Reviewerly/exHarmony huggingface dataset:
Luby-Rackoff Backwards with More Users and More Security

It is known, from the work of Dai et al. (in CRYPTO’17), that the PRF advantage of \(\mathsf {XORP}\) (bitwise-xor of two outputs of n-bit random permutations with domain separated inputs), against an adversary making q queries, is about \(q/2^n\) for \(q \le 2^{n- 5}\). The same bound can be easily shown to hold for \(\mathsf {XORP}[k]\) (bitwise-xor of k outputs n-bit pseudorandom random permutations with domain separated inputs), for \(k \ge 3\). In this work, we first consider multi-user security of \(\mathsf {XORP}[3]\). We show that the multi-user PRF advantage of \(\mathsf {XORP}[3]\) is about \(\sqrt{uq_{\max }}/2^n\) for all \(q_{\max } \le 2^{n}/12\), where u is the number of users and \(q_{\max }\) is the maximum number of queries the adversary can make to each user. In the multi-user setup, this implies that \(\mathsf {XORP}[3]\) gives security for \(O(2^n)\) users even allowing almost \(O(2^n)\) queries to each user. This also indicates significant improvement in the single-user setup (i.e., when \(u =1\)), where the distinguishing advantage of the adversary even after making \(O(2^n)\) queries is \(O({1 \over \sqrt{2^n}})\), i.e., negligible. Subsequently, we consider a simple efficient variant of \(\mathsf {XORP}[3]\) in which we use five calls to produce 2n bit output (instead of six calls in the case of \(\mathsf {XORP}[3]\)). This variant also achieves similar level of security. As an immediate application, we can construct a variant of block cipher based counter mode which provides much higher security (both in the single-user and the multi-user setup) compared to the security of the encryption part of GCM at the cost of efficiency.
Multi-scale Object Detection Algorithm in Smart City Based on Mixed Dilated Convolution Pyramid

In the current smart city scene, the positioning, detection and tracking of the objects are essential technologies in urban security. As the most prominent problem in the field of object detection, occlusion and multi-scale seriously affect the recall and accuracy of the algorithm. In response to the above problems, this paper starts from the receptive field and proposes an object detector based on the Mixed Dilated Convolution Pyramid Network (MDCPN). First of all, the dilated convolutional layers of different sizes are introduced into the feature pyramid to construct a Mixed Receptive Field Module (MRFM), which aims to obtain more global feature information by increasing the receptive field under the condition of controlling the amount of parameters, then solve the problem of occlusion of the object; secondly, improve the structure of the feature pyramid, and design a Lower Embedding Feature Pyramid Module (LEFPM), which combines low-level feature detail information and high-level feature semantic information to improve the representation ability of feature maps, and enhance the model scale adaptability; in particular, for the problem of missed detection, the anchor free mechanism of the FCOS algorithm is introduced, which effectively reduces the redundancy of candidate frames and further improves the positioning accuracy. Tested on the public dataset VOC, the algorithm has greatly improved the detection accuracy.
Art Online Design Based on Digital Simulation Technology

In order to improve the humanization and convenience of online art design, this paper applies digital simulation technology to the art online design system and establishes a set of sequential multi-free-form surface design methods. Based on the obtained front free-form surfaces, this paper establishes the relationship between the discrete points on the subsequent free-form surfaces and their spatial solid angles and, through extremely complex theoretical deduction, finally obtains the subsequent free-form surfaces. In addition, by combining the two free-form surfaces to enter the 3D modeling software, we can obtain an optical lens with multiple free-form surfaces to improve the digital simulation effect. Finally, this paper uses the intelligent system constructed in this paper to conduct multiple sets of simulation experiments to evaluate the digital effect and artistic design effect of the system constructed in this paper. From the experimental research, it can be known that the art online design system based on digital simulation technology constructed in this paper basically meets the expected goals of the system constructed in this paper.
Fake Feedback Detection to Enhance Trust in Cloud Using Supervised Machine Learning Techniques

Lots of enterprises have commenced to move toward a cloud-based infrastructure for their services. Organizations of distinct types, sizes, and industry are adopting cloud services for a broad range of use cases, such as software development, product deployment, backup of data, email services, and numerous tasks. Although there are various advantages of using cloud computing as the ability to scale rapidly, store data remotely, reducing the overall cost. Cloud computing has some major trust concerns due to privacy issues such as lack of transparency, lack of user control over user’s data. Thus, lack of trust is a big issue for cloud service providers and cloud infrastructure users. This paper centers on how to achieve a trust management system around cloud services by finding fake feedbacks on CloudArmour’s Trust Feedback Dataset. Post preprocessing and data cleaning, multiple models like Support vector machine, Naive Bayes, Random Forest, and Decision Tree were trained on our dataset to find the most accurate model. An ensemble model has also been proposed which has proved to be best algorithm for identifying fake feedbacks and has shown accuracy, precision & recall value of 93.55%, 92.90%, and 92.82%, respectively.
Towards sensing urban-scale COVID-19 policy compliance in new york city

