Miracle-dz/UniFAR-FTUs huggingface dataset:
Auditing Algorithmic Bias on Twitter

Digital media platforms are reshaping our habits, how we access information, and how we interact with others. As a result, algorithms used by platforms, for example, to recommend content, play an increasingly important role in our access to information. Due to practical difficulties of accessing how platforms present content to their users, relatively little is known about how recommendation algorithms affect the information people receive. In this paper we implement a sock-puppet audit, a computational framework to audit black-box social media systems so as to quantify the impact of algorithmic curation on the information people see. We evaluate this framework by conducting a study on Twitter. We demonstrate that Twitter’s timeline curation algorithms skew the popularity and novelty of content people see and increase the inequality of their exposure to friends’ tweets. Our work provides evidence that algorithmic curation of content systematically distorts the information people see.
Electricity Demand Activation Extraction: From Known to Unknown Signatures, Using Similarity Search

A powerful tool for reducing energy consumption is energy disaggregation (also called NILM Non-Intrusive Load Monitoring), where the goal is to disaggregate the smart meter readings of a household's total electricity consumption to the consumption of that household's individual appliances. State-of-the-art machine learning methods are widely used to solve the NILM problem, but in order to generalize well they require a large amount of data, which are not readily available. We thus need labeled electricity consumption readings from individual appliance activations. Though, manually annotating the start and end of single-appliance activations is extremely laborious and time consuming. Therefore, automated activation extraction methods are needed. Earlier approaches to solve this problem suffer from limitations, such as incomplete signatures, double signatures, and outliers. In this work, we introduce three scalable methods based on techniques that use time series similarity search. The first method is Cartesio that (improves on earlier work that relies on known features of the appliance) and separately detects the start and end times of an appliance activation. The second method is ValmA, a method for identifying previously unknown candidate signatures of variable length, which is essentially parameter-free. The third method is SimBA, a similarity search based method for efficient detection of known signatures in large datasets. These signatures can be computed from the activations extracted using the previous methods. Our experimental results with real 6 and 10 seconds-sampling data demonstrate that, compared to a state-of-the-art solution, our methods improve the accuracy and robustness of appliance activation extraction in very large time series collections. To compare these methods, we also describe a new accuracy measure that takes into account the special characteristics of subsequences, leading to more precise performance evaluation results.
A High Linearity Process Angle Interference Resistant Multi-phase Output Digital Control Oscillator with Negative Feedback

In this paper, we have proposed a high linearity process angle interference resistant multi-phase output digital control oscillator with negative feedback. The proposed digital control oscillator uses negative feedback structure to improve the output frequency linearity and resist process angle interference, while this paper optimizes and designs the circuit for the digital control current source differential cross-coupling structure basic delay unit, negative feedback structure. The DCO mentioned has beenimplemented in standard 130nm CMOS. The DCO designed in this paper has 8-phase output with frequency tuning in the range of 0.2 GHz-2.5 GHz and gain in the range of 2.14 MHz/LSB-5.5 MHz/LSB, with good linearity. At 1.5 GHz, the process leads to a frequency jitter size of only 6.6%, which has the property of resistance to process corner interference. The cycle integration jitter is 2.79ps, and the power consumption is 1.65mW.
Persuasiveness of a Game to Promote the Adoption of COVID-19 Precautionary Measures and the Moderating Effect of Gender

Persuasive games are widely being implemented in the healthcare domain to drive behaviour change among individuals. Previous research has shown that there are differences in how males and females respond to persuasive attempts. However, there is little knowledge on whether gender moderates the effectiveness of persuasive games for health, specifically, games for promoting the adoption of COVID-19 precautionary measures. To address this gap, we designed COVID Pacman-R – a persuasive game to promote the adoption of COVID-19 precautionary measures. This paper presents the design and evaluation of COVID Pacman-R to examine its overall perceived persuasiveness as well as gender differences in persuasiveness to establish whether there is a need to tailor the game to various gender groups. Study results (N=131) revealed that the game is perceived as highly persuasive overall as well as by the different gender groups. The findings also revealed that there are no significant differences in the persuasiveness across gender groups with respect to COVID-19 ability to motivate them to adopt the precautionary measures.
Inclusive Visual Design to Support People with Physical Impairments

