Reviewerly/exHarmony huggingface dataset:
Multi-task attribute-fusion model for fine-grained image recognition

Attribute information in fine-grained image recognition often provides more accurate and rich information related to categories. How to effectively combine such knowledge to guide image classification tasks has been one of the research hotspots in computer vision in recent years. We believe that using the association relationship between attributes to fuse attribute information can obtain a more accurate representation of the image. In this paper, we propose a novel Multi-Task Attribute Fusion Model (MTAF) which makes two major improvements to the traditional multi-task learning framework: 1) Attribute-Aware Feature Discrimination: combine the spatial attention and the channel attention mechanism to enhance the feature map of the CNN, so that attribute can be associated to important positions and important channels of the image; 2) Transformer-Based Feature Fusion: introduce the Transformer model to better learn the logical association between attributes, so that the reconstructed features are able to achieve a best classification performance. We have verified our algorithm on two datasets, one is the own-collected medical dataset for thyroid benign and malignant identification, and the other is an open dataset widely used for fine-grained image recognition. Experimental results on both datasets demonstrate that the proposed method can achieve higher classification accuracy than baselines.
Predicting Human Activity from Mobile Sensor Data Using CNN Architecture

Having a model for predicting motion related activities of humans have tremendous applications. Quite often a simple smartphone is enough for monitoring the liveliness of a person. This can be achieved by using Activity Recognition (AR). Smartphones are employed in a wider manner and it becomes one amongst the ways to spot the human's environmental changes by using the sensors in smart mobile. A variety of sensors are embedded in a smartphone - for instance gyroscope, accelerometer. The contraption is demonstrated to look at the state of a person. Here, a framework Human Activity Recognition (HAR) collects the raw data from sensors and movements of humans observed using a deep learning approach. Deep learning models are proposed to spot motions of humans with plausible high accuracy by using sensed data. The performance of a framework is analyzed using Convolutional Neural Network. The act of the model is analyzed in terms of exactness and efficiency. The designed activity recognition model is manipulated to detect the activities of elderly humans at home and it can detect the activities of persons in a crowded area when the area is authorized.
Sociological assessment of student activity potential as a subject of the educational process

Abstract The current stage of education modernization involves increasing student activity as a subject of the educational process, which should help to increase its independence and responsibility for the result. The research problem is the insufficient level of motivation and involvement in modern educational forms and technologies offered by the university and used in the learning process, which is noted today among young students. The research method is a monitoring sociological survey of Peter the Great St. Petersburg Polytechnic University students. The opinion poll is aimed at assessing students’ satisfaction with their education and at determining the level of their activity in the choice of academic disciplines, the implementation of educational forms and technologies, and the practical and creative development of the professional field competencies. The results of sociological surveys reflect the unused reserve of student activity, creativity potential along with the positive dynamics of independence, which is combined with a stable level of satisfaction with the training. This allows us to raise the question of increasing flexibility in the management of educational space, the wider inclusion of creative technologies and self-organizing processes, and taking advantage of a practice-oriented approach.
GeoClone: Online Task Replication and Scheduling for Geo-Distributed Analytics under Uncertainties

The execution and completion of analytics jobs can be significantly inflated by the slowest tasks contained. Despite task replication is well-adopted to reduce such straggler latency, existing replication strategies are unsuitable for geo-distributed analytics environments that are highly dynamic, uncertain, and heterogeneous. In this paper, we firstly model the task replication and scheduling problem over time, capturing the geo-analytics features. Afterwards, we design an online algorithm, GeoClone, to select tasks to replicate and select sites to execute the task replicas in an irrevocably online manner, through jointly considering the execution progress of each job and the resource performance in each site. We rigorously prove the competitive ratio to exhibit the theoretical performance guarantee of GeoClone, compared against the offline optimal algorithm which knows all the inputs at once beforehand. Finally, we implement GeoClone with Spark and Yarn for experiments and also conduct extensive large-scale simulations, which confirms GeoClone's practical superiority over multiple state-of-the-art replication strategies.
A 4D trajectory follower based on the ’Carrot chasing’ algorithm for UAS within the U-space context

