Reviewerly/exHarmony huggingface dataset:
A Deep Learning-Based Intelligent Face Recognition Method in the Internet of Home Things for Security Applications

At the beginning of the 21st century, human life has become increasingly dependent on security. At this point, the cost is the essential consideration. This approach can reduce monitoring costs. This research proposes a real-time recognition system to deal with photos as soon as possible. We hope that by identifying people, we can keep our homes and offices safe. One of the primary goals of this research was to develop a method of intelligent face recognition for smart homes based on deep learning. To demonstrate the usefulness of our study, we also examine and contrast this model with other methodologies deemed contemporary. This research proposes a tree-based deep model for cloud-based face recognition. The proposed deep model is less computationally demanding without affecting accuracy. Trees for each volume are built in the model's input volume, split into many ones. The number of branches and their height characterizes trees. Residual functions are used to represent each branch, and they are built from a convolutional layer and two non-linear functions. In various openly accessible databases, the proposed model is put to the test. It also compares to the best deep facial recognition models in the industry today.
Differentiable Neural Network Pruning to Enable Smart Applications on Microcontrollers

Wearable, embedded, and IoT devices are a centrepiece of many ubiquitous computing applications, such as fitness tracking, health monitoring, home security and voice assistants. By gathering user data through a variety of sensors and leveraging machine learning (ML), applications can adapt their behaviour: in other words, devices become "smart". Such devices are typically powered by microcontroller units (MCUs). As MCUs continue to improve, smart devices become capable of performing a non-trivial amount of sensing and data processing, including machine learning inference, which results in a greater degree of user data privacy and autonomy, compared to offloading the execution of ML models to another device. Advanced predictive capabilities across many tasks make neural networks an attractive ML model for ubiquitous computing applications; however, on-device inference on MCUs remains extremely challenging. Orders of magnitude less storage, memory and computational ability, compared to what is typically required to execute neural networks, impose strict structural constraints on the network architecture and call for specialist model compression methodology. In this work, we present a differentiable structured pruning method for convolutional neural networks, which integrates a model's MCU-specific resource usage and parameter importance feedback to obtain highly compressed yet accurate models. Compared to related network pruning work, compressed models are more accurate due to better use of MCU resource budget, and compared to MCU specialist work, compressed models are produced faster. The user only needs to specify the amount of available computational resources and the pruning algorithm will automatically compress the network during training to satisfy them. We evaluate our methodology using benchmark image and audio classification tasks and find that it (a) improves key resource usage of neural networks up to 80x; (b) has little to no overhead or even improves model training time; (c) produces compressed models with matching or improved resource usage up to 1.4x in less time compared to prior MCU-specific model compression methods.
A comparison of different support vector machine kernels for artificial speech detection

As the emergence of the voice biometric provides enhanced security and convenience, voice biometric-based applications such as speaker verification were gradually replacing the authentication techniques that were less secure. However, the automatic speaker verification (ASV) systems were exposed to spoofing attacks, especially artificial speech attacks that can be generated with a large amount in a short period of time using state-of-the-art speech synthesis and voice conversion algorithms. Despite the extensively used support vector machine (SVM) in recent works, there were none of the studies shown to investigate the performance of different SVM settings against artificial speech detection. In this paper, the performance of different SVM settings in artificial speech detection will be investigated. The objective is to identify the appropriate SVM kernels for artificial speech detection. An experiment was conducted to find the appropriate combination of the proposed features and SVM kernels. Experimental results showed that the polynomial kernel was able to detect artificial speech effectively, with an equal error rate (EER) of 1.42% when applied to the presented handcrafted features.
Lightweight Dual Mutual-Feedback Network for Artificial Intelligence in Medical Image Super-Resolution

As a result of hardware resource constraints, it is difficult to obtain medical images with a sufficient resolution to diagnose small lesions. Recently, super-resolution (SR) was introduced into the field of medicine to enhance and restore medical image details so as to help doctors make more accurate diagnoses of lesions. High-frequency information enhances the accuracy of the image reconstruction, which is demonstrated by deep SR networks. However, deep networks are not applicable to resource-constrained medical devices because they have too many parameters, which requires a lot of memory and higher processor computing power. For this reason, a lightweight SR network that demonstrates good performance is needed to improve the resolution of medical images. A feedback mechanism enables the previous layers to perceive high-frequency information of the latter layers, but no new parameters are introduced, which is rarely used in lightweight networks. Therefore, in this work, a lightweight dual mutual-feedback network (DMFN) is proposed for medical image super-resolution, which contains two back-projection units that operate in a dual mutual-feedback manner. The features generated by the up-projection unit are fed back into the down-projection unit and, simultaneously, the features generated by the down-projection unit are fed back into the up-projection unit. Moreover, a contrast-enhanced residual block (CRB) is proposed as each cell block used in projection units, which enhances the pixel contrast in the channel and spatial dimensions. Finally, we designed a unity feedback to down-sample the SR result as the inverse process of SR. Furthermore, we compared it with the input LR to narrow the solution space of the SR function. The final ablation studies and comparison results show that our DMFN performs well without utilizing a large amount of computing resources. Thus, it can be used in resource-constrained medical devices to obtain medical images with better resolutions.
A Fundamental Framework for Identifying Profitable Cryptocurrencies