People with physical impairments can experience significant challenges in using traditional input devices (e.g. a mouse, keyboard, stylus, etc.) to control mainstream creative design software (e.g. Adobe XD, Photoshop, and Illustrator). The use of speech recognition presents new opportunities to make these applications more accessible, although there has been little work exploring this area to date. This doctoral research is addressing this lack of work through investigating how different speech interaction techniques can be used to support people with physical impairments in controlling and manipulating graphical objects. Initial work around examining new techniques for supporting the movement of visual objects around a design canvas is presented, along with details outlining other key areas that require further investigation in this field.
Goal Modeling-based Evaluation for Personalized Recommendation Systems

Designing and evaluating a recommendation algorithm are typically user-centric operations. However, users are not the sole party of real-world applications. Therefore, designing, deploying, and evaluating a personalized system should consider all parties (or stakeholders). Dealing with recommender systems as multistakeholders systems is a relatively new research direction. In particular, considering the requirements of multiple stakeholders in selecting the best algorithm among a set of alternatives has not been discussed extensively. An adaptive evaluation approach that can handle personalized needs from all parties is therefore required. This paper aims to fill this gap by introducing the use of goal modeling to support the selection of a recommendation algorithm. Through an illustrative example, we show the feasibility of modeling the recommendation alternatives and their contributions to multiple stakeholders’ goals so that the selected algorithm is well-aligned with the overall system requirements. Accordingly, we say that the goal modeling approach has the potential of helping practitioners and researchers to better reason about algorithms selection and, therefore, advances the development of recommender systems.
Understanding Credibility of Adversarial Examples against Smart Grid: A Case Study for Voltage Stability Assessment

Stability assessment is an important task for maintaining reliable operations of power grids. With increased system complexity, deep learning-based stability assessment approaches are promising to address the shortfalls of the traditional time-domain simulation-based approaches. However, in the field of computer vision, the deep learning models are shown vulnerable to adversarial examples. Although this vulnerability has been noticed by the energy informatics research, the domain-specific analysis on the requirements imposed for implementing effective adversarial examples is still lacking. These attack requirements, albeit reasonable in computer vision tasks, can be too stringent in the context of power grids. In this paper, we systematically investigate the requirements and discuss the credibility of six representative adversarial example attacks for a case study of voltage stability assessment for the New England 10-machine 39-bus system. We show that (1) compromising the voltage traces of half of transmission system buses is a rule of thumb requirement; (2) the universal adversarial perturbations that are independent of the original clean voltage trajectory have the same credibility as the widely studied false data injection attacks on power grid state estimation, while other adversarial example attacks are less credible; (3) the universal perturbations can be effectively defended with strong adversarial training.
Discovering emergency call pitfalls for cellular networks with formal methods

Availability and security problems in cellular emergency call systems can cost people their lives, yet this topic has not been thoroughly researched. Based on our proposed Seed-Assisted Specification method, we start to investigate this topic by looking closely into one emergency call failure case in China. Using what we learned from the case as prior knowledge, we build a formal model of emergency call systems with proper granularity. By running model checking, four public-unaware scenarios where emergency calls cannot be correctly routed are discovered. Additionally, we extract configurations of two major U.S. carriers and incorporate them as model constraints into the model. Based on the augmented model, we find two new attacks leveraging the privileges of emergency calls. Finally, we present a solution with marginal overhead to resolve issues we can foresee.
Using Social Media for Personalizing the Cultural Heritage Experience

This article presents a personalized recommendation approach of textual and multimedia resources related to artistic and cultural points of interest (POIs). This approach exploits linked open data to retrieve content related to POIs and social media to personalize their recommendation to the target user. The similarity evaluation between the social user profile and the related material occurs based on the classic doc2vec model. A preliminary comparative analysis conducted on 20 real users showed encouraging experimental results in terms of perceived accuracy and beyond-accuracy metrics.
One model fits all: Individualized household energy demand forecasting with a single deep learning model

Energy demand forecasting at the household level is an important issue in smart energy grids to facilitate applications such as residential Demand Response (DR). However, if a separate machine learning model is trained for each house, the erratic nature of some consumers will lead to significant inaccuracies for the respective models, while predictions for new households with scarce data will not be possible to generate. In this work, we propose an approach with a single deep learning model that is trained on multiple households, which can create hourly energy consumption forecasts for individual households. We present a novel architecture that combines a Recurrent Neural Network (RNN) encoder and a Multilayer Perceptron (MLP). Our approach captures both the impact of past consumption time-series and that of energy profiles on future energy demand. Our model incorporates energy profiles to derive different characteristics between consumers, and it features a "double" clustering procedure that is specially designed for a mixture of time-series and non-time-series data. Experiments with real smart meter data show that the proposed neural network architecture achieves high performance in predicting energy consumption both for known and new consumers not present in the training dataset, with a Mean Absolute Percentage Error (MAPE) of 10.1% and 12.5% respectively.
Energon: A Data Acquisition System for Portable Building Analytics

