OpenRaiser/Intern-Atlas huggingface dataset:
Approximately Envy-Free Budget-Feasible Allocation
Venue: arXiv cs.GT 2021
Year: 2021

In the budget-feasible allocation problem, a set of items with varied sizes and values are to be allocated to a group of agents. Each agent has a budget constraint on the total size of items she can receive. The goal is to compute a feasible allocation that is envy-free (EF), in which the agents do not envy each other for the items they receive, nor do they envy a charity, who is endowed with all the unallocated items. Since EF allocations barely exist even without budget constraints, we are interested in the relaxed notion of envy-freeness up to one item (EF1). The computation of both exact and approximate EF1 allocations remains largely open, despite a recent effort by Wu et al. (IJCAI 2021) in showing that any budget-feasible allocation that maximizes the Nash Social Welfare (NSW) is 1/4-approximate EF1. In this paper, we move one step forward by showing that for agents with identical additive valuations, a 1/2-approximate EF1 allocation can be computed in polynomial time. For the uniform-budget and two-agent cases, we propose efficient algorithms for computing an exact EF1 allocation. We also consider the large budget setting, i.e., when the item sizes are infinitesimal compared with the agents' budgets, and show that both the NSW maximizing allocation and the allocation our polynomial-time algorithm computes have an approximation close to 1 regarding EF1.
Verification of a Smart Contract for a Simple Casino
Venue: arXiv cs.LO 2021
Year: 2021

We describe the verification of an existing smart contract for a simple casino application, using the Whiley specification and programming language, with a fully automated verification engine based on Boogie and Z3. After finding and fixing several specification and code issues in the smart contract, we are able to verify all the operations of the smart contract.
A More Compact Object Detector Head Network with Feature Enhancement and Relational Reasoning
Venue: arXiv cs.CV 2021
Year: 2021

Modeling implicit feature interaction patterns is of significant importance to object detection tasks. However, in the two-stage detectors, due to the excessive use of hand-crafted components, it is very difficult to reason about the implicit relationship of the instance features. To tackle this problem, we analyze three different levels of feature interaction relationships, namely, the dependency relationship between the cropped local features and global features, the feature autocorrelation within the instance, and the cross-correlation relationship between the instances. To this end, we propose a more compact object detector head network (CODH), which can not only preserve global context information and condense the information density, but also allows instance-wise feature enhancement and relational reasoning in a larger matrix space. Without bells and whistles, our method can effectively improve the detection performance while significantly reducing the parameters of the model, e.g., with our method, the parameters of the head network is 0.6 times smaller than the state-of-the-art Cascade R-CNN, yet the performance boost is 1.3% on COCO test-dev. Without losing generality, we can also build a more lighter head network for other multi-stage detectors by assembling our method.
Unsupervised Continual Learning via Self-Adaptive Deep Clustering Approach
Venue: arXiv cs.LG 2021
Year: 2021

Unsupervised continual learning remains a relatively uncharted territory in the existing literature because the vast majority of existing works call for unlimited access of ground truth incurring expensive labelling cost. Another issue lies in the problem of task boundaries and task IDs which must be known for model's updates or model's predictions hindering feasibility for real-time deployment. Knowledge Retention in Self-Adaptive Deep Continual Learner, (KIERA), is proposed in this paper. KIERA is developed from the notion of flexible deep clustering approach possessing an elastic network structure to cope with changing environments in the timely manner. The centroid-based experience replay is put forward to overcome the catastrophic forgetting problem. KIERA does not exploit any labelled samples for model updates while featuring a task-agnostic merit. The advantage of KIERA has been numerically validated in popular continual learning problems where it shows highly competitive performance compared to state-of-the art approaches. Our implementation is available in \textit{\url{https://github.com/ContinualAL/KIERA}}.
A keyword-driven approach to science
Venue: arXiv cs.CL 2021
Year: 2021

To a good extent, words can be understood as corresponding to patterns or categories that appeared in order to represent concepts and structures that are particularly important or useful in a given time and space. Words are characterized by not being completely general nor specific, in the sense that the same word can be instantiated or related to several different contexts, depending on specific situations. Indeed, the way in which words are instantiated and associated represents a particularly interesting aspect that can substantially help to better understand the context in which they are employed. Scientific words are no exception to that. In the present work, we approach the associations between a set of particularly relevant words in the sense of being not only frequently used in several areas, but also representing concepts that are currently related to some of the main standing challenges in science. More specifically, the study reported here takes into account the words "prediction", "model", "optimization", "complex", "entropy", "random", "deterministic", "pattern", and "database". In order to complement the analysis, we also obtain a network representing the relationship between the adopted areas. Many interesting results were found. First and foremost, several of the words were observed to have markedly distinct associations in different areas. Biology was found to be related to computer science, sharing associations with databases. Furthermore, for most of the cases, the words "complex", "model", and "prediction" were observed to have several strong associations.
An Adversarial Learning based Multi-Step Spoken Language Understanding System through Human-Computer Interaction
Venue: arXiv cs.CL 2021
Year: 2021

