Reviewerly/exHarmony huggingface dataset:
Designing a Web-Based Information System for Scholarship Management: Supporting Access and Rapid Dissemination of Information

This abstract presents the design of a web-based information system aimed at enhancing the management of scholarships by facilitating quick access to information and efficient dissemination of relevant updates. Scholarships play a crucial role in providing financial assistance to students and enabling them to pursue quality education. However, the existing manual processes and fragmented information systems often lead to challenges in accessing scholarship details and timely dissemination of information. The proposed system addresses these challenges by leveraging web-based technologies and designing an integrated platform for efficient scholarship management. The system incorporates features such as a centralized database, user-friendly interface, and robust search functionality to enable seamless access to scholarship information. By adopting a web-based approach, the system ensures accessibility from various devices, including desktop computers, laptops, and mobile devices. Moreover, the system emphasizes the importance of fast information dissemination. It enables real-time updates and notifications to keep students, scholarship providers, and other stakeholders informed about application deadlines, eligibility criteria, and any changes in the scholarship programs. Additionally, the system allows for personalized notifications, ensuring that users receive relevant updates based on their preferences and areas of interest. The design of the web-based information system prioritizes security and privacy by implementing stringent authentication mechanisms and encrypted data transmission. It also incorporates features for efficient application submission, document management, and evaluation processes, streamlining the entire scholarship lifecycle. The expected benefits of implementing this system include improved accessibility to scholarship opportunities, reduced administrative overhead, increased transparency in the selection process, and enhanced collaboration among scholarship providers and educational institutions. By leveraging the power of web technologies, the proposed system aims to revolutionize the management of scholarships, ultimately promoting equal educational opportunities and fostering academic excellence.
DRFM-Based Jamming Signal Recognition Method Guided by Target Detection

Digital radio frequency memory (DRFM) based jammer equipped on target can generate multiple false targets by intercepting and forwarding the signal transmitted by radar, which lead to a serious threat to the performance of a radar system. Therefore, the recognition of radar jamming signal type is a key step of radar countermeasure. The existing jamming recognition methods do not take into account the factor of real-time performance. Since it uses the raw time-domain signals and complex network structures, which incurs dense computational complexity. To tackle this problem, this paper analyzes the time-frequency distribution maps of jamming signals after signal processing, and proposes a radar jamming recognition method based on the guidance of target detection. In the proposed method, firstly, based on the target detection information, the time-frequency image of the jamming region is obtained by performing short-time Fourier transformation (STFT); Secondly, a lightweight convolutional neural network (CNN) model was constructed to achieve jamming recognition. Finally, effectiveness of this method was evaluated using the simulated datasets with six typical radar jamming signals. The results show that the proposed method performed well in accuracy and efficiency, which is practical for real-time anti-jamming.
A Graph-Based Stratified Sampling Methodology for the Analysis of (Underground) Forums

[Context] Researchers analyze underground forums to study abuse and cybercrime activities. Due to the size of the forums and the domain expertise required to identify criminal discussions, most approaches employ supervised machine learning techniques to automatically classify the posts of interest. [Goal] Human annotation is costly. How to select samples to annotate that account for the structure of the forum? [Method] We present a methodology to generate stratified samples based on information about the centrality properties of the population and evaluate classifier performance. [Result] We observe that by employing a sample obtained from a uniform distribution of the post degree centrality metric, we maintain the same level of precision but significantly increase the recall (+30%) compared to a sample whose distribution is respecting the population stratification. We find that classifiers trained with similar samples disagree on the classification of criminal activities up to 33% of the time when deployed on the entire forum.
IMPROVING WRITING ABILITY THROUGH COOPERATIVE INTEGRATED READING AND COMPOSITION (CIRC)

