Reviewerly/exHarmony huggingface dataset:
Invoice Classification Using Deep Features and Machine Learning Techniques

Invoices are issued by companies, banks and different organizations in different forms including handwritten and machine-printed ones; sometimes, receipts are included as a separated form of invoices. In current practice, normally, classifying these types is done manually, since each needs a special kind of processing such as making them suitable for optical character recognition systems (OCR). Classifying the invoices manually to different categories is a hard and time-consuming task. Therefore, we propose an automatic approach to classify invoices into three types: handwritten, machine-printed and receipts. The proposed method is based on extracting features using the deep convolutional neural network AlexNet. The features are classified using various machine learning algorithms, namely including Random Forests, K-nearest neighbors (KNN), and Naive Bayes. Different cross-validation approaches are applied in the experiments to ensure the effectiveness of the proposed solution. The best classification result was 98.4% (total accuracy), which was achieved by the KNN, such an almost perfect performance allows the proposed method to be used in practice as a preprocess for OCR systems, or as a standalone application.
Assessing the Meaning of Emojis for Emotional Awareness - A Pilot Study

Emojis are increasingly being used in today’s social communication - both formally in team messaging systems as well as informally via text messages on phones. Besides being used in social communication, emojis might also be a suitable mechanism for emotion (self-)assessment. Indeed, emojis can be expected to be familiar to people of different social groups and do not depend on the mastery of a specific language. However, emojis could be interpreted very differently from their actual intent. In order to determine whether people interpret emojis (specific to emotional states) in a consistent manner, we conducted an online survey on nine emojis with 386 people. The results show that the emojis representing anger, sadness, joy, surprise, and neutral state are interpreted as they were intended, independent of age and gender. Interpretations of other emojis such as Unamused Face and Face Screaming in Fear depend on age, and thus are not as useful for probing for emotion in a study setting unless all participants belong to the same age category. The Face with Rolling Eyes emoji is interpreted differently by gender and finally the Nauseated Face emoji resulted in no conclusive interpretation.
Boosting Convolutional Neural Networks Performance Based on FPGA Accelerator

Convolutional Neural Network (CNN) has been extensively used for image recognition due to its great accuracy. This accuracy is achieved through emulating the optic nerves behavior in living human beings. The speedy progress of the current applications derived from deep learning algorithms has extra enhanced research and developments. More specifically, several deep CNN accelerators have been planned on FPGA-based platform, due to its fast development round, reconfigure-ability, and high performance. The FPGA is extremely faster than the CPU because it based on parallel mechanism, as well as, consumes very low energy. This paper employs the FPGA in establishing the CNN architecture of type VGG16 model. The FPGA solves the convolutional computations for accelerating the computation time by 11%, without losing much fidelity when using 16-bit fixed-point data format rather than 32-bit floating-point data format.
A Novel Hybrid Meta-Heuristic Algorithm Based on the Cross-Entropy Method and Firefly Algorithm for Global Optimization

Global optimization, especially on a large scale, is challenging to solve due to its nonlinearity and multimodality. In this paper, in order to enhance the global searching ability of the firefly algorithm (FA) inspired by bionics, a novel hybrid meta-heuristic algorithm is proposed by embedding the cross-entropy (CE) method into the firefly algorithm. With adaptive smoothing and co-evolution, the proposed method fully absorbs the ergodicity, adaptability and robustness of the cross-entropy method. The new hybrid algorithm achieves an effective balance between exploration and exploitation to avoid falling into a local optimum, enhance its global searching ability, and improve its convergence rate. The results of numeral experiments show that the new hybrid algorithm possesses more powerful global search capacity, higher optimization precision, and stronger robustness.
A lighten CNN-LSTM model for speaker verification on embedded devices

Augmented by deep learning methods, the performance of speaker recognition pipeline has been drastically boosted. For the scenario of smart home, the algorithms of speaker recognition should be user friendly and has high speed, high precision and low resource demand. However, most of the existing algorithms are designed without considering these four performance requirements simultaneously. To fill this gap, this paper proposes a text-independent speaker verification model. Specifically, the lighten network scheme is constructed using one convolution layer, two bilateral Long Short-term Memory (LSTM) layers and one fully connected layer. Utterance segments are mapped to a hypersphere where cosine similarity is used to measure the degree of difference between speakers. Then we analyze the defects of Additive Angular Margin (AAM) loss and propose a 3-stage training method. Softmax pre-training is used for avoiding divergence. After pre-training, AAM loss is adopted to boost training process. In the end, we use triplet loss to further fine-tune the model. Short-term speech utterances are used in training and testing. The experimental results demonstrate that the proposed model reaches 1.17% Equal Error Rate (EER) on a 200 persons benchmark with real-time inference speed on a generic embedded device.
Pec: Proactive Elastic Collaborative Resource Scheduling in Data Stream Processing

