Reviewerly/exHarmony huggingface dataset:
Mask3D: Mask Transformer for 3D Semantic Instance Segmentation

Modern 3D semantic instance segmentation approaches predominantly rely on specialized voting mechanisms followed by carefully designed geometric clustering techniques. Building on the successes of recent Transformer-based methods for object detection and image segmentation, we propose the first Transformer-based approach for 3D semantic instance segmentation. We show that we can leverage generic Transformer building blocks to directly predict instance masks from 3D point clouds. In our model - called Mask3D - each object instance is represented as an instance query. Using Transformer decoders, the instance queries are learned by iteratively attending to point cloud features at multiple scales. Combined with point features, the instance queries directly yield all instance masks in parallel. Mask3D has several advantages over current state-of-the-art approaches, since it neither relies on (1) voting schemes which require hand-selected geometric properties (such as centers) nor (2) geometric grouping mechanisms requiring manually-tuned hyper-parameters (e.g. radii) and (3) enables a loss that directly optimizes instance masks. Mask3D sets a new state-of-the-art on ScanNet test (+6.2mAP), S3DIS 6-fold (+10.1 mAP), STPLS3D (+11.2 mAP) and ScanNet200 test (+12.4 mAP).
SoK: Taxonomy of Attacks on Open-Source Software Supply Chains

The widespread dependency on open-source software makes it a fruitful target for malicious actors, as demonstrated by recurring attacks. The complexity of today’s open-source supply chains results in a significant attack surface, giving attackers numerous opportunities to reach the goal of injecting malicious code into open-source artifacts that is then downloaded and executed by victims.This work proposes a general taxonomy for attacks on open-source supply chains, independent of specific programming languages or ecosystems, and covering all supply chain stages from code contributions to package distribution. Taking the form of an attack tree, it covers 107 unique vectors, linked to 94 real-world incidents, and mapped to 33 mitigating safeguards.User surveys conducted with 17 domain experts and 134 software developers positively validated the correctness, comprehensiveness and comprehensibility of the taxonomy, as well as its suitability for various use-cases. Survey participants also assessed the utility and costs of the identified safeguards, and whether they are used.
Diffusion Probabilistic Model Made Slim

Despite the recent visually-pleasing results achieved, the massive computational cost has been a long-standing flaw for diffusion probabilistic models (DPMs), which, in turn, greatly limits their applications on resource-limited platforms. Prior methods towards efficient DPM, however, have largely focused on accelerating the testing yet overlooked their huge complexity and sizes. In this paper, we make a dedicated attempt to lighten DPM while striving to preserve its favourable performance. We start by training a small-sized latent diffusion model (LDM) from scratch, but observe a significant fidelity drop in the synthetic images. Through a thorough assessment, we find that DPM is intrinsically biased against high-frequency generation, and learns to recover different frequency components at different time-steps. These properties make compact networks unable to represent frequency dynamics with accurate high-frequency estimation. Towards this end, we introduce a customized design for slim DPM, which we term as Spectral Diffusion (SD), for light-weight image synthesis. SD incorporates wavelet gating in its architecture to enable frequency dynamic feature extraction at every reverse step, and conducts spectrum-aware distillation to promote high-frequency recovery by inverse weighting the objective based on spectrum magnitude. Experimental results demonstrate that, SD achieves 8–18 × computational complexity reduction as compared to the latent diffusion models on a series of conditional and unconditional image generation tasks while retaining competitive image fidelity.
MTEB: Massive Text Embedding Benchmark

Text embeddings are commonly evaluated on a small set of datasets from a single task not covering their possible applications to other tasks. It is unclear whether state-of-the-art embeddings on semantic textual similarity (STS) can be equally well applied to other tasks like clustering or reranking. This makes progress in the field difficult to track, as various models are constantly being proposed without proper evaluation. To solve this problem, we introduce the Massive Text Embedding Benchmark (MTEB). MTEB spans 8 embedding tasks covering a total of 58 datasets and 112 languages. Through the benchmarking of 33 models on MTEB, we establish the most comprehensive benchmark of text embeddings todate. We find that no particular text embedding method dominates across all tasks. This suggests that the field has yet to converge on a universal text embedding method and scale it up sufficiently to provide state-of-theart results on all embedding tasks. MTEB comes with open-source code and a public leaderboard at https://github.com/embeddings-benchmark/mteb.
Constrained Symbolic Search: On Mutexes, BDD Minimization and More