AbstractThe study is aimed at improving the students’ writing ability in writing using Group Discussion and Cooperative Integrated Reading and Composition (CIRC) models of Cooperative Learning Strategy. The study employs Collaborative Classroom Action Research design. The subjects of the study are 5 students of fourth semester of the English Department of Kutai Kartanegara University in 2021-2022. The result of this study shows that Cooperative Learning strategy with Group Discussion and Cooperative Integrated Reading and Composition (CIRC) models is effective in improving the students’ writing ability. In the pretest, the average score of the students’ writing achievement was 4.15 on a 1.00 to 6.00 scale. Five students obtained a ‘poor’ level. After Cooperative Learning strategy was implemented, the average score of the class in Cycle 1 increased slightly to 4.65. In Cycle 2, the average score of the class increased to 5.04, and all subjects achieved the criteria of success. Keywords: writing ability, cooperative learning, strategy Abstrak Penelitian ini bertujuan untuk meningkatkan kemampuan menulis mahasiswa dalam menulis Bahasa Inggris dengan menggunakan model strategi pembelajaran Cooperatif Group Discussion dan Cooperative Integrated Reading and Composition. Peneliti menggunakan desain Penelitian Tindakan Kelas Kolaboratif dan subjek penelitian adalah 5 mahasiswa semester 4 Prodi pendidikan Bahasa Inggris Universitas Kutai Kartanegara tahun ajaran 2021-2022. Hasil penelitian ini menunjukkan bahwa strategi Cooperative Learning dengan model Group Discussion dan Cooperative Integrated Reading and Composition (CIRC) efektif dalam meningkatkan kemampuan menulis siswa. Pretest sebelum penerapan strategi ini, nilai rata-rata prestasi menulis siswa adalah 4,15 pada skala 1,00 sampai 6,00. Lima siswa memperoleh tingkat 'buruk'. Setelah penerapan strategi Cooperative Learning, nilai rata-rata kelas pada Siklus 1 meningkat tipis menjadi 4,65. Hanya 2 mahasiswa yang tidak mencapai kriteria keberhasilan. Siklus 2 nilai rata-rata kelas meningkat menjadi 5,04 dan semua subjek mencapai kriteria keberhasilan. Mahasiswa terlibat aktif dan sangat termotivasi untuk mengikuti kelas menulis dengan sistem ini. Kata-kata kunci: kemampuan menulis, pembelajaran kooperatif, strategi
A Logic-based Explanation Generation Framework for Classical and Hybrid Planning Problems (Extended Abstract)

In human-aware planning systems, a planning agent might need to explain its plan to a human user when that plan appears to be non-feasible or sub-optimal. A popular approach, called model reconciliation, has been proposed as a way to bring the model of the human user closer to the agent's model. In this paper, we approach the model reconciliation problem from a different perspective, that of knowledge representation and reasoning, and demonstrate that our approach can be applied not only to classical planning problems but also hybrid systems planning problems with durative actions and events/processes.
Large Decision Models

Over recent decades, sequential decision-making tasks are mostly tackled with expert systems and reinforcement learning. However, these methods are still incapable of being generalizable enough to solve new tasks at a low cost. In this article, we discuss a novel paradigm that leverages Transformer-based sequence models to tackle decision-making tasks, named large decision models. Starting from offline reinforcement learning scenarios, early attempts demonstrate that sequential modeling methods can be applied to train an effective policy given sufficient expert trajectories. When the sequence model goes large, its generalization ability over a variety of tasks and fast adaptation to new tasks has been observed, which is highly potential to enable the agent to achieve artificial general intelligence for sequential decision-making in the near future.
Quality-agnostic Image Captioning to Safely Assist People with Vision Impairment

