OpenRaiser/Intern-Atlas huggingface dataset:
Exploiting Social Media Content for Self-Supervised Style Transfer
Venue: arXiv cs.CL 2022
Year: 2022

Recent research on style transfer takes inspiration from unsupervised neural machine translation (UNMT), learning from large amounts of non-parallel data by exploiting cycle consistency loss, back-translation, and denoising autoencoders. By contrast, the use of self-supervised NMT (SSNMT), which leverages (near) parallel instances hidden in non-parallel data more efficiently than UNMT, has not yet been explored for style transfer. In this paper we present a novel Self-Supervised Style Transfer (3ST) model, which augments SSNMT with UNMT methods in order to identify and efficiently exploit supervisory signals in non-parallel social media posts. We compare 3ST with state-of-the-art (SOTA) style transfer models across civil rephrasing, formality and polarity tasks. We show that 3ST is able to balance the three major objectives (fluency, content preservation, attribute transfer accuracy) the best, outperforming SOTA models on averaged performance across their tested tasks in automatic and human evaluation.
BlueSky: Activity Control: A Vision for "Active" Security Models for Smart Collaborative Systems
Venue: arXiv cs.CR 2022
Year: 2022

Cyber physical ecosystem connects different intelligent devices over heterogeneous networks. Various operations are performed on smart objects to ensure efficiency and to support automation in smart environments. An Activity (defined by Gupta and Sandhu) reflects the current state of an object, which changes in response to requested operations. Due to multiple running activities on different objects, it is critical to secure collaborative systems considering run-time decisions impacted due to related activities (and other parameters) supporting active enforcement of access control decision. Recently, Gupta and Sandhu proposed Activity-Centric Access Control (ACAC) and discussed the notion of activity as a prime abstraction for access control in collaborative systems. The model provides an active security approach that considers activity decision factors such as authorizations, obligations, conditions, and dependencies among related device activities. This paper takes a step forward and presents the core components of an ACAC model and compares with other security models differentiating novel properties of ACAC. We highlight how existing models do not (or in limited scope) support `active' decision and enforcement of authorization in collaborative systems. We propose a hierarchical structure for a family of ACAC models by gradually adding the properties related to notion of activity and discuss states of an activity. We highlight the convergence of ACAC with Zero Trust tenets to reflect how ACAC supports necessary security posture of distributed and connected smart ecosystems. This paper aims to gain a better understanding of ACAC in collaborative systems supporting novel abstractions, properties and requirements.
A Method for Self-Service Rehabilitation Training of Human Lower Limbs
Venue: arXiv cs.RO 2022
Year: 2022

In recent years, the research of rehabilitation robot technology has become a hotspot in the field of rehabilitation medicine engineering and robotics. To assist active rehabilitation in patients with unilateral lower extremity injury, we propose a new self-service rehabilitation training method to control the injured lower extremity through its contralateral healthy upper limbs. Firstly, the movement data of upper limbs and lower limbs of healthy people in normal walking state are obtained by gait measurement experiment. Secondly, the eigenvectors of upper limb and lower limb movements in a single movement cycle are extracted respectively. Thirdly, the linear mapping relationship between the upper limbs movement and the lower limbs movement is identified using the least squares method. Finally, the simulation experiment of self-service rehabilitation training is implemented on MATLAB/Simulink. The results indicate that the identified linear mapping model can achieve good accuracy and adaptability. The self-service rehabilitation training method is effective for helping patients with unilateral limb injury to make rehabilitation training on themselves.
A Sequence Repetition Node-Based Successive Cancellation List Decoder for 5G Polar Codes: Algorithm and Implementation
Venue: arXiv cs.IT 2022
Year: 2022

Due to the low-latency and high-reliability requirements of 5G, low-complexity node-based successive cancellation list (SCL) decoding has received considerable attention for use in 5G communications systems. By identifying special constituent codes in the decoding tree and immediately decoding these, node-based SCL decoding provides a significant reduction in decoding latency compared to conventional SCL decoding. However, while there exists many types of nodes, the current node-based SCL decoders are limited by the lack of a more generalized node that can efficiently decode a larger number of different constituent codes to further reduce the decoding time. In this paper, we extend a recent generalized node, the sequence repetition (SR) node to SCL decoding and we describe the first implementation of an SR-List decoder. By merging certain SR-List decoding operations and applying various optimizations for 5G New Radio (NR) polar codes, our optimized SR-List decoding algorithm increases the throughput by almost ${2\times}$ compared to a similar state-of-the-art node-based SCL decoder. We also present our hardware implementation of the optimized SR-List decoding algorithm which supports all 5G NR polar codes. Synthesis results show that our SR-List decoder can achieve a $2.94 \, \mathrm{Gbps}$ throughput and $6.70\, \mathrm{Gbps} / \mathrm{mm}^2$ area efficiency for ${L=8}$.
BFCAI at SemEval-2022 Task 6: Multi-Layer Perceptron for Sarcasm Detection in Arabic Texts
Venue: arXiv cs.CL 2022
Year: 2022

