Reviewerly/exHarmony huggingface dataset:
Verifying Functional Correctness Properties at the Level of Java Bytecode

The breakneck evolution of modern programming languages aggravates the development of deductive verification tools, which struggle to timely and fully support all new language features. To address this challenge, we present ByteBack: a verification technique that works on Java bytecode. Compared to high-level languages, intermediate representations such as bytecode offer a much more limited and stable set of features; hence, they may help decouple the verification process from changes in the source-level language. ByteBack offers a library to specify functional correctness properties at the level of the source code, so that the bytecode is only used as an intermediate representation that the end user does not need to work with. Then, ByteBack reconstructs some of the information about types and expressions that is erased during compilation into bytecode but is necessary to correctly perform verification. Our experiments with an implementation of ByteBack demonstrate that it can successfully verify bytecode compiled from different versions of Java, and including several modern language features that even state-of-the-art Java verifiers (such as KeY and OpenJML) do not directly support—thus revealing how ByteBack 's approach can help keep up verification technology with language evolution.
Runtime Monitoring for Out-of-Distribution Detection in Object Detection Neural Networks

Runtime monitoring provides a more realistic and applicable alternative to verification in the setting of real neural networks used in industry. It is particularly useful for detecting out-of-distribution (OOD) inputs, for which the network was not trained and can yield erroneous results. We extend a runtime-monitoring approach previously proposed for classification networks to perception systems capable of identification and localization of multiple objects. Furthermore, we analyze its adequacy experimentally on different kinds of OOD settings, documenting the overall efficacy of our approach.
Are VR and AR Really Viable in Military Education?: A Position Paper

When people talk about virtual reality (VR) or augmented reality (AR), they usually associate these novel technologies with leisure activities. In the case of the armed forces, more often than not, enthusiasts tend to relate videos games (e.g., Call of Duty) to military training and education. However, little is known about the true potential, these technologies may bring to modern-day armed forces specially in the Latin American context. This position paper thoroughly presents some real possibilities about VR and AR in the military education and training, how they are being used by defense forces and what is required in the short term to foresee the possibilities other armies are already harnessing to date. In addition, a brief comparative analysis between advantages and disadvantages in the adoption of both technologies was made to know a more accurate scenario of them. Issues that may arise from the acceptance of such technologies are also discussed as well as how research can support a sustainable pathway toward the utilization of VR and AR in the context of Latin American armed forces.
On the Design and Implementation of a Quantum Architectures Knowledge Unit for a CS Curriculum

Sixteen years ago, Scott Aaronson remarked (in the presence of Ray Laflamme) that quantum mechanics (QM) resembles an operating system on which the rest of Physics is running its application software (except for general relativity "which has not yet been successfully ported to this particular OS''). Prior to that, it took the insight of an educator and eminent computer scientist (Umesh Vazirani) to realize that a complete and consistent introduction to QM can be given via the language of qubits and quantum gates. Closer to the present, it took the profound intuition of another polymath (Terry Rudolph) to realize that the linear algebra normally at the foundation of such an approach can be replaced with a simple rewriting system accessible to middle school students. Rewriting systems are at the foundation of Computer Science, they are, in fact, the very fabric of it (e.g., Turing machines and lambda calculus), so these are very fortunate developments. Furthermore, a linear algebra prerequisite is now shared firmly in the CS undergraduate curriculum with Machine Learning, a topic that has known a very deep and sudden revival. Quantum Information Science and Technology (QIST) is inherently interdisciplinary and spans physics, computer science, mathematics, engineering, chemistry and materials science. We present three curricular plans for incorporating QIST topics (via Quantum Computing) into the CS undergraduate curriculum. Such plans have been constructed with a preliminary consultation with QED-C members (industry, academia, national labs, and government agencies) asking for comments, suggestions and general input on these three curricular plans.
Detecting the Reasons for Program Decomposition in CS1 and Evaluating Their Impact

Decomposition is considered one of the four cornerstones of computational thinking, which is essential to software development [36]. It requires the ability to assess a problem at a high level, develop a strategy to combat it, and then design a solution. Our study focuses on the metacognitive aspect of decomposition. We try to understand the learner's thought process and, specifically, what makes the novice programmer decide to break down a function.
Recognizing geochemical patterns related to mineralization using a self-organizing map

