OpenRaiser/Intern-Atlas huggingface dataset:
The Software Heritage License Dataset (2022 Edition)
Venue: arXiv cs.SE 2023
Year: 2023

Context: When software is released publicly, it is common to include with it either the full text of the license or licenses under which it is published, or a detailed reference to them. Therefore public licenses, including FOSS (free, open source software) licenses, are usually publicly available in source code repositories.Objective: To compile a dataset containing as many documents as possible that contain the text of software licenses, or references to the license terms. Once compiled, characterize the dataset so that it can be used for further research, or practical purposes related to license analysis.Method: Retrieve from Software Heritage-the largest publicly available archive of FOSS source code-all versions of all files whose names are commonly used to convey licensing terms. All retrieved documents will be characterized in various ways, using automated and manual analyses.Results: The dataset consists of 6.9 million unique license files. Additional metadata about shipped license files is also provided, making the dataset ready to use in various contexts, including: file length measures, MIME type, SPDX license (detected using ScanCode), and oldest appearance. The results of a manual analysis of 8102 documents is also included, providing a ground truth for further analysis. The dataset is released as open data as an archive file containing all deduplicated license files, plus several portable CSV files with metadata, referencing files via cryptographic checksums.Conclusions: Thanks to the extensive coverage of Software Heritage, the dataset presented in this paper covers a very large fraction of all software licenses for public code. We have assembled a large body of software licenses, characterized it quantitatively and qualitatively, and validated that it is mostly composed of licensing information and includes almost all known license texts. The dataset can be used to conduct empirical studies on open source licensing, training of automated license classifiers, natural language processing (NLP) analyses of legal texts, as well as historical and phylogenetic studies on FOSS licensing. It can also be used in practice to improve tools detecting licenses in source code.
Orthogonal Constant-Amplitude Sequence Families for System Parameter Identification in Spectrally Compact OFDM
Venue: arXiv cs.IT 2023
Year: 2023

In rectangularly-pulsed orthogonal frequency division multiplexing (OFDM) systems, constant-amplitude (CA) sequences are desirable to construct preamble/pilot waveforms to facilitate system parameter identification (SPI). Orthogonal CA sequences are generally preferred in various SPI applications like random-access channel identification. However, the number of conventional orthogonal CA sequences (e.g., Zadoff-Chu sequences) that can be adopted in cellular communication without causing sequence identification ambiguity is insufficient. Such insufficiency causes heavy performance degradation for SPI requiring a large number of identification sequences. Moreover, rectangularly-pulsed OFDM preamble/pilot waveforms carrying conventional CA sequences suffer from large power spectral sidelobes and thus exhibit low spectral compactness. This paper is thus motivated to develop several order-I CA sequence families which contain more orthogonal CA sequences while endowing the corresponding OFDM preamble/pilot waveforms with fast-decaying spectral sidelobes. Since more orthogonal sequences are provided, the developed order-I CA sequence families can enhance the performance characteristics in SPI requiring a large number of identification sequences over multipath channels exhibiting short-delay channel profiles, while composing spectrally compact OFDM preamble/pilot waveforms.
Up-to-date Threat Modelling for Soft Privacy on Smart Cars
Venue: arXiv cs.CR 2023
Year: 2023

Physical persons playing the role of car drivers consume data that is sourced from the Internet and, at the same time, themselves act as sources of relevant data. It follows that citizens' privacy is potentially at risk while they drive, hence the need to model privacy threats in this application domain. This paper addresses the privacy threats by updating a recent threat-modelling methodology and by tailoring it specifically to the soft privacy target property, which ensures citizens' full control on their personal data. The methodology now features the sources of documentation as an explicit variable that is to be considered. It is demonstrated by including a new version of the de-facto standard LINDDUN methodology as well as an additional source by ENISA which is found to be relevant to soft privacy. The main findings are a set of 23 domain-independent threats, 43 domain-specific assets and 525 domain-dependent threats for the target property in the automotive domain. While these exceed their previous versions, their main value is to offer self-evident support to at least two arguments. One is that LINDDUN has evolved much the way our original methodology already advocated because a few of our previously suggested extensions are no longer outstanding. The other one is that ENISA's treatment of privacy aboard smart cars should be extended considerably because our 525 threats fall in the same scope.
Improving Knot Prediction in Wood Logs with Longitudinal Feature Propagation
Venue: arXiv cs.CV 2023
Year: 2023