Most of the existing spoken language understanding systems can perform only semantic frame parsing based on a single-round user query. They cannot take users' feedback to update/add/remove slot values through multiround interactions with users. In this paper, we introduce a novel multi-step spoken language understanding system based on adversarial learning that can leverage the multiround user's feedback to update slot values. We perform two experiments on the benchmark ATIS dataset and demonstrate that the new system can improve parsing performance by at least $2.5\%$ in terms of F1, with only one round of feedback. The improvement becomes even larger when the number of feedback rounds increases. Furthermore, we also compare the new system with state-of-the-art dialogue state tracking systems and demonstrate that the new interactive system can perform better on multiround spoken language understanding tasks in terms of slot- and sentence-level accuracy.
A Theory of Language Learning
Venue: arXiv cs.CL 2021
Year: 2021

A theory of language learning is described, which uses Bayesian induction of feature structures (scripts) and script functions. Each word sense in a language is mentally represented by an m-script, a script function which embodies all the syntax and semantics of the word. M-scripts form a fully-lexicalised unification grammar, which can support adult language. Each word m-script can be learnt robustly from about six learning examples. The theory has been implemented as a computer model, which can bootstrap-learn a language from zero vocabulary. The Bayesian learning mechanism is (1) Capable: to learn arbitrarily complex meanings and syntactic structures; (2) Fast: learning these structures from a few examples each; (3) Robust: learning in the presence of much irrelevant noise, and (4) Self-repairing: able to acquire implicit negative evidence, using it to learn exceptions. Children learning language are clearly all of (1) - (4), whereas connectionist theories fail on (1) and (2), and symbolic theories fail on (3) and (4). The theory is in good agreement with many key facts of language acquisition, including facts which are problematic for other theories. It is compared with over 100 key cross-linguistic findings about acquisition of the lexicon, phrase structure, morphology, complementation and control, auxiliaries, verb argument structures, gaps and movement - in nearly all cases giving unforced agreement without extra assumptions.
An evaluation of template and ML-based generation of user-readable text from a knowledge graph
Venue: arXiv cs.CL 2021
Year: 2021

Typical user-friendly renderings of knowledge graphs are visualisations and natural language text. Within the latter HCI solution approach, data-driven natural language generation systems receive increased attention, but they are often outperformed by template-based systems due to suffering from errors such as content dropping, hallucination, or repetition. It is unknown which of those errors are associated significantly with low quality judgements by humans who the text is aimed for, which hampers addressing errors based on their impact on improving human evaluations. We assessed their possible association with an experiment availing of expert and crowdsourced evaluations of human authored text, template generated text, and sequence-to-sequence model generated text. The results showed that there was no significant association between human authored texts with errors and the low human judgements of naturalness and quality. There was also no significant association between machine learning generated texts with dropped or hallucinated slots and the low human judgements of naturalness and quality. Thus, both approaches appear to be viable options for designing a natural language interface for knowledge graphs.
Rate-Splitting Random Access Mechanism for Massive Machine Type Communications in 5G Cellular Internet-of-Things
Venue: arXiv cs.IT 2021
Year: 2021

The cellular Internet-of-Things has resulted in the deployment of millions of machine-type communication (MTC) devices. These massive number of devices must communicate with a single gNodeB (gNB) via the random access channel (RACH) mechanism. However, existing RACH mechanisms are inefficient when dealing with such large number of devices. To address this issue, we propose the rate-splitting random access (RSRA) mechanism, which uses rate splitting and decoding in rate-splitting multiple access (RSMA) to improve RACH success rates. The proposed mechanism divides the message into common and private messages and enhances the decoding performance. We demonstrate, using extensive simulations, that the proposed RSRA mechanism significantly improves the success rate of MTC in cellular IoT networks. We also evaluate the performance of the proposed mechanism with increasing number of devices and received power difference.
The Associative Multifractal Process: A Novel Model for Computer Network Traffic Flows
Venue: arXiv cs.NI 2021
Year: 2021