The recognition of geochemical patterns, including the spatial association of geochemical patterns, geochemical element associations, and geochemical anomalies related to mineralization, requires different methods to meet the corresponding requirements. In this study, a self-organizing map (SOM), an unsupervised learning technique, was employed to realize the whole processes of geochemical pattern recognition in southeastern Hubei Province, China. First, the clustering ability of SOM was adopted to reveal the spatial association of geochemical patterns; then, geochemical elemental associations were adopted according to the association between variables and clusters derived from the correlation analysis between the unified distance matrix (U-matrix) and SOM maps, and finally the quantification error (QE) of the SOM was adopted as an anomalous index to detect multivariate geochemical anomalies related to mineralization. The obtained results show that two of the clusters are spatially closely related to ore-forming geological bodies, that is Precambrian strata and Yanshanian intrusive rocks. The associations of Au–As–Ba–Cd–Sb–V and Au–Ag–Pb–Zn–Bi–Cu–Mo–Na2O–P–Sr–W are responsible for the characterization of the two clusters, and their combination was further used for multivariate geochemical anomaly detection. The extracted multivariate geochemical anomalies are closely spatially correlated with known gold polymetallic deposits. Both the receiver operating characteristic (ROC) and success-rate curves suggest a satisfactory anomaly detection results, which can provide guidance for further mineral exploration. The results of this study also suggest that SOM is an effective and interpretable tool for a better recognition of geochemical patterns related to mineralization.
Robust interacting multiple model cubature Kalman filter for nonlinear filtering with unknown non-Gaussian noise

Estimating noise covariance matrices and suppressing outliers simultaneously is a huge challenge because they are coupled. In order to solve the nonlinear filtering problem with unknown non-Gaussian noise, the maximum correntropy criterion (MCC) is used to suppress the outliers, while the variational Bayesian (VB) technique is used to estimate the one-step prediction error covariance matrix (PECM) and the measurement noise covariance matrix (MNCM), and the nonlinear problem is solved by the third-order spherical radial cubature rule. In this paper, nominal measurement is constructed to eliminate the influence of outliers on the recursive estimation of MNCM. Through these operations, the variational Bayesian and maximum correntropy based cubature Kalman filter (VBMCCKF) and the maximum correntropy cubature Kalman filter (MCCKF) are derived. The interacting multiple model (IMM) fusion framework is used to promote the collaborative work of VBMCCKF and MCCKF, thus a novel filter is proposed in this paper. The simulation verifies the validity and universal applicability of the proposed filter. The estimation error of the proposed filter in spacecraft autonomous celestial navigation is about 30% less than that of the existing filter with the best performance.
Assessing Fine-Grained Explicitness of Song Lyrics

Music plays a crucial role in our lives, with growing consumption and engagement through streaming services and social media platforms. However, caution is needed for children, who may be exposed to explicit content through songs. Initiatives such as the Parental Advisory Label (PAL) and similar labelling from streaming content providers aim to protect children from harmful content. However, so far, the labelling has been limited to tagging the song as explicit (if so), without providing any additional information on the reasons for the explicitness (e.g., strong language, sexual reference). This paper addresses this issue by developing a system capable of detecting explicit song lyrics and assessing the kind of explicit content detected. The novel contributions of the work include (i) a new dataset of 4000 song lyrics annotated with five possible reasons for content explicitness and (ii) experiments with machine learning classifiers to predict explicitness and the reasons for it. The results demonstrated the feasibility of automatically detecting explicit content and the reasons for explicitness in song lyrics. This work is the first to address explicitness at this level of detail and provides a valuable contribution to the music industry, helping to protect children from exposure to inappropriate content.
Privacy-Preserving Method for Trajectory Data Publication Based on Local Preferential Anonymity

With the rapid development of mobile positioning technologies, location-based services (LBSs) have become more widely used. The amount of user location information collected and applied has increased, and if these datasets are directly released, attackers may infer other unknown locations through partial background knowledge in their possession. To solve this problem, a privacy-preserving method for trajectory data publication based on local preferential anonymity (LPA) is proposed. First, the method considers suppression, splitting, and dummy trajectory adding as candidate techniques. Second, a local preferential (LP) function based on the analysis of location loss and anonymity gain is designed to effectively select an anonymity technique for each anonymous operation. Theoretical analysis and experimental results show that the proposed method can effectively protect the privacy of trajectory data and improve the utility of anonymous datasets.
Storytelling with Image Data: A Systematic Review and Comparative Analysis of Methods and Tools

