Reviewerly/exHarmony huggingface dataset:
A Low Power DNN-based Speech Recognition Processor with Precision Recoverable Approximate Computing

This paper proposes a low power speech recognition processor based on an optimized DNN with precision recoverable approximate computing. In order to accelerate and improve energy utilization of DNN, an approximate multiplier based on cartesian genetic programming with weight pre-classification and mismatch compensation is proposed. A partial retraining scheme based on approximate noise is proposed to recover the accuracy loss caused by approximate computing. Experimental results show that the proposed approximate multiplier reduces power consumption by 42.9%, and the partial retraining scheme can recover accuracy of 3.03%~4.34%. Implemented under 22nm, the proposed processor can support the recognition of 10 keywords under different noise types and signal-to-noise ratios (5dB~clean), while the recognition accuracy is 83.36% ~89.82% and power consumption is 8.6μ W.
Achieving Efficient and Privacy-Preserving Arbitrary Geographic Range Query for Cloud

Geographic range query, as a basic query function, has been widely leveraged in location-based services. To adapt to the explosive growth of location data, more and more businesses and individuals choose to store their massive amounts of data on the powerful cloud, which however may raise severe threats to users' privacy. To resolve this problem, the location data is often encrypted before outsourcing, but this may sacrifice the availability and utility of the location data. In this paper, we design a geographic range query scheme to support efficient and privacy-preserving arbitrary geographic range queries over encrypted location data. Specifically, we first utilize the polynomial fitting technique to generate trapdoors for arbitrary geographic query ranges. Then, we design a randomizable matrix multiplication method based on matrix decomposition to achieve geographic range queries between data owners and data requesters. Through rigorous security analysis, we demonstrate the privacy of location data and queries is well protected in our scheme. Extensive experiments and performance evaluations show that our proposed scheme is highly efficient in terms of computational cost and communication overhead.
A Hybrid Combinatorial-Boolean Approach Toward Perfect Secrecy in IoT

This chapter completes our discussions in this book via presenting the hybrid combinatorial-Boolean approach for perfectly secure cryptography in IoT. To this end, we first propose a Boolean description along with an enumeration method as well as an encoding scheme for encryption/decryption algorithms. We especially apply the description, enumeration, and encoding schemes on perfectly secure cryptographic algorithms. The description, enumeration, and encoding schemes are then connected to Latin squares. We present a one-to-one mapping between perfectly secure algorithms and Latin squares. This leads to the following two significant achievements. At the end of this chapter, we reason why the approach is proper for use in IoT systems.
DigComp Helping Shape the Education Ecosystem in Europe

The Digital Competence Framework for Citizens (DigComp) provides a common understanding of what digital competence is and can thus support curriculum planning, instruction and assessment both at EU level and in Member States. Additionally, a reference framework such as DigComp can be a tool to support policy-making and monitoring (e.g. setting targets for digital up/re-skilling) and certification processes. All these components are integral parts of education ecosystems (e.g. curriculum planning, assessment, certification, policy-support). In this paper, a number of examples will be given and it will be argued that especially when digital transformation is shaping education ecosystems in Europe, common reference frameworks can support education actors to collaborate, share good practices and learn from one another both within a country and across them. This contribution first outlines the DigComp 2.2 update and then focuses on the uptake at EU level and in Member States.
Sparse LiDAR and Binocular Stereo Fusion Network for 3D Object Detection

3D object detection is an essential task in autonomous driving and virtual reality. Existing approaches largely rely on expensive LiDAR sensors for accurate depth information to have high performance. While much lower-cost stereo cameras have been introduced as a promising alternative, there is still a notable performance gap. In this paper, we explore the idea to leverage sparse LiDAR and stereo images obtained by low-cost sensors for 3D object detection. We propose a novel multi-modal attention fusion end-to-end learning framework for 3D object detection, which effectively integrate the complementarities of sparse LiDAR and stereo images. Instead of directly fusing LiDAR and stereo modalities, we introduce a deep attention feature fusion module, which enables interactions between intermediate layers of LiDAR and stereo image paths by exploring the interdependencies of channel features. These fused features connect higher layer features after upsampling and lower layer features from the stereo image pathway and sparse LiDAR pathway. Hence, the fused features have high-level semantics with higher resolution, which is beneficial for the following object detection network. We provide detailed experiments on KITTI benchmark and achieve state-of-the-art performance compared with the low-cost based methods.
The Application of AES in International Chinese Language Smart Education