Major academic research related to cryptocurrencies has been developed by technologists who mainly focus on feasibility and security. Research in the area of economics and finance has provided limited insight in guiding investors to profit from cryptocurrencies, however, this article proposes a framework that depicts how rational investing can be achieved with the help of historical information published by cryptocurrency projects. Trading strategies based on fundamentals can generate economically significant gains relative to a buy-and-hold position.
Research on online evaluation method of MOOC teaching quality based on decision tree-based big data classification

In order to improve the online evaluation ability to massive open online course (MOOC) teaching quality, an online evaluation method of MOOC teaching quality based on decision tree-based big data classification is proposed. First, a big data statistical analysis model is built to identify fuzzy degree parameters. Then, the quality index system is obtained to realise big data fusion and cluster analysis. It is concluded that this method has high accuracy in online evaluation. In this paper, the method shows that the accuracy as high as 0.996 when the number of iterations reaches 500. Its innovation lies in the analysis of the global optimal solution of online evaluation of distance MOOC teaching quality by using the big data decision tree model, which improves the information management of MOOC online evaluation.
SOFTWARE PROCESS ASSESSMENT AND CERTIFICATION: APPLICATION OF THE ANALYTIC HIERARCHY PROCESS FOR PRIORITY DETERMINATION

Software certification involves assessing and certifying the quality of the software process based on multiple evaluation criteria where each criterion has different importance values on the quality of the software. However, the different importance values of the evaluation criteria have not been addressed in the existing software process certification models. A systematic technique is needed to ensure that the certification results are consistent, accurate and not made arbitrarily. To address this issue, the Extended Software Process Certification (ESPAC) model was introduced by adopting the Analytic Hierarchy Process (AHP) technique to determine the priorities of the evaluation criteria. There were three main phases in this study: (a) theoretical study, (b) expert review and (c) focus group discussion. Ultimately, a mutual agreement was achieved about the evaluation criteria and the AHP was shown to be a suitable technique to be employed in software process assessment and certification. Furthermore, the acquired priorities were used as the ideal priorities for the ESPAC model, which can be used by assessors during the assessment and certification process. The outcome of this study benefits researchers in the AHP and software process assessment fields.
Meta-Extreme Learning Machine for Short-Term Traffic Flow Forecasting

The traffic flow forecasting proposed for a series of problems, such as urban road congestion and unreasonable road planning, aims to build a new smart city, improve urban infrastructure, and alleviate road congestion. The problems encountered in traffic flow forecasting are also relatively difficult; the reason is that traffic flow forecasting is uncertain, dynamic, and nonlinear. It is challenging to build a reliable and safe model. Aiming at this complex and nonlinear traffic flow forecasting problem, this paper proposes a solution of an ABC-ELM model optimized by an artificial bee colony algorithm to solve the above problem. It uses the characteristics of the artificial bee colony algorithm to optimize the model so that the model can better and faster find the optimal solution in space. Moreover, it also uses the characteristics of the limit learning machine to quickly deal with this nonlinear specific problem. Experimental results on the Amsterdam road traffic flow dataset show that the traffic flow prediction model proposed in this paper has higher prediction accuracy and is more sensitive to data changes.
Transmission estimation at the quantum Cramér-Rao bound with macroscopic quantum light

Abstract The field of quantum metrology seeks to apply quantum techniques and/or resources to classical sensing approaches with the goal of enhancing the precision in the estimation of a parameter beyond what can be achieved with classical resources. Theoretically, the fundamental minimum uncertainty in the estimation of a parameter for a given probing state is bounded by the quantum Cramér-Rao bound. From a practical perspective, it is necessary to find physical measurements that can saturate this fundamental limit and to show experimentally that it is possible to perform measurements with the required precision to do so. Here we perform experiments that saturate the quantum Cramér-Rao bound for transmission estimation over a wide range of transmissions when probing the system under study with a continuous wave bright two-mode squeezed state. To properly take into account the imperfections in the generation of the quantum state, we extend our previous theoretical results to incorporate the measured properties of the generated quantum state. For our largest transmission level of 84%, we show a 62% reduction over the optimal classical protocol in the variance in transmission estimation when probing with a bright two-mode squeezed state with −8 dB of intensity-difference squeezing. Given that transmission estimation is an integral part of many sensing protocols, such as plasmonic sensing, spectroscopy, calibration of the quantum efficiency of detectors, etc., the results presented promise to have a significant impact on a number of applications in various fields of research.
Practical Anonymous Multi-hop Locks for Lightning Network Compatible Payment Channel Networks

