Reviewerly/exHarmony huggingface dataset:
Functional code building genetic programming

General program synthesis has become an important application area for genetic programming (GP), and for artificial intelligence more generally. Code Building Genetic Programming (CBGP) is a recently introduced GP method for general program synthesis that leverages reflection and first class specifications to support the evolution of programs that may use arbitrary data types, polymorphism, and functions drawn from existing codebases. However, neither a formal description nor a thorough benchmarking of CBGP have yet been reported. In this work, we formalize the method of CBGP using algorithms from type theory. Specially, we show that a functional programming language and a Hindley-Milner type system can be used to evolve type-safe programs using the process abstractly described in the original CBGP paper. Furthermore, we perform a comprehensive analysis of the search performance of this functional variant of CBGP compared to other contemporary GP program synthesis methods.
Präzi: from package-based to call-based dependency networks

Modern programming languages such as Java, JavaScript, and Rust encourage software reuse by hosting diverse and fast-growing repositories of highly interdependent packages (i.e., reusable libraries) for their users. The standard way to study the interdependence between software packages is to infer a package dependency network by parsing manifest data. Such networks help answer questions such as “How many packages have dependencies to packages with known security issues?” or “What are the most used packages?”. However, an overlooked aspect in existing studies is that manifest-inferred relationships do not necessarily examine the actual usage of these dependencies in source code. To better model dependencies between packages, we developed Präzi, an approach combining manifests and call graphs of packages. Präzi constructs a dependency network at the more fine-grained function-level, instead of at the manifest level. This paper discusses a prototypical Präzi implementation for the popular system programming language Rust. We use Präzi to characterize Rust’s package repository, Crates.io, at the function level and perform a comparative study with metadata-based networks. Our results show that metadata-based networks generalize how packages use their dependencies. Using Präzi, we find packages call only 40% of their resolved dependencies, and that manual analysis of 34 cases reveals that not all packages use a dependency the same way. We argue that researchers and practitioners interested in understanding how developers or programs use dependencies should account for its context—not the sum of all resolved dependencies.
PO-ELIC: Perception-Oriented Efficient Learned Image Coding

In the past years, learned image compression (LIC) has achieved remarkable performance. The recent LIC methods outperform VVC in both PSNR and MS-SSIM. However, the low bit-rate reconstructions of LIC suffer from artifacts such as blurring, color drifting and texture missing. Moreover, those varied artifacts make image quality metrics correlate badly with human perceptual quality. In this paper, we propose PO-ELIC, i.e., Perception-Oriented Efficient Learned Image Coding. To be specific, we adapt ELIC, one of the state-of-the-art LIC models, with adversarial training techniques. We apply a mixture of losses including hinge-form adversarial loss, Charbonnier loss, and style loss, to finetune the model towards better perceptual quality. Experimental results demonstrate that our method achieves comparable perceptual quality with HiFiC with much lower bitrate.
Noiseless linear amplification in quantum target detection using Gaussian states

Abstract Quantum target detection aims to utilise quantum technologies to achieve performances in target detection not possible through purely classical means. Quantum illumination is an example of this, based on signal–idler entanglement, promising a potential 6 dB advantage in error exponent over its optimal classical counterpart. So far, receiver designs achieving this optimal reception remain elusive with many proposals based on Gaussian processes appearing unable to utilise quantum information contained within Gaussian state sources. This paper considers the employment of a noiseless linear amplifier at the detection stage of a quantum illumination-based quantum target detection protocol. Such a non-Gaussian amplifier offers a means of probabilistically amplifying an incoming signal without the addition of noise. Considering symmetric hypothesis testing, the quantum Chernoff bound is derived and limits on detection error probability is analysed for both the two-mode squeezed vacuum state and the coherent state classical benchmark. Our findings show that in such a scheme the potential quantum advantage is amplified even in regimes where quantum illumination alone offers no advantage, thereby extending its potential use. The same cannot be said for coherent states, whose performances are generally bounded by that without amplification.
Vulpedia: Detecting vulnerable ethereum smart contracts via abstracted vulnerability signatures

