OpenRaiser/Intern-Atlas huggingface dataset:
Effective Anomaly Detection in Smart Home by Integrating Event Time Intervals
Venue: arXiv cs.CR 2022
Year: 2022

Smart home IoT systems and devices are susceptible to attacks and malfunctions. As a result, users' concerns about their security and safety issues arise along with the prevalence of smart home deployments. In a smart home, various anomalies (such as fire or flooding) could happen, due to cyber attacks, device malfunctions, or human mistakes. These concerns motivate researchers to propose various anomaly detection approaches. Existing works on smart home anomaly detection focus on checking the sequence of IoT devices' events but leave out the temporal information of events. This limitation prevents them to detect anomalies that cause delay rather than missing/injecting events. To fill this gap, in this paper, we propose a novel anomaly detection method that takes the inter-event intervals into consideration. We propose an innovative metric to quantify the temporal similarity between two event sequences. We design a mechanism to learn the temporal patterns of event sequences of common daily activities. Delay-caused anomalies are detected by comparing the sequence with the learned patterns. We collect device events from a real-world testbed for training and testing. The experiment results show that our proposed method achieves accuracies of 93%, 88%, 89% for three daily activities.
A Systematic Literature Review of Blockchain Technology Adoption in Bangladesh
Venue: arXiv cs.CR 2022
Year: 2022

The spirit of "blockchain technology" is a distributed database in which saved data is transparent, accountable, public, immutable, and traceable. This base-level disruptive technology can boost the security and privacy-related efficiency of various domains. As Bangladesh is currently aiming for sustainable development, blockchain technology adoption by local researchers is growing robustly. However, in Bangladesh, the blockchain Technology Acceptance Model (TAM) is not yet well structured which is also limiting the perspective of local developers and researchers. Therefore, sectors like governance, healthcare, security, privacy, farming, information authentication, cryptocurrencies, internet architecture, data, and so on are unable to utilize the full potential of this technology. In this research, the authors conduct an in-depth review of such types of blockchain technology-related research articles that have been published recently and are also solely focused on Bangladesh. From 5 publishers (IEEE Xplore, ACM, ScienceDirect, Taylor & Francis, and SpringerLink) this study analyses 70 articles published during the year 2016-2020. The study results find the top 13 sectors where Bangladeshi researchers are currently focusing on. Those studies identify that the rigid policy by the government, scarcity of expert researchers, and lack of resources are the main reasons why Bangladesh is still struggling to accommodate blockchain extensively. In addition, published papers are mostly based on theoretical concepts without an appropriate implementation. Finally, this study will be a great resource to the developers, entrepreneurs, and technology enthusiasts to determine the strategic plan for adopting blockchain technology in Bangladesh or even in any other developing country.
AstBERT: Enabling Language Model for Financial Code Understanding with Abstract Syntax Trees
Venue: arXiv cs.AI 2022
Year: 2022

Using the pre-trained language models to understand source codes has attracted increasing attention from financial institutions owing to the great potential to uncover financial risks. However, there are several challenges in applying these language models to solve programming language-related problems directly. For instance, the shift of domain knowledge between natural language (NL) and programming language (PL) requires understanding the semantic and syntactic information from the data from different perspectives. To this end, we propose the AstBERT model, a pre-trained PL model aiming to better understand the financial codes using the abstract syntax tree (AST). Specifically, we collect a sheer number of source codes (both Java and Python) from the Alipay code repository and incorporate both syntactic and semantic code knowledge into our model through the help of code parsers, in which AST information of the source codes can be interpreted and integrated. We evaluate the performance of the proposed model on three tasks, including code question answering, code clone detection and code refinement. Experiment results show that our AstBERT achieves promising performance on three different downstream tasks.
PRMI: A Dataset of Minirhizotron Images for Diverse Plant Root Study
Venue: arXiv cs.CV 2022
Year: 2022

Understanding a plant's root system architecture (RSA) is crucial for a variety of plant science problem domains including sustainability and climate adaptation. Minirhizotron (MR) technology is a widely-used approach for phenotyping RSA non-destructively by capturing root imagery over time. Precisely segmenting roots from the soil in MR imagery is a critical step in studying RSA features. In this paper, we introduce a large-scale dataset of plant root images captured by MR technology. In total, there are over 72K RGB root images across six different species including cotton, papaya, peanut, sesame, sunflower, and switchgrass in the dataset. The images span a variety of conditions including varied root age, root structures, soil types, and depths under the soil surface. All of the images have been annotated with weak image-level labels indicating whether each image contains roots or not. The image-level labels can be used to support weakly supervised learning in plant root segmentation tasks. In addition, 63K images have been manually annotated to generate pixel-level binary masks indicating whether each pixel corresponds to root or not. These pixel-level binary masks can be used as ground truth for supervised learning in semantic segmentation tasks. By introducing this dataset, we aim to facilitate the automatic segmentation of roots and the research of RSA with deep learning and other image analysis algorithms.
Predicting Vegetation Stratum Occupancy from Airborne LiDAR Data with Deep Learning
Venue: arXiv cs.CV 2022
Year: 2022

