Reviewerly/exHarmony huggingface dataset:
Using Segmentation With Multi-Scale Selective Kernel for Visual Object Tracking

Generic visual object tracking is challenging due to various difficulties, e.g. scale variations and deformations. To solve those problems, we propose a novel multi-scale selective kernel module for tracking, which contains small-scale and large-scale branches to model the target at different scales and attention mechanism to capture the more effective appearance information of the target. In our module, we cascade multiple small-scale convolutional blocks as an equivalent large-scale branch to extract large-scale features of the target effectively. Besides, we present a hybrid strategy for feature selection to extract significant information from features of different scales. Based on the current excellent segmentation tracking framework, we propose a novel tracking network that leverages our module at multiple places in the up-sample phase to construct a more accurate and robust appearance model. Extensive experimental results show that our tracker outperforms other state-of-the-art trackers on multiple challenging benchmarks including VOT2018, TrackingNet, DAVIS-2017, and YouTube-VOS-2018 while achieves real-time tracking.
Discovering Periodic-Frequent Patterns in Uncertain Temporal Databases

Periodic-frequent pattern mining aims to discover all periodically occurring frequent patterns in a temporal database. Most previous studies focused on finding these patterns by disregarding the items' uncertainty nature in the data. This paper proposes a novel model of periodic-frequent patterns that exist in an uncertain temporal database. We introduce a new tree-structure and an algorithm to find all desired patterns in the database effectively. We have also presented two tighter upper bound measures to reduce the computational cost effectively. Experimental results on various databases demonstrate that our algorithm is memory and runtime efficient.
Determinants of Trust in Higher Education

The article addresses the factors that influence students’ trust in the teachers, faculty administration and university administration. Establishing trusting relationships at the university leads to a number of positive effects: for example, it facilitates cooperation between members of educational organization, making them more united, open to communication and motivated; it is a prerequisite for academic excellence, high-quality higher education and loyalty. This is the reason of the researchers’ interest to this topic. However, the factors influencing students’ trust in teachers and university management have yet to be defined. This paper examines the individual, socio-economic and institutional characteristics of student trust. The emphasis is placed on subjective factors – the perception and assessment by young people of what happens with them in the learning process. It is shown that students’ trust in the teachers and university management is positively correlated with their justified expectations (satisfaction), regarding higher education, and negatively correlated with the course of study. The influence of gender is significant when students trust in the administration of the faculty, while the influence of family income is significant when they trust in the administration of the university. Moreover, the last two factors are regionally specific: their effects are significant only for the trust of students studying at Moscow and St. Petersburg universities.
MultAV: Multiplicative Adversarial Videos

The majority of adversarial machine learning research focuses on additive attacks, which add adversarial perturbation to input data. On the other hand, unlike image recognition problems, only a handful of attack approaches have been explored in the video domain. In this paper, we propose a novel attack method against video recognition models, Multiplicative Adversarial Videos (MultAV), which imposes perturbation on video data by multiplication. MultAV has different noise distributions to the additive counterparts and thus challenges the defense methods tailored to resisting additive adversarial attacks. Moreover, it can be generalized to not only $\ell_{p}$-norm attacks with a new adversary constraint called ratio bound, but also different types of physically realizable attacks. Experimental results show that the model adversarially trained against additive attack is less robust to MultAV.
Approaches for Non-Functional Requirement Modeling: A Literature Survey

The term ‘non-functional requirement’ plays an important role for the success and failure of any software project. It have been studied since over 4 decades, however there are many consensus in software engineering community due to that many of research questions are significantly important to researchers. Non-Functional Requirements (NFRs) such as security, accuracy, safety and performance are crucial for the system in order to deliver good and reliable product and to achieve customer satisfaction. These aspects should consider developing and maintaining a system successfully. This paper surveys the existing techniques and tools in terms of their working, applicability while considering the context of the project. The main focus of this study is to identify and analyze the approaches available in literature to elicit, model and integrate non-functional requirements (NFRs). We performed a detailed analysis of existing approaches on the basis of our significant research questions and derive the suitability of these approaches in specific contexts and projects.
A Large-Scale <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>k</mi> </math>-Nearest Neighbor Classification Algorithm Based on Neighbor Relationship Preservation