Bitcoin, among other blockchain-based cryptocurrencies, has become increasingly popular. The expensive consensus process, however, severely limits the throughput of these systems. Allowing majority of the payment transactions to be settled off-chain, Payment channels (PC) have become a promising approach to address the scalability problem. Payment channel network (PCN) enables the payment between two users who do not have a direct PC through multi-hop payment across several payment channels. Lightning Network (LN), which handles around one trillion transactions per day, is the most well-known PCN deployed in practice. Improving the security and privacy of payment via PCN is an active research area. Recently, Malavolta et al. formalised a new cryptographic primitive known as anonymous multi-hop locks (AMHL) and demonstrated how it can be used to build a secure and privacy-preserving PCN. In this paper, we give a new construction of AMHL with the following features: (1) LN-compatible, i.e., it can be deployed into LN seamlessly; (2) secure in the universal composable framework; (3) highly efficient. Using our AMHL, a multi-hop payment with 5 users requires only 1458 bytes of off-chain communication. It compares favorably to state-of-the-art LN-compatible solutions, e.g., Fulgor and AMHL based on ECDSA, which requires 5 MB and 1.8 MB respectively. Furthermore, our solution is round-efficient. Specifically, the sender only needs to send one message to each node along the payment route.
On Label Granularity and Object Localization

Weakly supervised object localization (WSOL) aims to learn representations that encode object location using only image-level category labels. However, many objects can be labeled at different levels of granularity. Is it an animal, a bird, or a great horned owl? Which image-level labels should we use? In this paper we study the role of label granularity in WSOL. To facilitate this investigation we introduce iNatLoc500, a new large-scale fine-grained benchmark dataset for WSOL. Surprisingly, we find that choosing the right training label granularity provides a much larger performance boost than choosing the best WSOL algorithm. We also show that changing the label granularity can significantly improve data efficiency.
Can Shuffling Video Benefit Temporal Bias Problem: A Novel Training Framework for Temporal Grounding

Temporal grounding aims to locate a target video moment that semantically corresponds to the given sentence query in an untrimmed video. However, recent works find that existing methods suffer a severe temporal bias problem. These methods do not reason the target moment locations based on the visual-textual semantic alignment but over-rely on the temporal biases of queries in training sets. To this end, this paper proposes a novel training framework for grounding models to use shuffled videos to address temporal bias problem without losing grounding accuracy. Our framework introduces two auxiliary tasks, cross-modal matching and temporal order discrimination, to promote the grounding model training. The cross-modal matching task leverages the content consistency between shuffled and original videos to force the grounding model to mine visual contents to semantically match queries. The temporal order discrimination task leverages the difference in temporal order to strengthen the understanding of long-term temporal contexts. Extensive experiments on Charades-STA and ActivityNet Captions demonstrate the effectiveness of our method for mitigating the reliance on temporal biases and strengthening the model’s generalization ability against the different temporal distributions. Code is available at https://github.com/haojc/ShufflingVideosForTSG .
Multimodal sentiment analysis based on multi-head self-attention and convolutional block attention module

Sarcasm is a type of emotional expression. Sarcasm is commonly used on social media to express the inverse of what appears to be a statement and what is said. Previous automatic sarcasm detection mainly focused on text. With the rise of image sharing mode on social media platforms, text cannot fully reveal users’ emotions, so people begin to study multimodal sentiment analysis by combining text and images. Previous researches on sarcasm detection have used Bidirectional Long Short-term Memory Network (Bi-LSTM) and Residual Network (ResNet) to extract text and image feature vectors, respectively. While Multi-Head Self-Attention (MH-SA) is added to the Bi-LSTM model to perform relation extraction, which can effectively avoid complex feature engineering in traditional tasks. In the process of image extraction, the channel attention module (CAM) and the spatial attention module (SAM) are used to weight different spatial and channel features and focus on different regions and features of the image. The two complement each other, greatly improving the network’s ability to express features. On the Twitter dataset, our proposed model has a sarcasm detection accuracy of 87.55 %, which outperforms most models proposed in current papers.
Domain Adaptive Person Search