This paper is focused on the trajectory following problem for unmanned aerial systems in the context of the U-space and the 4D trajectory based operations (4D-TBO). A trajectory follower implementation for UASs, based on the carrot chasing algorithm, is presented with the twofold objective of minimizing the mean normal distance to the user-defined path and the mean difference with respect to the defined arrival times through the whole flight. The paper presents simulated and real flights carried out under the ROS (Robotic Operating System) framework that allow us to compare and analyze the behavior of the proposed solution for different policies.
Bringing Machine Learning Predictive Models Based on Machine Learning Closer to Non-technical Users

Today, data science has positioned as an area of interest for decision makers in many organizations. Advances in Machine Learning (ML) allow training predictive models based on the analysis of datasets in multiple domains such as: business, medicine, marketing, among others. These models are able to learn and predict future behaviors which helps in the decision-making process. However, many of the ML tools such as Python, Matlab, R Suite, and even their libraries, require that every action must be performed as a sequence of commands by means of scripts. These software packages require extensive technical knowledge of statistics, artificial intelligence, algorithms and computer programming that generally only computer engineers are skilled at. In this research we propose the development of a process complemented with the assistance of a set of user graphic interfaces (GUIs) to help non-sophisticated users to train and test ML models without writing scripts. A tool compatible with Python and Matlab was developed with a set of GUIs adapted to professionals of the business area that generally require to apply ML models in their jobs, but they do not have time to learn programming.
Investigation of the effect of impact direction on Schmidt rebound values by multivariate regression and neuro-fuzzy model

Schmidt hammer test is a nondestructive test used in rock characterization and estimation of mechanical properties of rocks. It is an important advantage that the experiment can be carried out for different impact directions. However, a normalization should be performed on the test results because of variation of effect of gravitational forces on rebound value. On the other hand, some weak, highly porous and weathered rocks have different energy absorbance levels that may not confirm the assumption of normalization formula of the suggested method. So, in this study it is aimed to examine the prediction performance of suggested normalization formulation for weak and highly porous rocks. For this purpose, Schmidt hammer tests for five different impact directions were performed on eight different rock types. The test results indicated that, in general, the rebound values obtained for an impact direction other than horizontal can be converted to the rebound value of horizontal impact direction with a high accuracy by using suggested normalization formula and/or chart. However, existence of some exception was also determined for weak and highly porous rock types. Multivariate regression analyses and adaptive-neuro-fuzzy inference system (ANFIS) were used to explain the variation of ratio between the normalized and tested rebound values for rock types used in this study. The results obtained from statistical and soft computing methods showed that the ratio between the normalized and tested rebound values is predictable and higher for weak and highly porous rock types.
Serendipity adjustable application recommendation via joint disentangled recurrent variational auto-encoder

In this study, we propose an advanced mobile application recommendation method that can systematically adjust the level of serendipity. Previous studies focused on generating recommendations that are relevant to users’ interests. However, this leads to a lack of serendipity and novelty for the user and is therefore not sufficient to achieve high levels of user satisfaction. Our model is trained to predict the next application to be recommended based on a disentangled representation of the sequence in which applications were previously used. We introduce variety into our model by varying the attributes of the disentangled representation. The results of our experiments indicate that the proposed method can systematically and effectively adjust the level of serendipity by varying the values and dimensions of the attribute variables in the disentangled representation. Additionally, our method produces recommendations that are superior to those of other benchmark methods in terms of serendipity and relevance.
Ship Detection in SAR Images Based on Region Growing and Multi-scale Saliency

Synthetic aperture radar (SAR) is one of the most widely used remote sensing monitoring methods for large-scale marine activities. Due to the influence of speckle noise, sea clutter and complex environment, ship detection of SAR images is still a challenging task. Based on the multi-layer selective cognition characteristics of the human visual system, we propose a ship target detection algorithm based on region growing and multi-scale saliency. First, the layered rough-fine land-sea segmentation is used to remove the effect of land scattering. Second, the non-subsampled Laplacian pyramid (NSLP) filter is applied to decompose the image at different scales. Then, the saliency region of the transformed coefficients is extracted by spectral residual (SR). And the constant false alarm rate (CFAR) algorithm is used to further filter the false alarm and extract target more accurately. Finally, saliency sub-images of different scales are fused to get the final detection results. Experimental results show that the algorithm not only effectively suppresses the influence of land and sea clutter, but also can improve the detection rate.
Determining the informativeness of comments: a natural language study of F1000Research open peer review reports

