Reviewerly/exHarmony huggingface dataset:
Uncertainty modelling and computational aspects of data association

Abstract A novel solution to the smoothing problem for multi-object dynamical systems is proposed and evaluated. The systems of interest contain an unknown and varying number of dynamical objects that are partially observed under noisy and corrupted observations. In order to account for the lack of information about the different aspects of this type of complex system, an alternative representation of uncertainty based on possibility theory is considered. It is shown how analogues of usual concepts such as Markov chains and hidden Markov models (HMMs) can be introduced in this context. In particular, the considered statistical model for multiple dynamical objects can be formulated as a hierarchical model consisting of conditionally independent HMMs. This structure is leveraged to propose an efficient method in the context of Markov chain Monte Carlo (MCMC) by relying on an approximate solution to the corresponding filtering problem, in a similar fashion to particle MCMC. This approach is shown to outperform existing algorithms in a range of scenarios.
Convolutional Neural Networks Demystified: A Matched Filtering Perspective-Based Tutorial

Deep neural networks (DNNs) and especially convolutional neural networks (CNNs) have revolutionized the way we approach the analysis of large quantities of data. However, the largely ad hoc fashion of their development, albeit one reason for their rapid success, has also brought to light the intrinsic limitations of CNNs—in particular, those related to their black box nature. In addition, the ability to “explain” both the way such systems behave and the results they produce is increasingly becoming an imperative in many practical applications. Therefore, it would be particularly useful to establish physically meaningful mechanisms underpinning the operation of CNNs, thus helping to resolve the issue of interpretability of the processing steps and explain their input-output relationship. To this end, we revisit the operation of CNNs from first principles and show that their very backbone—the convolution operation—represents a matched filter which examines the input for the presence of characteristic patterns in data. Our treatment is based on temporal signals, naturally generated by physical sensors, which admit rigorous analysis through systems science. This serves as a vehicle for a unifying account on the overall functionality of CNNs, whereby both the convolution-activation-pooling chain and learning strategies are shown to admit a compact and elegant interpretation under the umbrella of matched filtering. In addition to helping reveal the physical principles underpinning CNNs and providing an intuitive understanding of their operation, the treatment of CNNs from a matched filtering perspective is also shown to offer a platform to support further developments in this area.
The Fermi–Dirac distribution provides a calibrated probabilistic output for binary classifiers

Binary classification is one of the central problems in machine-learning research and, as such, investigations of its general statistical properties are of interest. We studied the ranking statistics of items in binary classification problems and observed that there is a formal and surprising relationship between the probability of a sample belonging to one of the two classes and the Fermi-Dirac distribution determining the probability that a fermion occupies a given single-particle quantum state in a physical system of noninteracting fermions. Using this equivalence, it is possible to compute a calibrated probabilistic output for binary classifiers. We show that the area under the receiver operating characteristics curve (AUC) in a classification problem is related to the temperature of an equivalent physical system. In a similar manner, the optimal decision threshold between the two classes is associated with the chemical potential of an equivalent physical system. Using our framework, we also derive a closed-form expression to calculate the variance for the AUC of a classifier. Finally, we introduce FiDEL (Fermi-Dirac-based ensemble learning), an ensemble learning algorithm that uses the calibrated nature of the classifier's output probability to combine possibly very different classifiers.
Fine-Grained Multi-Class Object Counting

Many animal species in the wild are at the risk of extinction. To deal with this situation, ecologists have monitored the population changes of endangered species. However, the current wildlife monitoring method is extremely laborious as the animals are counted manually. Automated counting of animals by species can facilitate this work and further renew the ways for ecological studies. However, to the best of our knowledge, few works and publicly available datasets have been proposed on multi-class object counting which is applicable to counting several animal species. In this paper, we propose a fine-grained multi-class object counting dataset, named KR-GRUIDAE, which contains endangered red-crowned crane and white-naped crane in the family Gruidae. We also propose a specialized network for multi-class object counting and line segment density maps, and show their effectiveness by comparing results of existing crowd counting methods on the KR-GRUIDAE dataset.
Two-Stage Seamless Text Erasing On Real-World Scene Images

Erasing text from images is a common image-editing task in film industry and shared media. Existing text-erasing models either tend to produce artifacts or fail to remove all the text in real-world images. In this paper, we follow a two-stage text erasing framework that first masks the text by segmentation, and then inpaints the masked region to create a text-erased image. Our proposed text mask generator is designed to accurately cover text, which combined with inpainting, can produce reliable text-erased results. In the inpainting model, we propose a Multiscale Gradient Reconstruction Loss to generate sharp realistic-looking images. Our model achieves state-of-the-art results on both synthetic and real world data in both quantitative and qualitative measures.
User Privacy Protection Scheme Based on Verifiable Outsourcing Attribute-Based Encryption

