Reviewerly/exHarmony huggingface dataset:
Tracking Different Ant Species: An Unsupervised Domain Adaptation Framework and a Dataset for Multi-object Tracking

Tracking individuals is a vital part of many experiments conducted to understand collective behaviour. Ants are the paradigmatic model system for such experiments but their lack of individually distinguishing visual features and their high colony densities make it extremely difficult to perform reliable racking automatically. Additionally, the wide diversity of their species' appearances makes a generalized approach even harder. In this paper, we propose a data-driven multi-object tracker that, for the first time, employs domain adaptation to achieve the required generalisation. This approach is built upon a joint-detection-and-tracking framework that is extended by a set of domain discriminator modules integrating an adversarial training strategy in addition to the tracking loss. In addition to this novel domain-adaptive tracking framework, we present a new dataset and a benchmark for the ant tracking problem. The dataset contains 57 video sequences with full trajectory annotation, including 30k frames captured from two different ant species moving on different background patterns. It comprises 33 and 24 sequences for source and target domains, respectively. We compare our proposed framework against other domain-adaptive and non-domain-adaptive multi-object tracking baselines using this dataset and show that incorporating domain adaptation at multiple levels of the tracking pipeline yields significant improvements. The code and the dataset are available at https://github.com/chamathabeysinghe/da-tracker.
DEIR: Efficient and Robust Exploration through Discriminative-Model-Based Episodic Intrinsic Rewards

Exploration is a fundamental aspect of reinforcement learning (RL), and its effectiveness is a deciding factor in the performance of RL algorithms, especially when facing sparse extrinsic rewards. Recent studies have shown the effectiveness of encouraging exploration with intrinsic rewards estimated from novelties in observations. However, there is a gap between the novelty of an observation and an exploration, as both the stochasticity in the environment and the agent's behavior may affect the observation. To evaluate exploratory behaviors accurately, we propose DEIR, a novel method in which we theoretically derive an intrinsic reward with a conditional mutual information term that principally scales with the novelty contributed by agent explorations, and then implement the reward with a discriminative forward model. Extensive experiments on both standard and advanced exploration tasks in MiniGrid show that DEIR quickly learns a better policy than the baselines. Our evaluations on ProcGen demonstrate both the generalization capability and the general applicability of our intrinsic reward.
Actor-Multi-Scale Context Bidirectional Higher Order Interactive Relation Network for Spatial-Temporal Action Localization

The key to video action detection lies in the understanding of interaction between persons and background objects in a video. Current methods usually employ object detectors to extract objects directly or use grid features to represent objects in the environment, which underestimate the great potential of multi-scale context information (e.g., objects and scenes of different sizes). How to exactly represent the multi-scale context and make full utilization of it still remains an unresolved challenge for spatial-temporal action localization. In this paper, we propose a novel Actor-Multi-Scale Context Bidirectional Higher Order Interactive Relation Network (AMCRNet) that extracts multi-scale context through multiple pooling layers with different sizes. Specifically, we develop an Interactive Relation Extraction module to model the higher-order relation between the target person and the context (e.g., other persons and objects). Along this line, we further propose a History Feature Bank and Interaction method to achieve better performance by modeling such relation across continuing video clips. Extensive experimental results on AVA2.2 and UCF101-24 demonstrate the superiority and rationality of our proposed AMCRNet.
Learning 3D Photography Videos via Self-supervised Diffusion on Single Images

3D photography renders a static image into a video with appealing 3D visual effects. Existing approaches typically first conduct monocular depth estimation, then render the input frame to subsequent frames with various viewpoints, and finally use an inpainting model to fill those missing/occluded regions. The inpainting model plays a crucial role in rendering quality, but it is normally trained on out-of-domain data. To reduce the training and inference gap, we propose a novel self-supervised diffusion model as the inpainting module. Given a single input image, we automatically construct a training pair of the masked occluded image and the ground-truth image with random cycle rendering. The constructed training samples are closely aligned to the testing instances, without the need for data annotation. To make full use of the masked images, we designed a Masked Enhanced Block (MEB), which can be easily plugged into the UNet and enhance the semantic conditions. Towards real-world animation, we present a novel task: out-animation, which extends the space and time of input objects. Extensive experiments on real datasets show that our method achieves competitive results with existing SOTA methods.
Adaptive Path-Memory Network for Temporal Knowledge Graph Reasoning

