OpenRaiser/Intern-Atlas huggingface dataset:
Drone Flocking Optimization using NSGA-II and Principal Component Analysis
Venue: arXiv cs.RO 2022
Year: 2022

Individual agents in natural systems like flocks of birds or schools of fish display a remarkable ability to coordinate and communicate in local groups and execute a variety of tasks efficiently. Emulating such natural systems into drone swarms to solve problems in defence, agriculture, industry automation and humanitarian relief is an emerging technology. However, flocking of aerial robots while maintaining multiple objectives, like collision avoidance, high speed etc. is still a challenge. In this paper, optimized flocking of drones in a confined environment with multiple conflicting objectives is proposed. The considered objectives are collision avoidance (with each other and the wall), speed, correlation, and communication (connected and disconnected agents). Principal Component Analysis (PCA) is applied for dimensionality reduction, and understanding the collective dynamics of the swarm. The control model is characterised by 12 parameters which are then optimized using a multi-objective solver (NSGA-II). The obtained results are reported and compared with that of the CMA-ES algorithm. The study is particularly useful as the proposed optimizer outputs a Pareto Front representing different types of swarms which can applied to different scenarios in the real world.
Reinforced Swin-Convs Transformer for Underwater Image Enhancement
Venue: arXiv cs.CV 2022
Year: 2022

Underwater Image Enhancement (UIE) technology aims to tackle the challenge of restoring the degraded underwater images due to light absorption and scattering. To address problems, a novel U-Net based Reinforced Swin-Convs Transformer for the Underwater Image Enhancement method (URSCT-UIE) is proposed. Specifically, with the deficiency of U-Net based on pure convolutions, we embedded the Swin Transformer into U-Net for improving the ability to capture the global dependency. Then, given the inadequacy of the Swin Transformer capturing the local attention, the reintroduction of convolutions may capture more local attention. Thus, we provide an ingenious manner for the fusion of convolutions and the core attention mechanism to build a Reinforced Swin-Convs Transformer Block (RSCTB) for capturing more local attention, which is reinforced in the channel and the spatial attention of the Swin Transformer. Finally, the experimental results on available datasets demonstrate that the proposed URSCT-UIE achieves state-of-the-art performance compared with other methods in terms of both subjective and objective evaluations. The code will be released on GitHub after acceptance.
Uniform Interpolation in Coalgebraic Modal Logic
Venue: arXiv cs.LO 2022
Year: 2022

A logic has uniform interpolation if its formulas can be projected down to given subsignatures, preserving all logical consequences that do not mention the removed symbols; the weaker property of (Craig) interpolation allows the projected formula -- the interpolant -- to be different for each logical consequence of the original formula. These properties are of importance, e.g., in the modularization of logical theories. We study interpolation in the context of coalgebraic modal logics, i.e. modal logics axiomatized in rank 1, restricting for clarity to the case with finitely many modalities. Examples of such logics include the modal logics K and KD, neighbourhood logic and its monotone variant, finite-monoid-weighted logics, and coalition logic. We introduce a notion of one-step (uniform) interpolation, which refers only to a restricted logic without nesting of modalities, and show that a coalgebraic modal logic has uniform interpolation if it has one-step interpolation. Moreover, we identify preservation of finite surjective weak pullbacks as a sufficient, and in the monotone case necessary, condition for one-step interpolation. We thus prove or reprove uniform interpolation for most of the examples listed above.
The use of Semantic Technologies in Computer Science Curriculum: A Systematic Review
Venue: arXiv cs.CY 2022
Year: 2022

Semantic technologies are evolving and being applied in several research areas, including the education domain. This paper presents the outcomes of a systematic review carried out to provide an overview of the application of semantic technologies in the context of the Computer Science curriculum and discuss the limitations in this field whilst offering insights for future research. A total of 4,510 studies were reviewed, and 37 were analysed and reported. As a result, while semantic technologies have been increasingly used to develop Computer Science curricula, the alignment of ontologies and accurate curricula assessment appears to be the most significant limitations to the widespread adoption of such technologies.
Many a little Makes a Mickle: Probing Backscattering Energy Recycling for Backscatter Communications
Venue: arXiv cs.IT 2022
Year: 2022

