Reviewerly/exHarmony huggingface dataset:
A General Theory of IR Evaluation Measures

Interval scales are assumed by several basic descriptive statistics, such as mean and variance, and by many statistical significance tests which are daily used in IR to compare systems. Unfortunately, so far, there has not been any systematic and formal study to discover the actual scale properties of IR measures. Therefore, in this paper, we develop a theory of <i>Information Retrieval (IR)</i> evaluation measures, based on the representational theory of measurements, to determine whether and when IR measures are interval scales. We found that common set-based retrieval measures—namely Precision, Recall, and F-measure—always are interval scales in the case of binary relevance while this happens also in the case of multi-graded relevance only when the relevance degrees themselves are on a ratio scale and we define a specific partial order among systems. In the case of rank-based retrieval measures—namely AP, gRBP, DCG, and ERR—only gRPB is an interval scale when we choose a specific value of the parameter <inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula> and define a specific total order among systems while all the other IR measures are not interval scales. Besides the formal framework itself and the proof of the scale properties of several commonly used IR measures, the paper also defines some brand new set-based and rank-based IR evaluation measures which ensure to be interval scales.
A Comprehensive Study of the Effect of Spatial Resolution and Color of Digital Images on Vehicle Classification

Vehicle-type classification is considered a core module for many intelligent transportation applications, such as speed monitoring, smart parking systems, and traffic analysis. In this paper, many vision-based classification techniques were presented relying only on a digital camera without the need for any extra hardware components. Dimension and color are two important characteristics of any digital image that affect the cost of the digital camera used in the image acquisition. In this paper, we present a comprehensive study of the effect of these two characteristics on the vehicle classification process in terms of accuracy and performance. We apply a set of different state-of-the-art image classifiers to the BIT-Vehicle and LabelMe data sets. Each data set is downscaled into different scales to generate a variety of spatial resolutions of each data set. Besides, we examine the effect of color by converting each color version to a gray-scale one. At last, we draw a valid conclusion in regards to the impact of these two characteristics (i.e., dimension and color) on the classification accuracy and performance of the image classification methods using more than 46 000 individual experiments. Experimental results show that there is no significant influence of both color and spatial resolutions of the vehicle images on the classification results obtained by most state-of-the-art image classification methods. However, there is a correlation between the spatial resolution and the processing time required by most image classification methods. Our findings can play an important role in saving not only money, but also time for vehicle-type classification systems.
Distributed Mean-Field-Type Filters for Traffic Networks

Traffic surveillance plays an important role in the development of a smart city, and it is a fundamental part in many applications such as security monitoring and traffic analysis. People are thrilled by the abundant data generated from the huge traffic networks but have difficulty in using them. In this paper, we propose a distributed mean-field-type filtering (DMF) framework to handle those noisy, partial-observed, and high-dimensional data. The filter incorporates a mean-field term into the system model and decomposes the state space into highly independent parts; filtering is performed in each part and then integrated. Our approach iterates through four operations: sampling, prediction, decomposition, and correction. Theoretical analysis provides a linear bound for the global error, which is independent of the network's cardinality. We implemented DMF in aircraft and vehicle tracking scenarios. Performance evaluation on synthetic and real-world data demonstrates the advantage of our approach over traditional mean-field free filters.
A Hierarchical Scheme for Vehicle Make and Model Recognition From Frontal Images of Vehicles

This paper presents a novel recognition scheme for vehicle make and model recognition (VMMR) from frontal images of vehicles. In general, we introduce some domain knowledge to cope with this task. The structural components contained in the frontal appearance of vehicles present different visual characteristics and their discriminating ability varies when vehicle models belonging to the same brand or different brands are compared. In light of the particularities, we take advantage of the varying discriminating ability of these structural components to perform the recognition task sequentially in two stages. At the first stage, the logo sub-region (which is one of the component-related sub-regions in the region of interest) is applied to classify the vehicle models at the brand level. Different from the traditional brand-level classification that the models of the same brand are considered as a single class, in this paper, multiple sub-classes in one brand class are allowed, since the intra-brand models also exhibit a certain degree of diversity. In this way, the problem of inter-class similarity is remitted. At the second stage, several customized classifiers are trained for each sub-class in the light of the discriminant ability of the remaining sub-regions. The proposed approach has been tested on a large-scale vehicle image database collected in this paper and has achieved the state-of-the-art results.
Spatial and Motion Saliency Prediction Method Using Eye Tracker Data for Video Summarization

