Reviewerly/exHarmony huggingface dataset:
On-Device Deep Multi-Task Inference via Multi-Task Zipping

Future mobile devices are anticipated to perceive, understand and react to the world on their own by running multiple correlated deep neural networks locally on-device. Yet the complexity of these deep models needs to be trimmed down both within-model and cross-model to fit in mobile storage and memory. Previous studies squeeze the redundancy within a single model. In this work, we aim to reduce the redundancy across multiple models. We propose Multi-Task Zipping (MTZ), a framework to automatically merge correlated, pre-trained deep neural networks for cross-model compression. Central in MTZ is a layer-wise neuron sharing and incoming weight updating scheme that induces a minimal change in the error function. MTZ inherits information from each model and demands light retraining to re-boost the accuracy of individual tasks. MTZ supports typical network layers (fully-connected, convolutional and residual) and applies to inference tasks with different input domains. Evaluations show that MTZ can fully merge the hidden layers of two VGG-16 networks with a 3.18% increase in the test error averaged on ImageNet for object classification and CelebA for facial attribute classification, or share <inline-formula><tex-math notation="LaTeX">$39.61\%$</tex-math></inline-formula> parameters between the two networks with <inline-formula><tex-math notation="LaTeX">$&lt;0.5\%$</tex-math></inline-formula> increase in the test errors. The number of iterations to retrain the combined network is at least <inline-formula><tex-math notation="LaTeX">$17.8\times$</tex-math></inline-formula> lower than that of training a single VGG-16 network. Moreover, MTZ can effectively merge nine residual networks for diverse inference tasks and models for different input domains. And with the model merged by MTZ, the latency to switch between these tasks on memory-constrained devices is reduced by <inline-formula><tex-math notation="LaTeX">$8.71{\times}$</tex-math></inline-formula> .
Real-Time Mexican Sign Language Interpretation Using CNN and HMM

Mexican Sign Language (MSL) is the primary form of communication for the deaf community in Mexico. MSL has a different grammatical structure than Spanish; furthermore, facial expression plays a determining role in complementing context-based meaning. This turns it difficult for a hearing person without prior knowledge of the language to understand what is to be transmitted, representing an important communication barrier for deaf people. In order to face this, we present the first architecture to consider facial features as indicators of grammatical tense to develop a real-time interpreter from MSL to written Spanish. Our model uses the open source MediaPipe library to extract marks from the face, body position and hands. Three 2D convolutional neural networks are used to encode individually and extract patterns, the networks converge to a multilayer perceptron for classification. Finally, a Hidden Markov Model is used to morphosyntactically predict the most probable sequence of words based on a preloaded knowledge base. From the experiments were carried out, a precision of 94.9% was obtained with \(\sigma = 0.07\) for the recognition of 75 isolated words and 94.1% with \(\sigma = 0.09\) for the interpretation of 20 sentences in MSL in a medical context. Being an approach based on camera inputs and observing that even with a few samples an adequate generalization can be achieved, it would be feasible to scale our architecture to other sign languages and offer possibilities of efficient communication to millions of people with hearing disability.
Photovoltaic-Thermoelectric Generator Monitoring System using Arduino Based Data Acquisition system Technique

This paper presents the design and development of a data acquisition system (DAQ) for a hybrid photovoltaic-thermoelectric generator (PV-TEG) to monitor and store system parameters collected from the PV-TEG source in large memory storage. A DAQ is an electronic device that collects and records data through a time-based microcontroller (DS1307 Real-Time Clock (RTC) chip). It utilizes the Arduino Mega 328P board in conjunction with the ATmega 328P chip for monitoring the system parameters such as voltage, current and power every second of the logging process. The hybrid system comprises 200 W panels and a combination of 192 TEG connected in series and parallel for higher output power. The system converts the original data into digital input for data acquisition and stores it on a secure digital card (SD card). The hybrid system performance was examined. The results from the DAQ shows that data were stored on the SD Card at a 1-second update cycle. The DAQ recorded maximum system parameter values as 39 V, 4.9 A for the hybrid system, 33 V, 4.7 A for the PV subsystem, and 6 V, 0.2 A for the TEG subsystem. The maximum power of 191.1 W was computed across a resistive load of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$8\ \Omega$</tex> , 300 W. This shows an increase of 15.38 % than the PV subsystem.
A Verified Decision Procedure for Orders in Isabelle/HOL