Recent years have seen smart contracts are getting increasingly popular in building trustworthy decentralized applications. Previous research has proposed static and dynamic techniques to detect vulnerabilities in smart contracts. These tools check vulnerable contracts against several predefined rules. However, the emerging new vulnerable types and programming skills to prevent possible vulnerabilities emerging lead to a large number of false positive and false negative reports of tools. To address this, we propose Vulpedia, which mines expressive vulnerability signatures from contracts. Vulpedia is based on the relaxed assumption that the owner of contract is not malicious. Specifically, we extract structural program features from vulnerable and benign contracts as vulnerability signatures, and construct a systematic detection method based on detection rules composed of vulnerability signatures. Compared with the rules defined by state-of-the-arts, our approach can extract more expressive rules to achieve better completeness (i.e., detection recall) and soundness (i.e., precision). We further evaluate Vulpedia with four baselines (i.e., Slither, Securify, SmartCheck and Oyente) on the testing dataset consisting of 17,770 contracts. The experiment results show that Vulpedia achieves best performance of precision on 4 types of vulnerabilities and leading recall on 3 types of vulnerabilities meanwhile exhibiting the great efficiency performance.
3D medical images security via light-field imaging

This Letter proposes a selective encryption scheme for three-dimensional (3D) medical images using light-field imaging and two-dimensional (2D) Moore cellular automata (MCA). We first utilize convolutional neural networks (CNNs) to obtain the saliency of each elemental image (EI) originating from a 3D medical image with different viewpoints, and successfully extract the region of interest (ROI) in each EI. In addition, we use 2D MCA with balanced rule to encrypt the ROI of each EI. Finally, the decrypted elemental image array (EIA) can be reconstructed into a full-color and full-parallax 3D image using the display device, which can be visually displayed to doctors so that they can observe from different angles to design accurate treatment plans and improve the level of medical treatment. Our work also requires no preprocessing of 3D images, which is more efficient than the method of using slices for encryption.
Learning Motion Primitives Automata for Autonomous Driving Applications

Motion planning methods often rely on libraries of primitives. The selection of primitives is then crucial for assuring feasible solutions and good performance within the motion planner. In the literature, the library is usually designed by either learning from demonstration, relying entirely on data, or by model-based approaches, with the advantage of exploiting the dynamical system’s property, e.g., symmetries. In this work, we propose a method combining data with a dynamical model to optimally select primitives. The library is designed based on primitives with highest occurrences within the data set, while Lie group symmetries from a model are analysed in the available data to allow for structure-exploiting primitives. We illustrate our technique in an autonomous driving application. Primitives are identified based on data from human driving, with the freedom to build libraries of different sizes as a parameter of choice. We also compare the extracted library with a custom selection of primitives regarding the performance of obtained solutions for a street layout based on a real-world scenario.
Evaluating Similarity of Spectrogram-like Images of DC Motor Sounds by Pearson Correlation Coefficient

Three main approaches on how audio signals can be used as input to a deep learning model are: extracting hand-crafted features from audio signals, mapping audio signals into appropriate images such as spectrogram-like ones, and using directly raw audio signals. Among these approaches, the usage of spectrogram-like images represents a compromise regarding the bias enforced by the processing (seen in hand-crafted features) and computational demands (seen in raw audio signals). When any of the spectrogram-like images is used as a deep learning model input, then different techniques for image processing become available and can be implemented. They include techniques for assessing the image similarity, implementing image matching, and image recognition. The topic of this paper is similarity of spectrogram-like images obtained from DC motor sounds. In that respect, relevant measures of image similarity are first reviewed, and then one of them - the Pearson correlation coefficient - is applied for evaluating the similarity within the same class and between two classes of different spectrogram-like images.
A Self Supervised StyleGAN for Image Annotation and Classification With Extremely Limited Labels

The recent success of learning-based algorithms can be greatly attributed to the immense amount of annotated data used for training. Yet, many datasets lack annotations due to the high costs associated with labeling, resulting in degraded performances of deep learning methods. Self-supervised learning is frequently adopted to mitigate the reliance on massive labeled datasets since it exploits unlabeled data to learn relevant feature representations. In this work, we propose SS-StyleGAN, a self-supervised approach for image annotation and classification suitable for extremely small annotated datasets. This novel framework adds self-supervision to the StyleGAN architecture by integrating an encoder that learns the embedding to the StyleGAN latent space, which is well-known for its disentangled properties. The learned latent space enables the smart selection of representatives from the data to be labeled for improved classification performance. We show that the proposed method attains strong classification results using small labeled datasets of sizes 50 and even 10. We demonstrate the superiority of our approach for the tasks of COVID-19 and liver tumor pathology identification.
You Only Infer Once: Cross-Modal Meta-Transfer for Referring Video Object Segmentation

We present YOFO (You Only inFer Once), a new paradigm for referring video object segmentation (RVOS) that operates in an one-stage manner. Our key insight is that the language descriptor should serve as target-specific guidance to identify the target object, while a direct feature fusion of image and language can increase feature complexity and thus may be sub-optimal for RVOS. To this end, we propose a meta-transfer module, which is trained in a learning-to-learn fashion and aims to transfer the target-specific information from the language domain to the image domain, while discarding the uncorrelated complex variations of language description. To bridge the gap between the image and language domains, we develop a multi-scale cross-modal feature mining block that aggregates all the essential features required by RVOS from both domains and generates regression labels for the meta-transfer module. The whole system can be trained in an end-to-end manner and shows competitive performance against state-of-the-art two-stage approaches.
Fast Sparse Decision Tree Optimization via Reference Ensembles

