Reviewerly/exHarmony huggingface dataset:
Video anomaly detection via improved future frame prediction

Authors:
raw_author_name: Bo Li
raw_affiliation_string: Nanjing Univ. of Science and Technology (China)
raw_author_name: Zefeng YIN
raw_affiliation_string: Nanjing Univ. of Science and Technology (China)

Abstract: Video anomaly detection is extensively utilized across a variety of domains including public transportation, industrial production, city management, and military fields to mitigate risks and bolster enhance safety. To tackle the challenges associated with video anomaly detection in intricate environments, we propose a light but efficient framework that builds upon future frame prediction techniques. Our framework incorporates Convolutional Long Short-Term Memory (ConvLSTM), masked convolution, and attention mechanisms to enhance the detection accuracy. Furthermore, to simplify the model's complexity, we replace the convolutional layers in the network with depthwise separable convolutions (DSC). Through evaluation on public datasets such as CUHK Avenue, UCSD Peds1, and UCSD Peds2, our proposed network model exhibits both high accuracy and real-time performance.
Ultra-short-term forecast of distributed photovoltaic power based on satellite cloud image and LSTM model

Authors:
raw_author_name: Jinjin Ding
raw_affiliation_string: State Grid Anhui Electric Power Research Institute, 299 Ziyun Road, Hefei, 230601, China

Abstract: Abstract Photovoltaic (PV) power generation, with its volatile, intermittent, and random characteristics, and large-scale PV access pose a threat to grid stability. For this reason, predicting the photovoltaic output will help keep the grid safe and stable. On the basis of the influence of cloud groups on solar radiation, a very short-term forecast of distributed PV energy will be made using satellite cloud picture information to improve the forecast accuracy of PV energy production. The paper presents a method to predict distributed PV power at very short notice based on satellite clouds and a network model with Long Short-Term Memory (LSTM). First, extract a subset of meteorological and PV power data from the forecast area as training samples., and the abnormal part of the samples is cleaned by an isolated forest algorithm. Secondly, the occlusion feature is extracted from the satellite cloud image in the same period. Finally, the measured solar irradiance, meteorological information, and obscuration features are input into the LSTM network for prediction, and the photovoltaic power prediction results in the next 4 hours are obtained. The measured PV power of Jinghai Guangfu Power Station in Hefei, Anhui province on the 5th day was the training sample for the prediction of PV power on the 6th day. The prediction results show that the prediction error is 2.73% when a satellite cloud image is added, and 16.15% when a satellite cloud image is not added, and the prediction error is reduced by 13.42%.
The effect of PIQMAS application on the engagement processes of upper basic stage mathematics and science students

Authors:
raw_author_name: Wajeeh Daher
raw_affiliation_string: Al-Qasemi Academic College of Education, Baqa, ISRAEL; An-Najah National University, Nablus, PALESTINE

Abstract: New strategies, methods and projects for the learning of the mathematics and science disciplines are needed. One such project is the project for improving quality of mathematics and science (PIQMAS) education. In the present research, we consider the project implementation in Palestine. Specifically, we examine students’ engagement when implementing the project. To do that, we consider three components of engagement: the cognitive, the behavioral and the emotional. To collect the data, we used interviews and observations, which ensured the triangulation of data collection. We interviewed 13 science and mathematics teachers who carried out the project. We used deductive and inductive content analysis to analyze the interviews and observations transcripts. The research results indicated that the project impacted positively the three components of students’ engagement, which indicates the importance of incorporating new teaching methods, especially PIQMAS, in the teaching of science and mathematics. It is recommended to study different aspects of students’ learning in PIQMAS environment.
Modelling crypto markets by multi-agent reinforcement learning

Authors:
raw_author_name: Lussange, Johann
raw_author_name: Palminteri, Stefano
raw_author_name: Gutkin, Boris

