Reviewerly/exHarmony huggingface dataset:
Robust one-stage object detection with location-aware classifiers

Recent progress on one-stage detectors focuses on improving the quality of bounding boxes, while they pay less attention to the classification head. In this work, we focus on investigating the influence of the classification head. To understand the behavior of the classifier in one-stage detectors, we resort to the methods of the Explainable deep learning area. We visualize its learned representations via activation maps and analyze its robustness to image scene context. Based on the analysis, we observe that the classifier limits the performance of the detector due to its limited receptive field and the lack of object locations. Then, we design a simple but efficient location-aware multi-dilation module (LAMD) to enhance the weak classifier. We conduct extensive experiments on the COCO benchmark to validate the effectiveness of LAMD. The results suggest that our LAMD can achieve consistent improvements and leads to robust detection across various one-stage detectors with different backbones.
A cognition-driven framework for the evaluation of startups in the digital economy

Purpose Capital restrictions normally exist in the creation of a startup, requiring investors to analyze funding alternatives in a highly competitive climate. Although different types of incentives to support startups exist, these incentives are only available to those companies that fulfill the requirements of the funding program to which they have applied. Due to social and economic changes introduced by the digital economy, however, existing mechanisms for assessing the potential growth of startups are scarce, outdated or simply incomplete, distorting the results of such evaluations. Design/methodology/approach Evaluating business opportunities and how to exploit them are critical activities for an entrepreneur. This study sought to address this issue through the combined use of cognitive mapping and the Decision EXpert (DEX) technique. Assuming a constructivist stance, the study brought together a panel of experienced entrepreneurs and business investors to identify and articulate the criteria to be considered in the evaluation and classification of startups. Findings The evaluation system created in this study was tested, and the results were validated by the expert panel on a collective basis, demonstrating that the dual methodology used can increase our understanding of the decision problem at hand and lead to more informed and potentially better evaluations of the potential growth of startups. Originality/value The authors know of no prior work reporting the integrated use of cognitive mapping and DEX in this study context.
Deep Multi-sphere Support Vector Data Description

Deep learning is increasingly used for unsupervised feature extraction and anomaly detection in big datasets.Most deep learning based anomaly detection techniques separately train a neural network for feature extraction, then apply a traditional anomaly detection method on the extracted features.These hybrid techniques have achieved higher accuracy than traditional anomaly detection methods and reconstruction-error-based deep autoencoders.However, recent research demonstrates that jointly optimising the objectives of the deep network and the anomaly detection technique in a hybrid architecture substantially improves detection performance.Existing methods that use this objective assume that the normal (i.e., non-anomalous) data comes from a single distribution.In this paper, we show that violation of this assumption negatively affects performance of these methods and creates model bias in the favour of anomalies.We propose Deep Multi-sphere Support Vector Data Description, which jointly optimises the objectives of the deep network and anomaly detection.It generates useful and discriminative features by embeding normal data with a multi-modal distribution into multiple data-enclosing hyperspheres with minimum volume.We empirically show that our proposed method outperforms state-of-the-art shallow and deep anomaly detection methods.
Partial image encryption using format-preserving encryption in image processing systems for Internet of things environment

Concomitant with advances in technology, the number of systems and devices that utilize image data has increased. Nowadays, image processing devices incorporated into systems, such as the Internet of things, drones, and closed-circuit television, can collect images of people and automatically share them with networks. Consequently, the threat of invasion of privacy by image leakage has increased exponentially. However, traditional image-security methods, such as privacy masking and image encryption, have several disadvantages, including storage space wastage associated with data padding, inability to decode, inability to recognize images without decoding, and exposure of private information after decryption. This article proposes a method for partially encrypting private information in images using FF1 and FF3-1. The proposed method encrypts private information without increasing the data size, solving the problem of wasted storage space. Furthermore, using the proposed method, specific sections of encrypted images can be decrypted and recognized before decryption of the entire information, which addresses the problems besetting traditional privacy masking and image encryption methods. The results of histogram analysis, correlation analysis, number of pixels change rate, unified average change intensity, information entropy analysis, and NIST SP 800-22 verify the security and overall efficacy of the proposed method.
You shouldn’t trust me: Learning models which conceal unfairness from multiple explanation methods.

