Reviewerly/exHarmony huggingface dataset:
Detection of Classical Cipher Types with Feature-Learning Approaches

To break a ciphertext, as a first step, it is essential to identify the cipher used to produce the ciphertext. Cryptanalysis has acquired deep knowledge on cryptographic weaknesses of classical ciphers, and modern ciphers have been designed to circumvent these weaknesses. The American Cryptogram Association (ACA) standardized so-called classical ciphers, which had historical relevance up to World War II. Identifying these cipher types using machine learning has shown promising results, but the state of the art relies on engineered features based on cryptanalysis. To overcome this dependency on domain knowledge, we explore in this paper the applicability of the two feature-learning algorithms long short-term memory (LSTM) and Transformer, for 55 classical cipher types from ACA. To lower the necessary data and the training time, various transfer-learning scenarios are investigated. Over a dataset of 10 million ciphertexts with a text length of 100 characters, Transformer correctly identified 72.33% of the ciphers, which is a slightly worse result than the best feature-engineering approach. Furthermore, with an ensemble model of feature-engineering and feature-learning neural network types, 82.78% accuracy over the same dataset has been achieved, which is the best known result for this significant problem in the field of cryptanalysis.
Adaptive Video Streaming with Scalable Video Coding using Multipath QUIC

The multi-path protocol improves end-to-end throughput and becomes one of the solutions to improve video streaming Quality of Experience (QoE). Scalable Video Coding (SVC) encodes a video segment into multi-layer, and different layers can be transmitted on heterogeneous paths. SVC has great potential to make use of the multipath protocol. Meanwhile, multipath QUIC (MPQUIC) provides multiplexing and instant handshake compared with multipath transmission control protocol (MPTCP). Thus MPQUIC has great potential in Dynamic Adaptive Streaming over HTTP (DASH) with SVC. However, mismatches between streams with different priorities and paths, network congestion caused by the suddenly increased data traffic are new challenges of MPQUIC for DASH-SVC. Adaptive Stream-scheduler Multipath QUIC (ASMQ) framework is proposed to improve the user’s QoE in DASH-SVC, prioritizing the streams based on the DASH-SVC application information and evaluates the qualities of multipath. ASMQ schedule prioritized streams to the path with different qualities. In addition, the server-client feedback mechanism of ASMQ can adapt to network congestion conditions in real-time. Emulation with real network traces demonstrates that the ASMQ improves user’s QoE compared to MPTCP and traditional MPQUIC.
Self-Attentional Multi-Field Features Representation and Interaction Learning for Person–Job Fit

Person–job fit, which aims to predict the matching degree between a resume and a job, has become an effective way to overcome information overload in the recruitment market. Existing studies on person–job fit usually focus on the representation learning of textual data in jobs and resumes. Person–job fit is a highly nonlinear complex problem that is affected by several fields of features. We assume that it would bring benefits to comprehensively consider the numerical features, categorical features, and textual features of resumes and jobs. To this end, we propose a novel model based on the self-attention mechanism, named MUlti-Field Features representation and INteraction (MUFFIN) learning for person–job fit. The key idea is to explore meaningful feature representations and interactions. Specifically, we group all the features of resumes and jobs into several fields. And a module is introduced to learn the hidden vectors of feature correlations in each feature field. Along this line, we propose a module with the multi-head self-attention mechanism and a residual connection to further model the feature field interactions. Moreover, we utilize a multi-layer perceptron (MLP) to measure the matching score between a resume and a job. Finally, the experimental results on a real-world dataset validate the effectiveness of MUFFIN for person–job fit.
Sharing emotions or/and making allies: the emoji’s interpersonal function in Chinese social media news comments

This article explores the semiotic capabilities of emoji in realizing attitude and building alignment via Chinese social media. The news comments section on Weibo is filled with readers’ evaluative responses, making it a suitable site for examining the interpersonal function of emoji. Drawing on appraisal, the analysis of emoji and emoji-text interactions in the news comments is associated with a car quality story. The findings show that a commenter’s position is contingently forged through the concordant or discordant coupling of ideation with attitude across language and emoji. Instead of construing particular attitude, some emoji serve as a bondicon featuring friendlies to convene readers around negative appraisal, irony or humor in the text. Besides, a few emoji perform a dual part of carrying evaluation and signaling membership in the comments. For Weibo news readers, the multifunctional emoji become a strategical tool of public participation in sociopolitical issues.
AI Charades: Language Models as Interactive Game Environments

