Reviewerly/exHarmony huggingface dataset:
XDQN: Inherently Interpretable DQN through Mimicking

Although deep reinforcement learning (DRL) methods have been successfully applied in challenging tasks, their application in real-world operational settings is challenged by methods' limited ability to provide explanations. Among the paradigms for explainability in DRL is the interpretable box design paradigm, where interpretable models substitute inner constituent models of the DRL method, thus making the DRL method "inherently" interpretable. In this paper we explore this paradigm and we propose XDQN, an explainable variation of DQN, which uses an interpretable policy model trained through mimicking. XDQN is challenged in a complex, real-world operational multi-agent problem, where agents are independent learners solving congestion problems. Specifically, XDQN is evaluated in three MARL scenarios, pertaining to the demand-capacity balancing problem of air traffic management. XDQN achieves performance similar to that of DQN, while its abilities to provide global models' interpretations and interpretations of local decisions are demonstrated.
Removing Non-Stationary Knowledge From Pre-Trained Language Models for Entity-Level Sentiment Classification in Finance

Extraction of sentiment signals from news text, stock message boards, and business reports, for stock movement prediction, has been a rising field of interest in finance. Building upon past literature, the most recent works attempt to better capture sentiment from sentences with complex syntactic structures by introducing aspect-level sentiment classification (ASC). Despite the growing interest, however, fine-grained sentiment analysis has not been fully explored in non-English literature due to the shortage of annotated finance-specific data. Accordingly, it is necessary for non-English languages to leverage datasets and pre-trained language models (PLM) of different domains, languages, and tasks to best their performance. To facilitate finance-specific ASC research in the Korean language, we build KorFinASC, a Korean aspect-level sentiment classification dataset for finance consisting of 12,613 human-annotated samples, and explore methods of intermediate transfer learning. Our experiments indicate that past research has been ignorant towards the potentially wrong knowledge of financial entities encoded during the training phase, which has overestimated the predictive power of PLMs. In our work, we use the term "non-stationary knowledge'' to refer to information that was previously correct but is likely to change, and present "TGT-Masking'', a novel masking pattern to restrict PLMs from speculating knowledge of the kind. Finally, through a series of transfer learning with TGT-Masking applied we improve 22.63% of classification accuracy compared to standalone models on KorFinASC.
Optimistic Meta-Gradients

We study the connection between gradient-based meta-learning and convex op-timisation. We observe that gradient descent with momentum is a special case of meta-gradients, and building on recent results in optimisation, we prove convergence rates for meta-learning in the single task setting. While a meta-learned update rule can yield faster convergence up to constant factor, it is not sufficient for acceleration. Instead, some form of optimism is required. We show that optimism in meta-learning can be captured through Bootstrapped Meta-Gradients (Flennerhag et al., 2022), providing deeper insight into its underlying mechanics.
mRpostman: An IMAP Client for R

Internet Message Access Protocol (IMAP) clients are a common feature in several programming languages. Despite having some packages for electronic messages retrieval, the R language, until recently, lacked a broader solution, capable of coping with different IMAP servers and providing a wide spectrum of features. mRpostman covers most of the IMAP 4rev1 functionalities by implementing tools for message searching, selective fetching of message attributes, mailbox management, attachment extraction, and several other IMAP features that can be executed in virtually any mail provider. By doing so, it enables users to perform data analysis based on e-mail content. The goal of this article is to showcase the toolkit provided with the mRpostman package, to describe its key features and provide some application examples.
A comprehensive review of automatic text summarization techniques: method, data, evaluation and coding

We provide a literature review about Automatic Text Summarization (ATS) systems. We consider a citation-based approach. We start with some popular and well-known papers that we have in hand about each topic we want to cover and we have tracked the "backward citations" (papers that are cited by the set of papers we knew beforehand) and the "forward citations" (newer papers that cite the set of papers we knew beforehand). In order to organize the different methods, we present the diverse approaches to ATS guided by the mechanisms they use to generate a summary. Besides presenting the methods, we also present an extensive review of the datasets available for summarization tasks and the methods used to evaluate the quality of the summaries. Finally, we present an empirical exploration of these methods using the CNN Corpus dataset that provides golden summaries for extractive and abstractive methods.
Latent Spectral Regularization for Continual Learning

