Reviewerly/exHarmony huggingface dataset:
Audio-visual-based history learning media materials about human life in the literary age

This study aims to develop and test the feasibility of audio-visual-based learning media in history learning class X in senior high school. This study is a research and development (RD) type with the analyze, design, development, implementation, evaluation (ADDIE) model. The research involved 96 students of class X IPS or social science at State Senior High School 1 Bandar Lampung, Indonesia. The data collection techniques as well as the instruments employed questionnaires, observation, interviews, and documentation. Data analysis techniques employed qualitative and quantitative analysis. The results of the study show that: i) The development of audio-visual-based learning media on material about human life in Indonesia’s literacy period was made using the Adobe Photoshop application; and ii) The feasibility of the validation results of media experts, material experts, and learning experts (history teachers) successively with an average result of 4.73 (very good), 4.28 (very good), 4.50 (very good) and the results of the assessment students at 4.37 (very good). Then, the audio-visual-based learning media is declared feasible to use. This audio-visual-based historical learning media has its charm in being able to encourage students to become higher in learning history.
An Effective Personality-Based Model for Short Text Sentiment Classification Using BiLSTM and Self-Attention

While user-generated textual content on social platforms such as Weibo provides valuable insights into public opinion and social trends, the influence of personality on sentiment expression has been largely overlooked in previous studies, especially in Chinese short texts. To bridge this gap, we propose the P-BiLSTM-SA model, which integrates personalities into sentiment classification by combining BiLSTM and self-attention mechanisms. We grouped Weibo texts based on personalities and constructed a personality lexicon using the Big Five theory and clustering algorithms. Separate sentiment classifiers were trained for each personality group using BiLSTM and self-attention, and their predictions were combined by ensemble learning. The performance of the P-BiLSTM-SA model was evaluated on the NLPCC2013 dataset and showed significant accuracy improvements. In particular, it achieved 82.88% accuracy on the NLPCC2013 dataset, a 7.51% improvement over the baseline BiLSTM-SA model. The results highlight the effectiveness of incorporating personality factors into sentiment classification of short texts.
ANALISIS KETERAMPILAN BERBICARA SISWA KELAS V PADA PEMBELAJARAN BAHASA INDONESIA SDN KARANG TENGAH 06

This study aims to 1) find out the speaking skills of fifth grade students at SDN Karang Tengah 06, 2) find out the factors that influence speaking skills in learning Indonesian for fifth grade students at SDN Karang Tengah 06. This research uses qualitative methods so that the data presented is in the form of analysis, the subjects used in this study amounted to 34 students. Data collection techniques used are observation, interviews, tests, and documentation. The results of the study can be concluded 1) in aspects of speaking skills possessed by VB class students at SDN Karang Tengah 06 they are still lacking in the intonation, courage, and demeanor sections, for aspects of pronunciation, vocabulary and memorization are in the good category but still need to be improved again so that later students can master all aspects of speaking skills well and even very well. 2) the factors that affect students' speaking skills consist of linguistic factors, such as students' lack of knowledge about how to pronounce, intonation, vocabulary or sentences properly and correctly, while non-linguistics such as students' lack of courage when speaking or reciting rhymes in front of the class, lack of students' knowledge about how to have a good attitude when speaking or reciting rhymes in front of the class, and still lack of fluency or mastery of topics owned by VB class students at SDN Karang Tengah 06
Teachers’ perspectives on technology-based learning for the kindergarten students

Ever since the pandemic occured, almost every sector utilize technology to accommodate learning. To tackle every possibility that may interfere the learning process, perspectives in using technology from teachers needed to be taken into account. Thereofore, the needs of finding the perspecives of teachers in teaching and learning especially in kindergarten are crucial since they are still new to the settings. Qualitative research design was applied through Google Form as the media to reach the participants. In collecting the data, the participants were asked about the use of the technology in the teaching and learning process using English as the language of instruction. This research involved eight teachers (T1, T2, T3, T4, T5, T6, T7, T8) of English for young learners in three different schools. Results revealed that there were four main themes which indicated the use of technology in teaching in the kindergarten students, namely: i) Technology motivates children to learn; ii) Technology effects children’s health; iii) Technology is an optional medium; and iv) Suitable application are needed for better learning. These findings can be useful for the present and future stakeholders of kindergarten schools especially in the use of information and communication technology (ICT) in the teaching of English as a foreign language (EFL) at kindergarten settings.
Causal Policy Gradient for Whole-Body Mobile Manipulation

