Reviewerly/exHarmony huggingface dataset:
QPDet: A Queuing People Detector for Aerial Images

Object detection in aerial images is a fundamental and active research topic with astonishing achievement in recent years. However, tiny object detection in aerial images remains a challenging problem due to the small sizes and limited features of the objects. In this paper, we propose QPDet, a queuing people detector for aerial images. Firstly, we create a large scale dataset of queuing people (named QP) using UAV. Secondly, a new label assigner is proposed based on normally distributed receptive field. Thirdly, a random sub-image segmentation and augmentation strategy is developed to facilitate the training of different queuing patterns. Our method is simple yet effective. Extensive experiments have been conducted to verify the effectiveness of our model, where we outperform most of the state-of-the-art methods in accuracy.
Exploring Planner-Guided Swarms Running on Real Robots

Robot swarms have been proposed as a way to take advantage of the scalability, robustness, and adaptability of natural large-scale multiagent systems in order to solve engineering challenges. However, accomplishing complex tasks while remaining flexible and decentralized has proven elusive. Our prior work on planner-guided robot swarms successfully combined a distributed swarm algorithm implementing low-level behaviors with automated parallel planners and executives selecting high-level actions for the swarm to perform as a whole, but had only been tested in simplistic grid-world simulations. Here we demonstrate our approach on physical robots augmented with experiments in continuous-space simulation, showing that it is an effective and efficient mechanism for achieving difficult task objectives to which swarms are rarely applied.
Semantic Annotation in Collaborative Document Transcription: A Gamified Citizen Science Approach

Human or manual transcription is the task where a person reads a handwritten document and types in a digital environment the text she is reading. The manual transcription task is a long and time-consuming process for one person. However, the intelligence of the transcriber is provided with better results than automatic alternatives. This article introduces a collaborative transcription platform called Transcriptor, in which the community members can upload digitized manuscripts and collaborate in transcribing them, defining different transcription layers which will be represented using semantic web technologies and gamification techniques. The article provides two experimentation that shows that Transcriptor had a good acceptance and novelty.
Impact of Using a Privacy Model on Smart Buildings Data for CO$$_2$$ Prediction

There is a constant trade-off between the utility of the data collected and processed by the many systems forming the Internet of Things (IoT) revolution and the privacy concerns of the users living in the spaces hosting these sensors. Privacy models, such as the SITA (Spatial, Identity, Temporal, and Activity) model, can help address this trade-off. In this paper, we focus on the problem of $$CO_2$$ prediction, which is crucial for health monitoring but can be used to monitor occupancy, which might reveal some private information. We apply a number of transformations on a real dataset from a Smart Building to simulate different SITA configurations on the collected data and transformed that data with multiple Machine Learning (ML) techniques to analyse the performance of the models to predict $$CO_{2}$$ levels. Our results show that different SITA configurations do not make one algorithm perform better or worse than others; also, the temporal dimension was particularly sensitive, with scores decreasing up to $$18.9\%$$ between the original and the transformed data. This shows the effect of different levels of data privacy on the data utility of IoT applications, and can also help to identify which parameters are more relevant for those systems so that higher privacy settings can be set while data utility is still preserved.
Bimodal Style Transference from Musical Composition to Image Using Deep Generative Models

Deep generative models have caused quite a stir due to their excellent performance in generating original images from different realms of the real world. An example of the application of these models is style transfer, where the style of one object is transferred to the content of another. In this study, an innovative proposal is made for transferring the multimodal style of songs to album covers, which consists of a pipeline structured in three parts. First, it is proposed to train a multimodal latent space from a triplet network model that receives a dataset of cover images and songs represented as spectrograms, around 18 genres. Then, with this latent space, the knn algorithm is computed, and the closest cover art to a query song is obtained. Finally, fine-tuning is performed on a pretrained Spectral Normalized GAN model on ImageNet, training only the batch parameters to avoid overfitting. And later, the original cover art is sampled. This way, the pipeline is executed for songs of 10 different genres, obtaining covers of similar genres in the 100 closest neighbors and obtaining images with an average Frechet Inception Distance of 20.89.
Probabilistic Fingerprinting Scheme for Correlated Data

In order to receive personalized services, individuals share their personal data with a wide range of service providers, hoping that their data will remain confidential. Thus, in case of an unauthorized distribution of their personal data by these service providers, data owners want to identify the source of such data leakage. We show that applying existing fingerprinting schemes to personal data sharing is vulnerable to the attacks utilizing the correlations in the data. To provide liability for unauthorized sharing of personal data, we propose a probabilistic fingerprinting scheme that efficiently generates the fingerprint by considering a fingerprinting probability (to keep the data utility high) and publicly known inherent correlations between data points. To improve the robustness of the proposed scheme against colluding malicious service providers, we also utilize the Boneh-Shaw fingerprinting codes as a part of the proposed scheme. We implement and evaluate the performance of the proposed scheme on real genomic data. Our experimental results show the efficiency and robustness of the proposed scheme.
An Analysis of Hybrid Consensus in Blockchain Protocols for Correctness and Progress

