Reviewerly/exHarmony huggingface dataset:
End-to-End Learnable Multi-Scale Feature Compression for VCM

The proliferation of deep learning-based machine vision applications has given rise to a new type of compression, so called video coding for machine (VCM). VCM differs from traditional video coding in that it is optimized for machine vision performance instead of human visual quality. In the feature compression track of MPEG-VCM, multi-scale features extracted from images are subject to compression. Recent feature compression works have demonstrated that the versatile video coding (VVC) standard-based approach can achieve a BD-rate reduction of up to 96% against MPEG-VCM feature anchor. However, it is still sub-optimal as VVC was not designed for extracted features but for natural images. Moreover, the high encoding complexity of VVC makes it difficult to design a lightweight encoder without sacrificing performance. To address these challenges, we propose a novel multi-scale feature compression method that enables both the end-to-end optimization on the extracted features and the design of lightweight encoders. The proposed model combines a learnable compressor with a multi-scale feature fusion network so that the redundancy in the multi-scale features is effectively removed. Instead of simply cascading the fusion network and the compression network, we integrate the fusion and encoding processes in an interleaved way. Our model first encodes a larger-scale feature to obtain a latent representation and then fuses the latent with a smaller-scale feature. This process is successively performed until the smallest-scale feature is fused and then the encoded latent at the final stage is entropy-coded for transmission. The results show that our model outperforms previous approaches by at least 52% BD-rate reduction and has ×5 to ×27 times less encoding time for object detection. It is noteworthy that our model can attain near-lossless task performance with only 0.002-0.003% of the uncompressed feature data size.
LEARNING LOSS IN THE TEACHING OF ENGLISH SUBJECT AT A VOCATIONAL SCHOOL IN TANJUNG JABUNG BARAT: TEACHERS’ PERCEPTION

Abstract&#x0D; The covid-19 Pandemic has caused an unexpected shift in teaching and learning at schools around the world. The teaching and learning process has mostly switched to distance learning and causes problems for all parties involved, including students and their parents, teachers, and school management. While research has been carried out to observe the implementation of distance learning and its challenges and problems, only a few studies have been focused on investigating a possible phenomenon called ‘learning loss’. Learning loss is generally defined as a lack of student interest in the learning process because there is only little to zero direct interaction process during their learning. This research is designed to investigate the possible occurrence of learning loss in a vocational school in Tanjung Jabung Barat. The research was carried out qualitatively. Data were collected through in-depth interviews with participants that consist of two English teachers in the school. Questions addressed in the interview were focused on investigating the shift in the teachers’ teaching process and students’ learning activities, including the signs of a decrease or loss of interest in learning and the impact on learning achievement. Teachers’ understanding of students’ situations and efforts to mitigate them will also be investigated. Data collected from the interview was analyzed using theories relevant to the issues.&#x0D; Key Word: Pandemic, English Teaching, Teachers Perception, Learning Loss
Introduction to the Crypto Revolution

In 2010, a man named Laszlo Hanyecz made history by becoming the first person to use Bitcoin to buy a tangible item: two pizzas. At the time, Bitcoin was worth less than a cent, and Hanyecz famously paid 10,000 bitcoins for his pizza order.
Pengaruh Penggunaan Media Komik Terhadap Hasil dan Minat Belajar Siswa dalam Pembelajaran Teks Negosiasi Kurikulum Merdeka

The Influence Digital Comic Media on The Results and Interest Students in Learning The Negotiation Text The Independent Curriculum ABSTRAKHasil dari pengamatan yang dilakukan di kelas X SMA Dr. Soetomo Surabaya, didapati permasalahan yang terjadi adalah rendahnya hasil belajar siswa, hal tersebut terjadi karena proses pembelajaran yang monoton, untuk itu diperlukan alternatif yang dapat mengubah proses pembelajaran agar lebih menarik, yaitu menggunakan media komik digital. Tujuan dari penelitian ini adalah untuk melihat pengaruh media pembelajaran komik digital dalam meningkatkan hasil serta minat belajar peserta didik kelas 10. Penelitian ini menerapkan metode quasi eksperimen, dengan dua kelas sampel yang di teliti yaitu kelas X-4 dan kelas X-2. Uji analisis menggunakan uji independent sample t-test dan analisis deskriptif kuesioner, hasil dari penelitian ini yaitu nilai rerata kelas eksperimen lebih tinggi 77,36 dan rerata kelas kontrol adalah 53,51, berdasarkan uji hipotesis terdapat perbedaan dengan nilai sig. 0,000&lt;0,05 jadi H_1 diterima dan H_0 ditolak, sedangkan pada uji analisis statistik deskriptif mendapat nilai rerata 3,03 dengan kriteria setuju pada pernyataan kuesioner.Kata kunci: Media Komik, Hasil Belajar, Minat BelajarABSTRACTThe results of observations made in class X SMA Dr. Soetomo Surabaya, it was found that the problem that occurred was the low student learning outcomes, this occurred because the learning process was monotonous, for this reason an alternative was needed that could change the learning process to make it more interesting, namely using digital comic media. The purpose of this study was to see the effect of digital comic learning media in improving the learning outcomes and interest of grade 10 students. This study applied a quasi-experimental method, with two sample classes being studied, namely class X-4 and class X-2. The analytical test used is the independent sample t-test and questionnaire descriptive analysis. The results of this study are that the average value of the experimental class is 77.36 higher and the control class average is 53.51. Based on the hypothesis test, there is a difference with a significant value of 0.000 &lt;0. .05 then is accepted and is rejected, while in the descriptive statistical analysis test it gets an average value of 3.03 with the criterion of agreeing on the questionnaire statement.Keyword: Comic Media, Learning Outcomes, Interest to learn
Auto-Summarization of text by using Natural Language Processing

