Reviewerly/exHarmony huggingface dataset:
Lookalike Disambiguation: Improving Face Identification Performance at Top Ranks

A face identification system compares an unknown input probe image to a gallery of labeled face images in order to determine the identity of the probe image. The result of identification is a ranked match list with the most similar gallery face image at the top (rank 1) and the least similar gallery face image at the bottom. In many systems, the top ranked gallery images may look very similar to the probe image as well as to each other and can sometimes result in the misidentification of the probe image. Such similar looking faces pertaining to different identities are referred to as lookalike faces. We hypothesize that a matcher specifically trained to disambiguate lookalike face images when combined with a regular face matcher will improve overall identification performance. This work proposes reranking the initial ranked match list using a disambiguator especially for lookalike face pairs. This work also evaluates schemes to select gallery images in the initial ranked match list that should be re- ranked. Experiments on the challenging TinyFace dataset shows that the proposed approach improves the closed-set identification accuracy of a state-of-the-art face matcher.
Uncertainty-sensitive Activity Recognition: A Reliability Benchmark and the CARING Models

Beyond assigning the correct class, an activity recognition model should also be able to determine, how certain it is in its predictions. We present the first study of how well the confidence values of modern action recognition architectures indeed reflect the probability of the correct outcome and propose a learning-based approach for improving it. First, we extend two popular action recognition datasets with a reliability benchmark in form of the expected calibration error and reliability diagrams. Since our evaluation highlights that confidence values of standard action recognition architectures do not represent the uncertainty well, we introduce a new approach which learns to transform the model output into realistic confidence estimates through an additional calibration network. The main idea of our Calibrated Action Recognition with Input Guidance (CARING) model is to learn an optimal scaling parameter depending on the video representation. We compare our model with the native action recognition networks and the temperature scaling approach - a wide spread calibration method utilized in image classification. While temperature scaling alone drastically improves the reliability of the confidence values, our CARING method consistently leads to the best uncertainty estimates in all benchmark settings.
Efficient Online Subclass Knowledge Distillation for Image Classification

Deploying state-of-the-art deep learning models on embedded systems dictates certain storage and computation limitations. During the recent few years Knowledge Distillation (KD) has been recognized as a prominent approach to address this issue. That is, KD has been effectively proposed for training fast and compact deep learning models by transferring knowledge from more complex and powerful models. However, knowledge distillation, in its conventional form, involves multiple stages of training, rendering it a computationally and memory demanding procedure. In this paper, a novel single-stage self knowledge distillation method is proposed, namely Online Subclass Knowledge Distillation (OSKD), that aims at revealing the similarities inside classes, so as to improve the performance of any deep neural model in an online manner. Hence, as opposed to existing online distillation methods, we are able to acquire further knowledge from the model itself, without building multiple identical models or using multiple models to teach each other, rendering the proposed OSKD approach more efficient. The experimental evaluation on two datasets validates that the proposed method improves the classification performance.
Natural Scene Text Detection using Deep Neural Networks

The text imparts a high level of information quickly and concisely. Therefore, retrieval of this information plays a vital role in learning and understanding for humans as it has various applications in computer vision. Various methodologies for text extraction from documents and natural scene text have been proposed in the past few decades. Two of these architectures, namely, Faster R-CNN and Effective and Accurate Scene Text detector, have been executed, analyzed, and assessed by the authors. The aim was to compare them based on their nature of pipelines, number of layers, speed of execution, and extent of accuracy. While both the networks are not perfect, the EAST detector showed exceptional results compared to other architectures. Due to a high receptive field and deeper network of DenseNet201, the inclusion of dice loss function, the model showed almost 78% accuracy.
Classification of Hoax/Non-Hoax News Articles on Social Media using an Effective Deep Neural Network

The internet has become a medium to share information rapidly across the globe. On the other hand, this immense growth of the internet has also led to the sharing of excessive misleading and fake news. There have been many classifiers in the existing literature to classify fraudulent news and legitimate news. Still, they require a lot of data before effective learning, which is present in the most significant cases and it is very challenging to obtain. As deep understanding is one of the emerging technologies, in this paper, we discuss how fake news on the web can be classified using deep learning techniques with testing on several datasets and implementing using several classification models. With our proposed deep learning model, we gained an accuracy of 96.25%. The word embedding techniques have improved the text classification methods. The model architecture for the classification of hoax information has optimized to give better results by reducing the loss value. Since the model has trained on two different real-world datasets, it requires hyperparameters to classify the information.
The Proposed of a Smart Traceability System for Teak Supply Chain Based on Blockchain Technology