One of the key ideas for enhancing the scalability of blockchain lies in having consensus among a smaller set of nodes rather than the set of all nodes be it PoW (Proof of Work) or PoS (Proof of Stake). Such a transformation calls for analysis of trust in the transformed consensus, forking, progress, fairness, etc. Thus, one major requirement is to ensure the correct functioning of PoW or PoS under the BFT (Byzantine Fault Tolerance) of the network remains invariant in spite of the transformation. Note that the conditions for scalability of PoW and BFT are somewhat contradictory, in the sense that PoW is good for a large network with very low throughput and BFT is good for a small network with high throughput. For scalability, we need high node scalability as in permissionless blockchain, and high transaction throughput as in permissioned blockchain. In this paper, we analyse different consensus mechanisms used in blockchain platforms like Ripple, Algorand, Red Belly, etc, for the correctness and also conditions required for overcoming forking or avoiding not-making progress. Our analysis is based on the widely used Rand Index used similarity measurement of data clusters. Our results show that the scalability of blockchain platforms requires a subtle assessment of correctness, forking, not making progress, or unfairness issues and cannot be just based on the experimental evaluation.
Participatory Design with Migrants in Language Learning Contexts in Brazil: A Systematic Literature Review

The increasing international migration in Brazil poses a series of challenges regarding the resettlement process of migrant people, one of which is learning the local language. This investigation concerns the role technology design might play in this context. We place a focus on how Participatory Design (PD) methods may serve this purpose, as this approach aims towards the development of technological solutions to a given issue by the individuals it affects, through democratic participation. We conducted a systematic literature review in order to examine the handling of PD projects with migrant people during 2018 and 2022. Results were found across several domains of knowledge, including that of language learning, and demonstrated how this methodological approach may create openness in technology design to account for linguistic and cultural diversity. In our analysis, opportunities and gaps were also found regarding effective participation and the handling of power imbalances, to which we discuss how theories on Science, Technology and Society studies may bring insightful solutions.
Bimodal Neural Style Transfer for Image Generation Based on Text Prompts

Neural networks have become one of the essential areas in Artificial Intelligence due to their extraordinary capacity to address problems in different domains. This ability led to the proposal of novel architectures and models to tackle challenging tasks such as neural style transfer. We propose a novel methodology for bimodal style transfer using text as input. We initially retrieve one image and a short descriptive text, which are mapped into a multimodal common latent space. Then, a new image is retrieved using an image retrieval engine. Finally, we use a generative model, which allows us to create artistic images by combining content and style. The proposed system can retrieve semantically similar images concerning a descriptive text (prompt), achieving great precision rates in image retrieval applied to the SemArt dataset. The transfer style neural model also preserves the image’s high quality, combining style and content.
Adaptive Consumption by Continuous Negotiation

In this paper, we study the problem of allocating concurrent jobs composed of situated tasks, underlying the distributed deployment of the MapReduce design pattern on a cluster. In order to implement our multi-agent strategy which aims at minimising the mean flowtime of jobs, we propose a modular agent architecture that allows the concurrency of negotiation and consumption. Our experiments show that our reallocation strategy, when executed continuously during the consumption process: (1) improves the flowtime; (2) does not penalise the consumption; (3) is robust against execution hazards.
Ontology-Based Mediation with Quality Criteria

Information integration has a long history since humans started using and collecting information. But, it has been a strong focus of IT research since many recent years. It deals with providing a unified and transparent access to a collection of heterogeneous data sources. In information integration, the formulization of a global schema is a difficult task that manages multiple, autonomous and heterogeneous data sources. This paper presents a semantic system named OntMed for an ontology-based data integration of heterogeneous data sources to achieve interoperability between them. Our system is based on the quality criteria (consistency, completeness and conciseness) for building the reliable analysis contexts to provide an accurate unified view of data to the end user. The generation of an error-free global analysis context with the semantic validation of initial mappings generates accuracy, and provides the means to access and exchange information in semantically sound manner. In addition, data integration in this way becomes more practical for dynamic situations and helps decision makers to work within a more consistent and reliable virtual data warehouse.
AMOE: A Tool to Automatically Extract and Assess Organizational Evidence for Continuous Cloud Audit