The quality of a wood log in the wood industry depends heavily on the presence of both outer and inner defects, including inner knots that are a result of the growth of tree branches. Today, locating the inner knots require the use of expensive equipment such as X-ray scanners. In this paper, we address the task of predicting the location of inner defects from the outer shape of the logs. The dataset is built by extracting both the contours and the knots with X-ray measurements. We propose to solve this binary segmentation task by leveraging convolutional recurrent neural networks. Once the neural network is trained, inference can be performed from the outer shape measured with cheap devices such as laser profilers. We demonstrate the effectiveness of our approach on fir and spruce tree species and perform ablation on the recurrence to demonstrate its importance.
BHSD: A 3D Multi-Class Brain Hemorrhage Segmentation Dataset
Venue: arXiv cs.CV 2023
Year: 2023

Intracranial hemorrhage (ICH) is a pathological condition characterized by bleeding inside the skull or brain, which can be attributed to various factors. Identifying, localizing and quantifying ICH has important clinical implications, in a bleed-dependent manner. While deep learning techniques are widely used in medical image segmentation and have been applied to the ICH segmentation task, existing public ICH datasets do not support the multi-class segmentation problem. To address this, we develop the Brain Hemorrhage Segmentation Dataset (BHSD), which provides a 3D multi-class ICH dataset containing 192 volumes with pixel-level annotations and 2200 volumes with slice-level annotations across five categories of ICH. To demonstrate the utility of the dataset, we formulate a series of supervised and semi-supervised ICH segmentation tasks. We provide experimental results with state-of-the-art models as reference benchmarks for further model developments and evaluations on this dataset.
Octopus: A Heterogeneous In-network Computing Accelerator Enabling Deep Learning for network
Venue: arXiv cs.AR 2023
Year: 2023

Deep learning (DL) for network models have achieved excellent performance in the field and are becoming a promising component in future intelligent network system. Programmable in-network computing device has great potential to deploy DL for network models, however, existing device cannot afford to run a DL model. The main challenges of data-plane supporting DL-based network models lie in computing power, task granularity, model generality and feature extracting. To address above problems, we propose Octopus: a heterogeneous in-network computing accelerator enabling DL for network models. A feature extractor is designed for fast and efficient feature extracting. Vector accelerator and systolic array work in a heterogeneous collaborative way, offering low-latency-highthroughput general computing ability for packet-and-flow-based tasks. Octopus also contains on-chip memory fabric for storage and connecting, and Risc-V core for global controlling. The proposed Octopus accelerator design is implemented on FPGA. Functionality and performance of Octopus are validated in several use-cases, achieving performance of 31Mpkt/s feature extracting, 207ns packet-based computing latency, and 90kflow/s flow-based computing throughput.
Boundary-RL: Reinforcement Learning for Weakly-Supervised Prostate Segmentation in TRUS Images
Venue: arXiv cs.CV 2023
Year: 2023

We propose Boundary-RL, a novel weakly supervised segmentation method that utilises only patch-level labels for training. We envision the segmentation as a boundary detection problem, rather than a pixel-level classification as in previous works. This outlook on segmentation may allow for boundary delineation under challenging scenarios such as where noise artefacts may be present within the region-of-interest (ROI) boundaries, where traditional pixel-level classification-based weakly supervised methods may not be able to effectively segment the ROI. Particularly of interest, ultrasound images, where intensity values represent acoustic impedance differences between boundaries, may also benefit from the boundary delineation approach. Our method uses reinforcement learning to train a controller function to localise boundaries of ROIs using a reward derived from a pre-trained boundary-presence classifier. The classifier indicates when an object boundary is encountered within a patch, as the controller modifies the patch location in a sequential Markov decision process. The classifier itself is trained using only binary patch-level labels of object presence, which are the only labels used during training of the entire boundary delineation framework, and serves as a weak signal to inform the boundary delineation. The use of a controller function ensures that a sliding window over the entire image is not necessary. It also prevents possible false-positive or -negative cases by minimising number of patches passed to the boundary-presence classifier. We evaluate our proposed approach for a clinically relevant task of prostate gland segmentation on trans-rectal ultrasound images. We show improved performance compared to other tested weakly supervised methods, using the same labels e.g., multiple instance learning.
ULGss: A Strategy to construct a Library of Universal Logic Gates for $N$-variable Boolean Logic beyond NAND and NOR
Venue: arXiv cs.ET 2023
Year: 2023