Abstract: Building on a previous foundation work (Lussange et al. 2020), this study introduces a multi-agent reinforcement learning (MARL) model simulating crypto markets, which is calibrated to the Binance's daily closing prices of $153$ cryptocurrencies that were continuously traded between 2018 and 2022. Unlike previous agent-based models (ABM) or multi-agent systems (MAS) which relied on zero-intelligence agents or single autonomous agent methodologies, our approach relies on endowing agents with reinforcement learning (RL) techniques in order to model crypto markets. This integration is designed to emulate, with a bottom-up approach to complexity inference, both individual and collective agents, ensuring robustness in the recent volatile conditions of such markets and during the COVID-19 era. A key feature of our model also lies in the fact that its autonomous agents perform asset price valuation based on two sources of information: the market prices themselves, and the approximation of the crypto assets fundamental values beyond what those market prices are. Our MAS calibration against real market data allows for an accurate emulation of crypto markets microstructure and probing key market behaviors, in both the bearish and bullish regimes of that particular time period.
Detecting DeFi securities violations from token smart contract code

Authors:
raw_author_name: Arianna Trozze
raw_affiliation_string: Department of Computer Science, University College London, Gower Street WC1E 6EA, London, UK
raw_author_name: Bennett Kleinberg
raw_affiliation_string: Department of Security and Crime Science, University College London, 35 Tavistock Square, WC1H 9EZ, London, UK
raw_author_name: Toby Davies
raw_affiliation_string: Department of Security and Crime Science, University College London, 35 Tavistock Square, WC1H 9EZ, London, UK

Abstract: Abstract Decentralized Finance (DeFi) is a system of financial products and services built and delivered through smart contracts on various blockchains. In recent years, DeFi has gained popularity and market capitalization. However, it has also been connected to crime, particularly various types of securities violations. The lack of Know Your Customer requirements in DeFi poses challenges for governments trying to mitigate potential offenses. This study aims to determine whether this problem is suited to a machine learning approach, namely, whether we can identify DeFi projects potentially engaging in securities violations based on their tokens’ smart contract code. We adapted prior works on detecting specific types of securities violations across Ethereum by building classifiers based on features extracted from DeFi projects’ tokens’ smart contract code (specifically, opcode-based features). Our final model was a random forest model that achieved an 80% F-1 score against a baseline of 50%. Notably, we further explored the code-based features that are the most important to our model’s performance in more detail by analyzing tokens’ Solidity code and conducting cosine similarity analyses. We found that one element of the code that our opcode-based features can capture is the implementation of the SafeMath library, although this does not account for the entirety of our features. Another contribution of our study is a new dataset, comprising (a) a verified ground truth dataset for tokens involved in securities violations and (b) a set of legitimate tokens from a reputable DeFi aggregator. This paper further discusses the potential use of a model like ours by prosecutors in enforcement efforts and connects it to a wider legal context.
Interval reservoir computing: theory and case studies

Authors:
raw_author_name: Zhen-Hua Li
raw_affiliation_string: Key Laboratory of Technology on Intelligent Transportation Systems, Ministry of Transport, China; Research Institute of Highway, China
raw_author_name: Ling Xu
raw_affiliation_string: Key Laboratory of Technology on Intelligent Transportation Systems, Ministry of Transport, China; Research Institute of Highway, China

Abstract: The time series data in many applications, for example, wind power and vehicle trajectory, show significant uncertainty. Using a single prediction value of wind power as feedback information for wind turbine control or unit commitment is not enough since the uncertainty of the prediction is not described. This paper addresses the uncertainty issue in time series data forecasting by proposing the novel interval reservoir computing method. The proposed interval reservoir computing can capture the underlying evolution of the stochastic dynamical system for time series data using the recurrent neural network (RNN). On the other hand, by formulating a chance-constrained optimization problem, interval reservoir computing outputs a set of parameters in the RNN, which maps to an interval of prediction values. The capacity of the interval is the smallest one satisfying the condition that the probability of having a prediction inside the interval is lower than the required level. The scenario approach solves the formulated chance-constrained optimization problem. We implemented an experimental data-based validation to evaluate the proposed method. The validation results show that the proposed interval reservoir computing can give a tight interval of time series data forecasting values for wind power and traffic trajectory. In addition, the confidence probability over the feasibility goes to 1 very quickly as the sample number increases.
Joint optimization of operational cost and carbon emission in multiple data center micro-grids

