Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantifying the Invisible Labor in Crowd Work

Crowdsourcing markets provide workers with a centralized place to find paid work. What may not be obvious at first glance is that, in addition to the work they do for pay, crowd workers also have to shoulder a variety of unpaid invisible labor in these markets, which ultimately reduces workers' hourly wages. Invisible labor includes finding good tasks, messaging requesters, or managing payments. However, we currently know little about how much time crowd workers actually spend on invisible labor or how much it costs them economically. To ensure a fair and equitable future for crowd work, we need to be certain that workers are being paid fairly for all of the work they do. In this paper, we conduct a field study to quantify the invisible labor in crowd work. We build a plugin to record the amount of time that 100 workers on Amazon Mechanical Turk dedicate to invisible labor while completing 40,903 tasks. If we ignore the time workers spent on invisible labor, workers' median hourly wage was $3.76. But, we estimated that crowd workers in our study spent 33% of their time daily on invisible labor, dropping their median hourly wage to $2.83. We found that the invisible labor differentially impacts workers depending on their skill level and workers' demographics. The invisible labor category that took the most time and that was also the most common revolved around workers having to manage their payments. The second most time-consuming invisible labor category involved hyper-vigilance, where workers vigilantly watched over requesters' profiles for newly posted work or vigilantly searched for labor. We hope that through our paper, the invisible labor in crowdsourcing becomes more visible, and our results help to reveal the larger implications of the continuing invisibility of labor in crowdsourcing.
EVALUATION OF CONDUCTIVE THREADS FOR OPTIMIZING PERFORMANCE OF EMBROIDERED RFID ANTENNAS

MUSTASIN MAHMOOD SAKIF: EVALUATION OF CONDUCTIVE THREADS FOR OPTIMIZING PERFORMANCE OF EMBROIDERED RFID ANTENNAS Tampere University Master of Science Thesis, 55 Pages September 2019 Master’s Degree Program in Electrical Engineering Examiners: Academy Research Fellow Johanna Virkki and Postdoctoral Research Fellow Han He Radio frequency identification (RFID) refers to a technology that utilizes radio signals for identifying objects automatically. This technology consists of a reader that detects the objects and a transponder that gets attached to the object and it is called tag. The tag is an enclosure that houses the antenna and an IC that stores the necessary information on that object. This thesis focuses tag antennas made for embroidered RFID. Embroidered antennas are made by sewing antenna using conductive thread onto a fabric using a computerized sewing machine. This enables us to extend the field of RFID technologies to textiles. Conventional RFID systems that use metal conductors are easy to model but the same cannot be said for embroidered RFID. The reason being conductive threads and embroidered antennas don’t have definite conductivity. The conductivity of an embroidered antenna depends multiple factors like thread conductivity, thread density, stitch density, sewing pattern etc. The target of this thesis is experimenting with conductive threads physically and for their conductivity followed by evaluating them for the use of embroidered RFID antenna fabrication for optimizing the performance. In this thesis, using same antenna pattern and technique, tags were fabricated from 6 different conductive threads onto the same cotton fabric. The conductive threads were investigated for their conductivity, thread thickness and their strength. The antennas were tested for their read range and the effect of different threads on the antenna were analysed. The threads with the highest conductive nature gave the highest read range of 6.2 meters. The threads were also evaluated for their usability for embroidery. Some threads were too thick, some had exposed structures leading to malfunction in the sewing machine and others were too thin and ripped easily during sewing. The selected thread should not only have great performance, but also it needs to be practical. It is seen that the conductivity of antenna and hence the performance is easily improved with using high conductive thread. After taking all the factors into account, finally a thread was selected that can be used to make high performance embroidered RFID antennas and also highly suitable for embroidery process. In the future, the same work can be revisited or extended to other more versatile and higher conductivity threads. Also, the advancement is embroidery techniques will allow for more conductive threads to be compatible for embroidery opening more options for optimization.
Coping with Larp-Related Stress

Larping has been described as extreme social sports. In practice, larping often gives players most when players devote time to the game, expose themselves as characters to situations both pleasant and distressing and attempt to immerse themselves in the experiences. The players are expected, or expect themselves, to put quite an effort into the larp. These expectations can include ​cognitive demands (such as ability to memorize background materials and switching one’s attention between relevant information during the game), emotional demands (for example, immersion into the character and how the character feels about the transpiring events), ​physical demands (including preparing props or doing physical feats in the game), and ​social demands (getting to know new people and adapting one’s behavior to new social setting). These expectations may cause stress for the players.
Monopod robot locomotion using data-driven genetic algorithms