Developing the next generation of household robot helpers requires combining locomotion and interaction capabilities, which is generally referred to as mobile manipulation (MoMa).MoMa tasks are difficult due to the large action space of the robot and the common multi-objective nature of the task, e.g., efficiently reaching a goal while avoiding obstacles.Current approaches often segregate tasks into navigation without manipulation and stationary manipulation without locomotion by manually matching parts of the action space to MoMa subobjectives (e.g.base actions for locomotion objectives and arm actions for manipulation).This solution prevents simultaneous combinations of locomotion and interaction degrees of freedom and requires human domain knowledge for both partitioning the action space and matching the action parts to the subobjectives.In this paper, we introduce Causal MoMa, a new framework to train policies for typical MoMa tasks that makes use of the most favorable subspace of the robot's action space to address each sub-objective.Causal MoMa automatically discovers the causal dependencies between actions and terms of the reward function and exploits these dependencies in a causal policy learning procedure that reduces gradient variance compared to previous state-of-the-art policy gradient algorithms, improving convergence and results.We evaluate the performance of Causal MoMa on three types of simulated robots across different MoMa tasks and demonstrate success in transferring the policies trained in simulation directly to a real robot, where our agent is able to follow moving goals and react to dynamic obstacles while simultaneously and synergistically controlling the whole-body: base, arm, and head.More information at https://sites.google.com/view/causal-moma
Non-Euclidean Motion Planning with Graphs of Geodesically-Convex Sets

Computing optimal, collision-free trajectories for high-dimensional systems is a challenging problem.Samplingbased planners struggle with the dimensionality, whereas trajectory optimizers may get stuck in local minima due to inherent nonconvexities in the optimization landscape.The use of mixedinteger programming to encapsulate these nonconvexities and find globally optimal trajectories has recently shown great promise, thanks in part to tight convex relaxations and efficient approximation strategies that greatly reduce runtimes.These approaches were previously limited to Euclidean configuration spaces, precluding their use with mobile bases or continuous revolute joints.In this paper, we handle such scenarios by modeling configuration spaces as Riemannian manifolds, and we describe a reduction procedure for the zero-curvature case to a mixed-integer convex optimization problem.We demonstrate our results on various robot platforms, including producing efficient collision-free trajectories for a PR2 bimanual mobile manipulator.
Bandit Submodular Maximization for Multi-Robot Coordination in Unpredictable and Partially Observable Environments

We study the problem of multi-agent coordination in unpredictable and partially observable environments, that is, environments whose future evolution is unknown a priori and that can only be partially observed.We are motivated by the future of autonomy that involves multiple robots coordinating actions in dynamic, unstructured, and partially observable environments to complete complex tasks such as target tracking, environmental mapping, and area monitoring.Such tasks are often modeled as submodular maximization coordination problems due to the information overlap among the robots.We introduce the first submodular coordination algorithm with bandit feedback and bounded tracking regret -bandit feedback is the robots' ability to compute in hindsight only the effect of their chosen actions, instead of all the alternative actions that they could have chosen instead, due to the partial observability; and tracking regret is the algorithm's suboptimality with respect to the optimal timevarying actions that fully know the future a priori.The bound gracefully degrades with the environments' capacity to change adversarially, quantifying how often the robots should re-select actions to learn to coordinate as if they fully knew the future a priori.The algorithm generalizes the seminal Sequential Greedy algorithm by Fisher et al. to the bandit setting, by leveraging submodularity and algorithms for the problem of tracking the best action.We validate our algorithm in simulated scenarios of multi-target tracking.
Application of Dual-Energy X-Ray Image Detection of Dangerous Goods Based on YOLOv7

X-ray security equipment is currently a more commonly used dangerous goods detection tool, due to the increasing security work tasks, the use of target detection technology to assist security personnel to carry out work has become an inevitable trend.With the development of deep learning, object detection technology is becoming more and more mature, and object detection framework based on convolutional neural networks has been widely used in industrial, medical and military fields.In order to improve the efficiency of security staff, reduce the risk of dangerous goods missed detection.Based on the data collected in X-ray security equipment, this paper uses a method of inserting dangerous goods into an empty package to balance all kinds of dangerous goods data and expand the data set.The high-low energy images are combined using the high-low energy feature fusion method.Finally, the dangerous goods target detection technology based on the YOLOv7 model is used for model training.After the introduction of the above method, the detection accuracy is improved by 6% compared with the direct use of the original data set for detection, and the speed is 93FPS, which can meet the requirements of the online security system, greatly improve the work efficiency of security personnel, and eliminate the security risks caused by missed detection.
Revisiting Lattice Tiling Decomposition and Dithered Quantisation