As International Chinese Language education has progressed into the era of smart education, the technology of language intelligence has been extensively used in teaching Chinese as a second language. This paper uses the multiple linear regression method to train an automatic scoring model for Chinese L2 learners. The article selects 15 of the 126 composition text features as effective parameters, and trains 7 scoring models based on these parameters, finally obtains the composition score by averaging. This survey also analyzes the teaching application of the smart learning platform from the perspective of teaching, learning, evaluation and management. The study findings of teaching experiments indicate that the automatic essay scoring technology can help Chinese L2 learners to develop their writing ability.
A Deep-Neural-Network-Based Approach To Detecting Forgery Images Generated From Various Generative Adversarial Networks

In this paper, the deep learning-based method for forgery image detection is presented. First, we respectively do discrete Fourier transform for both real images and the forgery images generated from the generative adversarial networks. Then the obtained Fourier spectrums are fed to deep neural networks for model training. In order to enhance the detection capability of the model, we incorporate contrastive learning to make the model directly learns the difference between real and forgery images. Four kinds of generative adversarial networks (GANs), namely DCGAN, CycleGAN, AutoGAN, and Mixed GAN, are chosen to generate forgery images for testing our proposed method. The experimental results reveal that the average accuracy rate reaches 99.5% using our proposed method to detect the four kinds of GAN-generated images. Compared with the state-of-the-art forgery image detection method, our proposed method can more widely detect forgery images derived from different sources.
Peruvian Sign Language Recognition Using Recurrent Neural Networks

Deaf people generally face difficulties in their daily lives when they try to communicate with hearing people, this is due to the lack of sign language knowledge in the country. Deaf people have to go on their everyday lives in company of a interpreter to be able to communicate, even wanting to go to buy bread every morning becomes a challenge for them and being treated in health centers also becomes a challenge, a challenge which should not exist since they have the fundamental right to health. For that reason this paper attempts to present a system for dynamic sign recognition for Peruvian Sign Language and our main goal is to detect which model and processing technique is the most appropriate to solve this problem. So that this system can be used in deaf people everyday life and help them communicate. There have been many projects around the world trying to address this situation. However, each Sign Language is unique in its own way and, therefore, a global and complete solution is not possible. There have also been similar projects in Peru, but all of them share the same flaw of only recognizing static signs. Since sign language is not just the static signs like the alphabet, a solution which addresses also words that can be used in sentences is needed. For this a dynamic recognition is needed, and this is the system that will be presented in this paper.
A Model Checking Based Approach to Detect Safety-Critical Adversarial Examples on Autonomous Driving Systems

The safety of autonomous driving systems (ADS) with machine learning (ML) components is threatened by adversarial examples. The mainstream defending technique against such threats concerns the adversarial examples that make the ML model fail. However, such an adversarial example does not necessarily cause safety problems for the entire ADS. Therefore a method for detecting the adversarial examples that will lead the ADS to unsafe states will be helpful to improve the defending technique. This paper proposes an approach to detect such safety-critical adversarial examples in typical autonomous driving scenarios based on the model checking technique. The scenario of autonomous driving and the semantic effect of adversarial attacks on object detection is specified with the Network of Timed Automata model. The safety properties of ADS are specified and verified through the UPPAAL model checker to show whether the adversarial examples lead to safety problems. The result from the model checking can reveal the critical time interval of adversarial attacks that will lead to an unsafe state for a given scenario. The approach is demonstrated on a popular adversarial attack algorithm in a typical autonomous driving scenario. Its effectiveness is shown through a series of simulations on the CARLA platform.
Identification of blurred objects in real time Video using deep learning neural networks

Our method employs a novel strategy for blur detection. The algorithm has been split into two stages. In the first stage, we used the YOLO object detection algorithm to detect the biggest object in the image. We also trained a Convolutional neural network (CNN) model with an external dataset for blur detection and finetuned it to suit our application. We captured the real-time video and the trained CNN model was able to detect whether the frames were blurred or not.
A Double-layer Attention Heterogeneous Graph Neural Network Based on Coupled P System

Heterogeneous graphs have various types of nodes and edges and its different types of nodes have features or attributes of different dimensions. Therefore, they can express rich and complex semantic information. This feature of heterogeneous graphs makes them widely used in researches of recent years. At the same time, the abundant semantic information also brings challenges to select heterogeneous neighbors of nodes for information processing process. It is difficult to aggregate nodes of different dimensions and types into the same feature space to compute node embedding. In addition, we also have difficulties in assigning different weights to various node types when aggregating heterogeneous neighbors. In this paper, we propose a double-layer attention heterogeneous graph neural network based on coupled P system (PDAHN) to compute the node embedding. The attention mechanism in this method is divided into two layers, node-layer attention and meta-path-layer attention. PDAHN can assign different weights to different nodes and meta-paths according to the different importance of them by weighted aggregation. The whole process is carried out in the coupled P system. Experiments on two datasets show that the method proposed in this paper outperforms the performance of state-of-the-art methods.
IOWA Rough-Fuzzy Support Vector Data Description