Emerging building analytics rely on data-driven machine learning algorithms. However, writing these analytics is still challenging---developers not only need to know what data are required by the analytics but also how to reach the data in each individual building, despite the existing solutions to standardizing data and resource management in buildings. To bridge the gap between analytics development and the specific details of reaching the actual data in each building, we present Energon, an open-source system that enables portable building analytics. The core of Energon is a new data organization of building data, as well as the tools that can effectively manage building data and support building analytics development. More specifically, we propose a new "logic partition" of data resources in buildings, and this abstraction universally applies to all buildings. We develop a declarative query language to find data resources in this new logic views with high-level queries, thus substantially reducing development efforts. We also develop a query engine with automatic data extraction by traversing building ontology that widely exists in buildings. In this way, Energon enables analytics requirements to be mapped to building resources in a building-agnostic manner. Using four types of real-world building analytics, we demonstrate the use of Energon as well as its effectiveness in reducing development efforts.
Leg Fidgeting During Prolonged Sitting Improves Postprandial Glycemic Control in People with Obesity

Studies have shown that fidgeting augments metabolic demand and increases blood flow to the moving limbs, whereas prolonged sitting suppresses these factors and exacerbates postprandial glucose excursions. Therefore, the hypothesis of this study was that leg fidgeting during prolonged sitting would improve postprandial glycemic control.
Age-related changes in the brain functional connectivity during motor initiation

Healthy aging affects structural and neurochemical properties of the human brain neural network. It also changes the brain functioning via the transformation of neural interactions both within and between functionally distinct brain areas. The age-related degradation of the brain functioning is evident on the behavioral level in terms of the decline in reaction time, low ability to execute and control complex motor actions, weak flexibility in learning new skills. In this paper we apply functional connectivity analysis to reveal the age-related changes in the integrative brain dynamic during the motor initiation before the dominant hand movements accompanied. Analyzing the whole-scalp electroencephalography (EEG) signals on the sensor level, we find higher theta-band coupling in the ipsilateral hemisphere.
A First Look into the Natural History of the Sierra Box Turtle (Terrapene nelsoni klauberi) in Southeast Sonora, Mexico

Abstract. The Sierra Box Turtle, Terrapene nelsoni, comprises 2 recognized subspecies that are distributed across the Sierra Madre Occidental mountain chain in western Mexico and little is known about their natural history. We present new information on the natural history of Terrapene nelsoni klauberi. Our study area was the Monte Mojino reserve located within the larger Sierra de Alamos–Río Cuchujaqui federal protected area in southeastern Sonora. We located 49 individuals in 3 different vegetation types: 4 were found at 600 m above sea level (masl) in the tropical dry forest, 4 at 1000 masl in grassy oak (Quercus spp.) savannah, and 41 at 1300 masl in pine(Pinus spp.)–oak forest. Seven turtles were monitored using radiotelemetry and have the average home range size of 1.7 ha. The areas we sampled appear to consist of mainly adults and the sex ratio is female-biased (1♂:1.5♀). Cloacal temperatures of active turtles and their immediate surroundings suggest that T. n. klauberi actively maintain their body temperature above the surrounding temperatures. Males also have higher average body temperatures than females (29.58°C ± 2.26°C vs. 27.35°C ± 2.71°C). We found no significant patterns of sexual dimorphism in carapace length (i.e., sexual size dimorphism) but did find sexual dimorphism in head size. We also found that 44% of males and 22% of females had severe damage to their marginal scutes, possibly from a local predator or from enduring bites over time from other turtles. Lastly, we observed T. n. klauberi foraging on beetles, mushrooms, grass, and wildflowers. These findings provide a first insight into the natural history of T. n. klauberi and how this species is distributed across different elevations and vegetation types. This information provides a starting point to assess the conservation status of this species. However, this species remains unstudied throughout the rest of its geographic range.
)-linear Model for the MaxCut Problem ∗