This paper describes the systems submitted to iSarcasm shared task. The aim of iSarcasm is to identify the sarcastic contents in Arabic and English text. Our team participated in iSarcasm for the Arabic language. A multi-Layer machine learning based model has been submitted for Arabic sarcasm detection. In this model, a vector space TF-IDF has been used as for feature representation. The submitted system is simple and does not need any external resources. The test results show encouraging results.
Hyperion: A Case for Unified, Self-Hosting, Zero-CPU Data-Processing Units (DPUs)
Venue: arXiv cs.AR 2022
Year: 2022

Since the inception of computing, we have been reliant on CPU-powered architectures. However, today this reliance is challenged by manufacturing limitations (CMOS scaling), performance expectations (stalled clocks, Turing tax), and security concerns (microarchitectural attacks). To re-imagine our computing architecture, in this work we take a more radical but pragmatic approach and propose to eliminate the CPU with its design baggage, and integrate three primary pillars of computing, i.e., networking, storage, and computing, into a single, self-hosting, unified CPU-free Data Processing Unit (DPU) called Hyperion. In this paper, we present the case for Hyperion, its design choices, initial work-in-progress details, and seek feedback from the systems community.
Analysing and strengthening OpenWPM's reliability
Venue: arXiv cs.CR 2022
Year: 2022

Automated browsers are widely used to study the web at scale. Their premise is that they measure what regular browsers would encounter on the web. In practice, deviations due to detection of automation have been found. To what extent automated browsers can be improved to reduce such deviations has so far not been investigated in detail. In this paper, we investigate this for a specific web automation framework: OpenWPM, a popular research framework specifically designed to study web privacy. We analyse (1) detectability of OpenWPM, (2) prevalence of OpenWPM detection, and (3) integrity of OpenWPM's data recording. Our analysis reveals OpenWPM is easily detectable. We measure to what extent fingerprint-based detection is already leveraged against OpenWPM clients on 100,000 sites and observe that it is commonly detected (~14% of front pages). Moreover, we discover integrated routines in scripts to specifically detect OpenWPM clients. Our investigation of OpenWPM's data recording integrity identifies novel evasion techniques and previously unknown attacks against OpenWPM's instrumentation. We investigate and develop mitigations to address the identified issues. In conclusion, we find that reliability of automation frameworks should not be taken for granted. Identifiability of such frameworks should be studied, and mitigations deployed, to improve reliability.
Enhancing data locality of the conjugate gradient method for high-order matrix-free finite-element implementations
Venue: arXiv cs.MS 2022
Year: 2022

This work investigates a variant of the conjugate gradient (CG) method and embeds it into the context of high-order finite-element schemes with fast matrix-free operator evaluation and cheap preconditioners like the matrix diagonal. Relying on a data-dependency analysis and appropriate enumeration of degrees of freedom, we interleave the vector updates and inner products in a CG iteration with the matrix-vector product with only minor organizational overhead. As a result, around 90% of the vector entries of the three active vectors of the CG method are transferred from slow RAM memory exactly once per iteration, with all additional access hitting fast cache memory. Node-level performance analyses and scaling studies on up to 147k cores show that the CG method with the proposed performance optimizations is around two times faster than a standard CG solver as well as optimized pipelined CG and s-step CG methods for large sizes that exceed processor caches, and provides similar performance near the strong scaling limit.
Finite two-dimensional proof systems for non-finitely axiomatizable logics
Venue: arXiv cs.LO 2022
Year: 2022

The characterizing properties of a proof-theoretical presentation of a given logic may hang on the choice of proof formalism, on the shape of the logical rules and of the sequents manipulated by a given proof system, on the underlying notion of consequence, and even on the expressiveness of its linguistic resources and on the logical framework into which it is embedded. Standard (one-dimensional) logics determined by (non-deterministic) logical matrices are known to be axiomatizable by analytic and possibly finite proof systems as soon as they turn out to satisfy a certain constraint of sufficient expressiveness. In this paper we introduce a recipe for cooking up a two-dimensional logical matrix (or B-matrix) by the combination of two (possibly partial) non-deterministic logical matrices. We will show that such a combination may result in B-matrices satisfying the property of sufficient expressiveness, even when the input matrices are not sufficiently expressive in isolation, and we will use this result to show that one-dimensional logics that are not finitely axiomatizable may inhabit finitely axiomatizable two-dimensional logics, becoming, thus, finitely axiomatizable by the addition of an extra dimension. We will illustrate the said construction using a well-known logic of formal inconsistency called mCi. We will first prove that this logic is not finitely axiomatizable by a one-dimensional (generalized) Hilbert-style system. Then, taking advantage of a known 5-valued non-deterministic logical matrix for this logic, we will combine it with another one, conveniently chosen so as to give rise to a B-matrix that is axiomatized by a two-dimensional Hilbert-style system that is both finite and analytic.
The games we play: critical complexity improves machine learning
Venue: arXiv cs.CY 2022
Year: 2022

