Reviewerly/exHarmony huggingface dataset:
Chronological Age Estimation Under the Guidance of Age-Related Facial Attributes

Although the researches of facial attributes' analysis have been launched for decades, the estimation of chronological age attribute remains a big challenge. Previous researchers have found that some facial attributes (e.g., gender and race attributes) have close connections with the age attribute and make age estimation under a specific condition decided by various combinations of those age-related attributes which should be more reasonable. In this paper, we propose a generic framework based on a convolutional neural network, which can consider different conditions for age estimation and jointly output age and age-related facial attributes in the end. Compared with conventional methods, it is more efficient and universal. Besides, we view age estimation as a special multi-class ordinal classification problem and use a losses combination function to optimize the predicted probability distribution of individual age classes. These operations further improve the performance of age estimation. Finally, the proposed method achieves state-of-the-art results on both controlled and wild face datasets.
Is Bitcoin the Only Problem? A Scenario Model for the Power Demand of Blockchains

\begin{abstract} When an author under the pseudonym Satoshi Nakamoto published the paper `Bitcoin: A Peer-to-Peer Electronic Cash System' in 2008, the first cryptocurrency using the new blockchain technology was introduced. Over the last decade, more than 1000 different cryptocurrencies, such as Ethereum, Ripple, and Litecoin were developed and Bitcoin's currency had almost reached an equivalent value of \SI{20000}{\$\per BTC}. After recognizing the disrupting momentum that the blockchain technology generated, scientists started to develop blockchain use cases for the energy sector. However, the scientific literature so far offers only rough and incomplete estimations when questions about the current and future energy consumption of the Bitcoin network are raised. This paper introduces a new scenario model to estimate the mining power demand of the Bitcoin and Ethereum network. Six scenarios are developed on the basis of mining hardware efficiency and network parameter data. The results show that an increase of the mining hardware efficiency will only have a limited impact on the overall power demand of blockchain networks. Furthermore, the current power demand of the Ethereum network is in the range from \SIrange{0.6}{3}{\giga\watt} and therefore, is similar to the one of Bitcoin. In case of linear growth of the block difficulty and sigmoidal increase of the hardware efficiency until the year of 2025, the mining power demand for the Bitcoin blockchain will be approximately \SI{3}{\giga\watt}. Furthermore, the model and the scenarios are adaptable to other cryptocurrencies that use the proof-of-work consensus algorithm to create scenarios for their future power demand.
Cybersecurity for eMaintenance in railway infrastructure: risks and consequences

Recently, due to the advancements in the Information and Communication Technology, there has been lot of emphasis on digitization of the existing and newly developed infrastructure. In transportation infrastructure, in general, 80% of the assets are already in place and there has been tremendous push to move to the digital era. For efficient and effective design, construction, operation and maintenance of the infrastructure, due to this digitization, there is increasing research trend in data-driven decision-making algorithms that are proved to be effective because of several advantages. Since railway is the backbone of the society, the data-driven approaches will ensure the continuous operation, efficient maintenance, planning and potential future investments. The breach and leak of this potential data to the wrong hands might result in havoc, risk, trust, hazards and serious consequences. Hence, the main purpose of this paper is to stress the potential challenges, consequences, threats, vulnerabilities and risk management of data security in the railway infrastructure in context of eMaintenance. In addition, this paper also identifies the research methods to obtain and secure this data for potential possible research.
Local-Entropy Based Approach for X-Ray Image Segmentation and Fracture Detection

The paper proposes a segmentation and classification technique for fracture detection in X-ray images. This novel rotation-invariant method introduces the concept of local entropy for de-noising and removing tissue from the analysed X-ray images, followed by an improved procedure for image segmentation and the detection of regions of interest. The proposed local Shannon entropy was calculated for each image pixel using a sliding 2D window. An initial image segmentation was performed on the entropy representation of the original image. Next, a graph theory-based technique was implemented for the purpose of removing false bone contours and improving the edge detection of long bones. Finally, the paper introduces a classification and localisation procedure for fracture detection by tracking the difference between the extracted contour and the estimation of an ideal healthy one. The proposed hybrid method excels at detecting small fractures (which are hard to detect visually by a radiologist) in the ulna and radius bones-common injuries in children. Therefore, it is imperative that a radiologist inspecting the X-ray image receives a warning from the computerised X-ray analysis system, in order to prevent false-negative diagnoses. The proposed method was applied to a data-set containing 860 X-ray images of child radius and ulna bones (642 fracture-free images and 218 images containing fractures). The obtained results showed the efficiency and robustness of the proposed approach, in terms of segmentation quality and classification accuracy and precision (up to 91.16 % and 86.22 % , respectively).
Nesting Classical Actuarial Models into Neural Networks