Transparency of algorithmic systems is an important area of research, which has been discussed as a way for end-users and regulators to develop appropriate trust in machine learning models. One popular approach, LIME [23], even suggests that model expla- nations can answer the question “Why should I trust you?”. Here we show a straightforward method for modifying a pre-trained model to manipulate the output of many popular feature importance explana- tion methods with little change in accuracy, thus demonstrating the danger of trusting such explanation methods. We show how this ex- planation attack can mask a model’s discriminatory use of a sensitive feature, raising strong concerns about using such explanation meth- ods to check fairness of a model.
Decentralized Recommendation Based on Matrix Factorization: A Comparison of Gossip and Federated Learning

Federated learning is a well-known machine learning approach over edge devices with relatively limited resources, such as mobile phones. A key feature of the approach is that no data is collected centrally; instead, data remains private and only models are communicated between a server and the devices. Gossip learning has a similar application domain; it also assumes that all the data remains private, but it requires no aggregation server or any central component. However—one would assume—gossip learning must pay a price for the extra robustness and lower maintenance cost it provides due to its fully decentralized design. Here, we examine this natural assumption empirically. The application we focus on is making recommendations based on private logs of user activity, such as viewing or browsing history. We apply low rank matrix decomposition to implement a common collaborative filtering method. First, we present similar algorithms for both frameworks to efficiently solve this problem without revealing any raw data or any user-specific parts of the model. We then examine the aggregated cost in both cases for several algorithm-variants in various simulation scenarios. These scenarios include a real churn trace collected over mobile phones. Perhaps surprisingly, gossip learning is comparable to federated learning in all the scenarios and, especially in large networks, it can even outperform federated learning when the same subsampling-based compression technique is applied in both frameworks.
Enhancement of Security and Handling the Inconspicuousness in IoT Using a Simple Size Extensible Blockchain

Blockchain technology is increasingly used worldwide to enhance the performance and profit of any environment through its defining characteristics, such as security, auditability, immutability, and inconspicuousness. Owing to these characteristics, the blockchain can be used in various non-financial operations of some domains, such as the Internet of Things (IoT) and distributed computing. However, implementing blockchain technology in IoT is not always a feasible solution because blockchain deployment is costly, it has limited extensibility and provides irregular bandwidth and latency. In this regard, a simple size extensible (SSE) blockchain has been proposed to provide an optimal solution for IoT environments by satisfying the needs of the IoT environment as well as ensuring end-to-end security. The implementation of the proposed blockchain develops an overlay network to obtain a distributed environment where the blockchain is handled by the resources present therein. Two novel algorithms were introduced into the proposed system to minimize the irregularity and latency on one hand, and to maximize the throughput of the system on the other. The shared-time depending agreement algorithm (STD) minimizes the irregularity in the extraction operation and latency. The other, the shared throughput administration algorithm (STA) justifies the overall collection of the transmission load in the network and maintains the performance of the blockchain. The proposed system was applied to smart home IoT appliances to test the performance of the proposed system. The experimental results show that the proposed blockchain system minimizes nearly 70% of the data irregularity, latency, and furthermore, 30% of the blockchain extensibility is maximized as compared to the existing systems.
Simulation and Modeling of Telocytes Behavior in Signaling and Intercellular Communication Processes

Background: Telocytes (TCs) are unique interstitial or stromal cells of mesodermal origin, defined by long cellular extensions called telopodes (Tps) which form a network, connecting them to surrounding cells. TCs were previously found around stem and progenitor cells, and were thought to be most likely involved in local tissue metabolic equilibrium and regeneration. The roles of telocytes are still under scientific scrutiny, with existing studies suggesting they possess various functions depending on their location. Methods: Human myometrium biopsies were collected from pregnant and non-pregnant women, telocytes were then investigated in myometrial interstitial cell cultures based on morphological criteria and later prepared for time-lapse microscopy. Semi-analytical and numerical solutions were developed to highlight the geometric characteristics and the behavior of telocytes. Results: Results were gathered in a database which would further allow efficient telocyte tracking and indexing in a content-based image retrieval (CBIR) of digital medical images. Mathematical analysis revealed pivotal information regarding the homogeneity, hardness and resistance of telocytes’ structure. Cellular activity models were monitored in vitro, therefore supporting the creation of databases of telocyte images. Conclusions: The obtained images were analyzed, using segmentation techniques and mathematical models in conjunction with computer simulation, in order to depict TCs behavior in relation to surrounding cells. This paper brings an important contribution to the development of bioinformatics systems by creating software-based telocyte models that could be used both for diagnostic and educational purposes.
Investigation of Specaugment for Deep Speaker Embedding Learning

