Reviewerly/exHarmony huggingface dataset:
Verifying Belief-Based Programs via Symbolic Dynamic Programming

Belief-based programming is a probabilistic extension of the Golog programming language family, where every action and sensing could be noisy and every test refers to the subjective beliefs of the agent. Such characteristics make it rather suitable for robot control in a partial-observable uncertain environment. Recently, efforts have been made in providing formal semantics for belief programs and investigating the hardness of verifying belief programs. Nevertheless, a general algorithm that actually conducts the verification is missing. In this paper, we propose an algorithm based on symbolic dynamic programming to verify belief programs, an approach that generalizes the dynamic programming technique for solving (partially observable) Markov decision processes, i.e. (PO)MDP, by exploiting the symbolic structure in the solution of first-order (PO)MDPs induced by belief program execution.
Evolutionary Explainable Rule Extraction from (Modal) Random Forests

Symbolic learning is the subfield of machine learning concerned with learning predictive models with knowledge represented in logical form, such as decision tree and decision list models. Ensemble learning methods, such as random forests, are usually deployed to improve the performance of decision trees; unfortunately, interpreting tree ensembles is challenging. In order to deal with unstructured (e.g., temporal or spatial) data, moreover, decision trees and random forests have been recently generalized to the use of modal logics, which are harder to interpret than their propositional counterpart. Recently, a methodology for extracting simple rules from propositional random forests, based on a sequence of optimization steps, was proposed. In this work, we generalize this approach along two directions: from propositional to modal logic and from a sequence of optimization steps to a single multi-objective optimization problem. Even if confined to the temporal domain, our experimental results, based on open-source implementations and public data, show that our method is robust and able to extract small, accurate, and informative decision lists even for complex classification problems.
Comment-Aware Multi-Modal Heterogeneous Pre-Training for Humor Detection in Short-Form Videos

Conventional humor analysis normally focuses on text, text-image pair, and even long video (e.g., monologue) scenarios. However, with the recent rise of short-form video sharing, humor detection in this scenario has not yet gained much exploration. To the best of our knowledge, there are two primary issues associated with short-form video humor detection (SVHD): 1) At present, there are no ready-made humor annotation samples in this scenario, and it takes a lot of manpower and material resources to obtain a large number of annotation samples; 2) Unlike the more typical audio and visual modalities, the titles (as opposed to simultaneous transcription in the lengthy film) and associated interactive comments in short-form videos may convey apparent humorous clues. Therefore, in this paper, we first collect and annotate a video dataset from DouYin (aka. TikTok in the world), namely DY24h, with hierarchical comments. Then, we also design a novel approach with comment-aided multi-modal heterogeneous pre-training (CMHP) to introduce comment modality in SVHD. Extensive experiments and analysis demonstrate that our CMHP beats several existing video-based approaches on DY24h, and that the comments modality further aids a better comprehension of humor. Our dataset, code and pre-trained models are available at https://github.com/yliu-cs/CMHP.
Conflict-Aware Active Automata Learning

Active automata learning algorithms cannot easily handle conflict in the observation data (different outputs observed for the same inputs).This inherent inability to recover after a conflict impairs their effective applicability in scenarios where noise is present or the system under learning is mutating.We propose the Conflict-Aware Active Automata Learning (C 3AL) framework to enable handling conflicting information during the learning process.The core idea is to consider the so-called observation tree as a first-class citizen in the learning process.Though this idea is explored in recent work, we take it to its full effect by enabling its use with any existing learner and minimizing the number of tests performed on the system under learning, specially in the face of conflicts.We evaluate C 3AL in a large set of benchmarks, covering over 30 different realistic targets, and over 18,000 different scenarios.The results of the evaluation show that C 3AL is a suitable alternative framework for closed-box learning that can better handle noise and mutations.
PRACTICAL SESSIONS OF FIRST-YEAR STUDENTS ON THE TERRITORY OF ST. SOPHIA CATHEDRAL

The article provides an analysis of the results of educational measurement and design work conducted within the territory of the National Conservation Area "St. Sophia of Kyiv" by first-year students of the Department of Fundamentals of Architecture and Architectural Design at Kyiv National University of Construction and Architecture. The structure of student work and the methodology of its implementation are described, along with collected samples of student projects and feedback on task execution. The obtained results aim to document the most successful approaches and tools in the educational process, with the goal of improving it in the future.
Power Load Forecasting Based on BP Neural Network Optimized by Genetic Algorithm

