Reviewerly/exHarmony huggingface dataset:
On the Effect of User Faults on her Perception of Agent’s Faults in Collaborative Settings

In various human-agent collaborative settings the agent is fault-prone. In many of these settings, it is possible that the human user will also account to failure, hindering task execution. In this paper we study the effect of the latter type of failures over the user’s satisfaction with the agent in the collaborative setting. We report the results of two experiments, differing in the number of agent’s faults and the way faults influence task progress and attract players’ focus, with 264 subjects recruited and interacted through Amazon Mechanical Turk. We find that when the user accounts for some faults during the collaborative execution of the task, she becomes more forgiving to the agent faults, and consequently more satisfied with the collaboration, compared to the case where she makes no faults. The importance of this finding becomes most apparent in the design of collaborative agents.
Optimization of Corner Detection Algorithm for Video Stream Based on FAST

For the FAST algorithm commonly used in corner detection of video stream, there are problems of image edge missing and corner redundancy. This paper proposes a constant filling method to fill the target image. Then judge the pixels points of 90 degrees Bresenham circle, reduce by a quarter the number of discrete contrast points for Bresenham circles in FAST algorithm. Finally, SAD non-maximum suppression is used to obtain corner points in video stream. The experimental results show that under the premise of detecting the same image and ensuring that the real corners of the original image are not lost, compared with the FAST algorithm, the number of fictitious corners detected by the proposed algorithm is reduced by 94.08% on average, and the detection time of corners is shortened by 23.33 %, and the missing detection of image edges is effectively avoided.
Dueling-DDQN Based Virtual Machine Placement Algorithm for Cloud Computing Systems

Virtual machine placement (VMP) in large-scale cloud computing clusters is a challenging problem with practical importance. Deep Q-learning (DQN) based algorithm is a promising means to solve difficult VMP problems with complex optimization goals and dynamically changing environments. However, native DQN algorithms suffer from shortcomings such as Q value overestimation, difficulty in convergence, and failure to maximize long-term reward. To overcome these shortcomings, this paper proposes an advanced VMP algorithm based on Dueling-DDQN. Moreover, specific optimization techniques are introduced to enhance the exploration strategy and the capability of achieving long-term reward. Experiment results show that the proposed algorithm outperforms native DQN in terms of convergence speed, Q-value estimation accuracy and stability. Meanwhile, the proposed algorithm can achieve multiple optimization goals such as reducing power consumption, ensuring resource load balance and Improving user service Quality.
Flexible and Efficient Blockchain-Based Cloud Storage

Immutability is a vital feature of blockchain. The introduction of blockchain into cloud storage provides a trusted method for data verification. Data are stored on the cloud, while its hash is recorded in blockchain transaction which is accessible to everyone. However, it also brings much inconvenience to data modification. For any change of data, the hash would become different. The data owner needs to update the whole data that is stored on the cloud and create another new blockchain transaction for the new data hash, which causes lots of extra costs. It is impractical when there is a need for frequent data modification. Therefore, we put forward a new scheme that makes blockchain-based cloud storage more flexible and efficient. Our approach designs the Merkle Tree based on pairing-based Chameleon hash and describes in detail how to integrate it into blockchain to realize data storage, modification and verification in a reliable and less-cost way. Moreover, we encapsulate the related operations into a virtual machine (VM) to improve the performance of the system. We also provide a real implementation of the scheme. The experimental results and comparison show that the proposed scheme is practical and efficient.
A Fault-Tolerant Navigation Method for Multirotor UAVs Based on Federal Adaptive Kalman Filter

With the rapid development of multi-sensor data fusion technology, the integrated navigation system is becoming more and more complex, so the faulty of a single component will affect the whole navigation system. To improve the fault tolerance performance of integrated navigation system, a fault-tolerant integrated navigation algorithm combining Federal Kalman Filter (FKF) and Adaptive Kalman Filter (AKF) is proposed in this paper. Introducing adaptive factors into the structure of FKF can reduce the influence of sensor failures and modeling errors. The real flight data from multirotor unmanned aerial vehicles (UAVs) is used for simulation. And the result of simulation shows that the proposed federal adaptive fault-tolerant integrated navigation algorithm based on FKF and AKF can increase the filtering accuracy and reliability significantly.
Creating a Web Corpus Using GO

The Web contains large amounts of textual data which could be used as a source to create new corpora, yet there are not many plug and play solutions for scraping specific parts of the websites. This paper presents a new open-source solution for downloading and parsing HTML websites which can be configured from one configuration file. As a demonstration of this method, a new ad hoc corpus was built. The corpus contains a total of 2,395,735 titles and articles collected from 14 most popular Croatian websites.
Lightweight and irreversible speech pseudonymization based on data-driven optimization of cascaded voice modification modules