Big data on the urban scale can enable many applications for improving city life and provide a more holistic understanding of urban life to researchers. While there are approaches to sense and model urban occupant behaviors using sound, radio frequency, and vision, how such behaviors are altered due to city governance and policies in response to emergencies such as a natural disaster or a public health crisis has been less explored. In this paper, we present a computer vision-based approach to capture patterns and interference in the urban life of New York City dwellers from March 2020 to August 2020. Using ~1 million images gathered with cameras mounted on ride-sharing vehicles throughout the city, we approximated the social proximity of pedestrians to understand policy compliance on the street. Our analysis reveals a correlation between policy violation and virus transmission. We believe that such big data-driven city-scale citizen modeling can inform policy design and crisis management schemes for urban scale smart infrastructure.
Topic modeling of maintenance logs for linac failure modes and trends identification

Medical linear accelerators (linacs) can fail in a multitude of different manners due to complex structures. An unclear identification of failure modes occurring constantly is a major obstacle to maintenance arrangements, thereby may increasing downtime. This study aims to use natural language processing techniques to deal with the unformatted maintenance logs to identify the linac failure modes and trends over time.The data used in our study are unformatted narrative maintenance logs recording linac conditions and repair actions. The latent Dirichlet allocation-based topic modeling method was used to identify topics and keywords regarding the failure modes. The temporal analysis method was applied to examine the variation of failure modes over 20 years.Based on the output of the topic modeling, 28 topics and keywords with frequency ranking were generated automatically. The latent failure modes in topics were identified and classified into six main subsystems of linacs. Furthermore, by using the temporal analysis method, the trends of all failure modes over 20 years were illustrated. Half of the topics demonstrated variations with three different patterns, namely periodic, increasing, and decreasing.The results of our study validated the effectiveness of using the topic modeling method to automatically analyze narrative maintenance logs. With domain knowledge, failure modes of linacs can be identified and categorized quantitatively.
Graph Matching Based Privacy-Preserving Scheme in Social Networks

The increasing popularity of social networks has inspired recent research to explore social graphs for data mining. Because social graph data contains sensitive information about users, publishing the graph data directly will cause privacy leakage of users. In this paper, we assume that attackers might re-identify targets with 1-neighborhood graph attacks. To prevent such attacks, we propose a Graph Matching based Privacy-preserving Scheme, named GMPS, to anonymize the social graphs. We utilize Jensen-Shannon Divergence to compute node structure similarity to improve the accuracy of node clustering. And then, utilize the graph modification to achieve k-anonymity and use graph matching to measure the similarity of graphs. The experiment results on HepTh and Facebook show that the proposed approach achieves k-anonymity with low information loss and high data utility.
Application of Bi-directional Extreme Learning Machine in Predicting Stability of Slope of Railway Embankment in Seismic Condition

This study presents the application of machine learning technique in predicting the stability of slope of railway embankment. As we know that most of the railway tracks are built on the natural ground surface and the track system is installed on the prepared formation called Permanent Way, constructed either earthwork in formation or earthwork in cutting or in a combination of both. In contrast, the installation of a railway system requires healthy investment as well. Hence, to run the entire system satisfactorily, a detailed analysis on account of the stability of slopes of Permanent Way is always necessary, especially to prevent damages in seismic conditions. For this purpose, Bi-directional extreme learning machine is used for the prediction of factor of safety in seismic conditions. It is interpreted from the results that the developed model is capable enough to predict the factor of safety. The value of R2 was obtained as 0.9983 in both the training and testing phases. The lower values of RMSE (training = 0.0184 and testing = 0.0181) in both phases justify the generalization capability of the model. The finding of this research concludes that the developed model can be used as a simple computational approach in predicting the stability of slopes particularly in the introductory stages of railway Projects.
Motion-from-Blur: 3D Shape and Motion Estimation of Motion-blurred Objects in Videos

We propose a method for jointly estimating the 3D motion, 3D shape, and appearance of highly motion-blurred objects from a video. To this end, we model the blurred appearance of a fast moving object in a generative fashion by parametrizing its 3D position, rotation, velocity, acceleration, bounces, shape, and texture over the duration of a predefined time window spanning multiple frames. Using differentiable rendering, we are able to estimate all parameters by minimizing the pixel-wise reprojection error to the input video via backpropagating through a rendering pipeline that accounts for motion blur by averaging the graphics output over short time intervals. For that purpose, we also estimate the camera exposure gap time within the same optimization. To account for abrupt motion changes like bounces, we model the motion trajectory as a piece-wise polynomial, and we are able to estimate the specific time of the bounce at sub-frame accuracy. Experiments on established benchmark datasets demonstrate that our method outperforms previous methods for fast moving object deblurring and 3D reconstruction.
Evaluation of User Reactions and Verification of the Authenticity of the User’s Identity during a Long Web Survey

