Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting Bias in News Articles using NLP Models

This project analyses various natural language processing (NLP) algorithms in order to build a deeper understanding of the machine learning techniques required to detect biased political leanings in news sources. Since news is the first and most direct source from which people learn about current unfolding events, the introduction of unjust subjectivity can be very harmful. Thus, the ability to detect bias in news sources is key to maintaining truth when disseminating information. The first classification model we implement is a Tensorflow deep neural network (DNN) using bag of words (BOW) to represent the input sentences. Then, we build on this deep learning algorithm by adding term frequency–inverse document frequency (TD-IDF) as a weighting factor to the DNN input data. In an attempt to further improve results, we shift towards an unsupervised K-Means clustering algorithm to analyze patterns discovered amongst articles from the various news source. Finally, we implement SimCSE, a contrastive learning framework for sentence embeddings. We find that contrastive learning is the most accurate NLP model of those tested for detecting the nuances of political bias in news article sentences.
Fast Fuzzing for Memory Errors

Greybox fuzzing is a proven effective testing method for the detection of security vulnerabilities and other bugs in modern software systems. Greybox fuzzing can also be used in combination with a sanitizer , such as AddressSanitizer (ASAN), to further enhance the detection of certain classes of bug such as buffer overﬂow and use-after-free errors. However, sanitizers also introduce additional performance overheads, and this can degrade the performance of greybox fuzzing—measured in the order of 2 . 36 × for fuzzing with ASAN—potentially negating the beneﬁt of using a sanitizer in the ﬁrst place. Recent research attributes this to extra overheads to additional page faults that are generated when the disjoint sanitizer metadata is accessed at runtime. In this paper, we present a new design that can detect memory errors without a proliferation of page faults. The basic idea is to track memory validity using randomized tokens that are stored directly in the memory itself, rather than in disjoint metadata. All read/write operations are instrumented to check for the token, and if present, a memory error will be detected. We implement our design in the form of the R E ZZ AN —a sanitizer speciﬁcally optimized for fuzz testing. Since there is no disjoint metadata access, no additional page faults are generated, minimizing the performance overhead to around 1 . 14-1 . 27 × (depending on the conﬁguration).
Paying More Attention to Self-attention: Improving Pre-trained Language Models via Attention Guiding

Pre-trained language models (PLM) have demonstrated their effectiveness for a broad range of information retrieval and natural language processing tasks. As the core part of PLM, multi-head self-attention is appealing for its ability to jointly attend to information from different positions. However, researchers have found that PLM always exhibits fixed attention patterns regardless of the input (e.g., excessively paying attention to [CLS] or [SEP]), which we argue might neglect important information in the other positions. In this work, we propose a simple yet effective attention guiding mechanism to improve the performance of PLM by encouraging attention towards the established goals. Specifically, we propose two kinds of attention guiding methods, i.e., map discrimination guiding (MDG) and attention pattern decorrelation guiding (PDG). The former definitely encourages the diversity among multiple self-attention heads to jointly attend to information from different representation subspaces, while the latter encourages self-attention to attend to as many different positions of the input as possible. We conduct experiments with multiple general pre-trained models (i.e., BERT, ALBERT, and Roberta) and domain-specific pre-trained models (i.e., BioBERT, ClinicalBERT, BlueBert, and SciBERT) on three benchmark datasets (i.e., MultiNLI, MedNLI, and Cross-genre-IR). Extensive experimental results demonstrate that our proposed MDG and PDG bring stable performance improvements on all datasets with high efficiency and low cost.
Electrostatic Brakes Enable Individual Joint Control of Underactuated, Highly Articulated Robots

Highly articulated organisms serve as blueprints for incredibly dexterous mechanisms, but building similarly capable robotic counterparts has been hindered by the difficulties of developing electromechanical actuators with both the high strength and compactness of biological muscle. We develop a stackable electrostatic brake that has comparable specific tension and weight to that of muscles and integrate it into a robotic joint. Compared to electromechanical motors, our brake-equipped joint is four times lighter and one thousand times more power efficient while exerting similar holding torques. Our joint design enables a ten degree-of-freedom robot equipped with only one motor to manipulate multiple objects simultaneously. We also show that the use of brakes allows a two-fingered robot to perform in-hand re-positioning of an object 45% more quickly and with 53% lower positioning error than without brakes. Relative to fully actuated robots, our findings suggest that robots equipped with such electrostatic brakes will have lower weight, volume, and power consumption yet retain the ability to reach arbitrary joint configurations.
Multi-objective evolutionary beer optimisation

