OpenRaiser/Intern-Atlas huggingface dataset:
Resonating Minds -- Emergent Collaboration Through Hierarchical Active Inference
Venue: arXiv cs.MA 2021
Year: 2021

Working together on complex collaborative tasks requires agents to coordinate their actions. Doing this explicitly or completely prior to the actual interaction is not always possible nor sufficient. Agents also need to continuously understand the current actions of others and quickly adapt their own behavior appropriately. Here we investigate how efficient, automatic coordination processes at the level of mental states (intentions, goals), which we call belief resonance, can lead to collaborative situated problem-solving. We present a model of hierarchical active inference for collaborative agents (HAICA). It combines efficient Bayesian Theory of Mind processes with a perception-action system based on predictive processing and active inference. Belief resonance is realized by letting the inferred mental states of one agent influence another agent's predictive beliefs about its own goals and intentions. This way, the inferred mental states influence the agent's own task behavior without explicit collaborative reasoning. We implement and evaluate this model in the Overcooked domain, in which two agents with varying degrees of belief resonance team up to fulfill meal orders. Our results demonstrate that agents based on HAICA achieve a team performance comparable to recent state of the art approaches, while incurring much lower computational costs. We also show that belief resonance is especially beneficial in settings were the agents have asymmetric knowledge about the environment. The results indicate that belief resonance and active inference allow for quick and efficient agent coordination, and thus can serve as a building block for collaborative cognitive agents.
Invisible Data Curation Practices: A Case Study from Facility Management
Venue: arXiv cs.CY 2021
Year: 2021

Facility management, which concerns the administration, operations, and mainte-nance of buildings, is a sector undergoing significant changes while becoming digitalized and data driven. In facility management sector, companies seek to ex-tract value from data about their buildings. As a consequence, craftsmen, such as janitors, are becoming involved in data curation. Data curation refers to activities related to cleaning, assembling, setting up, and stewarding data to make them fit existing templates. Craftsmen in facility management, despite holding a pivotal role for successful data curation in the domain, are understudied and disregarded. To remedy this, our holistic case study investigates how janitors' data curation practices shape the data being produced in three facility management organiza-tions. Our findings illustrate the unfortunate that janitors are treated more like a sensor than a human data curator. This treatment makes them less engaged in data curation, and hence do not engage in a much necessary correction of essential fa-cility data. We apply the conceptual lens of invisible work - work that blends into the background and is taken for granted - to explain why this happens and how data comes to be. The findings also confirm the usefulness of a previously pro-posed analytical framework by using it to interpret data curation practices within facility management. The paper contributes to practitioners by proposing training and education in data curation.
AI-Fuzzy Markup Language with Computational Intelligence for High-School Student Learning
Venue: arXiv cs.CY 2021
Year: 2021

Computational Intelligence (CI), which includes fuzzy logic (FL), neural network (NN), and evolutionary computation (EC), is an imperative branch of artificial intelligence (AI). As a core technology of AI, it plays a vital role in developing intelligent systems, such as games and game engines, neural-based systems including a variety of deep network models, evolutionary-based optimization methods, and advanced cognitive techniques. The 2021 IEEE CIS Summer School on CI for High-School Student Learning was held physically at the JanFuSun Resort Hotel, Taiwan, and virtually on Zoom, on August 10-12, 2021. The main contents of the Summer School were lectures focused on the basics of FL, NN, and EC and the workshop on AIoT (Artificial Intelligence of Things). Invited speakers gave nine courses covering topics like CI real-world applications, fundamentals of FL, and the introduction to NN and EC. The 2021 Summer School was supported by the 2021 IEEE CIS High School Outreach Subcommittee. We also invited students and teachers of high and elementary schools from Taiwan, Japan, and Indonesia. They attended the school and participated in AIoT workshop, gaining experience in applications of AIoT-FML learning tools. According to the short report and feedback from the involved students and teachers, we find out that most participants have quickly understood the principles of CI, FL, NN, and EC. In addition, one of the teachers sent the following remark to the organizers: "This is a great event to introduce students to computational intelligence at a young age, stimulate them to be involved in rapidly evolving fields, and foster participation in future research adventures."
Designing a Framework for Digital KYC Processes Built on Blockchain-Based Self-Sovereign Identity
Venue: arXiv cs.CY 2021
Year: 2021