Rough-Fuzzy Support Vector Data Description is a novel soft computing derivative of the classical Support Vector Data Description algorithm used in many real-world applications successfully. However, its current version treats all data points equally when constructing the classifier. If the data set contains outliers, they will substantially affect the decision boundary. To overcome this issue, we present a novel approach based on the induced ordered weighted average operator and linguistic quantifier functions to weigh data points depending on their closeness to the lower approximation of the target class. In this way, we determine the weights for the data points without using any external procedure. Our computational experiments emphasize the strength of the proposed approach underlining its potential for outlier detection.
Development and Acceptability of Math Based Reading Module for Enhancing Mathematical Vocabulary of Grade VI Pupils: Inputs in the Brigada Pag-Basa Reading Program 2022

The study focused on determining the acceptability of the Reading Module in Mathematics: in enhancing the Mathematical Vocabulary for Grade Six Pupils of Kapalaran Elementary School during the conduct of BE Brigada Pag-basa School year 2022. The study's respondents were the thirty (30) selected Grade Six pupils in Mathematics subject of Kapalaran Elementary School using simple random sampling utilizing the fish bowl technique, and twenty (20) selected teacher respondents using stratified sampling technique. More so, the researchers used quasi-experimental and descriptive method of research. The level of acceptability of the developed reading module was assessed in terms of its content, clarity, appeal to the target user, originality, knowledge questions, applicability, and format using the descriptive method of research. The study found out that there is an increase in the posttest means score of the grade six pupils at after being exposed to the Reading Module which means that the respondents gained more Vocabulary Understanding and their performance in will be improved. Then, the null hypothesis was rejected for the significant difference on the level of Vocabulary Understanding of grade six pupils at before and after exposure to the reading module in mathematic VI as revealed by the pretest and posttest with respect to the different topics. Finally, the Reading Module in Mathematics were found ‘Much Acceptable. The study concluded that the pupils obtained a high performance in the post-test as revealed by the respondents with the used of the Reading Module in Mathematics VI. Lastly, the Reading Module in Mathematics VI is an effective tool for reading intervention and in elevating the progress of vocabulary and understanding on the part of the pupils.
RF Reflectometry of NEMS Motional Capacitance with Micromanipulator Probe

Adiabatic reversible computing can dramatically reduce heat generation by switching circuits slowly, relative to their RC time constants, and using reversible logic. Nano-electro-mechanical systems (NEMS) are a promising approach to implement reversible computing as they don't have leakage current and can be used as pull-up and pull-down networks to generate digital gates. We present the measurement of NEMS motional capacitance devices with radio frequency (RF) reflectometry using a micromanipulator probe. The probe includes an on-board matching network that can be tuned to match the impedance of the NEMS devices under test. The NEMS are operated with a DC gate voltage and the reflectometry measurements verify their functionality paving the way for adiabatic reversible computing.
Reachability analysis for a quadrotor UAV based on zonotopes

The issue of obtaining an optimal solution for a quadrotor UAV system is investigated in this paper for a set of initial states. The reachable set of states can be calculated if an initial set is given for a quadrotor system with disturbances. Moreover, the generated reference path is enclosed by zonotopes. The local linearization approach is employed to deal with the nonlinear system, while the linearization error is given in the form of Lagrange remainders which belongs to a zonotopic set. The effectiveness of the main results is illustrated by a quadrotor UAV example.
VDSSA: Ventral &amp; Dorsal Sequential Self-attention AutoEncoder for Cognitive-Consistency Disentanglement

Learning compact and interpretable disentangled representation is considered to be the key challenge in unsupervised learning. Although current methods which could capture the essence of the input from the perspective of Bayesian inference, have achieved some promising results, there still exists a gap between cognitive mechanisms and disentanglement techniques. To tackle this problem, inspired by the two-streams hypothesis in the visual cortex, we propose a Ventral & Dorsal Sequential Self-attention AutoEncoder (VDSSA), which introduces a self-attention based disentangled representation to capture both cognitive-consistent and semantic consistent factors in sequential data. In particular, cognitive-consistent factors include Global Shape Information (GSI) in the Ventral stream and Global Dynamic Information (GDI) in the Dorsal stream. We introduce a modified self-attention module to capture the GSI which denotes the cognitive-consistent factor in the spatial domain, allowing to overcome the limitation of receptive field in CNN. The GDI is encoded by Self-attention LSTM to obtain cognitive-consistent factors in the temporal domain. To the best of our knowledge, this is the first work to use cognitive-consistency in disentanglement model. The experiments on artificially generated cartoon video clips show that the proposed VDSSA could disentangle ventral and dorsal stream information from video sequences. Furthermore, the experimental results on the MNIST dataset show that the proposed method also has the ability to disentangle style and content factors from static data, and the encoded content features have better clustering characteristics.
Spark Partition Strategy Based on Genetic Mutation Algorithm