We propose a new deep learning-based method for estimating the occupancy of vegetation strata from airborne 3D LiDAR point clouds. Our model predicts rasterized occupancy maps for three vegetation strata corresponding to lower, medium, and higher cover. Our weakly-supervised training scheme allows our network to only be supervised with vegetation occupancy values aggregated over cylindrical plots containing thousands of points. Such ground truth is easier to produce than pixel-wise or point-wise annotations. Our method outperforms handcrafted and deep learning baselines in terms of precision by up to 30%, while simultaneously providing visual and interpretable predictions. We provide an open-source implementation along with a dataset of 199 agricultural plots to train and evaluate weakly supervised occupancy regression algorithms.
LEMON: Language-Based Environment Manipulation via Execution-Guided Pre-training
Venue: arXiv cs.CL 2022
Year: 2022

Language-based environment manipulation requires agents to manipulate the environment following natural language instructions, which is challenging due to the huge space of the environments. To address this challenge, various approaches have been proposed in recent work. Although these approaches work well for their intended environments, they are difficult to generalize across environments. In this work, we propose LEMON, a general framework for language-based environment manipulation tasks. Specifically, we first specify a task-agnostic approach for language-based environment manipulation tasks, which can deal with various environments using the same generative language model. Then we propose an execution-guided pre-training strategy to inject prior knowledge of environments to the language model with a pure synthetic pre-training corpus. Experimental results on tasks including Alchemy, Scene, Tangrams, ProPara and Recipes demonstrate the effectiveness of LEMON: it achieves new state-of-the-art results on four of the tasks, and the execution-guided pre-training strategy brings remarkable improvements on all experimental tasks.
DeepGalaxy: Testing Neural Network Verifiers via Two-Dimensional Input Space Exploration
Venue: arXiv cs.SE 2022
Year: 2022

Deep neural networks (DNNs) are widely developed and applied in many areas, and the quality assurance of DNNs is critical. Neural network verification (NNV) aims to provide formal guarantees to DNN models. Similar to traditional software, neural network verifiers could also contain bugs, which would have a critical and serious impact, especially in safety-critical areas. However, little work exists on validating neural network verifiers. In this work, we propose DeepGalaxy, an automated approach based on differential testing to tackle this problem. Specifically, we (1) propose a line of mutation rules, including model level mutation and specification level mutation, to effectively explore the two-dimensional input space of neural network verifiers; and (2) propose heuristic strategies to select test cases. We leveraged our implementation of DeepGalaxy to test three state-of-the-art neural network verifies, Marabou, Eran, and Neurify. The experimental results support the efficiency and effectiveness of DeepGalaxy. Moreover, five unique unknown bugs were discovered
Learning robust perceptive locomotion for quadrupedal robots in the wild
Venue: arXiv cs.RO 2022
Year: 2022

Legged robots that can operate autonomously in remote and hazardous environments will greatly increase opportunities for exploration into under-explored areas. Exteroceptive perception is crucial for fast and energy-efficient locomotion: perceiving the terrain before making contact with it enables planning and adaptation of the gait ahead of time to maintain speed and stability. However, utilizing exteroceptive perception robustly for locomotion has remained a grand challenge in robotics. Snow, vegetation, and water visually appear as obstacles on which the robot cannot step~-- or are missing altogether due to high reflectance. Additionally, depth perception can degrade due to difficult lighting, dust, fog, reflective or transparent surfaces, sensor occlusion, and more. For this reason, the most robust and general solutions to legged locomotion to date rely solely on proprioception. This severely limits locomotion speed, because the robot has to physically feel out the terrain before adapting its gait accordingly. Here we present a robust and general solution to integrating exteroceptive and proprioceptive perception for legged locomotion. We leverage an attention-based recurrent encoder that integrates proprioceptive and exteroceptive input. The encoder is trained end-to-end and learns to seamlessly combine the different perception modalities without resorting to heuristics. The result is a legged locomotion controller with high robustness and speed. The controller was tested in a variety of challenging natural and urban environments over multiple seasons and completed an hour-long hike in the Alps in the time recommended for human hikers.
Interval-Memoized Backtracking on ZDDs for Fast Enumeration of All Lower Cost Solutions
Venue: arXiv cs.DS 2022
Year: 2022

