are conducted. Suggested by the results, our proposed method can provide smooth vehicular trajectories, avoid road obstacles, and simultaneously follow traffic regulations in different scenarios. Moreover, our method is robust to the spatial change of road geometries and other potential disturbances by the road curvature, work zone, and speed limit change.
Exploration on the Application Path of College English MOOCS Teaching under the Background of Internet of Things

While the development of information and communication technology has brought great changes to social industrial structure and economic development, it has also reshaped people's cognition and thinking modes to a great extent and has paid attention to the field of education under the guidance of technology. The reform of education is taking place. According to the background of the Internet of things, the teaching quality evaluation index is reconstructed with the help of principal component analysis, support vector machine, and fuzzy algorithms, and the application performance and practical results of the improved teaching path are analyzed. The results show that the evaluation accuracy of the improved hybrid algorithm for teaching quality reaches more than 90%, and its evaluation time is far less than that of the SVM algorithm (10.23 s), and it has a good application effect on the evaluation of teaching quality. The increase of students' satisfaction with the improved teaching quality has reached 5.8%, and their professional skills have improved by 6-25 points. After expert evaluation, it is found that the maximum satisfaction frequency of teaching content and teaching effect can reach 96.7% and 91.3%, respectively. Reconstructing the index system of the English path under the background of the Internet of things can better grasp students' learning needs and integrate multiple methods to provide new research ideas for follow-up English teaching.
Malicious encrypted traffic features extraction model based on unsupervised feature adaptive learning

As network traffic is increasingly valued for privacy protection and the encrypted SSL/TLS (Secure Sockets Layer/Transport Layer Security) traffic is surging, more and more malicious behaviors are hidden in it. Current detection methods are less accurate in detecting new and unknown malicious traffic. Although the method based on the supervised machine learning model has excellent accuracy performance, it has low detection strength and poor scalability for new and unknown malicious traffic. Therefore, this paper proposes a malicious SSL/TLS traffic detection method based on feature adaptive learning. The model can automatically learn key classification information from the unmarked malicious SSL/TLS encrypted traffic, and uses the 5-Tuple-Masking technology to optimize the input data, which greatly enhances the model's adaptation ability to new malicious traffic in complex network environments. After experimental verification, its comprehensive accuracy rate reaches 89.25%. Moreover, the supervised convolutional neural network detection method is used to compare and test the feasibility of this model in the field of malicious SSL/TLS traffic detection.