When mathematical modelling is applied to capture a complex system, multiple models are often created that characterize different aspects of that system. Often, a model at one level will produce a prediction which is contradictory at another level but both models are accepted because they are both useful. Rather than aiming to build a single unified model of a complex system, the modeller acknowledges the infinity of ways of capturing the system of interest, while offering their own specific insight. We refer to this pragmatic applied approach to complex systems -- one which acknowledges that they are incompressible, dynamic, nonlinear, historical, contextual, and value-laden -- as Open Machine Learning (Open ML). In this paper we define Open ML and contrast it with some of the grand narratives of ML of two forms: 1) Closed ML, ML which emphasizes learning with minimal human input (e.g. Google's AlphaZero) and 2) Partially Open ML, ML which is used to parameterize existing models. To achieve this, we use theories of critical complexity to both evaluate these grand narratives and contrast them with the Open ML approach. Specifically, we deconstruct grand ML `theories' by identifying thirteen 'games' played in the ML community. These games lend false legitimacy to models, contribute to over-promise and hype about the capabilities of artificial intelligence, reduce wider participation in the subject, lead to models that exacerbate inequality and cause discrimination and ultimately stifle creativity in research. We argue that best practice in ML should be more consistent with critical complexity perspectives than with rationalist, grand narratives.
Features of Perceived Metaphoricity on the Discourse Level: Abstractness and Emotionality
Venue: arXiv cs.CL 2022
Year: 2022

Research on metaphorical language has shown ties between abstractness and emotionality with regard to metaphoricity; prior work is however limited to the word and sentence levels, and up to date there is no empirical study establishing the extent to which this is also true on the discourse level. This paper explores which textual and perceptual features human annotators perceive as important for the metaphoricity of discourses and expressions, and addresses two research questions more specifically. First, is a metaphorically-perceived discourse more abstract and more emotional in comparison to a literally-perceived discourse? Second, is a metaphorical expression preceded by a more metaphorical/abstract/emotional context than a synonymous literal alternative? We used a dataset of 1,000 corpus-extracted discourses for which crowdsourced annotators (1) provided judgements on whether they perceived the discourses as more metaphorical or more literal, and (2) systematically listed lexical terms which triggered their decisions in (1). Our results indicate that metaphorical discourses are more emotional and to a certain extent more abstract than literal discourses. However, neither the metaphoricity nor the abstractness and emotionality of the preceding discourse seem to play a role in triggering the choice between synonymous metaphorical vs. literal expressions. Our dataset is available at https://www.ims.uni-stuttgart.de/data/discourse-met-lit.
SoK: The Impact of Unlabelled Data in Cyberthreat Detection
Venue: arXiv cs.CR 2022
Year: 2022

Machine learning (ML) has become an important paradigm for cyberthreat detection (CTD) in the recent years. A substantial research effort has been invested in the development of specialized algorithms for CTD tasks. From the operational perspective, however, the progress of ML-based CTD is hindered by the difficulty in obtaining the large sets of labelled data to train ML detectors. A potential solution to this problem are semisupervised learning (SsL) methods, which combine small labelled datasets with large amounts of unlabelled data. This paper is aimed at systematization of existing work on SsL for CTD and, in particular, on understanding the utility of unlabelled data in such systems. To this end, we analyze the cost of labelling in various CTD tasks and develop a formal cost model for SsL in this context. Building on this foundation, we formalize a set of requirements for evaluation of SsL methods, which elucidates the contribution of unlabelled data. We review the state-of-the-art and observe that no previous work meets such requirements. To address this problem, we propose a framework for assessing the benefits of unlabelled data in SsL. We showcase an application of this framework by performing the first benchmark evaluation that highlights the tradeoffs of 9 existing SsL methods on 9 public datasets. Our findings verify that, in some cases, unlabelled data provides a small, but statistically significant, performance gain. This paper highlights that SsL in CTD has a lot of room for improvement, which should stimulate future research in this field.
DL4DS -- Deep Learning for empirical DownScaling
Venue: arXiv cs.LG 2022
Year: 2022