A lattice tiling decomposition induces dual operations: quantisation and wrapping, which map the Euclidean space to the lattice and to one of its fundamental domains, respectively. Applying such decomposition to random variables over the Euclidean space produces quantised and wrapped random variables. In studying the characteristic function of those, we show a ‘frequency domain’ characterisation for deterministic quantisation, which is dual to the known ‘frequency domain’ characterisation of uniform wrapping. In a second part, we apply the tiling decomposition to describe dithered quantisation, which consists in adding noise during quantisation to improve its perceived quality. We propose a non-collaborative type of dithering and show that, in this case, a wrapped dither minimises the Kullback-Leibler divergence to the original distribution. Numerical experiments illustrate this result.
On the Correspondence Between Monotonic Max-Sum GNNs and Datalog

Although there has been significant interest in applying machine learning techniques to structured data, the expressivity (i.e., a description of what can be learned) of such techniques is still poorly understood. In this paper, we study data transformations based on graph neural networks (GNNs). First, we note that the choice of how a dataset is encoded into a numeric form processable by a GNN can obscure the characterisation of a model's expressivity, and we argue that a canonical encoding provides an appropriate basis. Second, we study the expressivity of monotonic max-sum GNNs, which cover a subclass of GNNs with max and sum aggregation functions. We show that, for each such GNN, one can compute a Datalog program such that applying the GNN to any dataset produces the same facts as a single round of application of the program's rules to the dataset. Monotonic max-sum GNNs can sum an unbounded number of feature vectors which can result in arbitrarily large feature values, whereas rule application requires only a bounded number of constants. Hence, our result shows that the unbounded summation of monotonic max-sum GNNs does not increase their expressive power. Third, we sharpen our result to the subclass of monotonic max GNNs, which use only the max aggregation function, and identify a corresponding class of Datalog programs.
Fuzzy Truth, Fuzzy Support and Fuzzy Information States for Inquisitive Semantics

In logic, the meaning of a sentence is usually reduced to its truth conditions. However, this makes sense only for declarative sentences. In order to model also the meaning of questions, inquisitive semantics replaces the truth-conditional approach relating sentences to possible worlds with a support-conditional approach that relates sentences to information states. The standard framework of inquisitive semantics is based on a crisp notion of an information state, defined as a set of possible worlds, and a crisp relation of informational support. This paper introduces and studies two refinements of the standard framework. The first refinement takes into account fuzzy information states (defined as fuzzy sets of possible worlds) and the second one introduces a notion of fuzzy informational support. The main result of this paper shows that in the resulting framework that fuzzifies inquisitive semantics in two different directions we obtain an abstract and very general version of a principle known from the basic inquisitive semantics as Truth-Support Bridge.
A Framework for Combining Entity Resolution and Query Answering in Knowledge Bases

We propose a new framework for combining entity resolution and query answering in knowledge bases (KBs) with tuple-generating dependencies (tgds) and equality-generating dependencies (egds) as rules. We define the semantics of the KB in terms of special instances that involve equivalence classes of entities and sets of values. Intuitively, the former collect all entities denoting the same real-world object, while the latter collect all alternative values for an attribute. This approach allows us to both resolve entities and bypass possible inconsistencies in the data. We then design a chase procedure that is tailored to this new framework and has the feature that it never fails; moreover, when the chase procedure terminates, it produces a universal solution, which in turn can be used to obtain the certain answers to conjunctive queries. We finally discuss challenges arising when the chase does not terminate.
Streamlining Input/Output Logics with Sequent Calculi

Input/Output (I/O) logic is a general framework for reasoning about conditional norms and/or causal relations. We streamline Bochman’s causal I/O logics via proof-search-oriented sequent calculi. Our calculi establish a natural syntactic link between the derivability in these logics and in the original I/O logics. As a consequence of our results, we obtain new, simple semantics for all these logics, complexity bounds, embeddings into normal modal logics, and efficient deduction methods. Our work encompasses many scattered results and provides uniform solutions to various unresolved problems.
Interactive Explanations by Conflict Resolution via Argumentative Exchanges

As the field of explainable AI (XAI) is maturing, calls for interactive explanations for (the outputs of) AI models are growing, but the state-of-the-art predominantly focuses on static explanations. In this paper, we focus instead on interactive explanations framed as conflict resolution between agents (i.e. AI models and/or humans) by leveraging on computational argumentation. Specifically, we define Argumentative eXchanges (AXs) for dynamically sharing, in multi-agent systems, information harboured in individual agents’ quantitative bipolar argumentation frameworks towards resolving conflicts amongst the agents. We then deploy AXs in the XAI setting in which a machine and a human interact about the machine’s predictions. We identify and assess several theoretical properties characterising AXs that are suitable for XAI. Finally, we instantiate AXs for XAI by defining various agent behaviours, e.g. capturing counterfactual patterns of reasoning in machines and highlighting the effects of cognitive biases in humans. We show experimentally (in a simulated environment) the comparative advantages of these behaviours in terms of conflict resolution, and show that the strongest argument may not always be the most effective.
Analysis of Deep Learning based Optimization Techniques for Oral Cancer Detection

