Reviewerly/exHarmony huggingface dataset:
Forensic speaker recognition: A new method based on extracting accent and language information from short utterances

This paper presents a new method for Forensic Speaker Recognition (FSR). The new method is based on extracting accent and language information from short utterances. Accent Classification (AC) and Language Identification (LI) play important role in the identification of people of different groups, communities and origins due to different speaking styles and native languages. In a multilingual society, the forensic experts use AC and LI to reduce search space for suspect recognition to regional and ethnic groups. In this paper, we use different baseline and deep learning methods to automate this process. The baseline methods used are Gaussian Mixture Model-Universal Background Model (GMM-UBM), i-vector and Gaussian Mixture Model-Support Vector Machine (GMM-SVM). The Mel-Frequency Cepstral Coefficients (MFCC) are used as speech features in the baseline methods. The deep learning methods used are Convolutional Neural Network (CNN) and Deep Neural Network (DNN). The recently proposed CNN based methods like VGGVox and GMM-CNN are used. VGGVox and GMM-CNN use speech spectrograms. In case of DNN, x-vectors method is used, which is based on DNN embedding. The experimental results show that GMM-SVM demonstrates better FSR performance compared to GMM-UBM and i-vector methods. Whereas, x-vectors method performs better than GMM-CNN and VGGVox methods. It also performs better than GMM-SVM method. The experimental results show that x-vectors method demonstrates 80.4% FSR accuracy. With AC, it achieves 85.4% accuracy. With LI, its accuracy is 90.2%. Whereas by combining AC and LI it obtains 95.1% accuracy. This shows that the proposed method based on AC and LI gives promising results.
Security Analysis on Tangle-Based Blockchain Through Simulation

Tangle provides an enlightening paradigm for DAG-based structures. We build a simple but flexible simulation network for Tangle by identifying its features. Based on that, we construct three types of attack strategies via defining basic actions and behaviours. We further evaluate these attacks in multi-dimensions with 12 sets of experiments, followed by comprehensive discussions. The results show the trend under different strategies and configurations. Our work provides an educational example for both attack and defense towards Tangle-based blockchains.
Learning modulation filter networks for weak signal detection in noise

Weak signal detection is a challenging yet significant problem in the field of radio communication. Although hand-crafted filters are widely used in signal processing, they are challenged by the weak signal detection task with unknown background noise especially in the range of 0-5dB. In this paper, we propose the learning modulation filter networks (LMFNs) to improve the detection performance. The approach is based on a two-stage optimization scheme which addresses filter learning, attention mechanism and classification in a unified framework. Modulation filters are built to enhance the capacity of the learned filters, and the attention mechanism further characterizes the saliency properties of the input signal. LMFNs reduce the storage size of the network while achieving the state-of-the-art performance by a significant margin compared to traditional cognitive radio approaches. We establish a weak signal dataset that contains unmanned aerial vehicle (UAV) communication signals in a real-terrain environment. The source code and dataset will be made publicly available soon.
Online Automatic Speech Recognition With Listen, Attend and Spell Model

The Listen, Attend and Spell (LAS) model and other attention-based automatic speech recognition (ASR) models have known limitations when operated in a fully online mode. In this letter, we analyze the online operation of LAS models to demonstrate that these limitations stem from the handling of silence regions and the reliability of online attention mechanism at the edge of input buffers. We propose a novel and simple technique that can achieve fully online recognition while meeting accuracy and latency targets. For the Mandarin dictation task, our proposed approach can achieve a character error rate in online operation that is within 4% relative to an offline LAS model. The proposed online LAS model operates at 12% lower latency relative to a conventional neural network hidden Markov model hybrid of comparable accuracy. We have validated the proposed method through a production scale deployment, which, to the best of our knowledge, is the first such deployment of a fully online LAS model.
Multi-Horizon Forecasting of Global Horizontal Irradiance Using Online Gaussian Process Regression: A Kernel Study

