Reviewerly/exHarmony huggingface dataset:
Learning How Long to Wait: Adaptively-Constrained Monotonic Multihead Attention for Streaming ASR

Monotonic Multihead Attention, which allows multiple heads to learn their own alignments per head, shows great performance on simultaneous machine translation and streaming speech recognition. However, it causes high latency waiting for the slowest head. Some recent advances such as Head-Synchronous Beam Search Decoding and its learnable version Mutually-Constrained Monotonic Multihead Attention, try to address this issue by restricting the difference in times of chosen frames among multi-heads to a fixed waiting time threshold. In this paper, we hypothesis that the optimal threshold for high performance with low latency depends on the input sequence, and propose an adaptive algorithm that learns how long to wait depending on input tokens by introducing a threshold prediction module. We evaluate our approach on two benchmark datasets for online Automatic Speech Recognition task and demonstrate that our method reduces the latency together with even improving the recognition accuracy.
A Novel Approach Integrating Design Thinking Techniques in Cyber Exercise Development

The increasing cyber security compliance requirements (e.g. NIS directive or GDPR in the EU) and the growing dependence on ICT systems have made cyber resilience to a top priority around the globe. Especially during the pandemic, the demand for secure and constantly available systems increased as companies had to change the way they work using home office and remote working capabilities. For satisfying the compelling need for resilient systems, it is vital to ensure that systems work even under adverse events. A key element in this context are cyber exercises. However, planning and conducting an effective cyber exercise is a complex and challenging task. To support this endeavor, in this paper we introduce a novel approach, which integrates design thinking techniques in planning process to improve the exercise development and to tailor exercises to the specific requirements of the organisations. To gain first insights, we evaluated the approach with 50 part-time cyber security students.
Design of a 3D Reconstruction System with Automatic Correction Function

In view of the problem that it is difficult to prepare for measuring the external dimensions of materials after heat treatment such as extrusion, stretching and straightening in factories, which is not conducive to further processing of materials in the later period, this paper designs a 3D reconstruction system which can quickly scan and reconstruct materials, obtain the deformation of materials, and measure the dimensions such as edge distance and surface flatness of materials. It can not only realize the continuous reconstruction of three-dimensional system of single material and the automatic reconstruction of starting and stopping root material by using the presence or absence of monitoring sensor of material in on-line non-stop state; At the same time, the basic size, deformation size and smoothness parameters of the reconstructed model are calculated In order to calibrate the center position deviation of materials with different sizes, reduce the influence of position information on the dimensional accuracy of material reconstruction, and increase the automatic correction function of center position. This design can supervise and feedback the processing effect of materials processing, and provide effective data reference for the further processing of materials and the reasonable combination of processing procedures. This design can improve the automation degree of processing and the quality of processed materials.
Resource Allocation for Cloud-Edge-End Simulation System

Abstract Aiming at the problem of simulating service requests and optimal scheduling during the operation of the cloud computing/edge computing environment, the real-time optimization scheduling technology of computing resources is studied, and elastic resource optimization scheduling is realized through data feature mining analysis, service load prediction, and collaborative resource management. Ensure that the simulation service quality meets the mission requirements and provide support.
Virtual Reality-Based Height Simulation With Method Multimedia Development Life Cycle

Virtual Reality is a technology that allows users to interact with a computer-generated simulation, so the user feels like being in another dimension. In this study, a height simulation based on VR is used to find out the phobia level of prospective general employees at STMIK PPKIA Tarakanita Rahwamawati. This simulation implemented becomes a challenge consisting of three levels. Candidate employees will be stated to have phobia if they cannot complete the first challenge within the specified time. This simulation is not a determinant of the acceptance of someone to work but for the general employee complementary components at the selection process. Multimedia Development Life Cycle is a method used to build a new system and develop a program. The method consists of 6 stages: concept, design, material collecting, assembly, testing, and distribution. The first stage must be done is the concept while the next stages can be run randomly according to the needs. The testing process was carried out using Blackbox and Likert Scale. The Blackbox testing result showed that the system was successfully implemented according to the scenario created while the Likert scale questionnaire showed that 86.90&#x0025; of 20 respondents gave good responses to the simulation.
Data Analytics for Data Warehousing

This chapter describes data analytics for data warehousing and how it differs from traditional data mining. Traditional data mining techniques are often restricted by the structure of the input dataset they require, whereas data analytics for data warehousing takes the data from the warehouse as input to the analytic engine. Three main data analytics techniques are explored in this chapter: statistical analysis using regression, clustering analysis and classification using regression trees.
Semantical-Info based Virtual Try-on NetWork with Generative Adversarial Nets