Purpose Social comments are rich in information and useful in evaluating, ranking or retrieving different kinds of materials. However, their merits in representing or providing added values to scientific articles have not yet been studied. Therefore, the present study investigates the informativeness of open review reports as a kind of social comments in a scholarly setting. Design/methodology/approach A test collection was built consisting of 100 randomly selected queries, 1,962 reviewed documents and their reviewers' open reports from F1000Research. They were analyzed using natural language techniques. The comments' salient words were compared to the documents' and also to the Medical Subject Headings (MeSH) salient words. The receiver operating characteristic (ROC) curve was used to test the accuracy of the comments in representing their related articles. Findings The papers' contents and comments have a considerable number of salient words in common. The comments' salient words are also largely found in the MeSH, signifying their consistency with the knowledge tree and their potential to add some complementary features to their related items. The ROC curves confirm the accuracy of the comments in retrieving their related papers. Originality/value This research is the first to reveal the merits of open review reports on scientific papers, in terms of their relatedness to their mother articles, in specific, and to the knowledge tree, in general. They are found informative in not only representing the reviewed papers but also in adding values to the contents of the papers.
On Senders’s Models of Visual Sampling Behavior

We review the sampling models described in John Senders's doctoral thesis on "visual sampling processes" via a ready and accessible exposition.John Senders left a significant imprint on human factors/ergonomics (HF/E). Here, we focus on one preeminent aspect of his career, namely visual attention.We present, clarify, and expand the models in his thesis through computer simulation and associated visual illustrations.One of the key findings of Senders's work on visual sampling concerns the linear relationship between signal bandwidth and visual sampling rate. The models that are used to describe this relationship are the periodic sampling model (PSM), the random constrained sampling model (RCM), and the conditional sampling model (CSM). A recent replication study that used results from modern eye-tracking equipment showed that Senders's original findings are manifestly replicable.Senders's insights and findings withstand the test of time and his models continue to be both relevant and useful to the present and promise continued impact in the future.The present paper is directed to stimulate a broad spectrum of researchers and practitioners in HF/E and beyond to use these important and insightful models.
Select the model who knows the image best: a multi-model method

In the current popular visual tasks, one single model is usually used to output the final results. But no model is perfect, in this paper, we propose a simple and general multi-model method. To combine the advantages of multiple models, we design a familiarity prediction network to output the model's familiarity of images, then select the optimal model based on the familiarity value. Since the loss value is a single value, the familiarity value of any task can be reflected in the loss value, so the output of the familiarity prediction network can be regarded as an estimate of the loss value. The accuracy of the multi-model exceeds any single model that composes the multi-model. By limiting the number of feature layers input into the familiarity network, the sacrifice of computation and detection speed is acceptable. Our method is general and task-agnostic, it not only performs well on classification tasks but also on object detection tasks and other vision tasks.
Automated work efficiency analysis for smart manufacturing using human pose tracking and temporal action localization

In this paper, we aim to develop an automatic system to monitor and evaluate worker's efficiency for smart manufacturing based on human pose tracking and temporal action localization. First, we explore the generative adversarial networks (GANs) to achieve significantly improved estimation of human body joints. Second, we formulate the automated worker efficiency analysis into a temporal action localization problem in which the action video performed by the worker is matched against a reference video performed by a teacher. We extract invariant spatio-temporal features from the human body pose sequences and perform cross-video matching using dynamic time warping. Our proposed human pose estimation method achieves state-of-the-art performance on the benchmark dataset. Our automated work efficiency analysis is able to achieve action localization with an average IoU (intersection over union) score large than 0.9. This represents one of the first systems to provide automated worker efficiency evaluation.
Mimicry Voice Detection using Convolutional Neural Networks

The primary purpose of the automatic speaker verification (ASV) system is for user authentication basing on speech samples. There are four spoofing attacks on the ASV system. They are Replay attack, Speech synthesis, Voice conversion, and impersonation attacks. Impersonation means mimicry attack. There are different databases available to detect and developa countermeasure forspoofing attacks. The available databases are concentrated on three spoofing attacks only. For impersonation attacks, there is no standard database available. The present work concentrated on collecting some samples for detecting mimicry attacks. For this purpose, two celebrity speakers are selected, and the direct speech samples from the speakers are collected as genuine samples. And also, the same speakers imitated samples are collected from the professional imitators, and these samples are taken as spoofed samples. The phantom highlights of the discourse tests are created from Mel Frequency Cepstral Coefficients (MFCC) include extraction procedure. For detecting the corresponding sample as a genuine or spoof sample, a classifier Convolutional Neural Network (CNN) is used. The evaluation of the system is carried out by taking Equal Error Rate (EER), and Receiver Operating Characteristics (ROC). With the collected genuine and spoof samples for mimicry spoofing attack, the EER for development data is obtained as 0.207, and for test data it is 0.3585.
Lattice Klepto Revisited

