Reviewerly/exHarmony huggingface dataset:
Joint Event Extraction with Contextualized Word Embeddings for the Portuguese Language

Event Extraction (EE) is the task of identifying mentions of particular event types and their arguments in text, and it constitutes an important and challenging task within the area of Information Extraction (IE). However, in the context of the Portuguese language, very little work has been conducted on this topic. In this paper, we propose a neural-based method for EE, as well as a data resource to mitigate this research gap. We also present a data augmentation strategy for EE, employing an Open Information Extraction (OIE) system, aiming to overcome the shortage in annotated data for the problem in the Portuguese language. Our experimental results show that our method is able to predict event types and arguments automatically, and the proposed method of data augmentation, in one of the two evaluated samples, contributes to the performance of the tested models in the subtask of argument role prediction. Further, an implementation of our method is available to the community, as the models trained in our experiments (https://github.com/FORMAS/TEFE).
An analysis of privacy leakage in DoQ traffic

Increased demand for DNS privacy has driven the creation of several encrypted DNS protocols, such as DNS over HTTPS (DoH), DNS over TLS (DoT), and DNS over QUIC (DoQ). Recently, the DoT and DoH have been deployed by some vendors like Google and Cloudflare. However, it is pointed out that DoT/DoH still have privacy leakage problems. Our goal is aiming at comparing the level of privacy leakage in encrypted DNS. This preliminary work focuses on analyzing the DoQ traffic to determine whether the adversary can infer the category of websites users visit. We find that information leakage is still possible even in the DoQ traffic. Moreover, we identify that important features are mainly related to inter-arrival times of queries, and the accuracy score slightly decreases for more categories.
Deepfake Detection Method Based on Cross-Domain Fusion

In recent years, despite its wide use in various fields, deepfake has been abused to generate hazardous contents such as fake movies, rumors, and fake news by manipulating or replacing facial information of the original sources and, thus, exerts huge security threats to the society. Facing the continuous evolution of deepfake, research on active detection and prevention technology becomes particularly important. In this paper, we propose a new deepfake detection method based on cross-domain fusion, which, on the basis of traditional spatial domain features, realizes the fusion of cross-domain image features by introducing edge geometric features of the frequency domain and, therefore, achieves considerable improvements on classification accuracy. Further evaluations of this method have been performed on publicly deepfake datasets, and the results show that our method is effective particularly on the Meso-4 DeepFake Database.
A note on cut-elimination for classical propositional logic

Abstract In Schwichtenberg (Studies in logic and the foundations of mathematics, vol 90, Elsevier, pp 867–895, 1977), Schwichtenberg fine-tuned Tait’s technique (Tait in The syntax and semantics of infinitary languages, Springer, pp 204–236, 1968) so as to provide a simplified version of Gentzen’s original cut-elimination procedure for first-order classical logic (Gallier in Logic for computer science: foundations of automatic theorem proving, Courier Dover Publications, London, 2015). In this note we show that, limited to the case of classical propositional logic, the Tait–Schwichtenberg algorithm allows for a further simplification. The procedure offered here is implemented on Kleene’s sequent system G4 (Kleene in Mathematical logic, Wiley, New York, 1967; Smullyan in First-order logic, Courier corporation, London, 1995). The specific formulation of the logical rules for G4 allows us to provide bounds on the height of cut-free proofs just in terms of the logical complexity of their end-sequent.
Generating Diverse 3D Reconstructions from a Single Occluded Face Image

Occlusions are a common occurrence in unconstrained face images. Single image 3D reconstruction from such face images often suffers from corruption due to the pres-ence of occlusions. Furthermore, while a plurality of 3D reconstructions is plausible in the occluded regions, existing approaches are limited to generating only a single so-lution. To address both of these challenges, we present Diverse3DFace, which is specifically designed to simulta-neously generate a diverse and realistic set of 3D reconstructions from a single occluded face image. It comprises three components; a global+local shape fitting process, a graph neural network-based mesh VAE, and a determinan-tal point process based diversity-promoting iterative opti-mization procedure. Quantitative and qualitative comparisons of 3D reconstruction on occluded faces show that Di-verse3DFace can estimate 3D shapes that are consistent with the visible regions in the target image while exhibiting high, yet realistic, levels of diversity in the occluded regions. On face images occluded by masks, glasses, and other random objects, Diverse3DFace generates a distri-bution of 3D shapes having ~50% higher diversity on the occluded regions compared to the baselines. Moreover, our closest sample to the ground truth has ~40% lower MSE than the singular reconstructions by existing approaches. Code and data available at: https://github.com/human-analysis/diverse3dface
Quantum Fan-out: Circuit Optimizations and Technology Modeling