Recently proposed image-based virtual try-on (VTON) aims at transferring a target clothing image onto a reference person image, which is a challenging task because the human body and clothes hava high flexibility in shapes and details. A common processing pipeline for this is composed of two stages: first, warping the target try-on clothes to align with the corresponding clothes region on the reference person image, and then the warped clothing is blended with the target person image. However, existing methods suffer from artifacts and distortions, also fails to preserve the corresponding region features due to the the complex semantics. In this paper, we propose a framework based on semantically conditional generative adversarial network for virtual try-on (SCVTON). Firstly, we introduce a semantic labels synthesis module to predict the desired semantic segmentation after try-on. Then a generative adversarial network fuses all information (e.g. reference person image, semantic labels, target clothes) to generate the final try-on result. Moreover, we propose using the synthetic semantic labels for modulating the activations in normalization layers through a semantic spatially-adaptive normalizations, which can effectively propagate the semantic information through the networks. Qualitative and quantitative results show significant improvements on VTON tasks.
When Sketch Face Recognition Meets Mask Obfuscation: Database and Benchmark

During this unprecedented time of the COVID19 pandemic, wearing face masks has become a necessity. While these masks aim to secure an individual from getting infected by any kind of viruses including COVID-19; they significantly obfuscate the identity. The situation becomes even worse when an attacker performs a crime and the place does not have any surveillance cameras. The identification of criminals in such conditions highly depends on the witnesses and generation of sketches based on their description. To the best of our knowledge, in the literature, no work has been performed for matching sketch images with masks. In this research, we have first created the mask sketch face database using more than 50 identities. The sketch images are generated using a different variant of pencils, which can be seen as different sketch artists. The recognition experiments are performed using state-of-the-art face embedding networks including ArcFace and DeepID which show that the recognition performance degrades significantly when the sketch mask images are used for identification. In another set of experiments, it is observed that the recognition algorithm is robust in handling the digital face mask images. However, the ineffectiveness in handling the variations that occurred due to sketches is a serious concern and needs attention.
Modeling of Cognitive Bias of Video Viewing Users Based on Quantum Decision Making

Recently, the need for watching videos as comfortably as possible in streaming services is increasing with the growing amount of video traffic. User satisfaction depends not only on the network quality but also on various factors such as the video content. Estimating the user’s Quality of Experience(QoE) and using it for network control is effective to improve user satisfaction. Therefore, we need to estimate the QoE accurately. Various QoE models using network quality have been studied. However, the QoE can vary with cognitive bias, which is a bias that occurs in our cognitive processes. For the QoE control, we need a QoE model with cognitive bias and quantum decision-making has gained attention as a method for modeling cognitive biases. In this paper, we propose a QoE model that includes cognitive bias with quantum decision-making. Then we simulate the QoE to show that our proposed method with quantum decision-making can estimate the QoE and represent video viewers’ cognitive bias.
Social Distancing Surveillance System via Inverse Perspective Mapping and Fixed-point Quantization

During the Coronavirus Disease 2019 (COVID-19) pandemic, many countries have introduced the social distancing policy in public areas to stop the spread of disease by maintaining a physical distance between people. This paper proposes an Artificial Intelligence (AI)-powered social distancing surveillance system that can detect pedestrians through video surveillance and monitor the social distance between them via Inverse Perspective Mapping (IPM) in real-time. The proposed system was deployed on the devices located at the network edge such as IoT devices and mobile devices to enable real-time response with low data transmission latency. To bypass the restriction on the computational and memory capacity for the edge devices, the proposed system was optimized through fixed-point quantization. From the evaluation results, the optimized models are almost 4 times smaller as compared to the original models. The best trade-off between speed and accuracy can be achieved with a 27.1&#x0025; improvement in speed and 2&#x0025; degradation in accuracy.
Poster: Design and Evaluation of an Extracurricular Educational Program for Developing the Basic Skills as a Member of Society