We present the first verified implementation of a decision procedure for the quantifier-free theory of partial and linear orders. We formalise the procedure in Isabelle/HOL and provide a specification that is made executable using Isabelle's code generator. The procedure is already part of the development version of Isabelle as a sub-procedure of the simplifier.
HiCoVA

The task of keyphrase generation, unlike extraction, aims to generate the phrases which succinctly capture the key information of the source text, that are even absent in the document (i.e., do not match any contiguous sub-sequence of source text). Despite the significant progress achieved by sequence-to-sequence (seq2seq) models in modelling such high entropy task, they are limited by their deterministic modelling capability which limits the generation of a diverse set of keyphrases. To address the above limitation, in this paper, we propose to incorporate Conditional Variational Autoencoder (CoVA) into seq2seq models for its ability to represent a set of keyphrases as a probabilistic distribution which improves the diversity of the generated keyphrases. We model the probabilistic distribution using a hierarchical latent structure where a global latent variable tries to model the diversity among the keyphrases and local latent variables control the generation of each keyphrase to make them coherent. Experimental results on four benchmark datasets of research papers demonstrate the effectiveness of our proposed approach in achieving a large improvement in diversity along with modest gains in quality with respect to previous models.
Fundamental Concepts on Fuzzy Sets

As a first step, it is important to postulate the fundamental concepts of fuzzy sets as proposed in Zadeh (Inf Control 8:338–353, 1965). This chapter is then devoted to basic notions of fuzzy sets, the logical and arithmetic related-operations, and to the well-known extension principle. Detailed examples illustrate the different concepts.
How can reasoning improve ontology-based context-aware system

Over the past two decades, the large evolution of software engineering, telecommunication and pervasive devices has lead to emergence of a new vision of development aiming at building systems to meet more complex and personalised needs known as context-aware systems. This type of systems is becoming the next computing paradigm in which infrastructure and services are sensitive to any change of the context, so that plays a crucial role to provide interactive intelligent environments. In parallel, contextual situation refers to a higher level of information inferred from different context data flow that can be extracted from physical and virtual sensors. The power of using situation is lies in their ability to provide a simple and comprehensible representation of context property, which preserve the services that manipulate them from the complexity of sensor readings, data transmission errors and inferencing activities. In this work, we aim to explore the added value of using ontology-based reasoning, focusing on first-order logic and fuzzy logic, to produce contextual situations.
Comprehensively Computing Link-based Similarities by Building A Random Surfer Graph

Link-based similarity computation arises in many real applications, including web search, clustering and recommender system. Lots of similarity measures are devoted recently, but there is one undesirable drawback, called ''path missing'' issue, i.e., the paths between objects are not fully considered for similarity computation. For example, SimRank considers only in-coming paths of equal length from a common ''center'' object, and a large portion of other paths are fully neglected. A comprehensive measure can be modeled by tallying all the possible paths between objects, but a large number of traverses would be required for these paths to fetch the similarities, which might increase the computational difficulty. In this paper, we propose a comprehensive similarity measure, namely RG-SimRank (Random surfer Graph-based SimRank), which resolves the "path missing'' issue with inheriting the philosophy of SimRank. We build a random surfer graph by allowing the surfer to stay at current object, go to other objects against in-links or along out-links. RG-SimRank adopts SimRank to compute similarities in random surfer graph instead of the original network, which has a same form of SimRank and hence inherits the optimization techniques on similarity computation. We prove that RG-SimRank considers all the possible paths of any direction and any length. And it provides a general solution to assess similarities, under which lots of existing similarity measures become its special cases. Other similarity measures besides SimRank can also be enhanced similarly using random surfer graph. Extensive experiments on real datasets demonstrate the performance of the proposed approach.
Energy-efficient VM opening algorithms for real-time workflows in heterogeneous clouds

Minimizing energy consumption is a critical challenge for real-time workflows, particularly in heterogeneous cloud computing systems. State-of-the-art algorithms aim to minimize the energy consumed for processing such applications by choosing virtual machines (VMs) to shut down from all opened VMs (i.e., VM merging). However, such VM merging through an “on-to-close” approach usually incurs high computational complexity. This paper proposes an energy-efficient VM opening (EEVO) algorithm that is capable of choosing VMs to turn on from all closed VMs while satisfying the real-time constraint of applications. Considering that there are slacks that can be eliminated or reduced between adjacently scheduled tasks after using the EEVO algorithm, a dynamic scaling down EEVO algorithm (DEEVO) is further proposed. DEEVO is implemented by scaling down the frequency of VMs executing each task based on the dynamic voltage and frequency scaling (DVFS) technique. Experimental results demonstrate that, with the above-mentioned improvements, DEEVO achieves lower energy consumption for real-time workflows than state-of-the-art algorithms do. In addition, DEEVO outperforms state-of-the-art algorithms in the computational efficiency of accomplishing task scheduling.
Parallel <scp>Multi‐Scale</scp> Network with Attention Mechanism for Pancreas Segmentation