A polytope P is a model for a combinatorial problem on finite graphs G whose variables are indexed by the edge set E of G if the points of P with (0,1)-coordinates are precisely the characteristic vectors of the subset of edges inducing the feasible configurations for the problem. In the case of the (simple) MaxCut Problem, which is the one that concern us here, the feasible subsets of edges are the ones inducing the bipartite subgraphs of G. In this paper we introduce a new polytope P12 ⊂ R|E| given by at most 11|E| inequalities, which is a model for the MaxCut Problem on G. Moreover, the left side of each inequality is the sum of at most 4 edge variables with coefficients ±1 and right side 0,1, or 2. We restrict our analysis to the case of G = Kz, the complete graph in z vertices, where z is an even positive integer z ≥ 4. This case is sufficient to study because the simple MaxCut problem for general graphs G can be reduced to the complete graph Kz by considering the obective function of the associated integer programming as the characteristic vector of the edges in G ⊆ Kz. This is a polynomial algorithmic transformation. 1 Notation and Preliminaries The MaxCut Problem [3] is one of the first NP-complete problems. This problem can be stated as follows. Given a graph G does it has a bipartite subgraph with n edges? It is a very special problem which has been acting as a paradigm for great theoretical developments. See, for instance [4], where an algorithm with a rather peculiar worse case performance (greater than 87%) can be established as a fraction of type (solution found/optimum solution). This result constitutes a landmark in the theory of approximation algorithms. Our approach is a theoretical investigation on polytopes associated to complete graphs. The main result is that there is a set of at most 11|E| short inequalities (each involving no more than 4 edge variables with coefficients ±1) so that the polytope in R|E| formed by these inequalities has its all integer coordinate points in 1-1 correpondence with the characteristic vectors of the complete bipartite subgraphs of Kz, z even. Thick graphs into closed surfaces. A surface is closed if it is compact and has no boundary. A closed surface is characterized by its Euler characterisitic and the information whether or not is orientable. We use the following combinatorial counterpart for a graph G cellularly ∗2010 Mathematics Subject Classification: 68Q25 (primary), 68R10, 05C85 (secondary).
MRG_UWaterloo Participation in the TREC 2020 Precision Medicine Track

Our baseline run (uwbm25) used the BM25 relevance ranking method, as implemented by the Wumpus Search Engine, 1 , with default parameters. The remaining runs examined the impact of various forms of pseudo-relevance feedback and manual relevance feedback, using relevance assessments by one author (Pham) who has extensive experience in conducting rapid systematic reviews, but not with precision medicine. To our surprise, we found that none of the feedback methods di(cid:27)ered substantially in e(cid:27)ectiveness from our baseline run. Based on preliminary NIST feedback, the e(cid:27)ectiveness of all runs was near the median of all TREC submissions. Our pseudo-relevance feedback run (uwpr) selected the 20 highest-ranked documents from uwbm25 as positive training examples, and 100 randomly selected documents from the corpus as negative training examples. These training examples were converted to tf-idf feature vectors and used as input to So(cid:28)a-ML 2 to compute a log-likelihood score for each document. Documents were sorted by score, and the top-scoring 1,000 documents were submitted as run uwpr. Ourpositive-only manual feedback run (uwr) started with the same documents as uwpr, but the 20 highest-ranked documents from uwbm25 were assessed for relevance by our expert (Pham), and only those assessed to be relevant were included as positive training examples. Those documents assessed to be non-relevant were excluded from the training set. Our postive-and-negative manual feedback run (uwrn) used the documents and relevance assessments from uwpr, treating documents assessed to be relevant as positive training examples, and documents assessed to be non-relevant, as well as the 100 random documents, as negative training examples. Our Continuous Active Learning ((cid:16)CAL(cid:17)
Face anti-spoofing based on weighted neighborhood pixel difference pattern

Abstract. The vulnerability of face recognition-based unlocking systems to spoofing attacks is a serious problem. With the development of science and technology, face pictures on the Internet are particularly easy to obtain and recreate. A face spoofing attack happens when someone fakes a face and tries to pass the verification of the face recognition system, which seriously threatens the security of users’ information. We propose a method based on quantifying the difference between neighborhood pixels around the center of a local region, namely, weighted neighborhood difference quantization local binary pattern (LBP), against print attacks, and video-replay attacks. The proposed method quantifies the differences between neighborhood pixels without using the center one and uses a linear weighting scheme to improve the discriminant capability of the traditional LBP. The combination of the proposed algorithm and the spatial pyramid further improves the performance of face spoofing detection. We also conducted a lot of experiments in different color spaces to illustrate the role of color in face spoofing detection. The improved method achieves better results in three challenging face anti-spoofing databases, CASIA FASD, Replay-Attack, and Replay-Mobile, respectively.