In the present paper, global horizontal irradiance (GHI) is modelled and forecasted at time horizons ranging from 30 min to 48 h, thus covering intrahour, intraday and intraweek cases, using online Gaussian process regression (OGPR) and online sparse Gaussian process regression (OSGPR). The covariance function, also known as the kernel, is a key element that deeply influences forecasting accuracy. As a consequence, a comparative study of OGPR and OSGPR models based on simple kernels or combined kernels defined as sums or products of simple kernels has been carried out. The classic persistence model is included in the comparative study. Thanks to two datasets composed of GHI measurements (45 days), we have been able to show that OGPR models based on quasiperiodic kernels outperform the persistence model as well as OGPR models based on simple kernels, including the squared exponential kernel, which is widely used for GHI forecasting. Indeed, although all OGPR models give good results when the forecast horizon is short-term, when the horizon increases, the superiority of quasiperiodic kernels becomes apparent. A simple online sparse GPR (OSGPR) approach has also been assessed. This approach gives less precise results than standard GPR, but the training computation time is decreased to a great extent. Even though the lack of data hinders the training process, the results still show the superiority of GPR models based on quasiperiodic kernels for GHI forecasting.
Modeling Data Reuse in Deep Neural Networks by Taking Data-Types into Cognizance

In recent years, researchers have focused on reducing the model size and number of computations (measured as “multiply-accumulate” or MAC operations) of DNNs. The energy consumption of a DNN depends on both the number of MAC operations and the energy efficiency of each MAC operation. The former can be estimated at design time; however, the latter depends on the intricate data reuse patterns and underlying hardware architecture. Hence, estimating it at design time is challenging. This article shows that the conventional approach to estimate the data reuse, viz. arithmetic intensity, does not always correctly estimate the degree of data reuse in DNNs since it gives equal importance to all the data types. We propose a novel model, termed “data type aware weighted arithmetic intensity” (DI), which accounts for the unequal importance of different data types in DNNs. We evaluate our model on 25 state-of-the-art DNNs on two GPUs. We show that our model accurately models data-reuse for all possible data reuse patterns for different types of convolution and different types of layers. We show that our model is a better indicator of the energy efficiency of DNNs. We also show its generality using the central limit theorem.
BlockHR

Electronic Health Records (EHRs) have become a popular method to store and manage patients' data in hospitals. Sharing these records make the current healthcare system more accurate and cost-efficient. Currently, EHRs are stored using the client-server architecture allowing the hospitals or a cloud service provider to retain the stewardship of the patients' data. In addition, the records of a patient are scattered among different hospitals using heterogeneous databases. Consequently, patients face difficulties to construct a cohesive view of their medical data history to focus on the details of their healthcare. The replication mechanism and security features of the blockchain has a promising future in the healthcare domain presenting solutions to the complexity, confidentiality, integrity, interoperability, and privacy issues of the current client-server architecture-based EHRs management system. In this paper, we propose a blockchain-based framework for health records management (BlockHR); a medical support system for medical professionals for better prognosis/diagnosis and follow-up on patients. BlockHR incorporates tools for patients to upload medical and lifestyle data used to predict their risk of developing chronic diseases.
On Succinct Arguments and Witness Encryption from Groups

Succinct non-interactive arguments (SNARGs) enable proofs of $$\mathsf {NP} $$ statements with very low communication. Recently, there has been significant work in both theory and practice on constructing SNARGs with very short proofs. Currently, the state-of-the-art in succinctness is due to Groth (Eurocrypt 2016) who constructed a SNARG from bilinear maps where the proof consists of just 3 group elements. In this work, we first construct a concretely-efficient designated-verifier (preprocessing) SNARG with inverse polynomial soundness, where the proof consists of just 2 group elements in a standard (generic) group. This leads to a 50% reduction in concrete proof size compared to Groth's construction. We follow the approach of Bitansky et al. (TCC 2013) who describe a compiler from linear PCPs to SNARGs in the preprocessing model. Our improvement is based on a new linear PCP packing technique that allows us to construct 1-query linear PCPs which can then be compiled into a SNARG (using ElGamal encryption over a generic group). An appealing feature of our new SNARG is that the verifier can precompute a statement-independent lookup table in an offline phase; verifying proofs then only requires 2 exponentiations and a single table lookup. This makes our new designated-verifier SNARG appealing in settings that demand fast verification and minimal communication. We then turn to the question of constructing arguments where the proof consists of a single group element. Here, we first show that any (possibly interactive) argument for a language $$\mathcal {L}$$ where the verification algorithm is "generic" (i.e., only performs generic group operations) and the proof consists of a single group element, implies a witness encryption scheme for $$\mathcal {L}$$ . We then show that under a yet-unproven, but highly plausible, hypothesis on the hardness of approximating the minimal distance of linear codes, we can construct a 2-message laconic argument for $$\mathsf {NP} $$ where the proof consists of a single group element. Under the same hypothesis, we obtain a witness encryption scheme for $$\mathsf {NP} $$ in the generic group model. Along the way, we show that under a conceptually-similar but proven hardness of approximation result, there is a 2-message laconic argument for $$\mathsf {NP} $$ with negligible soundness error where the prover's message consists of just 2 group elements. In both settings, we obtain laconic arguments (and linear PCPs) with linear decision procedures. Our constructions circumvent a previous lower bound by Groth on such argument systems with linear decision procedures by relying on imperfect completeness. Namely, our constructions have vanishing but not negligible completeness error, while the lower bound of Groth implicitly assumes negligible completeness error of the underlying argument. Our techniques thus highlight new avenues for designing linear PCPs, succinct arguments, and witness encryption schemes.
Static internal representation of dynamic situations reveals time compaction in human cognition