With the advent of the digital age and rapidly developing technologies, we find ourselves teeming with data.Organisations and enterprises across the globe generate increasingly vast amounts of information on daily.The rise of big data as a concept has led to a new field of advanced analytics centred around mining this data for helpful information.A method to automatically parse through these large blocks of data is the need of the hour.Textual data is a significant component of this flood of data.Considering the sheer scope of the texts available to us, particularly through the internet, it becomes difficult for humans to sift through it for important information.Time being a more scarce commodity than ever before, the applications of automatic summarization span various fields, both technical and non-technical.This ranges from news aggregators and chatbots to legal contract analysis and medical record parsing.The problem of Automatic Text Summarization has been chosen as the project topic.This project intends to use extractive and abstractive text summarization techniques to produce concise and coherent accounts of the relevant information.This project will be using several NLP modules in Python such as NLTK and spaCy in conjunction with supervised and unsupervised machine learning algorithms.It will also investigate using transformers and neural networks to augment summarization capabilities.
A Note on Non-interactive Zero-Knowledge from CDH

We build non-interactive zero-knowledge (NIZK) and ZAP arguments for all $$\textsf{NP} $$ where soundness holds for infinitely-many security parameters, and against uniform adversaries, assuming the subexponential hardness of the Computational Diffie-Hellman (CDH) assumption. We additionally prove the existence of NIZK arguments with these same properties assuming the polynomial hardness of both CDH and the Learning Parity with Noise (LPN) assumption. In both cases, the CDH assumption does not require a group equipped with a pairing. Infinitely-often uniform security is a standard byproduct of commonly used non-black-box techniques that build on disjunction arguments on the (in)security of some primitive. In the course of proving our results, we develop a new variant of this non-black-box technique that yields improved guarantees: we obtain explicit constructions (previous works generally only obtained existential results) where security holds for a relatively dense set of security parameters (as opposed to an arbitrary infinite set of security parameters). We demonstrate that our technique can have applications beyond our main results.
Smart helmet with integrated Airbag system

An integrated helmet airbag system is a revolutionary technology that aims to provide additional protection for motorcycle riders in the event of an accident.It is a safety device that integrates an airbag system into a helmet, providing an extra layer of protection for the head and neck.This innovative technology has the potential to save countless lives, and its development is a significant step forward in motorcycle safety.The integrated helmet airbag system consists of a set of sensors that are strategically placed throughout the motorcycle.These sensors are designed to detect the sudden deceleration or change in speed that typically occurs during a crash.When a crash is detected, the sensors trigger the deployment of the airbag system.The airbag system in the helmet works in the same way as the airbag in a car.It is designed to inflate rapidly upon impact, creating a cushion of air around the head and neck of the rider.This cushion of air helps to reduce the risk of serious injury by absorbing the impact of the crash.The integrated helmet airbag system is an excellent addition to existing safety measures such as helmets and protective clothing.The system works to protect the head and neck, which are the most vulnerable areas of the body in the event of an accident.
An FPGA-Based Physical Layer Approach for a CV-QKD Transmitter