In the Distributed Parallel Stream Processing Systems (DPSPS), elastic resource allocation allows applications to dynamically response to workload fluctuations. However, resource provisioning can be particularly challenging, due to the unpredictability of the workload. In addition, unlike CPU resources, bandwidth resources are often ignored in resource allocation. Moreover, resource allocation and resource placement are considered separately. In this paper, we investigate the proactive elastic resource scheduling problem for computation-intensive and communication-intensive applications, which aims at meeting the latency requirement with the minimal energy cost, and propose a dynamic collaborative strategy from the systemic perspective. Specifically, we first model a collaborative workload prediction pattern to accurately predict the upcoming workload, and construct a latency estimation model to estimate the latency of the application. Then, we design an energy-efficient resource pre-allocation method, in which the CPU frequency adjustment and the stability of resource reconfigurations are both considered. Finally, we present a communication-aware resource placement approach. Simulation results show that, compared with the reactive strategies, our strategy achieves an obviously better latency performance, and effectively avoids unnecessary resource adjustments. Meanwhile, the energy consumption is about saved by 50 percent on average, and the communication cost is maintained at a very low level of 4 percent.
Self-Supervised Quantization of Pre-Trained Neural Networks for Multiplierless Acceleration

To host intelligent algorithms such as Deep Neural Networks on embedded devices, it is beneficial to transform the data representation of neural networks into a fixed-point format with reduced bit-width. In this paper we present a novel quantization procedure for parameters and activations of pre-trained neural networks. For 8 bit linear quantization, our procedure achieves close to original network performance without retraining and consequently does not require labeled training data. Additionally, we evaluate our method for power-of-two quantization as well as for a two-hot quantization scheme, enabling shift-based inference. To underline the hardware benefits of a multiplierless accelerator, we propose the design of a shift-based processing element.
Automatic Sign Language Translation to Improve Communication

Over the last years, there has been an increase in hearing-impaired students who use sign language as their main form of communication attending higher education institutions around the world. The knowledge that their comprehension of texts is reduced due to sentence structure differences causes a need for more solutions to improve communication and support students in environments where they are unable to be accompanied by sign interpreters. This article details the improvements and current structure of the VirtualSign platform, a bidirectional sign language to text translation tool that has been in development since 2015. The platform is divided into two main parts, sign to text and text to sign, and both components are described and explained. The solution has received positive feedback on several tests and a pilot experiment, and is being developed with partnerships with sign interpreters from six different European countries. Some planned improvements and future functionalities for the tool are also mentioned and detailed.
Emoji as social semiotic resources for meaning-making in discourse: Mapping the functions of the toilet emoji in Cher’s tweets about Donald Trump

Emoji have emerged as a significant feature of modern discourse through the proliferation of social media. Noted for their ability to create meaning across linguistic boundaries, emoji are strongly associated with informal interaction where they serve to build and maintain social bonds. Following in the footsteps of Zappavigna (2011, 2015, 2018) I use a social semiotic approach to analyze how the celebrity, Cher, creates meaning across the strata of discourse and builds affinity with her followers through the use of the toilet emoji in relation to Donald Trump. The approach is successful at elucidating the complex layers of meaning which a single emoji can enact, providing further evidence of the value of the social semiotic approach in the context of global media. The affinity that is created by the toilet emoji can be understood through attitude-ideation couplings, where the coupling is made more 'bondable' to her ambient audience through the system of promotion. The possibility of an emoji enacting both attitude and ideation in a coupling is suggested and implications for how we understand certain semiotic choices discussed.
A Novel Deep Fuzzy Classifier by Stacking Adversarial Interpretable TSK Fuzzy Sub-classifiers with Smooth Gradient Information

Different from our previous stacked-structure-based deep fuzzy classifier, in this paper, we explore the distinctive role of adversarial outputs of training samples in enhancing the classification performance of a stacked-structure-based deep fuzzy classifier. In order to achieve such goals, an adversarial Takagi-Sugeno-Kang (TSK) fuzzy classifier, which is denoted as TSKa, is proposed. With the TSKa, interpretable IF parts of first-order fuzzy rules can be generated by the random selection of fixed linguistic terms along each feature. According to our theoretical analysis, adversarial outputs of training samples enhance TSKa's generalization capability, thereby, resulting in the potential feasibility of leveraging their smooth gradient information with respect to the inputs in the training input space to construct a stacked-structure-based deep fuzzy classifier. In this paper, a novel deep fuzzy classifier is devised by stacking a series of TSKa sub-classifiers and training them by a deep learning strategy. An advantage of the proposed deep fuzzy classifier is its easy yet fast training. The training of each layer consists of two basic steps: computation of the smooth gradient information of adversarial outputs with respect to the inputs, and fast training of each corresponding TSKa by the least learning machine method. Comprehensive experiments on both benchmark datasets and an industrial case demonstrate the promising performance and advantages of the proposed deep fuzzy classifier.
Scalable and Forward Secure Network Attestation With Privacy-Preserving in Cloud-Assisted Internet of Things