—This paper presents a novel implementation of genetic algorithms for locomotion control of robots. The main difference is that, instead of assuming a feedback model for the controller and using genetic algorithm to ﬁnd the optimal control gains, the proposed algorithm directly generates the optimal joint torques for the robot. This new form of genetic algorithm is called data-driven genetic algorithm (DDGA) in this paper. We implement our proposed control algorithm on a planar hopping robot in simulation. The results show that the robot is able to perform hopping motions on a ﬂat surface as well as climbing the stairs. We also show that, using our proposed controller, the robot can execute a range of highly dynamic manoeuvres such as somersault and backﬂip.
Spatiotemporal Multimodal Network for Dynamic Gesture Recognition

Human gesture recognition is among interesting topics of visual understanding, which benefits a variety of applications like user interface design and robotic perception. With the goal of deriving an effective model to recognize human gestures, we propose a deep neural network architecture for describing and recognizing important spatial-temporal information across different video data domains. Our proposed network is realized by using a technique of transfer learning, in which the network can be trained and evaluated on a relative small dataset while possible overfitting can be also mitigated. In our experiments, our method is able to achieve state-of-the-art accuracy on a challenging gesture recognition benchmark.
Dynamic adsorption of In(III) and Fe(III) on solvent impregnated resins containing trioctylmethylammonium chloride

Solvent impregnated resins (SIRs) were prepared by dry impregnation using styrene-divinylbenzene type macroreticular resin (HZ-818) as support and trioctylmethylammonium chloride as extractant. Adsorption and separation performance of In(III) and Fe(III) with SIRs was studied on HCl medium by static and dynamic adsorption method. The effect of HCl concentration on the adsorption of In(III) and Fe(III) was investigated by static adsorption method. The effect of feed flow rate, bed height, and metal ion concentration (In(III) and Fe(III)) was investigated by dynamic adsorption method. The optimized acidity for In(III) and Fe(III) adsorption with SIRs was 4 mol/L HCl in single-component and bi-component systems containing In(III) and Fe(III). The dynamic adsorption study revealed that when the concentration of HCl, feed flow rate, bed height, and metal ion concentration were 4 mol/L, 1.4 mL/min, 140 mm, and 90 mg/L at ambient temperature, the adsorption capacities of In(III) and Fe(III) in single-component system were 13.1 and 37.8 mg/g, respectively, and the adsorption capacities of In(III) and Fe(III) in bi-component system were 1.3 and 35.8 mg/g, respectively. Hence, SIRs could selectively adsorb Fe(III), and the separation of In(III) and Fe(III) in HCl solution could be achieved.
Deep Emotion Change Detection for Human-robot Interaction

—Emotion change detection via facial expression information is an important clue to non-verbal communication that can uncover emotional context. In a human-robot interaction scenario, ﬁguring out the timing of emotion changes using facial expression information from an user has three advantages on: 1) providing a start point to obtain time-consistent multi-modal information, 2) reducing search space in time series, and 3) producing feedback information to improve the scenario. In this regard, we introduce an initial investigation to an automatic emotion change detection framework in the ﬁeld of human-robot interaction. To tackle this issue, we propose a novel method of deep emotion change detection for inferencing emotion status and detecting multiple points of emotion changes. Incorporating these ideas, we provide evaluation methods to validate the framework and the baseline performance of our approach.
NEW RESEARCH Effect of Preventive Interventions in Mentally Ill Parents on the Mental Health of the Offspring: Systematic Review and Meta-Analysis

Objective: Mental illness in parents affects the mental health of their children. A systematic review and a meta-analysis of the effectiveness of interventions to prevent mental disorders or psychological symptoms in the offspring were performed. Method: The Cochrane, MEDLINE, EMBASE, and PsycINFO databases were searched for randomized controlled trials of interventions in parents with mental disorders. Outcomes in the child included incident mental disorders of the same nature and internalizing (negative emotions, depressive symptoms, anxiety) or externalizing (hyperactivity, aggressiveness, behavioral problems) symptoms. Relative risks and standardized mean differences in symptom scores were combined in random-effects metaanalysis. Results: Thirteen trials including 1,490 children were analyzed. Interventions included cognitive, behavioral, or psychoeducational components. Seven trials assessed the incidence of mental disorders and seven trials assessed symptoms. In total 161 new diagnoses of mental illness were recorded, with interventions decreasing the risk by 40% (combined relative risk 0.60, 95% CI 0.45–0.79). Symptom scores were lower in the intervention groups: standardized mean differences were 0.22 (95% CI 0.37 to 0.08) for internalizing symptoms (p .003) and 0.16 (95% confidence interval 0.36 to 0.04) for externalizing symptoms (p .12). Conclusions: Interventions to prevent mental disorders and psychological symptoms in the offspring of parents with mental disorders appear to be effective. J. Am. Acad. Child Adolesc. Psychiatry, 2012;51(1):8–17.
Words Meet Photos: When and Why Visual Content Increases Review Helpfulness