A common task in Earth Sciences is to infer climate information at local and regional scales from global climate models. Dynamical downscaling requires running expensive numerical models at high resolution which can be prohibitive due to long model runtimes. On the other hand, statistical downscaling techniques present an alternative approach for learning links between the large- and local-scale climate in a more efficient way. A large number of deep neural network-based approaches for statistical downscaling have been proposed in recent years, mostly based on convolutional architectures developed for computer vision and super-resolution tasks. This paper presents DL4DS, Deep Learning for empirical DownScaling, a python library that implements a wide variety of state-of-the-art and novel algorithms for downscaling gridded Earth Science data with deep neural networks. DL4DS has been designed with the goal of providing a general framework for training convolutional neural networks with configurable architectures and learning strategies to facilitate the conduction of comparative and ablation studies in a robust way. We showcase the capabilities of DL4DS on air quality CAMS data over the western Mediterranean area. The DL4DS library can be found in this repository: https://github.com/carlos-gg/dl4ds
Attention-based Multimodal Feature Representation Model for Micro-video Recommendation
Venue: arXiv cs.IR 2022
Year: 2022

In recommender systems, models mostly use a combination of embedding layers and multilayer feedforward neural networks. The high-dimensional sparse original features are downscaled in the embedding layer and then fed into the fully connected network to obtain prediction results. However, the above methods have a rather obvious problem, that is, the features directly input are treated as independent individuals, and in fact there are internal correlations between features and features, and even different features have different importance in the recommendation. In this regard, this paper adopts a self-attentive mechanism to mine the internal correlations between features as well as their relative importance. In recent years, as a special form of attention mechanism, self-attention mechanism is favored by many researchers. The self-attentive mechanism captures the internal correlation of data or features by learning itself, thus reducing the dependence on external sources. Therefore, this paper adopts a multi-headed self-attentive mechanism to mine the internal correlations between features and thus learn the internal representation of features. At the same time, considering the rich information often hidden between features, the new feature representation obtained by crossover between the two is likely to imply the new description of the user likes the item. However, not all crossover features are meaningful, i.e., there is a problem of limited expression of feature combinations. Therefore, this paper adopts an attention-based approach to learn the external cross-representation of features.
A weakly supervised framework for high-resolution crop yield forecasts
Venue: arXiv cs.LG 2022
Year: 2022

Predictor inputs and label data for crop yield forecasting are not always available at the same spatial resolution. We propose a deep learning framework that uses high resolution inputs and low resolution labels to produce crop yield forecasts for both spatial levels. The forecasting model is calibrated by weak supervision from low resolution crop area and yield statistics. We evaluated the framework by disaggregating regional yields in Europe from parent statistical regions to sub-regions for five countries (Germany, Spain, France, Hungary, Italy) and two crops (soft wheat and potatoes). Performance of weakly supervised models was compared with linear trend models and Gradient-Boosted Decision Trees (GBDT). Higher resolution crop yield forecasts are useful to policymakers and other stakeholders. Weakly supervised deep learning methods provide a way to produce such forecasts even in the absence of high resolution yield data.
Stop the Spread: A Contextual Integrity Perspective on the Appropriateness of COVID-19 Vaccination Certificates
Venue: arXiv cs.CY 2022
Year: 2022

We present an empirical study exploring how privacy influences the acceptance of vaccination certificate (VC) deployments across different realistic usage scenarios. The study employed the privacy framework of Contextual Integrity, which has been shown to be particularly effective in capturing people's privacy expectations across different contexts. We use a vignette methodology, where we selectively manipulate salient contextual parameters to learn whether and how they affect people's attitudes towards VCs. We surveyed 890 participants from a demographically-stratified sample of the US population to gauge the acceptance and overall attitudes towards possible VC deployments to enforce vaccination mandates and the different information flows VCs might entail. Analysis of results collected as part of this study is used to derive general normative observations about different possible VC practices and to provide guidance for the possible deployments of VCs in different contexts.
Rank-based Heuristics for Optimizing the Execution of Product Data Models
Venue: arXiv cs.DB 2022
Year: 2022

The Product Data Model (PDM) is an example of a data-centric approach to modelling information-intensive business processes, which offers exibility and facilitates process optimization. Because the approach is declarative in nature, there may be multiple, alternative execution plans that can produce the desired end product. To generate such plans, several heuristics have been proposed in the literature. The contributions of this work are twofold: (i) we propose new heuristics that capitalize on established techniques for optimizing data-intensive work ows in terms of execution time and cost and transfer them to business processes; and (ii) we extensively evaluate the existing solutions. Our results shed light on the merits of each heuristic and show that our new heuristics can yield significant benefits.