In this paper, we address the task of segmenting small organs (i.e., the pancreas) from abdominal CT scans. As the target often occupies a relatively small region in the input image, deep neural networks can be easily confused by complex and variable backgrounds. We propose a method that uses a parallel multi‐scale network with an attention mechanism for pancreas segmentation, which can better grasp the balance between the semantic segmentation, classification, and localization tasks. We use a parallel network to connect the feature maps between different bottleneck layers, which contain rich semantic information and complete spatial information. We apply an attention module to enhance the key features of semantic information. Then, we fuse the two modules and apply the fused module as attention information on the feature map to ensure the full fusion between contextual semantic information and spatial information, thereby improving segmentation accuracy. We conduct extensive experiments on the NIH pancreas segmentation data set. In particular, our model achieves a mean coefficient Dice of 86.6. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
Liver tumor detection based on objects as points

The automatic detection of liver tumors by computed tomography is challenging, owing to their wide variations in size and location, as well as to their irregular shapes. Existing detection methods largely rely on two-stage detectors and use CT images marked with bounding boxes for training and detection. In this study, we propose a single-stage detector method designed to accurately detect multiple tumors simultaneously, and provide results demonstrating its increased speed and efficiency compared to prior methods. The proposed model divides CT images into multiple channels to obtain continuity information and implements a bounding box attention mechanism to overcome the limitation of inaccurate prediction of tumor center points and decrease redundant bounding boxes. The model integrates information from various channels using an effective Squeeze-and-Excitation attention module. The proposed model obtained a mean average precision result of 0.476 on the Decathlon dataset, which was superior to that of the prior methods examined for comparison. This research is expected to enable physicians to diagnose tumors very efficiently; particularly, the prediction of tumor center points is expected to enable physicians to rapidly verify their diagnostic judgments. The proposed method is considered suitable for future adoption in clinical practice in hospitals and resource-poor areas because its superior performance does not increase computational cost; hence, the equipment required is relatively inexpensive.
Preserving the Privacy of COVID-19 Infected Patients Data Using a Divergent-Scale Supervised Learning for Publishing the Informative Data

The ongoing COVID-19 pandemic has raised many important questions about sharing and usage of personal information of the infected patients for data analysis, research, or any other decision-making. When we want to share the data with others, we want to provide the privacy of the individuals too. There are many situations where the preservation of COVID-19 patient's identity or personal data becomes important. Thus the essence of preserving personal information should be carefully considered. Data digitization is the key factor of the distributed computer and communication systems of the present era, which enabled phenomenal opportunities and benefits in almost all contexts of the new age requirements of human life. However, the volume of weaknesses is also essential to the violation of digital data, which are available to data losses, a confidential crack, and other use of ethics and unauthorized data on a digital platform. In regards to defend the data breaching in the data publishing process, many contemporary models endeavored to improve the anonymization and diversification of the sensitive elements of the data used in the publishing process. However, these methods cannot be generous for large data. At the same time, some of the scalable methods are recognized to store confidentiality data published in the literature. In addition, most of the methods worked based on the 1-diversity and k-anonymity principles. These methods are susceptible to data breaches that are mostly uninformative, which reduces the scope of research to develop privacy-preserving methods for data publishing that prevent the vulnerabilities caused by data breaches or resulted in the publishing of uninformative data. Hence, the proposed work intended to define a novel supervised learning-based privacy-preserving technique for secure yet informative data publishing for the COVID-19 pandemic situation. The proposed method considers the values specified as the sensitive data as labels and verifies the diversity of the other elements such as n-gram quasi-identifiers of the corresponding data, which expose the values (considered as labels) of sensitive elements. Experimental results show the significance of the proposal by comparing it with other contemporary diversity-based techniques.
A Deep Learning Approach for Bangla Speech to Text Conversion