Is visual-verbal word-of-mouth more persuasive? If so, do consumers find communication more helpful when photos and text convey similar or different information? This paper examines the effect of photo-text similarity on review helpfulness and its underlying drivers. Using a dataset of 6.8M reviews including 3.3M photos from Yelp and applying state-of-the-art machine learning algorithms, we quantify the similarity of the content between text and photos. We find that it is not only the mere presence of a photo that increases helpfulness but also the similarity between the photo content and the review text. We replicate our main findings and examine the underlying drivers in two laboratory experiments. When the photos and text convey similar (vs. dissimilar) information, consumers find the review more helpful because 1) the information in the review becomes easier to process, and 2) quality inferences of the focal attribute are heightened. However, greater similarity 3) limits the total amount of information conveyed, reducing helpfulness. We find that drivers (1) and (2) outweigh (3). Therefore, the totality of these three distinct processes allows greater photo-text similarity to heighten persuasion. These findings provide novel insights into the persuasiveness of visual-verbal word-of-mouth and its underlying psychological drivers.
Automatic Rule Generation for Time Expression Normalization

The understanding of time expressions includes two sub-tasks: recognition and normalization. In recent years, significant progress has been made in the recognition of time expressions while research on normalization has lagged behind. Existing SOTA normalization methods highly rely on rules or grammars designed by experts, which limits their performance on emerging corpora, such as social media texts. In this paper, we model time expression normalization as a sequence of operations to construct the normalized temporal value, and we present a novel method called ARTime, which can automatically generate normalization rules from training data without expert interventions. Specifically, ARTime automatically captures possible operation sequences from annotated data and generates normalization rules on time expressions with common surface forms. The experimental results show that ARTime can significantly surpass SOTA methods on the Tweets benchmark, and achieves competitive results with existing expert-engineered rule methods on the TempEval-3 benchmark.
Automated Tracking of Primate Behavior

Understanding primate behavior is a mission-critical goal of both biology and biomedicine. Despite the importance of behavior, our ability to rigorously quantify it has heretofore been limited to low-information measures like preference, looking time, and reaction time, or to non-scaleable measures like ethograms. However, recent technological advances have led to a major revolution in behavioral measurement. Specifically, digital video cameras and automated pose tracking software can provide detailed measures of full body position (i.e., pose) of multiple primates over time (i.e., behavior) with high spatial and temporal resolution. Posetracking technology in turn can be used to detect behavioral states, such as eating, sleeping, and mating. The availability of such data has in turn spurred developments in data analysis techniques. Together, these changes are poised to lead to major advances in scientific fields that rely on behavioral as a dependent variable. In this review, we situate the tracking revolution in the history of the study of behavior, argue for investment in and development of analytical and research techniques that can profit from the advent of the era of big behavior, and propose that zoos will have a central role to play in this era.
TrackerSift: untangling mixed tracking and functional web resources

Trackers have recently started to mix tracking and functional resources to circumvent privacy-enhancing content blocking tools. Such mixed web resources put content blockers in a bind: risk breaking legitimate functionality if they act and risk missing privacy-invasive advertising and tracking if they do not. In this paper, we propose TrackerSift to progressively classify and untangle mixed web resources (that combine tracking and legitimate functionality) at multiple granularities of analysis (domain, hostname, script, and method). Using TrackerSift, we conduct a large-scale measurement study of such mixed resources on 100K websites. We find that more than 17% domains, 48% hostnames, 6% scripts, and 9% methods observed in our crawls combine tracking and legitimate functionality. While mixed web resources are prevalent across all granularities, TrackerSift is able to attribute 98% of the script-initiated network requests to either tracking or functional resources at the finest method-level granularity. Our analysis shows that mixed resources at different granularities are typically served from CDNs or as in-lined and bundled scripts, and that blocking them indeed results in breakage of legitimate functionality. Our results highlight opportunities for finer-grained content blocking to remove mixed resources without breaking legitimate functionality.
In-Body Bionanosensor Localization for Anomaly Detection via Inertial Positioning and THz Backscattering Communication