The Project based learning (PBL) projects implemented by colleges are generally intended to focus on actual problems with enterprises and local communities and give proposals for solving them. Students’ activities in such PBL projects sometimes develop into extracurricular activities according to the progress of the projects. Engaging in extracurricular activities in addition to learning through a curriculum helps students improve their basic skills as a member of society. In this study, we designed an extracurricular educational program titled “Science Communication Project” for the purpose of honing the basic skills of students majoring in science or engineering as a member of society, and put it into practice. In Japan, students self-evaluate their basic skills by utilizing the 12 elements of the basic skills as a member of society suggested by the Ministry of Economy, Trade and Industry in 2006. In this study, twenty students who participated in our educational program during the 8-year period from 2013 to 2020 were instructed to recall their own activities in the program and self-evaluate their basic skills as a member of society. We requested students who participated in our extracurricular activity to self-evaluate 12 items regarding the basic skills as a member of society while recalling their school days around the time of graduation, and also self-evaluate them while assuming that they had not participated in the extracurricular activity. Then, we studied which of the 12 items regarding the basic skills as a member of society improved through our educational program and which items did not improve sufficiently.
Lightweight Facial Expression Recognition Based on Multi-scale Dense Convolutional Neural Network

Facial expression recognition has a wide range of applications in various human-computer interaction fields. In view of the problems of a large number of network model parameters, insufficient generalization ability in a complex environment, high requirements on hardware equipment and difficulty in the practical deployment of the current deep learning models applied to facial expression recognition, this paper proposes a lightweight face expression recognition model based on multi-scale dense connection convolutional neural network. First of all, the model uses Densenet's idea for reference in feature learning, and the dense convolutional layer realizes the reuse of feature maps so that the network can learn more original features. Secondly, based on the deep separable convolution, a multi-scale deep separable convolution is proposed to expand the receptive field of the convolution and enhance the richness of the characteristic channels of the convolution. Different scales of the convolution kernel can get different scales of receptive fields, and eventually fuse the features of different scales, which is conducive to model recognition of facial expression images of different scales and improve the classification accuracy. At the same time, deep separable convolution can further reduce the number of parameters and the amount of calculation. Finally, a channel attention mechanism is added to improve the accuracy of the model. Experimental results show that, compared with other classical convolutional neural networks, the lightweight model proposed in this paper can ensure a higher accuracy while reducing the number of network parameters and computation in the facial expression recognition task, which is of great significance for the practical deployment of the model.
A New Method To Find All The High-Probability Word-Oriented Truncated Differentials: Application To <tt>Midori</tt>, <tt>SKINNY</tt> And <tt>CRAFT</tt>

Abstract This paper proposes a new method to find high-probability truncated differentials using matrix muliplication. For Markov cipher with similar round function, suppose that the transition probability matrix of round function is $\mathcal{D}$, then $\mathcal{D}^{r}$ contains all the differential probabilities of an $r$-round block cipher. To reduce the matrix dimension, we consider the word-oriented truncated differential and the truncated transition probability matrix $\mathcal{T}$. Regardless of the effect of the $S$-box, we focus on whether there is a non-zero difference on one cell instead of the value of the difference. In this case, the matrix dimension reduces significantly and we can calculate $\mathcal{T}^{r}$ using a workstation. Then all the $r$-round truncated differential probabilities can be found from $\mathcal{T}^{r}$. And the probability in $\mathcal{T}^{r}$ is the probability of the whole truncated differential hull but not a single or several truncated differential characteristics. Besides, we make a more accurate probability estimation of the truncated differential of lightweight block cipher. Combined with the truncated differential hull, we found some longer truncated differential distinguishers. And as $\mathcal{T}^{r}$ stores all the truncated differential probabilities, we can also find all the impossible truncated differentials.
A Covert Communication Method Based on Gradient Model

Pdf, word, etc. have always been used for malicious code transmission and data leakage, and academia have performed a lot of research on the detection of these files. However, with the evolution of big data, artificial intelligence and other new technologies, federated learning, as a new generation of distributed machine learning training method, is widely used in cross domain joint training by exchanging model parameters rather than raw data. This makes it possible to transmit malicious code and disclose sensitive data in federated learning. Therefore, this paper innovatively proposes a covert communication method based on gradient model, which uses the least significant bit steganography algorithm to embed data during model parameters exchanges between clients and central server. Experiments demonstrate that the proposed method has nearly no influence on federated learning global model, apart from strong robustness and large covert channel capacity.
Autonomous object detection and tracking robot using Kinect v2