Instruction scheduling is a key compiler optimization in quantum computing, just as it is for classical computing. Current schedulers optimize for data parallelism by allowing simultaneous execution of instructions, as long as their qubits do not overlap. However, on many quantum hardware platforms, instructions on overlapping qubits can be executed simultaneously through global interactions. For example, while fan-out in traditional quantum circuits can only be implemented sequentially when viewed at the logical level, global interactions at the physical level allow fan-out to be achieved in one step. We leverage this simultaneous fan-out primitive to optimize circuit synthesis for NISQ (Noisy Intermediate-Scale Quantum) workloads. In addition, we introduce novel quantum memory architectures based on fan-out.Our work also addresses hardware implementation of the fan-out primitive. We perform realistic simulations for trapped ion quantum computers. We also demonstrate experimental proof-of-concept of fan-out with superconducting qubits. We perform depth (runtime) and fidelity estimation for NISQ application circuits and quantum memory architectures under realistic noise models. Our simulations indicate promising results with an asymptotic advantage in runtime, as well as 7–24% reduction in error.
HPGAB3C: A Novel Hybridized Optimization Approach

This paper proposes a novel soft computing based hybrid approach named HPGAB3C for global optimization. The proposed approach is based upon the nature inspired hybridized approaches used for optimization in various domains. In the proposed approach, a combination of genetic algorithms (GA) and big bang big crunch (BBBC) approach is used where parallel/multiple populations will be passed to genetic algorithm and the best population evaluated by genetic algorithm will be passed on to big bang big crunch approach to compute the optimized solution. We implemented the approach in MATLAB. Its performance for optimal route evaluation was tested on WMNs with node sizes varying from 1000 to 10,000 nodes. Performance of the proposed approach is compared with 9 other approaches. We observed that the proposed approach outperforms all the other approaches.
API-PROGRAM: An API Package Recommendation Model Based on the Graph Representation Learning Method

To combine multiple services together using technologies such as mashup to produce a composite service has become a popular practice. However, with the increasing number of services and the diversification of service types, how to select suitable services and ensure these service combinations meet the needs of users has become an increasingly challenging topic. At present, although there are many recommendation algorithms for service selection, the semantics of the composed Web services have not been sufficiently modeled. This paper proposes an API package recommendation model based on the graph representation learning method (API-PROGRAM) which uses the historical data to learn more comprehensive semantics of Web APIs, construct the composite features of Web API collaborations and then recommend Web API packages for new mashups. The experimental results show that, compared with the existing algorithms, API-PROGRAM achieves better performance.
Model Predictive Control-Based Multirotor Three-Dimensional Motion Planning with Point Cloud Obstacle

This study proposes a collision-free motion planning framework for indoor autonomous flight of multirotor unmanned aerial vehicle (UAV) based on the convex model predictive control (MPC) approach under a three-dimensional point cloud environment. The suggested framework is divided into three steps: full reference path generation, piecewise flight corridor (PFC) creation, and MPC-based motion planning. The framework begins with reconstructing boundary surfaces that can encapsulate the given point cloud in order to generate a full reference path by applying Dijkstra and Voronoi diagram algorithms. Then PFC that represents locally convex and feasible flight corridor is generated using the current vehicle state, triangulized obstacle, and full reference path. In such a way, the entire problem breaks down into a series of discretized convex motion planning problems whose solution can be found by applying MPC iteratively until the UAV reaches its final destination. The constraints of the MPC are set up with the dynamics of the UAV, PFC, and the performance limitation of the platform. The framework is verified with simulation under a MATLAB environment. As a result, the UAV can find the control variable needed to reach the final destination with the suggested framework. Also, the computational time of the suggested framework is shorter than those of full reference path optimization methods.
Single Channel EOG Measurement System and Interface Design

In this study, a low cost single channel electrooculography (EOG) amplifier circuit and interface software have been created. This interface was created using the Python programming language. In this interface, EOG signals can be drawn and recorded in real time. These recorded signals can be redrawn later on offline interface. In addition, class information of blink, looking up and down eye movements was obtained through 3 warning lamps added to the interface. Thresholding method was used to classify these eye movements.Unlike the 2 channel EOG measurement circuits in the literature, a single channel EOG amplifier circuit was used in this study. Eye movements in vertical and horizontal planes with a small change in the electrode placement areas were visualized with this single channel amplifier circuit. The EOG signals received in response to these eye movements were compared with the signals received with 2 channel EOG amplifier circuits in the literature. There was little decrease in the amplitude values of the signals. However, there was no change in the general shape of the signals.
Freehand Interaction in Virtual Reality: Bimanual Gestures for Cross-Workspace Interaction