In this paper, we investigate and analyze full-duplex-based backscatter communications with multiple backscatter devices (BDs). Different from previous works where only the energy from the energy source is harvested, BDs are also allowed to harvest energy from previous BDs by recycling the backscattering energy. Our objective is to maximize the total energy efficiency (EE) of the system via joint time scheduling, beamforming design, and reflection coefficient (RC) adjustment while satisfying the constraints on the total time, the transmit energy consumption, the circuit energy consumption and the achievable throughput for each BD by taking the causality and the non-linearity of energy harvesting into account. To deal with this intractable non-convex problem, we reformulate the problem by utilizing the Dinkelbach's method. Subsequently, an alternative iterative algorithm is designed to solve it. Simulation results show that the proposed algorithm achieves a much better EE than the benchmark algorithms.
Preserve Pre-trained Knowledge: Transfer Learning With Self-Distillation For Action Recognition
Venue: arXiv cs.CV 2022
Year: 2022

Video-based action recognition is one of the most popular topics in computer vision. With recent advances of selfsupervised video representation learning approaches, action recognition usually follows a two-stage training framework, i.e., self-supervised pre-training on large-scale unlabeled sets and transfer learning on a downstream labeled set. However, catastrophic forgetting of the pre-trained knowledge becomes the main issue in the downstream transfer learning of action recognition, resulting in a sub-optimal solution. In this paper, to alleviate the above issue, we propose a novel transfer learning approach that combines self-distillation in fine-tuning to preserve knowledge from the pre-trained model learned from the large-scale dataset. Specifically, we fix the encoder from the last epoch as the teacher model to guide the training of the encoder from the current epoch in the transfer learning. With such a simple yet effective learning strategy, we outperform state-of-the-art methods on widely used UCF101 and HMDB51 datasets in action recognition task.
Deep vs. Shallow Learning: A Benchmark Study in Low Magnitude Earthquake Detection
Venue: arXiv cs.LG 2022
Year: 2022

While deep learning models have seen recent high uptake in the geosciences, and are appealing in their ability to learn from minimally processed input data, as black box models they do not provide an easy means to understand how a decision is reached, which in safety-critical tasks especially can be problematical. An alternative route is to use simpler, more transparent white box models, in which task-specific feature construction replaces the more opaque feature discovery process performed automatically within deep learning models. Using data from the Groningen Gas Field in the Netherlands, we build on an existing logistic regression model by the addition of four further features discovered using elastic net driven data mining within the catch22 time series analysis package. We then evaluate the performance of the augmented logistic regression model relative to a deep (CNN) model, pre-trained on the Groningen data, on progressively increasing noise-to-signal ratios. We discover that, for each ratio, our logistic regression model correctly detects every earthquake, while the deep model fails to detect nearly 20 % of seismic events, thus justifying at least a degree of caution in the application of deep models, especially to data with higher noise-to-signal ratios.
Improved Low-Depth Set-Multilinear Circuit Lower Bounds
Venue: arXiv cs.CC 2022
Year: 2022

