Reviewerly/exHarmony huggingface dataset:
Transfer Learning For Crowed Counting

Transfer learning for crowd counting via CNN is explored in this research to minimize training time and computational cost. The Mall dataset is used to evaluate the effectiveness of the transfer learning approach and is compared with other recent techniques that use their own deep architecture. By using transfer learning, less computation is involved because the pretrained model has already learned the necessary weightage and architecture thus reducing time consumption for training. The ResNet50 model is fine-tuned to be applying to the Mall dataset. The result of this project shows that using ResNet50 for transfer learning achieved mediocre MAE and MSE compared to other recent techniques such as the DeepCount and DecideNet models. Therefore, application of transfer learning in crowd counting using CNN for ResNet50 on Mall dataset was proven inefficient and further improvements needs to be carried out for this application to be beneficial
HAMRAZ: Resilient Partitioning and Replication

Inter-organizational systems where subsystems with partial trust need to cooperate are common in healthcare, finance and military. In the face of malicious Byzantine attacks, the ultimate goal is to assure end-to-end policies for the three aspects of trustworthiness: confidentiality, integrity and availability. In contrast to confidentiality and integrity, provision and validation of availability has been often sidestepped. This paper guarantees end-to-end policies simultaneously for all the three aspects of trustworthiness. It presents a security-typed object-based language, a partitioning transformation, an operational semantics, and an information flow type inference system for partitioned and replicated classes. The type system provably guarantees that well-typed methods enjoy noninterference for the three properties, and that their types quantity their resilience to Byzantine attacks. Given a class and the specification of its end-to-end policies, the HAMRAZ tool applies type inference to automatically place and replicate the fields and methods of the class on Byzantine quorum systems, and synthesize trustworthy-by-construction distributed systems. The experiments show the resiliency of the resulting systems; they can gracefully tolerate attacks that are as strong as the specified policies.
Security Foundations for Application-Based Covert Communication Channels

We introduce the notion of an application-based covert channel—or ABCC—which provides a formal syntax for describing covert channels that tunnel messages through existing protocols. Our syntax captures many recent systems, including DeltaShaper (PETS 2017) and Protozoa (CCS 2020). We also define what it means for an ABCC to be secure against a passive eavesdropper, and prove that suitable abstractions of existing censorship circumvention systems satisfy our security notion. In doing so, we define a number of important non-cryptographic security assumptions that are often made implicitly in prior work. We believe our formalisms may be useful to censorship circumvention developers for reasoning about the security of their systems and the associated security assumptions required.
Domains Do Change Their Spots: Quantifying Potential Abuse of Residual Trust

When domains expire and are released to the public, adversaries can re-register them with the hope of exploiting residual trust from clients that are unaware of the change in ownership. Because domain name resolution is integral to the web, possible clients run the gamut from humans browsing the web to automated and periodic processes such as system updates. For an adversary, this trivially yields access to an attack vector that can affect a multitude of diverse systems and devices. We reason that some domains which experience residual trust and are valuable from a security perspective are not valuable from a dropcatching perspective and, as such, can be re-registered by an adversary without participating in fiercely competitive auctions for expired domains. In this paper, we present an investigation into this attack vector using a top-down, opportunistic approach, as opposed to bottom-up approaches used by prior work that target specific systems and infrastructure. Throughout a one-month re-registration period, we identify potentially valuable dropped domains using a threshold of passive DNS resolutions, re-register, and deploy them with basic honeypot services. We then analyze the traffic to these domains to find instances of residual trust that can be exploited. Our honeypot services recorded, over a four-month period, 650,737,621 requests from 5,540,379 unique IP addresses situated in 22,744 different autonomous systems to the 201 re-registered domains. Although a majority of these domains may not pose significant security risks, we are most concerned with and thus focus our discussion on unusual domains which receive orders of magnitude more traffic and types of traffic that are significantly different from the other domains. These include domains which previously functioned as a torrent tracker, an API for a computer lab usage statistics service used by many universities, an API that was a point of contact for a common Android haptics library, security company DNS sinkhole servers, an Internet radio and music station, command-and-control servers for malware and potentially unwanted programs, and an email tracker. Our findings demonstrate that expired domains pose a real threat to the security of the Internet ecosystem and that adversaries with modest budgets can compromise a wide range of systems and services by merely registering previously-popular domains that were left to expire.
Study of a linear-physical-programming-based approach for web service selection under uncertain service quality