Neural network modeling often suffers the deficiency of not using a systematic way of improving classical statistical regression models. In this tutorial we exemplify the proposal of the editorial of ASTIN Bulletin 2019/1. We embed a classical generalized linear model into a neural network architecture, and we let this nested network approach explore model structure not captured by the classical generalized linear model. In addition, if the generalized linear model is already close to optimal, then the maximum likelihood estimator of the generalized linear model can be used as initialization of the fitting algorithm of the neural network. This saves computational time because we start the fitting algorithm in a reasonable parameter. As a by-product of our derivations, we present embedding layers and representation learning which often provides a more efficient treatment of categorical features within neural networks than dummy and one-hot encoding.
Mixed Reality Deictic Gesture for Multi-Modal Robot Communication

In previous work, researchers have repeatedly demonstrated that robots' use of deictic gestures enables effective and natural human-robot interaction. However, new technologies such as augmented reality head mounted displays enable environments in which mixed-reality becomes possible, and in such environments, physical gestures become but one category among many different types of mixed reality deictic gestures. In this paper, we present the first experimental exploration of the effectiveness of mixed reality deictic gestures beyond physical gestures. Specifically, we investigate human perception of videos simulating the display of allocentric gestures, in which robots circle their targets in users' fields of view. Our results suggest that this is an effective communication strategy, both in terms of objective accuracy and subjective perception, especially when paired with complex natural language references.
Metaheuristic FIR filter with game theory based compression technique- A reliable medical image compression technique for online applications

In image processing, there are many inventions results in processing the images effectively suitable for applications like change detection, SAR image analysis, Television i.e. for compressing videos for effective transmission etc. In Social network, exchange of bulk information needs an effective compression technique to compress, transmit, decompress and reconstruct the original quality of source information. Compression technique can reduce the storage deficiency, energy consumption. In this proposed methodology, an effective Finite Impulse Response filter designed with meta-heuristic techniques-a game theory-based approach provides a compromising data compression ratio for standard input images. Effective quality indicators such as compression ratio, Mean Square Error stands as proof for the implications of the anticipated technique in medical applications as it affords extraordinary results for compression of Ultrasound and MRI images, thus providing a pathway for a trans receiving data with minimum space required.
Correction of Barrel Distortion in Fisheye Lens Images Using Image-Based Estimation of Distortion Parameters

Images acquired by a fisheye lens camera contain geometric distortion that results in deformation of the object's shape. To correct the lens distortion, existing methods use prior information, such as calibration patterns or lens design specifications. However, the use of a calibration pattern works only when an input scene is a 2-D plane at a prespecified position. On the other hand, the lens design specifications can be understood only by optical experts. To solve these problems, we present a novel image-based algorithm that corrects the geometric distortion. The proposed algorithm consists of three stages: i) feature detection, ii) distortion parameter estimation, and iii) selection of the optimally corrected image out of multiple corrected candidates. The proposed method can automatically select the optimal amount of correction for a fisheye lens distortion by analyzing characteristics of the distorted image using neither prespecified lens design parameters nor calibration patterns. Furthermore, our method performs not only on-line correction by using facial landmark points, but also off-line correction described in subsection III-C. As a result, the proposed method can be applied to a virtual reality (VR) or augmented reality (AR) camera with two fisheye lenses in a field-of-view (FOV) of 195°, autonomous vehicle vision systems, wide-area visual surveillance systems, and unmanned aerial vehicle (UAV) cameras.
Entropy Estimation for ADC Sampling-Based True Random Number Generators

True random number generators (TRNGs) are widely used in cryptographic systems, and their security is the base of many cryptographic algorithms and protocols. At present, entropy estimation based on a stochastic model is a well-recommended approach to evaluate the security of a specific TRNG structure. Besides, the generation speed is also an important property for TRNGs. For this purpose, an analog-to-digital converter (ADC) can be employed to sample the noisy signal to achieve high bit rate. However, no research focuses on the entropy estimation on the basis of the stochastic model toward ADC sampling. In this paper, we propose an entropy estimation for the ADC sampling-based TRNG through extending an existing model. In particular, we present an equivalent model to estimate the entropy of any single bit in the converted sample obtained by the ADC sampling. Furthermore, we propose a method of the entropy estimation for the multi-bit ADC output, which provides the lower bound of the entropy. By conducting simulations and hardware experiments on this type of TRNG, we confirm the correctness of the proposed entropy estimation theory. The prototype chip is fabricated in the SMIC 65-nm process, and the consumed power is 34 mW. The random bit sequences compatible with the AIS 31 standard are generated at a speed of 132.3 Mb/s. The sequences are able to pass the rigorous statistical test suites, including NIST SP 800-22, Diehard, and TestU01 (containing the Big Crush test), after simple post-processing at a bit rate of around 33 Mb/s.
Crowd panic state detection using entropy of the distribution of enthalpy