In our digital age, data are generated constantly from public and private sources, social media platforms, and the Internet of Things. A significant portion of this information comes in the form of unstructured images and videos, such as the 95 million daily photos and videos shared on Instagram and the 136 billion images available on Google Images. Despite advances in image processing and analytics, the current state of the art lacks effective methods for discovering, linking, and comprehending image data. Consider, for instance, the images from a crime scene that hold critical information for a police investigation. Currently, no system can interactively generate a comprehensive narrative of events from the incident to the conclusion of the investigation. To address this gap in research, we have conducted a thorough systematic literature review of existing methods, from labeling and captioning to extraction, enrichment, and transforming image data into contextualized information and knowledge. Our review has led us to propose the vision of storytelling with image data, an innovative framework designed to address fundamental challenges in image data comprehension. In particular, we focus on the research problem of understanding image data in general and, specifically, curating, summarizing, linking, and presenting large amounts of image data in a digestible manner to users. In this context, storytelling serves as an appropriate metaphor, as it can capture and depict the narratives and insights locked within the relationships among data stored across different islands. Additionally, a story can be subjective and told from various perspectives, ranging from a highly abstract narrative to a highly detailed one.
A Modular Architecture of Command-and-Control Software in Multi-Sensor Systems Devoted to Public Security

Preventing terrorist attacks at soft targets has become a priority for our society. The realization of sensor systems for automatic threat detection in crowded spaces, such as airports and metro stations, is challenged by the limited sensing coverage capability of the devices in place due to the variety of dangerous materials, to the scanning rate of the devices, and to the detection area covered. In this context, effectiveness of the physical configuration of the system based on the detectors used, the coordination of the sensor data collection, and the real time data analysis for threat identification and localization to enable timely reactions by the security guards are essential requirements for such integrated sensor-based applications. This paper describes a modular distributed architecture of a command-and-control software, which is independent from the specific detectors and where sensor data fusion is supported by two intelligent video systems. Furthermore, the system installation can be replicated at different locations of a public space. Person tracking and later re-identification in a separate area, and tracking hand-over between different video components, provide the command-and-control with localization information of threats to timely activate alarm management and support the activity of subsequent detectors. The architecture has been implemented for the NATO-funded DEXTER program and has been successfully tested in a big city trial at a metro station in Rome both when integrated with two real detectors of weapons and explosives and as a stand-alone system. The discussion focuses on the software functions of the command-and-control and on the flexibility and re-use of the system in wider settings.
Deep Learning Architecture Improvement Based on Dynamic Pruning and Layer Fusion

The heavy workload of current deep learning architectures significantly impedes the application of deep learning, especially on resource-constrained devices. Pruning has provided a promising solution to compressing the bloated deep learning models by removing the redundancies of the networks. However, existing pruning methods mainly focus on compressing the superfluous channels without considering layer-level redundancies, which results in the channel-pruned models still suffering from serious redundancies. To mitigate this problem, we propose an effective compression algorithm for deep learning models that uses both the channel-level and layer-level compression techniques to optimize the enormous deep learning models. In detail, the channels are dynamically pruned first, and then the model is further optimized by fusing the redundant layers. Only a minor performance loss results. The experimental results show that the computations of ResNet-110 are reduced by 80.05%, yet the accuracy is only decreased by 0.72%. Forty-eight convolutional layers could be discarded from ResNet-110 with no loss of performance, which fully demonstrates the efficiency of the proposal.
Nested relation extraction via self-contrastive learning guided by structure and semantic similarity

The conventional Relation Extraction (RE) task involves identifying whether relations exist between two entities in a given sentence and determining their relation types. However, the complexity of practical application scenarios and the flexibility of natural language demand the ability to extract nested relations, i.e., the recognized relation triples may be components of the higher-level relations. Previous studies have highlighted several challenges that affect the nested RE task, including the lack of abundant labeled data, inappropriate neural networks, and underutilization of the nested relation structures. To address these issues, we formalize the nested RE task and propose a hierarchical neural network to iteratively identify the nested relations between entities and relation triples in a layer by layer manner. Moreover, a novel self-contrastive learning optimization strategy is presented to adapt our method to low-data settings by fully exploiting the constraints due to the nested structure and semantic similarity between paired input sentences. Our method outperformed the state-of-the-art baseline methods in extensive experiments, and ablation experiments verified the effectiveness of the proposed self-contrastive learning optimization strategy.
Multi-Objective Service Composition Using Enhanced Multi-Objective Differential Evolution Algorithm