Symbolic search allows saving large amounts of memory compared to regular explicit-state search algorithms. This is crucial in optimal settings, in which common search algorithms often exhaust the available memory. So far, the most successful uses of symbolic search have been bidirectional blind search and the generation of abstraction heuristics like Pattern Databases. Despite its usefulness, several common techniques in explicit-state search have not been employed in symbolic search. In particular, mutexes and other constraining invariants, techniques that have been proven essential when doing regression, are yet to be exploited in conjunction with BDDs. In this paper we analyze the use of such constraints in symbolic search and its combination with minimization techniques common in BDD manipulation. Experimental results show a significant increase in performance, considerably above the current state of the art in optimal planning.
Automata and quantum computing

Quantum computing is a new model of computation, based on quantum physics. Quantum computers can be exponentially faster than conventional computers for problems such as factoring. Besides full-scale quantum computers, more restricted models such as quantum versions of finite automata have been studied. In this paper, we survey various models of quantum finite automata and their properties. We also provide some open questions and new directions for researchers. Keywords: quantum finite automata, probabilistic finite automata, nondeterminism, bounded error, unbounded error, state complexity, decidability and undecidability, computational complexity
Efficient Bronchoscopic Video Summarization

Bronchoscopy enables many minimally invasive chest procedures for diseases such as lung cancer and asthma. Guided by the bronchoscope's video stream, a physician can navigate the complex three-dimensional (3-D) airway tree to collect tissue samples or administer a disease treatment. Unfortunately, physicians currently discard procedural video because of the overwhelming amount of data generated. Hence, they must rely on memory and anecdotal snapshots to document a procedure. We propose a robust automatic method for summarizing an endobronchial video stream. Inspired by the multimedia concept of the video summary and by research in other endoscopy domains, our method consists of three main steps: 1) shot segmentation, 2) motion analysis, and 3) keyframe selection. Overall, the method derives a true hierarchical decomposition, consisting of a shot set and constituent keyframe set, for a given procedural video. No other method to our knowledge gives such a structured summary for the raw, unscripted, unedited videos arising in endoscopy. Results show that our method more efficiently covers the observed endobronchial regions than other keyframe-selection approaches and is robust to parameter variations. Over a wide range of video sequences, our method required on average only 6.5% of available video frames to achieve a video coverage = 92.7%. We also demonstrate how the derived video summary facilitates direct fusion with a patient's 3-D chest computed-tomography scan in a system under development, thereby enabling efficient video browsing and retrieval through the complex airway tree.
Fast and robust multiple ColorChecker detection using deep convolutional neural networks

ColorCheckers are reference standards that professional photographers and filmmakers use to ensure predictable results under every lighting condition. The objective of this work is to propose a new fast and robust method for automatic ColorChecker detection. The process is divided into two steps: (1) ColorCheckers localization and (2) ColorChecker patches recognition. For the ColorChecker localization, we trained a detection convolutional neural network using synthetic images. The synthetic images are created with the 3D models of the ColorChecker and different background images. The output of the neural networks are the bounding box of each possible ColorChecker candidates in the input image. Each bounding box defines a cropped image which is evaluated by a recognition system, and each image is canonized with regards to color and dimensions. Subsequently, all possible color patches are extracted and grouped with respect to the center's distance. Each group is evaluated as a candidate for a ColorChecker part, and its position in the scene is estimated. Finally, a cost function is applied to evaluate the accuracy of the estimation. The method is tested using real and synthetic images. The proposed method is fast, robust to overlaps and invariant to affine projections. The algorithm also performs well in case of multiple ColorCheckers detection.
EXCHANge: Securing IoT via channel anonymity

Establishing confidentiality between communicating peers is still an issue in contexts where solutions based on asymmetric keys are not viable, such as in dynamic Internet of Things (IoT) systems made up of heterogeneous and resource constrained devices. From the current literature, channel anonymity emerges as a promising methodology able to support key-establishment protocols. But, to the best of authors’ knowledge, no works already demonstrated its practical adoption over a concrete communication technology. To bridge this gap, we experimentally show that a lightweight key-establishment protocol based on channel anonymity is viable. The contributions of this work are mainfold. First, we introduce EXCHANge, a protocol that achieves key-establishment exploiting channel anonymity despite the presence of either a passive or active global-eavesdropper adversary. Second, we evaluate the performance of EXCHANge through an extensive experimental campaign involving real world IoT devices (OpenMote-CC2538). Our results demonstrate that the proposed solution introduces a limited overhead, thus being able to meet the requirements of resource constrained devices Finally, we experimentally demonstrate the security of the EXCHANge protocol against passive and active adversaries. Overall, this paper proves that channel anonymity can be a powerful tool in the IoT setting, to achieve a secure, effective, and efficient key-establishment.
Face Recognition Using Simultaneous Discriminative Feature and Adaptive Weight Learning Based on Group Sparse Representation