The motion state of a crowd can be described using the change of energy information of pedestrians’ motion. Enthalpy can be used to describe the state of a system by consider the energy of the system. The distribution of enthalpy will change follow the change of a crowd state. Entropy is very suitable for measure the degree of disorder of a system Based on this idea, a crowd panic state detection method is proposed in this paper according to the entropy of the distribution of enthalpy. Firstly, the optical flow of two frames is calculated to get the motion information of a crowd. Secondly, based on the results of optical flow, the pedestrian moving region can be gained based on flow field visualization and texture segmentation method. Therefore the enthalpy in a tiny image region can be gained in the effective crowd movement region. The distribution of the enthalpy for the motion field with moving pedestrians can be gained. Based on the distribution of enthalpy, entropy of each frame can be calculated to describe the crowd state. Experimental results show the panic crowd motion state has higher entropy, and normal crowd state has lower entropy.
Computer-assisted Acute Lymphoblastic Leukemia detection and diagnosis

Leukemia is a cancer of white blood cells (WBCs) which damages blood and bone marrow of human body. It can be fatal disease if not diagnose at earlier stage. Generally complete blood count (CBC) or morphological image analysis is used to manually diagnose the leukemia cells. These methods are time consuming and less accurate which needs to be fixed. In this paper we have proposed an automated technique for the detection of acute lymphoblastic leukemia by microscopic blood image analysis. This approach first segment out the different types of cells from the image i-e. white blood cells, red blood cells and platelets. After that Lymphocytes are separated from the white blood cells. Then shape and color features are extracted from these lymphocytes which are given to SVM classifier to classify the cells into normal and blast. We have achieved an overall accuracy of 93.7%, senstivity of 92% and specificity of 91% by testing our proposed algorithm over the ALL-IDB-1 dataset. This automated leukemia detection system found to be more effective, fast and accurate as compare to manual diagnosing methods.
Long Document Classification From Local Word Glimpses via Recurrent Attention Learning

Document classification requires to extract high-level features from low-level word vectors. Typically, feature extraction by deep neural networks makes use of all words in a document, which cannot scale well for a long document. In this paper, we propose to tackle the long document classification task by incorporating the recurrent attention learning framework, which can produce the discriminative features with significantly less words. Specifically, the core work is to train a recurrent neural network (RNN)-based <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">controller</i> , which can focus its attention on the discriminative parts. Then, the glimpsed feature is extracted by a typical short text level convolutional neural network (CNN) from the focused group of words. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">controller</i> locates its attention according to the context information, which consists of the coarse representation of the original document and the memorized glimpsed features. By glimpsing a few groups, the document can be classified by aggregating these glimpsed features and the coarse representation. For our collected 11-class 10 000-word arXiv paper data set, the proposed method outperforms two subsampled deep CNN baseline models by a large margin given much less observed words.
Accelerating content-defined-chunking based data deduplication by exploiting parallelism

Data deduplication, a data reduction technique that efficiently detects and eliminates redundant data chunks and files, has been widely applied in large-scale storage systems. Most existing deduplication-based storage systems employ content-defined chunking (CDC) and secure-hash-based fingerprinting (e.g., SHA1) to remove redundant data at the chunk level (e.g., 4 KB/8 KB chunks), which are extremely compute-intensive and thus time-consuming for storage systems. Therefore, we present P-Dedupe, a pipelined and parallelized data deduplication system that accelerates deduplication process by dividing the deduplication process into four stages (i.e., chunking, fingerprinting, indexing, and writing), pipelining these four stages with chunks & files (the processing data units for deduplication), and then parallelizing CDC and secure-hash-based fingerprinting stages to further alleviate the computation bottleneck. More important, to efficiently parallelize CDC with the requirements of both maximal and minimal chunk sizes and inspired by the MapReduce model, we first split the data stream into several segments (i.e., “Map”), where each segment will be running CDC in parallel with an independent thread, and then re-chunk and join the boundaries of these segments (i.e., “Reduce”) to ensure the chunking effectiveness of parallelized CDC. Experimental results of P-Dedupe with eight datasets on a quad-core Intel i7 processor suggest that P-Dedupe is able to accelerate the deduplication throughput near linearly by exploiting parallelism in the CDC-based deduplication process at the cost of only 0.02% decrease in the deduplication ratio. Our work provides contributions to big data science to ensure all files go through deduplication process quickly and thoroughly, and only process and analyze the same file once, rather than multiple times.
Distributed Deep Learning Strategies for Automatic Speech Recognition