Video summarization is the process to extract the most significant contents of a video and to represent it in a concise form. The existing methods for video summarization could not achieve a satisfactory result for a video with camera movement and significant illumination changes. To solve these problems, in this paper, a new framework for video summarization is proposed based on eye tracker data, as human eyes can track moving object accurately in these cases. The smooth pursuit is the state of eye movement when a user follows a moving object in a video. This motivates us to implement a new method to distinguish smooth pursuit from other type of gaze points, such as fixation and saccade. The smooth pursuit provides only the location of moving objects in a video frame; however, it does not indicate whether the located moving objects are very attractive (i.e., salient regions) to viewers or not, as well as the amount of motion of the moving objects. The amount of salient regions and object motions are the two important features to measure the viewer's attention level for determining the key frames for video summarization. To find the most attractive objects, a new spatial saliency prediction method is also proposed by constructing a saliency map around each smooth pursuit gaze point based on human visual field, such as fovea, parafoveal, and perifovea regions. To identify the amount of object motions, the total distances between the current and the previous gaze points of viewers during smooth pursuit are measured as a motion saliency score. The motivation is that the movement of eye gaze is related to the motion of the objects during smooth pursuit. Finally, both spatial and motion saliency maps are combined to obtain an aggregated saliency score for each frame and a set of key frames are selected based on user selected or system default skimming ratio. The proposed method is implemented on Office video data set that contains videos with camera movements and illumination changes. Experimental results confirm the superior performance of the proposed spatial and motion saliency prediction method compared with the state-of-the-art methods.
Vocal effort compensation for MFCC feature extraction in a shouted versus normal speaker recognition task

In shouting, speakers use increased vocal effort to convey spoken messages over distance or above environmental noise. For automatic speaker recognition systems trained using normal speech, shouting causes a severe vocal effort mismatch between the enrollment and test hence reducing the recognition performance. In this study, two compensation methods are proposed to tackle the mismatch in a shouted versus normal speaker recognition task. These techniques are applied in the feature extraction stage of a speaker recognition system to modify the spectral envelopes of shouts to be closer to those in normal speech. The techniques modify the all-pole power spectrum of the MFCC computation chain with shouted-to-normal compensation filtering that is obtained using a GMM-based statistical mapping. In an evaluation using the state-of-the-art i-vector based recognition system, the proposed techniques provided considerable improvements in identification rates compared to the case when shouted speech spectra were not processed.
A Novel Secure and Efficient Data Aggregation Scheme for IoT

We define the following problem termed n × 1-out-of-n oblivious transfer (n × 1-out-of-n OT): in a system with one server and n clients, how to securely and efficiently assign n secrets to n clients by the server, with each client getting a unique secret from the server, and the server and clients remain unknown of how the secrets are distributed? This is a novel problem that is fundamentally different than 1-out-of-n OT repeated n times, and is different than k-out-of-n OT as well. Nevertheless, the proposed OT has many practical applications such as privacy-preserving data aggregation in smart grids. It can also be employed to design crypto protocols for anonymous communications and group signatures. In this paper, we propose the first algorithm to efficiently and effectively implement the n × 1-out-of-n OT. We construct hidden permutation circuits to obliviously assign n secrets to n clients by the server within O(lg(n)) time. A rigorous theoretical analysis is also carried out to investigate the security strength and performance of the protocol.
Arithmetic Considerations for Isogeny-Based Cryptography

In this paper we investigate various arithmetic techniques which can be used to potentially enhance the performance in the supersingular isogeny Diffie-Hellman (SIDH) key-exchange protocol which is one of the more recent contenders in the post-quantum public-key arena. First, we give a systematic overview of techniques to compute efficient arithmetic modulo 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sup> p <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">y</sup> ± 1. Our overview shows that in the SIDH setting, where arithmetic over a quadratic extension field is required, the approaches based on the Montgomery reduction for such primes of a special shape are to be preferred. Moreover, the outcome of our investigation reveals that there exist moduli which allow even faster implementations. Second, we investigate if it is beneficial to use other curve models to speed up the elliptic curve scalar multiplication. The use of twisted Edwards curves allows one to search for efficient addition-subtraction chains for fixed scalars while this is not possible with the differential addition law when using Montgomery curves. Our preliminary results show that despite the fact that we found such efficient chains, using twisted Edwards curves does not result in faster scalar multiplication arithmetic in the setting of SIDH.
Aligned to the Object, Not to the Image: A Unified Pose-Aligned Representation for Fine-Grained Recognition