Know your customer (KYC) processes place a great burden on banks, because they are costly, inefficient, and inconvenient for customers. While blockchain technology is often mentioned as a potential solution, it is not clear how to use the technology's advantages without violating data protection regulations and customer privacy. We demonstrate how blockchain-based self-sovereign identity (SSI) can solve the challenges of KYC. We follow a rigorous design science research approach to create a framework that utilizes SSI in the KYC process, deriving nascent design principles that theorize on blockchain's role for SSI.
A Neuroscience Approach regarding Student Engagement in the Classes of Microcontrollers during the COVID19 Pandemic
Venue: arXiv cs.CY 2021
Year: 2021

The process of teaching has been greatly changed by the COVID-19 pandemic. It is possible that studying will not resemble anymore the process known by the previous generations of students. As the current generations learn by doing and use their intuition, new platforms need to be involved in the teaching process. The current paper proposes a new method to keep the students engaged while learning by involving neuroscience during the classes of Microcontrollers. Arduino and Raspberry Pi boards are studied at the course of Microcontrollers using online simulation environments. The Emotiv Insight headset is used by the professor during the theoretical and practical hours of the Microcontrollers course. The analysis performed on the brainwaves generated by the headset provides numerical values for the mood, focus, stress, relaxation, engagement, excitement and interest levels of the professor. The approaches used during teaching were inquiry-based learning, game-based learning and personalized learning. In this way, professors can determine how to improve the connection with their students based on the use of technology and virtual simulation platforms. The results of the test show that the game-based learning was be best approach because students had to become problem solves and start to use the software skills which they will need as future software engineers. The emphasis is put on mastering the mindset by having to choose their actions and to experiment along the way. According to their achievement, students receive experience points in a gamified environment. Professors need to adjust to a new era of teaching and refine their practices and learning philosophy. They need to be able to use virtual platforms with ease, as well as to engage with their students in order to determine and satisfy their needs.
Borrowing from Similar Code: A Deep Learning NLP-Based Approach for Log Statement Automation
Venue: arXiv cs.SE 2021
Year: 2021

Software developers embed logging statements inside the source code as an imperative duty in modern software development as log files are necessary for tracking down runtime system issues and troubleshooting system management tasks. However, the current logging process is mostly manual, and thus, proper placement and content of logging statements remain as challenges. To overcome these challenges, methods that aim to automate log placement and predict its content, i.e., 'where and what to log', are of high interest. Thus, we focus on predicting the location (i.e., where) and description (i.e., what) for log statements by utilizing source code clones and natural language processing (NLP), as these approaches provide additional context and advantage for log prediction. Specifically, we guide our research with three research questions (RQs): (RQ1) how similar code snippets, i.e., code clones, can be leveraged for log statements prediction? (RQ2) how the approach can be extended to automate log statements' descriptions? and (RQ3) how effective the proposed methods are for log location and description prediction? To pursue our RQs, we perform an experimental study on seven open-source Java projects. We introduce an updated and improved log-aware code-clone detection method to predict the location of logging statements (RQ1). Then, we incorporate natural language processing (NLP) and deep learning methods to automate the log statements' description prediction (RQ2). Our analysis shows that our hybrid NLP and code-clone detection approach (NLP CC'd) outperforms conventional clone detectors in finding log statement locations on average by 15.60% and achieves 40.86% higher performance on BLEU and ROUGE scores for predicting the description of logging statements when compared to prior research (RQ3).
The Impact of Data Distribution on Fairness and Robustness in Federated Learning
Venue: arXiv cs.LG 2021
Year: 2021

Federated Learning (FL) is a distributed machine learning protocol that allows a set of agents to collaboratively train a model without sharing their datasets. This makes FL particularly suitable for settings where data privacy is desired. However, it has been observed that the performance of FL is closely related to the similarity of the local data distributions of agents. Particularly, as the data distributions of agents differ, the accuracy of the trained models drop. In this work, we look at how variations in local data distributions affect the fairness and the robustness properties of the trained models in addition to the accuracy. Our experimental results indicate that, the trained models exhibit higher bias, and become more susceptible to attacks as local data distributions differ. Importantly, the degradation in the fairness, and robustness can be much more severe than the accuracy. Therefore, we reveal that small variations that have little impact on the accuracy could still be important if the trained model is to be deployed in a fairness/security critical context.
Advancing Artificial Intelligence and Machine Learning in the U.S. Government Through Improved Public Competitions
Venue: arXiv cs.CY 2021
Year: 2021

