Reviewerly/exHarmony huggingface dataset:
Controlled Quantum Secure Direct Communication Based on Four-Qubit Cluster States and Quantum Search Algorithm

A controlled quantum secure direct communication protocol based on four-qubit cluster states and quantum search algorithm is put forward, in which four users, a sender, a receiver and two controllers, are involved in achieving the secure transmission of secret message. The four-qubit cluster state can ensure the feasibility and security of the protocol because of its large persistency of entanglement. Meanwhile, the idea of quantum search algorithm is used to accomplish the task of encoding and decoding secret message. The proposed protocol can successfully avoid the information leakage problem and resist some common attacks including the outsider attacks and the internal attacks, and its qubit efficiency is up to 20%. Furthermore, compared with the previous quantum secure direct communication protocols, it can effectively resist the attacks from the dishonest receiver.
Aging Residual Factorization Machines: A Multi-Layer Residual Network Based on Aging Mechanisms

With the rapid development of recommendation systems, models and algorithms supporting the core of recommendation systems have emerged one after another, and researchers have attempted to optimize them. However, the structure of these models is complex. Popular deep neural networks often achieve the highest utilization of data by increasing the number of hidden layers, ignoring the problems of exploding and vanishing gradients and even the entire degradation of the networks. However, researchers pay too much attention to algorithms and models and do not consider the dataset itself. Methods for processing data and finding possible connections between the data and models have become new explorable points. Cold start is also a problem that researchers have been trying to solve and optimize since the birth of the recommendation system. Recent studies also provide good ideas for solving cold start, but the problem is that researchers still do not focus on datasets. In order to fill the gap in the exploration and research of datasets, this paper takes the long tail distribution and cold start problems that are common in recommendation systems such as the starting point, combines the residual network in computer vision with deep learning, and proposes the aging mechanism of datasets. In this paper, a multi-layer residual network based on aging mechanisms called Aging Residual Factorization Machines (ARFM) is proposed. Parallel experiments with other model algorithms are carried out on three datasets of different sizes and categories. Experimental results show that ARFM achieve performance advantages under the premise of different recommendation tasks.
Design and Research of College English Reading, Writing and Translation Teaching Class Based on 5G Technology

Abstract Traditional college English lectures, writing, and translation through wireless networks have numerous drawbacks. As a result, investigation and advancement for fifth-generation (5G) technologies is already underway. High data rates, reduced delay, energy saving, minimum price, improved system potential, and large-scale devices interconnection are all features of 5G, the fifth generation of mobile communications technology. The 5G wireless network interaction methodology for digital cellular network that allows high amounts of information traffic and a large count of wireless connections, is known as the 5G network. The use of the latest 5G technologies must be integrated into the curriculum to achieve higher order educating capabilities. Educators must make complete utilization of network sources, particularly 5G network interaction tools, to enhance English lecture, writing, and translation, as the rapid growth of 5G network interaction methodology offers a novel perspective for English lecture, writing, and translation. Based on 5G technology, this article suggests the design of soft computing approaches such as Genetic Algorithm (GA) for college English lecture, writing, and translation. This study claims that network interaction technologies shall promote resource sharing, empower teacher-student connection, and improve students' overall English writing performance by researching this novel kind of network. To increase the performance of the suggested system, 5G network-dependent English writing and interaction tools are employed in this paper.
Research on Chinglish in the Internet Buzzwords from the Perspective of Sociolinguistics

The Internet buzzword containing a lot of Chinglish is a new form of language generated in the context of the rapid development and wide spread of the Internet. A large number of Internet buzzwords, which is different from the traditional media language, correspond with the trend of the time as well as meet the satisfaction of people’s language demands. As an academic discipline, the main purpose of sociolinguistic is to study the relationship between people’s language and the social condition. This paper will illustrate the Chinglish in the Internet buzzword from the perspective of sociolinguistics. Keywords: Chinglish, Internet buzzword, sociolinguistics
Integrating Companding and Deep Learning on Bandwidth-Limited Image Transmission

&lt;p&gt;The image companding is a simple image compression technique which is very easy to be implemented in the bandwidth-limited environment. This paper presents a simple way for improving the quality of decompressed image in the image companding task. The proposed method consists of two networks, namely Sub-band Network (SubNet) and Pixel Network (PixNet), for performing an image reconstruction. The SubNet module exploits the effectiveness of Stationary Wavelet Transform (SWT) and Convolutional Neural Network (CNN) in order to recover the lost information in the wavelet sub-bands basis. Whilst, the PixNet part applies CNN with identity mapping to improve the quality of initial reconstructed image obtained from the SubNet module. As reported in this paper, the proposed method outperforms the former existing schemes in the image companding task. It has also been proven that the proposed method is able to improve the quality of reconstructed image with some simple steps.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Classification of Quasars, Galaxies, and Stars in the Mapping of the Universe Multi-modal Deep Learning