Kleptography introduced by Young and Yung is about using an embedded backdoor to perform attacks on a cryptosystems. At SAC'17, Kwantet al. proposed a kleptographic backdoor on NTRU encryption scheme and thought that the backdoor can not be detected. However, in this paper we show that the user can detect the backdoor very efficiently and hence the problem of constructing a kleptographic backdoor on NTRU stays open. Moreover, we also design a universal method to embed a kleptographic backdoor for RLWE-based scheme, such as NewHope. Our construction is shown to be strongly undetectable, which reveals the threats of the kleptographic attacks on lattice-based schemes.
Traffic Classification Based on Graph Convolutional Network

Traffic classification is the first step of network QoS control mechanism and traffic anomaly detection and is an important research branch of congestion control and network security. In complex network analysis, the traffic classification scheme based on machine learning often requires a large number of correctly labeled samples, which requires a lot of manual operations. Therefore, it is still a tough thing for traffic classification with higher accuracy at low identification rates. In this work, two network representation based on graph convolution network (GCN) are tried to avoid a large number of labeled samples. The research method is convenient to combine the representation learning based on the graph structure network with the network traffic problems, and obtain a certain classification accuracy of network traffic with relatively few marking samples. The preliminary experimental results show that the proposed method achieves good performance under small network scale, and the classification accuracy rate reaches 97.35%.
Handwritten Mathematical Expression Recognition: A Survey

While the handwritten character recognition has reached a point of maturity, the recognition of handwritten mathematics is still a challenging problem. The problem usually consists of three major parts: strokes segmentation, single symbol recognition and structural analysis. In this paper, we present a review on handwritten mathematical expression recognition to show how the recognition technique is developed. In particular, we put emphasis on the differences between systems.
A Proof-of-Majority Consensus Protocol for Blockchain-enabled Collaboration Infrastructure of 5G Network Slice Brokers

With network slicing technology, 5G changes the classical mobile network business model from a network-operator-oriented business to a more open system with multiple stakeholders. In this context, a network slice broker plays the role of agency between network resource providers and vertical service providers, to lease and provide on-demand network slice services. In practice, a single network slice broker normally owns limited types of network slices. And a collaboration infrastructure could be built up to enable network slice sharing among multiple network slice brokers, so as to improve user experience, and enhance the resource utilization rate. However, a major challenge with such an infrastructure is how to support fair and secure slice trading process among multiple un-trusted network slice providers. Blockchain technology, on the other side, brings in high opportunities to implement a fair and secure trading system without a trusted third-party. In view of this observation, this paper proposes a design of blockchain-enabled collaboration infrastructure for network slice brokers, where a group of registered brokers can trade their network slices in a fair and secure manner. To enhance the accountability of slice trading records, transactions are verified and sorted by each broker node, which are further packed into blocks and appended to a shared ledger maintained by each member in the collaboration network. More specifically, we design a Proof-of-Majority (PoM) consensus protocol to sort slice trading transactions before packing transaction blocks of the shared ledger. Furthermore, an optimization for the basic PoM protocol is discussed, with dependency resolving strategy to improve system performance. Experiments are conducted to verify the feasibility of our proposal.
Multiscale WS-SSIM for panoramic video quality assessment

Objective quality assessment plays a vital role in the evaluation and optimization of panoramic video. However, most of the current methods only consider the structural distortion caused by the projection format, and do not consider the effect of clarity on quality evaluation. For this reason, we propose a new objective video quality assessment method for panoramic video. First, the source image and the distorted image are down-sampled to obtain five sets of images with different scales. Second, calculate WS-SSIM at different scales. Finally, according to the degree of influence of different scales on the subjective evaluation, different coefficients are assigned to the corresponding WS-SSIM, and the overall score is calculated. Experiments on the database established in our laboratory have proved its effectiveness through comparison.