Language models in recent years have shown astounding growth in modeling future text prediction. From a games perspective, language models present richly semantic environments, in which players can deeply influence the model's behavior through text input. This work presents a framework for building Language Model Games, games which center around a player manipulating a language model into behaving in a desired manner. A small demo, AI Charades, showcases a proof-of-concept game in which a player must communicate a secret word to an AI without mentioning it directly. Later discussion explores language models as a general tool for human-AI interfacing, along with their capabilities as design tools.
Extraction of a computer-certified ODE solver

Reliably determining system trajectories is essential in many analysis and control design approaches. To this end, an initial value problem has to be usually solved via numerical algorithms which rely on a certain software realization. Because software realizations can be error-prone, proof assistants may be used to verify the underlying mathematical concepts and corresponding algorithms. In this work we present a computer-certified formalization of the solution of the initial value problem of ordinary differential equations. The concepts are performed in the framework of constructive analysis and the proofs are written in the Minlog proof system. We show the extraction of a program, which solves an ODE numerically and provide some possible optimization regarding the efficiency. Finally, we provide numerical experiments to demonstrate how programs of a certain high level of abstraction can be obtained efficiently. The presented concepts may also be viewed as a part of preliminary work for the development of formalized nonlinear control theory, hence offering the possibility of computer-assisted controller design and program extraction for the controller implementation.
Measuring Shift-Invariance of Convolutional Neural Network with a Probability-Incorporated Metric

In image classification tasks, convolutional neural networks (CNNs) have exhibited higher performance than many other methods. However, some recent studies have shown that CNNs are not shift-invariant, that is, even small spatial shifts in the image can drastically change the output distribution. This finding is non-intuitive because CNNs have long been believed to be shift-invariant. Therefore, we need to measure the shift-invariance of CNNs to better understand the performance of CNNs with respect to image classification. Previous research proposed the utilization of consistency as a metric for measuring shift-invariance. The consistency metric measures how often a CNN classifies the same top-1 class, given two different shifts on the same image. Herein, we identify two shortcomings of the consistency-based approach. First, consistency cannot perfectly capture the change in the output distribution of the CNN because it only considers the top-1 class from the distribution; therefore, other relevant information is lost. Second, the consistency metric is biased by the classification accuracy because the consistency value increases when the images are classified correctly; otherwise, it decreases. To overcome these shortcomings, this paper proposes the correctness-aware distribution distance (CADD). In CADD, we use the Jensen-Shannon (JS) divergence to measure the difference among the entire output distributions. Furthermore, the JS divergence on the correct and incorrect subsets that are split by the classification correctness is separately calculated and averaged. The experimental results demonstrate that the CADD is more stable and interpretable than the consistency-based approach.
COVID-19 Mandatory self-quarantine wearable device for authority monitoring with edge AI reporting &amp; flagging system

A mandatory self-quarantine is necessary for those who return from overseas or any red zone areas. It is important that the self-quarantine is conducted without the non-adherence issue occurring and causes the self-quarantine individual to be the carrier of the COVID-19 in the community. To navigate and resolve this issue, most countries have implemented a series of COVID-19 monitoring and tracing systems. However, there are some restrictions and limitation which can lead to intentional non-adherence. The quarantined individuals can still travel within the community by removing the wristband or simply providing an incorrect contact status in the tracing application. In this paper, a novel configuration for mandatory self-quarantine system is proposed. It will enable interaction between the wearable and contact tracing technologies to ensure that the authorities have total control of the system. The hardware of the proposed system in the wearable device is low in cost, lightweight and safe to use for the next user after the quarantine is completed. The software (software and database) that linked between the quarantine user and normal user utilizes edge artificial intelligence (AI) for reporting and flagging mechanisms.
An Algorithm for Sensor Data Uncertainty Quantification

This letter presents an algorithm for reducing measurement uncertainty of one physical quantity when oversampling measurements of two physical quantities with correlated noise. The algorithm assumes that the aleatoric measurement uncertainty in both physical quantities follows a Gaussian distribution and relies on sampling faster than it is possible for the measurand (the true value of the physical quantity that we are trying to measure) to change (due to the system thermal time constant) to calculate the parameters of the noise distribution. In contrast to the Kalman and particle filters, which, respectively, require state update equations and a map of one physical quality, our algorithm requires only the oversampled sensor measurements. When applied to temperature-compensated humidity sensors, it provides reduced uncertainty in humidity estimates from correlated temperature and humidity measurements. In an experimental evaluation, the algorithm achieves average uncertainty reduction of 10.3%. The algorithm incurs an execution time overhead of 5.3% when compared to the minimum algorithm required to measure and calculate the uncertainty. Detailed instruction-level emulation of a C-language implementation compiled to the RISC-V architecture shows that the uncertainty reduction program required 0.05% more instructions per iteration than the minimum operations required to calculate the uncertainty.
Glance: A Generative Approach to Interactive Visualization of Voluminous Satellite Imagery

