OpenRaiser/Intern-Atlas huggingface dataset:
Joint Wireless and Computing Resources Allocation in Multi-Cell MEC
Venue: arXiv cs.IT 2021
Year: 2021

This paper addresses join wireless and computing resource allocation in mobile edge computing (MEC) systems with several access points and with the possibility that users connect to many access points, and utilize the computation capability of many servers at the same time. The problem of sum transmission energy minimization under response time constraints is considered. It is proved, that the optimization problem is non-convex. The complexity of optimization of a part of the system parameters is investigated, and based on these results an Iterative Resource Allocation procedure is proposed, that converges to a local optimum. The performance of the joint resource allocation is evaluated by comparing it to lower and upper bounds defined by less or more flexible multi-cell MEC architectures. The results show that the free selection of the access point is crucial for good system performance.
Predicting Document Coverage for Relation Extraction
Venue: arXiv cs.CL 2021
Year: 2021

This paper presents a new task of predicting the coverage of a text document for relation extraction (RE): does the document contain many relational tuples for a given entity? Coverage predictions are useful in selecting the best documents for knowledge base construction with large input corpora. To study this problem, we present a dataset of 31,366 diverse documents for 520 entities. We analyze the correlation of document coverage with features like length, entity mention frequency, Alexa rank, language complexity and information retrieval scores. Each of these features has only moderate predictive power. We employ methods combining features with statistical models like TF-IDF and language models like BERT. The model combining features and BERT, HERB, achieves an F1 score of up to 46%. We demonstrate the utility of coverage predictions on two use cases: KB construction and claim refutation.
Benchmarking Shadow Removal for Facial Landmark Detection and Beyond
Venue: arXiv cs.CV 2021
Year: 2021

Facial landmark detection is a very fundamental and significant vision task with many important applications. In practice, facial landmark detection can be affected by a lot of natural degradations. One of the most common and important degradations is the shadow caused by light source blocking. While many advanced shadow removal methods have been proposed to recover the image quality in recent years, their effects to facial landmark detection are not well studied. For example, it remains unclear whether shadow removal could enhance the robustness of facial landmark detection to diverse shadow patterns or not. In this work, for the first attempt, we construct a novel benchmark to link two independent but related tasks (i.e., shadow removal and facial landmark detection). In particular, the proposed benchmark covers diverse face shadows with different intensities, sizes, shapes, and locations. Moreover, to mine hard shadow patterns against facial landmark detection, we propose a novel method (i.e., adversarial shadow attack), which allows us to construct a challenging subset of the benchmark for a comprehensive analysis. With the constructed benchmark, we conduct extensive analysis on three state-of-the-art shadow removal methods and three landmark detectors. The observation of this work motivates us to design a novel detection-aware shadow removal framework, which empowers shadow removal to achieve higher restoration quality and enhance the shadow robustness of deployed facial landmark detectors.
Quantifying human mixing patterns in Chinese provinces outside Hubei after the 2020 lockdown was lifted
Venue: arXiv cs.SI 2021
Year: 2021

Contact patterns play a key role in the spread of respiratory infectious diseases in human populations. During the COVID-19 pandemic the regular contact patterns of the population has been disrupted due to social distancing both imposed by the authorities and individual choices. Here we present the results of a contact survey conducted in Chinese provinces outside Hubei in March 2020, right after lockdowns were lifted. We then leveraged the estimated mixing patterns to calibrate a model of SARS-CoV-2 transmission, which was used to estimate different metrics of COVID-19 burden by age. Study participants reported 2.3 contacts per day (IQR: 1.0-3.0) and the mean per-contact duration was 7.0 hours (IQR: 1.0-10.0). No significant differences were observed between provinces, the number of recorded contacts did not show a clear-cut trend by age, and most of the recorded contacts occurred with family members (about 78%). Our findings suggest that, despite the lockdown was no longer in place at the time of the survey, people were still heavily limiting their contacts as compared to the pre-pandemic situation. Moreover, the obtained modeling results highlight the importance of considering age-contact patterns to estimate COVID-19 burden.
Average Outward Flux Skeletons for Environment Mapping and Topology Matching
Venue: arXiv cs.RO 2021
Year: 2021