In this paper, we propose a fast method for exactly enumerating a very large number of all lower cost solutions for various combinatorial problems. Our method is based on backtracking for a given decision diagram which represents all the feasible solutions. The main idea is to memoize the intervals of cost bounds to avoid duplicate search in the backtracking process. In contrast to usual pseudo-polynomial-time dynamic programming approaches, the computation time of our method does not directly depend on the total cost values, but is bounded by the input and output size of the decision diagrams. Therefore, it can be much faster if the cost values are large but the input/output decision diagrams are well-compressed. We demonstrate its practical efficiency by comparing our method to current available enumeration methods: for nontrivial size instances of the Hamiltonian path problem, our method succeeded in exactly enumerating billions of all lower cost solutions in a few seconds, which was hundred or much more times faster. Our method can be regarded as a novel search algorithm which integrates the two classical techniques, branch-and-bound and dynamic programming. This method would have many applications in various fields, including operations research, data mining, statistical testing, hardware/software system design, etc.
DRTCI: Learning Disentangled Representations for Temporal Causal Inference
Venue: arXiv cs.LG 2022
Year: 2022

Medical professionals evaluating alternative treatment plans for a patient often encounter time varying confounders, or covariates that affect both the future treatment assignment and the patient outcome. The recently proposed Counterfactual Recurrent Network (CRN) accounts for time varying confounders by using adversarial training to balance recurrent historical representations of patient data. However, this work assumes that all time varying covariates are confounding and thus attempts to balance the full state representation. Given that the actual subset of covariates that may in fact be confounding is in general unknown, recent work on counterfactual evaluation in the static, non-temporal setting has suggested that disentangling the covariate representation into separate factors, where each either influence treatment selection, patient outcome or both can help isolate selection bias and restrict balancing efforts to factors that influence outcome, allowing the remaining factors which predict treatment without needlessly being balanced.
Physically Embodied Deep Image Optimisation
Venue: arXiv cs.RO 2022
Year: 2022

Physical sketches are created by learning programs to control a drawing robot. A differentiable rasteriser is used to optimise sets of drawing strokes to match an input image, using deep networks to provide an encoding for which we can compute a loss. The optimised drawing primitives can then be translated into G-code commands which command a robot to draw the image using drawing instruments such as pens and pencils on a physical support medium.
A Systematic Analysis on the Impact of Contextual Information on Point-of-Interest Recommendation
Venue: arXiv cs.IR 2022
Year: 2022

As the popularity of Location-based Social Networks (LBSNs) increases, designing accurate models for Point-of-Interest (POI) recommendation receives more attention. POI recommendation is often performed by incorporating contextual information into previously designed recommendation algorithms. Some of the major contextual information that has been considered in POI recommendation are the location attributes (i.e., exact coordinates of a location, category, and check-in time), the user attributes (i.e., comments, reviews, tips, and check-in made to the locations), and other information, such as the distance of the POI from user's main activity location, and the social tie between users. The right selection of such factors can significantly impact the performance of the POI recommendation. However, previous research does not consider the impact of the combination of these different factors. In this paper, we propose different contextual models and analyze the fusion of different major contextual information in POI recommendation. The major contributions of this paper are: (i) providing an extensive survey of context-aware location recommendation (ii) quantifying and analyzing the impact of different contextual information (e.g., social, temporal, spatial, and categorical) in the POI recommendation on available baselines and two new linear and non-linear models, that can incorporate all the major contextual information into a single recommendation model, and (iii) evaluating the considered models using two well-known real-world datasets. Our results indicate that while modeling geographical and temporal influences can improve recommendation quality, fusing all other contextual information into a recommendation model is not always the best strategy.
Data-Driven Innovation: What Is It
Venue: arXiv cs.CY 2022
Year: 2022

The future of innovation processes is anticipated to be more data-driven and empowered by the ubiquitous digitalization, increasing data accessibility and rapid advances in machine learning, artificial intelligence, and computing technologies. While the data-driven innovation (DDI) paradigm is emerging, it has yet been formally defined and theorized and often confused with several other data-related phenomena. This paper defines and crystalizes "data-driven innovation" as a formal innovation process paradigm, dissects its value creation, and distinguishes it from data-driven optimization (DDO), data-based innovation (DBI), and the traditional innovation processes that purely rely on human intelligence. With real-world examples and theoretical framing, I elucidate what DDI entails and how it addresses uncertainty and enhance creativity in the innovation process and present a process-based taxonomy of different data-driven innovation approaches. On this basis, I recommend the strategies and actions for innovators, companies, R&D organizations, and governments to enact data-driven innovation.
High Performance Parallel I/O and In-Situ Analysis in the WRF Model with ADIOS2
Venue: arXiv cs.DC 2022
Year: 2022