In the last two years, the U.S. government has emphasized the importance of accelerating artificial intelligence (AI) and machine learning (ML) within the government and across the nation. In particular, the National Artificial Intelligence Initiative Act of 2020, which became law on January 1, 2021, provides for a coordinated program across the entire federal government to accelerate AI research and application. The U.S. government can benefit from public artificial intelligence and machine learning challenges through the development of novel algorithms and participation in experiential training. Although the public, private, and non-profit sectors have a history of leveraging crowdsourcing initiatives to generate novel solutions to difficult problems and engage stakeholders, interest in public competitions has waned in recent years as a result of at least three major factors: (1) a lack of high-quality, high-impact data; (2) a narrow engagement focus on specialized groups; and (3) insufficient operationalization of challenge results. Herein we identify common issues and recommend approaches to increase the effectiveness of challenges. To address these barriers, enabling the use of public competitions for accelerating AI and ML practice, the U.S. government must leverage methods that protect sensitive data while enabling modelling, enable easier participation, empower deployment of validated models, and incentivize engagement from broad sections of the population.
FedRAD: Federated Robust Adaptive Distillation
Venue: arXiv cs.LG 2021
Year: 2021

The robustness of federated learning (FL) is vital for the distributed training of an accurate global model that is shared among large number of clients. The collaborative learning framework by typically aggregating model updates is vulnerable to model poisoning attacks from adversarial clients. Since the shared information between the global server and participants are only limited to model parameters, it is challenging to detect bad model updates. Moreover, real-world datasets are usually heterogeneous and not independent and identically distributed (Non-IID) among participants, which makes the design of such robust FL pipeline more difficult. In this work, we propose a novel robust aggregation method, Federated Robust Adaptive Distillation (FedRAD), to detect adversaries and robustly aggregate local models based on properties of the median statistic, and then performing an adapted version of ensemble Knowledge Distillation. We run extensive experiments to evaluate the proposed method against recently published works. The results show that FedRAD outperforms all other aggregators in the presence of adversaries, as well as in heterogeneous data distributions.
Altering Facial Expression Based on Textual Emotion
Venue: arXiv cs.CV 2021
Year: 2021

Faces and their expressions are one of the potent subjects for digital images. Detecting emotions from images is an ancient task in the field of computer vision; however, performing its reverse -- synthesizing facial expressions from images -- is quite new. Such operations of regenerating images with different facial expressions, or altering an existing expression in an image require the Generative Adversarial Network (GAN). In this paper, we aim to change the facial expression in an image using GAN, where the input image with an initial expression (i.e., happy) is altered to a different expression (i.e., disgusted) for the same person. We used StarGAN techniques on a modified version of the MUG dataset to accomplish this objective. Moreover, we extended our work further by remodeling facial expressions in an image indicated by the emotion from a given text. As a result, we applied a Long Short-Term Memory (LSTM) method to extract emotion from the text and forwarded it to our expression-altering module. As a demonstration of our working pipeline, we also create an application prototype of a blog that regenerates the profile picture with different expressions based on the user's textual emotion.
Unity is Strength: A Formalization of Cross-Domain Maximal Extractable Value
Venue: arXiv cs.CR 2021
Year: 2021

The multi-chain future is upon us. Modular architectures are coming to maturity across the ecosystem to scale bandwidth and throughput of cryptocurrency. One example of such is the Ethereum modular architecture, with its beacon chain, its execution chain, its Layer 2s, and soon its shards. These can all be thought as separate blockchains, heavily inter-connected with one another, and together forming an ecosystem. In this work, we call each of these interconnected blockchains "domains", and study the manifestation of Maximal Extractable Value (MEV, a generalization of "Miner Extractable Value") across them. In other words, we investigate whether there exists extractable value that depends on the ordering of transactions in two or more domains jointly. We first recall the definitions of Extractable and Maximal Extractable Value, before introducing a definition of Cross-Domain Maximal Extractable Value. We find that Cross-Domain MEV can be used to measure the incentive for transaction sequencers in different domains to collude with one another, and study the scenarios in which there exists such an incentive. We end the work with a list of negative externalities that might arise from cross-domain MEV extraction and lay out several open questions. We note that the formalism in this work is a work in progress, and we hope that it can serve as the basis for formal analysis tools in the style of those presented in Clockwork Finance, as well as for discussion on how to mitigate the upcoming negative externalities of substantial cross-domain MEV.
Improving mathematical questioning in teacher training
Venue: arXiv cs.HC 2021
Year: 2021