SpecAugment is a newly proposed data augmentation method for speech recognition. By randomly masking bands in the log Mel spectogram this method leads to impressive performance improvements. In this paper, we investigate the usage of SpecAugment for speaker verification tasks. Two different models, namely 1-D convolutional TDNN and 2-D convolutional ResNet34, trained with either Softmax or AAM-Softmax loss, are used to analyze SpecAugment's effectiveness. Experiments are carried out on the Voxceleb and NIST SRE 2016 dataset. By applying SpecAugment to the original clean data in an on-the-fly manner without complex off-line data augmentation methods, we obtained 3.72% and 11.49% EER for NIST SRE 2016 Cantonese and Tagalog, respectively. For Voxceleb1 evaluation set, we obtained 1.47% EER.
Observation of Broadband Entanglement in Microwave Radiation from a Single Time-Varying Boundary Condition

Entangled pairs of microwave photons are commonly produced in the narrow frequency band of a resonator, which represents a modified vacuum density of states. We use a broadband, semi-infinite transmission line terminated by a superconducting quantum interference device (SQUID). A weak pump signal modulates the SQUID inductance, resulting in a single time-varying boundary condition. We detect both quadratures of the microwave radiation emitted at two different frequencies separated by 0.7~GHz. We determine the type and purity of entanglement from the noise correlations and an in-situ noise and power calibration.
Quantifying Dynamical Coherence with Dynamical Entanglement

Coherent superposition and entanglement are two fundamental aspects of nonclassicality. Here we provide a quantitative connection between the two on the level of operations by showing that the dynamical coherence of an operation upper bounds the dynamical entanglement that can be generated from it with the help of additional incoherent operations. In case a particular choice of monotones based on the relative entropy is used for the quantification of these dynamical resources, this bound can be achieved. In addition, we show that an analog to the entanglement potential exists on the level of operations and serves as a valid quantifier for dynamical coherence.
A Frame Detection Method for Real-Time Hand Gesture Recognition Systems Using CW-Radar

In this paper, a method to detect frames was described that can be used as hand gesture data when configuring a real-time hand gesture recognition system using continuous wave (CW) radar. Detecting valid frames raises accuracy which recognizes gestures. Therefore, it is essential to detect valid frames in the real-time hand gesture recognition system using CW radar. The conventional research on hand gesture recognition systems has not been conducted on detecting valid frames. We took the R-wave on electrocardiogram (ECG) detection as the conventional method. The detection probability of the conventional method was 85.04%. It has a low accuracy to use the hand gesture recognition system. The proposal consists of 2-stages to improve accuracy. We measured the performance of the detection method of hand gestures provided by the detection probability and the recognition probability. By comparing the performance of each detection method, we proposed an optimal detection method. The proposal detects valid frames with an accuracy of 96.88%, 11.84% higher than the accuracy of the conventional method. Also, the recognition probability of the proposal method was 94.21%, which was 3.71% lower than the ideal method.
Efficient verification of quantum processes

Quantum processes, such as quantum circuits, quantum memories, and quantum channels, are essential ingredients in almost all quantum information processing tasks. However, the characterization of these processes remains a daunting task due to the exponentially increasing amount of resources required by traditional methods. Here, by first proposing the concept of quantum process verification, we establish two efficient and practical protocols for verifying quantum processes which can provide an exponential improvement over the standard quantum process tomography and a quadratic improvement over the method of direct fidelity estimation. The efficacy of our protocols is illustrated with the verification of various quantum gates as well as the processes of well-known quantum circuits. Moreover, our protocols are readily applicable with current experimental techniques since only local measurements are required. In addition, we show that our protocols for verifying quantum processes can be easily adapted to verify quantum measurements.
Transfer Representation Learning With TSK Fuzzy System

Transfer learning can address the learning tasks of unlabeled data in the target domain by leveraging plenty of labeled data from a different but related source domain. A core issue in transfer learning is to learn a shared feature space where the distributions of the data from the two domains are matched. This learning process can be named as transfer representation learning (TRL). Feature transformation methods are crucial to ensure the success of TRL. The most commonly used feature transformation method in TRL is kernel-based nonlinear mapping to the high-dimensional space, followed by linear dimensionality reduction. But the kernel functions are lack of interpretability, and it is difficult to select kernel functions. To this end, this article proposes a more intuitive and interpretable method, called TRL with TSK-FS (TRL-TSK-FS), by combining TSK fuzzy system (TSK-FS) with transfer learning. Specifically, TRL-TSK-FS realizes TRL from two aspects. On one hand, the data in the source and target domains are transformed into the fuzzy feature space where the distribution distance of the data between the two domains is minimized. On the other hand, discriminant information and geometric properties of the data are preserved by linear discriminant analysis and principal component analysis. A further advantage is that nonlinear transformation is realized in the proposed method by constructing fuzzy mapping with the antecedent part of the TSK-FS instead of kernel functions, which are difficult to be selected. Extensive experiments are conducted on text and image datasets to demonstrate the superiority of the proposed method.
Cross Model Deep Learning Scheme for Automatic Modulation Classification