The human brain has evolved under the constraint of survival in complex dynamic situations. It makes fast and reliable decisions based on internal representations of the environment. Whereas neural mechanisms involved in the internal representation of space are becoming known, entire spatiotemporal cognition remains a challenge. Growing experimental evidence suggests that brain mechanisms devoted to spatial cognition may also participate in spatiotemporal information processing.The time compaction hypothesis postulates that the brain represents both static and dynamic situations as purely static maps. Such an internal reduction of the external complexity allows humans to process time-changing situations in real-time efficiently. According to time compaction, there may be a deep inner similarity between the representation of conventional static and dynamic visual stimuli. Here, we test the hypothesis and report the first experimental evidence of time compaction in humans.We engaged human subjects in a discrimination-learning task consisting in the classification of static and dynamic visual stimuli. When there was a hidden correspondence between static and dynamic stimuli due to time compaction, the learning performance was expected to be modulated. We studied such a modulation experimentally and by a computational model.The collected data validated the predicted learning modulation and confirmed that time compaction is a salient cognitive strategy adopted by the human brain to process time-changing situations. Mathematical modelling supported the finding. We also revealed that men are more prone to exploit time compaction in accordance with the context of the hypothesis as a cognitive basis for survival.The static internal representation of dynamic situations is a human cognitive mechanism involved in decision-making and strategy planning to cope with time-changing environments. The finding opens a new venue to understand how humans efficiently interact with our dynamic world and thrive in nature.
Research on sports action recognition system based on cluster regression and improved ISA deep network

The movements of sports athletes are complex and difficult to identify with current smart technologies. Therefore, in order to improve the sports athlete recognition rate, this paper analyze the sports action recognition system based on cluster regression and improved ISA deep network. Through literature investigation, this paper chooses ISA neural network as the basis of the algorithm. At the same time, this paper analyzes the shortcomings of traditional ISA neural network, combines the sports player’s motion recognition requirements to improve the traditional ISA neural network, and builds a sports player motion recognition system based on the improved ISA neural network algorithm. In addition, this paper uses the network data collection method to construct the sports player action video library and takes the basketball project as an example for analysis and identifies it through feature judgment. Finally, this paper builds experiments to perform model performance analysis. The research shows that the recognition rate of basketball action is greatly improved compared with the traditional algorithm model, the results verify that the improved ISA deep network proposed in this paper has significant effectiveness in the field of human behavior recognition research.
Modelling Human Reasoning in Practical Behavioural Contexts Using Real-Time Maude

In this paper we present an approach for modelling human reasoning using rewrite systems and we illustrate our approach in the context of human behaviour using a car driving example. Reasoning inference rules and descriptions of human activities are expressed using the Behaviour and Reasoning Description Language (BRDL). The BRDL model is then translated into Real-Time Maude. The object-oriented and equational logic aspects of Maude are exploited in order to define alternative semantic variations of BRDL that implement alternative theories of memory and cognition.
P4NIS: Improving network immunity against eavesdropping with programmable data planes

Due to improving computational capacity of supercomputers, transmitting encrypted packets via one single network path is vulnerable to brute-force attacks. The versatile attackers secretly eavesdrop all the packets, classify packets into different streams, performs an exhaustive search for the decryption key, and extract sensitive personal information from the streams. However, new Internet Protocol (IP) brings great opportunities and challenges for preventing eavesdropping attacks. In this paper, we propose a Programming Protocol-independent Packet Processors (P4) based Network Immune Scheme (P4NIS) against the eavesdropping attacks. Specifically, P4NIS is equipped with three lines of defense to improve the network immunity. The first line is promiscuous forwarding by splitting all the traffic packets in different network paths disorderly. Complementally, the second line encrypts transmission port fields of the packets using diverse encryption algorithms. The encryption could distribute traffic packets from one stream into different streams, and disturb eavesdroppers to classify them correctly. Besides, P4NIS inherits the advantages from the existing encryption-based countermeasures which is the third line of defense. Using a paradigm of programmable data planes-P4, we implement P4NIS and evaluate its performances. Experimental results show that P4NIS can increase difficulties of eavesdropping significantly, and increase transmission throughput by 31.7% compared with state-of-the-art mechanisms.
A Mixed-Signal Time-Domain Generative Adversarial Network Accelerator with Efficient Subthreshold Time Multiplier and Mixed-Signal On-Chip Training for Low Power Edge Devices