In this paper, the fourth version the Sloan Digital Sky Survey (SDSS-4), Data Release 16 dataset was used to classify the SDSS dataset into galaxies, stars, and quasars using machine learning and deep learning architectures. We efficiently utilize both image and metadata in tabular format to build a novel multi-modal architecture and achieve state-of-the-art results. In addition, our experiments on transfer learning using Imagenet weights on five different architectures (Resnet-50, DenseNet-121 VGG-16, Xception, and EfficientNet) reveal that freezing all layers and adding a final trainable layer may not be an optimal solution for transfer learning. It is hypothesized that higher the number of trainable layers, higher will be the training time and accuracy of predictions. It is also hypothesized that any subsequent increase in the number of training layers towards the base layers will not increase in accuracy as the pre trained lower layers only help in low level feature extraction which would be quite similar in all the datasets. Hence the ideal level of trainable layers needs to be identified for each model in respect to the number of parameters. For the tabular data, we compared classical machine learning algorithms (Logistic Regression, Random Forest, Decision Trees, Adaboost, LightGBM etc.,) with artificial neural networks. Our works shed new light on transfer learning and multi-modal deep learning architectures. The multi-modal architecture not only resulted in higher metrics (accuracy, precision, recall, F1 score) than models using only image data or tabular data. Furthermore, multi-modal architecture achieved the best metrics in lesser training epochs and improved the metrics on all classes.
Image Steganography

Abstract: The behavior of posting pictures on social network structures is befell everywhere and every single 2nd. for this reason, the conversation channels supplied through various social networks have a remarkable potential for covert verbal exchange. however, photos transmitted through such channels will typically be JPEG compressed, which fails most of the prevailing steganographic schemes. in this paper, we advise a singular photograph steganography framework this is robust to such channels. specifically, we first reap the channel compressed version (i.e., the channel output) of the unique picture. secret data is embedded into the channel compressed original photo by the use of any of the prevailing JPEG steganographic schemes, which produces the stego-image after the channel transmission. To generate the corresponding image earlier than the channel transmission (termed as the intermediate photograph), we recommend a coefficient adjustment scheme to barely regulate the authentic image based totally at the stego- image. The adjustment is carried out such that the channel compressed version of the intermediate photo is precisely similar to the stego-picture. therefore, after the channel transmission, the secret facts may be extracted from the stego-photo with 100Various experiments are conducted to reveal the effectiveness of the proposed framework for image steganography sturdy to JPEG compression. Keywords: Steganograpghy, Data hiding, Security, Image Steganogrpghy
Blackbird's language matrices (BLMs): a new benchmark to investigate disentangled generalisation in neural networks

Current successes of machine learning architectures are based on computationally expensive algorithms and prohibitively large amounts of data. We need to develop tasks and data to train networks to reach more complex and more compositional skills. In this paper, we illustrate Blackbird's language matrices (BLMs), a novel grammatical dataset developed to test a linguistic variant of Raven's progressive matrices, an intelligence test usually based on visual stimuli. The dataset consists of 44800 sentences, generatively constructed to support investigations of current models' linguistic mastery of grammatical agreement rules and their ability to generalise them. We present the logic of the dataset, the method to automatically construct data on a large scale and the architecture to learn them. Through error analysis and several experiments on variations of the dataset, we demonstrate that this language task and the data that instantiate it provide a new challenging testbed to understand generalisation and abstraction.
Multilingual Machine Translation with Hyper-Adapters

Multilingual machine translation suffers from negative interference across languages. A common solution is to relax parameter sharing with language-specific modules like adapters. However, adapters of related languages are unable to transfer information, and their total number of parameters becomes prohibitively expensive as the number of languages grows. In this work, we overcome these drawbacks using hyper-adapters -- hyper-networks that generate adapters from language and layer embeddings. While past work had poor results when scaling hyper-networks, we propose a rescaling fix that significantly improves convergence and enables training larger hyper-networks. We find that hyper-adapters are more parameter efficient than regular adapters, reaching the same performance with up to 12 times less parameters. When using the same number of parameters and FLOPS, our approach consistently outperforms regular adapters. Also, hyper-adapters converge faster than alternative approaches and scale better than regular dense networks. Our analysis shows that hyper-adapters learn to encode language relatedness, enabling positive transfer across languages.
How to Make AlphaGo’s Children Explainable