Authors:
raw_author_name: Xiaobo Hao
raw_affiliation_string: Ningxia Hongdunzi Coal Ind, NingXia, China
raw_author_name: Pengcheng Liu
raw_affiliation_string: Ningxia Hongdunzi Coal Ind, NingXia, China
raw_author_name: Yanhui Deng
raw_affiliation_string: Ningxia Hongdunzi Coal Ind, NingXia, China

Abstract: As the internet data centers are mushrooming, the energy consumption and carbon emission of data centers are increasing rapidly. To cut down the electricity cost and carbon emission of the data centers, we proposed an optimization method to reduce the electricity cost and carbon emission in geo-distributed multiple data centers. In the proposed method, the carbon tax is considered in the overall operation cost to optimize the carbon emission. The spatial and temporal flexibility of computational workload is fully utilized by considering the difference in renewable energy power output, local electricity and carbon emission of multiple geo-distributed data centers to achieve a better performance. Furthermore, the nonlinear characteristics of the power loss of uninterruptible power supply (UPS) are considered in the optimization. To verify the proposed optimization method, simulation of six cases is carried out with realistic data, and results have proved the proposed method can reduce the operational costs by 4.93%–12.7% and decrease carbon emissions by up to 10%.
Tutt la tosse in una figura

Authors:
raw_author_name: Giorgio Longo
raw_affiliation_string: Clinica pediatrica, IRCCS Materno-Infantile “Burlo Garofolo”, Trieste

Abstract: Cough is the most common cause of unscheduled paediatric visits. The figure in the article shows the main causes of paediatric coughs differentiated by duration, quality (dry or wet) and night-time coughing. With these few simple key anamnestic elements (and espe- cially if confirmed and reinforced by being able to see, even in a simple video, the child coughing) a diagnosis may be made with reasonable certainty, saving specialist visits and diagnostic tests that are unlikely useful and potentially harmful.
The Effect of Role Conflict, Role Ambiguity and Ethical Sensitivity on Auditor Performance with Intellectual Intelligence as a Moderating Variable

Authors:
raw_author_name: Muhammad Hasan

Abstract: This study aims to analyze the effect of role conflict, role ambiguity, and ethical sensitivity on auditor performance and intellectual intelligence as a moderating variable on the relationship between these factors and auditor performance. Data were collected from 200 respondents who were auditors at the Makassar City BPKP Office using a questionnaire. The data analysis technique used is multiple regression with a moderated regression analysis (MRA) approach. The results showed that role conflict and role ambiguity have a significant negative effect on auditor performance, while ethical sensitivity has a significant positive effect on their performance. In addition, intellectual intelligence moderates the relationship between role conflict and auditor performance so that the higher the intellectual intelligence of an auditor, the negative impact of role conflict on his performance will be reduced. However, intellectual intelligence does not moderate the relationship between role ambiguity and performance.
Self-Guided Robust Graph Structure Refinement

Authors:
raw_author_name: In, Yeonjun
raw_author_name: Yoon, Kanghoon
raw_author_name: Kim, Kibum
raw_author_name: Shin, Kijung
raw_author_name: Park, Chanyoung

Abstract: Recent studies have revealed that GNNs are vulnerable to adversarial attacks. To defend against such attacks, robust graph structure refinement (GSR) methods aim at minimizing the effect of adversarial edges based on node features, graph structure, or external information. However, we have discovered that existing GSR methods are limited by narrowassumptions, such as assuming clean node features, moderate structural attacks, and the availability of external clean graphs, resulting in the restricted applicability in real-world scenarios. In this paper, we propose a self-guided GSR framework (SG-GSR), which utilizes a clean sub-graph found within the given attacked graph itself. Furthermore, we propose a novel graph augmentation and a group-training strategy to handle the two technical challenges in the clean sub-graph extraction: 1) loss of structural information, and 2) imbalanced node degree distribution. Extensive experiments demonstrate the effectiveness of SG-GSR under various scenarios including non-targeted attacks, targeted attacks, feature attacks, e-commerce fraud, and noisy node labels. Our code is available at https://github.com/yeonjun-in/torch-SG-GSR.
WildFake: A Large-scale Challenging Dataset for AI-Generated Images  Detection

