OpenRaiser/Intern-Atlas huggingface dataset:
Generative models-based data labeling for deep networks regression: application to seed maturity estimation from UAV multispectral images
Venue: arXiv cs.CV 2022
Year: 2022

Monitoring seed maturity is an increasing challenge in agriculture due to climate change and more restrictive practices. Seeds monitoring in the field is essential to optimize the farming process and to guarantee yield quality through high germination. Traditional methods are based on limited sampling in the field and analysis in laboratory. Moreover, they are time consuming and only allow monitoring sub-sections of the crop field. This leads to a lack of accuracy on the condition of the crop as a whole due to intra-field heterogeneities. Multispectral imagery by UAV allows uniform scan of fields and better capture of crop maturity information. On the other hand, deep learning methods have shown tremendous potential in estimating agronomic parameters, especially maturity. However, they require large labeled datasets. Although large sets of aerial images are available, labeling them with ground truth is a tedious, if not impossible task. In this paper, we propose a method for estimating parsley seed maturity using multispectral UAV imagery, with a new approach for automatic data labeling. This approach is based on parametric and non-parametric models to provide weak labels. We also consider the data acquisition protocol and the performance evaluation of the different steps of the method. Results show good performance, and the non-parametric kernel density estimator model can improve neural network generalization when used as a labeling method, leading to more robust and better performing deep neural models.
The Right Kind of Non-Determinism: Using Concurrency to Verify C Programs with Underspecified Semantics
Venue: arXiv cs.PL 2022
Year: 2022

We present a novel and well automatable approach to formal verification of C programs with underspecified semantics, i.e., a language semantics that leaves open the order of certain evaluations. First, we reduce this problem to non-determinism of concurrent systems, automatically extracting a distributed Active Object model from underspecified, sequential C code. This translation process provides a fully formal semantics for the considered C subset. In the extracted model every non-deterministic choice corresponds to one possible evaluation order. This step also automatically translates specifications in the ANSI/ISO C Specification Language (ACSL) into method contracts and object invariants for Active Objects. We then perform verification on the specified Active Objects model, using the Crowbar theorem prover, which verifies the extracted model with respect to the translated specification and ensures the original property of the C code for all possible evaluation orders. By using model extraction, we can use standard tools, without designing a new complex program logic to deal with underspecification. The case study used is highly underspecified and cannot be handled correctly by existing tools for C.
Branching Pomsets for Choreographies
Venue: arXiv cs.PL 2022
Year: 2022

Choreographic languages describe possible sequences of interactions among a set of agents. Typical models are based on languages or automata over sending and receiving actions. Pomsets provide a more compact alternative by using a partial order over these actions and by not making explicit the possible interleaving of concurrent actions. However, pomsets offer no compact representation of choices. For example, if an agent Alice can send one of two possible messages to Bob three times, one would need a set of 2 * 2 * 2 distinct pomsets to represent all possible branches of Alice's behaviour. This paper proposes an extension of pomsets, named branching pomsets, with a branching structure that can represent Alice's behaviour using 2 + 2 + 2 ordered actions. We encode choreographies as branching pomsets and show that the pomset semantics of the encoded choreographies are bisimilar to their operational semantics.
Decolonisation, Global Data Law, and Indigenous Data Sovereignty
Venue: arXiv cs.CY 2022
Year: 2022

This research examines the impact of digital neo-colonialism on the Global South and encourages the development of legal and economic incentives to protect Indigenous cultures globally. Data governance is discussed in an evolutionary context while focusing on data sharing and data mining. Case studies that exemplify the need to steer global data law towards protecting the earth, while addressing issues of data access, privacy, rights, and colonialism in the global South are explored. The case studies highlight connections to indigenous people's rights, in regard to the protection of environmental ecosystems, thus establishing how data law can serve the earth from an autochthonous lens. This framework examines histories shaped by colonialism and suggests how data governance could be used to create healthier balances of power.
Towards a General Pre-training Framework for Adaptive Learning in MOOCs
Venue: arXiv cs.CY 2022
Year: 2022

Adaptive learning aims to stimulate and meet the needs of individual learners, which requires sophisticated system-level coordination of diverse tasks, including modeling learning resources, estimating student states, and making personalized recommendations. Existing deep learning methods have achieved great success over statistical models; however, they still lack generalization for diverse tasks and suffer from insufficient capacity since they are composed of highly-coupled task-specific architectures and rely on small-scale, coarse-grained recommendation scenarios. To realize the idea of general adaptive systems proposed in pedagogical theory, with the emerging pre-training techniques in NLP, we try to conduct a practical exploration on applying pre-training to adaptive learning, to propose a unified framework based on data observation and learning style analysis, properly leveraging heterogeneous learning elements. Through a series of downstream tasks of Learning Recommendation, Learning Resource Evaluation, Knowledge Tracing, and Dropout Prediction, we find that course structures, text, and knowledge are helpful for modeling and inherently coherent to student non-sequential learning behaviors and that indirectly relevant information included in the pre-training foundation can be shared across downstream tasks to facilitate effectiveness. We finally build a simplified systematic application of adaptive learning and reflect on the insights brought back to pedagogy. The source code and dataset will be released.
The Transform-o-meter: A method to forecast the transformative impact of innovation
Venue: arXiv cs.CY 2022
Year: 2022