For Cambodia, Lao PDR, Myanmar, Thailand, Vietnam, and around 70 nations in Asia, Africa, Latin America, and Oceania, teak is one of several developing valued hardwood species that has been grown progressively in established forests. Thailand is the second-largest area of natural teak forests and the logging ban is still in force and it has strong political support. The problem of smuggling teak timber and illegal activities or illegal planting areas due to its high value has a long history in Thailand. Nowadays, a traceability system for agricultural commodities is mandatory requirement in all supply chains. This paper discusses the traceability method for identifying the validity of teak wood within the Teak Supply Chain with Distributed Ledger Technology (DLT). DLT is also called Blockchain Technology (BCT) and has the core characteristics of preserving a disseminated archive, digital encryption, traceable data and block material that cannot be absolute, which is appropriate for use in the teak supply chain and could offer a remedy. Ethereum DApp is suggested for use in this paper. Supply chain stakeholders understand that using blockchain technologies and the communication of data stored in the blockchain supply chain can guarantee that data cannot be compromised. Based on the experiment result that the traceability system with DLT is relatively supported to helps stakeholders of the teak supply chain to complete their traceability system capabilities for the benefit of teak timber and legality of teak.
Normalization of data for training and analysis by the MaskRCNN model using the k-means method for a smart refrigerator’s computer vision

Abstract The authors propose to use the k-means method for semantic image segmentation in the artificial vision of a smart refrigerator. The authors have developed a new two-tier architecture for the semantic segmentation system. In the proposed architecture, various sets of image contrast optimization settings are used to classify pixels belonging to fragments of the studied image. Experiments have shown that the proposed method has decisive advantages over existing works. The k-means method was used to cluster pixels directly into semantic groups. The clustered data is used to solve the semantic segmentation problem. For the correct selection of the number of centroids of the k-means method, the mean shift method was used. Extensive experiments with the traditional k-means algorithm and mean shift method have shown the advantage of the proposed approach in accuracy and performance.
Proximity Isolation Forests

Isolation Forests are a very successful approach for solving outlier detection tasks. Isolation Forests are based on classical Random Forest classifiers that require feature vectors as input. There are many situations where vectorial data is not readily available, for instance when dealing with input sequences or strings. In these situations, one can extract higher level characteristics from the input, which is typically hard and often loses valuable information. An alternative is to define a proximity between the input objects, which can be more intuitive. In this paper we propose the Proximity Isolation Forests that extend the Isolation Forests to non-vectorial data. The introduced methodology has been thoroughly evaluated on 8 different problems and it achieves very good results also when compared to other techniques.
Preserving Privacy with Federated Learning in Route Choice Behavior Modeling

While big data helps improve decision-making and model developments, it often runs into privacy concerns. An example would be retrieving drivers’ origin and destination information from smartphone navigation apps for developing a route choice behavior model. To conserve privacy, yet to take advantage of big data in navigation applications, the authors propose to apply a federated learning approach, which has shown promising application in predicting smartphone keyboard’s next word without sending text to the server. Additional benefits of using federated learning is to save on data communications, by sending model parameters instead of entire raw data, and to distribute the computational burden to each smartphone instead of to the main server. The results from real-world route navigation usage data from about 30,000 drivers over one year showed that the proposed federated learning approach was able to achieve very similar accuracy to the traditional centralized global model and yet assures privacy.
Secure AAC steganography scheme based on multi-view statistical distortion (SofMvD)

Advanced Audio Coding (AAC) is currently the most widely used audio compression standard in Internet applications. In this paper, a large capacity secure steganography algorithm based on the compression parameters of AAC QMDCT is proposed. In order to improve the statistical security and the imperceptibility of steganography. Based on multi-view statistical distortion, which combines the statistical distribution of Huffman codewords, QMDCT’s intra-frame and inter-frame statistical correlation, and the frequency perceptual masking in the psychoacoustic model, this paper presents a secure AAC steganography scheme SofMvD based on STCs. In the experimental results, the maximum embedding capacity of the proposed scheme is about 12 kbps. Compared with the existing methods, this method can improve steganalysis’s ability to 8%−30% and improve the auditory concealment of steganalysis without changing the embedding capacity. This scheme can also be extended to MP3.
Optimum criterion for lightweight nonlinear confusion component with multi-criteria decision making