Food production is a complex process which can benefit from many optimisation approaches. However, there is growing interest in methods that support customisation of food properties to satisfy individual consumer preferences. This paper addresses the personalisation of beer properties. Having identified components of the production process for beers, we introduce a system which enables brewers to map the desired beer properties into ingredients dosage and combination. Previously explored approaches include direct use of structural equations as well as global machine learning methods. We introduce a framework which uses an evolutionary method supporting multi-objective optimisation. This work identifies problem-dependent objectives, their associations, and proposes a workflow to automate the discovery of multiple novel recipes based on user-defined criteria. The quality of the solutions generated by the multi-objective optimiser is compared against solutions from multiple runs of the method, and those of a single objective evolutionary technique. This comparison provides a road-map allowing the users to choose among more varied options or to finetune one of the preferred solutions. The experiments presented here demonstrate the usability of the framework as well as the transparency of its criteria.
Risk-based analysis of a detention basin for urban runoff control

Various uncertainties exist in detention basin design which contribute to the potential failure or unsatisfactory performance of the system. Risk-based analysis provides a systematic framework to explicitly consider the trade-off among the cost of facility, its failure probability, and failure consequences. Conventional risk-based analysis requires the information of failure damage function. Such information is often unavailable and this hinders the implementation of risk-based analysis. Under this circumstance, a viable procedure is presented according to marginal cost analysis for risk-based design of a detention basin. The procedure utilizes the physical characteristics of system failure (overflow volume) which generally can be straightforwardly obtained from flow simulations. In addition to using the mean value of annual overflow volume, the suggested procedure further incorporates variability of overflow volume in decision-making. For illustration, this study applies the modified rational method in the risk-based framework for sizing the detention basin.
DeFTA: A Plug-and-Play Decentralized Replacement for FedAvg

Federated learning (FL) is identified as a crucial enabler for large-scale distributed machine learning (ML) without the need for local raw dataset sharing, substantially reducing privacy concerns and alleviating the isolated data problem. In reality, the prosperity of FL is largely due to a centralized framework called FedAvg, in which workers are in charge of model training and servers are in control of model aggregation. However, FedAvg's centralized worker-server architecture has raised new concerns, be it the low scalability of the cluster, the risk of data leakage, and the failure or even defection of the central server. To overcome these problems, we propose Decentralized Federated Trusted Averaging (DeFTA), a decentralized FL framework that serves as a plug-and-play replacement for FedAvg, instantly bringing better security, scalability, and fault-tolerance to the federated learning process after installation. In principle, it fundamentally resolves the above-mentioned issues from an architectural perspective without compromises or tradeoffs, primarily consisting of a new model aggregating formula with theoretical performance analysis, and a decentralized trust system (DTS) to greatly improve system robustness. Note that since DeFTA is an alternative to FedAvg at the framework level, \textit{prevalent algorithms published for FedAvg can be also utilized in DeFTA with ease}. Extensive experiments on six datasets and six basic models suggest that DeFTA not only has comparable performance with FedAvg in a more realistic setting, but also achieves great resilience even when 66% of workers are malicious. Furthermore, we also present an asynchronous variant of DeFTA to endow it with more powerful usability.
Towards a shared use case repository - the swimming initiative started in the framework of the EU H2020 R&DI programme

Data exchange and data sharing are one of the big challenges in the Architecture, Engineering, and Construction (AEC) industry and energy efficient building (EeB) domain. BIM open standards and lately the use of Semantic Web technologies provide a sound basis to implement exchange requirements derived from typical EeB use cases. However, the challenge remains to identify what models are available and how to align these with a particular use cases data requirements. This paper focuses on the application of an established methodology (Information Delivery Manual) adapted for the EeB domain and the application of the BIM*Q tool, which applies this methodology. The paper proposes building a shared use case repository that collects detailed data Exchange Requirements as well as alignments to existing models to support projects, when developing new, novel use cases, in the difficult task of aligning data requirements with models and standards.
X-CAR: An Experimental Vehicle Platform for Connected Autonomy Research

Autonomous vehicles (AVs) promise a future with safer, cleaner, more efficient, and more reliable transportation. However, the current approach to autonomy has focused on building small, disparate intelligences that are closed off to the rest of the world. Vehicle connectivity has been proposed as a solution, relying on a vision of the future where a mix of connected autonomous and human–driven vehicles populate the road. Developed by the U.S. Department of Transportation Federal Highway Administration (FHWA) as a reusable, extensible platform for controlling connected autonomous vehicles (CAVs), the CARMA Platform, is one of the technologies enabling this connected future. Nevertheless, the adoption of CARMA has been slow, with one contributing factor being the limited, expensive, and relatively old vehicle configurations that are officially supported. To alleviate this problem, we propose the eXperimental vehicle platform for Connected Autonomy Research (X-CAR). By implementing the CARMA Platform on more-affordable, high-quality hardware, X-CAR aims to increase the versatility of the FHWA platform and facilitate its adoption for research and development into connected driving automation.
Sampling-based Fast Gradient Rescaling Method for Highly Transferable Adversarial Attacks