Temporal knowledge graph (TKG) reasoning aims to predict the future missing facts based on historical information and has gained increasing research interest recently. Lots of works have been made to model the historical structural and temporal characteristics for the reasoning task. Most existing works model the graph structure mainly depending on entity representation. However, the magnitude of TKG entities in real-world scenarios is considerable, and an increasing number of new entities will arise as time goes on. Therefore, we propose a novel architecture modeling with relation feature of TKG, namely aDAptivE path-MemOry Network (DaeMon), which adaptively models the temporal path information between query subject and each object candidate across history time. It models the historical information without depending on entity representation. Specifically, DaeMon uses path memory to record the temporal path information derived from path aggregation unit across timeline considering the memory passing strategy between adjacent timestamps. Extensive experiments conducted on four real-world TKG datasets demonstrate that our proposed model obtains substantial performance improvement and outperforms the state-of-the-art up to 4.8% absolute in MRR.
Don't Ignore Alienation and Marginalization: Correlating Fraud Detection

The anonymity of online networks makes tackling fraud increasingly costly. Thanks to the superiority of graph representation learning, graph-based fraud detection has made significant progress in recent years. However, upgrading fraudulent strategies produces more advanced and difficult scams. One common strategy is synergistic camouflage —— combining multiple means to deceive others. Existing methods mostly investigate the differences between relations on individual frauds, that neglect the correlation among multi-relation fraudulent behaviors. In this paper, we design several statistics to validate the existence of synergistic camouflage of fraudsters by exploring the correlation among multi-relation interactions. From the perspective of multi-relation, we find two distinctive features of fraudulent behaviors, i.e., alienation and marginalization. Based on the finding, we propose COFRAUD, a correlation-aware fraud detection model, which innovatively incorporates synergistic camouflage into fraud detection. It captures the correlation among multi-relation fraudulent behaviors. Experimental results on two public datasets demonstrate that COFRAUD achieves significant improvements over state-of-the-art methods.
ROBOTICS AND BLOCK PROGRAMMING IN INFORMATION TECHNOLOGY LEARNING AT THE HIGH SCHOOL LEVEL

According to the latest changes, according to the Law on Preschool and School Education in Bulgaria, programming training at the high school level is implemented only as a profiled or elective school subject. This creates problems in building key digital competencies related to programming, according to the European Union’s DigComp strategic framework. Since information technology is studied at all levels of education and participates as a profile subject in many professions and profile classes as compulsory or elective modules, this discipline can be used to fill these gaps. The article examines an approach for applying block programming and robotics in elective modules in profiled IT training at the second-high school level. Solving specific practical tasks by programming the behaviour and activity of robotic devices increases the activity and motivation of students and allows them to build the key competencies necessary for their successful future professional realization as EU citizens.
The Proof and Decryption of Goldbach Conjecture

In this paper, a few mathematical bases are given firstly. Then, the step thinness τp of primes is given as τp = Lo1 = P1ΠP/(P−1) for the inner character, having an lnX logarithmic outer character. The “best estimation and mutual exchange equivalent” are easily obtained as Lo1 = P1Π P/(P−1)~τp~lnX(ε). This is the principal contradiction, and P is the main aspect. Then, the sparsity τt of twin primes is defined as Lo2 = τt = P2 Π P/(P−2) = C2Lo1² = C2ln²X(C2 = 0.75739006). Then, the sparsity τg of the Goldbach pair and τb of both the twin and Goldbach pairs are obtained as Lo3 = τg = {2Lo1, Lo1², 2C2Lo1²} and Lo4 = τb =4.7Lo1³ (or they are omitted). Lastly, all conjectures can be proved with the same frame formula, N = X/LOK. The twin prime conjecture and Goldbach conjecture low bound are clearly and accurately proved with T = X/(C2Lo1²) = 1.32032X/ln²X and Gd = X/(2C2Lo1²) = 0.66016X/ln²X. Using Lo1 + C2 to decrypt the Selberg formula C(ω) = 2C(N) obtains the totally same results.
Eksplorasi Penggunaan ChatGPT dalam Penulisan Artikel Pendidikan Matematika

This study aims to explore the potential of ChatGPT, an artificial intelligence platform developed by OpenAI, in assisting the process of writing research articles on mathematics education. This research is a qualitative descriptive study that collects a sample of data which is then checked using Turnitin to determine the plagiarism level of the contents of articles created by ChatGPT. Nonetheless, this study has limitations in the limited number of samples due to time and resource constraints. The purpose of this research is to find out how ChatGPT can help in the process of making math education research articles and whether it can improve the quality of the research articles that are made. The results of this research are expected to contribute to the latest studies on ChatGPT's ability to assist in the process of writing math education research articles, as well as provide input for teachers, lecturers, and other researchers in utilizing ChatGPT as a tool in the process of writing research articles
California Digital Library invests in OpenCitations

OpenCitations is excited to announce that the California Digital Library (CDL) has joined our growing list of contributors. CDL’s commitment to sustainable open scholarship has great value for the global scholarly community.
Provably Secure Blockchain Protocols from Distributed Proof-of-Deep-Learning