Owing to the absence of hypotheses of the underlying distributions of the data and the strong generation ability, the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>k</mi> </math> -nearest neighbor (kNN) classification algorithm is widely used to face recognition, text classification, emotional analysis, and other fields. However, kNN needs to compute the similarity between the unlabeled instance and all the training instances during the prediction process; it is difficult to deal with large-scale data. To overcome this difficulty, an increasing number of acceleration algorithms based on data partition are proposed. However, they lack theoretical analysis about the effect of data partition on classification performance. This paper has made a theoretical analysis of the effect using empirical risk minimization and proposed a large-scale <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>k</mi> </math> -nearest neighbor classification algorithm based on neighbor relationship preservation. The process of searching the nearest neighbors is converted to a constrained optimization problem. Then, it gives the estimation of the difference on the objective function value under the optimal solution with data partition and without data partition. According to the obtained estimation, minimizing the similarity of the instances in the different divided subsets can largely reduce the effect of data partition. The minibatch <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mi>k</mi> </math> -means clustering algorithm is chosen to perform data partition for its effectiveness and efficiency. Finally, the nearest neighbors of the test instance are continuously searched from the set generated by successively merging the candidate subsets until they do not change anymore, where the candidate subsets are selected based on the similarity between the test instance and cluster centers. Experiment results on public datasets show that the proposed algorithm can largely keep the same nearest neighbors and no significant difference in classification accuracy as the original kNN classification algorithm and better results than two state-of-the-art algorithms.
Fast datalog evaluation for batch and stream graph processing

Abstract Implementing complex algorithms for big data, artificial intelligence, and graph processing requires enormous effort. Succinct, declarative programs to solve complex problems that can be efficiently executed for batching and streaming data are in demand. This paper presents Nexus, a distributed Datalog evaluation system. It evaluates Datalog programs using the semi-naive algorithm for batch and streaming data using incremental and asynchronous iteration. Furthermore, we evaluate Datalog programs with aggregates to determine the advantages of implementing the semi-naive algorithm using incremental iteration on its performance. Our experimental results show that Nexus significantly outperforms acyclic dataflow-based systems.
Learning Phone Recognition From Unpaired Audio and Phone Sequences Based on Generative Adversarial Network

ASRhas been shown to achieve great performance recently. However, most of them rely on massive paired data, which is not feasible for low-resource languages worldwide. This paper investigates how to learn directly from unpaired phone sequences and speech utterances. We design a two-stage iterative framework. GAN training is adopted in the first stage to find the mapping relationship between unpaired speech and phone sequence. In the second stage, another HMM model is introduced to train from the generator’s output, which boosts the performance and provides a better segmentation for the next iteration. In the experiment, we first investigate different choices of model designs. Thenwe compare the framework to different types of baselines: (i) supervised methods (ii) acoustic unit discovery based methods (iii) methods learning from unpaired data. Our framework performs consistently better than all acoustic unit discovery methods and previous methods learning from unpaired data based on the TIMIT dataset.
HENet: Forcing a Network to Think More for Font Recognition

Although lots of progress were made in Text Recognition /OCR in recent years, the task of font recognition is remaining challenging. The main challenge lies in the subtle difference between these similar fonts, which is hard to distinguish. This paper proposes a novel font recognizer with a pluggable module solving the font recognition task. The pluggable module hides the most discriminative accessible features and forces the network to consider other complicated features to solve the hard examples of similar fonts, called HE Block. Compared with the available public font recognition systems, our proposed method does not require any interactions at the inference stage. Extensive experiments demonstrate that HENet achieves encouraging performance, including on character-level dataset Explor all and word-level dataset AdobeVFR.
VSTAR: Visual Semantic Thumbnails and tAgs Revitalization

Nowadays, video-sharing portals’ popularity has entailed massive growth in data uploads over the Internet. For several applications (e.g., browsing, retrieval, or recommendation of videos), dealing with vast data volumes has become a critical issue. In a video-sharing scenario, the devising of tools and infrastructures able to completely satisfy users’ interests and requests is becoming increasingly crucial to influence their online experiences. On the one hand, annotating a video with meaningful human-friendly words (i.e., tags) plays an essential role in matching users’ interests. On the other hand, providing a condensed and straightforward preview of the video content (i.e., thumbnails) is crucial to capture the user’s attention immediately. In this context, we propose VSTAR (Visual Semantic Thumbnails and tAgs Revitalization), a novel approach in video optimization aimed at generating both suitable tags and thumbnails from a different perspective than classical approaches. The novelty lies in: (i) exploiting image captioning to extract visual and semantic information for generating both tags and thumbnails; (ii) identifying semantically related popular search queries (i.e., trends) to be suggested as new tags; (iii) giving the final user the control on a trade-off between quality and quantity of the generated items (tags and thumbnails); (iv) creating a proper dataset and making it publicly available. Experiments demonstrate the viability of our proposal.
Anomaly Rule Detection in Sequence Data