Challenges in interactive visualizations over satellite data collections stem primarily from their inherent data volumes. Enabling interactive visualizations of such data results in both processing and I/O (network and disk) on the server side. These are further exacerbated by multiple, concurrent requests issued by different clients. Hotspots may also arise when multiple users are interested in a particular geographical extent. We propose a novel methodology to support interactive visualizations over voluminous satellite imagery. Our system, codenamed Glance, generates models that once installed on the client side, substantially alleviate resource requirements on the server side. Our system dynamically generates imagery during zoom-in operations. Glance also supports image refinements using partial high-resolution information when available. Glance is based broadly on a deep Generative Adversarial Network, and our model is space-efficient to facilitate memory-residency at the clients. We supplement Glance with a module to estimate rendering errors when using the model to generate imagery as opposed to a resource-intensive query-and-retrieve operation to the server. Benchmarks to profile our methodology show substantive improvements in interactivity with up to 23x reduction in time lags without utilizing GPU and 297x-6627x reduction while harnessing GPU. Further, the perceptual quality of the images from our generative model is robust with PSNR values ranging from 32.2-40.5, depending on the scenario and upscale factor.
Translation of Array-Based Graph Programs to Spark SQL on Block Arrays

Many graph algorithms can be expressed as repetitive computations that resemble matrix multiplication in which the addition and multiplication operations have been replaced with generalized operations that form an algebraic structure known as a semiring. Similar to matrix multiplication, these graph algorithms can be implemented in a distributed system using block arrays, which are distributed collections of non-overlapping dense arrays. In a distributed system, computations on block matrices are significantly faster than the same computations in the coordinate format in terms of computation and communication cost. Given the rise of Big Data and data-driven decision-making, our goal is to rewrite sequential programs on large graphs that are initially written to run on a single computer using loops and standard array operations to data-parallel programs on array blocks that can run on a distributed system. Our framework, called OSQLgen, automatically parallelizes graph programs with loops and arrays to distributed data-parallel programs. OSQLgen recognizes programs that are equivalent to a semiring structure and translates them to fast Spark SQL programs on block arrays that can run on a computer cluster in a distributed mode. We compare the performance of OSQLgen with GraphX, GraphFrames, and hand-written Spark SQL programs on coordinate and block arrays on various graph problems. On certain graph problems, OSQLgen is up to 36x faster than GraphX, 25x faster than GraphFrames, and up to 99x faster than hand-written Spark SQL programs on coordinate arrays, giving performance close to that of hand-written Spark SQL programs on block arrays.
Adaptive decision making using a chaotic semiconductor laser for multi-armed bandit problem with time-varying hit probabilities

We numerically demonstrate the principle of adaptive decision making for solving multi-armed bandit problems in dynamically changing reward environments. We use the tug-of-war method by comparing a threshold and a chaotic temporal waveform generated from a semiconductor laser observed in an experiment. We propose a method for detecting dynamic changes in hit probabilities by evaluating short-term standard deviations of the estimated hit probabilities. Furthermore, the threshold is forced to be initialized when changes in the hit probabilities are detected. We perform adaptive decision making in time-varying hit probabilities, including cases in which the differences in the hit probabilities are small. The proposed method paves the way for ultrafast photonic decision making in dynamically changing environments for various applications, such as cognitive wireless communications and robot control using reinforcement learning.
A Pruning Neural Network for Automatic Modulation Classification

Automatic modulation classification (AMC) is a promising technology for non-cooperative communication systems in both military and civilian scenarios. Nowadays, More and more scholars apply deep learning (DL) framework to AMC. However, most of the papers do not consider that the typical deep learning model is difficult to deploy on the resource constrained devices. In this paper, a lightweight DL based Average percentage of zeros (APoZ) is used with pruning neural. We introduce a novel method of generating modulation signals called contour stellar image (CSI). We train the data through some scaling factors in convolution neural network (CNN) especially the AlexNet. It can screen out the inconsequential neurons which can be pruned. Experimental results suggest that using APoZ to prune can not only slim the network but also stabilize the average error about 1.05% compared with the original network.
Superclass-aware network for few-shot learning