Proof-of-useful-work (PoUW), an alternative to the widely used proof-of-work (PoW), aims to re-purpose the network’s computing power. Namely, users evaluate meaningful computational problems, e.g., solving optimization problems, instead of computing numerous hash function values as in PoW. A recent approach utilizes the training process of deep learning as “useful work”. However, these works lack security analysis when deploying them with blockchain-based protocols, let alone the informal and over-complicated system design. This work proposes a distributed proof-of-deep-learning (D-PoDL) scheme concerning PoUW’s requirements. With a novel hash-traininßg-hash structure and model-referencing mechanism, our scheme is the first deep learning-based PoUW scheme that enables achieving better accuracy distributively. Next, we introduce a transformation from the D-PoDL scheme to a generic D-PoDL blockchain protocol which can be instantiated with two chain selection rules, i.e., the longest-chain rule and the weight-based blockchain framework (LatinCrypt’ 21). This work is the first to provide formal proofs for deep learning-involved blockchain protocols concerning the robust ledger properties, i.e., chain growth, chain quality, and common prefix. Finally, we implement the D-PoDL scheme to discuss the effectiveness of our design.
Brazilian Intermunicipal Water and Sewage Consortia Management: Harnessing the Power of Artificial Intelligence

This article proposes the implementation of Artificial Intelligence (AI) algorithms in the management of Brazilian intermunicipal water and sewage service consortia, utilizing coordinated and strategic programming in Python. The primary significance of this approach lies in its ability to enhance decision-making processes and offer well-informed and precise guidelines for intermunicipal management. Within this context, the theoretical foundations of intermunicipal consortia and AI are thoroughly explored, providing the basis for this qualitative study with both descriptive and propositional features. The study's results demonstrate that the utilization of AI in managing intermunicipal consortia presents a promising and innovative approach. It exhibits potential in optimizing processes, improving operational efficiency, reducing costs, and enhancing the quality of services provided to the population in areas such as data consumption analysis, leak monitoring, water quality control, and demand forecasting. For future research, it is recommended to investigate the economic feasibility, assess social and environmental impacts, and consider the ethical and legal aspects associated with the use of AI techniques. Additionally, the integration of emerging technologies such as cloud computing, Internet of Things (IoT), and blockchain should be explored to complement AI approaches.
Development of object identification model with deep reinforcement learning algorithm

This research work presents an object identification method based on the machine learning technique based on human vision system. The objective is to prevent processing a complete image in sort to locate objects. Presently, the-state-of-the-art techniques divide an image into sub-regions and search for an object in all the subparts. This is ineffective for applications like embedded systems where the computation power is restricted or the resolution of the images are high. To address this issue, an object identification task was formulated as a decision-making problem. Followed the concept of DRL proposed, accepted RL algorithm DQL was applied to learn a policy from input data, i.e. images, to identify objects in a scene. In this manner, with the policy learned, a set of actions that transforms a box was apply in order to make tighter a bounding box around the target object.
Hubungan efikasi diri dengan self-regulated learning siswa SMA dalam pembelajaran daring

In the field of education during the pandemic, students must do online learning. The online learning process raises several problems in the form of low motivation, decreased academic achievement and grades, and less than optimal student activity during online learning to affect the less than optimal self-efficacy produced. Self-efficacy is a factor that can affect self-regulated learning in online learning. Students who have high self-efficacy, will increase student success in learning. However, if self-efficacy is low, students feel pressured in completing tasks that make it difficult to carry out learning strategies, which has an impact on self-efficacy. The purpose of this study was to determine the relationship between self-efficacy and self-regulated learning students in this online learning. The sample of this study amounted to 102 samples using accidental sampling techniques. The instruments used are the Self-Efficacy Scale and the Self-Regulated Learning Scale. The analysis technique used is the pearson product moment correlation test. The results showed that there was a significant positive relationship between self-efficacy and self-regulated learning of r = 0.675 (p &lt; 0.05) where the higher the self-efficacy, the higher the self-regulated learning. Keywords: High school students, online, self-efficacy, self-regulated learning
Swiss Institutions pledge 89,250 Euros to OpenCitations

We want to express our gratitude to the 18 institutional members and customers of the Consortium of Swiss Academic Libraries which have now pledged 89,250 euros to support OpenCitations over the next three years. This generous donation is part of a total funding of 320,250 euros destined for the three services currently being promoted by SCOSS: DOAB and OAPEN, PKP, and OpenCitations.
OpenCitations’ compliance with the Principles of Open Scholarly Infrastructure

What should an open scholarly infrastructure look like? An answer to this tough question can be found in the original February 2015 blog post by Geoffrey Bilder, Jennifer Lin and Cameron Neylon Bilder G., Lin J., Neylon C. (2015) Principles for Open Scholarly Infrastructure , http://dx.doi.org/10.6084/m9.figshare.1314859 and in the summary of the principles to be found as: Bilder G, Lin J, Neylon C (2020),&lt;em&gt; The Principles of Open Scholarly Infrastructure&lt;/em&gt;, https://doi.org/10.24343/C34W2H : “&lt;em&gt;Infrastructure at its best is invisible.&lt;/em&gt;
The OpenCitations team is growing – announcing three new appointments!