Given the rapid development of cloud computing and cloud storage technology, a growing number of enterprises and individuals use cloud storage services to save data or back up data. In the cloud storage services, although attribute-based encryption can protect users’ data security, the computational cost of key generation, data encryption, and data decryption linearly increases with the complexity of access strategies, which becomes more critical for resource-constrained users. Therefore, this paper proposes a verifiable attribute-based encryption scheme for a fully outsourced ciphertext policy. The scheme can simultaneously realize the functions of key generation, data encryption, and data decryption outsourcing and verify the correctness of the outsourcing calculation results. This scheme can effectively reduce the computational burden of a cloud storage system. The security and verifiability of the scheme are indicated and proved with the random oracle model. The experimental results show that the scheme has the advantages of function and efficiency compared with other schemes. The research results have theoretical and practical significance.
Analysis of Skim Reading on Desktop versus Mobile Screen

Web readers usually skim through the text to keep up with the amount of available content. The effectiveness of skim reading is ensured by keeping the focus on the meaningful part of the text rather than the less meaningful part. To assure if the skim reading shows efficient results for a particular screen resolution, this research presents variations in the memory of significant data when a text is read on a mobile screen or a desktop screen. Moreover, the study aims to understand the knowledge gained from the text at a given time. In total, sixty participants contributed to the study and it is found that, relative to reading the text on a mobile screen, skimming showed improved memory for the ideas defined in the text on a computer screen. A software prototype is developed in this research study to analyze the impact of skim reading on a desktop computer screen versus a mobile device screen. The findings of the study have been interpreted as evidence in support of a skimming process called satisficing.
The physical origin of a photon-number parity effect in cavity quantum electrodynamics

The rapidly increasing capability to modulate the physicochemical properties of atomic groups and molecules by means of their coupling to radiation, as well as the revolutionary potential of quantum computing for materials simulation and prediction, fuel the interest for non-classical phenomena produced by atom-radiation interaction in confined space. One of such phenomena is a “parity effect” that arises in the dynamics of an atom coupled to two degenerate cavity field modes by two-photon processes and manifests itself as a strong dependence of the field dynamics on the parity of the initial number of photons. Here we identify the physical origin of this effect in the quantum correlations that produce entanglement among the system components, explaining why the system evolution depends critically on the parity of the total number of photons. Understanding the physical underpinnings of the effect also allows us to characterize it within the framework of quantum information theory and to generalize it. Since a single photon addition/removal has dramatic effects on the system behavior, this effect may be usefully applied, also for amplification purposes, to optoelectronics and quantum information processing.
An Investigate on Moving Object Tracking and Detection in Images

Object detection and tracking one of the most important areas of research due to routine change in motion of object and variation in scene size, occlusions, appearance variations, and ego-motion and illumination changes. Specifically, feature selection is the vital role in object tracking. Nowadays, moving objects tracking in real time is a new paradigm that has a great impact on video surveillance and monitoring to detect and trace objects, and due to the constant change in the motion of the object and changing the size of the scene, obstruction, changes in appearance and changes in movement and brightness; it is an important area of research and a very important task in the field of security automation monitoring systems automatically. On the other hand, object tracking in a film is a matter of estimating positions and other relevant information about the movement of objects in the film's visual sequences. The first step in such systems is to identify a moving object in the film. The second step is to track the detected object. The purpose of this paper is to analyze and review the previous approach towards object tracking, object recognition and object detection in Images.
Even Censors Have a Backup

The Great Firewall of China (GFW) has long censored HTTPS (via the Server Name Indication field, or SNI). Its mechanism for doing so has been studied, with various evasion strategies discovered in recent years. In this paper, we have evidence that suggests the GFW has deployed a second HTTPS censorship middlebox that runs in parallel to the first. We present a detailed analysis of this secondary censorship middlebox---how it operates, the content it blocks, and how it interacts with the primary middlebox---and present evidence that this has been in operation since at least September 2019. We also present several packet-based evasion strategies for the secondary middlebox and demonstrate that the primary censorship middlebox can be defeated independently from the secondary. Our code is publicly available.
The Research on Rejoining of the Oracle Bone Rubbings Based on Curve Matching