Spark is a general computing engine in the big data field. The shuffle phase is an important part of ensuring Spark’s operation. However, when the default partitioning strategy is used for partitioning in this phase, tasks with a large amount of data will cause data skew and cause calculation delays. Aiming at the problem of data skew, this paper proposes a partitioning strategy based on genetic mutation algorithm (GAPartition), sampling and predicting the data volume of each Task, and then using genetic mutation algorithm to map tasks and partitions into mathematical models, re-partitioning, and finally constructing the evaluation model of the degree of cluster tilt. Through experiments, the calculation time of Spark’s default HashPartition and RangePartition partitions at different degrees of tilt is compared. The experimental results show that GAPartition can effectively solve the problem of data tilt and shorten the execution time of tasks in the cluster.
Research on Image Information Restoration Algorithm of Printing Micro Dots Based on GAN

During printing and shooting, the degradation of printing micro dots significantly affects the decoding and reading of hidden anti-counterfeiting information. However, existing image restoration methods cannot effectively restore image information. Moreover, there are relatively few datasets related to halftone dot images, and most datasets differ from the real data. Therefore, we propose an end-to-end restoration model based on the single-image super-resolution information. Specifically, we constructed a PMD dataset for real printing of anti-counterfeiting scenes. Based on this dataset, we used the high-resolution image information as the target. The positional inclination of the degraded images is corrected using the blank and interline characteristics of the printing micro dots images. The restoration is completed with the help of feature extraction and upsample of ESRGAN. In addition, we propose evaluation measures suitable for error detection, correction, and decoding requirements for microscopic image information. The experimental results show that, within the noise tolerance range, the image information restored by our method has a maximum average bit error rate is 0.97% and a Euclidean distance is 0.00804 pixels, whereas traditional filtering measures cannot effectively restore image information. The experimental results verified the effectiveness and robustness of the proposed method.
Speaker Recognition using Multiple X-Vector Speaker Representations with Two-Stage Clustering and Outlier Detection Refinement

This paper presents a novel Variational Bayes x-vector Voice Print Extraction (VBxVPE) system, capable of capturing vocal variations using multiple x-vector representations with two-stage clustering and outlier detection for robust speaker recognition and verification. The presented approach demonstrates beyond the state-of-the-art results when evaluated against the ‘core-core’ and ‘core-multi’ evaluation conditions of the Speakers In the Wild dataset, achieving an Equal Error Rate of 1.06%, Cost of Detection score of 0.052, minimum Cost of Detection score of 0.010, Speaker Identification Accuracy of 95.84% with Precision, Recall and F1 score values of 0.964, 0.958 and 0.961, respectively on the ‘core-core’ evaluation condition and Equal Error Rate of 1.07%, Cost of Detection score of 0.066, minimum Cost of Detection score of 0.010 with Precision, Recall and F1 score values of 0.967, 0.963 and 0.965, respectively on the ‘core-multi’ evaluation condition.
Exploring Spatiotemporal Relationships for Improving Compressed Video Quality

The compressed video quality enhancement problem aims at restoring the distorted information of the compressed video. Details in high-quality reference frames are usually used to improve the detailed information and reduce the artifacts of the low-quality target frames. In this paper, we propose a novel network to extract helpful information from peak quality frames and then incorporate it into low-quality frames. The proposed network first integrates the information from both the target and reference frames, and then fully spatiotemporal deformable convolutions are used to better adapt the sampling strategy of the input frames. In addition, the temporal offset calibration procedure is also applied to reduce the loss of high-frequency components in the interpolation process. The results on benchmark datasets show that our method achieves competitive performance compared with other existing methods.
Machine Learning Approach to Patient Health and Stress Monitoring System

Over 90% of people have too many feelings of stress, and there is no external remedy to relieve stress other than being diverted to other cases. Stress can be good in helping the less fortunate, or it could be injurious, but it continues to suffer. If the demanding model of a human keeps going, he or she should die. After conducting research, the scientists discovered that using Internet of Things (IoT) devices and sometimes a Neural Network (NN) model to eradicate a person's stressful behavior. The gadgets used to gather information from the body of the patient would have a connection to the internet and be programmed to analyze the data using a Machine Learning(ML) algorithm. Now the whole system has been tested in several scenarios. Different parts sent out with standards have been managed and monitored. The goal of this study is to eliminate stress with continuous statistics and go through the whole process.