Abstract To promote the practical use of web service technologies, it is critical to select a proper web service that will meet users’ preferences from a pool of web services with similar functions. It is usually a core issue during web service selection to describe and evaluate the quality of these web services. Average value can be employed to describe certain service quality and to evaluate the quality with multiple criteria through the Simple Additive Weight Method . Based on an analysis of various deficiencies in existing approaches proposed in some studies, four indicators were used: Price, Response Time, Reliability and Credibility . On top of that, recent sampling data sets were introduced to describe the uncertain quality of service (QoS) criteria; linear physical programming was used to evaluate QoS with comprehensive criteria to meet the need to objectively describe uncertain QoS criteria, and preference regions were set to adapt to interval characteristics of criteria. Finally, a computational example is provided to demonstrate the feasibility and effectiveness of the method.
Generating Deontic Obligations From Utility-Maximizing Systems

This work gives a logical characterization of the (ethical and social) obligations of an agent trained with Reinforcement Learning (RL). An RL agent takes actions by following a utility-maximizing policy. We maintain that the choice of utility function embeds ethical and social values implicitly, and that it is necessary to make these values explicit. This work provides a basis for doing so. First, we propose a probabilistic deontic logic that is suited for formally specifying the obligations of a stochastic system, including its ethical obligations. We prove some useful validities about this logic, and how its semantics are compatible with those of Markov Decision Processes (MDPs). Second, we show that model checking allows us to prove that an agent has a given obligation to bring about some state of affairs - meaning that by acting optimally, it is seeking to reach that state of affairs. We develop a model checker for our logic against MDPs. Third, we observe that it is useful for a system designer to obtain a logical characterization of her system's obligations, which is potentially more interpretable and helpful in debugging than the expression of a utility function. Enumerating all the obligations of an agent is impractical, so we propose a Bayesian optimization routine that learns to generate a system's obligations that the system designer deems interesting. We implement the model checking and Bayesian optimization routines, and demonstrate their effectiveness with an initial pilot study. This work provides a rigorous method to characterize utility-maximizing agents in terms of the (ethical and social) obligations that they implicitly seek to satisfy.
AI Alignment Dialogues: An Interactive Approach to AI Alignment in Support Agents

This project proposes a different way of looking at AI alignment, namely by introducing AI Alignment Dialogues. We argue that alignment dialogues have a number of advantages in comparison to data-driven approaches, especially for behaviour support agents, which aim to support users in achieving their desired future behaviours rather than their current behaviours. The advantages of alignment dialogues include allowing the users to directly convey higher-level concepts to the agent and making the agent more transparent and trusted.
A Bio-Inspired Framework for Machine Bias Interpretation

Machine learning algorithms use the past and the present to predict the future. But when given biased historical data, these algorithms can quickly become discriminatory. The area of machine learning fairness has emerged to detect and de-bias these algorithms, but has received widespread criticism for its one-size-fits-all approach, which allows certain cases of bias to slip through the cracks. In this study, we take a deeper look at the mechanisms by which machine learning algorithms develop harmful bias. We introduce a new method to interpret discriminatory systems, an Evolutionary algorithm for Feature Interaction (EFI), which we apply to several commonly used machine learning algorithms in two real-world problem instances: violent crime and median house price prediction. In the results, we discover several complex forms of bias including the encoding of race through other seemingly unrelated attributes. Ultimately we suggest that more informative interpretation tools such as EFI can be used to not only explain machine learning outcomes, but supplement and improve existing machine bias detection approaches to provide a more robust and in-depth ethical evaluation of machine learning algorithms.
Piecemeal Knowledge Acquisition for Computational Normative Reasoning

We present a hybrid approach to knowledge acquisition and representation for machine ethics---or more generally, computational normative reasoning. Building on recent research in artificial intelligence and law, our approach is modeled on the familiar practice of decision-making under precedential constraint in the common law. We first provide a formal characterization of this practice, showing how a body of normative information can be constructed in a way that is piecemeal, distributed, and responsive to particular circumstances. We then discuss two possible applications: first, a robot childminder, and second, moral judgment in a bioethical domain.
Actual Causality and Responsibility Attribution in Decentralized Partially Observable Markov Decision Processes