Sparse decision tree optimization has been one of the most fundamental problems in AI since its inception and is a challenge at the core of interpretable machine learning. Sparse decision tree optimization is computationally hard, and despite steady effort since the 1960's, breakthroughs have been made on the problem only within the past few years, primarily on the problem of finding optimal sparse decision trees. However, current state-of-the-art algorithms often require impractical amounts of computation time and memory to find optimal or near-optimal trees for some real-world datasets, particularly those having several continuous-valued features. Given that the search spaces of these decision tree optimization problems are massive, can we practically hope to find a sparse decision tree that competes in accuracy with a black box machine learning model? We address this problem via smart guessing strategies that can be applied to any optimal branch-and-bound-based decision tree algorithm. The guesses come from knowledge gleaned from black box models. We show that by using these guesses, we can reduce the run time by multiple orders of magnitude while providing bounds on how far the resulting trees can deviate from the black box's accuracy and expressive power. Our approach enables guesses about how to bin continuous features, the size of the tree, and lower bounds on the error for the optimal decision tree. Our experiments show that in many cases we can rapidly construct sparse decision trees that match the accuracy of black box models. To summarize: when you are having trouble optimizing, just guess.
Unsupervised Anomaly Detection by Robust Density Estimation

Density estimation is a widely used method for unsupervised anomaly detection. However, the presence of anomalies in training data may severely impact the density estimation process, thereby hampering the use of more sophisticated density estimation methods such as those based on deep neural networks. In this work, we propose RobustRealNVP, a robust deep density estimation framework for unsupervised anomaly detection. Our approach differs from existing flow-based models from two perspectives. First, RobustRealNVP discards data points with low estimated densities during optimization to prevent them from corrupting the density estimation process. Furthermore, it imposes Lipschitz regularization to ensure smoothness in the estimated density function. We demonstrate the robustness of our algorithm against anomalies in training data from both theoretical and empirical perspectives. The results show that our algorithm outperforms state-of-the-art unsupervised anomaly detection methods.
Limitations of nearest-neighbor quantum networks

Quantum communication research has in recent years shifted to include multipartite networks for which questions of quantum network routing naturally emerge. To understand the potential for multipartite routing, we focus on the most promising architectures for future quantum networks---those connecting nodes close to each other. Nearest-neighbor networks, such as rings, lines, and grids, have been studied under different communication scenarios to facilitate the sharing of quantum resources especially in the presence of bottlenecks. We analyze the potential of nearest-neighbor entangling gate quantum networks and identify some serious limitations by demonstrating that rings and lines cannot overcome common bottleneck communication problems.
Training of future STEAM teachers: Comparison between primary degree students and secondary master's degree students

In recent years, the presence of the acronym STEAM has been growing in the educational field. All around the world we are faced with a growing demand for professional STEM skills, however, who has the responsibility to train future STEAM teachers? In this article, we explain the results of an investigation with university students pursuing a degree in primary education and Master’s degree students in secondary education, with technology speciality, participating in a seminar about STEAM projects design in two consecutive academic calendar years (18/19 and 19/20). We analyse the responses to a questionnaire which was specifically designed to discover their knowledge about STEAM and the predisposition towards its extended study with a set of semi-structured interviews to delve into the causes of the responses. Previous knowledge about the meaning of STEM and the predisposition of pre-service teachers towards the future design of projects based on the STEAM disciplines are compared. The results show significant differences between the previous knowledge and the perceptions of the teachers from both specializations: primary and secondary level. The results also confirm the low levels of prior knowledge with a slight tendency towards improvement when two consecutive courses are compared. The findings emphasize the need to integrate STEAM teacher training into official curricula.
Garbage collection makes rust easier to use

Rust is a general-purpose programming language that is both type-and memory-safe. Rust does not use a garbage collector, but rather achieves these properties through a sophisticated, but complex, type system. Doing so makes Rust very efficient, but makes Rust relatively hard to learn and use. We designed Bronze, an optional, library-based garbage collector for Rust. To see whether Bronze could make Rust more usable, we conducted a randomized controlled trial with volunteers from a 633--person class, collecting data from 428 students in total. We found that for a task that required managing complex aliasing, Bronze users were more likely to complete the task in the time available, and those who did so required only about a third as much time (4 hours vs. 12 hours). We found no significant difference in total time, even though Bronze users re-did the task without Bronze afterward. Surveys indicated that ownership, borrowing, and lifetimes were primary causes of the challenges that users faced when using Rust.
Deep-Learning-Based Multiple Model Tracking Method for Targets with Complex Maneuvering Motion