Deep neural networks are known to be vulnerable to adversarial examples crafted by adding human-imperceptible perturbations to the benign input. After achieving nearly 100% attack success rates in white-box setting, more focus is shifted to black-box attacks, of which the transferability of adversarial examples has gained significant attention. In either case, the common gradient-based methods generally use the sign function to generate perturbations on the gradient update, that offers a roughly correct direction and has gained great success. But little work pays attention to its possible limitation. In this work, we observe that the deviation between the original gradient and the generated noise may lead to inaccurate gradient update estimation and suboptimal solutions for adversarial transferability. To this end, we propose a Sampling-based Fast Gradient Rescaling Method (S-FGRM). Specifically, we use data rescaling to substitute the sign function without extra computational cost. We further propose a Depth First Sampling method to eliminate the fluctuation of rescaling and stabilize the gradient update. Our method could be used in any gradient-based attacks and is extensible to be integrated with various input transformation or ensemble methods to further improve the adversarial transferability. Extensive experiments on the standard ImageNet dataset show that our method could significantly boost the transferability of gradient-based attacks and outperform the state-of-the-art baselines.
LilNetX: Lightweight Networks with EXtreme Model Compression and Structured Sparsification

We introduce LilNetX, an end-to-end trainable technique for neural networks that enables learning models with specified accuracy-rate-computation trade-off. Prior works approach these problems one at a time and often require post-processing or multistage training which become less practical and do not scale very well for large datasets or architectures. Our method constructs a joint training objective that penalizes the self-information of network parameters in a reparameterized latent space to encourage small model size while also introducing priors to increase structured sparsity in the parameter space to reduce computation. We achieve up to 50% smaller model size and 98% model sparsity on ResNet-20 while retaining the same accuracy on the CIFAR-10 dataset as well as 35% smaller model size and 42% structured sparsity on ResNet-50 trained on ImageNet, when compared to existing state-of-the-art model compression methods. Code is available at https://github.com/Sharath-girish/LilNetX.
Federated Learning for Personalized Humor Recognition

Computational understanding of humor is an important topic under creative language understanding and modeling. It can play a key role in complex human-AI interactions. The challenge here is that human perception of humorous content is highly subjective. The same joke may receive different funniness ratings from different readers. This makes it highly challenging for humor recognition models to achieve personalization in practical scenarios. Existing approaches are generally designed based on the assumption that users have a consensus on whether a given text is humorous or not. Thus, they cannot handle diverse humor preferences well. In this article, we propose the FedHumor approach for the recognition of humorous content in a personalized manner through Federated Learning (FL). Extending a pre-trained language model, FedHumor guides the fine-tuning process by considering diverse distributions of humor preferences from individuals. It incorporates a diversity adaptation strategy into the FL paradigm to train a personalized humor recognition model. To the best of our knowledge, FedHumor is the first text-based personalized humor recognition model through federated learning. Extensive experiments demonstrate the advantage of FedHumor in recognizing humorous texts compared to nine state-of-the-art humor recognition approaches with superior capability for handling the diversity in humor labels produced by users with diverse preferences.
Incentivizing Truck Retrofitting in Port Drayage: A Study of Drivers at the Ports of Los Angeles and Long Beach

ii DISCLAIMER The contents of this report reflect the views of the authors, who are responsible for the facts and the accuracy of the information presented herein. This document is disseminated under the Department of Transportation in the interest of information exchange. The U.S. Government and California Department of Transportation assume no liability for the contents or use thereof. The contents do not necessarily reflect the official views or policies of the State of California or the Department of Transportation. This report does not constitute a standard, specification, or regulation. iii ABSTRACT Using data from detailed surveys of truck drivers, we analyze the wages and working conditions of drivers involved in port drayage. We also focus on drivers' willingness to pay for retrofitting and their preferences regarding different truck replacement programs. We find that drivers are willing to pay a portion of truck retrofitting costs. Drivers with higher annual incomes are willing to pay more for retrofitting. We also find that though a grant-based truck replacement program was ranked highest among drivers, drivers were somewhat polarized on this program; many drivers also ranked this program as their least favorite. A subsidized interest rate program had the most cumulative first and second rankings. DISCLOSURE This project was funded in entirety under this contract to California Department of Transportation. vii ACKNOWLEDGEMENTS Special thanks are due to Charlie Abbott and Chrissy Mulcahy for their data collection efforts.
Information technology capability as predictor of organizational intelligence in Libyan oil and gas companies