While biological intelligence grows organically as new knowledge is gathered throughout life, Artificial Neural Networks forget catastrophically whenever they face a changing training data distribution. Rehearsal-based Continual Learning (CL) approaches have been established as a versatile and reliable solution to overcome this limitation; however, sudden input disruptions and memory constraints are known to alter the consistency of their predictions. We study this phenomenon by investigating the geometric characteristics of the learner's latent space and find that replayed data points of different classes increasingly mix up, interfering with classification. Hence, we propose a geometric regularizer that enforces weak requirements on the Laplacian spectrum of the latent space, promoting a partitioning behavior. We show that our proposal, called Continual Spectral Regularizer (CaSpeR), can be easily combined with any rehearsal-based CL approach and improves the performance of SOTA methods on standard benchmarks. Finally, we conduct additional analysis to provide insights into CaSpeR's effects and applicability.
One-Shot Achievable Secrecy Rate Regions for Quantum Interference Wiretap Channel

In this paper, we want to derive achievable secrecy rate regions for quantum interference channel with classical inputs under one-shot setting. The main idea to this end is to use the combination of superposition and rate splitting for encoding scheme and constructing a decoding scheme based on simultaneous decoding.
ERNIE 3.0 Tiny: Frustratingly Simple Method to Improve Task-Agnostic Distillation Generalization

Task-agnostic knowledge distillation attempts to address the problem of deploying large pretrained language model in resource-constrained scenarios by compressing a large pretrained model called teacher into a smaller one called student such that the student can be directly finetuned on downstream tasks and retains comparable performance. However, we empirically find that there is a generalization gap between the student and the teacher in existing methods. In this work, we show that we can leverage multi-task learning in task-agnostic distillation to advance the generalization of the resulted student. In particular, we propose Multi-task Infused Task-agnostic Knowledge Distillation (MITKD). We first enhance the teacher by multi-task training it on multiple downstream tasks and then perform distillation to produce the student. Experimental results demonstrate that our method yields a student with much better generalization, significantly outperforms existing baselines, and establishes a new state-of-the-art result on in-domain, out-domain, and low-resource datasets in the setting of task-agnostic distillation. Moreover, our method even exceeds an 8x larger BERT$_{\text{Base}}$ on SQuAD and four GLUE tasks. In addition, by combining ERNIE 3.0, our method achieves state-of-the-art results on 10 Chinese datasets.
A Cyber Threat Intelligence Management Platform for Industrial Environments

Developing intelligent, interoperable Cyber Threat Information (CTI) sharing technologies can help build strong defences against modern cyber threats. CTIs allow the community to share information about cybercriminals' threats and vulnerabilities and countermeasures to defend themselves or detect malicious activity. A crucial need for success is that the data connected to cyber risks be understandable, organized, and of good quality. The receiving parties may grasp its content and utilize it effectively. This article describes an innovative cyber threat intelligence management platform (CTIMP) for industrial environments, one of the Cyber-pi project's significant elements. The suggested architecture, in particular, uses cyber knowledge from trusted public sources and integrates it with relevant information from the organization's supervised infrastructure in an entirely interoperable and intelligent way. When combined with an advanced visualization mechanism and user interface, the services mentioned above provide administrators with the situational awareness they require while also allowing for extended cooperation, intelligent selection of advanced coping strategies, and a set of automated self-healing rules for dealing with threats.
Unifying Nesterov's Accelerated Gradient Methods for Convex and Strongly Convex Objective Functions: From Continuous-Time Dynamics to Discrete-Time Algorithms

Although Nesterov's accelerated gradient (NAG) methods have been studied from various perspectives, it remains unclear why the most popular forms of NAG must handle convex and strongly convex objective functions separately. Motivated by this inconsistency, we propose an NAG method that unifies the existing ones for the convex and strongly convex cases. We first design a Lagrangian function that continuously extends the first Bregman Lagrangian to the strongly convex setting. As a specific case of the Euler--Lagrange equation for this Lagrangian, we derive an ordinary differential equation (ODE) model, which we call the unified NAG ODE, that bridges the gap between the ODEs that model NAG for convex and strongly convex objective functions. We then design the unified NAG, a novel momentum method whereby the continuous-time limit corresponds to the unified ODE. The coefficients and the convergence rates of the unified NAG and unified ODE are continuous in the strong convexity parameter $\mu$ on $[0, +\infty)$. Unlike the existing popular algorithm and ODE for strongly convex objective functions, the unified NAG and the unified NAG ODE always have superior convergence guarantees compared to the known algorithms and ODEs for non-strongly convex objective functions. This property is beneficial in practical perspective when considering strongly convex objective functions with small $\mu$. Furthermore, we extend our unified dynamics and algorithms to the higher-order setting. Last but not least, we propose the unified NAG-G ODE, a novel ODE model for minimizing the gradient norm of strongly convex objective functions. Our unified Lagrangian framework is crucial in the process of constructing this ODE. Fascinatingly, using our novel tool, called the differential kernel, we observe that the unified NAG ODE and the unified NAG-G ODE have an anti-transpose relationship.
A systematic literature review of capstone courses in software engineering