Web surveys are very popular in the Internet space. Web surveys are widely incorporated for gathering customer opinion about Internet services, for sociological and psychological research, and as part of the knowledge testing systems in electronic learning. When conducting web surveys, one of the issues to consider is the respondents’ authenticity throughout the entire survey process. We took 20,000 responses to an online questionnaire as experimental data. The survey took about 45 min on average. We did not take into account the given answers; we only considered the response time to the first question on each page of the survey interface, that is, only the users’ reaction time was taken into account. Data analysis showed that respondents get used to the interface elements and want to finish a long survey as soon as possible, which leads to quicker reactions. Based on the data, we built two neural network models that identify the records in which the respondent’s authenticity was violated or the respondent acted as a random clicker. The amount of data allows us to conclude that the identified dependencies are widely applicable.
Wildlife Small Object Detection based on Enhanced Network in Ecological Surveillance

Visual tele-observation is an effective way for intelligent monitoring and protection of ecology and the natural environment. Different from pedestrian or rigid body detection, wildlife detection in natural scenes face more complex problems, such as the existence of wild background clutter, local or global vegetation occlusion of animals, small object, rotation, deformation, and other interfering factors. In this paper, We mainly propose a novel method for the small object problem. For our small object wild species data set, we use an SSD detector for object detection. Firstly, the K-means algorithm is used to adjust the anchor box of the SSD network. Secondly, for the situation that SSD is not good for small object detection, in conv4_3 layers of SSD, the feature enhancement module is added. Finally, aiming at the size of a small object, the network level of SSD is deleted. We have proved the feasibility of each of the three methods through experiments and combined with the three methods to verify, the recognition rate of the small target has increased by 2.67%.
Efficient Detection of Large-Scale Multimedia Network Information Data Anomalies Based on the Rule-Extracting Matrix Algorithm

With the continuous development of multimedia social networks, online public opinion information is becoming more and more popular. The rule extraction matrix algorithm can effectively improve the probability of information data to be tested. The network information data abnormality detection is realized through the probability calculation, and the prior probability is calculated, to realize the detection of abnormally high network data. Practical results show that the rule-extracting matrix algorithm can effectively control the false positive rate of sample data, the detection accuracy is improved, and it has efficient detection performance.
The Acceptance of Augmented Reality as a Determining Factor in Intralogistics Planning

In the automotive industry, an innovative tool to support the intralogistics planning is essential. One possibility is the use of augmented reality (AR). AR can be a suitable tool for improving the planning of intralogistics processes. An improvement of the intralogistics planning processes can be realized by applying this technology. The application thereby dependents on the acceptance of AR. In this paper, the Technology Acceptance Model and a survey are used to verify the acceptance of AR in intralogistics planning. In addition, relevant factors are identified having an impact on the acceptance using AR in intralogistics.
Degradation-Driven Underwater Image Enhancement

The use of robotic equipment in modern underwater activities is an usual practice. Maintenance, robotic inspection and environment and biological research are some examples of applications and the acquisition of the images and videos is a common procedure. Frequently, these images suffer with light attenuation and turbidity of the water requiring enhancement or restoration. In this work, we present a method for underwater image enhancement based on deep learning and inspired in the Underwater Image Formation Model but color space-contextualized. The strategy misleads a neural network, generating a fake and distorted output image that replaces the real network output in the loss function. The algorithm does not estimate physical parameters, but explores similar representations in the color space. The only information required is the input image and the methodology require no ground-truth and unsupervised learning is adopted. Only real underwater images are used and the results indicate the effectiveness of the method in color preservation, sharpness and contrast improvement.
Age-related differences when searching in a real environment: The use of semantic contextual guidance and incidental object encoding