The rejoining of oracle bone rubbings is a fundamental topic for oracle research. However, it is a tough task to reassemble severely broken oracle bone rubbings because of detail loss in manual labeling, the great time consumption of rejoining, and the low accuracy of results. To overcome the challenges, we introduce a novel CFDA&amp;CAP algorithm that consists of the Curve Fitting Degree Analysis (CFDA) algorithm and the Correlation Analysis of Pearson (CAP) algorithm. First, the orthogonalization system is constructed to extract local features based on the curve features analysis. Second, the global feature descriptor is depicted by using coordinate points sequences. Third, we screen candidate curves based on the features as well as the CFDA algorithm, so the search range of the candidates is narrowed down. Finally, image recommendation libraries for target curves are generated by adopting the CAP algorithm, and the rank for each target matching curve generates simultaneously for result evaluation. With experiments, the proposed method shows a good effect in rejoining oracle bone rubbings automatically: (1) it improves the average accuracy rate of curve matching up to 84%, and (2) for a low-resource task, the accuracy of our method has 25% higher accuracy than that of other methods.
Weakly Supervised Temporal Anomaly Segmentation with Dynamic Time Warping

Most recent studies on detecting and localizing temporal anomalies have mainly employed deep neural networks to learn the normal patterns of temporal data in an unsupervised manner. Unlike them, the goal of our work is to fully utilize instance-level (or weak) anomaly labels, which only indicate whether any anomalous events occurred or not in each instance of temporal data. In this paper, we present WETAS, a novel framework that effectively identifies anomalous temporal segments (i.e., consecutive time points) in an input instance. WETAS learns discriminative features from the instance-level labels so that it infers the sequential order of normal and anomalous segments within each instance, which can be used as a rough segmentation mask. Based on the dynamic time warping (DTW) alignment between the input instance and its segmentation mask, WETAS obtains the result of temporal segmentation, and simultaneously, it further enhances itself by using the mask as additional supervision. Our experiments show that WETAS considerably outperforms other baselines in terms of the localization of temporal anomalies, and also it provides more informative results than point-level detection methods.
Prototyping Practices in Software Startups: Initial Case Study Results

Software startups use prototyping to develop and test business ideas and to validate market viability. While prototyping is emphasized in agile methods, there is little research on how startups can best utilise scarce resources to effectively use prototypes in their dynamic business context. We performed a case study of four startups and investigated how startups currently use prototyping to elicit, validate and communicate requirements through semi-structured interviews. Our initial results indicate that prototyping is a commonly applied practice that is implicitly required to obtain funding for early stage startups, that software engineering competence is required to produce interactive and fully functioning prototypes, and that it is a challenge to balance prototype scope against expected gains.
Environment-Aware Dense Video Captioning for IoT-Enabled Edge Cameras

In recent years, the Artificial Intelligence of Things (AIoT) has led to the rapid development of edge computing, and existing video-captioning systems can be deployed directly on AIoT-enabled cameras (hereafter referred to as edge cameras), which have increasingly powerful computing resources. Therefore, we propose a lightweight dense-video-captioning model based on the Transformer framework to improve execution efficiency for video-caption generation on edge cameras. In addition, to investigate the effect of concept drift on video captioning, we also propose an environment-aware adaptation to allow the system to respond to changes in the environment in order to produce more accurate video captions. Experimental results on the lightweight dense-video-captioning model show that the bilingual evaluation understudy (BLEU) metrics can increase by up to 23.5%, the computation time decreases by 46.4%, and the edge camera operates at a rate of 27.63 FPS, which is also faster than existing approaches by 4.7%. In addition, the mean average precision of the environment-aware adaptation is up to 11.3% higher than the existing approaches. In conclusion, the proposed approaches perform better than previous ones and are more flexible in different weather scenarios.
Learning 3D medical image keypoint descriptors with the triplet loss

We propose to learn a 3D keypoint descriptor which we use to match keypoints extracted from full-body CT scans. Our methods are inspired by 2D keypoint descriptor learning, which was shown to outperform hand-crafted descriptors. Adapting these to 3D images is challenging because of the lack of labelled training data and high memory requirements.We generate semi-synthetic training data. For that, we first estimate the distribution of local affine inter-subject transformations using labelled anatomical landmarks on a small subset of the database. We then sample a large number of transformations and warp unlabelled CT scans, for which we can subsequently establish reliable keypoint correspondences using guided matching. These correspondences serve as training data for our descriptor, which we represent by a CNN and train using the triplet loss with online triplet mining.We carry out experiments on a synthetic data reliability benchmark and a registration task involving 20 CT volumes with anatomical landmarks used for evaluation purposes. Our learned descriptor outperforms the 3D-SURF descriptor on both benchmarks while having a similar runtime.We propose a new method to generate semi-synthetic data and a new learned 3D keypoint descriptor. Experiments show improvement compared to a hand-crafted descriptor. This is promising as literature has shown that jointly learning a detector and a descriptor gives further performance boost.
Investigating Lexical Effects in Syntax with Regularized Regression (Lasso)