Quantum key distribution (QKD) provides a secure and effective way of exchanging cryptographic symmetric keys without the possibility of an eavesdropper being undetected. The transmission and detection of the quantum states at the physical layer is the first step to defining a common source of entropy, where both parties will extract a raw key. Over the last few years, continuous variable quantum key distribution (CV-QKD) protocols have become an attractive technology for QKD, providing a low-cost solution that can be easily integrated with the current optical network devices and infrastructure. Unlike other QKD technologies, a CV protocol requires complex and highly computationally demanding digital signal processing (DSP) operations. In that sense, parallel computation tools such as field programmable gate arrays (FPGAs) represent a key element for the practical implementation of this quantum technology and to scale its performance in terms of distance and secret key rate indicators. This work presents an FPGA-based implementation of the physical layer of a CV-QKD transmitter. The proposed architecture and employed protocols are described, and hardware implementation aspects are discussed, including resource occupation analysis, configuration parameters, and timing issues.
Oblivious Keys for Secure Multiparty Computation Obtained from a CV-QKD

We analyze the use of a Quantum Key Distribution (QKD) system to generate and distribute oblivious keys. Oblivious keys can be used to implement secure multiparty computation services. After defining oblivious keys, we show how they can be reduced to symmetric keys and used to implement secure links, and how they can also be used to implement secure computation services. Oblivious keys can also be reversed. In this context, we analyze the advantages of using quantum resources to generate oblivious keys. Particularly, we analyze a protocol to generate oblivious keys, considering both the efficiency and security of the process. We show that by using QKD, we can simultaneously obtain an efficient and quantum-resistant solution to generate and distribute oblivious keys. Finally, we discuss a field trial where a quantum secure multiparty computation service for calculating phylogenetic trees was implemented in the Quantum Madrid Network.
Enhanced Cross-Modal Transformer Model for Video Semantic Similarity Measurement

Video processing is critical to many industrial systems. Semantic similarity measures for videos aim to evaluate the semantic distance of videos, and its downstream applications seem obvious, such as deduplication, related matching, ranking and content diversity control. Despite sustainable efforts, many previous approaches require large-scale human annotation for the training purpose, which can be difficult to obtain in practical settings. Moreover, previous methods struggle to characterize the interactions between multiple modalities, which severely constraints the performance. To address aforementioned challenges, in this paper, we introduce a novel framework to measure the semantic similarity of the video. Specifically, to address the limited annotated datasets, we firstly propose a pre-training paradigm leveraging weakly-supervised label classification. Moreover, our approach proposes an Enhanced Cross-modal Transformer (ECT) block, to fully utilize the interaction information between video and textural features. Various optimization strategies have also been proposed to improve the performance of the model. Despite its conceptually simpleness, extensive experiments demonstrate the effectiveness of the proposed approach on the semantic similarity measurement tasks.
Machine learning and multinomal models: A simulation study

In recent times, the use of machine learning in academic research has become a more and more attractive option, an option that is becoming increasingly popular in the field of econometric research A significant amount of econometric literature has pitched the use of machine learning techniques for the purposes of improving research in roles such as variable selection or causal effect evaluation, in papers such as Athey and Imbens (2017) and Varian (2014).However, conversely, there is an equal amount of literature warning against blindly making use of these techniques without adjustments in papers such as Athey and Imbens ( 2019) and Mullainathan and Spiess (2017).In order to attain a better understanding of how the estimators provided by some of these machine learning techniques behave compared to their more traditional counterparts, this paper conducts a Monte Carlo Simulation study between Shrinkage based techniques such as LASSO and the Elastic Net, against Logistical Regression in a sparse multinomial model environment, comparing both selection consistency and estimator variance between different sample sizes.Overall, the results of this paper show that when dealing with sparse multinomial environments, of the methods tested, the Elastic Net provides the clearest advantage in selection consistency, both being able to mark strongly relevant variables as strongly relevant more often than LASSO and Logit, but also managing to avoid marking irrelevant variables as relevant.Moreover, when compared to its peers Elastic Net holds the greatest advantage when dealing with datasets with low samples.However, this advantage is not universal, as it holds a natural bias when assigning values to coefficients and its ability to pick out weakly relevant variables is comparable to that of Logit.
7 Convergence of AR &amp; VR with IoT

Augmented and virtual reality are two technological domains that provide and improve the real world simulation of the real objects. Internet of Things is another technological aspects which enables the integration of the physical objects upon the digital platforms. In this chapter we present the IoT based applications having the AR and VR technologies integration to enhance the sensitivity and quality of service of the applications in real world environments. The details of the devices based on the IoT are elaborated in context of AR and VR to create the mixed reality. Architectural frameworks of VR is defined with respect to market requirements like applications in the field of medical, entertainment, automotive, aerospace, and also for the electronic commerce. The useability and scope of the augmented reality with IoT for applications like connected vehicles, vehicle congestion management and future information of the congestion, smart grids, environment management, management of smart buildings and homes, smart cities, supply chain management and industrial, agricultural, and commercial applications and scopes are detailed for the future endeavour for the merged technologies of AR, VR and IoT. The technological frameworks of the IoT based objects and scalability of the AR and VR along with the IoT is detailed to focus on the communication and collaboration enhancements in industries and other public sector firms. Also, we present the challenges and cost pertaining to the IoT based Object simulation over the AR and VR technologies.
Chapter 8 Quantum computing in automata theory