We consider how to directly extract a road map (also known as a topological representation) of an initially-unknown 2-dimensional environment via an online procedure that robustly computes a retraction of its boundaries. In this article, we first present the online construction of a topological map and the implementation of a control law for guiding the robot to the nearest unexplored area, first presented in [1]. The proposed method operates by allowing the robot to localize itself on a partially constructed map, calculate a path to unexplored parts of the environment (frontiers), compute a robust terminating condition when the robot has fully explored the environment, and achieve loop closure detection. The proposed algorithm results in smooth safe paths for the robot's navigation needs. The presented approach is any time algorithm that has the advantage that it allows for the active creation of topological maps from laser scan data, as it is being acquired. We also propose a navigation strategy based on a heuristic where the robot is directed towards nodes in the topological map that open to empty space. We then extend the work in [1] by presenting a topology matching algorithm that leverages the strengths of a particular spectral correspondence method [2], to match the mapped environments generated from our topology-making algorithm. Here, we concentrated on implementing a system that could be used to match the topologies of the mapped environment by using AOF Skeletons. In topology matching between two given maps and their AOF skeletons, we first find correspondences between points on the AOF skeletons of two different environments. We then align the (2D) points of the environments themselves. We also compute a distance measure between two given environments, based on their extracted AOF skeletons and their topology, as the sum of the matching errors between corresponding points.
A Topological Approach for Computing Supremal Sublanguages for Some Language Equations in Supervisory Control Theory
Venue: arXiv cs.FL 2021
Year: 2021

In this paper, we shall present a topological approach for the computation of some supremal sublanguages, often specified by language equations, which arise from the study of the supervisory control theory. The basic idea is to identify the solutions of the language equations as open sets for some (semi)-topologies. Then, the supremal sublanguages naturally correspond to the supremal open subsets, i.e., the interiors. This provides an elementary and uniform approach for computing various supremal sublanguages encountered in the supervisory control theory and is closely related to a theory of approximation, known as the rough set theory, in artificial intelligence.
Normative Disagreement as a Challenge for Cooperative AI
Venue: arXiv cs.MA 2021
Year: 2021

Cooperation in settings where agents have both common and conflicting interests (mixed-motive environments) has recently received considerable attention in multi-agent learning. However, the mixed-motive environments typically studied have a single cooperative outcome on which all agents can agree. Many real-world multi-agent environments are instead bargaining problems (BPs): they have several Pareto-optimal payoff profiles over which agents have conflicting preferences. We argue that typical cooperation-inducing learning algorithms fail to cooperate in BPs when there is room for normative disagreement resulting in the existence of multiple competing cooperative equilibria, and illustrate this problem empirically. To remedy the issue, we introduce the notion of norm-adaptive policies. Norm-adaptive policies are capable of behaving according to different norms in different circumstances, creating opportunities for resolving normative disagreement. We develop a class of norm-adaptive policies and show in experiments that these significantly increase cooperation. However, norm-adaptiveness cannot address residual bargaining failure arising from a fundamental tradeoff between exploitability and cooperative robustness.
Assessing the Effectiveness of YARA Rules for Signature-Based Malware Detection and Classification
Venue: arXiv cs.CR 2021
Year: 2021

Malware often uses obfuscation techniques or is modified slightly to evade signature detection from antivirus software and malware analysis tools. Traditionally, to determine if a file is malicious and identify what type of malware a sample is, a cryptographic hash of a file is calculated. A more recent and flexible solution for malware detection is YARA, which enables the creation of rules to identify and classify malware based on a file's binary patterns. In this paper, the author will critically evaluate the effectiveness of YARA rules for signature-based detection and classification of malware in comparison to alternative methods, which include cryptographic and fuzzy hashing.
Controlling the Correctness of Aggregation Operations During Sessions of Interactive Analytic Queries
Venue: arXiv cs.DB 2021
Year: 2021

We present a comprehensive set of conditions and rules to control the correctness of aggregation queries within an interactive data analysis session. The goal is to extend self-service data preparation and BI tools to automatically detect semantically incorrect aggregate queries on analytic tables and views built by using the common analytic operations including filter, project, join, aggregate, union, difference, and pivot. We introduce aggregable properties to describe for any attribute of an analytic table which aggregation functions correctly aggregates the attribute along which sets of dimension attributes. These properties can also be used to formally identify attributes which are summarizable with respect to some aggregation function along a given set of dimension attributes. This is particularly helpful to detect incorrect aggregations of measures obtained through the use of non-distributive aggregation functions like average and count. We extend the notion of summarizability by introducing a new generalized summarizability condition to control the aggregation of attributes after any analytic operation. Finally, we define propagation rules which transform aggregable properties of the query input tables into new aggregable properties for the result tables, preserving summarizability and generalized summarizability.
Distributed Anomaly Detection in Edge Streams using Frequency based Sketch Datastructures
Venue: arXiv cs.DC 2021
Year: 2021