Analyzing sequence data usually leads to the discovery of interesting patterns and then anomaly detection. In recent years, numerous frameworks and methods have been proposed to discover interesting patterns in sequence data as well as detect anomalous behavior. However, existing algorithms mainly focus on frequency-driven analytics, and they are challenging to be applied in real-world settings. In this work, we present a new anomaly detection framework called DUOS that enables Discovery of Utility-aware Outlier Sequential rules from a set of sequences. In this pattern-based anomaly detection algorithm, we incorporate both the anomalousness and utility of a group, and then introduce the concept of utility-aware outlier sequential rule (UOSR). We show that this is a more meaningful way for detecting anomalies. Besides, we propose some efficient pruning strategies w.r.t. upper bounds for mining UOSR, as well as the outlier detection. An extensive experimental study conducted on several real-world datasets shows that the proposed DUOS algorithm has a better effectiveness and efficiency. Finally, DUOS outperforms the baseline algorithm and has a suitable scalability.
Multimodal feature fusion based on object relation for video captioning

Abstract Video captioning aims at automatically generating a natural language caption to describe the content of a video. However, most of the existing methods in the video captioning task ignore the relationship between objects in the video and the correlation between multimodal features, and they also ignore the effect of caption length on the task. This study proposes a novel video captioning framework (ORMF) based on the object relation graph and multimodal feature fusion. ORMF uses the similarity and Spatio‐temporal relationship of objects in video to construct object relation features graph and introduce graph convolution network (GCN) to encode the object relation. At the same time, ORMF also constructs a multimodal features fusion network to learn the relationship between different modal features. The multimodal feature fusion network is used to fuse the features of different modals. Furthermore, the proposed model calculates the length loss of the caption, making the caption get richer information. The experimental results on two public datasets (Microsoft video captioning corpus [MSVD] and Microsoft research‐video to text [MSR‐VTT]) demonstrate the effectiveness of our method.
iELMNet: Integrating Novel Improved Extreme Learning Machine and Convolutional Neural Network Model for Traffic Sign Detection

Traffic sign detection (TSD) in real-time environment holds great importance for applications such as automated-driven vehicles. Large variety of traffic signs, different appearances, and spatial representations causes a huge intraclass variation. In this article, an extreme learning machine (ELM), convolutional neural network (CNN), and scale transformation (ST)-based model, called improved extreme learning machine network, are proposed to detect traffic signs in real-time environment. The proposed model has a custom DenseNet-based novel CNN architecture, improved version of region proposal networks called accurate anchor prediction model (A2PM), ST, and ELM module. CNN architecture makes use of handcrafted features such as scale-invariant feature transform and Gabor to improvise the edges of traffic signs. The A2PM minimizes the redundancy among extracted features to make the model efficient and ST enables the model to detect traffic signs of different sizes. ELM module enhances the efficiency by reshaping the features. The proposed model is tested on three publicly available data sets, challenging unreal and real environments for traffic sign recognition, Tsinghua-Tencent 100K, and German traffic sign detection benchmark and achieves average precisions of 93.31%, 95.22%, and 99.45%, respectively. These results prove that the proposed model is more efficient than state-of-the-art sign detection techniques.
A New Approach for Secure Cloud-Based Electronic Health Record and its Experimental Testbed

The tremendous development of the Internet of Things (IoT) technology in the last decades has fostered advancement in automatic medical assistive devices to support the existing Electronic Health Record (EHR) system.As an integral part of the EHR electronic model, public cloud servers store medical data.Unfortunately, public cloud servers are prone to security and privacy breach.This paper introduces a novel non-cryptographic approach to preserve electrocardiograph (ECG) data confidentiality and integrity in the EHR environment.The main objective of the proposed anonymization algorithm is to obscure the patient's cardiac information during transmission and to protect information stored in the cloud database.Although we focus on ECG data, generalization to other types of clinical data can be derived using the proposed method.Performance evaluation of the proposed scheme showed that the algorithm conceals both fiducial and non-fiducial features of the data.Therefore, confidentiality feature is preserved.This paper examined confidentiality of the anonymized data using the Percentage Residual Difference (PRD) and investigated the integrity of the reconstructed data in terms of cross-correlation.Security analysis carried out using the PRD, brute force attack, and performance comparison between the proposed algorithm and existing methods.Evaluation showed that the proposed scheme offers a secure non-cryptographic model for transmission and storing clinical data in the cloud.Moreover, in terms of processing time, the proposed algorithm is ten times faster than the existing wavelet packet method when processing long ECG data, 65,536 sample points.In a real-time experimental testbed, the implemented proposed system was successful.
Time Series Forecasting of Cloud Resource Usage