In this paper, we propose a speech pseudonymization framework that utilizes cascaded and superposition-based voice modification modules. With increasing opportunities to use spoken dialogue systems nowadays, research regarding protecting the privacy of speaker information encapsulated in speech data is attracting attention. Pseudonymization, which is one method for voice privacy protection, aims to keep the intelligibility of speech while simultaneously suppressing speaker-specific information. One motivation of our framework is to achieve a reliable pseudonymization performance with light computation. To do this, we utilize the advantages of both machine learning-based and signal processing-based approaches. The advantages are (1) using signal processing-based methods parameterized with few hyperparameters and (2) using machine learning-based optimization to optimize all hyperparameters on the basis of black-box systems consisting of automatic speaker verification and automatic speech recognition. Our method of cascading signal processing modules, which are jointly optimized in a data-driven manner, can pseudonymize speech in a lightweight manner. Additionally, we discuss irreversible pseudonymization approaches and propose a superposition approach, yet another pseudonymization method that is more irreversible than the cascade method in terms of estimating the adequate parameters to recover the original signal. From the experimental results conducted under the VoicePrivacy 2020 protocols, we can demonstrate that (1) our cascade method succeeds in deteriorating the speaker recognition rate by over 24% while simultaneously improving the speech recognition rate by approximately 8% compared with a signal processing-based baseline system of VoicePrivacy 2020 and that (2) our superposition method works comparable to our cascade method in terms of pseudonymization performance.
Dynamic Content Generation for Augmented Technical Support

In the hardware technical support domain, scaling technician skills remains a prevalent problem. Given the large portfolio of hardware products service providers need to maintain, it is not possible for every technician to be expert at repairing every product. Augmented reality addresses this problem through virtual procedures, which are interactive 3D visual representations of text-based knowledge articles that describe how to perform step-by-step repair actions. Virtual procedures, thus, equip the technicians with the skills they need to support a wide range of hardware products. In this paper, we present a novel and scalable approach to dynamically construct virtual procedures, and we demonstrate the feasibility of our approach through a real-life implementation.
Design of a Skin Cancer Diagnosing Web Application Based on Convolutional Neural Network Model and Chatterbot Application Programming Interface

Abstract Skin cancer has become a great concern for people's wellness. With the popularization of machine learning, a considerable amount of data about skin cancer has been created. However, applications on the market featuring skin cancer diagnosis have barely utilized the data. In this paper, we have designed a web application to diagnose skin cancer with the CNN model and Chatterbot API. First, the application allows the user to upload an image of the user's skin. Next, a CNN model is trained with a huge amount of pre-taken images to make predictions about whether the skin is affected by skin cancer, and if the answer is yes, which kind of skin cancer the uploaded image can be classified. Last, a chatbot using the Chatterbot API is trained with hundreds of answers and questions asked and answered on the internet to interact with and give feedback to the user based on the information provided by the CNN model. The application has achieved significant performance in making classifications and has acquired the ability to interact with users. The CNN model has reached an accuracy of 0.95 in making classifications, and the chatbot can answer more than 100 questions about skin cancer. We have also done a great job on connecting the backend based on the CNN model as well as the Chatterbot API and the frontend based on the VUE Javascript framework.
Performance Analysis of Driver Abnormal Behavior Classification System Using Enhanced Multi-Layer Perceptron

Traffic accident is one of the contributing factors of the death that happened around the world. From various types of drivers that are usually found in the traffic, motorcycle drivers have a relatively higher risk compared with other types of drivers. Therefore, there is a need for a system that is capable to detect dangerous behavior from the driver and gives an alert when abnormal behavior is detected. To develop it, we choose the 2 basic sensors to detect the movement from a driver, which are Accelerometer and Gyroscope. These sensors have been integrated into a smartphone. For the classification process, we proposed an Enhanced Multi-Layer Perceptron (MLP). The complexity of the model is reduced to ensure that our proposed system will be able to work in real-time conditions and in a limited-resources environment. In our model, we use the combination of ReLU and Softmax function, two of the famous Activation function, to enhance the performance of our model. The level of accuracy of our model achieved 97.5% with an average computational time of 45 ms. This proved that our model works better than the previous research with the same dataset.
Neurotechnology and VR. Compatibility and risks