In this article, we have designed a new scheme for the construction of the nonlinear confusion component. Our mechanism uses the notion of a semigroup, Inverse LA-semigroup, and various other loops. With the help of these mathematical structures, we can easily build our confusion component namely substitution boxes (S-boxes) without having specialized structures. We authenticate our proposed methodology by incorporating the available cryptographic benchmarks. Moreover, we have utilized the technique for order of preference by similarity to ideal solution (TOPSIS) to select the best nonlinear confusion component. With the aid of this multi-criteria decision-making (MCDM), one can easily select the best possible confusion component while selecting among various available nonlinear confusion components.
Adversarial, yet Friendly Signal Design for Secured Wireless Communication

Whenever wireless communication link becomes a unique communication means, ensuring security is just as important as achieving fast and reliable communication especially for military and medical systems. A common approach for secure wireless communication is via encryption of the information but this may fail to provide complete security (e.g., vulnerable to side-channel attack) especially when it comes with computation-limited scenarios such as Internet of Things (IoT) case. A simpler, yet powerful alternative technique to improve security is by preventing eavesdroppers from knowing correct modulation and coding (MCS) scheme. In traditional wireless communication systems, this MCS information can be secured based on sophisticate design of mutually cooperative strategies between the transmitter and the receiver. However, for deep learning (DL)-based wireless communication systems, this cooperation can be significantly simplified by just adding a well-designed adversarial signal to the transmitted signal. In this paper, we propose a novel, transferable adversarial signal design that simultaneously prevents non-cooperative eavesdroppers from achieving correct modulation scheme while ensuring the intended, cooperative receiver's correct acquisition of the modulation scheme. Extensive numerical results validate secrecy of the proposed scheme along with transferability.
Modeling Long-Term Interactions to Enhance Action Recognition

In this paper, we propose a new approach to understand actions in egocentric videos that exploit the semantics of object interactions at both frame and temporal levels. At the frame level, we use a region-based approach that takes as input a primary region roughly corresponding to the user hands and a set of secondary regions potentially corresponding to the interacting objects and calculates the action score through a CNN formulation. This information is then fed to a Hierarchical Long Short-Term Memory Network (HLSTM) that captures temporal dependencies between actions within and across shots. Ablation studies thoroughly validate the proposed approach, showing in particular that both levels of the HLSTM architecture contribute to performance improvement. Furthermore, quantitative comparisons show that the proposed approach outperforms the state-of-the-art in terms of action recognition on standard benchmarks, without relying on motion information.
Writer Identification Using Deep Neural Networks: Impact of Patch Size and Number of Patches

Traditional approaches for the recognition or identification of the writer of a handwritten text image used to relay on heuristic knowledge about the shape and other features of the strokes of previously segmented characters. However, recent works have done significantly advances on the state of the art thanks to the use of various types of deep neural networks. In most of all of these works, text images are decomposed into patches, which are processed by the networks without any previous character or word segmentation. In this paper, we study how the way images are decomposed into patches impact recognition accuracy, using three publicly available datasets. The study also includes a simpler architecture where no patches are used at all - a single deep neural network inputs a whole text image and directly provides a writer recognition hypothesis. Results show that bigger patches generally lead to improved accuracy, achieving in one of the datasets a significant improvement over the best results reported so far.
Interactive re-ranking for cross-modal retrieval based on object-wise question answering

Cross-modal retrieval methods retrieve desired images from a query text by learning relationships between texts and images. This retrieval approach is one of the most effective ways in the easiness of query preparation. Recent cross-modal retrieval is convenient and accurate when users input a query text that can uniquely identify the desired image. Meanwhile, users frequently input ambiguous query texts, and these ambiguous queries make it difficult to obtain the desired images. To alleviate these difficulties, in this paper, we propose a novel interactive cross-modal retrieval method based on question answering (QA) with users. The proposed method analyses candidate images and asks users about information that can narrow retrieval candidates effectively. By only answering the questions generated by the proposed method, users can reach their desired images even from an ambiguous query text. Experimental results show the effectiveness of the proposed method.
Learning-Based Dynamic Graph Stream Sketch