Deep Neural Networks (DNNs) have achieved remarkable accuracy improvements for automatic modulation classification. However, the employed networks often have millions of parameters and need very high computation, which makes it difficult to deploy these models on portable devices with limited resources. We propose a cross model deep learning scheme to build a lightweight deep network for accurate modulation classification. Firstly, a large Hybrid DNN (HDNN) that is composed of convolutional and recurrent layers is constructed and trained for automatic and accurate classification of signals. Then we build a smaller Layered Resnet Network (LRN) with shallow layers and few nodes. The HDNN and LRN are taken as a Teacher Model (TM) and a Student Model (SM) respectively. Finally, a knowledge distillation method is proposed to guide the learning of the SM, by formulating a teaching loss from the prediction of the TM to train the SM. The performances of the proposed HDNN and LRN are investigated on the public RadioML2016.10a and RadioML2016.10b data sets. The experimental results show that the trained HDNN presents state-of-the-art classification results and the LRN trained in this scheme takes only about a sixth of the HDNN's inference time and consumes only 472.3KB for storage, with a slight accuracy decrease compared with the large HDNN.
Experimentally Accessible Lower Bounds for Genuine Multipartite Entanglement and Coherence Measures

Experimentally quantifying entanglement and coherence is extremely important in quantum information processing. In a multipartite quantum system, usually the fidelity of the system's state compared to a target state is measured to detect its entanglement. The authors present a fidelity-based method to derive experimentally accessible lower bounds for measures of genuine multipartite entanglement and coherence, allowing quick quantification of system states without quantum state tomography in experiments. The method works generally, for several entanglement measures and coherence measures, and examples of real experimental states are analyzed in detail.
Recognition of the Driving Style in Vehicle Drivers

This paper presents three different approaches to recognize driving style based on a hierarchical-model. Specifically, it proposes a hierarchical model for the recognition of the driving style for advanced driver-assistance systems (ADAS) for vehicles. This hierarchical model for the recognition of the style of the car driving considers three aspects: the driver emotions, the driver state, and finally, the driving style itself. In this way, the proposed hierarchical pattern is composed of three levels of descriptors/features, one to recognize the emotional states, another to recognize the driver state, and the last one to recognize the driving style. Each level has a set of descriptors, which can be sensed in a real context. Finally, the paper presents three driving style recognition algorithms based on different paradigms. One is based on fuzzy logic, another is based on chronicles (a temporal logic paradigm), and the last is based on an algorithm that uses the idea of the recognition process of the neocortex, called Ar2p (Algoritmo Recursivo de Reconocimiento de Patrones, for its acronym in Spanish). In the paper, these approaches are compared using real datasets, using different metrics of interest in the context of the Internet of the Things, in order to determine their capabilities of reasoning, adaptation, and the communication of information. In general, the initial results are encouraging, specifically in the cases of chronicles and Ar2p, which give the best results.
Satellite Video Super-Resolution Based on Adaptively Spatiotemporal Neighbors and Nonlocal Similarity Regularization

Recently, super-resolution (SR) of satellite videos has received increasing attention as it can overcome the limitation of spatial resolution in applications of satellite videos to dynamic analysis. The low quality of satellite videos presents big challenges to the development of the spatial SR techniques, e.g., accurate motion estimation and motion compensation for multiframe SR. Therefore, reasonable image priors in maximum a posteriori (MAP) framework, where motion information among adjacent frames is involved, are needed to regularize the solution space and generate the corresponding high-resolution frames. In this article, an effective satellite video SR framework based on locally spatiotemporal neighbors and nonlocal similarity modeling is proposed. Firstly, local prior knowledge is represented by means of adaptively exploiting spatiotemporal neighbors. In this way, implicitly local motion information can be captured without explicit motion estimation. Secondly, the nonlocal spatial similarity is integrated into the proposed SR framework to enhance texture details. Finally, the locally spatiotemporal regularization and nonlocal similarity modeling bring out a complex optimization problem, which is solved via the iterated reweighted least squares in the proposed SR framework. The videos from the Jilin-1 satellite and the OVS-1A satellite are used for evaluating the proposed method. Experimental results show that the proposed method demonstrates better SR performance in preserving edges and texture details compared with the-state-of-art video SR methods.