Authors:
raw_author_name: Hong, Yan
raw_author_name: Zhang, Jianfu

Abstract: The extraordinary ability of generative models enabled the generation of images with such high quality that human beings cannot distinguish Artificial Intelligence (AI) generated images from real-life photographs. The development of generation techniques opened up new opportunities but concurrently introduced potential risks to privacy, authenticity, and security. Therefore, the task of detecting AI-generated imagery is of paramount importance to prevent illegal activities. To assess the generalizability and robustness of AI-generated image detection, we present a large-scale dataset, referred to as WildFake, comprising state-of-the-art generators, diverse object categories, and real-world applications. WildFake dataset has the following advantages: 1) Rich Content with Wild collection: WildFake collects fake images from the open-source community, enriching its diversity with a broad range of image classes and image styles. 2) Hierarchical structure: WildFake contains fake images synthesized by different types of generators from GANs, diffusion models, to other generative models. These key strengths enhance the generalization and robustness of detectors trained on WildFake, thereby demonstrating WildFake's considerable relevance and effectiveness for AI-generated detectors in real-world scenarios. Moreover, our extensive evaluation experiments are tailored to yield profound insights into the capabilities of different levels of generative models, a distinctive advantage afforded by WildFake's unique hierarchical structure.
ComFusion: Personalized Subject Generation in Multiple Specific Scenes  From Single Image

Authors:
raw_author_name: Hong, Yan
raw_author_name: Zhang, Jianfu

Abstract: Recent advancements in personalizing text-to-image (T2I) diffusion models have shown the capability to generate images based on personalized visual concepts using a limited number of user-provided examples. However, these models often struggle with maintaining high visual fidelity, particularly in manipulating scenes as defined by textual inputs. Addressing this, we introduce ComFusion, a novel approach that leverages pretrained models generating composition of a few user-provided subject images and predefined-text scenes, effectively fusing visual-subject instances with textual-specific scenes, resulting in the generation of high-fidelity instances within diverse scenes. ComFusion integrates a class-scene prior preservation regularization, which leverages composites the subject class and scene-specific knowledge from pretrained models to enhance generation fidelity. Additionally, ComFusion uses coarse generated images, ensuring they align effectively with both the instance image and scene texts. Consequently, ComFusion maintains a delicate balance between capturing the essence of the subject and maintaining scene fidelity.Extensive evaluations of ComFusion against various baselines in T2I personalization have demonstrated its qualitative and quantitative superiority.
Application of BiLSTM-CRF model with different embeddings for product name extraction in unstructured Turkish text

Authors:
raw_author_name: Serdar Arslan

Abstract: Abstract Named entity recognition (NER) plays a pivotal role in Natural Language Processing by identifying and classifying entities within textual data. While NER methodologies have seen significant advancements, driven by pretrained word embeddings and deep neural networks, the majority of these studies have focused on text with well-defined grammar and structure. A significant research gap exists concerning NER in informal or unstructured text, where traditional grammar rules and sentence structure are absent. This research addresses this crucial gap by focusing on the detection of product names within unstructured Turkish text. To accomplish this, we propose a deep learning-based NER model which combines a Bidirectional Long Short-Term Memory (BiLSTM) architecture with a Conditional Random Field (CRF) layer, further enhanced by FastText embeddings. To comprehensively evaluate and compare our model’s performance, we explore different embedding approaches, including Word2Vec and Glove, in conjunction with the Bidirectional Long Short-Term Memory and Conditional Random Field (BiLSTM-CRF) model. Furthermore, we conduct comparisons against BERT to assess the efficacy of our approach. Our experimentation utilizes a Turkish e-commerce dataset gathered from the internet, where traditional grammatical and structural rules may not apply. The BiLSTM-CRF model with FastText embeddings achieved an F1 score value of 57.40%, a precision value of 55.78%, and a recall value of 59.12%. These results indicate promising performance in outperforming other baseline techniques. This research contributes to the field of NER by addressing the unique challenges posed by unstructured Turkish text and opens avenues for improved entity recognition in informal language settings, with potential applications across various domains.
Question Answering Over Spatio-Temporal Knowledge Graph