A graph stream is a kind of dynamic graph representation that consists of a consecutive sequence of edges where each edge is represented by two endpoints and a weight. Graph stream is widely applied in many application scenarios to describe the relationships in social networks, communication networks, academic collaboration networks, etc. Graph sketch mechanisms were proposed to summarize large-scale graphs by compact data structures with hash functions to support fast queries in a graph stream. However, the existing graph sketches use fixed-size memory and inevitably suffer from dramatic performance drops after a massive number of edge updates. In this paper, we propose a novel Dynamic Graph Sketch (DGS) mechanism, which is able to adaptively extend graph sketch size to mitigate the performance degradation caused by memory overload. The proposed DGS mechanism incorporates deep neural network structures with graph sketch to actively detect the query errors, and dynamically expand the memory size and hash space of a graph sketch to keep the error below a pre-defined threshold. We conducted extensive experiments on three real-world graph stream datasets, which show that DGS outperforms the state-of-the-arts with regard to the accuracy of different kinds of graph queries.
Cross-Lingual Text Image Recognition via Multi-Task Sequence to Sequence Learning

This paper considers recognizing texts shown in a source language and translating into a target language, without generating the intermediate source language text image recognition results. We call this problem Cross-Lingual Text Image Recognition (CLTIR). To solve this problem, we propose a multi-task system containing a main task of CLTIR and an auxiliary task of Mono-Lingual Text Image Recognition (MLTIR) simultaneously. Two different sequence to sequence learning methods, a convolution based attention model and a Bidirectional Long Short-Term Memory (BLSTM) model with Connectionist Temporal Classification (CTC), are adopted for these tasks respectively. We evaluate the system on a newly collected Chinese-English bilingual movie subtitle image dataset. Experimental results demonstrate the multi-task learning framework performs superiorly in both languages.
Short-Term Master-Slave Forecast Method for Distributed Photovoltaic Plants Based on the Spatial Correlation

With the large-scale integration of distributed photovoltaic (DPV) power plants, the uncertainty of photovoltaic generation is intensively influencing the secure operation of power systems. Improving the forecast capability of DPV plants has become an urgent problem to solve. However, most of the DPV plants are not able to make generation forecast on their own due to the constraints of the investment cost, data storage condition, and the influence of microscope environment. Therefore, this paper proposes a master-slave forecast method to predict the power of target plants without forecast ability based on the power of DPV plants with comprehensive forecast system and the spatial correlation between these two kinds of plants. First, a characteristics pattern library of DPV plants is established with K-means clustering algorithm considering the time difference. Next, the pattern most spatially correlated to the target plant is determined through online matching. The corresponding spatial correlation mapping relationship is obtained by numerical fitting using least squares support vector machine (LS-SVM), and the short-term generation forecast for target plants is achieved with the forecast of reference plants and mapping relationship. Simulation results demonstrate that the proposed method could improve the overall forecast accuracy by more than 52% for univariate prediction and by more than 22% for multivariate prediction and obtain short-term generation forecast for DPV or newly built DPV plants with low investment.
Using Probabilistic Methods in the Analysis of Information Security of Distributed Ledger Systems

In this paper, using probabilistic methods of the queuing theory for blockchain-based distributed ledger systems mathematical models are constructed that allow calculating a number of characteristics of these systems. The obtained mathematical models are intended for private blockchains and can be used to analyze and optimize the operation of distributed ledger systems in order to ensure their information security.
Distantly Supervised Relation Extraction via Recursive Hierarchy-Interactive Attention and Entity-Order Perception

Wrong-labeling problem and long-tail relations severely affect the performance of distantly supervised relation extraction task. Many studies mitigate the effect of wrong-labeling through selective attention mechanism and handle long-tail relations by introducing relation hierarchies to share knowledge. However, almost all existing studies ignore the fact that, in a sentence, the appearance order of two entities contributes to the understanding of its semantics. Furthermore, they only utilize each relation level of relation hierarchies separately, but do not exploit the heuristic effect between relation levels, i.e., higher-level relations can give useful information to the lower ones. Based on the above, in this paper, we design a novel Recursive Hierarchy-Interactive Attention network (RHIA) to further handle long-tail relations, which models the heuristic effect between relation levels. From the top down, it passes relation-related information layer by layer, which is the most significant difference from existing models, and generates relation-augmented sentence representations for each relation level in a recursive structure. Besides, we introduce a newfangled training objective, called Entity-Order Perception (EOP), to make the sentence encoder retain more entity appearance information. Substantial experiments on the popular New York Times (NYT) dataset are conducted. Compared to prior baselines, our RHIA-EOP achieves state-of-the-art performance in terms of precision-recall (P-R) curves, AUC, Top-N precision and other evaluation metrics. Insightful analysis also demonstrates the necessity and effectiveness of each component of RHIA-EOP.