In literature, NAND and NOR are two logic gates that display functional completeness, hence regarded as Universal gates. So, the present effort is focused on exploring a library of universal gates in binary that are still unexplored in literature along with a broad and systematic approach to classify the logic connectives. The study shows that the number of Universal Gates in any logic system grows exponentially with the number of input variables $N$. It is revealed that there are $56$ Universal gates in binary for $N=3$. It is shown that the ratio of the count of Universal gates to the total number of Logic gates is $\approx $ $\frac{1}{4}$ or 0.25. Adding constants $0,1$ allow for the creation of $4$ additional (for $N=2$) and $169$ additional Universal Gates (for $N=3$). In this article, the mathematical and logical underpinnings of the concept of universal logic gates are presented, along with a search strategy $ULG_{SS}$ exploring multiple paths leading to their identification. A fast-track approach has been introduced that uses the hexadecimal representation of a logic gate to quickly ascertain its attribute.
Aspect-oriented Opinion Alignment Network for Aspect-Based Sentiment Classification
Venue: arXiv cs.CL 2023
Year: 2023

Aspect-based sentiment classification is a crucial problem in fine-grained sentiment analysis, which aims to predict the sentiment polarity of the given aspect according to its context. Previous works have made remarkable progress in leveraging attention mechanism to extract opinion words for different aspects. However, a persistent challenge is the effective management of semantic mismatches, which stem from attention mechanisms that fall short in adequately aligning opinions words with their corresponding aspect in multi-aspect sentences. To address this issue, we propose a novel Aspect-oriented Opinion Alignment Network (AOAN) to capture the contextual association between opinion words and the corresponding aspect. Specifically, we first introduce a neighboring span enhanced module which highlights various compositions of neighboring words and given aspects. In addition, we design a multi-perspective attention mechanism that align relevant opinion information with respect to the given aspect. Extensive experiments on three benchmark datasets demonstrate that our model achieves state-of-the-art results. The source code is available at https://github.com/AONE-NLP/ABSA-AOAN.
Unsupervised Prototype Adapter for Vision-Language Models
Venue: arXiv cs.CV 2023
Year: 2023

Recently, large-scale pre-trained vision-language models (e.g. CLIP and ALIGN) have demonstrated remarkable effectiveness in acquiring transferable visual representations. To leverage the valuable knowledge encoded within these models for downstream tasks, several fine-tuning approaches, including prompt tuning methods and adapter-based methods, have been developed to adapt vision-language models effectively with supervision. However, these methods rely on the availability of annotated samples, which can be labor-intensive and time-consuming to acquire, thus limiting scalability. To address this issue, in this work, we design an unsupervised fine-tuning approach for vision-language models called Unsupervised Prototype Adapter (UP-Adapter). Specifically, for the unannotated target datasets, we leverage the text-image aligning capability of CLIP to automatically select the most confident samples for each class. Utilizing these selected samples, we generate class prototypes, which serve as the initialization for the learnable prototype model. After fine-tuning, the prototype model prediction is combined with the original CLIP's prediction by a residual connection to perform downstream recognition tasks. Our extensive experimental results on image recognition and domain generalization show that the proposed unsupervised method outperforms 8-shot CoOp, 8-shot Tip-Adapter, and also the state-of-the-art UPL method by large margins.
Target-Grounded Graph-Aware Transformer for Aerial Vision-and-Dialog Navigation
Venue: arXiv cs.CV 2023
Year: 2023

This report details the methods of the winning entry of the AVDN Challenge in ICCV CLVL 2023. The competition addresses the Aerial Navigation from Dialog History (ANDH) task, which requires a drone agent to associate dialog history with aerial observations to reach the destination. For better cross-modal grounding abilities of the drone agent, we propose a Target-Grounded Graph-Aware Transformer (TG-GAT) framework. Concretely, TG-GAT first leverages a graph-aware transformer to capture spatiotemporal dependency, which benefits navigation state tracking and robust action planning. In addition,an auxiliary visual grounding task is devised to boost the agent's awareness of referred landmarks. Moreover, a hybrid augmentation strategy based on large language models is utilized to mitigate data scarcity limitations. Our TG-GAT framework won the AVDN Challenge, with 2.2% and 3.0% absolute improvements over the baseline on SPL and SR metrics, respectively. The code is available at https://github.com/yifeisu/TG-GAT.
Towards Universal Interaction for Extended Reality
Venue: arXiv cs.HC 2023
Year: 2023