Object detection and tracking are one of the key features of a robust autonomous mobile robot, allowing it to navigate places and avoid obstacles. The Mobile robotics market and proliferation has been growing and the Covid-19 era has added another boost to this area where more and more interest is being drawn to the autonomous capabilities of these machines. In this paper we propose a hardware based model to detect and track objects based on color. We propose robust object detection and tracking with minimum environmental constraints to improve accuracy using our algorithm, and capable of behaving well in unknown environmental conditions. At the end of the analysis, the robot was able to detect the object and track it well. We also show frequency analysis, compression and error analysis of the underlying technique. Experimental outcomes verify improved accuracy of our algorithm.
Lightweight Data Integration and Fusion Method Based on a Convolutional Neural Network

In view of the large amount of integration and low data sharing efficiency in traditional data integration and fusion methods, this paper proposes a lightweight data integration and fusion method based on convolutional neural networks. First, convolutional neural network training is used to fit the given data, and then a lightweight data integration framework is constructed, combined with the ontology mapping algorithm to establish the mapping relationship between the local ontologies, and the integration process is designed to form global information containing global information. Metadata. Finally, the Gaussian membership function is selected to fuzzify the global metadata for the measurement and estimator. A lightweight data fusion result is obtained on the basis of the initialization parameter construction process. It can be seen from the experimental results that the data sharing efficiency of this method can reach 97%, which effectively improves the lightweight data sharing effect.
Approximation Algorithms for Matroid and Knapsack Means Problems

In this paper, we concentrate on studying the [Formula: see text]-means problem with a matroid or a knapsack constraint. In the matroid means problem, given an observation set and a matroid, the goal is to find a center set from the independent sets to minimize the cost. By using the linear programming [Formula: see text]-rounding technology, we obtain a constant approximation guarantee. For the knapsack means problem, we adopt a similar strategy to that of matroid means problem, whereas the difference is that we add a knapsack covering inequality to the relaxed LP in order to decrease the unbounded integrality gap.
Covid Alert System: A Smart Security System to Alert Violations of Covid Protocol Using OpenCV

According to the World Health Organization, the COVID-19 pandemic is causing overall prosperity into crisis. The pandemic made all countries across the world commence lockdowns to decrease the transfer of the virus. The protection procedure is wearing a face mask in open areas. Records show that wearing face masks in crowded areas diminishes the threat of virus transfer. A novel framework is proposed in this paper to minimize the spreading of COVID-19 in crowded places like schools, malls, theaters, etc., by identifying individuals without wearing a mask. A face masks algorithm and CNN algorithm have been developed the usage of machine learning which acknowledges the faces besides masks and signals the safety system. It can detect face masks with high accuracy of 99%. The methodology proposed exhibits its high effectiveness in detecting facial masks.
The implementation of Herringbone technique to improve reading comprehension

The objectives of this research were to find out whether there was a significant difference of the students’ reading comprehension after the implementation of Herringbone technique and to investigate which aspect of reading that improves the most and improves the least after the implementation of the technique. The population of this research was the first grade of SMA Tri Sukses Natar in the academic year 2019/2020. The sample of this research was X science 2 which consisted of 30 students. The data were taken from the tests and they were analyzed by using paired sample t-test. The result showed that there was a significant difference in the students’ reading comprehension of pre-test (61.40) to post-test (72.30) in which t-value&gt;t-table (11.882&gt;2.452) or sig p&lt;0.05 (0.000&lt;0.05). In addition, main idea was the aspect of reading that improved the most after the implementation of the technique and the aspect which improved the least was vocabulary. Based on the result, it can be concluded that Herringbone technique is effective to improve students’ reading comprehension. Keywords: Herringbone technique, Reading comprehension, Narrative text
Using Self Attention DNNs to Discover Phonemic Features for Audio Deep Fake Detection

With the advancement in natural-sounding speech production models, it is becoming important to develop models that can detect spoofed audios. Synthesized speech models do not explicitly account for all factors affecting speech production, such as the shape, size and structure of a speaker's vocal tract. In this paper, we hypothesize that due to practical limitations of audio corpora (including size, distribution, and balance of variables like gender, age, and accents), there exist certain phonemes that synthesized models are not able to replicate as well as the human articulation system and such phonemes differ in their spectral characteristics from bonafide speech. To discover such phonemes and quantify their effectiveness in distinguishing between spoofed and bonafide speech, we use a deep learning model with self-attention, and analyze the attention weights of the trained model. We use the ASVSpoof2019 dataset for our analysis and find that the attention mechanism picks most on fricatives: /S/,/SH/, nasals: /M/,/N/, vowels: /Y/, and stops: /D/. Furthermore, we obtain 7.54% EER on train and 11.98% on dev data when using only the top-16 most attended phonemes from input audio, better than when any other phoneme classes are used.