The main aim is to examine the application of deep learning technique in detecting oral cancer at an early stage. The focus is on evaluating the performance of various optimization techniques to deep learning technique through analyzing images of the mouth and throat for oral cancer detection. The goal is to determine the most effective method for identifying oral cancer and gain insights into potential improvements. The importance of early detection of oral cancer cannot be overstated, as it plays a crucial role in improving patient outcomes through earlier treatment and higher survival rates. The findings have the potential to contribute to the development of more accurate and efficient methods for oral cancer diagnosis and make a positive impact in the field.
Non-ellipsoidal extended object tracking using PMBM filter

The Poisson multi-Bernoulli mixture (PMBM) filter is an effective tracking framework for tracking multiple extended objects. However, methods based on this framework typically assume that the object's shape is an ellipse, which cannot adequately describe the object's shape. When two objects are spatially close, problems such as difficulty correctly distinguishing the object trajectory and insufficient utilization of the object shape's feature information arise. To address the aforementioned issues, this paper describes the shape of the object in greater detail using the multi-ellipse model. On this premise, the extended object shape will be divided into multiple Gaussian inverse Wishart components, and the likelihood will be calculated. Furthermore, a new partitioning method is proposed to divide the measurements into several most linearly dependent components, which aids in estimating the object shape composed of multiple ellipses. The simulation results show that the new algorithm outperforms the original PMBM algorithm in terms of accuracy.
A Tale of Additives and Concurrency in Game Semantics

Twenty years ago, Abramsky and Melliès published their famous paper, Concurrent Games and Full Completeness. In that paper, they advocated the switch to a truly concurrent canvas to address the issue known as the Blass problem, the non-associativity of composition in Blass’ games model for Linear Logic, diagnosed as an excess of sequentiality. Their model, concurrent games, was the first of a family of positional or causal game semantics which has since then shown merits far beyond the full completeness problem for Linear Logic. In this paper, we tell and revisit the story of models of in game semantics, in the modern clothes of concurrent games on event structures, from Blass games to Melliès’ approach to fully complete models of Linear Logic.
Traditional pattern enhancement based on improved singular value decomposition and gamma function in the frequency domain

A novel enhancement method to improve resolution and contrast has been proposed to address the issues of blurring and distortion commonly encountered in traditional patterns. Initially, a discrete wavelet transform, a stationary wavelet transform, and an interpolation algorithm are used to obtain high-resolution images of traditional patterns. Subsequently, improved singular value matrix coefficients and reconstructed gamma function are used to enhance the image contrast to obtain high-resolution and contrast-enhanced patterns. Experimental results demonstrate the efficacy of this method, as evidenced by improved evaluation indexes, such as mean square error, peak signal-to-noise ratio, and structural similarity, in comparison to other existing methods. The proposed method effectively improves the quality of traditional patterns and offers significant contributions to research on the restoration and protection of traditional patterns.
Urban Farming Prototype Design and Development with IoT System using STEMSEL and Runlinc IDE

Urban farming technology is getting more acceptable with the increase in population. Running urban farming with IoT becomes more important as urban farming has many constraints. This project aimed to design and develop an automated urban farming system operated with an IoT system using Science Technology Engineering Maths Social Enterprise Learning Artificial Intelligence (STEMSEL AI), and Runlinc Integrated Development Environment (IDE) to ease urban farming. The prototype consists of three major components: the structural part, the water piping system, and the IoT operating system. Idea generation was applied to develop various conceptual designs in the structural component. Concept evaluation was performed to choose the final design. The IoT operation system was designed and developed using the STEMSEL module and Runlinc IDE. A few challenges were identified throughout the design process. Overall, STEMSEL is much simpler and easier to set up than other controllers.
Multi-angle truck model recognition based on detail awareness

A detail-aware multi-angle vehicle recognition algorithm is proposed to address the problem of detail information loss due to pooling operation in multi-angle vehicle recognition. Firstly, considering that the differences between different vehicles are concentrated in the vehicle length and axle regions, the mid-level features are selected to build a local perception module, and the ECA attention mechanism is embedded to enhance the network's discrimination of details in local regions and suppress the interference of low discrimination features; secondly, a void space pyramid pooling model is introduced to increase the perceptual field to aggregate multi-scale contextual information, which reduces the problem of detail information loss and improves the global The second is the introduction of the null space pyramid pooling model, which increases the perceptual field to aggregate multi-scale contextual information, reduces the loss of detail information and improves the perception of global features. The experimental results show that the detection accuracy mAP of the improved algorithm reaches 96.1%, and the proposed method can effectively obtain local features with discrimination and improve the perception ability of the global features.