The Libyan oil and gas companies are in dispersed locations, which necessitate an efficient and effective management system to improve their competitiveness. They need to pool resources in the organization to create organizational intelligence as to be competitive. Organizational intelligence is organizational practices which are influenced IT capability. The objective of the study is to investigate how IT capability influences organizational intelligence in the Libyan oil and gas companies. A quantitative research method based on survey questionnaire was used to collect numerical data that were statistically analyzed using descriptive and inferential statistics based on Statistical Package of Social Science (SPSS) software and AMOS module. A total of 163 managers in the Libyan oil and gas companies were randomly selected as the respondents. The result of the analysis showed that IT capability and organizational intelligence has a significant correlation and a predictor of OI with the value of p=0.05 and B=1.562. This study contributes towards new knowledge through the insights from the role of IT capability in influencing organizational intelligence in the context of Libyan oil and gas companies. The predictor’s model of IT capability contributes towards better decision making where the oil and gas companies management can decide on their investment for their companies in the future in order to increase their organizational intelligence.
Calculation of sectoral economic-tax impacts caused by tax changes: computable general equilibrium model applied to the Brazilian economy Notebooks, v. 1, maio 2021 Notebooks, Brasília, v. 21, n. 1, p. 1-76, maio 2021

This work aims to simulate the main proposals for tax reforms currently under discussion in the country as well as other changes suggested, through CGE model adapted to Brazilian needs. The model used was ORANI-G , widely used for public policy analysis. The model was calibrated with data from the Brazilian economy for 2015. The changes in the model were aimed at providing it with greater tax breakdown, as well as allow simulating the implementation of a tax on value-added. The implemented simulations deal with the replacement of employers' social security contributions for a new contribution on gross or a contribution on the value-added revenue, besides the tax on replacement products or services by a VAT. It is concluded that the changes bring positive effects, both in relation to the main macroeconomic variables and in relation to the performance of the great majority of economic sectors. characteristics of a good value added tax (VAT), a model adopted by most countries for taxing the consumption of goods and services.
LIQMA: Natural Language Inference to correct Question-answer Model Assertions

In this project, we aim to improve language model consistency in binary question-answering tasks by identifying and negating contradictory assertions via natural language inference and a variety of correction algorithms. To do so, we apply a pretrained Question Answering (QA) model to judge whether or not statements about a chosen entity are true, and generate assertions based on those judgments. We then apply a Natural Language Inference (NLI) model to the produced statements in pairwise fashion, determining the probability of QA assertions being contradictory, entailed, or consistent with one another. Due to QA models’ incomplete knowledge base or their inability to interpret queries, they generate a significant proportion of incorrect assertions, which often contradict with correct assertions. Our goal is therefore to use the outputs and confidences of the QA model, as well as the contradiction probabilities output by the NLI model, to determine which assertions should be negated to minimize contradictions and thus improve consistency. We implement several different families of algorithms to determine which judgments to negate; these methods fall under the categories of either constraint satisfiability problems, contradiction-or entailment-based probabilistic estimation, and requerying of the QA model.
This paper is included in the Proceedings of the 19th USENIX Symposium on Networked Systems Design and Implementation.

Containers, originally designed for cloud environments, are increasingly popular for provisioning workers outside the cloud, for example in mobile and edge computing. These settings, however, bring new challenges: high latency links, limited bandwidth, and resource-constrained workers. The result is longer provisioning times when deploying new workers or updating existing ones, much of it due to network trafﬁc. Our analysis shows that current piecemeal approaches to reducing provisioning time are not always sufﬁcient, and can even make things worse as round-trip times grow. Rather, we ﬁnd that the very same layer-based structure that makes containers easy to develop and use also makes it more difﬁcult to optimize deployment. Addressing this issue thus requires rethinking the container deployment pipeline as a whole. Based on our ﬁndings, we present Starlight: an accelerator for container provisioning. Starlight decouples provisioning from development by redesigning the container deployment protocol, ﬁlesystem, and image storage format. Our evaluation using 21 popular containers shows that, on average, Starlight deploys and starts containers 3.0 × faster than the current state-of-the-art implementation while incurring no runtime overhead and little (5%) storage overhead. Finally, it is backwards compatible with existing workers and uses standard container registries.