Within usage-based theory, notably in construction grammar though also elsewhere, the role of the lexicon and of lexically-specific patterns in morphosyntax is well recognized. The methodology, however, is not always sufficiently suited to get at the details, as lexical effects are difficult to study under what are currently the standard methods for investigating grammar empirically. In this short article, we propose a method from machine learning: regularized regression (Lasso) with k-fold cross-validation, and compare its performance with a Distinctive Collexeme Analysis.
MPANET: Multi-Scale Pyramid Aggregation Network For Stereo Matching

At present, the performance of the end-to-end stereo matching networks based on CNN greatly exceed the traditional stereo matching networks, but the accuracy in those ill-posed regions like foreground areas is still not optimistic. In this paper, we propose a novel design to improve the prediction performance of disparity in foreground. First, a multi-scale pyramid aggregation module with hourglass-like structure is designed to effectively utilize the aggregation information of different scales. In addition, we propose MPANet, a novel end-to-end stereo matching network, which significantly alleviates the disparity mismatch problem in foreground areas. Experimental results on Scene Flow and KITTI datasets also demonstrate that our network has competitive performance among existing first-class methods.
Progressive Knowledge Distillation For Early Action Recognition

We present a novel framework to train a recurrent neural network for early recognition of human actions, which is an important but challenging task given the need to recognize an on-going action based on partial observation. Our framework is based on knowledge distillation, where the network for early recognition is viewed as a student model. The student is trained using knowledge distilled from a more knowledgeable teacher model that can peek into the future and incorporate extra observations about the action in consideration. This framework can be used in both supervised and semi-supervised learning settings, being able to utilize both the labeled and unlabeled training data. Experiments on the UCF101, SYSU 3DHOI, and NTU RGB-D datasets show the effectiveness of knowledge distillation for early recognition, including when we only have a small amount of annotated training data.
Intelligent container reallocation at Microsoft 365

The use of containers in microservices has gained popularity as it facilitates agile development, resource governance, and software maintenance. Container reallocation aims to achieve workload balance via reallocating containers over physical machines. It affects the overall performance of microservice-based systems. However, container scheduling and reallocation remain an open issue due to their complexity in real-world scenarios. In this paper, we propose a novel Multi-Phase Local Search (MPLS) algorithm to optimize container reallocation. The experimental results show that our optimization algorithm outperforms state-of-the-art methods. In practice, it has been successfully applied to Microsoft 365 system to mitigate hotspot machines and balance workloads across the entire system.
Opposing Data Exploitation: Behaviour Biometrics for Privacy-Preserving Authentication in IoT Environments

Multimodal data harvested by the Internet of Things sensors has recently been utilised for behavioural biometrics and consequently user authentication. While strengthening the security, these data nevertheless present a privacy threat to users whose behaviour can now be modelled in detail, thus allowing the authenticating authority to not only know who is present, but also what the present person is doing. In this work we reconsider IoT sensor-based authentication and provide a solution mitigating the privacy risk associated with unnecessary information leaks. Our approach harnesses adversarial learning and identifies such a projection of the data that maintains identity separability, yet obfuscates activity separability, thus ensuring that the authenticating authority can successfully identify the user, but not her actions within the sensed environment. We evaluate our approach on a real-world dataset of three activities performed by fifteen users and show that the activity obfuscation is achieved without compromising identification capabilities.
Multi-label classification with local pairwise and high-order label correlations using graph partitioning

In multi-label learning problems, the class labels are correlated and the label correlations can be leveraged to improve the predictive performance of a classifier. Methods that consider high-order correlations in the label space, usually do not utilize pairwise correlations. In most of these methods, label correlations are considered as prior knowledge, which can be misleading in problems with noisy or missing labels. In such cases, learning the label correlation as part of the model training task is more effective. In this paper, a rule-based evolutionary multi-label classification method is proposed that incorporates the local label correlations through the high-order label subsets and pairwise dependencies. Graph structures are employed to model the label dependencies and the estimated label similarities are used to obtain more accurate label sets for the classification rules. To refine the high-order label relations, a novel hierarchical density-based clustering method is proposed to obtain a k-way partitioning for the label graphs based on their pairwise correlations. The effectiveness of the proposed method is experimented on multiple benchmark datasets from different domains and compared with several well-known multi-label classification algorithms. The proposed method has shown the highest average rank along multiple metrics and the results are consistently better than or similar to the compared methods with statistical significance.