This work presents the design and evaluation of three bimanual interaction modalities for cross-workspace interaction in virtual reality (VR), in which the user can move items between a personal workspace and a shared workspace. We conducted an empirical study to understand three modalities and their suitability for cross-workspace interaction in VR.
A Protocol for Argumentation-Based Persuasive Negotiation Dialogues

Argumentation-based persuasive negotiation is a form of negotiation dialogue in which agents, with different interests and goals, exchange proposals that are supported by rhetorical arguments such as threats, rewards, or appeals. Besides rhetorical arguments, additional kinds of illocutions may also be exchanged during the dialogue, for instance, agents may ask for explanations, give explanations, or attack (or contradict) previous arguments. This paper presents a formal protocol for argumentation-based persuasive negotiation dialogues in which a proponent agent tries to persuade his opponent to perform a given action and the opponent tries to maintain his position. The protocol is modelled as a dialogue game (i.e. the interactions between the proponent and the opponent are governed by a set of rules) and the outcome of the dialogue is determined by applying an argumentation semantics. We prove the soundness and completeness of our proposal and illustrate the proposed protocol by using an example.
Open set anomaly classification

Anomaly classification can provide a discrimination of the type of anomaly at hand, that can help the user to narrow down the search space during troubleshooting. A limitation of most machine learning (ML) classification models is being under the closed set assumption and thereby failing to recognize unknown data. Literature refers to this issue as the Opens Set Recognition (OSR) problem. In this paper, the given use case is the detection of technical faults in HVAC systems in commercial buildings. Here, the OSR problem is of high relevance, because it is very unrealistic that all possible faults are already known beforehand. This paper proposes an ML pipeline for open set anomaly classification. To address the OSR problem, the pipeline makes use of three common neural network architecture models that successively build on each other, namely two autoencoders and one ANN classifier. The advantage of using common well-known models is the ease of implementation for solution developers. The validation is made against an HVAC dataset published by LBNL that contains 7 fault cases, where we assume 3 of them as unknown. Our validation results show that the proposed pipeline can significantly reduce the risk of false class predictions in the presence of unknown data.
Rate-constrained learning-based image compression

Rate control is a desirable feature, sometimes a requirement, for several applications in still image coding. Usually, the objective is to achieve rate control for every input data with minimal impact on rate–distortion performance. However, this task can be quite challenging. Learning-based image compression is a new paradigm that needs to be competitive with conventional image coding techniques. A learning-based lossy codec may require several trained models for different quality requirements. Therefore, a coding tool providing the ability to achieve a specific rate can be a deterministic factor to apply such models in practical application scenarios. Hence, in this work, we present a non-constrained solution to solve the constrained problem of training a learning-based image codec for a specific bitrate. The proposed solution requires a modified loss function for autoencoder optimization. This modification allows controlling the deviation from the specified target rate. Experiments performed in Kodak and JPEG AI datasets show that autoencoders trained with the proposed loss function can achieve rate constrained encoding with negligible losses in terms of SSIM and MS-SSIM.
Explore the Performance of Capsule Neural Network Learning Discrete Features

The continuous breakthrough of deep learning model in image processing, natural language processing and other fields is mainly due to the strong ability of deep neural network in feature extraction. Based on the idea of capsule neural network, this paper proposes a capsule neural network for general classification problems, and explores the learning ability of capsule network model for classification problems of discrete feature. In order to evaluate the capsule network model, this paper verifies the effect of the model on real datasets, and makes a comparative analysis with common machine learning classification algorithms.
Multi-Keyword Classification: A Case Study in Finnish Social Sciences Data Archive

In this paper, we consider the task of assigning relevant labels to studies in the social science domain. Manual labelling is an expensive process and prone to human error. Various multi-label text classification machine learning approaches have been proposed to resolve this problem. We introduce a dataset obtained from the Finnish Social Science Archive and comprised of 2968 research studies’ metadata. The metadata of each study includes attributes, such as the “abstract” and the “set of labels”. We used the Bag of Words (BoW), TF-IDF term weighting and pretrained word embeddings obtained from FastText and BERT models to generate the text representations for each study’s abstract field. Our selection of multi-label classification methods includes a Naive approach, Multi-label k Nearest Neighbours (ML-kNN), Multi-Label Random Forest (ML-RF), X-BERT and Parabel. The methods were combined with the text representation techniques and their performance was evaluated on our dataset. We measured the classification accuracy of the combinations using Precision, Recall and F1 metrics. In addition, we used the Normalized Discounted Cumulative Gain to measure the label ranking performance of the selected methods combined with the text representation techniques. The results showed that the ML-RF model achieved a higher classification accuracy with the TF-IDF features and, based on the ranking score, the Parabel model outperformed the other methods.
Automated Use-After-Free Detection and Exploit Mitigation: How Far Have We Gone?

