Reviewerly/exHarmony huggingface dataset:
Sentiment analysis on Education transformation during Covid-19 using Arabic tweets in KSA.

The emergence of COVID-19 pandemic has changed the whole world. To prevent the spread of the virus, different precautionary measure and policies have been defined, one of them is distance learning. It has led to the educational transformation from physical education to online learning. Similarly, in KSA online education is adopted since March 2020. Inorder to extract the individual perception about the online education in KSA, twitter data was used. Arabic tweets were collected using twitter API. Furthermore, several machine learning models such as Support Vector Machine (SVM), Naïve Bayes (NB), Random Forest (RF), Logistic regression, K-nearest neighbors was used develop an automated sentiment analysis of online education related tweets in KSA. For feature extraction and selection N-gram and Term Frequency–Inverse Document Frequency (TF-IDF) was used. Logistic Regression achieved the highest accuracy of 69.33% for multiclass and Random Forest achieved accuracy of 80.35%. According to the dataset, most of the individuals have negative opinion about the online learning as the number of negative tweets are higher as compared to positive and neutral class
Strategic Abilities of Forgetful Agents in Stochastic Environments

In this paper, we investigate the probabilistic variants of the strategy logics ATL and ATL* under imperfect information. Specifically, we present novel decidability and complexity results when both the model transitions and the strategies played by agents are stochastic. That is, the semantics of the logics are based on multi-agent, stochastic transition systems with imperfect information, which combine two sources of uncertainty, namely, the partial observability agents have on the environment, and the likelihood of transitions to occur from a system state. Since the model checking problem is undecidable in general in this setting, we restrict our attention to agents with memoryless (positional) strategies. The resulting setting captures the situation in which agents have qualitative uncertainty of the local state and quantitative uncertainty about the occurrence of future events. We illustrate the usefulness of this setting with meaningful examples.
Analysis of Faculty Teaching Performance Based On Student Feedback Using Fuzzy Mamdani Inference System

The objective of this study was to develop an Inference System based on the Fuzzy Mamdani system for assessing the teaching skills of faculty members in academic or educational institutions. Nowadays, many academic institutions have adopted web-based methods to gather students' feedback on faculty members' teaching effectiveness. Evaluating faculty performance in teaching activities is crucial for establishing a more equitable academic environment. The primary goal of assessing faculty performance is to identify areas of strength and weakness in their professional development. This application of soft computing, specifically the Fuzzy Mamdani Inference System (FMIS), in evaluating faculty teaching performance can be valuable for organizational management in assessing faculty capabilities and their impact on student outcomes.
Unified Foundations of Team Semantics via Semirings

Semiring semantics for first-order logic provides a way to trace how facts represented by a model are used to deduce satisfaction of a formula. Team semantics is a framework for studying logics of dependence and independence in diverse contexts such as databases, quantum mechanics, and statistics by extending first-order logic with atoms that describe dependencies between variables. Combining these two, we propose a unifying approach for analysing the concepts of dependence and independence via a novel semiring team semantics, which subsumes all the previously considered variants for first-order team semantics. In particular, we study the preservation of satisfaction of dependencies and formulae between different semirings. In addition we create links to reasoning tasks such as provenance, counting, and repairs.
On Training Neurons with Bounded Compilations

Knowledge compilation offers a formal approach to explaining and verifying the behavior of machine learning systems, such as neural networks. Unfortunately, compiling even an individual neuron into a tractable representation such as an Ordered Binary Decision Diagram (OBDD), is an NP-hard problem. In this paper, we consider the problem of training a neuron from data, subject to the constraint that it has a compact representation as an OBDD. Our approach is based on the observation that a neuron can be compiled into an OBDD in polytime if (1) the neuron has integer weights, and (2) its aggregate weight is bounded. Unfortunately, we first show that it is also NP-hard to train a neuron, subject to these two constraints. On the other hand, we show that if we train a neuron generatively, rather than discriminatively, a neuron with bounded aggregate weight can be trained in pseudo-polynomial time. Hence, we propose the first efficient algorithm for training a neuron that is guaranteed to have a compact representation as an OBDD. Empirically, we show that our approach can train neurons with higher accuracy and more compact OBDDs.
Towards an Embedded Trust Blockchain Architecture for 6G Networks

In recent years, blockchain technology has received considerable attention from academia, financiers and governments. Blockchain is considered to be a key disruptive technology for the 21st Century, with a very wide array of application domains that impact all areas of our lives. That such a technology exists just as the world is transitioning towards 6G mobile networks that will offer unprecedented levels of performance, but will come an attendant set of ethical and other implications, is highly fortuitous. We offer in this paper a proposal to integrate blockchain technology into the anticipated 6G feature set to address the problem of ensuring and automating Trust to a high degree.
Verification of Covid-19 Social Assistance Recipients using Naïve Bayes Classifier