Automated image captioning has the potential to be a useful tool for people with vision impairments. Images taken by this user group are often noisy, which leads to incorrect and even unsafe model predictions. In this paper, we propose a quality-agnostic framework to improve the performance and robustness of image captioning models for visually impaired people. We address this problem from three angles: data, model, and evaluation. First, we show how data augmentation techniques for generating synthetic noise can address data sparsity in this domain. Second, we enhance the robustness of the model by expanding a state-of-the-art model to a dual network architecture, using the augmented data and leveraging different consistency losses. Our results demonstrate increased performance, e.g. an absolute improvement of 2.15 on CIDEr, compared to state-of-the-art image captioning networks, as well as increased robustness to noise with up to 3 points improvement on CIDEr in more noisy settings. Finally, we evaluate the prediction reliability using confidence calibration on images with different difficulty / noise levels, showing that our models perform more reliably in safety-critical situations. The improved model is part of an assisted living application, which we develop in partnership with the Royal National Institute of Blind People.
Stability and Generalization of lp-Regularized Stochastic Learning for GCN

Graph convolutional networks (GCN) are viewed as one of the most popular representations among the variants of graph neural networks over graph data and have shown powerful performance in empirical experiments. That l2-based graph smoothing enforces the global smoothness of GCN, while (soft) l1-based sparse graph learning tends to promote signal sparsity to trade for discontinuity. This paper aims to quantify the trade-off of GCN between smoothness and sparsity, with the help of a general lp-regularized (1&lt;p&lt;= 2) stochastic learning proposed within. While stability-based generalization analyses have been given in prior work for a second derivative objectiveness function, our lp-regularized learning scheme does not satisfy such a smooth condition. To tackle this issue, we propose a novel SGD proximal algorithm for GCNs with an inexact operator. For a single-layer GCN, we establish an explicit theoretical understanding of GCN with the lp-regularized stochastic learning by analyzing the stability of our SGD proximal algorithm. We conduct multiple empirical experiments to validate our theoretical findings.
NeoMaPy: A Framework for Computing MAP Inference on Temporal Knowledge Graphs

Markov Logic Networks (MLN) are used for reasoning on uncertain and inconsistent temporal data. We proposed the TMLN (Temporal Markov Logic Network) which extends them with sorts/types, weights on rules and facts, and various temporal consistencies. The NeoMaPy framework integrates it as a knowledge graph based on conflict graphs which offers flexibility for reasoning with parametric Maximum A Posteriori (MAP) inferences, efficiency with an optimistic heuristic and interactive graph visualization for results explanation.
Recent Advances in Direct Speech-to-text Translation

Recently, speech-to-text translation has attracted more and more attention and many studies have emerged rapidly. In this paper, we present a comprehensive survey on direct speech translation aiming to summarize the current state-of-the-art techniques. First, we categorize the existing research work into three directions based on the main challenges --- modeling burden, data scarcity, and application issues. To tackle the problem of modeling burden, two main structures have been proposed, encoder-decoder framework (Transformer and the variants) and multitask frameworks. For the challenge of data scarcity, recent work resorts to many sophisticated techniques, such as data augmentation, pre-training, knowledge distillation, and multilingual modeling. We analyze and summarize the application issues, which include real-time, segmentation, named entity, gender bias, and code-switching. Finally, we discuss some promising directions for future work.
Towards Formal Verification of Neuro-symbolic Multi-agent Systems

This paper outlines some of the key methods we developed towards the formal verification of multi- agent systems, covering both symbolic and connectionist systems. It discusses logic-based methods for the verification of unbounded multi-agent systems (i.e., systems composed of an arbitrary number of homogeneous agents, e.g., robot swarms), optimisation approaches for establishing the robustness of neural network models, and methods for analysing properties of neuro-symbolic multi-agent systems.
Causal Deep Reinforcement Learning Using Observational Data