To ensure the normal work of the whole system in the context of the Internet of Things (IoT), remote attestation for each smart device should be guaranteed. However, considering the huge number of smart devices, the traditional individual attestation schemes cannot meet the requirements in terms of efficiency. In addition, protecting the sensitive identity information related to the devices also needs to be supported. Furthermore, avoiding security risks caused by the secret key exposures is very important in the context of the IoT, where the compromise of terminal devices frequently occurs. To solve these challenges, in this paper, we present a new network attestation scheme that, for the first time, provides scalability, forward-security, and privacy preserving simultaneously. The new scheme allows anyone to publicly verify a collective attestation, and any identity information of the provers will not be revealed to the verifier. Moreover, it provides unforgeability and confidentiality of the previous messages even though the current secret key is exposed. Considering the limited resources of lightweight devices in the context of the IoT, our scheme outsources the main computational task to an untrusted cloud server. Finally, our security proof and performance evaluation show that our scheme is secure and feasible.
Residual convolutional neural network with attentive feature pooling for end-to-end language identification from short-duration speech

The problem of language identification from speech is tackled in this work. Residual convolutional neural networks are employed to this end, aiming at exploiting the ability of such architectures to take into account large contextual segments of input data. Moreover, learnable attention mechanisms are introduced on top of the convolutional stack for data-driven feature pooling across time, enabling the computation of fixed-dimension representations given varying-length speech segments as input. Training is performed using a combination of language identification and metric learning via triplet loss minimization, aimed at enforcing class separability within the embeddings space. Evaluation is performed across different conditions, such as multi-class classification, short-duration test utterances, and confusing languages, for the closed-set case, while open-set performance is evaluated with the introduction of unseen languages. At test time, end-to-end scoring along with cosine similarity and PLDA are employed, outperforming state-of-the-art benchmark methods, such as i-vectors by improving the average cost by 30% to 40% depending on the evaluation condition.
Vehicle Speed Estimation Based on 3D ConvNets and Non-Local Blocks

Vehicle speed estimation is an important problem in traffic surveillance. Many existing approaches to this problem are based on camera calibration. Two shortcomings exist for camera calibration-based methods. First, camera calibration methods are sensitive to the environment, which means the accuracy of the results are compromised in some situations where the environmental condition is not satisfied. Furthermore, camera calibration-based methods rely on vehicle trajectories acquired by a two-stage tracking and detection process. In an effort to overcome these shortcomings, we propose an alternate end-to-end method based on 3-dimensional convolutional networks (3D ConvNets). The proposed method bases average vehicle speed estimation on information from video footage. Our methods are characterized by the following three features. First, we use non-local blocks in our model to better capture spatial–temporal long-range dependency. Second, we use optical flow as an input in the model. Optical flow includes the information on the speed and direction of pixel motion in an image. Third, we construct a multi-scale convolutional network. This network extracts information on various characteristics of vehicles in motion. The proposed method showcases promising experimental results on commonly used dataset with mean absolute error (MAE) as 2.71 km/h and mean square error (MSE) as 14.62 .
Different Model for Hand Gesture Recognition with a Novel Line Feature Extraction

Hand gestures are commonly used for communication between both impaired community and normal people. Sign languages stand for the human languages of deaf people. They form the most growing domain of research worldwide. A number of techniques was developed in this area lately. It is recognized by means of deducing the features involved in the use of hand gesture. As a matter of fact, various approaches, namely the vision-based, the data-glove-based, the colored-marker and the Electromyogram (EMG) approaches have been utilized by researchers to recognize the different hand gestures implemented in many different fields such as the whole approaches which can be divided into four main categories, viz. Data Collection, Image Processing, Feature Extraction and Gesture Recognition. Only few of those categories have been discussed in this paper to be compared between the accuracy rates by applying Artificial Neural Network (ANN) classification. This classification is based on different models and a novel method for Real-Time Hand Gesture Recognition System (RTHGRS). The latter has used one line (fifty features) extracted from black and white processed images to recognize the numbers from (1-10) in Kurdish Sign Language (KurdSL) using one hand only with accuracy 98%.
E-victimization and e-predation theory as the dominant aggressive communication: the case of cyber bullying