The Indonesian government launches the Covid-19 social assistance program to reduce the impacts of the economic downturn during the pandemic. The recipients of social assistance in Sukabumi Selatan District of Jakarta Province is collected form Neighborhood Association (RT/RW). However, this mechanism has limitations in terms of feasibility assessment through direct verification which is not optimal due to social restriction activities. At the same time, data is also collected through the regular recipients of social aid program, so there is a data discrepancy that causing a bias in determining the recipients’ feasibility. Therefore, a mechanism is required to assess the eligibility of the recipients. This study aims to assist Social Service Agency of Sukabumi Selatan district, in assessing the eligibility of the recipients using Naïve Bayes classifier and K-Nearest Neighbors (K-NN) classifier as comparison. Experiments using Cross-Industry Standard Process for Data Mining (CRISP-DM) model were carried out on a collected dataset, and the results show that Naïve Bayes classifier shows the best result with 93% accuracy, 86% precision and 100% recall, while K-NN has 90% accuracy, 82% precision and 98% recall. This research may assist the Social Service Agency of the district to determining more accurately the target recipients.
Verification of Semantic Key Point Detection for Aircraft Pose Estimation

We analyse Semantic Segmentation Neural Networks running on an autonomous aircraft to estimate its 6DOF pose during landing. We show that automated reasoning techniques from neural network verification can be used to analyse the conditions under which the networks can operate safely, thus providing enhanced assurance guarantees on the behaviour of the overall pose estimation systems.
Standpoint Linear Temporal Logic

Many complex scenarios require the coordination of agents holding different points of view, possibly cooperating and not necessarily agreeing. For this reason, standpoint logic (SL) has been recently introduced in the context of knowledge integration, allowing one to reason with diverse and potentially conflicting viewpoints held by different agents. Linear temporal logic (LTL) is the most widely known formalism to express temporal properties of systems and processes, both in formal methods and artificial intelligence related fields. In this paper, we present 'standpoint linear temporal logic' (SLTL), a new logic that combines the temporal features of LTL with the multi-perspective modelling capacity of SL. We define the logic SLTL, its syntax, its semantics, establish its decidability and complexity, and provide a terminating tableau calculus to automate SLTL reasoning. Conveniently, this offers a clear path to extend existing LTL reasoners to provide practical reasoning support for temporal reasoning in multi-perspective settings.
Leveraging Large Language Models to Generate Answer Set Programs

Large language models (LLMs), such as GPT-3 and GPT-4, have demonstrated exceptional performance in various natural language processing tasks and have shown the ability to solve certain reasoning problems. However, their reasoning capabilities are limited and relatively shallow, despite the application of various prompting techniques. In contrast, formal logic is adept at handling complex reasoning, but translating natural language descriptions into formal logic is a challenging task that non-experts struggle with. This paper proposes a neuro-symbolic method that combines the strengths of large language models and answer set programming. Specifically, we employ an LLM to transform natural language descriptions of logic puzzles into answer set programs. We carefully design prompts for an LLM to convert natural language descriptions into answer set programs in a step by step manner. Surprisingly, with just a few in-context learning examples, LLMs can generate reasonably complex answer set programs. The majority of errors made are relatively simple and can be easily corrected by humans, thus enabling LLMs to effectively assist in the creation of answer set programs.
Do Repeat Yourself: Understanding Sufficient Conditions for Restricted Chase Non-Termination

The disjunctive restricted chase is a sound and complete procedure for solving boolean conjunctive query entailment over knowledge bases of disjunctive existential rules. Alas, this procedure does not always terminate and checking if it does is undecidable. However, we can use acyclicity notions (sufficient conditions that imply termination) to effectively apply the chase in many real-world cases. To know if these conditions are as general as possible, we can use cyclicity notions (sufficient conditions that imply non-termination). In this paper, we discuss some issues with previously existing cyclicity notions, propose some novel notions for non-termination by dismantling the original idea, and empirically verify the generality of the new criteria.
Practical Abstraction for Model Checking of Multi-Agent Systems

Model checking of multi-agent systems (MAS) is known to be hard, both theoretically and in practice. A smart abstraction of the state space may significantly reduce the model,and facilitate the verification. In this paper, we propose and study an intuitive agent-based abstraction scheme, based on the removal of variables in the representation of a MAS. This allows to achieve a desired reduction of a state space without generating the global model of the system. Moreover, the process is easy to understand and control even for domain experts with little knowledge of computer science. We formally prove the correctness of the approach, and evaluate the gains experimentally on a family of a postal voting models.
Prelims