This is a summer of great news for OpenCitations. Thanks to the generous support received from the scholarly community during the first year of SCOSS adoption, we’re happy to announce the appointment of three new colleagues to work for OpenCitations at the Research Centre for Open Scholarly Metadata (University of Bologna). Here’s some information about their backgrounds, positions and roles at OpenCitations.
Artificial Intelligence Powered Chatbot for Business

Text has become an essential interaction manner between people. The use of chatbots improved quickly in business area including marketing, customer service and e-commerce. Users value chatbots because they are fast, intuitive and convenient. This paper discussed about the artificial intelligence technology that used to develop and implement chatbots which can the organization used to benefit in their businesses. A chatbot is a computer program that can interact with a human by using natural language. The main three areas in business that using chatbot the most are marketing, customer service and e-commerce fields. The roles of chatbot in mentioned areas has been discussed in this paper. AI powered chatbots transform business by reducing costs, increasing revenue and enhancing the customer experience. The benefits and limitation of chatbots have been also discussed in this paper.
An efficient long-text semantic retrieval approach via utilizing presentation learning on short-text

Abstract Although the short-text retrieval model by BERT achieves significant performance improvement, research on the efficiency and performance of long-text retrieval still faces challenges. Therefore, this study proposes an efficient long-text retrieval model based on BERT (called LTR-BERT). This model achieves speed improvement while retaining most of the long-text retrieval performance. In particular, The LTR-BERT model is trained by using the relevance between short texts. Then, the long text is segmented and stored off-line. In the retrieval stage, only the coding of the query and the matching scores are calculated, which speeds up the retrieval. Moreover, a query expansion strategy is designed to enhance the representation of the original query and reserve the encoding region for the query. It is beneficial for learning missing information in the representation stage. The interaction mechanism without training parameters takes into account the local semantic details and the whole relevance to ensure the accuracy of retrieval and further shorten the response time. Experiments are carried out on MS MARCO Document Ranking dataset, which is specially designed for long-text retrieval. Compared with the interaction-focused semantic matching method by BERT-CLS, the MRR@10 values of the proposed LTR-BERT method are increased by 2.74%. Moreover, the number of documents processed per millisecond increased by 333 times.
Purification-enabled Routing with Guaranteed Fidelity in Entanglement Distribution Networks

A purification-enabled routing algorithm was proposed to support remote entangled pairs sharing with limited quantum resources, upon satisfying the fidelity threshold for generated remote entanglements, and the proposed algorithm is further verified via simulation.
Towards AI-Native Vehicular Communications

The role of fast yet reliable wireless communications in various application domains is getting ever more important. At the same time, as use cases are becoming more and more complex, application requirements are getting ever more stringent. One example is intelligent transportation, where the efficiency and reliability of wireless data delivery is essential for effective service support. As a consequence, in this context the adoption of AI techniques is widely considered crucial for enabling vehicular communications to adapt to dynamic changes of the environment. In this position paper, we discuss some representative applications of advanced AI tools in vehicular communications. In particular, we elaborate on the potential of distributed learning based on federated learning, of proactive service provisioning, and of graph neural network for enabling AI-native vehicular communications.
Adaptive Quantum Key Distribution for Ultra-Long-Distance Secure Services Based on Satellite Networks

Targeting the secret-key provision mismatch with the security requirements of ultra-long-distance services, this paper designs an adaptive quantum key distribution scheme based on satellite networks, improving more than 23% of service success probability compared to the benchmark.
Implementation of a High Available Database on Virtualizing Cloud Platform

Cloud infrastructure is the core focus of all enterprises today, and databases are an indispensable service for companies today. The use of cloud technology can get rid of the limitations of traditional construction and reduce the problem of high investment costs in the construction environment, due to the various advantages of cloudification, our society's demand for cloud resources is continuing to rise, these changes also illustrate the trend of cloudification, effective and secure storage of a company's large number of database services are also an indispensable part, and it is important to establish a high-availability mechanism to prevent database corruption. This paper builds a highly available service platform, which includes a capacity transfer mechanism and a real-time monitoring system for Web server services, two physical machines for accessing data, and other service systems are built in virtual machines, and the actual operation uses PostgreSQL and Microsoft SQL Server databases, they are installed in this system. Through the WAL (Write-Ahead-Logging) mechanism of the distributed storage system (DRBD) for remote backup and reuse of the database, recover the data lost by the database system after the power outage, and then verify whether the system automatically resumes the service in a short time when it faces a sudden power outage in the service.