Humans can learn to recognize a novel object by just going through its images a few times. It might because that they do not recognize the novel object purely on the visual information, but also based on their prior knowledge. Inspired from this, we propose a novel framework named Superclass-aware Network (Sup-Net) to tackle the few-shot learning problem. We first present a knowledge extraction schema in Sup-Net, which can acquire superclass information, and compute superclass semantic relations between different categories. We introduce a novel soft label supervised contrastive loss to help extract discriminative superclass features from images so that the superclass relation can be captured by these features. A novel model architecture that is jointly trained by images and prior knowledge has been proposed. The model encodes image features that minimize the cross-entropy loss at the category level, while it also extracts the superclass feature that minimizes the soft label contrastive loss at the superclass level. Experimental results demonstrate that Sup-Net achieves competitive results on miniImageNet datasets. In addition, we conduct experiments on a large-scale dataset tieredImageNet; the results further demonstrate the effectiveness of our Sup-Net.
Quantile-based Random Kaczmarz for corrupted linear systems of equations

Abstract We consider linear systems $Ax = b$ where $A \in \mathbb{R}^{m \times n}$ consists of normalized rows, $\|a_i\|_{\ell ^2} = 1$, and where up to $\beta m$ entries of $b$ have been corrupted (possibly by arbitrarily large numbers). Haddock, Needell, Rebrova &amp; Swartworth propose a quantile-based Random Kaczmarz method and show that for certain random matrices $A$ it converges with high likelihood to the true solution. We prove a deterministic version by constructing, for any matrix $A$, a number $\beta _A$ such that there is convergence for all perturbations with $\beta &amp;lt; \beta _A$. Assuming a random matrix heuristic, this proves convergence for tall Gaussian matrices with up to $\sim 0.5\%$ corruption (a number that can likely be improved).
A survey on blockchain-based student certificate management system

Blockchain Technology's first application is Bitcoin. It started its journey with cryptocurrency in 2008 by Satoshi Nakamoto. After that, it travelled in many areas such as Government, healthcare, academics, supply chain management, Intellectual property management, social welfare, and energy system. Australia, UAE, Japan are some of the top countries using blockchain technology in various government projects. One can use blockchain technology with decentralised storage of data, which is distributed, immutable, tamper resistance, and securable with a consensus mechanism. Today, many government sectors, organisations, companies, and institutions follow blockchain technology using smart contracts that do not require third-party agreements. We have conducted a systematic survey on blockchain-based digital certification to find the research gap in this research work. We carry out the following: (i) Investigation on the reasons to use the blockchain in education system (ii) Classification of blockchain projects into five categories based on services provided by the projects and platforms used for development (iii) Identification of the services provided by the project and the technologies used for the development.
Automatic Static Vulnerability Detection Approaches and Tools: State of the Art

Software vulnerabilities are behind many cybersecurity threats. In fact, if they are exploited, they could cause severe damage to an organization. Thus, finding vulnerabilities in software is a crucial process. However, it often needs both time and expertise. Fortunately, many researchers have focused on automating this process to accelerate the finding of vulnerabilities. Some have worked on making static vulnerability discovery more effective; others have concentrated their research on developing dynamic analysis techniques. This paper presents some of the research that focuses on static analysis approaches and discusses the main differences between them. At the end of this paper, we experiment and analyze the most popular tools that have implemented these approaches and their main weaknesses.
Adaptive Consensus-Based Distributed System for Multisensor Multitarget Tracking

In this article, a new comprehensive system for distributed multisensor multitarget tracking is proposed. All of its functions, including track initiation, confirmation, maintenance and termination, and track-to-track association and fusion, are built around the concept of the probability of target existence (PTE). A new track maintenance algorithm is composed of the correction part, with data association requiring computations linearly depending on the number of measurements and tracks, and the prediction part, containing an adaptive consensus scheme, coping with limited sensor observability. A new track-to-track association and fusion algorithm based on an approximation of track association probability is also proposed, ensuring consistency and continuity of individual tracks. Track initiation and termination algorithms are derived on the basis of local PTEs. Stability of the proposed system is studied for the steady state and time-varying regimes. The system as a whole achieves high performance close to the centralized solution, outperforming all the comparable existing state-of-the-art approaches, keeping much lower communication and computation requirements.
Citation Analysis on Scientific Articles Using Cosine Similarity

Citations have a vital role in scientific articles. Every sentence that is listed must be obtained from the reference. There is a measure that is similarity. The problem is, the likeness of a sentence in a scientific article to the source it refers to can have different meanings. The high level of similarity can undoubtedly increase the similarity value if it is checked using the plagiarism (similarity checker) tool, where the higher the value is more similar to other articles on the contrary with low similarity remaining. However, a low level of similarity can also mean that a statement in a scientific paper has referred to a completely irrelevant source. This research aims to analyze the level of similarity between scientific articles and other articles or sources it refers to. Checking the input text starts with text preprocessing, weighting the TF-IDF, and determining the similarity level using Cosine Similarity. Based on the test results, it is found that the results in a scientific article document have a high level of similarity with the article it refers to, and the other 30% have a low level of similarity, or in other words, there is no relationship.