In the contemporary era we dwell in, every now and then, new technologies are developed by master brains such as researchers and scientists. This also paves the way to discover replacements to classical computers. One such progression among the great inventions is the essence of quantum computing. They are brought to light for the purpose of solving problems and scenarios solved by classical computers but in a more effective way and in the least span of time. The study of quantum computing focuses on finding new ways to compute by using quantum physics phenomena. A qubit, in contrast to a typical computer bit, can be either 0 or 1, or it can be a superposition of both 0 and 1. Quantum theory commemorates with automata theory, thereby formulating quantum finite automata. Quantum finite automaton, or basically a deterministic finite automaton, is one of the simplest models of computation, which is an input-driven model, which takes an input, and according to the input, we just read the machine, and it changes its state. This proves that any quantum finite automata are completely capable of recognizing languages like Tally with given cut point too. Such languages recognized by a cut point, by quantum finite automata, are called stochastic languages. It further collaborates with the chemical industry with its qubit power over molecular collide, interactive models, and collaborative string processing using nonregular languages.
Determinate Student Final Project Supervisor Based AHP and SAW

Determining competent supervisors for student research projects is one of the factors that play the most important role because it can affect the success of student education, so it deserves attention.However, the process of determining a supervisor is not an easy thing because it involves various complex criteria and sub-criteria for making decisions consistently and objectively.Therefore, we propose AHP and SAW methods be utilized simultaneously with the criteria for education level, educational background, guiding experience, lecturer experience area, publication, guide quota, and student concentration, along with Forty-Three (43) other sub-criteria.This research purpose is to provide knowledge about how the AHP-SAW methods can be utilized together to cover each other's weaknesses in determining supervisors for student research projects.Where the AHP method works to calculate the priority level of criteria and sub-criteria that will be used by the SAW method in forming a matrix of criteria and alternatives and calculates the consistency value of the criteria and sub-criteria, while the SAW method works to calculate the matrix normalization value and ranking value for each alternative by utilizing the value of priority level of the criteria obtained from work of AHP.The results showed that the two methods were able to complement each other in determining the main supervisor of student research projects, with a ranking score of 1,00 for alternative ALec_002 and a co-supervisor ranking score of 0,97 for alternative ALec_007 out of 35 candidates.
Multimodal image registration and mosaicking of artworks: an approach based on mutual information

We present an automated method for registration and mosaicking of multimodal technical images of artworks based on mutual information. We focus on the registration of element distribution maps resulting from macro X-ray fluorescence (MA-XRF) scanning, which can be considered as a layered stack and treated as the moving image. The target fixed image is the visible image of the same artwork. In consecutive stages, a unique, optimised transformation that provides the highest average mutual information across all images in the stack is identified with consensus. This transformation can be applied to the moving image to obtain the best alignment between the moving and fixed images when overlapped.
Graph Sparsification by Approximate matrix Multiplication

Graphs arising in statistical problems, signal processing, large networks, combinatorial optimization, and data analysis are often dense, which causes both computational and storage bottlenecks. One way of sparsifying a weighted graph, while sharing the same vertices as the original graph but reducing the number of edges, is through spectral sparsification. We study this problem through the perspective of RandNLA. Specifically, we utilize randomized matrix multiplication to give a clean and simple analysis of how sampling according to edge weights gives a spectral approximation to graph Laplacians, without requiring spectral information. Through the CR−MM algorithm, we attain a simple and computationally efficient sparsifier whose resulting Laplacian estimate is unbiased and of minimum variance. Furthermore, we define a new notion of additive spectral sparsifiers, which has not been considered in the literature.
Intellectual Property Protection of Deep Learning Systems via Hardware/Software Co-design

Recent advances in model piracy have uncovered a new security hole for malicious attacks endangering the Intellectual Property (IP) of Deep Learning (DL) systems. This manuscript features our research titled “DeepAttest: An End-to-End Attestation Framework for Deep Neural Networks” [1] that is selected for the 2021 Top Picks in hardware and embedded security. DeepAttest is the first end-to-end framework that achieves reliable and efficient IP protection of DL devices with hardware-bounded usage control. We leverage device-specific model fingerprinting and Trusted Execution Environment (TEE) to ensure that only DL models with the device-specific fingerprint can run inference on protected hardware.