OpenRaiser/Intern-Atlas huggingface dataset:
Improving Vulnerability Prediction of JavaScript Functions Using Process Metrics
Venue: arXiv cs.CR 2021
Year: 2021

Due to the growing number of cyber attacks against computer systems, we need to pay special attention to the security of our software systems. In order to maximize the effectiveness, excluding the human component from this process would be a huge breakthrough. The first step towards this is to automatically recognize the vulnerable parts in our code. Researchers put a lot of effort into creating machine learning models that could determine if a given piece of code, or to be more precise, a selected function, contains any vulnerabilities or not. We aim at improving the existing models, building on previous results in predicting vulnerabilities at the level of functions in JavaScript code using the well-known static source code metrics. In this work, we propose to include several so-called process metrics (e.g., code churn, number of developers modifying a file, or the age of the changed source code) into the set of features, and examine how they affect the performance of the function-level JavaScript vulnerability prediction models. We can confirm that process metrics significantly improve the prediction power of such models. On average, we observed a 8.4% improvement in terms of F-measure (from 0.764 to 0.848), 3.5% improvement in terms of precision (from 0.953 to 0.988) and a 6.3% improvement in terms of recall (from 0.697 to 0.760).
Algorithmic Principles of Camera-based Respiratory Motion Extraction
Venue: arXiv cs.CV 2021
Year: 2021

Measuring the respiratory signal from a video based on body motion has been proposed and recently matured in products for video health monitoring. The core algorithm for this measurement is the estimation of tiny chest/abdominal motions induced by respiration, and the fundamental challenge is motion sensitivity. Though prior arts reported on the validation with real human subjects, there is no thorough/rigorous benchmark to quantify the sensitivities and boundary conditions of motion-based core respiratory algorithms that measure sub-pixel displacement between video frames. In this paper, we designed a setup with a fully-controllable physical phantom to investigate the essence of core algorithms, together with a mathematical model incorporating two motion estimation strategies and three spatial representations, leading to six algorithmic combinations for respiratory signal extraction. Their promises and limitations are discussed and clarified via the phantom benchmark. The insights gained in this paper are intended to improve the understanding and applications of camera-based respiration measurement in health monitoring.
fybrrStream: A WebRTC based Efficient and Scalable P2P Live Streaming Platform
Venue: arXiv cs.NI 2021
Year: 2021

The demand for streaming media and live video conferencing is at peak and expected to grow further, thereby the need for low-cost streaming services with better quality and lower latency is essential. Therefore, in this paper, we propose a novel peer-to-peer (P2P) live streaming platform, called fybrrStream, where a logical mesh and physical tree i.e., hybrid topology-based approach is leveraged for low latency streaming. fybrrStream distributes the load on participating peers in a hierarchical manner by considering their network bandwidth, network latency, and node stability. fybrrStream costs as low as the cost of just hosting a light-weight website and the performance is comparable to the existing state-of-the-art media streaming services. We evaluated and tested the proposed fybrrStream platform with real-field experiments using 50+ users spread across India and results obtained show significant improvements in the live streaming performance over other schemes.
DeepMerge: Learning to Merge Programs
Venue: arXiv cs.SE 2021
Year: 2021

In collaborative software development, program merging is the mechanism to integrate changes from multiple programmers. Merge algorithms in modern version control systems report a conflict when changes interfere textually. Merge conflicts require manual intervention and frequently stall modern continuous integration pipelines. Prior work found that, although costly, a large majority of resolutions involve re-arranging text without writing any new code. Inspired by this observation we propose the first data-driven approach to resolve merge conflicts with a machine learning model. We realize our approach in a tool DeepMerge that uses a novel combination of (i) an edit-aware embedding of merge inputs and (ii) a variation of pointer networks, to construct resolutions from input segments. We also propose an algorithm to localize manual resolutions in a resolved file and employ it to curate a ground-truth dataset comprising 8,719 non-trivial resolutions in JavaScript programs. Our evaluation shows that, on a held out test set, DeepMerge can predict correct resolutions for 37% of non-trivial merges, compared to only 4% by a state-of-the-art semistructured merge technique. Furthermore, on the subset of merges with upto 3 lines (comprising 24% of the total dataset), DeepMerge can predict correct resolutions with 78% accuracy.
Rethinking "Batch" in BatchNorm
Venue: arXiv cs.CV 2021
Year: 2021

BatchNorm is a critical building block in modern convolutional neural networks. Its unique property of operating on "batches" instead of individual samples introduces significantly different behaviors from most other operations in deep learning. As a result, it leads to many hidden caveats that can negatively impact model's performance in subtle ways. This paper thoroughly reviews such problems in visual recognition tasks, and shows that a key to address them is to rethink different choices in the concept of "batch" in BatchNorm. By presenting these caveats and their mitigations, we hope this review can help researchers use BatchNorm more effectively.
Towards Novel Multipath Data Scheduling For Future IoT Systems: A Survey
Venue: arXiv cs.NI 2021
Year: 2021