High-fidelity, AI-based simulated classroom systems enable teachers to rehearse effective teaching strategies. However, dialogue-oriented open-ended conversations such as teaching a student about scale factors can be difficult to model. This paper builds a text-based interactive conversational agent to help teachers practice mathematical questioning skills based on the well-known Instructional Quality Assessment. We take a human-centered approach to designing our system, relying on advances in deep learning, uncertainty quantification, and natural language processing while acknowledging the limitations of conversational agents for specific pedagogical needs. Using experts' input directly during the simulation, we demonstrate how conversation success rate and high user satisfaction can be achieved.
Is Approximation Universally Defensive Against Adversarial Attacks in Deep Neural Networks?
Venue: arXiv cs.CR 2021
Year: 2021

Approximate computing is known for its effectiveness in improvising the energy efficiency of deep neural network (DNN) accelerators at the cost of slight accuracy loss. Very recently, the inexact nature of approximate components, such as approximate multipliers have also been reported successful in defending adversarial attacks on DNNs models. Since the approximation errors traverse through the DNN layers as masked or unmasked, this raises a key research question-can approximate computing always offer a defense against adversarial attacks in DNNs, i.e., are they universally defensive? Towards this, we present an extensive adversarial robustness analysis of different approximate DNN accelerators (AxDNNs) using the state-of-the-art approximate multipliers. In particular, we evaluate the impact of ten adversarial attacks on different AxDNNs using the MNIST and CIFAR-10 datasets. Our results demonstrate that adversarial attacks on AxDNNs can cause 53% accuracy loss whereas the same attack may lead to almost no accuracy loss (as low as 0.06%) in the accurate DNN. Thus, approximate computing cannot be referred to as a universal defense strategy against adversarial attacks.
Wearable Affective Memory Augmentation
Venue: arXiv cs.HC 2021
Year: 2021

Human memory prioritizes the storage and recall of information that is emotionally-arousing and/or important in a process known as value-directed memory. When experiencing a stream of information (e.g. conversation, book, lecture, etc.), the individual makes conscious and subconscious value assessments of the incoming information and uses this as a metric to determine what to remember. In order to improve automatic recall of memory, previous memory augmentation systems have sensed users' physiological state to determine which sensory media should be prioritized. Here, we propose to prioritize memories using the affective state of individuals that the user is interacting with. Thereby, the proposed wearable Affective Memory Augmentation system uses affective information from the user's social companions in order to facilitate value-directed memory.
Online Search With Best-Price and Query-Based Predictions
Venue: arXiv cs.DS 2021
Year: 2021

In the online (time-series) search problem, a player is presented with a sequence of prices which are revealed in an online manner. In the standard definition of the problem, for each revealed price, the player must decide irrevocably whether to accept or reject it, without knowledge of future prices (other than an upper and a lower bound on their extreme values), and the objective is to minimize the competitive ratio, namely the worst-case ratio between the maximum price in the sequence and the one selected by the player. The problem formulates several applications of decision-making in the face of uncertainty on the revealed samples. Previous work on this problem has largely assumed extreme scenarios in which either the player has almost no information about the input, or the player is provided with some powerful, and error-free advice. In this work, we study learning-augmented algorithms, in which there is a potentially erroneous prediction concerning the input. Specifically, we consider two different settings: the setting in which the prediction is related to the maximum price in the sequence, as well as the setting in which the prediction is obtained as a response to a number of binary queries. For both settings, we provide tight, or near-tight upper and lower bounds on the worst-case performance of search algorithms as a function of the prediction error. We also provide experimental results on data obtained from stock exchange markets that confirm the theoretical analysis, and explain how our techniques can be applicable to other learning-augmented applications.
Probabilistic Contrastive Loss for Self-Supervised Learning
Venue: arXiv cs.LG 2021
Year: 2021

This paper proposes a probabilistic contrastive loss function for self-supervised learning. The well-known contrastive loss is deterministic and involves a temperature hyperparameter that scales the inner product between two normed feature embeddings. By reinterpreting the temperature hyperparameter as a quantity related to the radius of the hypersphere, we derive a new loss function that involves a confidence measure which quantifies uncertainty in a mathematically grounding manner. Some intriguing properties of the proposed loss function are empirically demonstrated, which agree with human-like predictions. We believe the present work brings up a new prospective to the area of contrastive learning.