Actual causality and a closely related concept of responsibility attribution are central to accountable decision making. Actual causality focuses on specific outcomes and aims to identify decisions (actions) that were critical in realizing an outcome of interest. Responsibility attribution is complementary and aims to identify the extent to which decision makers (agents) are responsible for this outcome. In this paper, we study these concepts under a widely used framework for multi-agent sequential decision making under uncertainty: decentralized partially observable Markov decision processes (Dec-POMDPs). Following recent works in RL that show correspondence between POMDPs and Structural Causal Models (SCMs), we first establish a connection between Dec-POMDPs and SCMs. This connection enables us to utilize a language for describing actual causality from prior work and study existing definitions of actual causality in Dec-POMDPs. Given that some of the well-known definitions may lead to counter-intuitive actual causes, we introduce a novel definition that more explicitly accounts for causal dependencies between agents' actions. We then turn to responsibility attribution based on actual causality, where we argue that in ascribing responsibility to an agent it is important to consider both the number of actual causes in which the agent participates, as well as its ability to manipulate its own degree of responsibility. Motivated by these arguments we introduce a family of responsibility attribution methods that extends prior work, while accounting for the aforementioned considerations. Finally, through a simulation-based experiment, we compare different definitions of actual causality and responsibility attribution methods. The empirical results demonstrate the qualitative difference between the considered definitions of actual causality and their impact on attributed responsibility.
Judging Instinct Exploitation in Statistical Data Explanations Based on Word Embedding

This paper proposes 18 types of statistical data explanations and three kinds of procedures to investigate credibility in unethical and biased explanations due to exploitation of the 10 instincts proposed by Rosling et al. The explanation "women have lower math scores than men'' accompanied with the averages and the distributions of their scores is an example of such an explanation, as it exploits the gap instinct, i.e., our tendency to divide all kinds of things into two distinct and often conflicting groups. It becomes much less credible if we replace the word "math'' with "English'', even if we keep the data as they are, as the exploitation seems to fail. Our judging procedures are based on phrase embedding and carefully designed comparisons to judge the credibility. The results of our experiments comparing the 18 types with their variants show promising results and clues for further developments.
Constructing Meaningful Explanations: Logic-based Approaches

Machine learning (ML) models are ubiquitous: we encounter them when using a search engine, behind online text translation, etc. However, these models have to be used with care, as they are susceptible to social biases. Further, most ML models are inherently opaque, another obstacle to understand and verify them.
Stochastic Policies in Morally Constrained (C-)SSPs

Stochastic policies often outperform deterministic ones. This is especially true for Constrained Stochastic Shortest Path (C-SSP) problems, a popular approach to planning under uncertainty with multiple objectives. Nevertheless, there are moral concerns about stochastic policies that should deter us from selecting them. In this paper, we identify some of these moral concerns and offer 'acceptability constraints' that allow only certain stochastic policies to be selected. We propose a novel C-SSP solver able to integrate our moral acceptability constraints, we evaluate its performance in a relevant test problem, and we show that our approach can successfully produce acceptable policies in morally significant domains.
Towards Better Detection of Biased Language with Scarce, Noisy, and Biased Annotations

Biased language is prevalent in today's online social media. To reduce the amount of online biased language, one critical first step is to accurately detect such biased language, ideally automatically. This is a challenging problem, however, as the annotated data necessary for training a biased language classifier is either scarce and costly (e.g., when collected from experts), or noisy and potentially biased on their own (e.g., when collected from crowd workers). The biased language classifier built based on these annotations may thus be inaccurate, and sometimes unfair (e.g., have systematic accuracy disparities across texts with different political leanings). In this paper, we propose a novel method, CLEARE, for biased language detection, in which we utilize self-supervised contrastive learning to enhance the biased language classifier---we learn a robust encoder of the textual data through solving a min-max optimization problem, so that the encoder could help achieve the best classification performance even if the worst data augmentation strategy is selected. Extensive evaluations suggest that CLEARE shows substantial improvements compared to the state-of-art biased language detection methods on several benchmark datasets, in terms of improving both the accuracy and the fairness of the detection.
Investigating Debiasing Effects on Classification and Explainability