In recent cloud computing research, the allocation of virtual resources has become a major concern. The Virtual Machine (VM) dynamic deployment technique is one of the key virtual resource management techniques. Virtual resource management predicts the resources on the virtual machine and manages accordingly. The study of the Virtual Machine resource dynamic deployment focuses on the fine-grained resource adjustment strategy. So, there is a need for predicting the usage of resources for the virtual machine to avoid delays, overprovisioning and under provisioning. Time series forecasting models are used because predictions of cloud resources are dependent on time. In this paper, different time series models and ensemble techniques have been fitted to predict resource usage for successive hours. Different time series analysis techniques were implemented to study the dataset and to choose the appropriate model. Various types of resources such as CPU usage, disk read times, disk write times and memory usage are predicted using the above models.
Secure Federated Learning Model Verification: A Client-side Backdoor Triggered Watermarking Scheme

Federated learning (FL) has become an emerging distributed framework to build deep learning models with collaborative efforts from multiple participants. Consequently, copyright protection of FL deep model is urgently required because too many participants have access to the joint-trained model. Recently, Secure FL framework is developed to address data leakage issue when central node is not fully trustable. This encryption process has made existing DL model watermarking schemes impossible to embed watermark at the central node. In this paper, we propose a novel client-side Federated Learning watermarking method to tackle the model verification issue under the Secure FL framework. In specific, we design a backdoor-based watermarking scheme to allow model owners to embed their pre-designed noise patterns into the FL deep model. Thus, our method provides reliable copyright protection while ensuring the data privacy because the central node has no access to the encrypted gradient information. The experimental results have demonstrated the efficiency of our method in terms of both FL model performance and watermarking robustness.
GomokuNet: A Novel UNet-style Network for Gomoku Zero Learning via Exploiting Positional Information and Multiscale Features

Since the tremendous success of the AlphaGo family (AlphaGo, AlphaGo Zero, and Alpha Zero), zero learning has become the baseline method for many board games, such as Gomoku and Chess. Recent works on zero learning have demonstrated that improving the performance of neural networks used in zero learning programs remains nontrivial and challenging. Considering both positional information and multiscale features, this paper presents a novel positional attention-based UNet-style model (GomokuNet) for Gomoku AI. An encoder-decoder architecture is adopted as the backbone network to guarantee the fusion of multiscale features. Positional information modules are incorporated into our model in order to further capture the location information of the board. Quantitative results obtained by ablation analysis indicate that our GomokuNet outperforms previous state-of-the-art zero learning networks on the RenjuNet dataset. Our method shows the potential to improve zero learning efficiency and AI engine performance.
Transfer learning for regression via latent variable represented conditional distribution alignment

Since labelled data are expensive to generate both computationally and experimentally, how to establish data-driven models with limited data has become an important challenge in various engineering and scientific applications. Transfer learning has been used to improve the performance of many tasks by leveraging rich labelled data from related domains. This paper focuses on the transfer learning problem for regression under the situation of conditional distribution shift, which is a common scenario in manufacturing industry and has not been fully studied. Hence, we first propose a Latent variable represented Conditional Distribution Alignment method (LCDA), which exploits the low-dimensional latent representation to describe the global conditional distribution discrepancy. Then the properties of the latent variables are optimised by matching the kernel embedding conditional distribution between domains, so that the global distribution discrepancy can be aligned with the residual function generated by the latent variables. By combining domain priors with machine learning, the proposed method provides a potential direction to reduce labelling consumption for the intelligent manufacturing. Series of experiments on two industrial applications are conducted to validate the effectiveness and analyse the characteristics of the proposed method.
Semi-supervised multi-label classification using an extended graph-based manifold regularization

Graph-based algorithms are known to be effective approaches to semi-supervised learning. However, there has been relatively little work on extending these algorithms to the multi-label classification case. We derive an extension of the Manifold Regularization algorithm to multi-label classification, which is significantly simpler than the general Vector Manifold Regularization approach. We then augment our algorithm with a weighting strategy to allow differential influence on a model between instances having ground-truth vs. induced labels. Experiments on four benchmark multi-label data sets show that the resulting algorithm performs better overall compared to the existing semi-supervised multi-label classification algorithms at various levels of label sparsity. Comparisons with state-of-the-art supervised multi-label approaches (which of course are fully labeled) also show that our algorithm outperforms all of them even with a substantial number of unlabeled examples.