Abstract Over the past century, technology has tightly entered our daily lives, penetrating into all spheres of society. Developing, they are increasingly changing our usual world and lifestyle. One of these technologies is virtual reality systems and neurotechnologies, in connection with which this article is devoted to them. In particular, this work provides information about neurotechnologies and virtual reality. The associated risks arising from the use of these technologies are considered. The principle of interaction between neurogarnituresand virtual reality is given.
Removing Snow From a Single Image Using a Residual Frequency Module and Perceptual RaLSGAN

Removing snow particles from an image is a complicated task due to the shape, size, and color of the particles. The latest snow removal methods remove snow from a single image but still retain some snow and salt-and-pepper particles. Some approaches, while trying to remove snow from a single image, produce blurry artifacts. In this paper, we solve these problems by designing a network model that consists of a residual generative network, a snow-free image generative network, and a perceptual relativistic discriminative network. In both generative networks, we assign the residual frequency network (ReFNet) as our bottleneck module. Our network model learns to map two relationships. First, the input snowy image is trained to map the snow mask image in the dataset. Then, a retained image resulting from subtraction between an input image and the estimated residual image is concatenated with the input snowy image and mapped to the desired snow-free ground truth. Moreover, we use a perceptual identical-paired adversarial network based on a relativistic discriminative network to make our training results more robust. Our results achieve greater performance than state-of-the-art methods on both synthetic and real-world snowy images.
A Deep Learning-Based Method for Image Tampering Detection

This paper proposes a method to detect the fake objects in images by combining You Only Look Once (YOLO) to define the objects and image post-processing techniques to authenticate the forgery manipulations on objects. YOLO is developed in 2016 and improved in different versions. For the object detection in images, authors used the first version of YOLO. In the proposed method, YOLO is firstly used to identify objects on the image and these objects are removed from the background to reduce computational complexity. The boundaries of objects will be then detected, and their sharpness distribution are calculated as the traces for determining the object tampering. The objects with higher sharpness at the boundaries will be compared the objects in the same group. If there is an object’s features similarity, there will be a copy-move object; otherwise, a spliced object. These steps are integrated into a neural network model trained by both spliced and copy-move image datasets. The combination of YOLO and the proposed neural network model has solved the problem of detecting fake objects with average accuracy of 95.3% for copy-move images and 93.8% for spliced images. The proposed method can be efficient not only for copy-move images and spliced images, but also for the mixed images.
TooLate

The design of an access control technique for devices not connected to the Internet can present some very difficult challenges. Among others, how to properly handle user revocation stands out. In this paper, we present TooLate, a cryptosystem that allows an authority to grant access to a user over a set of encrypted files stored on an offline device. The authority has no connection with the device but is able to centrally manage access control to the remote datastore, providing users with decryption keys whose validity is limited in time and only to specific resources. The system uses a lightweight cryptographic technique suitable for non-tamper-proof, low-power devices. An active security module periodically re-encrypts the entire datastore using keys dynamically generated from a hash chain. Key rotation ensures that, even by tampering with the device, attackers cannot gain access to the data without a valid key, as the re-encryption occurs and they arrive too late.
Toward AI-Based Scenario Management for Cyber Range Training

There is an immediate need for a greater number of highly skilled cybersecurity personnel to meet intensified cyber attacks. We propose a cyber range exercise management architecture that employs machine reasoning to structure the design, execution and analysis of cyber range training scenarios. The scenarios are then used in simulation-based training in an emulated IT infrastructure environment. The machine reasoning is obtained by combining four AI methods: attack-defence trees, formal argumentation theory, answer set programming and multiagent systems. We argue that this type of advanced functionality that supports exercise managers in their design and analysis of scenarios is strictly necessary to improve current exercise management systems and build the required cybersecurity expertise.
The Realization and Optimization Technology of Recognition Algorithm Based on Tensorflow Deep Learning Mechanism

Abstract With the rapid development of today’s technological society, recognition algorithms have received more and more attention. In addition, in recent years, deep learning algorithms have developed rapidly at the theoretical level, and related new technologies have also been applied to various industries. TensorFlow is a deep learning framework that performs well in all aspects. The purpose of this article is to study the realization of recognition algorithms based on TensorFlow’s deep learning mechanism and their optimization techniques. The target detection algorithm used in the system in this paper combines deep learning technology to replace the traditional method based on convolutional filtering. The paper is based on the TensorFlow deep learning framework. TensorFlow is an open source software library for machine intelligence. The learning software library of the network learning framework. This article uses a semi-automatic labeling method combined with an incremental learning algorithm to label the data set. After labeling the data, the parameters are set, the model is trained, and the model is finally trained and applied to the detection system. Studies have shown that: in the recognition algorithm, only the single sub-analysis stream is considered, and the short video sequence analysis stream can get the most excellent accuracy. Compared with the second best long video sequence analysis stream, it can also increase by about 3%.
Techniques for Speeding up H-Core Protein Fitting