During the initial years of its inception, the Internet was widely used for transferring data packets between users and respective data sources by using IP addresses. With the advancements in technology, the Internet has been used to share data within several small and resource-constrained devices connected in billions to create the framework for the so-called Internet of Things (IoT). These systems were known for the presentation of a large quantum of data emerging within these devices. On the flip side, these devices are known to impose huge overheads on the IoT network. Therefore, it was essential to develop solutions concerning different network-related problems as a part of IoT networking. In this paper, we review these challenges emerge in routing, congestion, energy conservation, scalability, heterogeneity, reliability, security, and quality of service (QoS). This can be leverage to use the available network optimally. As part of this research work, a detailed survey is to be conducted on the network optimization process within IoT, as presented in another research. Owing to the advances in wireless networking, relevant Internet-of-Things (IoT) devices were equipped with several elements, including multiple network access interfaces. The adoption of multipath TCP (MPTCP) technology would improve the total throughput of data transmission. On the other hand, leveraging traditional MPTCP path management algorithms lead to other problems in data transport areas along with even buffer blockage. This shall lead to massive issues in areas of reduction of transmission performance across the entire IoT network. To this end, we develop a novel multipath algorithm that would efficiently manage the data transport in an intelligently scheduled and seamless manner using multiple wireless/wireline paths.
Intrablock Interleaving for Batched Network Coding with Blockwise Adaptive Recoding
Venue: arXiv cs.IT 2021
Year: 2021

Batched network coding (BNC) is a low-complexity solution to network transmission in multi-hop packet networks with packet loss. BNC encodes the source data into batches of packets. As a network coding scheme, the intermediate nodes perform recoding on the received packets belonging to the same batch instead of just forwarding them. A recoding scheme that may generate more recoded packets for batches of a higher rank is also called adaptive recoding. Meanwhile, in order to combat burst packet loss, the transmission of a block of batches can be interleaved. Stream interleaving studied in literature achieves the maximum separation among any two consecutive packets of a batch, but permutes packets across blocks and hence cannot bound the buffer size and the latency. To resolve the issue of stream interleaver, we design an intrablock interleaver for adaptive recoding that can preserve the advantages of using a block interleaver when the number of recoded packets is the same for all batches. We use potential energy in classical mechanics to measure the performance of an interleaver, and propose an algorithm to optimize the interleaver with this performance measure. Our problem formulation and algorithm for intrablock interleaving are also of independent interest.
A Fine-Grained Visual Attention Approach for Fingerspelling Recognition in the Wild
Venue: arXiv cs.CV 2021
Year: 2021

Fingerspelling in sign language has been the means of communicating technical terms and proper nouns when they do not have dedicated sign language gestures. Automatic recognition of fingerspelling can help resolve communication barriers when interacting with deaf people. The main challenges prevalent in fingerspelling recognition are the ambiguity in the gestures and strong articulation of the hands. The automatic recognition model should address high inter-class visual similarity and high intra-class variation in the gestures. Most of the existing research in fingerspelling recognition has focused on the dataset collected in a controlled environment. The recent collection of a large-scale annotated fingerspelling dataset in the wild, from social media and online platforms, captures the challenges in a real-world scenario. In this work, we propose a fine-grained visual attention mechanism using the Transformer model for the sequence-to-sequence prediction task in the wild dataset. The fine-grained attention is achieved by utilizing the change in motion of the video frames (optical flow) in sequential context-based attention along with a Transformer encoder model. The unsegmented continuous video dataset is jointly trained by balancing the Connectionist Temporal Classification (CTC) loss and the maximum-entropy loss. The proposed approach can capture better fine-grained attention in a single iteration. Experiment evaluations show that it outperforms the state-of-the-art approaches.
Open-set Recognition based on the Combination of Deep Learning and Ensemble Method for Detecting Unknown Traffic Scenarios
Venue: arXiv cs.CV 2021
Year: 2021

An understanding and classification of driving scenarios are important for testing and development of autonomous driving functionalities. Machine learning models are useful for scenario classification but most of them assume that data received during the testing are from one of the classes used in the training. This assumption is not true always because of the open environment where vehicles operate. This is addressed by a new machine learning paradigm called open-set recognition. Open-set recognition is the problem of assigning test samples to one of the classes used in training or to an unknown class. This work proposes a combination of Convolutional Neural Networks (CNN) and Random Forest (RF) for open set recognition of traffic scenarios. CNNs are used for the feature generation and the RF algorithm along with extreme value theory for the detection of known and unknown classes. The proposed solution is featured by exploring the vote patterns of trees in RF instead of just majority voting. By inheriting the ensemble nature of RF, the vote pattern of all trees combined with extreme value theory is shown to be well suited for detecting unknown classes. The proposed method has been tested on the highD and OpenTraffic datasets and has demonstrated superior performance in various aspects compared to existing solutions.
OntoEA: Ontology-guided Entity Alignment via Joint Knowledge Graph Embedding
Venue: arXiv cs.CL 2021
Year: 2021