Dramatic appearance variation due to pose constitutes a great challenge in fine-grained recognition, one which recent methods using attention mechanisms or second-order statistics fail to adequately address. Modern CNNs typically lack an explicit understanding of object pose and are instead confused by entangled pose and appearance. In this paper, we propose a unified object representation built from pose-aligned regions of varied spatial sizes. Rather than representing an object by regions aligned to image axes, the proposed representation characterizes appearance relative to the object's pose using pose-aligned patches whose features are robust to variations in pose, scale and viewing angle. We propose an algorithm that performs pose estimation and forms the unified object representation as the concatenation of pose-aligned region features, which is then fed into a classification network. The proposed algorithm attains state-of-the-art results on two fine-grained datasets, notably 89.2% on the widely-used CUB-200 dataset and 87.9% on the much larger NABirds dataset. Our success relative to competing methods shows the critical importance of disentangling pose and appearance for continued progress in fine-grained recognition.
Improving PRNU Compression Through Preprocessing, Quantization, and Coding

In the last decade, the extremely rapid proliferation of digital devices capable of acquiring and sharing images over the Web has significantly increased the amount of digital images publicly accessible by everyone with Internet access. Despite the obvious benefits of such technological improvements, it is becoming mandatory to verify the origin and trustfulness of such shared pictures. Photo response non-uniformity (PRNU) is the reference signal for forensic investigators when it comes to verifying or identifying which camera device shot a picture under analysis. In spite of this, PRNU is almost a white-shaped noise, thus being very difficult to compress for storage or large scale search purposes, which are frequent investigation scenarios. To overcome the issue, the forensic community has developed a series of compression algorithms. Lately, Gaussian random projections have proved to achieve state-of-the-art performance. In this paper, we propose two additional steps that help improving even more Gaussian random projections compression rate: 1) a decimation preprocessing step tailored at attenuating frequency components in which PRNU traces are already suppressed in JPEG compressed images and 2) a dead-zone quantizer (rather than the commonly used binary one) that enables an entropy coding scheme to save bitrate when storing PRNU fingerprints or sending residuals over a communication channel. Reported results show the effectiveness of proposed improvements, both under controlled JPEG compression and in a real case scenario.
Theory of supports for linear codes endowed with the sum-rank metric

The sum-rank metric naturally extends both the Hamming and rank metrics in coding theory over fields. It measures the error-correcting capability of codes in multishot matrix-multiplicative channels (e.g. linear network coding or the discrete memoryless channel on fields). Although this metric has already shown to be of interest in several applications, not much is known about it. In this work, sum-rank supports for codewords and linear codes are introduced and studied, with emphasis on duality. The lattice structure of sum-rank supports is given; characterizations of the ambient spaces (support spaces) they define are obtained; the classical operations of restriction and shortening are extended to the sum-rank metric; and estimates (bounds and equalities) on the parameters of such restricted and shortened codes are found. Three main applications are given: (1) Generalized sum-rank weights are introduced, together with their basic properties and bounds; (2) It is shown that duals, shortened and restricted codes of maximum sum-rank distance (MSRD) codes are in turn MSRD; (3) Degenerateness and effective lengths of sum-rank codes are introduced and characterized. In an Appendix, skew supports are introduced, defined by skew polynomials, and their connection to sum-rank supports is given.
Non-Markovian dynamics revealed at a bound state in the continuum

We propose a methodical approach to controlling and enhancing deviations from exponential decay in quantum and optical systems by exploiting recent progress surrounding another subtle effect: the bound states in the continuum, which have been observed in optical waveguide array experiments within this past decade. Specifically, we show that by populating an initial state orthogonal to that of the bound state in the continuum, it is possible to engineer system parameters for which the usual exponential decay process is suppressed in favor of inverse power law dynamics and coherent effects that typically would be extremely difficult to detect in experiment. We demonstrate our method using a model based on an optical waveguide array experiment and further show that the method is robust even in the face of significant detuning from the precise location of the bound state in the continuum.
Surface Material Retrieval Using Weakly Paired Cross-Modal Learning