Deep reinforcement learning (DRL) requires the collection of interventional data, which is sometimes expensive and even unethical in the real world, such as in the autonomous driving and the medical field. Offline reinforcement learning promises to alleviate this issue by exploiting the vast amount of observational data available in the real world. However, observational data may mislead the learning agent to undesirable outcomes if the behavior policy that generates the data depends on unobserved random variables (i.e., confounders). In this paper, we propose two deconfounding methods in DRL to address this problem. The methods first calculate the importance degree of different samples based on the causal inference technique, and then adjust the impact of different samples on the loss function by reweighting or resampling the offline dataset to ensure its unbiasedness. These deconfounding methods can be flexibly combined with existing model-free DRL algorithms such as soft actor-critic and deep Q-learning, provided that a weak condition can be satisfied by the loss functions of these algorithms. We prove the effectiveness of our deconfounding methods and validate them experimentally.
Finite Entailment of UCRPQs over ALC Ontologies (Extended Abstract)

We investigate the problem of finite entailment of ontology-mediated queries. We consider the expressive query language, unions of conjunctive regular path queries (UCRPQs), extending the well-known class of unions of conjunctive queries, with regular expressions over roles. We look at ontologies formulated using the description logic ALC, and show a tight 2ExpTime upper bound for finite entailment of UCRPQs.
Video Diffusion Models with Local-Global Context Guidance

Diffusion models have emerged as a powerful paradigm in video synthesis tasks including prediction, generation, and interpolation. Due to the limitation of the computational budget, existing methods usually implement conditional diffusion models with an autoregressive inference pipeline, in which the future fragment is predicted based on the distribution of adjacent past frames. However, only the conditions from a few previous frames can't capture the global temporal coherence, leading to inconsistent or even outrageous results in long-term video prediction. In this paper, we propose a Local-Global Context guided Video Diffusion model (LGC-VD) to capture multi-perception conditions for producing high-quality videos in both conditional/unconditional settings. In LGC-VD, the UNet is implemented with stacked residual blocks with self-attention units, avoiding the undesirable computational cost in 3D Conv. We construct a local-global context guidance strategy to capture the multi-perceptual embedding of the past fragment to boost the consistency of future prediction. Furthermore, we propose a two-stage training strategy to alleviate the effect of noisy frames for more stable predictions. Our experiments demonstrate that the proposed method achieves favorable performance on video prediction, interpolation, and unconditional video generation. We release code at https://github.com/exisas/LGC-VD.
On the Complexity of Counterfactual Reasoning

We study the computational complexity of counterfactual reasoning in relation to the complexity of associational and interventional reasoning on structural causal models (SCMs). We show that counterfactual reasoning is no harder than associational or interventional reasoning on fully specified SCMs in the context of two computational frameworks. The first framework is based on the notion of treewidth and includes the classical variable elimination and jointree algorithms. The second framework is based on the more recent and refined notion of causal treewidth which is directed towards models with functional dependencies such as SCMs. Our results are constructive and based on bounding the (causal) treewidth of twin networks---used in standard counterfactual reasoning that contemplates two worlds, real and imaginary---to the (causal) treewidth of the underlying SCM structure. In particular, we show that the latter (causal) treewidth is no more than twice the former plus one. Hence, if associational or interventional reasoning is tractable on a fully specified SCM then counterfactual reasoning is tractable too. We extend our results to general counterfactual reasoning that requires contemplating more than two worlds and discuss applications of our results to counterfactual reasoning with partially specified SCMs that are coupled with data. We finally present empirical results that measure the gap between the complexities of counterfactual reasoning and associational/interventional reasoning on random SCMs.
Locate, Refine and Restore: A Progressive Enhancement Network for Camouflaged Object Detection

Camouflaged Object Detection (COD) aims to segment objects that blend in with their surroundings. Most existing methods mainly tackle this issue by a single-stage framework, which tends to degrade performance in the face of small objects, low-contrast objects and objects with diverse appearances. In this paper, we propose a novel Progressive Enhancement Network (PENet) for COD by imitating the human visual detection system, which follows a three-stage detection process: locate objects, refine textures and restore boundary. Specifically, our PENet contains three key modules, i.e., the object location module (OLM), the group attention module (GAM) and the context feature restoration module (CFRM). The OLM is designed to position the object globally, the GAM is developed to refine both high-level semantic and low-level texture feature representation, and the CFRM is leveraged to effectively aggregate multi-level features for progressively restoring the clear boundary. Extensive results demonstrate that our PENet significantly outperforms 32 state-of-the-art methods on four widely used benchmark datasets
Synthesizing Resilient Strategies for Infinite-Horizon Objectives in Multi-Agent Systems