Under the rubric of understanding the problem of explainability of AI in terms of abductive cognition, I propose to review the lessons from AlphaGo and her more powerful successors. As AI players in Baduk (Go, Weiqi) have arrived at superhuman level, there seems to be no hope for understanding the secret of their breathtakingly brilliant moves. Without making AI players explainable in some ways, both human and AI players would be less-than omniscient, if not ignorant, epistemic agents. Are we bound to have less explainable AI Baduk players as they make further progress? I shall show that the resolution of this apparent paradox depends on how we understand the crucial distinction between abduction and inference to the best explanation (IBE). Some further philosophical issues arising from explainable AI will also be discussed in connection with this distinction.
Performance Comparison of Python Translators for a Multi-threaded CPU-Bound Application

Currently, Python is one of the most widely used languages in various application areas. However, it has limitations when it comes to optimizing and parallelizing applications due to the nature of its official CPython interpreter, especially for CPU-bound applications. To solve this problem, several alternative translators have emerged, each with a different approach and its own cost-performance ratio. Due to the absence of comparative studies, we have carried out a performance comparison of these translators using N-Body as a case study (a well-known problem with high computational demand). The results obtained show that CPython and PyPy presented poor performance due to their limitations when it comes to parallelizing algorithms; while Numba and Cython achieved significantly higher performance, proving to be viable options to speed up numerical algorithms.
An Empirical Study of Gradient-based Explainability Techniques for Self-admitted Technical Debt Detection

&lt;p&gt;Self-Admitted Technical Debt (SATD) is an intentionally introduced software code comment describing potential defects or other technical debt. Currently, deep learning is widely used in fields such as Natural Language Processing. Deep learning can be used for SATD detection, but there is a class imbalance problem and a large number of easily classified SATD instances that may potentially affect the loss value. As a result, we proposed a weighted focal loss function based on particle swarm to address the problem. Meanwhile, there is no empirical research based on local explanations for SATD detection. We have investigated local interpretation models such as Saliency Maps, Integrated Gradients, which are currently widely used in deep learning, and conducted empirical research for shared data sets. The research results show that our proposed weighted focal loss function can achieve the best performance for SATD detection; our model achieves 12.27%, 5.97%, and 5.62% improvement in Precision, Recall, and AUC compared to the baseline model, respectively; Local explanation models, including Saliency Maps and Integrated Gradients can cover nearly half of the manually labeled paradigms; these two interpretation models can also discover potential new paradigms.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Image Segmentation via Multiscale Perceptual Grouping

The human eyes observe an image through perceptual units surrounded by symmetrical or asymmetrical object contours at a proper scale, which enables them to quickly extract the foreground of the image. Inspired by this characteristic, a model combined with multiscale perceptual grouping and unit-based segmentation is proposed in this paper. In the multiscale perceptual grouping part, a novel total variation regularization is proposed to smooth the image into different scales, which removes the inhomogeneity and preserves the edges. To simulate perceptual units surrounded by contours, the watershed method is utilized to cluster pixels into groups. The scale of smoothness is determined by the number of perceptual units. In the segmentation part, perceptual units are regarded as the basic element instead of discrete pixels in the graph cut. The appearance models of the foreground and background are constructed by combining the perceptual units. According to the relationship between perceptual units and the appearance model, the foreground can be segmented through a minimum-cut/maximum-flow algorithm. The experiment conducted on the CMU-Cornell iCoseg database shows that the proposed model has a promising performance.
Classically optimized Hamiltonian simulation

Hamiltonian simulation is a promising application for quantum computers to achieve a quantum advantage. We present classical algorithms based on tensor network methods to optimize quantum circuits for this task. We show that, compared to Trotter product formulas, the classically optimized circuits can be orders of magnitude more accurate and significantly extend the total simulation time.
On Cryptocurrencies: An Assessment of Bitcoin’s Prospect As Legal Medium of Exchange

Abstract Digital currency has gained importance in recent times. But definitional issue made this currency ambiguous to society and financial institutes. Digital currencies are used in real-world transaction as well as in the dark web. Digital currency comes into discussion with the creation of bitcoin - an online payment method with no intermediary or financial institute. This online payment system has a strong security system. Consequently, bitcoin account holders has been increasing considerably. We can use bitcoins legally besides other legal tenders, if existing laws do not consider these coins immoral. Also, in the midst of the ongoing global pandemic (Novel Coronavirus or Covid-19), a paperless currency like bitcoin, which also involves contactless transaction, would go a long way in preserving wellbeing for individual and economy alike. Furthermore, we can try to find out and address the problems related to this currency. After proper assessment, if we acknowledge bitcoin, they can compete with the existing coins in the present currency system, complying with government rules, and contribute to the development of the currency system. They can also work as service providers or economic agents of virtual coins, and participate in the country’s economic activities through the exchange of the coins. JEL Classification: E42, G18, O38. Keywords: Cryptocurrencies, Bitcoin, Digital currencies, Virtual currencies, Cryptography, Fiat currency, Covid-19.
Topics in Graph Burning and Datalog