One of the most remarkable advantages of speech recognition (SR) technology includes speech to text conversion (STT). This paper's focal point is to recognize Bangla words among various speakers and noisy environments and convert them into text. Mel frequency cepstral coefficient (MFCC) has been used for extracting words from the audio file. Using gated recurrent units (GRU) along with directional long short term memory (LSTM), the continuity of words and next word-level prediction in a sentence have been implemented. An existing dataset that is enriched by recorded speech has been used here. Keras-Tensorflow toolkit has been used as a software apparatus for training data. The highest train accuracy has been obtained from the GRU architecture in the acoustic model (AM) is 94.44% while the test accuracy rate is only 47% due to the small test dataset. On the other hand, test accuracy is maximum up to 45.81% with the LSTM model in the language model (LM). The proposed method signifies good training and testing accuracy rate based on applying GRU model to the AM, LSTM to the LM and the numbers of total recordings of each sentence.
Extracting Mode Diagrams from Blech Code

Software visualization tools can improve the software development process by providing a graphical overview of source code and enhancing collaboration. We here propose a concept to automatically extract mode diagrams from Blech code, an imperative synchronous programming language for embedded, reactive and safety-critical systems. Our main findings are that the visualization is helpful to understand the stateful nature of the source code and that it can enhance the collaboration between developers. It is also found, however, that a good understanding of the precise diagram semantics meaning of the diagram elements is key. Lastly, the findings indicate that preference on different labeling options is highly subjective.
Automatic Recognition of Pointer Meter Reading Based on Yolov4 and Improved U-net Algorithm

Pointer meters are widely used in life and industrial production due to their simple structure, good reliability, high-cost performance, and intuitive and convenient reading. With the improvement of artificial intelligence and automation level, the application of drones in substation inspections has become more and more extensive, which has brought convenience to image collection, but the problem of the accuracy of reading recognition of pointer meters has not been well resolved. Using the current mainstream algorithms, due to the complexity of the surrounding environment, dust pollution, and other factors, the obtained image contains a lot of noise, which affects the accuracy of reading recognition. In this paper, YOLOV4 is used to detect the position of the dial in the images and classify the meters; then combined with the characteristics of the meter image, the improved U-Net image segmentation technology to effectively extract the pointers in the area, accurately identify the meter readings, and use the collected power equipment sample training data set to test the algorithm. The improved U-Net improves the learning ability of the shallow information of the image, reduces the loss of edge features, and improves the segmentation effect of the target.
Randomization-based Defenses against Data-Oriented Attacks

For nearly two decades now, the vast majority of critical software vulnerabilities have been memory corruption bugs in C and C++ programs[13, 14]. Attackers often exploit these bugs using control-flow hijacking techniques to seize control over vulnerable programs. This allows them to execute arbitrary code, exfiltrate sensitive data, or escalate the programs' privileges. However, thanks to the successful rollout of mitigations such as control-flow integrity and shadow stacks,[5] it is becoming increasingly harder to mount control-flow attacks.
Application of Artificial Intelligence and Big Data in Sports Event Service——Take Guilin as an Example

Abstract In recent years, many cities improve their brand awareness by holding sports events, but the traditional service mode of sports events cannot meet the needs of people at present. Through artificial intelligence and big data technology to improve and strengthen the service ability of event organizers is one of the current mainstream trends. However, there are not many practical application cases at present, so this paper puts forward the application research based on artificial intelligence and big data in sports event service, Taking Guilin as an example. This paper makes an in-depth investigation and Research on the demand of different ticket holders for the event service in Guilin. Through the analysis of the survey results, it can be seen that different ticket holders have different demand points for the event service. However, in the traditional service mode of sports events, it is difficult to meet the needs of users due to the lack of data support. In view of this, according to the characteristics of sports event service, combined with artificial intelligence and big data technology, this paper puts forward optimization and improvement measures. By improving the information gain in big data mining, this measure simplifies the algorithm and makes the calculation results more in line with the actual needs of sports event services. Analysis shows that under the background of information age, artificial intelligence and big data technology can further improve the comprehensive ability of China’s sports event service, meet the needs of users, and promote the development of sports.
An Effective Lightweight Cryptographic Algorithm to Secure Internet of Things Devices

The Internet of Things (IoT) connects billions of devices all over the world that are currently connected to the internet. IoT is essentially a combination of constrained devices that share sensitive data with each other without human involvement. Various security and privacy concerns exist with such a large number of connected devices, as they have low inbuilt resources. Hence, it is imperative to provide security solutions for such devices to secure them. This implies that the authentication factor protects the security and privacy of these devices and data by playing a vital role in data integrity. We have been analyzed in this paper the various lightweight solutions with the authentication requirements in IoT applications. This study has indicated that the top security issue is to provide less computation and be resistant to attacks, such as replay attacks, man-in-the-middle, forgery and chosen-ciphertext attacks, and denial of service (DoS) attacks.