With the advent of Transformative Artificial Intelligence, it is now more important than ever to be able to both measure and forecast the transformative impact/potential of innovation. However, current methods fall short when faced with this task. This paper introduces the Transform-o-meter; a methodology that can be used to achieve the aforementioned goal, and be applied to any innovation, both material and immaterial. While this method can effectively be used for the mentioned purpose, it should be taken as a first approach; to be iterated, researched, and expanded further upon.
Efficient Novelty Detection Methods for Early Warning of Potential Fatal Diseases
Venue: arXiv cs.CY 2022
Year: 2022

Fatal diseases, as Critical Health Episodes (CHEs), represent real dangers for patients hospitalized in Intensive Care Units. These episodes can lead to irreversible organ damage and death. Nevertheless, diagnosing them in time would greatly reduce their inconvenience. This study therefore focused on building a highly effective early warning system for CHEs such as Acute Hypotensive Episodes and Tachycardia Episodes. To facilitate the precocity of the prediction, a gap of one hour was considered between the observation periods (Observation Windows) and the periods during which a critical event can occur (Target Windows). The MIMIC II dataset was used to evaluate the performance of the proposed system. This system first includes extracting additional features using three different modes. Then, the feature selection process allowing the selection of the most relevant features was performed using the Mutual Information Gain feature importance. Finally, the high-performance predictive model LightGBM was used to perform episode classification. This approach called MIG-LightGBM was evaluated using five different metrics: Event Recall (ER), Reduced Precision (RP), average Anticipation Time (aveAT), average False Alarms (aveFA), and Event F1-score (EF1-score). A method is therefore considered highly efficient for the early prediction of CHEs if it exhibits not only a large aveAT but also a large EF1-score and a low aveFA. Compared to systems using Extreme Gradient Boosting, Support Vector Classification or Naive Bayes as a predictive model, the proposed system was found to be highly dominant. It also confirmed its superiority over the Layered Learning approach.
Secure Implementation of a Vehicular Application for the Symbian Platform
Venue: arXiv cs.NI 2022
Year: 2022

A vehicular ad-hoc network is a type of communication network where the nodes are vehicles. Their aim is to manage traffic efficiently in order to prevent unwanted events such as accidents, jams, etc. The research on this type of networks assumes the use of additional infrastructure installed on the roads, and special devices installed in vehicles. In this paper we propose a novel solution for the deployment of vehicular networks, which uses only existing smartphones with technologies such as Wi-Fi, GPS and Bluetooth. This approach will save the costs of road infrastructure deployment and extra vehicles devices, making it possible to deploy the first real-world vehicular ad-hoc network with a low cost. In particular, the developed implementation of the proposal for the Symbian platform is here examined in full detail.
Long-Term Mentoring for Computer Science Researchers
Venue: arXiv cs.CY 2022
Year: 2022

Early in the pandemic, we -- leaders in the research areas of programming languages (PL) and computer architecture (CA) -- realized that we had a problem: the only way to form new lasting connections in the community was to already have lasting connections in the community. Both of our academic communities had wonderful short-term mentoring programs to address this problem, but it was clear that we needed long-term mentoring programs. Those of us in CA approached this scientifically, making an evidence-backed case for community-wide long-term mentoring. In the meantime, one of us in PL had impulsively launched an unofficial long-term mentoring program, founded on chaos and spreadsheets. In January 2021, the latter grew to an official cross-institutional long-term mentoring program called SIGPLAN-M; in January 2022, the former grew to Computer Architecture Long-term Mentoring (CALM). The impacts have been strong: SIGPLAN-M reaches 328 mentees and 234 mentors across 41 countries, and mentees have described it as "life changing" and "a career saver." And while CALM is in its pilot phase -- with 13 mentors and 21 mentees across 7 countries -- it has received very positive feedback. The leaders of SIGPLAN-M and CALM shared our designs, impacts, and challenges along the way. Now, we wish to share those with you. We hope this will kick-start a larger long-term mentoring effort across all of computer science.
Digital health shopping assistant with React Native: a simple technological solution to a complex health problem
Venue: arXiv cs.CY 2022
Year: 2022