Tertiary education institutions aim to prepare their computer science and software engineering students for working life. While much of the technical principles are covered in lower-level courses, team-based capstone projects are a common way to provide students with hands-on experience and teach soft skills. This paper explores the characteristics of software engineering capstone courses presented in the literature. The goal of this work is to understand the pros and cons of different approaches by synthesising the various aspects of software engineering capstone courses and related experiences. In a systematic literature review for 2007-2022, we identified 127 primary studies. These studies were analysed based on their presented course characteristics and the reported course outcomes. The characteristics were synthesised into a taxonomy consisting of duration, team sizes, client and project sources, project implementation, and student assessment. We found out that capstone courses generally last one semester and divide students into groups of 4-5 where they work on a project for a client. For a slight majority of courses, the clients are external to the course staff and students are often expected to produce a proof-of-concept level software product as the main end deliverable. The courses also offer versatile assessments for students throughout the project. This paper provides researchers and educators with a classification of characteristics of software engineering capstone courses based on previous research. We further synthesise insights on the reported outcomes of capstone courses. Our review study aims to help educators to identify various ways of organising capstones and effectively plan and deliver their own capstone courses. The characterisation also helps researchers to conduct further studies on software engineering capstones.
Empirical analysis of the impact of Performance Appraisal System on employee's productivity: A study of Pakistan Telecommunication Limited (PTCL): Non-managerial employees' perspective

Organizations are acknowledging the importance of employee performance appraisals as an important measure for enhancing productivity in an organization. To evaluate the impact of performance mechanism for measuring employee performance was the main objective of this study, with a focus on Pakistan Telecommunication Limited (PTCL). The independent variable in this research is the Performance appraisal system, and the dependent variable is employee productivity. The research design used in this study was a descriptive survey. The structured questionnaires for this study were adapted and developed on a five-point Likert scale and distributed randomly to non-managerial employees. The number of respondents in this research study is 210 non-managerial employees and their responses are analyzed in Smart PLS 3 software. The study concluded that an effective performance appraisal system allows employees to share their thoughts and expectations for achieving the company's strategic goals. The ability of a company to achieve its objectives and improve its workers' productivity is influenced by its performance appraisal system. Employee productivity may increase if evaluation feedback is effective. Both measurement and structural model assessments and P-values are considerable, and the results demonstrate significant and favorable results so the study concluded that an effective performance appraisal system improves employee productivity in an organization.
Health Information Sharing in Cloud Environment Using Modular Encryption Standard

Despite the Cloud Environment’s (CE) numerous and obvious improvements in healthcare, these issues continue to impede CE’s development. In order to fully understand and utilise such issues, the utmost serious thinking is required. Global, national, and local health information are in demand. Setting up the required security protocols for handling security issues and breaches is essential for getting the most out of healthcare services. The Modular Encryption Standard (MES) is therefore being investigated for this research in order to provide requirement-based health data protection in light of the tiering of security efforts. According to the presentation evaluation, the suggested work outperforms other regularly used calculations against the security of health data in the CE environment in terms of enhanced performance and practical subjective security ensuring approaches.
Stochastic Computing Solutions Challenges and Application

High speed and power are required for modern applications, and low power consumption and a small integration area. So, this system will be consumed less power. To manipulate these problems either way of computing should be changed by another technique or the transistor should be changed by another device. One selective computing is stochastic. While loaded computing contributes high hardware cost, high speed. Stochastic computing accuracy is less than binary computing circuits. They are having some implementation basic operations of stochastic computing. They are complementary operation, multiplication operation, addition operation, subtraction operation, and division operation. These operations having some logic gates i.e., AND gate, NOT gate, XOR gate, an XNOR gate. And implement the hardware Tripartite synapse. It can be divided into parts. They are Astrocyte, Synapse, Input spiking, LIF neuron, and output spiking. Using stochastic circuits and conversion processes, this paper demonstrates the fundamental notions of stochastic computing. implementation of arithmetic operations and hardware tripartite synapse and tripartite synapse hardware architecture.
Classifying wireless signal modulation sorting using convolutional neural network