Citation (2023), "Prelims", Adeola, O. (Ed.) Casebook of Indigenous Business Practices in Africa, Emerald Publishing Limited, Bingley, pp. i-xix. https://doi.org/10.1108/978-1-80455-762-420231024
Finest Syntax Splittings of Ranking Functions and Total Preorders on Worlds

The notion of syntax splitting was initially introduced by Parikh for belief sets, and one key observation is that every belief set has a unique finest syntax splitting, i.e., a syntax splitting that refines every other syntax splitting of that belief set. Later, the notion of syntax splitting was extended to ranking functions and total preorders on worlds (TPOs), which are two common models for belief states in the context of iterated belief revision. In this paper, we prove that ranking functions also have unique finest syntax splittings, i.e., every ranking function has a syntax splitting that refines all other syntax splittings of that ranking function. Using this, we can show that the syntax splittings of a ranking function κ are exactly the coarsenings of the finest splitting of κ. For TPOs we show that, in contrast to ranking functions, the coarsening of a syntax splitting of a TPO ⪯ is not necessarily a syntax splitting of ⪯. Despite that we can prove that every TPO has a unique finest syntax splitting that refines all other syntax splittings of that TPO.
Shrink-Perturb Improves Architecture Mixing during Population Based Training for Neural Architecture Search

In this work, we show that simultaneously training and mixing neural networks is a promising way to conduct Neural Architecture Search (NAS). For hyperparameter optimization, reusing the partially trained weights allows for efficient search, as was previously demonstrated by the Population Based Training (PBT) algorithm. We propose PBT-NAS, an adaptation of PBT to NAS where architectures are improved during training by replacing poorly-performing networks in a population with the result of mixing well-performing ones and inheriting the weights using the shrink-perturb technique. After PBT-NAS terminates, the created networks can be directly used without retraining. PBT-NAS is highly parallelizable and effective: on challenging tasks (image generation and reinforcement learning) PBT-NAS achieves superior performance compared to baselines (random search and mutation-based PBT).
Approximating Weakly Preferred Semantics in Abstract Argumentation through Vacuous Reduct Semantics

We consider the recently introduced vacuous reduct semantics in abstract argumentation that allows the composition of arbitrary argumentation semantics through the notion of the reduct. We show that by recursively applying the principle of vacuous reduct semantics we are able to cover a broad range of semantical approaches. Our main result shows that we can recover the weakly preferred semantics as the unique solution of a fixed point equation involving an infinite application of the vacuous reduct semantics based only on the very simple property of conflict-freeness. We also conduct an extensive study of the computational complexity of the recursive application of vacuous reduct semantics, which shows that it completely covers each level of the polynomial hierarchy, depending on the recursion depth.
S3C2 Summit 2202-09: Industry Secure Suppy Chain Summit

Recent years have shown increased cyber attacks targeting less secure elements in the software supply chain and causing fatal damage to businesses and organizations. Past well-known examples of software supply chain attacks are the SolarWinds or log4j incidents that have affected thousands of customers and businesses. The US government and industry are equally interested in enhancing software supply chain security. We conducted six panel discussions with a diverse set of 19 practitioners from industry. We asked them open-ended questions regarding SBOMs, vulnerable dependencies, malicious commits, build and deploy, the Executive Order, and standards compliance. The goal of this summit was to enable open discussions, mutual sharing, and shedding light on common challenges that industry practitioners with practical experience face when securing their software supply chain. This paper summarizes the summit held on September 30, 2022.
Simulation and analysis of quantum phase estimation algorithm in the presence of incoherent quantum noise channels

The quantum phase estimation (QPE) is one of the fundamental algorithms based on the quantum Fourier transform. It has applications in order-finding, factoring, and finding the eigenvalues of unitary operators. The major challenge in running QPE and other quantum algorithms is the noise in quantum computers. In the present work, we study the impact of incoherent noise on QPE, modeled as trace-preserving and completely positive quantum channels. Different noise models such as depolarizing, phase flip, bit flip, and bit-phase flip are taken to understand the performance of the QPE in the presence of noise. The simulation results indicate that the standard deviation of the eigenvalue of the unitary operator has strong exponential dependence upon the error probability of individual qubits. However, the standard deviation increases only linearly with the number of qubits for fixed error probability when that error probability is small.
MeMOTR: Long-Term Memory-Augmented Transformer for Multi-Object Tracking

As a video task, Multiple Object Tracking (MOT) is expected to capture temporal information of targets effectively. Unfortunately, most existing methods only explicitly exploit the object features between adjacent frames, while lacking the capacity to model long-term temporal information. In this paper, we propose MeMOTR, a long-term memory-augmented Transformer for multi-object tracking. Our method is able to make the same object's track embedding more stable and distinguishable by leveraging long-term memory injection with a customized memory-attention layer. This significantly improves the target association ability of our model. Experimental results on DanceTrack show that MeMOTR impressively surpasses the state-of-the-art method by 7.9% and 13.0% on HOTA and AssA metrics, respectively. Furthermore, our model also outperforms other Transformer-based methods on association performance on MOT17 and generalizes well on BDD100K. Code is available at https://github.com/MCG-NJU/MeMOTR.
Learning General Policies with Policy Gradient Methods