A novel constructive mathematical model based on the multifractal formalism in order to accurately characterizing the localized fluctuations present in the course of traffic flows today high-speed computer networks is presented. The proposed model has the target to analyze self-similar second-order time series representative of traffic flows in terms of their roughness and impulsivity.
Developing a cyber security culture: Current practices and future needs
Venue: arXiv cs.CR 2021
Year: 2021

While the creation of a strong security culture has been researched and discussed for decades, it continues to elude many businesses. Part of the challenge faced is distilling pertinent, recent academic findings and research into useful guidance. In this article, we aim to tackle this issue by conducting a state-of-the-art study into organisational cyber security culture research. This work investigates four questions, including how cyber security culture is defined, what factors are essential to building and maintaining such a culture, the frameworks proposed to cultivate a security culture and the metrics suggested to assess it. Through the application of the PRISMA systematic literature review technique, we identify and analyse 58 research articles from the last 10 years (2010-2020). Our findings demonstrate that while there have been notable changes in the use of terms (e.g., information security culture and cyber security culture), many of the most influential factors across papers are similar. Top management support, policy and procedures, and awareness for instance, are critical in engendering cyber security culture. Many of the frameworks reviewed revealed common foundations, with organisational culture playing a substantial role in crafting appropriate cyber security culture models. Questionnaires and surveys are the most used tool to measure cyber security culture, but there are also concerns as to whether more dynamic measures are needed. For practitioners, this article highlights factors and models essential to the creation and management of a robust security culture. For research, we produce an up-to-date characterisation of the field and also define open issues deserving of further attention such as the role of change management processes and national culture in an enterprise's cyber security culture.
Realtime Robust Malicious Traffic Detection via Frequency Domain Analysis
Venue: arXiv cs.CR 2021
Year: 2021

Machine learning (ML) based malicious traffic detection is an emerging security paradigm, particularly for zero-day attack detection, which is complementary to existing rule based detection. However, the existing ML based detection has low detection accuracy and low throughput incurred by inefficient traffic features extraction. Thus, they cannot detect attacks in realtime especially in high throughput networks. Particularly, these detection systems similar to the existing rule based detection can be easily evaded by sophisticated attacks. To this end, we propose Whisper, a realtime ML based malicious traffic detection system that achieves both high accuracy and high throughput by utilizing frequency domain features. It utilizes sequential features represented by the frequency domain features to achieve bounded information loss, which ensures high detection accuracy, and meanwhile constrains the scale of features to achieve high detection throughput. Particularly, attackers cannot easily interfere with the frequency domain features and thus Whisper is robust against various evasion attacks. Our experiments with 42 types of attacks demonstrate that, compared with the state-of-theart systems, Whisper can accurately detect various sophisticated and stealthy attacks, achieving at most 18.36% improvement, while achieving two orders of magnitude throughput. Even under various evasion attacks, Whisper is still able to maintain around 90% detection accuracy.
Adding Interactivity to Education of Complex Wireless Networks Using Digital Game-Based Learning
Venue: arXiv cs.CY 2021
Year: 2021

Can we make undergraduate engineering education easier and more fun? This research aims to see if we can answer the ambitious question! The digital game-based learning (DGBL) has been found to increase the efficacy of learning when applied in engineering classes thanks to its ability to make students feel easy and fun. However, the state-of-the-art DGBL schemes still observe challenges in various aspects including cost, efficacy, readiness of instructors and students, etc. Motivated from the challenges, this research proposes to design a DGBL platform that features visualized and systematic views. Specifically, we identify the blockchain applied to wireless communications and networking systems as a key ecosystem that we capitalize the benefit of the proposed platform.. As such, in this paper, we lay out a comprehensive DGBL pedagogy that includes (i) creation of relevant assignment activities and class materials in a relevant course and (ii) evaluation of the pedagogical efficacy. In a long-term view, a successful delivery of this research will increase the confidence of undergraduate engineering students on the "in-concert" dynamics of various factors determining the performance of a blockchain system built on a wireless network.
Efficient Maximum Likelihood Decoding of Polar Codes Over the Binary Erasure Channel
Venue: arXiv cs.IT 2021
Year: 2021