We prove strengthened lower bounds for constant-depth set-multilinear formulas. More precisely, we show that over any field, there is an explicit polynomial $f$ in VNP defined over $n^2$ variables, and of degree $n$, such that any product-depth $Δ$ set-multilinear formula computing $f$ has size at least $n^{Ω\left( n^{1/Δ}/Δ\right)}$. The hard polynomial $f$ comes from the class of Nisan-Wigderson (NW) design-based polynomials. Our lower bounds improve upon the recent work of Limaye, Srinivasan and Tavenas (STOC 2022), where a lower bound of the form $(\log n)^{Ω(Δn^{1/Δ})}$ was shown for the size of product-depth $Δ$ set-multilinear formulas computing the iterated matrix multiplication (IMM) polynomial of the same degree and over the same number of variables as $f$. Moreover, our lower bounds are novel for any $Δ\geq 2$. For general set-multilinear formulas, a lower bound of the form $ n^{Ω(\log n)}$ was already obtained by Raz (J. ACM 2009) for the more general model of multilinear formulas. The techniques of LST give a different route to set-multilinear formula lower bounds, and allow them to obtain a lower bound of the form $(\log n)^{Ω(\log n)}$ for the size of general set-multilinear formulas computing the IMM polynomial. Our proof techniques are another variation on those of LST, and enable us to show an improved lower bound (matching that of Raz) of the form $n^{Ω(\log n)}$, albeit for the same polynomial $f$ in VNP (the NW polynomial). As observed by LST, if the same $n^{Ω(\log n)}$ size lower bounds for unbounded-depth set-multilinear formulas could be obtained for the IMM polynomial, then using the self-reducibility of IMM and using hardness escalation results, this would imply super-polynomial lower bounds for general algebraic formulas.
MUTR3D: A Multi-camera Tracking Framework via 3D-to-2D Queries
Venue: arXiv cs.CV 2022
Year: 2022

Accurate and consistent 3D tracking from multiple cameras is a key component in a vision-based autonomous driving system. It involves modeling 3D dynamic objects in complex scenes across multiple cameras. This problem is inherently challenging due to depth estimation, visual occlusions, appearance ambiguity, etc. Moreover, objects are not consistently associated across time and cameras. To address that, we propose an end-to-end \textbf{MU}lti-camera \textbf{TR}acking framework called MUTR3D. In contrast to prior works, MUTR3D does not explicitly rely on the spatial and appearance similarity of objects. Instead, our method introduces \textit{3D track query} to model spatial and appearance coherent track for each object that appears in multiple cameras and multiple frames. We use camera transformations to link 3D trackers with their observations in 2D images. Each tracker is further refined according to the features that are obtained from camera images. MUTR3D uses a set-to-set loss to measure the difference between the predicted tracking results and the ground truths. Therefore, it does not require any post-processing such as non-maximum suppression and/or bounding box association. MUTR3D outperforms state-of-the-art methods by 5.3 AMOTA on the nuScenes dataset. Code is available at: \url{https://github.com/a1600012888/MUTR3D}.
LoopStack: a Lightweight Tensor Algebra Compiler Stack
Venue: arXiv cs.LG 2022
Year: 2022

We present LoopStack, a domain specific compiler stack for tensor operations, composed of a frontend, LoopTool, and an efficient optimizing code generator, LoopNest. This stack enables us to compile entire neural networks and generate code targeting the AVX2, AVX512, NEON, and NEONfp16 instruction sets while incorporating optimizations often missing from other machine learning compiler backends. We evaluate our stack on a collection of full neural networks and commonly used network blocks as well as individual operators, and show that LoopStack generates machine code that matches and frequently exceeds the performance of in state-of-the-art machine learning frameworks in both cases. We also show that for a large collection of schedules LoopNest's compilation is orders of magnitude faster than LLVM, while resulting in equal or improved run time performance. Additionally, LoopStack has a very small memory footprint - a binary size of 245KB, and under 30K lines of effective code makes it ideal for use on mobile and embedded devices.
POLITICS: Pretraining with Same-story Article Comparison for Ideology Prediction and Stance Detection
Venue: arXiv cs.CL 2022
Year: 2022

Ideology is at the core of political science research. Yet, there still does not exist general-purpose tools to characterize and predict ideology across different genres of text. To this end, we study Pretrained Language Models using novel ideology-driven pretraining objectives that rely on the comparison of articles on the same story written by media of different ideologies. We further collect a large-scale dataset, consisting of more than 3.6M political news articles, for pretraining. Our model POLITICS outperforms strong baselines and the previous state-of-the-art models on ideology prediction and stance detection tasks. Further analyses show that POLITICS is especially good at understanding long or formally written texts, and is also robust in few-shot learning scenarios.
Attack Detection for Networked Control Systems Using Event-Triggered Dynamic Watermarking
Venue: arXiv cs.CR 2022
Year: 2022