Person search is a challenging task which aims to achieve joint pedestrian detection and person re-identification (ReID). Previous works have made significant advances under fully and weakly supervised settings. However, existing methods ignore the generalization ability of the person search models. In this paper, we take a further step and present Domain Adaptive Person Search (DAPS), which aims to generalize the model from a labeled source domain to the unlabeled target domain. Two major challenges arises under this new setting: one is how to simultaneously solve the domain misalignment issue for both detection and ReID tasks, and the other is how to train the ReID subtask without reliable detection results on the target domain. To address these challenges, we propose a strong baseline framework with two dedicated designs. 1) We design a domain alignment module including image-level and task-sensitive instance-level alignments, to minimize the domain discrepancy. 2) We take full advantage of the unlabeled data with a dynamic clustering strategy, and employ pseudo bounding boxes to support ReID and detection training on the target domain. With the above designs, our framework achieves 34.7% in mAP and 80.6% in top-1 on PRW dataset, surpassing the direct transferring baseline by a large margin. Surprisingly, the performance of our unsupervised DAPS model even surpasses some of the fully and weakly supervised methods. The code is available at https://github.com/caposerenity/DAPS .
A Blockchain-Based Scheme for Efficient Medical Data Sharing with Attribute-Based Hierarchical Encryption

Most of the traditional medical information management system adopt centralized storage and management. So, there are some problems such as single point of failure and data sharing difficulty. Considering the advantages of decentralization, non-tampering and traceability, the sharing of medical data can be realized based on the blockchain system. But the risk of privacy leakage will still be faced. In order to protect the privacy of data in sharing and guarantee the medical data controlled by the data owner, we propose an attribute-based hierarchical encryption scheme to realize the access control of medical data. In order to improve the query efficiency of users, we propose a blockchain query index with a combination of skip list and Bloom filter. However, users still may encounter the privacy leakage during the query process. Therefore, supervision institutions are introduced to supervise the historical inquiry records of user groups to prevent them from doing evil things. Finally, the scheme is proved to be effective by relevant experiments.
Statistical Security in Two-Party Computation Revisited

We present a new framework for building round-optimal one-sided statistically secure two party computation (2PC) protocols in the plain model. We demonstrate that a relatively weak notion of oblivious transfer (OT), namely a three round elementary oblivious transfer $$\textsf {eOT}$$ with statistical receiver privacy, along with a non-interactive commitment scheme suffices to build a one-sided statistically secure two party computation protocol with black-box simulation. Our framework enables the first instantiations of round-optimal one-sided statistically secure 2PC protocols from the CDH assumption and certain families of isogeny-based assumptions. As part of our compiler, we introduce the following new one-sided statistically secure primitives in the pre-processing model that might also be of independent interest: The above primitives are directly constructed from $$\textsf {eOT}$$ and hence we obtain their instantiations from the same set of assumptions as our 2PC.
Vision based Human Activity Recognition using Hybrid Deep Learning

Human activity recognition is a wide research area of computer vision that finds applications in smart surveillance system, healthcare, and human robotic interactions. Nowadays, deep learning methods have achieved more interest due to its ability of executing feature extraction and classification steps simultaneously. In this paper, we have focused on the vision based human activity recognition using deep learning algorithms. Long short term memory (LSTM) is a special form of recurrent neural networks (RNN), specifically designed for long term data dependencies. Also it is a known fact that among deep learning algorithms, convolutional neural networks (CNN) have earned high performance in image classification. To overcome the limitation of LSTM in case of classification of static images, a hybrid CNN-LSTM model is proposed in which features are firstly extracted through CNN and then feed to LSTM as a sequence by the means of time distributed layer. This model is utilized for classifying six activities from two datasets which have shown the accuracy of 96.24% and 93.39% on KTH and Weizmann datasets, respectively. We have also implemented the CNN and LSTM models separately on these datasets with same parameters as used in hybrid model to study their impact on accuracy and loss.
Universal Photometric Stereo Network using Global Lighting Contexts

This paper tackles a new photometric stereo task, named universal photometric stereo. Unlike existing tasks that assumed specific physical lighting models; hence, drastically limited their usability, a solution algorithm of this task is supposed to work for objects with diverse shapes and materials under arbitrary lighting variations without assum-ig any specific models. To solve this extremely challenging task, we present a purely data-driven method, which eliminates the prior assumption of lighting by replacing the recovery of physical lighting parameters with the extraction of the generic lighting representation, named global lighting contexts. We use them like lighting parameters in a calibrated photometric stereo network to recover surface normal vectors pixelwisely. To adapt our network to a wide variety of shapes, materials and lightings, it is trained on a new synthetic dataset which simulates the appearance of objects in the wild. Our method is compared with other state-of-the-art uncalibrated photometric stereo methods on our test data to demonstrate the significance of our method.