The paper will focus on the identification of several key criteria in e-discourses via the constitution of e-images of the Anonymous arising from e-medias (Facebook, twitter, Snapchat, WeChat, etc.) with a specific focus on youngsters and their (ab)uses of these communication channels to facilitate digital predation, and so to lead to e-victimization. I will explain the specifics of e-victimization discourse taking into consideration its triadic dimension. I will then be able to conceptualize a dominant e-communication and the e-victimization theory around central ideas of dynamics of violence, gender discrimination and power abuse leading to a semio-sphere with a deep focus on anonymity, exposure, frequency, and insecurity as indicators of collective e-delinquency and proneness to e-victimization.
Using developer eye movements to externalize the mental model used in code summarization tasks

Eye movements of developers are used to speculate the mental cognition model (i.e., bottom-up or top-down) applied during program comprehension tasks. The cognition models examine how programmers understand source code by describing the temporary information structures in the programmer's short term memory. The two types of models that we are interested in are top-down and bottom-up. The top-down model is normally applied as-needed (i.e., the domain of the system is familiar). The bottom-up model is typically applied when a developer is not familiar with the domain or the source code. An eye-tracking study of 18 developers reading and summarizing Java methods is used as our dataset for analyzing the mental cognition model. The developers provide a written summary for methods assigned to them. In total, 63 methods are used from five different systems. The results indicate that on average, experts and novices read the methods more closely (using the bottom-up mental model) than bouncing around (using top-down). However, on average novices spend longer gaze time performing bottom-up (66s.) compared to experts (43s.)
Towards Secure and Fair IIoT-Enabled Supply Chain Management via Blockchain-Based Smart Contracts

Integrating the Industrial Internet of Things (IIoT) into supply chain management enables flexible and efficient on-demand exchange of goods between merchants and suppliers. However, realizing a fair and transparent supply chain system remains a very challenging issue due to the lack of mutual trust among the suppliers and merchants. Furthermore, the current system often lacks the ability to transmit trade information to all participants in a timely manner, which is the most important element in supply chain management for the effective supply of goods between suppliers and the merchants. This paper presents a blockchain-based supply chain management system in the IIoT. The proposed system takes advantage of blockchain technology in terms of its transparency and tamper-proof nature to support fair goods exchange between merchants and suppliers. Additionally, the decentralization and pseudonymity property will play a significant role in preserving the privacy of participants in the blockchain. In particular, fairness in the IIoT is first defined. Then, a design for a smart contract for fair goods exchange is presented to prevent malicious behavior through imposing penalties. The proposed system was prototyped on Ethereum and experiments were conducted to demonstrate its feasibility.
Distributed H∞-consensus filtering for target state tracking over a wireless filter network with switching topology, channel fading and packet dropouts

This paper is concerned with the problem of distributed H∞-consensus filtering for target state tracking over a wireless filter network. The wireless filter network consists of a large number of filter nodes. The network topology is supposed to be switching or changeable over time. Data communication between filter nodes is subject to some network-induced constraints including packet dropouts and channel fading. Different from some existing results, packet dropouts and channel fading are assumed to occur not only in the measurement process for sensors but also in the transmission process among filter nodes. Within such settings, a distributed H∞-consensus filtering method is developed to ensure both the estimation accuracy of filters and the robustness of the filtering error system against network-induced constraints. Criteria on designing desired distributed H∞ consensus-based filter are derived in terms of a set of linear matrix inequalities. Numerical examples are given to verify the effectiveness of the proposed target state tracking filtering algorithm.
Video tampering localisation using features learned from authentic content

Video tampering detection remains an open problem in the field of digital media forensics. As video manipulation techniques advance, it becomes easier for tamperers to create convincing forgeries that can fool human eyes. Deep learning methods have already shown great promise in discovering effective features from data, particularly in the image domain; however, they are exceptionally data hungry. Labelled datasets of varied, state-of-the-art, tampered video which are large enough to facilitate machine learning do not exist and, moreover, may never exist while the field of digital video manipulation is advancing at such an unprecedented pace. Therefore, it is vital to develop techniques which can be trained on authentic or synthesised video but used to localise the patterns of manipulation within tampered videos. In this paper, we developed a framework for tampering detection which derives features from authentic content and utilises them to localise key frames and tampered regions in three publicly available tampered video datasets. We used convolutional neural networks to estimate quantisation parameter, deblock setting and intra/inter mode of pixel patches from an H.264/AVC sequence. Extensive evaluation suggests that these features can be used to aid localisation of tampered regions within video.