Today, more and more people are reporting allergies, which can range from simple reactions close to discomfort to anaphylactic shocks. Other people may not be allergic but avoid certain foods for personal reasons. Daily food shopping of these people is hampered by the fact that unwanted ingredients can be hidden in any food, and it is difficult to find them all. The paper presents a digital health shopping assistant called "Diet Helper", aimed to make life easier for such people by making it easy to determine whether a product is suitable for consumption, according to the specific dietary requirements of both types - existing diet and self-defined. This is achieved by capturing ingredient label, received by the app as an input, which the app analyses, converting the captured label to text, and filters out unwanted ingredients that according to the user should be avoided as either allergens or products to which the consumer is intolerant etc, helping the user decide if the product is suitable for consumption. This should make daily grocery shopping easier by providing the user with more accurate and simplified product selection in seconds, reducing the total time spent in the grocery stores, which is especially relevant in light of COVID-19, although it was and will remain out of it due to the busy schedules and active rhythm of life of modern society. The app is developed using the React Native framework and Google Firebase platform, which makes it easy to develop, use and extend such solutions thereby encouraging to start actively developing solutions that could improve wellbeing.
Integrating connection search in graph queries
Venue: arXiv cs.DB 2022
Year: 2022

Graph data management and querying has many practical applications. When graphs are very heterogeneous and/or users are unfamiliar with their structure, they may need to find how two or more groups of nodes are connected in a graph, even when users are not able to describe the connections. This is only partially supported by existing query languages, which allow searching for paths, but not for trees connecting three or more node groups. The latter is related to the NP-hard Group Steiner Tree problem, and has been previously considered for keyword search in databases. In this work, we formally show how to integrate connecting tree patterns (CTPs, in short) within a graph query language such as SPARQL or Cypher, leading to an Extended Query Language (or EQL, in short). We then study a set of algorithms for evaluating CTPs; we generalize prior keyword search work, most importantly by (i) considering bidirectional edge traversal and (ii) allowing users to select any score function for ranking CTP results. To cope with very large search spaces, we propose an efficient pruning technique and formally establish a large set of cases where our algorithm, MOLESP, is complete even with pruning. Our experiments validate the performance of our CTP and EQL evaluation algorithms on a large set of synthetic and real-world workloads.
An Unconstrained Symmetric Nonnegative Latent Factor Analysis for Large-scale Undirected Weighted Networks
Venue: arXiv cs.LG 2022
Year: 2022

Large-scale undirected weighted networks are usually found in big data-related research fields. It can naturally be quantified as a symmetric high-dimensional and incomplete (SHDI) matrix for implementing big data analysis tasks. A symmetric non-negative latent-factor-analysis (SNL) model is able to efficiently extract latent factors (LFs) from an SHDI matrix. Yet it relies on a constraint-combination training scheme, which makes it lack flexibility. To address this issue, this paper proposes an unconstrained symmetric nonnegative latent-factor-analysis (USNL) model. Its main idea is two-fold: 1) The output LFs are separated from the decision parameters via integrating a nonnegative mapping function into an SNL model; and 2) Stochastic gradient descent (SGD) is adopted for implementing unconstrained model training along with ensuring the output LFs nonnegativity. Empirical studies on four SHDI matrices generated from real big data applications demonstrate that an USNL model achieves higher prediction accuracy of missing data than an SNL model, as well as highly competitive computational efficiency.
Rapid Development of a Mobile Robot Simulation Environment
Venue: arXiv cs.RO 2022
Year: 2022

Robotics simulation provides many advantages during the development of an intelligent ground vehicle (IGV) such as testing the software components in varying scenarios without requiring a complete physical robot. This paper discusses a 3D simulation environment created using rapid application development and the Unity game engine to enable testing during a mobile robotics competition. Our experience shows that the simulation environment contributed greatly to the development of software for the competition. The simulator also contributed to the hardware development of the robot.
Global Evaluation for Decision Tree Learning
Venue: arXiv cs.LG 2022
Year: 2022

We transfer distances on clusterings to the building process of decision trees, and as a consequence extend the classical ID3 algorithm to perform modifications based on the global distance of the tree to the ground truth--instead of considering single leaves. Next, we evaluate this idea in comparison with the original version and discuss occurring problems, but also strengths of the global approach. On this basis, we finish by identifying other scenarios where global evaluations are worthwhile.
On the Importance of Critical Period in Multi-stage Reinforcement Learning
Venue: arXiv cs.AI 2022
Year: 2022

The initial years of an infant's life are known as the critical period, during which the overall development of learning performance is significantly impacted due to neural plasticity. In recent studies, an AI agent, with a deep neural network mimicking mechanisms of actual neurons, exhibited a learning period similar to human's critical period. Especially during this initial period, the appropriate stimuli play a vital role in developing learning ability. However, transforming human cognitive bias into an appropriate shaping reward is quite challenging, and prior works on critical period do not focus on finding the appropriate stimulus. To take a step further, we propose multi-stage reinforcement learning to emphasize finding ``appropriate stimulus" around the critical period. Inspired by humans' early cognitive-developmental stage, we use multi-stage guidance near the critical period, and demonstrate the appropriate shaping reward (stage-2 guidance) in terms of the AI agent's performance, efficiency, and stability.
PerD: Perturbation Sensitivity-based Neural Trojan Detection Framework on NLP Applications
Venue: arXiv cs.LG 2022
Year: 2022