To better address face recognition task with occlusions and illumination changes, this letter proposes a novel framework called simultaneous discriminative feature and adaptive weight learning (SDFAWL). Sepecifically, SDFAWL uses a novel unified objective function to simultaneously learn the discriminant features, adaptive feature weights, and classification coefficients. First, a discriminant feature projection is incorporated into group sparse representation model, which reduces the intra-class distance between training samples. Second, we integrate adaptive feature weights into our model to penalize the noisy pixels, which is simultaneously learned by our unified objective function. Besides, we derive an efficient algorithm to optimize the proposed objective function, where the simultaneously learning scheme can encourage obtained parameters combining better and decrease the information loss. Extensive experiments under different conditions including occlusion, random noise, and illumination changes are conducted on the famous Aleix Martinez and Robert Benavente and ExYale B database demonstrate the effectiveness of our model.
Global Software Engineering Education Practice Continuum Special Issue of the ACM Transactions on Computing Education

We are pleased to introduce this Special Issue on Global Software Engineering Education published by the ACM Transactions on Computing Education (TOCE) that focuses on educational practices to prepare students for a global workplace. This issue comes at a time when universities are recognizing the need to provide courses that address the challenges of distributed development and presents research that will facilitate course leaders currently running, or embarking on, Global Software Engineering Education (GSE-Ed).
Coalgebra Learning via Duality

Abstract Automata learning is a popular technique for inferring minimal automata through membership and equivalence queries. In this paper, we generalise learning to the theory of coalgebras. The approach relies on the use of logical formulas as tests, based on a dual adjunction between states and logical theories. This allows us to learn, e.g., labelled transition systems, using Hennessy-Milner logic. Our main contribution is an abstract learning algorithm, together with a proof of correctness and termination.
Opposition-Based Multi-objective Binary Differential Evolution for Multi-label Feature Selection

Multi-label learning problem is a data analytic task in which every sample is associated with more than single label. The complexity of such problems declares the importance of feature selection task as a preprocessing step prior for multi-label learning. Feature selection can make a better learning performance both in terms of reducing computational complexity and increasing classification accuracy. Selecting the best subset of features with two objectives, the smaller number of features and higher accuracy of classification can be treated as a binary multi-objective optimization problem. Since feature selection is inherently a binary optimization problem, applying continuous metaheuristic algorithms to solve this problem decreases the diversity of solutions in the optimal Pareto-front, because of many-to-one mapping and low exploration power, accordingly. This paper proposed a binary version of Generalized Differential Evolution (BGDE3) for multi-label feature selection based on majority voting of solutions and opposition-based learning (OBL). Experimental results show that the proposed algorithm outperforms the continuous GDE3 for multi-label feature selection.
A Cloud-Based Crime Reporting System with Identity Protection

Criminal activities have always been a part of human society, and even today, in a world of extremely advanced surveillance and policing capabilities, many different kinds of crimes are still committed in almost every social environment. However, since those who commit crimes are not representative of the majority of their community, members of these communities tend to wish to report crime when they see it; however, they are often reluctant to do so for fear of their own safety should the people they report identify them. Thus, a great deal of crime goes unreported, and investigations fail to gain key evidence from witnesses, which serves only to foster an environment in which criminal activity is more likely to occur. In order to address this problem, this paper proposes an online illegal event reporting scheme based on cloud technology, which combines digital certificates, symmetric keys, asymmetric keys, and digital signatures. The proposed scheme can process illegal activity reports from the reporting event to the issuing of a reward. The scheme not only ensures informers’ safety, anonymity and non-repudiation, but also prevents cases and reports being erased, and ensures data integrity. Furthermore, the proposed scheme is designed to be robust against abusive use, and is able to preclude false reports. Therefore, it provides a convenient and secure platform for reporting and fighting crime.
An analysis on the insights of the anti-vaccine movement from social media posts using k-means clustering algorithm and VADER sentiment analyzer