Extended Reality (XR) is a rapidly growing field offering unique immersive experiences, social networking, learning, and collaboration opportunities. The continuous advancements in XR technology and industry efforts are gradually moving this technology toward end consumers. However, a universal one-size-fits-all solution for seamless XR interaction still needs to be discovered. Currently, we face a diverse landscape of interaction modalities that depend on the environment, user preferences, task, and device capabilities. Commercially available input methods like handheld controllers, hand gestures, voice commands, and combinations of those need universal flexibility and expressiveness. Additionally, hybrid user interfaces, such as smartwatches and smartphones as ubiquitous input and output devices, expand this interaction design space. In this position paper, we discuss the idea of a universal interaction concept for XR. We present challenges and opportunities for implementing hybrid user interfaces, emphasizing Environment, Task, and User. We explore the potential to enhance user experiences, interaction capabilities, and the development of seamless and efficient XR interaction methods. We examine challenges and aim to stimulate a discussion on the design of generic, universal interfaces for XR.
Semantic Multi-Resolution Communications
Venue: arXiv cs.LG 2023
Year: 2023

Deep learning based joint source-channel coding (JSCC) has demonstrated significant advancements in data reconstruction compared to separate source-channel coding (SSCC). This superiority arises from the suboptimality of SSCC when dealing with finite block-length data. Moreover, SSCC falls short in reconstructing data in a multi-user and/or multi-resolution fashion, as it only tries to satisfy the worst channel and/or the highest quality data. To overcome these limitations, we propose a novel deep learning multi-resolution JSCC framework inspired by the concept of multi-task learning (MTL). This proposed framework excels at encoding data for different resolutions through hierarchical layers and effectively decodes it by leveraging both current and past layers of encoded data. Moreover, this framework holds great potential for semantic communication, where the objective extends beyond data reconstruction to preserving specific semantic attributes throughout the communication process. These semantic features could be crucial elements such as class labels, essential for classification tasks, or other key attributes that require preservation. Within this framework, each level of encoded data can be carefully designed to retain specific data semantics. As a result, the precision of a semantic classifier can be progressively enhanced across successive layers, emphasizing the preservation of targeted semantics throughout the encoding and decoding stages. We conduct experiments on MNIST and CIFAR10 dataset. The experiment with both datasets illustrates that our proposed method is capable of surpassing the SSCC method in reconstructing data with different resolutions, enabling the extraction of semantic features with heightened confidence in successive layers. This capability is particularly advantageous for prioritizing and preserving more crucial semantic features within the datasets.
Demand-driven provisioning of Kubernetes-like resources in OSG
Venue: arXiv cs.DC 2023
Year: 2023

The OSG-operated Open Science Pool is an HTCondor-based virtual cluster that aggregates resources from compute clusters provided by several organizations. Most of the resources are not owned by OSG, so demand-based dynamic provisioning is important for maximizing usage without incurring excessive waste. OSG has long relied on GlideinWMS for most of its resource provisioning needs but is limited to resources that provide a Grid-compliant Compute Entrypoint. To work around this limitation, the OSG Software Team has developed a glidein container that resource providers could use to directly contribute to the OSPool. The problem of that approach is that it is not demand-driven, relegating it to backfill scenarios only. To address this limitation, a demand-driven direct provisioner of Kubernetes resources has been developed and successfully used on the NRP. The setup still relies on the OSG-maintained backfill container image but automates the provisioning matchmaking and successive requests. That provisioner has also been extended to support Lancium, a green computing cloud provider with a Kubernetes-like proprietary interface. The provisioner logic has been intentionally kept very simple, making this extension a low-cost project. Both NRP and Lancium resources have been provisioned exclusively using this mechanism for many months.
A multi-case study of agile requirements engineering and the use of test cases as requirements
Venue: arXiv cs.SE 2023
Year: 2023

Context: It is an enigma that agile projects can succeed 'without requirements' when weak requirements engineering is a known cause for project failures. While agile development projects often manage well without extensive requirements test cases are commonly viewed as requirements and detailed requirements are documented as test cases. Objective: We have investigated this agile practice of using test cases as requirements to understand how test cases can support the main requirements activities, and how this practice varies. Method: We performed an iterative case study at three companies and collected data through 14 interviews and two focus groups. Results: The use of test cases as requirements poses both benefits and challenges when eliciting, validating, verifying, and managing requirements, and when used as a documented agreement. We have identified five variants of the test-cases-as-requirements practice, namely de facto, behaviour-driven, story-test driven, stand-alone strict and stand-alone manual for which the application of the practice varies concerning the time frame of requirements documentation, the requirements format, the extent to which the test cases are a machine executable specification and the use of tools which provide specific support for the practice of using test cases as requirements. Conclusions: The findings provide empirical insight into how agile development projects manage and communicate requirements. The identified variants of the practice of using test cases as requirements can be used to perform in-depth investigations into agile requirements engineering. Practitioners can use the provided recommendations as a guide in designing and improving their agile requirements practices based on project characteristics such as number of stakeholders and rate of change.