Deep Neural Networks (DNNs) have been shown to be susceptible to Trojan attacks. Neural Trojan is a type of targeted poisoning attack that embeds the backdoor into the victim and is activated by the trigger in the input space. The increasing deployment of DNNs in critical systems and the surge of outsourcing DNN training (which makes Trojan attack easier) makes the detection of Trojan attacks necessary. While Neural Trojan detection has been studied in the image domain, there is a lack of solutions in the NLP domain. In this paper, we propose a model-level Trojan detection framework by analyzing the deviation of the model output when we introduce a specially crafted perturbation to the input. Particularly, we extract the model's responses to perturbed inputs as the `signature' of the model and train a meta-classifier to determine if a model is Trojaned based on its signature. We demonstrate the effectiveness of our proposed method on both a dataset of NLP models we create and a public dataset of Trojaned NLP models from TrojAI. Furthermore, we propose a lightweight variant of our detection method that reduces the detection time while preserving the detection rates.
Visual Heart Rate Estimation from RGB Facial Video using Spectral Reflectance
Venue: arXiv cs.CV 2022
Year: 2022

Estimation of the Heart rate from the facial video has a number of applications in the medical and fitness industries. Additionally, it has become useful in the field of gaming as well. Several approaches have been proposed to seamlessly obtain the Heart rate from the facial video, but these approaches have had issues in dealing with motion and illumination artifacts. In this work, we propose a reliable HR estimation framework using the spectral reflectance of the user, which makes it robust to motion and illumination disturbances. We employ deep learning-based frameworks such as Faster RCNNs to perform face detection as opposed to the Viola Jones algorithm employed by previous approaches. We evaluate our method on the MAHNOB HCI dataset and found that the proposed method is able to outperform previous approaches.Estimation of the Heart rate from facial video has a number of applications in the medical and the fitness industries. Additionally, it has become useful in the field of gaming as well. Several approaches have been proposed to seamlessly obtain the Heart rate from the facial video, but these approaches have had issues in dealing with motion and illumination artifacts. In this work, we propose a reliable HR estimation framework using the spectral reflectance of the user, which makes it robust to motion and illumination disturbances. We employ deep learning-based frameworks such as Faster RCNNs to perform face detection as opposed to the Viola-Jones algorithm employed by previous approaches. We evaluate our method on the MAHNOB HCI dataset and found that the proposed method is able to outperform previous approaches.
Programming Nonlinear Propagation for Efficient Optical Learning Machines
Venue: arXiv cs.ET 2022
Year: 2022

The ever-increasing demand for processing data with larger machine learning models requires more efficient hardware solutions due to limitations such as power dissipation and scalability. Optics is a promising contender for providing lower power computation since light propagation through a non-absorbing medium is a lossless operation. However, to carry out useful and efficient computations with light, generating and controlling nonlinearity optically is a necessity that is still elusive. Multimode fibers (MMF) have been shown that they can provide nonlinear effects with microwatts of average power while maintaining parallelism and low loss. In this work, we propose an optical neural network architecture, which performs nonlinear optical computation by controlling the propagation of ultrashort pulses in MMF by wavefront shaping. With a surrogate model, optimal sets of parameters are found to program this optical computer for different tasks with minimal utilization of an electronic computer. We show a remarkable decrease of 97% in the number of model parameters, which leads to an overall 99% digital operation reduction compared to an equivalently performing digital neural network. We further demonstrate that a fully optical implementation can also be performed with competitive accuracies.
A Bivariate Invariance Principle
Venue: arXiv cs.IT 2022
Year: 2022

A notable result from analysis of Boolean functions is the Basic Invariance Principle (BIP), a quantitative nonlinear generalization of the Central Limit Theorem for multilinear polynomials. We present a generalization of the BIP for bivariate multilinear polynomials, i.e., polynomials over two n-length sequences of random variables. This bivariate invariance principle arises from an iterative application of the BIP to bound the error in replacing each of the two input sequences. In order to prove this invariance principle, we first derive a version of the BIP for random multilinear polynomials, i.e., polynomials whose coefficients are random variables. As a benchmark, we also state a naive bivariate invariance principle which treats the two input sequences as one and directly applies the BIP. Neither principle is universally stronger than the other, but we do show that for a notable class of bivariate functions, which we term separable functions, our subtler principle is exponentially tighter than the naive benchmark.