Semantic embedding has been widely investigated for aligning knowledge graph (KG) entities. Current methods have explored and utilized the graph structure, the entity names and attributes, but ignore the ontology (or ontological schema) which contains critical meta information such as classes and their membership relationships with entities. In this paper, we propose an ontology-guided entity alignment method named OntoEA, where both KGs and their ontologies are jointly embedded, and the class hierarchy and the class disjointness are utilized to avoid false mappings. Extensive experiments on seven public and industrial benchmarks have demonstrated the state-of-the-art performance of OntoEA and the effectiveness of the ontologies.
Hash-MAC-DSDV: Mutual Authentication for Intelligent IoT-Based Cyber-Physical Systems
Venue: arXiv cs.CR 2021
Year: 2021

Cyber-Physical Systems (CPS) connected in the form of Internet of Things (IoT) are vulnerable to various security threats, due to the infrastructure-less deployment of IoT devices. Device-to-Device (D2D) authentication of these networks ensures the integrity, authenticity, and confidentiality of information in the deployed area. The literature suggests different approaches to address security issues in CPS technologies. However, they are mostly based on centralized techniques or specific system deployments with higher cost of computation and communication. It is therefore necessary to develop an effective scheme that can resolve the security problems in CPS technologies of IoT devices. In this paper, a lightweight Hash-MAC-DSDV (Hash Media Access Control Destination Sequence Distance Vector) routing scheme is proposed to resolve authentication issues in CPS technologies, connected in the form of IoT networks. For this purpose, a CPS of IoT devices (multi-WSNs) is developed from the local-chain and public chain, respectively. The proposed scheme ensures D2D authentication by the Hash-MAC-DSDV mutual scheme, where the MAC addresses of individual devices are registered in the first phase and advertised in the network in the second phase. The proposed scheme allows legitimate devices to modify their routing table and unicast the one-way hash authentication mechanism to transfer their captured data from source towards the destination. Our evaluation results demonstrate that Hash- MAC-DSDV outweighs the existing schemes in terms of attack detection, energy consumption and communication metrics.
A CCG-Based Version of the DisCoCat Framework
Venue: arXiv cs.CL 2021
Year: 2021

While the DisCoCat model (Coecke et al., 2010) has been proved a valuable tool for studying compositional aspects of language at the level of semantics, its strong dependency on pregroup grammars poses important restrictions: first, it prevents large-scale experimentation due to the absence of a pregroup parser; and second, it limits the expressibility of the model to context-free grammars. In this paper we solve these problems by reformulating DisCoCat as a passage from Combinatory Categorial Grammar (CCG) to a category of semantics. We start by showing that standard categorial grammars can be expressed as a biclosed category, where all rules emerge as currying/uncurrying the identity; we then proceed to model permutation-inducing rules by exploiting the symmetry of the compact closed category encoding the word meaning. We provide a proof of concept for our method, converting "Alice in Wonderland" into DisCoCat form, a corpus that we make available to the community.
Microservices in IoT Security: Current Solutions, Research Challenges, and Future Directions
Venue: arXiv cs.CR 2021
Year: 2021

In recent years, the Internet of Things (IoT) technology has led to the emergence of multiple smart applications in different vital sectors including healthcare, education, agriculture, energy management, etc. IoT aims to interconnect several intelligent devices over the Internet such as sensors, monitoring systems, and smart appliances to control, store, exchange, and analyze collected data. The main issue in IoT environments is that they can present potential vulnerabilities to be illegally accessed by malicious users, which threatens the safety and privacy of gathered data. To face this problem, several recent works have been conducted using microservices-based architecture to minimize the security threats and attacks related to IoT data. By employing microservices, these works offer extensible, reusable, and reconfigurable security features. In this paper, we aim to provide a survey about microservices-based approaches for securing IoT applications. This survey will help practitioners understand ongoing challenges and explore new and promising research opportunities in the IoT security field. To the best of our knowledge, this paper constitutes the first survey that investigates the use of microservices technology for securing IoT applications.
Studying the association of online brand importance with museum visitors: An application of the semantic brand score
Venue: arXiv cs.CL 2021
Year: 2021