Power load forecasting has important research value for power grid coordination scheduling, power distribution load, regional production planning and so on. In order to improve the accuracy of power load forecasting, based on the BP neural network model, this paper optimizes the BP neural network parameters by genetic algorithm, organically combines their respective global search ability and local search ability, and updates and improves the weights and thresholds of the neural network. In the analysis of power load forecasting in a city, the prediction error MAPE value of the model is 2.0102 %, which is significantly higher than that of the single BP neural network model, and has reliable reliability and generalization ability.
An Event Early Warning Method Based on Knowledge Base and Semantic Fingerprint

The prevalence of the Internet and social networks has led to the low marginal cost of information replication, the widespread existence of duplicate content and the problem of “information disorientation”. Early detection of events helps relevant departments intervene and make decisions as soon as possible, thereby reducing the social harm cause by the aforementioned issues. Therefore, this paper proposes an event early warning method based on knowledge base and semantic fingerprint. Firstly, based on the idea of data mining, a classified event feature knowledge base is constructed. Next, using Simhash algorithm, a text deduplication method based on semantic fingerprints is proposed, which realizes the noise reduction and simplification of public opinion, and obtains the document feature value after deduplication. Finally, the document features are matched with the features in the knowledge base, realizing event classification and event early warning, facilitating relevant departments to take corresponding measures and reduce social impact.
Review Determinasi Sistem Informasi Manajemen: Database, Komputer dan Software

Management information systems have a close relationship with various components in the process of creating information systems that meet their needs. The proper implementation of these components is crucial because inaccurate, insufficiently detailed, untimely, and irrelevant information can lead to errors in the company's decision-making process. Therefore, a deep understanding of the components and management information systems, especially in terms of Databases, computers, and Software , is essential in order to generate valuable, effective, and high-quality information for the company. The purpose of this article is to develop hypotheses regarding the interplay between variables that can be used in further research. The findings of this study are as follows: 1) Databases have an influence on Management Information Systems; 2) Computers have an influence on Management Information Systems; and 3) Software has an influence on Management Information Systems.
Data Correlation behaviour on Privacy Leakage in Differential Privacy

Massive static or continuous data generated from day-to-day life activities has eventually led to increased analysis and statistical data publishing. Differential privacy has also seen a significant rise in attention as a privacy framework. Many of the existing privacy methods still use the traditional Differential privacy mechanism as a tool to publish private data to protect privacy. However, these privacy methods completely ignore the fact that there may exist some correlation between the data and assume that each data entity is independent, which is not the case majority of the time. Specifically, a temporal correlation may exist in data generated from the user’s continuous publication of data. If an adversary obtains such correlation information from other sources, then the adversary may infer users’ private data from the published data. Therefore, it is necessary to measure (or analyze) the privacy leakage of traditional differential privacy mechanisms, irrespective of whether the users’ data contains correlation over time as the data is published. And present a method under the protection of a differential privacy mechanism, which shows the maximum privacy leakage when the data involves correlation. Finally, we conduct an experiment to analyze the maximum privacy leakage, and the results show that user data’s privacy leakage can gradually increase over time as the data is published.
Analysis of the water-climatic balances by the Thornthwaite method in Dukan district

Water balances in any area are one of the main factors affecting water revenue, the natural environment, and vegetation cover. Water balance refers to the contribution of rainfall, temperature, evaporation, and impact on surface and groundwater in each region. In this research, an analytical and descriptive method was used to analyse the climate data used at Dukan climate station to determine the total amount of water between (2000-2022), which reached (1714.6 mm) For nine (9) months of the year, we have a shortage of water; only three (3) months have surpluses of water, which reached 130.1 mm. To prove whether the evaporation taken from Dukan climate station is the true evaporation of the water balance in the study area, we used the Thornthwaite equation to find out the true water balance based on temperature and annual rainfall for the same period. The results showed that the area had a water shortage of (1287.3 mm) for six (6) months and a surplus of (390.5 mm) for six (6) months. At the same time, the total evaporation, or evapotranspiration, reached (1461.9 mm). Based on the water balance equation, using scientific resources, and showing the relationship between precipitation and evaporation, the research concluded that the water balance varies from year to year depending on the amount of rainfall in the study area, but in general there is a water shortage in the study area during the year, and the highest water shortage in the region is in the summer months.
Agile Principle 6: “The Most Efficient and Effective Method of Conveying Information to and Within a Development Team Is Face-to-Face Conversation”