Dynamic watermarking schemes can enhance the cyber attack detection capability of networked control systems (NCSs). This paper presents a linear event-triggered solution to conventional dynamic watermarking (CDW) schemes. Firstly, the limitations of CDW schemes for event-triggered state estimation based NCSs are investigated. Secondly, a new event-triggered dynamic watermarking (ETDW) scheme is designed by treating watermarking as symmetric key encryption, based on the limit convergence theorem in probability. Its security property against the generalized replay attacks (GRAs) is also discussed in the form of bounded asymptotic attack power. Thirdly, finite sample ETDW tests are designed with matrix concentration inequalities. Finally, experimental results of a networked inverted pendulum system demonstrate the validity of our proposed scheme.
Reducing the Cost of Training Security Classifier (via Optimized Semi-Supervised Learning)
Venue: arXiv cs.CR 2022
Year: 2022

Background: Most of the existing machine learning models for security tasks, such as spam detection, malware detection, or network intrusion detection, are built on supervised machine learning algorithms. In such a paradigm, models need a large amount of labeled data to learn the useful relationships between selected features and the target class. However, such labeled data can be scarce and expensive to acquire. Goal: To help security practitioners train useful security classification models when few labeled training data and many unlabeled training data are available. Method: We propose an adaptive framework called Dapper, which optimizes 1) semi-supervised learning algorithms to assign pseudo-labels to unlabeled data in a propagation paradigm and 2) the machine learning classifier (i.e., random forest). When the dataset class is highly imbalanced, Dapper then adaptively integrates and optimizes a data oversampling method called SMOTE. We use the novel Bayesian Optimization to search a large hyperparameter space of these tuning targets. Result: We evaluate Dapper with three security datasets, i.e., the Twitter spam dataset, the malware URLs dataset, and the CIC-IDS-2017 dataset. Experimental results indicate that we can use as low as 10% of original labeled data but achieve close or even better classification performance than using 100% labeled data in a supervised way. Conclusion: Based on those results, we would recommend using hyperparameter optimization with semi-supervised learning when dealing with shortages of labeled security data.
SightSteeple: Agreeing to Disagree with Functional Blockchain Consensus
Venue: arXiv cs.CR 2022
Year: 2022

Classical and contemporary distributed consensus protocols, may they be for binary agreement, state machine replication, or blockchain consensus, require all protocol participants in a peer-to-peer system to agree on exactly the same information as part of the consensus payload. Although this model of consensus is extensively studied, and is useful for most consensus based decentralized applications, it falls short of defining correct distributed systems which mandate participant credential based privileged visibility into the consensus payload, through the consensus protocol itself. We introduce a new paradigm for distributed consensus, called functional blockchain consensus. Functional blockchain consensus allows each blockchain protocol participant to agree on some distinct sub-information of the list of transactions, as a function of the credentials of the participant in the blockchain system, instead of agreeing on the entire list of transactions. We motivate two adversary models, one with a standard crash-fault adversary and another with a novel rational-fault adversary, to compromise functional blockchain consensus. We then present two versions of a blockchain protocol called SightSteeple, that achieves functional blockchain consensus in the said fault models. SightSteeple relies on a novel combination of standard blockchain consensus and functional encryption, among other primitives, to achieve its goals of correctness. Finally, we discuss practical uses of functional blockchain consensus based asymmetric distributed ledgers, and motivate off-shoot constructions that can result from this new consensus paradigm.
Large Neighborhood Search based on Neural Construction Heuristics
Venue: arXiv cs.LG 2022
Year: 2022

We propose a Large Neighborhood Search (LNS) approach utilizing a learned construction heuristic based on neural networks as repair operator to solve the vehicle routing problem with time windows (VRPTW). Our method uses graph neural networks to encode the problem and auto-regressively decodes a solution and is trained with reinforcement learning on the construction task without requiring any labels for supervision. The neural repair operator is combined with a local search routine, heuristic destruction operators and a selection procedure applied to a small population to arrive at a sophisticated solution approach. The key idea is to use the learned model to re-construct the partially destructed solution and to introduce randomness via the destruction heuristics (or the stochastic policy itself) to effectively explore a large neighborhood.
More Programming Than Programming: Teaching Formal Methods in a Software Engineering Programme
Venue: arXiv cs.SE 2022
Year: 2022