&lt;p&gt;Graph burning studies how fast a contagion, modelled as a set of fires, spreads in a graph. The burning process takes place in discrete time-steps, or rounds. In each round, a fire breaks out at a given node (or vertex), thus burning it. Between rounds, fires from burning nodes spread to adjacent nodes. The burning number of a graph G, denoted b(G), is the minimum number of rounds necessary for every node of G to burn. We consider b(Gm,n), where Gm,n is the m × n Cartesian grid. For m = ω( √ n), the asymptotic value of b(Gm,n) was determined, but only the growth rate of b(Gm,n) was investigated in the case m = O( √ n). Accordingly, we provide new explicit bounds on b(Gc √ n,n) for valid c &gt; 0. Graph burning is analogous to a pebble game, which typically involves the placement of pebbles on nodes of a graph. Burning of a node is comparable to a pebbling step (or pebbling move): the removal of two pebbles from a node, where one of the removed pebbles is placed on an adjacent node while the other is discarded. In a certain pebble game iii variant (discussed in Section 1.7), the existence of a winning strategy has an interesting characterization: expressibility of the relevant constraint satisfaction problem (or CSP) in the logic programming language Datalog. If a structure with a non-empty domain is restricted to relation symbols only, then we call that structure a template. We show that the CSP for any finite template admitting terms of the weak J ́onsson type has a property known as bounded pathwidth duality. This implies the expressibility of the complement CSP in linear Datalog, and places the CSP in NL.&lt;/p&gt; &lt;p&gt;&lt;br&gt;&lt;/p&gt;
Revisiting the Design of Parallel Stream Joins on Trusted Execution Environments

The appealing properties of secure hardware solutions such as trusted execution environment (TEE) including low computational overhead, confidentiality guarantee, and reduced attack surface have prompted considerable interest in adopting them for secure stream processing applications. In this paper, we revisit the design of parallel stream join algorithms on multicore processors with TEEs. In particular, we conduct a series of profiling experiments to investigate the impact of alternative design choices to parallelize stream joins on TEE including: (1) execution approaches, (2) partitioning schemes, and (3) distributed scheduling strategies. From the profiling study, we observe three major high-performance impediments: (a) the computational overhead introduced with cryptographic primitives associated with page swapping operations, (b) the restrictive Enclave Page Cache (EPC) size that limits the supported amount of in-memory processing, and (c) the lack of vertical scalability to support the increasing workload often required for near real-time applications. Addressing these issues allowed us to design SecJoin, a more efficient parallel stream join algorithm that exploits modern scale-out architectures with TEEs rendering no trade-offs on security whilst optimizing performance. We present our model-driven parameterization of SecJoin and share our experimental results which have shown up to 4-folds of improvements in terms of throughput and latency.
Vulnerability of Saudi Private Sector Organisations to Cyber Threats and Methods to Reduce the Vulnerability

The Middle Eastern region has witnessed many cyber-attacks in recent years, especially in Saudi Arabia. Saudi Arabian organisations face problems anticipating, detecting, mitigating, or preventing cyber-attacks despite policies and regulations. The reasons for this have not been investigated adequately. This research aims to study the methods used to address cyber security issues in the private sector. A survey of IT managers of private organisations yielded 230 usable responses. The data were analysed for descriptive statistics and frequency estimations of responses, and the results are presented in this paper. Poor awareness of cyber security issues is reflected in the survey responses. The expenditure on cyber security, especially by large firms, was inadequate. There was a greater tendency to outsource many aspects of cyber security without concern about the risks. A very small percentage of IT managers considered the certainty of a cyber threat within the next year. It is important from the point of proactive strategies to prevent attacks. The findings highlight a lack of required knowledge and skills in performing their expected roles well. Additionally, many weaknesses have been detected in cyber security management in Saudi private organisations, and there is room to improve the quality of computer security systems. The published literature largely supported this. The findings from this study have implications for the stakeholders, especially IT managers working in the private sector of Saudi Arabia. The learnings from this study may be used to address the vulnerabilities identified. The findings clearly show the need to train IT managers of Saudi private organisations.