Delivering valuable software frequently is the name of the game. The most efficient and effective method for conveying information is that method—whether in person or virtual, synchronous or asynchronous—that maximizes your three most valuable resources: your time, your energy, and your attention. Efficient and effective communication matters because it is the sole means for causing and ensuring teams stay aligned on their objectives. Vignettes from leaders at Centene, UScellular, and Oracle illustrate the challenges of efficient and effective communication. The medium you choose for communicating with your team matters less than the content of your communication. With multiple mediums—face-to-face, video, instant message, email—at your disposal, you get to choose the medium best suited to your purpose. Recognize that with whichever medium you choose, you must be consistent in how you use that medium and what you use it for because with each message you send or dialogueDialogues you engage in, you’re setting an expectation with your team on how you will use the different mediums and for what purpose.
Exploring Credit Relationship Dynamics in an Interbank Market Benefiting from Blockchain-Based Distributed Trust: Insights from an Agent-Based Model

Trust is crucial in economic complex adaptive systems, where agents frequently change the other side of their interactions, which often leads to changes in the system’s structure. In such a system, agents who seek as much as possible to build lasting trust relationships for long-term confident interactions with their counterparts decide whom to interact with based on their level of trust in existing partners. A trust crisis refers to the time when the level of trust between agents drops so much that there is no incentive to interact, a situation that ultimately leads to the collapse of the system. This paper presents an agent-based model of the interbank market and evaluates the effects of using a voting-based consensus mechanism embedded in a blockchain-based loan system on maintaining trust between agents and system stability. In this paper, we rely on the fact that blockchain as a distributed system only manages the existing trust and does not create it on its own. Furthermore, this study uses actual blockchain technology in its simulation rather than simply presenting an abstraction.
Improving Facade Parsing with Vision Transformers and Line Integration

Facade parsing stands as a pivotal computer vision task with far-reaching applications in areas like architecture, urban planning, and energy efficiency. Despite the recent success of deep learning-based methods in yielding impressive results on certain open-source datasets, their viability for real-world applications remains uncertain. Real-world scenarios are considerably more intricate, demanding greater computational efficiency. Existing datasets often fall short in representing these settings, and previous methods frequently rely on extra models to enhance accuracy, which requires much computation cost. In this paper, we introduce Comprehensive Facade Parsing (CFP), a dataset meticulously designed to encompass the intricacies of real-world facade parsing tasks. Comprising a total of 602 high-resolution street-view images, this dataset captures a diverse array of challenging scenarios, including sloping angles and densely clustered buildings, with painstakingly curated annotations for each image. We introduce a new pipeline known as Revision-based Transformer Facade Parsing (RTFP). This marks the pioneering utilization of Vision Transformers (ViT) in facade parsing, and our experimental results definitively substantiate its merit. We also design Line Acquisition, Filtering, and Revision (LAFR), an efficient yet accurate revision algorithm that can improve the segment result solely from simple line detection using prior knowledge of the facade. In ECP 2011, RueMonge 2014, and our CFP, we evaluate the superiority of our method.
MedEdit: Model Editing for Medical Question Answering with External Knowledge Bases

Large Language Models (LLMs), although powerful in general domains, often perform poorly on domain-specific tasks like medical question answering (QA). Moreover, they tend to function as "black-boxes," making it challenging to modify their behavior. Addressing this, our study delves into model editing utilizing in-context learning, aiming to improve LLM responses without the need for fine-tuning or retraining. Specifically, we propose a comprehensive retrieval strategy to extract medical facts from an external knowledge base, and then we incorporate them into the query prompt for the LLM. Focusing on medical QA using the MedQA-SMILE dataset, we evaluate the impact of different retrieval models and the number of facts provided to the LLM. Notably, our edited Vicuna model exhibited an accuracy improvement from 44.46% to 48.54%. This work underscores the potential of model editing to enhance LLM performance, offering a practical approach to mitigate the challenges of black-box LLMs.
Handbook on Leveraging Lines for Two-View Relative Pose Estimation

We propose an approach for estimating the relative pose between calibrated image pairs by jointly exploiting points, lines, and their coincidences in a hybrid manner. We investigate all possible configurations where these data modalities can be used together and review the minimal solvers available in the literature. Our hybrid framework combines the advantages of all configurations, enabling robust and accurate estimation in challenging environments. In addition, we design a method for jointly estimating multiple vanishing point correspondences in two images, and a bundle adjustment that considers all relevant data modalities. Experiments on various indoor and outdoor datasets show that our approach outperforms point-based methods, improving AUC@10$^\circ$ by 1-7 points while running at comparable speeds. The source code of the solvers and hybrid framework will be made public.
AnyMAL: An Efficient and Scalable Any-Modality Augmented Language Model