C/C++ programs frequently encounter memory errors, such as Use-After-Free (UAF), buffer overflow, and integer overflow. Among these memory errors, UAF vulnerabilities are increasingly being exploited by attackers to disrupt critical software systems, leading to serious consequences, such as remote code execution and data breaches. Researchers have proposed dozens of approaches to detect UAFs in testing environments and to mitigate UAF exploit in production environments. However, to the best of our knowledge, no comprehensive studies have evaluated and compared these approaches. In this paper, we shed light on the current UAF detection and exploit mitigation approaches and provide a systematic overview, comprehensive comparison, and evaluation. Specifically, we evaluate the effectiveness and efficiency of publicly available UAF detection and exploit mitigation tools. The experimental results show that static UAF detectors are suitable for detecting intra-procedural UAFs but are not sufficient to detect inter-procedural UAFs in real-world programs. Dynamic UAF detectors are still the first choice for detecting inter-procedural UAFs. Our evaluation also demonstrates that the runtime overhead of existing UAF exploit mitigation tools is relatively stable whereas the memory overhead may vary dramatically with respect to different programs. Finally, we envision potential valuable future research directions.
Supervised Learning of Keyphrase Extraction Utilizing Prior Summarization

Keyphrase extraction is the task of selecting a set of phrases that can best represent a given document. Keyphrase extraction is utilized in document indexing and categorization, thus being one of core technologies of digital libraries. Supervised keyphrase extraction based on pretrained language models are advantageous thorough their contextualized text representations. In this paper, we show an adaptation of the pertained language model BERT to keyphrase extraction, called BERT Keyphrase-Rank (BK-Rank), based on a cross-encoder architecture. However, the accuracy of BK-Rank alone is suffering when documents contain a large amount of candidate phrases, especially in long documents. Based on the notion that keyphrases are more likely to occur in representative sentences of the document, we propose a new approach called Keyphrase-Focused BERT Summarization (KFBS), which extracts important sentences as a summary, from which BK-Rank can more easily find keyphrases. Training of KFBS is by distant supervision such that sentences lexically similar to the keyphrase set are chosen as positive samples. Our experimental results show that the combination of KFBS + BK-Rank show superior performance over the compared baseline methods on well-known four benchmark collections, especially on long documents.
Automatic Cause-Effect Relation Extraction from Dental Textbooks Using BERT

The ability to automatically identify causal relations from surgical textbooks could prove helpful in the automatic construction of ontologies for dentistry and building learning-assistant tools for dental students where questions about essential concepts can be auto-generated from the extracted ontologies. In this paper, we propose a neural network architecture to extract cause-effect relations from dental surgery textbooks. The architecture uses a transformer to capture complex causal sentences, specific semantics, and large-scale ontologies and solve sequence-to-sequence tasks while preserving long-range dependencies. Furthermore, we have also used BERT to learn word contextual relations. During pre-training, BERT is trained on enormous corpora of unannotated text on the web. These pre-trained models can be fine-tuned on custom tasks with specific datasets. We first detect sentences that contain cause-effect relations. Then, cause and effect clauses from each cause-effect sentence are identified and extracted. Both automatic and expert-rated evaluations are used to validate the efficacy of our proposed models. Finally, we discuss a prototype system that helps dental students learn important concepts from dental surgery textbooks, along with our future research directions.
Is Function-as-a-Service a Good Fit for Latency-Critical Services?

Function-as-a-Service (FaaS) is becoming an increasingly popular cloud-deployment paradigm for serverless computing that frees application developers from managing the infrastructure. At the same time, it allows cloud providers to assert control in workload consolidation, i.e., co-locating multiple containers on the same server, thereby achieving higher server utilization, often at the cost of higher end-to-end function request latency. Interestingly, a key aspect of serverless latency management has not been well studied: the trade-off between application developers' latency goals and the FaaS providers' utilization goals.
Parallel near-optimal pathfinding based on landmarks

We present a new approach for path finding in weighted graphs using pre-computed minimal distance fields. By selecting the most promising minimal distance field at any given node and switching between them, our algorithm aims to find the shortest path possible. As we show, this approach scales excellently for various topologies, graph sizes and hardware specifications while maintaining a mean length error below 1% and reasonable memory consumption. By utilizing a simplified structure and keeping backtracking to a minimum, we are able to leverage the same approach on the massively parallel GPUs or any other shared memory parallel architecture, reducing the run time even further.