The recent spread of cloud services has enabled many companies to take advantage of them. Nevertheless, the main concern about the adoption of cloud services remains the lack of transparency perceived by customers regarding security and privacy. To overcome this issue, Cloud Service Certifications (CSCs) have emerged as an effective solution to increase the level of trust in cloud services, possibly based on continuous auditing to monitor and evaluate the security of cloud services on an ongoing basis. Continuous auditing can be easily implemented for technical aspects, while organizational aspects can be challenging due to their generic nature and varying policies between service providers. In this paper, we propose an approach to facilitate the automatic assessment of organizational evidence, such as that extracted from security policy documents. The evidence extraction process is based on Natural Language Processing (NLP) techniques, in particular on Question Answering (QA). The implemented prototype provides promising results on an annotated dataset, since it is capable to retrieve the correct answer for more than half of the tested metrics. This prototype can be helpful for Cloud Service Providers (CSPs) to automate the auditing of textual policy documents and to help in reducing the time required by auditors to check policy documents.
Research on Online Theater Presentation Technology and Experience Based on the Relationship Between Viewers and Actors

In the context of media change, traditional theater is declining. As an indelible part of human civilization, in the post-epidemic era, theater has started to reinvent itself based on the diversified development of media. During the global new epidemic, the emergence of “online theater” is a product of the development of the times. It plays a role in the development of theater. In this paper, we focus on the relationship between viewing and performance, and study the possibility of online theater development under the empowerment of technology. On the premise of confirming that online theater does not destroy the basic structure of the viewing-acting relationship, we intend to look at three dimensions: the development history of the viewing-acting relationship, the breakthrough of online theater development in the post-epidemic era, and the prospect of the future development of online theater. Based on the real factors of the development of online theater offline, we explore the future possibilities of the presentation of online theater viewing relationships.
Investigation of Integrating Solution Techniques for Lifelong MAPD Problem Considering Endpoints

The lifelong multiagent pickup-and-delivery problem has been studied for autonomous carrier robots that perform tasks continuously generated on demand in warehouses. It is an extension of continuous multiagent pathfinding problems, and a set of move paths along which agents simultaneously travel without collision is continuously solved for dynamically updated source and destination locations. Fundamental solution methods employ endpoints (EPs) that can be either beginning or end locations of agents’ move paths. EPs are used to consider the situations where some paths are locked by reservation, and deadlocks among reserved paths need to be avoided. While several heuristic techniques have been proposed to improve the solution method, there have been opportunities to investigate their effect and influence in order to integrate these techniques. As an analysis of solution techniques with endpoints for lifelong MAPD problems, we integrated several solution techniques in stages and experimentally investigated their effects and influence. Here, we experimentally present the effect of the proposed approach and generally consider the possibilities of integrated efficient methods. Our results show how the redundancies of problems and move paths can be reduced by the integrated techniques. On the other hand, there are several trade-offs among the heuristics combined for the concurrency of tasks.
Enhancing Safety Checking Coverage with Multi-swarm Particle Swarm Optimization

Model checking analyzes behaviors of software systems for verifying the absence of errors formally. Traditionally, such errors are detected with exhaustive search algorithms. As an alternative approach, metaheuristics and Swarm Intelligence have been applied to model checking. However, because of their non-exhaustiveness and locality, it is difficult to cover a wide region of the entire search space to users’ satisfaction. In order to mitigate this incomplete coverage problem, we propose an incremental model checking approach that uses Multi-swarm Particle Swarm Optimization for error detection. The proposed approach conducts the optimization repeatedly while updating knowledge about the problem domain. The update guides the optimization to explore the search space more intensively as the repetition proceeds. Furthermore, we propose novel coverage metrics to measure diversity and intensity of the search by Swarm Intelligence. The proposed metrics make it easy for users to grasp how much model checking progresses. They can be used for large systems that tend to cause state space explosion, which is a major obstacle in model checking. Our experimental results show that the proposed incremental approach is effective as compared to simple random re-initialization approach in terms of diversity and intensity.
Saving Memory Space in Deep Neural Networks by Recomputing: A Survey

Training a multilayered neural network involves execution of the network on the training data, followed by calculating the error between the predicted and actual output, and then performing backpropagation to update the network’s weights in order to minimise the overall error. This process is repeated many times, with the network updating its weights until it produces the desired output with a satisfactory level of accuracy. It requires storage in memory of activation and gradient data for each layer during each training run of the network. This paper surveys the main approaches to recomputing the needed activation and gradient data instead of storing it in memory. We discuss how these approaches relate to reversible computation techniques.
Understanding and Compressing Music with Maximal Transformable Patterns