We consider the problem of synthesizing resilient and stochastically stable strategies for systems of cooperating agents striving to minimize the expected time between consecutive visits to selected locations in a known environment. A strategy profile is resilient if it retains its functionality even if some of the agents fail, and stochastically stable if the visiting time variance is small. We design a novel specification language for objectives involving resilience and stochastic stability, and we show how to efficiently compute strategy profiles (for both autonomous and coordinated agents) optimizing these objectives. Our experiments show that our strategy synthesis algorithm can construct highly non-trivial and efficient strategy profiles for environments with general topology.
RANCANG BANGUN PROTOTYPE SISTEM INFORMASI PERINGATAN DINI DAN PENCEGAHAN BANJIR LUAPAN AIR SUNGAI BENDUNGAN PAMARAYAN BERBASIS INTERNET OF THINGS

This research will raise a system of how to plan an early warning system for floods in areas around rivers, or what is commonly called an Early Warning System (EWS) to prevent flooding around rivers. This early warning system is built with hardware that uses the ESP 32 microcontroller to control the level of water release from the dam and select the floodgates to be opened automatically. Ultrasonic sensors are used to detect water levels, IOT modules are used to provide quick information to officers. The object of this research was carried out at the Gerak Pamarayan Dam, a dam that controls the two largest rivers in Banten, namely Ciujung and Ciberang. Two rivers flow in two districts in the Banten area. The method used in this study is the Prototype method. And the expected result of this research is to create an early warning system for overflowing river water that measures the water level of the river automatically and automatically reports the water level that is at the threshold to the officers and what is equally important is that this system can also prevent flooding due to overflow by how to open the floodgates automatically when the river water level is beyond the normal height threshold.
Choose your Data Wisely: A Framework for Semantic Counterfactuals

Counterfactual explanations have been argued to be one of the most intuitive forms of explanation. They are typically defined as a minimal set of edits on a given data sample that, when applied, changes the output of a model on that sample. However, a minimal set of edits is not always clear and understandable to an end-user, as it could constitute an adversarial example (which is indistinguishable from the original data sample to an end-user). Instead, there are recent ideas that the notion of minimality in the context of counterfactuals should refer to the semantics of the data sample, and not to the feature space. In this work, we build on these ideas, and propose a framework that provides counterfactual explanations in terms of knowledge graphs. We provide an algorithm for computing such explanations (given some assumptions about the underlying knowledge), and quantitatively evaluate the framework with a user study.
Temporal Datalog with Existential Quantification

Existential rules, also known as tuple-generating dependencies (TGDs) or Datalog+/- rules, are heavily studied in the communities of Knowledge Representation and Reasoning, Semantic Web, and Databases, due to their rich modelling capabilities. In this paper we consider TGDs in the temporal setting, by introducing and studying DatalogMTLE---an extension of metric temporal Datalog (DatalogMTL) obtained by allowing for existential rules in programs. We show that DatalogMTLE is undecidable even in the restricted cases of guarded and weakly-acyclic programs. To address this issue we introduce uniform semantics which, on the one hand, is well-suited for modelling temporal knowledge as it prevents from unintended value invention and, on the other hand, provides decidability of reasoning; in particular, it becomes 2-EXPSPACE-complete for weakly-acyclic programs but remains undecidable for guarded programs. We provide an implementation for the decidable case and demonstrate its practical feasibility. Thus we obtain an expressive, yet decidable, rule-language and a system which is suitable for complex temporal reasoning with existential rules.