During each stage of a dataset creation and development process, harmful biases can be accidentally introduced, leading to models that perpetuates marginalization and discrimination of minorities, as the role of the data used during the training is critical. We propose an evaluation framework that investigates the impact on classification and explainability of bias mitigation preprocessing techniques used to assess data imbalances concerning minorities' representativeness and mitigate the skewed distributions discovered. Our evaluation focuses on assessing fairness, explainability and performance metrics. We analyze the behavior of local model-agnostic explainers on the original and mitigated datasets to examine whether the proxy models learned by the explainability techniques to mimic the black-boxes disproportionately rely on sensitive attributes, demonstrating biases rooted in the explainers. We conduct several experiments about known biased datasets to demonstrate our proposal's novelty and effectiveness for evaluation and bias detection purposes.
Clustering Learner’s Metacognitive Judgment Accuracy and Bias to Explore Learning with AIEd Systems

Metacognitive monitoring and regulation are key dynamic psychological processes in predicting learning, reasoning, and problem solving across AIEd systems. They are impacted by one's metacognitive knowledge, skills, and experiences. Understanding the dynamical processes underlying metacognition are critical to design intelligent adaptive scaffolding. Metacognition may be assumed to function at hierarchical levels of abstraction including a local level (i.e., isolated metacognitive judgements) and global level (i.e., self-beliefs). However, metacognitive research for complex learning has traditionally disregarded the concept of a general metacognitive ability due to local level fluctuations in temporal metacognitive accuracy. In our study, we shift our analyses to reflect a global-level approach to study metacognitive judgment ability by measuring both accuracy and bias across a series of metacognitive judgments. Using hierarchical clustering on undergraduates' (n = 58) metacognitive judgments' accuracy and bias while learning about nine human biological systems with MetaTutor-IVH, a multimedia-based learning environment, we show that some learners show patterns of global-level metacognitive ability. Specifically, we find that learners who tended to have low metacognitive accuracy across all judgment types performed worse overall on learning outcomes. Examining the bias of these learners, we found they tended to be under-confident across all judgment types. Our work suggests that considering multiple metrics of local-level metacognitive judgments' accuracy and bias, can be aggregated to depict a global-level metacognitive ability of learners that is correlated to learning outcomes. We further discuss the impact of our findings for the design of adaptive scaffolding of AIEd systems to foster metacognition.
Extensive Reading at Home: Extracting Self-directed Reading Habits from Learning Logs

Habit is an important concept attracting researchers' attention in many fields. In the past, habit was commonly measured by tools such as questionnaires and diaries which involve subjects' self-reported behaviors. However, attempts to extract and support good learning habits with actual learners' data are still limited. Therefore, in this study, we aim to identify students' reading habits using learning analytics, which enables fine description of learners' behaviors with accumulated amounts of log data. We define students' reading logs based on a psychological framework in which habit is defined as a repetitive behavior responding to a certain context. The learning context in this study is the promotion of extensive reading in English implemented in a Japanese public junior high school and supported by a digital learning environment. We extract 175 students' daily reading logs recorded from May 31, 2020 to April 9, 2021 (314 days) to examine whether periodicity occurs in the data so as to identify the students who newly form an English reading habit as a result of the promotion as well as their behavior patterns in the process.
Student-Tutor Mixed-Initiative Decision-Making Supported by Deep Reinforcement Learning

One fundamental goal of education is to enable students to act independently in the world by continuously adapting and learning. Certain learners are less sensitive to learning environments and can always perform well, while others are more sensitive to variations in learning environments and may fail to learn. We refer to the former as high performers and the latter as low performers. Previous research showed that low performers benefit more from tutor-driven Intelligent Tutoring Systems (ITSs), in which the tutor makes pedagogical decisions, while the high ones often prefer to take control of their own learning by making decisions by themselves. We propose a student-tutor mixed-initiative (ST-MI) decision-making framework which balances allowing students some control over their own learning while ensuring effective pedagogical interventions. In an empirical study, ST-MI significantly improved student learning gains than an Expert-designed, tutor-driven pedagogical policy on an ITS. Furthermore, our ST-MI framework was found to offer low performers the same benefits as the Expert policy, while that for high performers was significantly greater than the Expert policy.
Assessing Readability by Filling Cloze Items with Transformers

Cloze items are a foundational approach to assessing readability. However, they require human data collection, thus making them impractical in automated metrics. The present study revisits the idea of assessing readability with cloze items and compares human cloze scores and readability judgments with predictions made by T5, a popular deep learning architecture, on three corpora. Across all corpora, T5 predictions significantly correlated with human cloze scores and readability judgments, and in predictive models, they could be used interchangeably with average word length, a common readability predictor. For two corpora, combining T5 and Flesch reading ease predictors improved model fit for human cloze scores and readability judgments.