In recent years, the optimization of multi-objective service composition in distributed systems has become an important issue. Existing work makes a smaller set of Pareto-optimal solutions to represent the Pareto Front (PF). However, they do not support complex mapping of the Pareto-optimal solutions to quality of service (QoS) objective space, thus having limitations in providing a representative set of solutions. We propose an enhanced multi-objective differential evolution algorithm to seek a representative set of solutions with good proximity and distributivity. Specially, we propose a dual strategy to adjust the usage of different creation operators, to maintain the evolutionary pressure toward the true PF. Then, we propose a reference vector neighbor search to have a fine-grained search. The proposed approach has been tested on a real-world dataset that locates a representative set of solutions with proximity and distributivity.
Metaheuristic algorithms in text clustering

This chapter provides an in-depth overview of the metaheuristic optimization algorithms used in the domain of document/text clustering as well as a description of their main processes. Because of their effective capability in dealing with machine learning difficulties, particularly text clustering problems, these metaheuristic algorithms are recognized as advantageous computational intelligence techniques. This chapter reviews all relevant studies in the literature on metaheuristic-based document/text clustering applications, including classical, improved, hybridized, and multiobjective metaheuristics algorithms used in this domain. It also presents the key processes involved in text/document clustering along with critical discussions. Finally, the chapter discusses challenges and potential future research directions.
Temporal-Aware Siamese Tracker: Integrate Temporal Context for 3D Object Tracking

Learning discriminative target-specific feature representation for object localization is the core of the 3D Siamese object tracking algorithms. Current Siamese trackers focus on aggregating the target information from the latest template into the search area for target-specific feature construction, which presents the limited performance in the case of object occlusion or object missing. To this end, in this paper, we propose a novel temporal-aware Siamese tracking framework, where the rich target clue lying in a set of historical templates is integrated into the search area for reliable target-specific feature aggregation. Specifically, our method consists of three modules, including a template set sampling module, a temporal feature enhancement module and a temporal-aware feature aggregation module. In the template set sampling module, an effective scoring network is proposed to evaluate the tracking quality of the template so that the high-quality templates are collected to form the historical template set. Then, with the initial feature embeddings of the historical templates, the temporal feature enhancement module concatenates all template embeddings as a whole and then feeds them into a linear attention module for cross-template feature enhancement. Furthermore, the temporal-aware feature aggregation module aggregates the target clue lying in each template into the search area to construct multiple historical target-specific search-area features. Particularly, we follow the collection orders of the templates to fuse all generated target-specific features via an RNN-based module so that the fusion weight of the previous template information can be discounted to better fit the current tracking state. Finally, we feed the temporal fused target-specific feature into a modified CenterPoint detection head for target position regression. Extensive experiments on KITTI, NuScenes and waymo open datasets show the effectiveness of our proposed method. Source code is available at https://github.com/tqsdyy/TAT .
Neural Deformable Voxel Grid for Fast Optimization of Dynamic View Synthesis

Recently, Neural Radiance Fields (NeRF) is revolutionizing the task of novel view synthesis (NVS) for its superior performance. In this paper, we propose to synthesize dynamic scenes. Extending the methods for static scenes to dynamic scenes is not straightforward as both the scene geometry and appearance change over time, especially under monocular setup. Also, the existing dynamic NeRF methods generally require a lengthy per-scene training procedure, where multi-layer perceptrons (MLP) are fitted to model both motions and radiance. In this paper, built on top of the recent advances in voxel-grid optimization, we propose a fast deformable radiance field method to handle dynamic scenes. Our method consists of two modules. The first module adopts a deformation grid to store 3D dynamic features, and a light-weight MLP for decoding the deformation that maps a 3D point in the observation space to the canonical space using the interpolated features. The second module contains a density and a color grid to model the geometry and density of the scene. The occlusion is explicitly modeled to further improve the rendering quality. Experimental results show that our method achieves comparable performance to D-NeRF using only 20 minutes for training, which is more than 70 $$\times $$ faster than D-NeRF, clearly demonstrating the efficiency of our proposed method.