Often logs hosted in large data centers represent network traffic data over a long period of time. For instance, such network traffic data logged via a TCP dump packet sniffer (as considered in the 1998 DARPA intrusion attack) included network packets being transmitted between computers. While an online framework is necessary for detecting any anomalous or suspicious network activities like denial of service attacks or unauthorized usage in real time, often such large data centers log data over long periods of time (e.g., TCP dump) and hence an offline framework is much more suitable in such scenarios. Given a network log history of edges from a dynamic graph, how can we assign anomaly scores to individual edges indicating suspicious events with high accuracy using only constant memory and within limited time than state-of-the-art methods? We propose MDistrib and its variants which provides (a) faster detection of anomalous events via distributed processing with GPU support compared to other approaches, (b) better false positive guarantees than state of the art methods considering fixed space and (c) with collision aware based anomaly scoring for better accuracy results than state-of-the-art approaches. We describe experiments confirming that MDistrib is more efficient than prior work.
A Recommender System-Inspired Cloud Data Filling Scheme for Satellite-based Coastal Observation
Venue: arXiv cs.LG 2021
Year: 2021

Filling missing data in cloud-covered areas of satellite imaging is an important task to improve data quantity and quality for enhanced earth observation. Traditional cloud filling studies focused on continuous numerical data such as temperature and cyanobacterial concentration in the open ocean. Cloud data filling issues in coastal imaging is far less studied because of the complex landscape. Inspired by the success of data imputation methods in recommender systems that are designed for online shopping, the present study explored their application to satellite cloud data filling tasks. A numerical experiment was designed and conducted for a LandSat dataset with a range of synthetic cloud covers to examine the performance of different data filling schemes. The recommender system-inspired matrix factorization algorithm called Funk-SVD showed superior performance in computational accuracy and efficiency for the task of recovering landscape types in a complex coastal area than the traditional data filling scheme of DINEOF (Data Interpolating Empirical Orthogonal Functions) and the deep learning method of Datawig. The new method achieved the best filling accuracy and reached a speed comparable to DINEOF and much faster than deep learning. A theoretical framework was created to analyze the error propagation in DINEOF and found the algorithm needs to be modified to converge to the ground truth. The present study showed that Funk-SVD has great potential to enhance cloud data filling performance and connects the fields of recommender systems and cloud filling to promote the improvement and sharing of useful algorithms.
Exploring Low-Cost Transformer Model Compression for Large-Scale Commercial Reply Suggestions
Venue: arXiv cs.CL 2021
Year: 2021

Fine-tuning pre-trained language models improves the quality of commercial reply suggestion systems, but at the cost of unsustainable training times. Popular training time reduction approaches are resource intensive, thus we explore low-cost model compression techniques like Layer Dropping and Layer Freezing. We demonstrate the efficacy of these techniques in large-data scenarios, enabling the training time reduction for a commercial email reply suggestion system by 42%, without affecting the model relevance or user engagement. We further study the robustness of these techniques to pre-trained model and dataset size ablation, and share several insights and recommendations for commercial applications.
Unsupervised Domain Adaptive Person Re-Identification via Human Learning Imitation
Venue: arXiv cs.CV 2021
Year: 2021

Unsupervised domain adaptive person re-identification has received significant attention due to its high practical value. In past years, by following the clustering and finetuning paradigm, researchers propose to utilize the teacher-student framework in their methods to decrease the domain gap between different person re-identification datasets. Inspired by recent teacher-student framework based methods, which try to mimic the human learning process either by making the student directly copy behavior from the teacher or selecting reliable learning materials, we propose to conduct further exploration to imitate the human learning process from different aspects, \textit{i.e.}, adaptively updating learning materials, selectively imitating teacher behaviors, and analyzing learning materials structures. The explored three components, collaborate together to constitute a new method for unsupervised domain adaptive person re-identification, which is called Human Learning Imitation framework. The experimental results on three benchmark datasets demonstrate the efficacy of our proposed method.
Statically Detecting Adversarial Malware through Randomised Chaining
Venue: arXiv cs.CR 2021
Year: 2021

With the rapid growth of malware attacks, more antivirus developers consider deploying machine learning technologies into their productions. Researchers and developers published various machine learning-based detectors with high precision on malware detection in recent years. Although numerous machine learning-based malware detectors are available, they face various machine learning-targeted attacks, including evasion and adversarial attacks. This project explores how and why adversarial examples evade malware detectors, then proposes a randomised chaining method to defend against adversarial malware statically. This research is crucial for working towards combating the pertinent malware cybercrime.
Cognitive factors that affect the adoption of autonomous agriculture
Venue: arXiv cs.HC 2021
Year: 2021