Restoration of the 3D structure of a protein from the sequence of its amino acids (“folding”) is one of the most important and challenging problems in computational biology. The most accurate methods require enormous computational resources due to the large number of variables determining a protein’s shape. Coarse-grained models combining several protein atoms into one unified globule partially mitigate this issue. The paper studies one of these models where globules are located in the nodes of the two-dimensional triangular lattice. In this model, folding is reduced to the discrete optimization problem: find positions of protein’s globules to maximize the number of contacts between them. We consider a standard procedure that finds an exact solution to this problem. It first generates an H-core—a set of positions for hydrophobic globules, which is followed by mapping of protein’s hydrophobic globules to these positions by the constraint satisfaction techniques. We propose a way to avoid unnecessary enumeration by skipping infeasible H-cores prior to mapping. Another contribution of our paper is a procedure that automatically generates constraints to simplify finding the feasible mapping of proteins globules to the lattice nodes. Experiments show that the proposed techniques tremendously accelerate the problem’s solving process.
Enhancing Effectiveness of Teaching Evaluation using Blockchain and Regulated Token Economy

To improve the effectiveness of teaching evaluation is an important objective at all teaching institutions. As a direct teaching assessment method, the core task of the evaluation is to elicit genuine and conscientious ratings and comments from students. However, the existing non-trivial teaching evaluation practices cannot assure students’ true anonymities as well as fall short at motivating students to perform high quality evaluations. Therefore, efforts on improving the effectiveness of teaching evaluation hit a bottleneck. In this paper we give and discuss our insights on how to use the blockchain technology and a regulated token economy to address these two issues in order to remove the bottleneck. We also offer our visions on the implementation details of the approach based on our insights.
Communication-Efficient and Personalized Federated Lottery Ticket Learning

The lottery ticket hypothesis (LTH) claims that a deep neural network (i.e., ground network) contains a number of subnetworks (i.e., winning tickets), each of which exhibiting identically accurate inference capability as that of the ground network. Federated learning (FL) has recently been applied in LotteryFL to discover such winning tickets in a distributed way, showing higher accuracy multi-task learning than Vanilla FL. Nonetheless, LotteryFL relies on unicast transmission on the downlink, and ignores mitigating stragglers, questioning scalability. Motivated by this, in this article we propose a personalized and communication-efficient federated lottery ticket learning algorithm, coined CELL, which exploits downlink broadcast for communication efficiency. Furthermore, it utilizes a novel user grouping method, thereby alternating between FL and lottery learning to mitigate stragglers. Numerical simulations validate that CELL achieves up to 3.6% higher personalized task classification accuracy with 4.3x smaller total communication cost until convergence under the CIFAR-10 dataset.
The modified weighted gauss-fitting algorithm for structured-illumination microscopy

Structured-illumination microscopy (SIM) is a novel instrument for measuring surface topography. After images captured by SIM, optical-sectioned images can be acquired by sectioned by section algorithm. Finally, the peak point can be obtained by gauss fitting algorithm based on sectioned images. However, classical gauss-fitting is an inaccurate method since it is sensitive to noise. In this paper, the modified weighted-gauss fitting algorithm (MWGFA) is applied to peak extract. Through simulation and wafer measurement experiment, MWGFA is demonstrated to be an effective method to fitting the SIM z-axis response curve obviously. Compared with CM, MWGFA behaves better in accuracy. Thus it is suitable for SIM well enough and improve the measurement accuracy in mirror and wafer with steps.
An Intelligent Evaluation Method of Information Course Teaching Effect Based on Image Analysis

Due to its own limitations, the traditional teaching quality evaluation method has been unable to adapt to the development of information-based curriculum teaching. Therefore, the establishment of a scientific and intelligent teaching effect evaluation method will help to improve the teaching quality of college teachers. To solve the above problems, a student fatigue state evaluation method based on the quantum particle swarm optimization artificial neural network is proposed. Firstly, face detection is realized by adding three Haar-like feature blocks and improving the AdaBoost algorithm of a weak classifier connection. Secondly, in order to effectively improve the image imbalance, the MSR algorithm is used to enhance the face data image, which is effectively suitable for network training. Then, by readjusting the connection mode, the DenseNet is improved to fully reflect the local detail feature information of the low level. Finally, quantum particle swarm optimization (QPSO) is used to optimize the DenseNet structure, which makes the optimization of network structure more automatic and solves the uncertainty of manual selection. The experimental results show that the proposed method has a good detection effect and prove the effectiveness and correctness of the proposed method.