This work presents a low-cost mixed-signal time-domain accelerator for generative adversarial network (GAN). A significant reduction in hardware cost was achieved through delicate architecture optimization for 8-bit GAN training on edge devices. An area efficient subthreshold time-domain multiplier was designed to eliminate excessive data conversion for mixed-signal computing enabling high throughput mixed-signal online training demonstrated in a 65nm CMOS test chip.
Validating Seed Data Samples for Synthetic Identities – Methodology and Uniqueness Metrics

This work explores the identity attribute of synthetic face samples derived from Generative Adversarial Networks. The goal is to determine if individual samples are unique in terms of identity, firstly with respect to the seed dataset that trains the GAN model and secondly with respect to other synthetic face samples. Two approaches are introduced to enable the comparative analysis of large sets of synthetic face samples. The first of these uses ROC curves to determine identity uniqueness using a number of large publicly available datasets of real facial samples to provide reference ROCs as a baseline. The second approach uses a thresholding technique utilizing again large publicly available datasets as a reference. For this approach, new metrics are introduced, and a technique is provided to remove the most connected data samples within a large synthetic dataset. The remaining synthetic samples can be considered as unique as data samples gathered from different real individuals. Several StyleGAN models are used to create the synthetic datasets, and variations in key model parameters are explored. It is concluded that the resulting synthetic data samples exhibit excellent uniqueness when compared with the original training dataset, but significantly less uniqueness when comparisons are made within the synthetic dataset. Nevertheless, it is possible to remove the most highly connected synthetic data samples. Thus, in some cases, up to 92% of the data samples in a 20k synthetic dataset can be shown to exhibit similar uniqueness to data samples taken from real public datasets.
Forgery document detection in information management system using cognitive techniques

Manually verifying the authenticity of the physical documents (personal identity card, certificates, passports, legal documents) increases the administrative overhead and takes a lot of time. Later image processing techniques were used. But most of the image processing based forgery document detection methods are less accurate. To improve the accuracy, this paper proposes an automatic document verification model using Convolutional Neural Networks (CNN). Furthermore, we use Optical Character Recognition (OCR) and Linear Binary Pattern (LBP) to extract the textual information and regional edges from the documents. Later, Oriented fast and Rotated Brief (ORB) is used to extract the images from the scanned documents. To train the CNN, MIDV-500 dataset of 256 Azerbaijani passport images, each with the size of 1040*744 pixels is taken. The proposed CNN model uses sliding window operations layers to evaluate the authenticity. The proposed model analyzes both the textual authenticity and image (seal, stamp and hologram) authenticity of the scanned document. The experimental analysis is carried out on the TensorFlow using python programming language. The results derived from the proposed CNN based forgery detection model is compared with existing models and the results are promising to implement on the real time applications
A Case for SmartNIC-accelerated Private Communication

Transport Layer Security (TLS) has become a key building block for private network communication in modern Internet. While recent advancement of CPU has substantially improved the data encryption performance, TLS key exchange still remains the bottleneck for short-lived transactions. Dedicated hardware crypto accelerators promise good performance, but they often require invasive modification of the application due to its inherent architecture of asynchronous processing.
ArAutoSenti: automatic annotation and new tendencies for sentiment classification of Arabic messages

A corpus-based sentiment analysis approach for messages written in Arabic and its dialects is presented and implemented. The originality of this approach resides in the automation construction of the annotated sentiment corpus, which relies mainly on a sentiment lexicon that is also constructed automatically. For the classification step, shallow and deep classifiers are used with features being extracted applying word embedding models. For the validation of the constructed corpus, we proceed with a manual reviewing and it was found that 85.17% were correctly annotated. This approach is applied on the under-resourced Algerian dialect and the approach is tested on two external test corpora presented in the literature. The obtained results are very encouraging with an F1 score that is up to 88% (on the first test corpus) and up to 81% (on the second test corpus). These results, respectively, represent a 20% and a 6% improvement, respectively, when compared with existing work in the research literature.