While reinforcement learning methods have delivered remarkable results in a number of settings, generalization, i.e., the ability to produce policies that generalize in a reliable and systematic way, has remained a challenge. The problem of generalization has been addressed formally in classical planning where provable correct policies that generalize over all instances of a given domain have been learned using combinatorial methods. The aim of this work is to bring these two research threads together to illuminate the conditions under which (deep) reinforcement learning approaches, and in particular, policy optimization methods, can be used to learn policies that generalize like combinatorial methods do. We draw on lessons learned from previous combinatorial and deep learning approaches, and extend them in a convenient way. From the former, we model policies as state transition classifiers, as (ground) actions are not general and change from instance to instance. From the latter, we use graph neural networks (GNNs) adapted to deal with relational structures for representing value functions over planning states, and in our case, policies. With these ingredients in place, we find that actor-critic methods can be used to learn policies that generalize almost as well as those obtained using combinatorial approaches while avoiding the scalability bottleneck and the use of feature pools. Moreover, the limitations of the DRL methods on the benchmarks considered have little to do with deep learning or reinforcement learning algorithms, and result from the well-understood expressive limitations of GNNs, and the tradeoff between optimality and generalization (general policies cannot be optimal in some domains). Both of these limitations are addressed without changing the basic DRL methods by adding derived predicates and an alternative cost structure to optimize.
ORLA: Learning Explainable Argumentation Models

This paper presents ORLA (Online Reinforcement Learning Argumentation), a new approach for learning explainable symbolic argumentation models through direct exploration of the world. ORLA takes a set of expert arguments that promote some action in the world, and uses reinforcement learning to determine which of those arguments are the most effective for performing a task by maximizing a performance score. Thus, ORLA learns a preference ranking over the expert arguments such that the resulting value-based argumentation framework (VAF) can be used as a reasoning engine to select actions for performing the task. Although model-extraction methods exist that extract a VAF by mimicking the behavior of some non-symbolic model (e.g., a neural network), these extracted models are only approximations to their non-symbolic counterparts, which may result in both a performance loss and non-faithful explanations. Conversely, ORLA learns a VAF through direct interaction with the world (online learning), thus producing faithful explanations without sacrificing performance. This paper uses the Keepaway world as a case study and shows that models trained using ORLA not only perform better than those extracted from non-symbolic models but are also more robust. Moreover, ORLA is evaluated as a strategy discovery tool, finding a better solution than the expert strategy proposed by a related study.
ANALISIS KEMAMPUAN AFEKTIF PEMBELAJAR DARING PADA PROGRAM STUDI PENDIDIKAN EKONOMI UNIVERSITAS MUHAMAMADIYAH BENGKULU

This study aims to analyze how children's learning achievement in online learning during the Covid-19 pandemic. The type of research used is qualitative research with a descriptive approach. The data collection techniques in this study used deep interview techniques and documentation which was based on valid data. The result of the study was that the Covid-19 pandemic had a major impact on children's learning achievement. In learning in online learning students experience more difficulties in learning. The learning achievement of children is difficult to monitor directly by the teacher. The application of effective and efficient learning methods in online learning will affect good learning achievement. There are other factors, namely external factors, namely the family which greatly influences children's learning achievement
An Efﬁcient Hybrid Bi-LSTM Attention Model for Claims Extraction from Research Articles through Deep Learning

The research literature is growing rapidly. A research article contains massive amounts of textual information. Claims are the most significant information in a research article that needs to be retrieved to understand the gist of the research work. A research article contains a number of claims in different sections (abstract, introduction, results, and discussion) of an article. A literature review shows that a few studies of claim extraction have been conducted and they are limited to extracting claims from the abstract section of the article only. In existing studies claims are classified either on the basis of the keywords or all the words. In existing works semantics and context are ignored, and Bag of Words (BoW) representation is used. Deep learning architectures such as Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) have the potential to produce better results through the use of deep learning. Attention mechanism and Bidirectional Long Short-Term Memory (Bi-LSTM) have been used for multiple tasks in Natural Language Processing (NLP) and give effective results. In this work, we propose a hybrid Bi-LSTM attention model. Bi-LSTM model captures the long-term sequences of words and the attention mechanism highlights the important words or keywords in text. A number of experiments have been performed on research articles claim and standard IBM datasets. We verified our proposed model on two datasets for claim/non-claim classification and our model gives 96.7% and 95% on both datasets. The results show that our proposed model through deep learning outperforms existing models.