We present Any-Modality Augmented Language Model (AnyMAL), a unified model that reasons over diverse input modality signals (i.e. text, image, video, audio, IMU motion sensor), and generates textual responses. AnyMAL inherits the powerful text-based reasoning abilities of the state-of-the-art LLMs including LLaMA-2 (70B), and converts modality-specific signals to the joint textual space through a pre-trained aligner module. To further strengthen the multimodal LLM's capabilities, we fine-tune the model with a multimodal instruction set manually collected to cover diverse topics and tasks beyond simple QAs. We conduct comprehensive empirical analysis comprising both human and automatic evaluations, and demonstrate state-of-the-art performance on various multimodal tasks.
E2Net: Resource-Efficient Continual Learning with Elastic Expansion Network

Continual Learning methods are designed to learn new tasks without erasing previous knowledge. However, Continual Learning often requires massive computational power and storage capacity for satisfactory performance. In this paper, we propose a resource-efficient continual learning method called the Elastic Expansion Network (E2Net). Leveraging core subnet distillation and precise replay sample selection, E2Net achieves superior average accuracy and diminished forgetting within the same computational and storage constraints, all while minimizing processing time. In E2Net, we propose Representative Network Distillation to identify the representative core subnet by assessing parameter quantity and output similarity with the working network, distilling analogous subnets within the working network to mitigate reliance on rehearsal buffers and facilitating knowledge transfer across previous tasks. To enhance storage resource utilization, we then propose Subnet Constraint Experience Replay to optimize rehearsal efficiency through a sample storage strategy based on the structures of representative networks. Extensive experiments conducted predominantly on cloud environments with diverse datasets and also spanning the edge environment demonstrate that E2Net consistently outperforms state-of-the-art methods. In addition, our method outperforms competitors in terms of both storage and computational requirements.
Geometrically Local Quantum and Classical Codes from Subdivision

A geometrically local quantum code is an error correcting code situated within $\mathbb{R}^D$, where the checks only act on qubits within a fixed spatial distance. The main question is: What is the optimal dimension and distance for a geometrically local code? This question was recently answered by Portnoy which constructed codes with optimal dimension and distance up to polylogs. This paper extends Portnoy's work by constructing a code which additionally has an optimal energy barrier up to polylogs. The key ingredient is a simpler code construction obtained by subdividing the balanced product codes. We also discuss applications to classical codes.
UVL: A Unified Framework for Video Tampering Localization

With the development of deep learning technology, various forgery methods emerge endlessly. Meanwhile, methods to detect these fake videos have also achieved excellent performance on some datasets. However, these methods suffer from poor generalization to unknown videos and are inefficient for new forgery methods. To address this challenging problem, we propose UVL, a novel unified video tampering localization framework for synthesizing forgeries. Specifically, UVL extracts common features of synthetic forgeries: boundary artifacts of synthetic edges, unnatural distribution of generated pixels, and noncorrelation between the forgery region and the original. These features are widely present in different types of synthetic forgeries and help improve generalization for detecting unknown videos. Extensive experiments on three types of synthetic forgery: video inpainting, video splicing and DeepFake show that the proposed UVL achieves state-of-the-art performance on various benchmarks and outperforms existing methods by a large margin on cross-dataset.
Random and Safe Cache Architecture to Defeat Cache Timing Attacks

Caches have been exploited to leak secret information due to the different times they take to handle memory accesses. Cache timing attacks include non-speculative cache side and covert channel attacks and cache-based speculative execution attacks. We first present a systematic view of the attack and defense space and show that no existing defense has addressed both speculative and non-speculative cache timing attack families, which we do in this paper. We propose Random and Safe (RaS) cache architectures to decorrelate the cache state changes from memory requests. RaS fills the cache with ``safe'' cache lines that are likely to be used in the future, rather than with demand-fetched, security-sensitive lines. RaS captures a group of safe addresses during runtime and fetches addresses randomly displaced from these addresses. Our proposed RaS architecture is flexible to allow security-performance trade-offs. We show different designs of RaS architectures that can defeat cache side-channel attacks and cache-based speculative execution attacks. The RaS variant against cache-based speculative execution attacks has 4.2% average performance overhead and other RaS variants against both attack families have 7.9% to 45.2% average overhead. For some benchmarks, RaS defenses improve the performance while providing security.
CoinRun: Solving Goal Misgeneralisation

Goal misgeneralisation is a key challenge in AI alignment -- the task of getting powerful Artificial Intelligences to align their goals with human intentions and human morality. In this paper, we show how the ACE (Algorithm for Concept Extrapolation) agent can solve one of the key standard challenges in goal misgeneralisation: the CoinRun challenge. It uses no new reward information in the new environment. This points to how autonomous agents could be trusted to act in human interests, even in novel and critical situations.