Formal methods for software correctness are critical to the future of software engineering - and so must be an essential part of software engineering education. Unfortunately, formal methods are often resisted by students due to perceived difficulty, mathematicity, and practical irrelevance. We redeveloped our software correctness course by taking a programming intensive approach, using the solver-aided language Dafny to provide instant formative feedback via automated assessment. Our redeveloped course increased student retention and resulted in the best evaluation for the course for at least ten years.
Semantic localization on BIM-generated maps using a 3D LiDAR sensor
Venue: arXiv cs.RO 2022
Year: 2022

Conventional sensor-based localization relies on high-precision maps, which are generally built using specialized mapping techniques involving high labor and computational costs. In the architectural, engineering and construction industry, Building Information Models (BIM) are available and can provide informative descriptions of environments. This paper explores an effective way to localize a mobile 3D LiDAR sensor on BIM-generated maps considering both geometric and semantic properties. First, original BIM elements are converted to semantically augmented point cloud maps using categories and locations. After that, a coarse-to-fine semantic localization is performed to align laser points to the map based on iterative closest point registration. The experimental results show that the semantic localization can track the pose successfully with only one LiDAR sensor, thus demonstrating the feasibility of the proposed mapping-free localization framework. The results also show that using semantic information can help reduce localization errors on BIM-generated maps.
A Novel Approach to Fairness in Automated Decision-Making using Affective Normalization
Venue: arXiv cs.CY 2022
Year: 2022

Any decision, such as one about who to hire, involves two components. First, a rational component, i.e., they have a good education, they speak clearly. Second, an affective component, based on observables such as visual features of race and gender, and possibly biased by stereotypes. Here we propose a method for measuring the affective, socially biased, component, thus enabling its removal. That is, given a decision-making process, these affective measurements remove the affective bias in the decision, rendering it fair across a set of categories defined by the method itself. We thus propose that this may solve three key problems in intersectional fairness: (1) the definition of categories over which fairness is a consideration; (2) an infinite regress into smaller and smaller groups; and (3) ensuring a fair distribution based on basic human rights or other prior information. The primary idea in this paper is that fairness biases can be measured using affective coherence, and that this can be used to normalize outcome mappings. We aim for this conceptual work to expose a novel method for handling fairness problems that uses emotional coherence as an independent measure of bias that goes beyond statistical parity.
Discrete models of continuous behavior of collective adaptive systems
Venue: arXiv cs.AI 2022
Year: 2022

Artificial ants are "small" units, moving autonomously on a shared, dynamically changing "space", directly or indirectly exchanging some kind of information. Artificial ants are frequently conceived as a paradigm for collective adaptive systems. In this paper, we discuss means to represent continuous moves of "ants" in discrete models. More generally, we challenge the role of the notion of "time" in artificial ant systems and models. We suggest a modeling framework that structures behavior along causal dependencies, and not along temporal relations. We present all arguments by help of a simple example. As a modeling framework we employ Heraklit; an emerging framework that already has proven its worth in many contexts.
On the Role of Postconditions in Dynamic First-Order Epistemic Logic
Venue: arXiv cs.LO 2022
Year: 2022

Dynamic Epistemic Logic (DEL) is a logic that models information change in a multi-agent setting through the use of action models with pre- and post-conditions. In a recent work, DEL has been extended to first-order epistemic logic (DFOEL), with a proof that the resulting Epistemic Planning Problem is decidable, as long as action models pre- and post-conditions are non-modal and the first-order domain is finite. Our contribution highlights the role post-conditions have in DFOEL. We show that the Epistemic Planning Problem with possibly infinite first-order domains is undecidable if the non-modal event post-conditions may contain first-order quantifiers, while, on the contrary, the problem becomes decidable when event post-conditions are quantifier-free. The latter result is non-trivial and makes an extensive use of automatic structures.