Deep learning has recently been used for this issue with superior results in automatic modulation classification. Previous studies state that it is challenging to categorize a variety of modulation formats using traditional approaches; however, modulation classification is a crucial component of non-cooperative communication in wireless communication. The deep learning network was applied to solve the issue and get decent outcomes. This work uses a deep learning convolutional neural network (DLCNN) to classify three analog and eight digital modulation techniques by generating channel-impaired and synthetic waveforms as training data. The obtained DLCNN is tested by over-the-air indicators and a Software Define Radio(SDR) platform. The trained DLCNN estimates the modulation kind of each frame by taking 1024 samples of channel-impaired signals. The method includes generating several frames of 4-arry pulse amplitude modulation (PAM4) that are impaired with sampling time drift, Additive white Gaussian noise (AWGN), center frequency, and Rician multipath fading. The DLCNN predicts real inputs when receiving a signal with complex samples of baseband. Before updating the network coefficients and on all iterations, the data store transforms data from files and records it. This network takes about 50 minutes to train using in-memory data and 110 minutes to train using disk data. The evaluation of the trained DLCNN is carried out by obtaining the classification accuracy for the test frames. The obtained outcome demonstrates that the developed network can achieve an accuracy of about 94.3 % in roughly 12 epochs for such types of waveforms, which elapsed about 26 minutes for training. This will increase the efficiency of spectrum usage and detect the modulation type of the wireless communication receivers
Model-independent methods for embedding directed networks into Euclidean and hyperbolic spaces

The arrangement of network nodes in hyperbolic spaces has become a widely studied problem, motivated by numerous results suggesting the existence of hidden metric spaces behind the structure of complex networks. Although several methods have already been developed for the hyperbolic embedding of undirected networks, approaches able to deal with directed networks are still in their infancy. Here, we propose a framework based on the dimension reduction of proximity matrices reflecting the network topology, coupled with a general conversion method transforming Euclidean node coordinates into hyperbolic ones even for directed networks. While proposing a new measure of proximity, we also incorporate an earlier Euclidean embedding method in our pipeline, demonstrating the widespread applicability of our Euclidean-hyperbolic conversion. Besides, we introduce a dimension reduction technique that maps the nodes directly into the hyperbolic space with the aim of reproducing a distance matrix measured on the given (un)directed network. According to mapping accuracy, graph reconstruction performance and greedy routing score, our methods are capable of producing high-quality embeddings for several real networks.
An intelligent campus simulation system based on game

Abstract As a densely populated place, the occurrence of accidents on campus causes serious concerns, while the construction of an intelligent and safe campus is also one of the hotly debated topics. Starting from the perspective of the development process of security events, this paper comprehensively perfects each stage and applies reasonable methods, hoping to reduce the cost of human and material resources in campus security. First, the abnormal behaviour data set and the identification model based on time and space fusion are constructed to identify the abnormal behaviours on campus. Further on, through the agent reward function construction and state transfer function solution and algorithm research, the multi-intelligent-agent tracking and cooperation based on the game idea are realised to provide efficient real-time security alarm and decision suggestion. Finally, the safety resource path planning platform is designed, and the simulation analysis is conducted combined with the traffic network of New Normal University. The results turn out to identify the abnormal behaviour, work out the optimal decision as well as figure out the optimal path of security resources planning and verify the effectiveness of the mathematical model in a simulation manner.
Human-Machine Shared Situational Awareness

Abstract As unmanned platforms are more and more present in battlefield, alongside humans, in different configurations (from tele-operated to fully autonomous platforms, manned-unmanned teaming, swarming, etc.) and domains (space, air, land, sea, and underwater), it is also necessary for decision-making process to adapt to this new reality. This process will no longer be exclusively human and this requires that humans and machines share a common, meaningful, and timely understanding of the context in which they act and interact, namely a common situational awareness. We will investigate how this objective could be achieved by means of AI-related techniques of inference and reasoning based on ontologies that will enable all levels of information sharing (data, knowledge, and models) among all participants, humans and non-humans. The success of this undertaking shall be reflected by the achievement of a high level of interoperability between heterogeneous entities, within which they will be able to take advantage of each other’s best developed abilities.