As the computing power of large-scale HPC clusters approaches the Exascale, the gap between compute capabilities and storage systems is ever widening. In particular, the popular High Performance Computing (HPC) application, the Weather Research and Forecasting Model (WRF) is being currently being utilized for high resolution forecasting and research which generate very large datasets, especially when investigating transient weather phenomena. However, the I/O options currently available in WRF have been found to be a bottleneck at scale. In this work, we demonstrate the impact of integrating a next-generation parallel I/O framework - ADIOS2, as a new I/O backend option in WRF. First, we detail the implementation considerations, setbacks, and solutions that were encountered during the integration. Next we examine the results of I/O write times and compare them with results of currently available WRF I/O options. The resulting I/O times show over an order of magnitude speedup when using ADIOS2 compared to classic MPI-I/O based solutions. Additionally, the node-local burst buffer write capabilities as well as in-line lossless compression capabilities of ADIOS2 are showcased, further boosting performance. Finally, usage of the novel ADIOS2 in-situ analysis capabilities for weather forecasting is demonstrated using a WRF forecasting pipeline, showing a seamless end-to-end processing pipeline that occurs concurrently with the execution of the WRF model, leading to a dramatic improvement in total time to solution.
A Brief Analysis of the Apollo Guidance Computer
Venue: arXiv cs.OH 2022
Year: 2022

The AGC was designed with the sole purpose of providing navigational guidance and spacecraft control during the Apollo program throughout the 1960s and early 1970s. The AGC sported 72kb of ROM, 4kb of RAM, and a whopping 14,245 FLOPS, roughly 30 million times fewer than the computer this report is being written on. These limitations are what make the AGC so interesting, as its programmers had to ration each individual word of memory due to the bulk of memory technology of the time. Despite these limitations (or perhaps due to them), the AGC was highly optimized, and arguably the most advanced computer of its time, as its computational power was only matched in the late 1970s by computers like the Apple II. It is safe to say that the AGC had no intended market, and was explicitly designed to enhance control of the Apollo Command Module and Apollo Lunar Module. The AGC was not entirely internal to NASA, however, and was designed in MIT's Instrumentation Laboratory, and manufactured by Raytheon, a weapons and defense contractor.
Simple Gray Coding and LLR Calculation for MDS Modulation Systems
Venue: arXiv cs.IT 2022
Year: 2022

Due to dependence between codeword elements, index modulation (IM) and related modulation techniques struggle to provide simple solutions for practical problems such as Gray coding between information bits and constellation points; and low-complexity log-likelihood ratio (LLR) calculations for channel-encoded information bits. In this paper, we show that a modulation technique based on a simple maximum distance separable (MDS) code, in other words, MDS modulation, can provide simple yet effective solutions to these problems, rendering the MDS techniques more beneficial in the presence of coding. We also compare the coded error performance of the MDS methods with that of the IM methods and demonstrate that MDS modulation outperforms IM.
LaMDA: Language Models for Dialog Applications
Venue: arXiv cs.CL 2022
Year: 2022

We present LaMDA: Language Models for Dialog Applications. LaMDA is a family of Transformer-based neural language models specialized for dialog, which have up to 137B parameters and are pre-trained on 1.56T words of public dialog data and web text. While model scaling alone can improve quality, it shows less improvements on safety and factual grounding. We demonstrate that fine-tuning with annotated data and enabling the model to consult external knowledge sources can lead to significant improvements towards the two key challenges of safety and factual grounding. The first challenge, safety, involves ensuring that the model's responses are consistent with a set of human values, such as preventing harmful suggestions and unfair bias. We quantify safety using a metric based on an illustrative set of human values, and we find that filtering candidate responses using a LaMDA classifier fine-tuned with a small amount of crowdworker-annotated data offers a promising approach to improving model safety. The second challenge, factual grounding, involves enabling the model to consult external knowledge sources, such as an information retrieval system, a language translator, and a calculator. We quantify factuality using a groundedness metric, and we find that our approach enables the model to generate responses grounded in known sources, rather than responses that merely sound plausible. Finally, we explore the use of LaMDA in the domains of education and content recommendations, and analyze their helpfulness and role consistency.