Authors:
raw_author_name: Dai, Xinbang
raw_author_name: Li, Huiying
raw_author_name: Qi, Guilin

Abstract: Spatio-temporal knowledge graphs (STKGs) extend the concept of knowledge graphs (KGs) by incorporating time and location information. While the research community's focus on Knowledge Graph Question Answering (KGQA), the field of answering questions incorporating both spatio-temporal information based on STKGs remains largely unexplored. Furthermore, a lack of comprehensive datasets also has hindered progress in this area. To address this issue, we present STQAD, a dataset comprising 10,000 natural language questions for spatio-temporal knowledge graph question answering (STKGQA). Unfortunately, various state-of-the-art KGQA approaches fall far short of achieving satisfactory performance on our dataset. In response, we propose STCQA, a new spatio-temporal KGQA approach that utilizes a novel STKG embedding method named STComplEx. By extracting temporal and spatial information from a question, our QA model can better comprehend the question and retrieve accurate answers from the STKG. Through extensive experiments, we demonstrate the quality of our dataset and the effectiveness of our STKGQA method.
Training Language Model Agents without Modifying Language Models

Authors:
raw_author_name: Zhang, Shaokun
raw_author_name: Zhang, Jieyu
raw_author_name: Liu, Jiale
raw_author_name: Song, Linxin
raw_author_name: Krishna, Ranjay

Abstract: Researchers and practitioners have recently reframed powerful Large Language Models (LLMs) as agents, enabling them to automate complex tasks largely via the use of specialized functions. To facilitate the development of LLM agents, we present a novel paradigm of training LLM agents without modifying the LLM weights, which is particularly useful when the LLMs are difficult or inaccessible for modifications. Inspired by how humans continuously forge tools to adapt to real-world tasks, rather than change our biological structure to fit a static set of tools, we propose to progressively forge agent's functions to better solve the downstream tasks instead of modifying the LLM weights. By treating the functions as learnable `agent parameters' and leveraging the fundamental idea of model training in artificial intelligence, we develop AgentOptimizer that employs the LLM to update agents' functions and devise an agent training algorithm with two strategies, roll-back, and early-stop, to streamline the training process. With extensive experiments, we showcase that the agent training paradigm could significantly improve the performance of representative LLM agents in various downstream tasks. We also study the behavior of the agent training regarding aspects like the learning curve and domain transferability.
I Learn Better If You Speak My Language: Enhancing Large Language Model  Fine-Tuning with Style-Aligned Response Adjustments

Authors:
raw_author_name: Ren, Xuan
raw_author_name: Wu, Biao
raw_author_name: Liu, Lingqiao

Abstract: Fine-tuning large language models (LLMs) with a small data set for particular tasks is a widely encountered yet complex challenge. The potential for overfitting on a limited number of examples can negatively impact the model's ability to generalize and retain its original skills. Our research explores the impact of the style of ground-truth responses during the fine-tuning process. We found that matching the ground-truth response style with the LLM's inherent style results in better learning outcomes. Building on this insight, we developed a method that minimally alters the LLM's pre-existing responses to correct errors, using these adjusted responses as training targets. This technique enables precise corrections in line with the model's native response style, safeguarding the model's core capabilities and thus avoid overfitting. Our findings show that this approach not only improves the LLM's task-specific accuracy but also crucially maintains its original competencies and effectiveness.