Robotic and Autonomous Agricultural Technologies (RAAT) are increasingly available yet may fail to be adopted. This paper focusses specifically on cognitive factors that affect adoption including: inability to generate trust, loss of farming knowledge and reduced social cognition. It is recommended that agriculture develops its own framework for the performance and safety of RAAT drawing on human factors research in aerospace engineering including human inputs (individual variance in knowledge, skills, abilities, preferences, needs and traits), trust, situational awareness and cognitive load. The kinds of cognitive impacts depend on the RAATs level of autonomy, ie whether it has automatic, partial autonomy and autonomous functionality and stage of adoption, ie adoption, initial use or post-adoptive use. The more autonomous a system is, the less a human needs to know to operate it and the less the cognitive load, but it also means farmers have less situational awareness about on farm activities that in turn may affect strategic decision-making about their enterprise. Some cognitive factors may be hidden when RAAT is first adopted but play a greater role during prolonged or intense post-adoptive use. Systems with partial autonomy need intuitive user interfaces, engaging system information, and clear signaling to be trusted with low level tasks; and to compliment and augment high order decision-making on farm.
Semantic Code Search for Smart Contracts
Venue: arXiv cs.SE 2021
Year: 2021

Semantic code search technology allows searching for existing code snippets through natural language, which can greatly improve programming efficiency. Smart contracts, programs that run on the blockchain, have a code reuse rate of more than 90%, which means developers have a great demand for semantic code search tools. However, the existing code search models still have a semantic gap between code and query, and perform poorly on specialized queries of smart contracts. In this paper, we propose a Multi-Modal Smart contract Code Search (MM-SCS) model. Specifically, we construct a Contract Elements Dependency Graph (CEDG) for MM-SCS as an additional modality to capture the data-flow and control-flow information of the code. To make the model more focused on the key contextual information, we use a multi-head attention network to generate embeddings for code features. In addition, we use a fine-tuned pretrained model to ensure the model's effectiveness when the training data is small. We compared MM-SCS with four state-of-the-art models on a dataset with 470K (code, docstring) pairs collected from Github and Etherscan. Experimental results show that MM-SCS achieves an MRR (Mean Reciprocal Rank) of 0.572, outperforming four state-of-the-art models UNIF, DeepCS, CARLCS-CNN, and TAB-CS by 34.2%, 59.3%, 36.8%, and 14.1%, respectively. Additionally, the search speed of MM-SCS is second only to UNIF, reaching 0.34s/query.
Mapping Industry 4.0 Technologies: From Cyber-Physical Systems to Artificial Intelligence
Venue: arXiv cs.CL 2021
Year: 2021

The fourth industrial revolution is rapidly changing the manufacturing landscape. Due to the growing research and fast evolution in this field, no clear definitions of these concepts yet exist. This work provides a clear description of technological trends and gaps. We introduce a novel method to create a map of Industry 4.0 technologies, using natural language processing to extract technology terms from 14,667 research articles and applying network analysis. We identified eight clusters of Industry 4.0 technologies, which served as the basis for our analysis. Our results show that Industrial Internet of Things (IIoT) technologies have become the center of the Industry 4.0 technology map. This is in line with the initial definitions of Industry 4.0, which centered on IIoT. Given the recent growth in the importance of artificial intelligence (AI), we suggest accounting for AI's fundamental role in Industry 4.0 and understanding the fourth industrial revolution as an AI-powered natural collaboration between humans and machines. This article introduces a novel approach for literature reviews, and the results highlight trends and research gaps to guide future work and help these actors reap the benefits of digital transformations.
Monoidal weak omega-categories as models of a type theory
Venue: arXiv cs.LO 2021
Year: 2021

Weak $ω$-categories are notoriously difficult to define because of the very intricate nature of their axioms. Various approaches have been explored, based on different shapes given to the cells. Interestingly, homotopy type theory encompasses a definition of weak $ω$-groupoid in a globular setting, since every type carries such a structure. Starting from this remark, Brunerie could extract this definition of globular weak $ω$\nobreakdash-groupoids, formulated as a type theory. By refining its rules, Finster and Mimram have then defined a type theory called CaTT, whose models are weak $ω$-categories. Here, we generalize this approach to monoidal weak $ω$-categories. Based on the principle that they should be equivalent to weak $ω$-categories with only one $0$-cell, we are able to derive a type theory MCaTT whose models are monoidal categories. This requires changing the rules of the theory in order to encode the information carried by the unique $0$-cell. The correctness of the resulting type theory is shown by defining a pair of translations between our type theory MCaTT and the type theory CaTT. Our main contribution is to show that these translations relate the models of our type theory to the models of the type theory CaTT consisting of $ω$-categories with only one $0$-cell, by analyzing in details how the notion of models interact with the structural rules of both type theories.