This study analyzes the insights and sentiments of the anti-vaccine movements in the social media. The data, comprising of tweets and excerpts, are pre-processed to omit noise and irrelevant data. They are clustered using the k-means clustering algorithm. Each word belonging to a cluster is processed by VADER sentiment analyzer. Prevalent sentiments per cluster label the mood associated in the cluster. The results suggest insights about vaccines such as: side effects, post-shot injuries, ineffectiveness, damage from ingredients, unvaccinated elite, reinforcement of the right to not vaccinate, toxic ingredients, big pharmaceuticals' profit maximization, links to autism, and health issues after getting vaccine shots. To evaluate k-means results, the silhouette score is determined to indicate how far a point is to other nearby clusters. The resulting average silhouette score of all points is 0.013540022 which indicates that the points are close to the decision boundaries.
Digital Signature Authentication Scheme with Message Recovery Based on the Use of Elliptic Curves

Digital signature authentication scheme provides secure communication between users. Digital signatures guarantee message integrity and authentication information about the origin of a message. In the present paper we describe existing algorithms for the formation and verification of the digital signature. The conducted studies made it possible to determine that the schemes with message recovery differ from the schemes with the addition that they do not completely hash messages, but instead use masking functions and redundancy of the message. It was determined that the most effective and optimal for further use is the Nyrberg-Rueppel scheme, which is based on elliptical curves (ECNR). In this paper, we present a new digital signature scheme with message recovery based on elliptic curve cryptograph on the base of the State standart 4145-2002. Elliptic curve cryptosystem provides greater security compared to integer factorization system and discrete logarithm system, for any given key size and bandwidth. The main difference between the proposed scheme was the replacement of the hash function with the hash token function, which makes the signature and verification procedure reversed and allows you to recove messages from the signature r-component.
Continuous Patient-Centric Sequence Generation via Sequentially Coupled Adversarial Learning

Analyzing massive patient-centric Electronic Health Records (EHRs) becomes a key to success for improving health care and treatment. However, the amount of these data is limited and the access to EHRs is difficult due to the issue of patient privacy. Thus high quality synthetic EHRs data is necessary to alleviate these issues. In this paper, we propose a Sequentially Coupled Generative Adversarial Network (SC-GAN) to generate continuous patient-centric data, including patient state and medication dosage data. SC-GAN consists of two generators which coordinate the generation of patient state and medication dosage in a unified model, revealing the clinical fact that the generation of patient state and medication dosage data have noticeable mutual influence on each other. To verify the quality of the synthetic data, we conduct comprehensive experiments to employ these data on real medical tasks, showing that data generated from SC-GAN leads to better performance than the data from other generative models.
A Novel Data Augmentation Method for Detection of Specific Aircraft in Remote Sensing RGB Images

In this paper, we propose a novel data augmentation method for the detection of specific aircraft in remote sensing RGB images. For object detection, the number of training data has great influence on the training of deep learning network. Researchers suggest that the extensive training samples in deep learning are indispensable. The extensive training samples can guarantee the accuracy and robustness of object detection. We refer to military aircraft and helicopter as specific aircraft. Due to the number of remote sensing images of specific aircraft is far less than that of civil aircraft, it is difficult to train an ideal detection model only by using the available specific aircraft images. Deep learning networks have the excellent ability on fault tolerance and generalization and can extract features from simulated aircraft samples. This means that the simulated aircraft samples can partly replace real images to some extent and reduce the need for detection models. Inspired by this, true remote sensing images are combined with specific aircraft three-dimensional models to form simulated images. Compared with previous data augmentation method, such as flipping and rotating, our method brings in new sample information. The experiments based on remote sensing images show the feasibility and effectiveness of our method. Meanwhile, the proposed method has compatibility with other data augmentation methods.
Real-time task allocation for a heterogeneous multi-UAV simultaneous attack

In this study, we address a coupled task allocation and path planning problem for a multi-unmanned aerial vehicle (UAV) reconnaissance/attack. Both coupled task allocation and path planning problem are addressed. We propose a task allocation algorithm for maximizing system utility and a path planning algorithm for simultaneous arrival. Moreover, we consider the targets resource requirement and UAVs resource constraints based on a contract net protocol for task allocation. Benefits of destroying the target and costs of UAV attacking target are considered in the objective function. To address the multi-UAV path planning problem, a combination of cooperative particle swarm optimization algorithm, coordination variables, and coordination functions is proposed. UAVs kinematics constraint is considered for the path planning method. Compared with a polynomial time coalition formation algorithm (PTCFA), simulation results show that the proposed algorithm improves the average performance by at least $8%$ with simultaneous arrival using the path planning method.