We present a polynomial-time algorithm that discovers all maximal patterns in a point set, $$D\subset \mathbb {R}^k$$ , that are related by transformations in a user-specified class, $$F$$ , of bijections over $$\mathbb {R}^k$$ . We also present a second algorithm that discovers the set of occurrences for each of these maximal patterns and then uses compact encodings of these occurrence sets to compute a losslessly compressed encoding of the input point set. This encoding takes the form of a set of pairs, $$E=\left\{ \left\langle P_1, T_1\right\rangle ,\left\langle P_2, T_2\right\rangle ,\ldots \left\langle P_{\ell }, T_{\ell }\right\rangle \right\} $$ , where each $$\langle P_i,T_i\rangle $$ consists of a maximal pattern, $$P_i\subseteq D$$ , and a set, $$T_i\subset F$$ , of transformations that map $$P_i$$ onto other subsets of $$D$$ . Each transformation is encoded by a vector of real values that uniquely identifies it within $$F$$ and the length of this vector is used as a measure of the complexity of $$F$$ . We evaluate the new compression algorithm with three transformation classes of differing complexity, on the task of classifying folk-song melodies into tune families. The most complex of the classes tested includes all combinations of the musical transformations of transposition, inversion, retrograde, augmentation and diminution. We found that broadening the transformation class improved performance on this task. However, it did not, on average, improve compression factor, which may be due to the datasets (in this case, folk-song melodies) being too short and simple to benefit from the potentially greater number of pattern relationships that are discoverable with larger transformation classes.
VIET: A Tool for Extracting Essential Information from Vulnerability Descriptions for CVSS Evaluation

Security vulnerabilities can be patched in order based on their severity as indicated by an assigned score, to minimize the chance and impact of potential exploits. However, it often takes several days for an analyst to assign a CVSS score, leaving a window of opportunity for attackers. Existing solutions heavily rely on the accuracy of current vulnerability scoring, which suffers from potential bias and errors introduced by human analysts. In this paper, we propose VIET, a tool that extracts essential information from vulnerability descriptions, which can be loosely mapped to CVSS metrics, and facilitates vulnerability evaluation. We trained a dedicated cybersecurity linguistic model based on 209,842 vulnerability descriptions and implemented a Bidirectional LSTM network trained on 800 labeled vulnerability descriptions. We evaluate the effectiveness of VIET through F1-scores and the efficiency of the models in terms of training time. The results show that VIET can extract essential entities to determining a vulnerability's severity level, reducing analysis time and addressing inconsistencies in the evaluation process.
Multi-agent Reinforcement Learning for Structured Symbolic Music Generation

Generating structured music using deep learning methods with symbolic representation is challenging due to the complex relationships between musical elements that define a musical composition. Symbolic representation of music, such as MIDI or sheet music, can help overcome some of these challenges by encoding the music in a format that allows manipulation and analysis. However, the symbolic representation of music still requires interpretation and understanding of musical concepts and theory. In this paper, we propose a method for symbolic music generation using a multi-agent structure built on top of growing hierarchical self-organizing maps and recurrent neural networks. Our model primarily focuses on music structure. It operates at a higher level of abstraction, enabling it to capture longer-term musical structure and dependency. Our approach involves using reinforcement learning as a self-learning method for agents and the human user as a musical expert to facilitate the agents' learning of global dependency and musical characteristics. We show how agents can learn and adapt to the user's preferences and musical style. Furthermore, we present and discuss the potential of our approach for agent communication, learning and adaptation, and distributed problem-solving in music generation.
Quantum String Matching Unfolded and Extended

The string matching problem is one of the fundamental problems in computer science with applications in a variety of fields. Basically, it consists in finding all occurrences of a given pattern within a larger text. Despite its straightforward formulation, it has given rise to a huge number of solutions based on very different approaches and varied computational paradigms. But it is only very recently that the first solution based on quantum computation has been proposed by Niroula and Nam, allowing the problem to be solved in $$\mathcal {O}(\sqrt{n}(\log ^2(n)+\log (m)))$$ time, with a quadratic speed-up compared to classical computation. To date, these two research fields have remained almost entirely separate, mainly because the technical aspects typical of the quantum computation field remain almost obscure to those involved in text processing. This paper aims to reconcile the two fields by unfolding the technical aspects of the Niroula-Nam quantum solution and providing a detailed general procedure working in $$\mathcal {O}(\sqrt{n}\log ^2(n))$$ time that can be used as a framework for solving other string matching problems, including nonstandard ones. In this direction, the paper also proposes an extension of the algorithm to the approximate string matching problem with swaps, reporting the configuration of the occurrence together with its position, and achieving a quasi-linear $$\mathcal {O}(\sqrt{n}\log ^2(n))$$ time complexity when $$m=\mathcal {O}(\log (n))$$ .