The effective detection of unmanned aerial vehicle (UAV) targets is of great significance to guarantee national military security and social stability. In recent years, with the development of communication and control technology, the movement of UAVs has become increasingly flexible and complex, presenting diverse trajectory forms and different motion models in different phases. The Gaussian mixture probability hypothesis density filter incorporating the linear Gaussian jump Markov system approach (LGJMS-GMPHD) provides an efficient method for tracking multiple maneuvering targets, as applied to the switching of motions between a set of models in a Markovian chain. However, in practice, the motion model parameters of targets are generally unknown and the model switching is uncertain. When the preset filtering model parameters are mismatched, the tracking performance is dramatically degraded. In this paper, within the framework of the LGJMS-GMPHD filter, a deep-learning-based multiple model tracking method is proposed. First, an adaptive turn rate estimation network is designed to solve the filtering model mismatch caused by unknown turn rate parameters in coordinate turn models. Second, a filter state modification network is designed to solve the large tracking errors in the maneuvering phase caused by uncertain motion model switching. Finally, based on simulations of multiple maneuvering targets in cluttered environments and experimental field data verification, it can be concluded that the proposed method has strong adaptability to multiple maneuvering forms and can effectively improve the tracking performance of targets with complex maneuvering motion.
Wear-free gesture recognition based on residual features of RFID signals

Traditionally, RFID is frequently used in identification and localization. In this paper, an extension application of RFID is designed to recognize gestures. Currently, gesture recognition is mainly used for feature extraction through wearable sensors and video cameras, which have shortcomings such as inconvenience to carry and interference with obstacles. This paper proposes a gesture recognition system based on radio frequency identification (RFID), where users do not need to wear devices. In the proposed model, the interference information generated by the gesture action on the tag signal is used as the fingerprint feature of the action. To obtain satisfactory recognition, the signal diversity is first increased through the tag array. Then, the RSSI and phase signal are normalized to eliminate offset and noise before training. Furthermore, a residual neural network (ResNet) is carefully built as a gesture classification model. The experimental results show that the recognition system achieves more recognition accuracy than existing methods, and the average gesture recognition accuracy reaches 95.5%.
Exploiting Playbacks in Unsupervised Domain Adaptation for 3D Object Detection in Self-Driving Cars

Self-driving cars must detect other traffic participants like vehicles and pedestrians in 3D in order to plan safe routes and avoid collisions. State-of-the-art 3D object detectors, based on deep learning, have shown promising accuracy but are prone to over-fit domain idiosyncrasies, making them fail in new environments-a serious problem for the robustness of self-driving cars. In this paper, we propose a novel learning approach that reduces this gap by fine-tuning the detector on high-quality pseudo-labels in the target domain - pseudo-labels that are automatically generated after driving based on replays of previously recorded driving sequences. In these replays, object tracks are smoothed forward and backward in time, and detections are interpolated and extrapolated-crucially, leveraging future information to catch hard cases such as missed detections due to occlusions or far ranges. We show, across five autonomous driving datasets, that fine-tuning the object detector on these pseudo-labels substantially reduces the domain gap to new driving environments, yielding strong improvements detection reliability and accuracy.
Semantic-aware Texture-Structure Feature Collaboration for Underwater Image Enhancement

Underwater image enhancement has become an attractive topic as a significant technology in marine engineering and aquatic robotics. However, the limited number of datasets and imperfect hand-crafted ground truth weaken its robustness to unseen scenarios, and hamper the application to high-level vision tasks. To address the above limitations, we develop an efficient and compact enhancement network in collaboration with a high-level semantic-aware pretrained model, aiming to exploit its hierarchical feature representation as an auxiliary for the low-level underwater image enhancement. Specifically, we tend to characterize the shallow layer features as textures while the deep layer features as structures in the semantic-aware model, and propose a multi-path Contextual Feature Refinement Module (CFRM) to refine features in multiple scales and model the correlation between different features. In addition, a feature dominative network is devised to perform channel-wise modulation on the aggregated texture and structure features for the adaptation to different feature patterns of the enhancement network. Extensive experiments on benchmarks demonstrate that the proposed algorithm achieves more appealing results and outperforms state-of-the-art methods by large margins. We also apply the proposed algorithm to the underwater salient object detection task to reveal the favorable semantic-aware ability for high-level vision tasks. The code is available at STSC.