A new algorithm for efficient exact maximum likelihood decoding of polar codes (which may be CRC augmented), transmitted over the binary erasure channel, is presented. The algorithm applies a matrix triangulation process on a sparse polar code parity check matrix, followed by solving a small size linear system over GF(2). To implement the matrix triangulation, we apply belief propagation decoding type operations. We also indicate how this decoder can be implemented in parallel for low latency decoding. Numerical simulations are used to evaluate the performance and computational complexity of the new algorithm.
Designing color symmetry in stigmergic art
Venue: arXiv cs.NE 2021
Year: 2021

Color symmetry is an extension of symmetry imposed by isometric transformations and means that the colors of geometrical objects are assigned according to the symmetry properties of the objects. A color symmetry permutes the coloring of the objects consistently with their symmetry group. We apply this concept to bio-inspired generative art. Therefore, we interpret the geometrical objects as motifs that may repeat themselves with a symmetry-consistent coloring. The motifs are obtained by design principles from stigmergy. We discuss a design procedure and present visual results.
Doing good by fighting fraud: Ethical anti-fraud systems for mobile payments
Venue: arXiv cs.CR 2021
Year: 2021

App builders commonly use security challenges, a form of step-up authentication, to add security to their apps. However, the ethical implications of this type of architecture has not been studied previously. In this paper, we present a large-scale measurement study of running an existing anti-fraud security challenge, Boxer, in real apps running on mobile devices. We find that although Boxer does work well overall, it is unable to scan effectively on devices that run its machine learning models at less than one frame per second (FPS), blocking users who use inexpensive devices. With the insights from our study, we design Daredevil, anew anti-fraud system for scanning payment cards that work swell across the broad range of performance characteristics and hardware configurations found on modern mobile devices. Daredevil reduces the number of devices that run at less than one FPS by an order of magnitude compared to Boxer, providing a more equitable system for fighting fraud. In total, we collect data from 5,085,444 real devices spread across 496 real apps running production software and interacting with real users.
Rethinking Token-Mixing MLP for MLP-based Vision Backbone
Venue: arXiv cs.CV 2021
Year: 2021

In the past decade, we have witnessed rapid progress in the machine vision backbone. By introducing the inductive bias from the image processing, convolution neural network (CNN) has achieved excellent performance in numerous computer vision tasks and has been established as \emph{de facto} backbone. In recent years, inspired by the great success achieved by Transformer in NLP tasks, vision Transformer models emerge. Using much less inductive bias, they have achieved promising performance in computer vision tasks compared with their CNN counterparts. More recently, researchers investigate using the pure-MLP architecture to build the vision backbone to further reduce the inductive bias, achieving good performance. The pure-MLP backbone is built upon channel-mixing MLPs to fuse the channels and token-mixing MLPs for communications between patches. In this paper, we re-think the design of the token-mixing MLP. We discover that token-mixing MLPs in existing MLP-based backbones are spatial-specific, and thus it is sensitive to spatial translation. Meanwhile, the channel-agnostic property of the existing token-mixing MLPs limits their capability in mixing tokens. To overcome those limitations, we propose an improved structure termed as Circulant Channel-Specific (CCS) token-mixing MLP, which is spatial-invariant and channel-specific. It takes fewer parameters but achieves higher classification accuracy on ImageNet1K benchmark.
Design for a blind stereoscopic picture taker
Venue: arXiv cs.RO 2021
Year: 2021

An Schematical Design for an Autonomous Picture taker used for obtaining Point clouds from pictures taken inside a House. In this case we are proposing the use of an equation programmed inside an embedded system that will be tracking the points inside a room and then, open the space between two cameras of same type in order to take pictures that later will be used to create the cloud points for the mathematical model that the latter user will apply to that pictures.
Object Detection Based Handwriting Localization
Venue: arXiv cs.CV 2021
Year: 2021

We present an object detection based approach to localize handwritten regions from documents, which initially aims to enhance the anonymization during the data transmission. The concatenated fusion of original and preprocessed images containing both printed texts and handwritten notes or signatures are fed into the convolutional neural network, where the bounding boxes are learned to detect the handwriting. Afterwards, the handwritten regions can be processed (e.g. replaced with redacted signatures) to conceal the personally identifiable information (PII). This processing pipeline based on the deep learning network Cascade R-CNN works at 10 fps on a GPU during the inference, which ensures the enhanced anonymization with minimal computational overheads. Furthermore, the impressive generalizability has been empirically showcased: the trained model based on the English-dominant dataset works well on the fictitious unseen invoices, even in Chinese. The proposed approach is also expected to facilitate other tasks such as handwriting recognition and signature verification.