Visual search is a crucial, everyday activity that declines with ageing. Here, referring to the environmental support account, we hypothesised that semantic contextual associations between the target and the neighbouring objects (e.g., a teacup near a tea bag and a spoon), acting as external cues, may counteract this decline. Moreover, when searching for a target, viewers may encode information about the co-present distractor objects, by simply looking at them. In everyday life, where viewers often search for several targets within the same environment, such distractor objects may often become targets of future searches. Thus, we examined whether incidentally fixating a target during previous trials, when it was a distractor, may also modulate the impact of ageing on search performance. We used everyday object arrays on tables in a real room, where healthy young and older adults had to search sequentially for multiple objects across different trials within the same array. We showed that search was quicker (1) in young than older adults; (2) for targets surrounded by semantically associated objects than unassociated objects, but only in older adults; and (3) for incidentally fixated targets than for targets that were not fixated when they were distractors, with no differences between young and older adults. These results suggest that older viewers use both environmental support based on object semantic associations and object information incidentally encoded to enhance efficiency of real-world search, even in relatively simple environments. This reduces, but does not eliminate, search decline related to ageing.
Path Planning for Unmanned Surface Vehicle (USV) in obstacle-filled environments

For robots with poor mobility such as high-speed unmanned surface vehicles (USVs) or Unmanned aerial vehicle (UAV), planning a reasonable path for Multi-waypoint missions is very essential. This paper will propose a method to create a smooth curve through given waypoints (WPs) under constraints of the vehicle's turning radius, and in complex obstacle-filled environments. This algorithm is combined with the Line-of-Sight (LOS) guidance and PID controller to become a complete GNC system. We also experimented on an USV model to verify the feasibility of this algorithm.
A YOLOv4-based Vehicle Detection Method from UAV Videos

In order to solve the positioning problem and missed detection caused by occlusion or small target when the UAV detects the target vehicle, this paper proposes an end-to-end vehicle detection method based on UAV video. The shallow fine-grained information and high-level semantic information are extracted through the YOLOv4 detection network and feature fusion is performed, the loss function is partially replaced with Mish to optimize the flow of gradient information. The initial anchor box obtained by K-means clustering is used to realize a multi-scale detection strategy. Through testing in different environments, the proposed method has high robustness in terms of different vehicle scale and texture changes caused by UAV camera angle movement or weather changes. The mean Average Precision (mAP) index of the algorithm reaches 90.3%, which has good detection effect and positioning accuracy.
Deep Learning-Based Approach Using Word and Character Embedding for Named Entity Recognition from Hindi-English Tweets

Named Entity Recognition (NER) is a task that identifies named entities from data written in Natural Language. It accepts sentences or paragraphs as input and identifies the relevant nouns like names of people, places, organizations, etc. that appear within the sentences or paragraph or an article. This model also belongs from the field of Information Extraction of Natural Language Processing (NLP). Lots of research works have been carried out on Named Entities Recognition (NER), but most of the works have been done on resource-rich languages and domains. It is very challenging to work with informal tweets which make the process more complex due to unstructured and incomplete data. In this paper, we use deep learning-based approach using bi-directional LSTM (Bi-LSTM) for recognizing named entities from tweets mixed in Hindi and English languages.
Web Service Quality Prediction Method Based on Recurrent Neural Network

For web services, QoS (Quality of Service, quality of service) is an important indicator for judging whether a web service is efficient. How to better predict the QoS value of the service to make appropriate service recommendations is the entire recommendation system and Issues that are being discussed in the service forecasting academia. At the same time, the timeliness and time relevance of QoS values are also affecting the prediction accuracy of Web services. A large amount of QoS data has potentially time-related attributes. This provides a new inspiration and thinking for service forecasting. Add the time characteristics of the data to the learning of the predictive model. Inspired by these factors, this paper proposes a deep neural network combination model that is sensitive to the time characteristics of QoS. At the same time, based on the final experimental results, the model proposed in this paper has obvious effects on the prediction of QoS values with time attributes.
PEDaLS: Persisting Versioned Data Structures

In this paper, we investigate how to automatically persist versioned data structures in distributed settings (e.g. cloud + edge) using append-only storage. By doing so, we facilitate resiliency by enabling program state to survive program activations and termination, and program-level data structures and their version information to be accessed programmatically by multiple clients (for replay, provenance tracking, debugging, and coordination avoidance, and more). These features are useful in distributed, failure-prone contexts such as those for heterogeneous and pervasive Internet of Things (IoT) deployments. We prototype our approach within an open-source, distributed operating system for IoT. Our results show that it is possible to achieve algorithmic complexities similar to those of in-memory versioning but in a distributed setting.
Comparison of Outlier Detection Methods on Astronomical Image Data

Among the many challenges posed by the huge data volumes produced by the new generation of astronomical instruments there is also the search for rare and peculiar objects. Unsupervised outlier detection algorithms may provide a viable solution. In this work we compare the performances of six methods: the Local Outlier Factor, Isolation Forest, k-means clustering, a measure of novelty, and both a normal and a convolutional autoencoder. These methods were applied to data extracted from SDSS stripe 82. After discussing the sensitivity of each method to its own set of hyperparameters, we combine the results from each method to rank the objects and produce a final list of outliers.