This paper explores the association between brand importance and growth in museum visitors. We analyzed 10 years of online forum discussions and applied the Semantic Brand Score (SBS) to assess the brand importance of five European Museums. Our Naive Bayes and regression models indicate that variations in the combined dimensions of the SBS (prevalence, diversity and connectivity) are aligned with changes in museum visitors. Results suggest that, in order to attract more visitors, museum brand managers should focus on increasing the volume of online posting and the richness of information generated by users around the brand, rather than controlling for the posts' overall positivity or negativity.
Automated Biodesign Engineering by Abductive Meta-Interpretive Learning
Venue: arXiv cs.AI 2021
Year: 2021

The application of Artificial Intelligence (AI) to synthetic biology will provide the foundation for the creation of a high throughput automated platform for genetic design, in which a learning machine is used to iteratively optimise the system through a design-build-test-learn (DBTL) cycle. However, mainstream machine learning techniques represented by deep learning lacks the capability to represent relational knowledge and requires prodigious amounts of annotated training data. These drawbacks strongly restrict AI's role in synthetic biology in which experimentation is inherently resource and time intensive. In this work, we propose an automated biodesign engineering framework empowered by Abductive Meta-Interpretive Learning ($Meta_{Abd}$), a novel machine learning approach that combines symbolic and sub-symbolic machine learning, to further enhance the DBTL cycle by enabling the learning machine to 1) exploit domain knowledge and learn human-interpretable models that are expressed by formal languages such as first-order logic; 2) simultaneously optimise the structure and parameters of the models to make accurate numerical predictions; 3) reduce the cost of experiments and effort on data annotation by actively generating hypotheses and examples. To verify the effectiveness of $Meta_{Abd}$, we have modelled a synthetic dataset for the production of proteins from a three gene operon in a microbial host, which represents a common synthetic biology problem.
Does "AI" stand for augmenting inequality in the era of covid-19 healthcare?
Venue: arXiv cs.CY 2021
Year: 2021

Among the most damaging characteristics of the covid-19 pandemic has been its disproportionate effect on disadvantaged communities. As the outbreak has spread globally, factors such as systemic racism, marginalisation, and structural inequality have created path dependencies that have led to poor health outcomes. These social determinants of infectious disease and vulnerability to disaster have converged to affect already disadvantaged communities with higher levels of economic instability, disease exposure, infection severity, and death. Artificial intelligence (AI) technologies are an important part of the health informatics toolkit used to fight contagious disease. AI is well known, however, to be susceptible to algorithmic biases that can entrench and augment existing inequality. Uncritically deploying AI in the fight against covid-19 thus risks amplifying the pandemic's adverse effects on vulnerable groups, exacerbating health inequity. In this paper, we claim that AI systems can introduce or reflect bias and discrimination in three ways: in patterns of health discrimination that become entrenched in datasets, in data representativeness, and in human choices made during the design, development, and deployment of these systems. We highlight how the use of AI technologies threaten to exacerbate the disparate effect of covid-19 on marginalised, under-represented, and vulnerable groups, particularly black, Asian, and other minoritised ethnic people, older populations, and those of lower socioeconomic status. We conclude that, to mitigate the compounding effects of AI on inequalities associated with covid-19, decision makers, technology developers, and health officials must account for the potential biases and inequities at all stages of the AI process.
The Flipped Classroom model for teaching Conditional Random Fields in an NLP course
Venue: arXiv cs.CY 2021
Year: 2021

In this article, we show and discuss our experience in applying the flipped classroom method for teaching Conditional Random Fields in a Natural Language Processing course. We present the activities that we developed together with their relationship to a cognitive complexity model (Bloom's taxonomy). After this, we provide our own reflections and expectations of the model itself. Based on the evaluation got from students, it seems that students learn about the topic and also that the method is rewarding for some students. Additionally, we discuss some shortcomings and we propose possible solutions to them. We conclude the paper with some possible future work.
A Review on Explainability in Multimodal Deep Neural Nets
Venue: arXiv cs.AI 2021
Year: 2021

Artificial Intelligence techniques powered by deep neural nets have achieved much success in several application domains, most significantly and notably in the Computer Vision applications and Natural Language Processing tasks. Surpassing human-level performance propelled the research in the applications where different modalities amongst language, vision, sensory, text play an important role in accurate predictions and identification. Several multimodal fusion methods employing deep learning models are proposed in the literature. Despite their outstanding performance, the complex, opaque and black-box nature of the deep neural nets limits their social acceptance and usability. This has given rise to the quest for model interpretability and explainability, more so in the complex tasks involving multimodal AI methods. This paper extensively reviews the present literature to present a comprehensive survey and commentary on the explainability in multimodal deep neural nets, especially for the vision and language tasks. Several topics on multimodal AI and its applications for generic domains have been covered in this paper, including the significance, datasets, fundamental building blocks of the methods and techniques, challenges, applications, and future trends in this domain