In this paper, we propose and investigate a variety of distributed deep learning strategies for automatic speech recognition (ASR) and evaluate them with a state-of-the-art Long short-term memory (LSTM) acoustic model on the 2000-hour Switchboard (SWB2000), which is one of the most widely used datasets for ASR performance benchmark. We first investigate what are the proper hyper-parameters (e.g., learning rate) to enable the training with sufficiently large batch size without impairing the model accuracy. We then implement various distributed strategies, including Synchronous (SYNC) , Asynchronous Decentralized Parallel SGD (ADPSGD) and the hybrid of the two HYBRID, to study their runtime/accuracy trade-off. We show that we can train the LSTM model using ADPSGD in 14 hours with 16 NVIDIA P100 GPUs to reach a 7.6% WER on the Hub5-2000 Switchboard (SWB) test set and a 13.1% WER on the Call-Home (CH) test set. Furthermore, we can train the model using HYBRID in 11.5 hours with 32 NVIDIA V100 GPUs without loss in accuracy.
End-to-end Code-switched TTS with Mix of Monolingual Recordings

State-of-the-art text-to-speech (TTS) synthesis models can produce monolingual speech with high intelligibility and naturalness. However, when the models are applied to synthesize code-switched (CS) speech, the performance declines seriously. Conventionally, developing a CS TTS system requires multilingual data to incorporate language-specific and cross-lingual knowledge. Recently, end-to-end (E2E) architecture has achieved satisfactory results in monolingual TTS. The architecture enables the training from one end of alphabetic text input to the other end of acoustic feature output. In this paper, we explore the use of E2E framework for CS TTS, using a combination of Mandarin and English monolingual speech corpus uttered by two female speakers. To handle alphabetic input from different languages, we explore two kinds of encoders: (1) shared multilingual encoder with explicit language embedding (LDE); (2) separated monolingual encoder (SPE) for each language. The two systems use identical decoder architecture, where a discriminative code is incorporated to enable the model to generate speech in one speaker's voice consistently. Experiments confirm the effectiveness of the proposed modifications on the E2E TTS framework in terms of quality and speaker similarity of the generated speech. Moreover, our proposed systems can generate controllable foreign-accented speech at character-level using only mixture of monolingual training data.
An Artificial Neural Network Approach to Power Consumption Model Construction for Servers in Cloud Data Centers

The power consumption estimation or prediction of cloud servers is the basis of energy-aware scheduling to realize energy saving in cloud datacenters. The existing works are mainly based on the static mathematical formulas which establish the relationship between the server power consumption and the system performance. However, these models are weak in adaptability and generalization ability, not adaptable to the changes and fluctuation of different workload, and demanding on the clear and profound understanding of the inner relationship among related power consumption parameters. Therefore, we propose the ANN (Artificial Neural Network) method to model the power consumption of the servers in datacenters, a kind of end-to-end black box model. We performed a fine-grained and in-depth analysis about the system performance and power consumption characteristics of the CPU, memory, and disk of the server running different types of task loads, and selected a set of performance counters that can fully reflect the status of system power consumption as the input of the model. Then, we establish power consumption models based on BP neural network, Elman neural network, and LSTM neural network, respectively. In order to get a better result, we use data collected from four different types of task loads (i.e., CPU-intensive, memory-intensive, I/O-intensive, and mixed load) to train, validate, and test our target models. The experimental results show that, compared with multiple linear regression and support vector regression, the proposed three power models have better performance in predicting the server's real-time power consumption.
DDLSTM: Dual-Domain LSTM for Cross-Dataset Action Recognition

Domain alignment in convolutional networks aims to learn the degree of layer-specific feature alignment beneficial to the joint learning of source and target datasets.While increasingly popular in convolutional networks, there have been no previous attempts to achieve domain alignment in recurrent networks.Similar to spatial features, both source and target domains are likely to exhibit temporal dependencies that can be jointly learnt and aligned.In this paper we introduce Dual-Domain LSTM (DDL-STM), an architecture that is able to learn temporal dependencies from two domains concurrently.It performs cross-contaminated batch normalisation on both input-tohidden and hidden-to-hidden weights, and learns the parameters for cross-contamination, for both single-layer and multi-layer LSTM architectures.We evaluate DDLSTM on frame-level action recognition using three datasets, taking a pair at a time, and report an average increase in accuracy of 3.5%.The proposed DDLSTM architecture outperforms standard, fine-tuned, and batch-normalised LSTMs.