Nanotechnology is enabling the development of a new generation of devices which are able to sense, process and communicate, while being in the scale of tens to hundreds of cubic nanometers. Such small, imperceptible devices enhance not only current applications but enable entirely new paradigms especially for in-body environments. This paper introduces a localization and tracking concept for bionanosensors floating in the human bloodstream to detect anomalies in the body. Besides the nanoscale sensors, the proposed system also comprises macroscale anchor nodes attached to the skin of the monitored person. To realize autonomous localization and resource-efficient wireless communication between sensors and anchors, we propose to exploit inertial positioning and sub-terahertz backscattering. The proposed system is a first step towards early disease detection as it aims at localizing body regions which show anomalies. Simulations are conducted to enable a systematical evaluation on the feasibility of the approach.
Cross-Lingual Text Classification of Transliterated Hindi and Malayalam

Transliteration is very common on social media, but transliterated text is not adequately handled by modern neural models for various NLP tasks. In this work, we combine data augmentation approaches with a Teacher-Student training scheme to address this issue in a cross-lingual transfer setting for fine-tuning state-of-the-art pre-trained multilingual language models such as mBERT and XLM-R. We evaluate our method on transliterated Hindi and Malayalam, also introducing new datasets for benchmarking on real-world scenarios: one on sentiment classification in transliterated Malayalam, and another on crisis tweet classification in transliterated Hindi and Malayalam (related to the 2013 North India and 2018 Kerala floods). Our method yielded an average improvement of +5.6% on mBERT and +4.7% on XLM-R in F1 scores over their strong baselines.1
Boosting the conformational sampling by combining replica exchange with solute tempering and well‐sliced metadynamics

Methods that combine collective variable (CV) based enhanced sampling and global tempering approaches are used in speeding‐up the conformational sampling and free energy calculation of large and soft systems with a plethora of energy minima. In this paper, a new method of this kind is proposed in which the well‐sliced metadynamics approach (WSMTD) is united with replica exchange with solute tempering (REST2) method. WSMTD employs a divide‐and‐conquer strategy wherein high‐dimensional slices of a free energy surface are independently sampled and combined. The method enables one to accomplish a controlled exploration of the CV‐space with a restraining bias as in umbrella sampling, and enhance‐sampling of one or more orthogonal CVs using a metadynamics like bias. The new hybrid method proposed here enables boosting the sampling of more slow degrees of freedom in WSMTD simulations, without the need to specify associated CVs, through a replica exchange scheme within the framework of REST2. The high‐dimensional slices of the probability distributions of CVs computed from the united WSMTD and REST2 simulations are subsequently combined using the weighted histogram analysis method to obtain the free energy surface. We show that the new method proposed here is accurate, improves the conformational sampling, and achieves quick convergence in free energy estimates. We demonstrate this by computing the conformational free energy landscapes of solvated alanine tripeptide and Trp‐cage mini protein in explicit water.
A Generative Approach for Mitigating Structural Biases in Natural Language Inference

Many natural language inference (NLI) datasets contain biases that allow models to perform well by only using a biased subset of the input, without considering the remainder features. For instance, models are able to classify samples by only using the hypothesis, without learning the true relationship between it and the premise. These structural biases lead discriminative models to learn unintended superficial features and generalize poorly out of the training distribution. In this work, we reformulate the NLI task as a generative task, where a model is conditioned on the biased subset of the input and the label and generates the remaining subset of the input. We show that by imposing a uniform prior, we obtain a provably unbiased model. Through synthetic experiments, we find that this approach is highly robust to large amounts of bias. We then demonstrate empirically on two types of natural bias that this approach leads to fully unbiased models in practice. However, we find that generative models are difficult to train and generally perform worse than discriminative baselines. We highlight the difficulty of the generative modeling task in the context of NLI as a cause for this worse performance. Finally, by fine-tuning the generative model with a discriminative objective, we reduce the performance gap between the generative model and the discriminative baseline, while allowing for a small amount of bias.
Pruning with Compensation: Efficient Channel Pruning for Deep Convolutional Neural Networks

Channel pruning is a promising technique to compress the parameters of deep convolutional neural networks(DCNN) and to speed up the inference. This paper aims to address the long-standing inefficiency of channel pruning. Most channel pruning methods recover the prediction accuracy by re-training the pruned model from the remaining parameters or random initialization. This re-training process is heavily dependent on the sufficiency of computational resources, training data, and human interference(tuning the training strategy). In this paper, a highly efficient pruning method is proposed to significantly reduce the cost of pruning DCNN. The main contributions of our method include: 1) pruning compensation, a fast and data-efficient substitute of re-training to minimize the post-pruning reconstruction loss of features, 2) compensation-aware pruning(CaP), a novel pruning algorithm to remove redundant or less-weighted channels by minimizing the loss of information, and 3) binary structural search with step constraint to minimize human interference. On benchmarks including CIFAR-10/100 and ImageNet, our method shows competitive pruning performance among the state-of-the-art retraining-based pruning methods and, more importantly, reduces the processing time by 95% and data usage by 90%.