In this paper, we investigate the cross-modal material retrieval problem, which permits the user to submit a multimodal query including tactile and auditory modalities, and retrieve the image results of visual modalities. Since multiple significantly different modalities are involved in this process, we encounter more challenges compared with the existing cross-modal retrieval tasks. Our focus is to learn cross-modal representations when the modalities are significantly different and with minimal supervision. A novelty is that we establish a framework that deals with weakly paired multimodal fusion method for heterogenous tactile and auditory modalities and weakly paired cross-modal transfer for visual modality. A structured dictionary learning method with a low rank and common classifier is developed to obtain the modal-invariant representation. Finally, some cross-modal validations on publicly available data sets are performed to show the advantages of the proposed method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Cross-modal retrieval is an important task for industrial intelligence. In this paper, we establish a framework to effectively solve the cross-modal material retrieval problem. In the developed framework, the user may submit a multimodal query including acceleration and sound about an object, and the system may return the most relevant retrieved images. Such a framework may find extensive applications in many fields, because it can be flexible to deal with a multiple-modal query and uses the minimal category label supervision without the need of strong sample pairing information between modalities. Compared with the previous material analysis systems, this paper goes beyond previously proposed surface material classification approaches as it returns an ordered list of perceptually similar surface materials for a query.
Studying Aging-Related Bug Prediction Using Cross-Project Models

In long running systems, software tends to encounter performance degradation and increasing failure rate during execution. This phenomenon has been named software aging, which is caused by aging-related bugs (ARBs). Testing resource allocation can be optimized by identifying ARB-prone modules with ARB prediction. However, due to the low presence and reproducing difficulty of ARBs, it is usually hard to collect sufficient training data to carry out within-project ARB prediction. In this paper, we propose an approach named transfer learning based aging-related bug prediction (TLAP) to perform cross-project ARB prediction. TLAP first takes advantage of transfer learning to reduce distribution difference between training and testing project. Then, class imbalance learning is conducted to mitigate the severe class imbalance between ARB-prone and ARB-free modules. Finally, machine learning methods are used to handle bug prediction tasks. The effectiveness of this approach is validated and evaluated by nine groups of experiments on real software systems. Major conclusions from the experiments include the following: first, TLAP improves cross-project ARB prediction on average compared with traditional machine learning methods; second, utilizing information from multiple-projects can further improve the prediction performance on average. In the best case, it outperforms within-project prediction; third, the number of ARB-prone files and distribution similarity can influence TLAP performance.
An efficient method for clustered multi-metric learning

Distance metric learning, which aims at finding a distance metric that separates examples of one class from examples of the other classes, is the key to the success of many machine learning tasks. Although there has been an increasing interest in this field, learning a global distance metric is insufficient to obtain satisfactory results when dealing with heterogeneously distributed data. A simple solution to tackle this kind of data is based on kernel embedding methods. However, it quickly becomes computationally intractable as the number of examples increases. In this paper, we propose an efficient method that learns multiple local distance metrics instead of a single global one. More specifically, the training examples are divided into several disjoint clusters, in each of which a distance metric is trained to separate the data locally. Additionally, a global regularization is introduced to preserve some common properties of different clusters in the learned metric space. By learning multiple distance metrics jointly within a single unified optimization framework, our method consistently outperforms single distance metric learning methods, while being more efficient than other state-of-the-art multi-metric learning methods.
Finding Integral Distinguishers with Ease

The division property method is a technique to determine integral distinguishers on block ciphers. While the complexity of finding these distinguishers is higher, it has recently been shown that MILP and SAT solvers can efficiently find such distinguishers. In this paper, we provide a framework to automatically find those distinguishers which solely requires a description of the cryptographic primitive. We demonstrate that by finding integral distinguishers for 30 primitives with different design strategies. We provide several new or improved bit-based division property distinguishers for ChaCha, Chaskey, DES, GIFT, LBlock, Mantis, Qarma, RoadRunner, Salsa and SM4. Furthermore, we present an algorithm to find distinguishers with lower data complexity more efficiently.
Immune-Endocrine System Inspired Hierarchical Coevolutionary Multiobjective Optimization Algorithm for IoT Service

The intelligent devices in Internet of Things (IoT) not only provide services but also consider how to allocate heterogeneous resources and reduce resource consumption and service time as far as possible. This issue becomes crucial in the case of large-scale IoT environments. In order for the IoT service system to respond to multiple requests simultaneously and provide Pareto optimal decisions, we propose an immune-endocrine system inspired hierarchical coevolutionary multiobjective optimization algorithm (IE-HCMOA) in this paper. In IE-HCMOA, a multiobjective immune algorithm based on global ranking with vaccine is designed to choose superior antibodies. Meanwhile, we adopt clustering in top population to make the operations more directional and purposeful and realize self-adaptive searching. And we use the human forgetting memory mechanism to design two-level memory storage for the choice problem of solutions to achieve promising performance. In order to validate the practicability and effectiveness of IE-HCMOA, we apply it to the field of agricultural IoT service. The simulation results demonstrate that the proposed algorithm can obtain the best Pareto, the strongest exploration ability, and excellent performance than nondominated neighbor immune algorithms and NSGA-II.