Miracle-dz/UniFAR-FTUs huggingface dataset:
Transition-based Parsing with Lighter Feed-Forward Networks

We explore whether it is possible to build lighter parsers, that are statistically equivalent to their corresponding standard version, for a wide set of languages showing different structures and morphologies. As testbed, we use the Universal Dependencies and transition-based dependency parsers trained on feed-forward networks. For these, most existing research assumes de facto standard embedded features and relies on pre-computation tricks to obtain speed-ups. We explore how these features and their size can be reduced and whether this translates into speed-ups with a negligible impact on accuracy. The experiments show that grand-daughter features can be removed for the majority of treebanks without a significant (negative or positive) LAS difference. They also show how the size of the embeddings can be notably reduced.
Molecular Evolution and Stress and Phytohormone Responsiveness of SUT Genes in Gossypium hirsutum

Sucrose transporters (SUTs) play key roles in allocating the translocation of assimilates from source to sink tissues. Although the characteristics and biological roles of SUTs have been intensively investigated in higher plants, this gene family has not been functionally characterized in cotton. In this study, we performed a comprehensive analysis of SUT genes in the tetraploid cotton Gossypium hirsutum. A total of 18 G. hirsutum SUT genes were identified and classified into three groups based on their evolutionary relationships. Up to eight SUT genes in G. hirsutum were placed in the dicot-specific SUT1 group, while four and six SUT genes were, respectively, clustered into SUT4 and SUT2 groups together with members from both dicot and monocot species. The G. hirsutum SUT genes within the same group displayed similar exon/intron characteristics, and homologous genes in G. hirsutum At and Dt subgenomes, G. arboreum, and G. raimondii exhibited one-to-one relationships. Additionally, the duplicated genes in the diploid and polyploid cotton species have evolved through purifying selection, suggesting the strong conservation of SUT loci in these species. Expression analysis in different tissues indicated that SUT genes might play significant roles in cotton fiber elongation. Moreover, analyses of cis-acting regulatory elements in promoter regions and expression profiling under different abiotic stress and exogenous phytohormone treatments implied that SUT genes, especially GhSUT6A/D, might participate in plant responses to diverse abiotic stresses and phytohormones. Our findings provide valuable information for future studies on the evolution and function of SUT genes in cotton.
Connected Homes and Online Retail: The Case of IoT Enabled Online Shopping

The emergence of the Internet of Things (IoT) is an important technology breakthrough with the potential to transform our lives. In this paper, we consider the connected home application domain of the IoT from an online retail perspective and explore factors that may affect a consumer’s behavioral intention to adopt IoT enabled online shopping (IoTOS) artifacts such as Amazon’s Dash Wand and the Google Home smart speaker. Based on the existing body of knowledge in the Information Systems (IS) field, we identify key factors including security concerns (composed of data confidentiality concerns and information privacy concerns), performance expectancy, effort expectancy, facilitating conditions, and price value, as deter minants of an individual’s behavioral intention to use IoTOS artifacts. In doing so, we provide both a foundation for future research pertaining to consumer adoption of connected home innovations as well as offer insights that may help shape such innovations and their associated services.
Personality Traits and the Digital Entrepreneur: A New Breed or Same Actor?

Interest in the mindset of the entrepreneur, one of the first popular topics of entrepreneurship research, has since specialized into studies of subsets of entrepreneurial manifestations including innovation-driven, necessity-driven, corporate, and social entrepreneurship. The societal and market impact of young companies with purely digital products or services, like Facebook, Uber, and Airbnb, has recently raised interest in an emerging subset, the so-called “digital entrepreneurs” and on new business models and ecosystems in which digital entrepreneurs seem to thrive. In this paper, we focus our attention back to the persistent question on whether psychological traits can help identify and characterize entrepreneurs and, in particular, these new digital entrepreneurs. Are they unique offspring of millennials and digital natives, or are they business as usual? We use the Five Factor Model (FFM) of personality and psycholinguistic text analysis of interviews with entrepreneurs at a business incubator to analyze startup performance. We find an increase in model predictive power when we distinguish digital entrepreneurs from traditional entrepreneurs. In particular, digital entrepreneurs are significantly less concerned about the future than traditional entrepreneurs, and appear to use the personality factor “neuroticism” to their advantage. Among traditional entrepreneurs, anxiety correlates strongly and positively with risk and negatively with firm performance, whereas anxiety in a digital entrepreneur correlates strongly and positively with firm performance. These findings show that digital entrepreneurs are sufficiently different from traditional ones to warrant further research on their drivers and motivation, and on policies and ecosystems that reflect these differences.
Towards an Open Ecosystem for Maturity Models in the Digital Era: The Example of the Data Quality Management Perspective

What many enterprises lack today is an appreciation of where they stand in terms of their own digitization level. Maturity models are tools recognized both in science and in practice as a means of determining the current state of development for an application domain. However, considering the complexity of digital transformation, an “attempt” to classify digitization levels with a single maturity model consistently fails. Therefore, we recommend an open ecosystem of maturity models focusing on digital transformation as holistic toolset. The contribution of our paper lies in providing a procedure model for identifying and selecting different maturity models to better assess an enterprise’s digitization level in its entirety. More practically, we include a step-by-step exertion of the procedure model. Since master data represents a valuable asset in the digital era and therefore, must be managed efficiently and effectively—we apply our procedure model to the perspective of data quality management.
Improving Scrum Adoption with Gamification

Despite the wide adoption of agile methodologies, software development teams still struggle to meet time, budget and scope, partially due to practitioners’ lack of motivation to apply agile techniques in practice. In this paper, we present a software tool based on gamification to make Scrum techniques more fun and engaging for practitioners. This paper presents the results of the first iteration of a larger research effort that follows the Design Science Research methodology, where a prototype was developed as a Jira Software app and evaluated with a Scrum team in practice. Results suggest that the team’s Scrum practices slightly improved after using the app, when compared against the baseline. Quantitative analysis and an interview with the Scrum Master allowed to understand that the proposal should be more challenging and the score system more customized. In the future, the app will be improved based on received feedback.
Condition Number Analysis of Logistic Regression, and its Implications for Standard First-Order Solution Methods

Logistic regression is one of the most popular methods in binary classification, wherein estimation of model parameters is carried out by solving the maximum likelihood (ML) optimization problem, and the ML estimator is defined to be the optimal solution of this problem. It is well known that the ML estimator exists when the data is non-separable, but fails to exist when the data is separable. First-order methods are the algorithms of choice for solving large-scale instances of the logistic regression problem. In this paper, we introduce a pair of condition numbers that measure the degree of non-separability or separability of a given dataset in the setting of binary classification, and we study how these condition numbers relate to and inform the properties and the convergence guarantees of first-order methods. When the training data is non-separable, we show that the degree of non-separability naturally enters the analysis and informs the properties and convergence guarantees of two standard first-order methods: steepest descent (for any given norm) and stochastic gradient descent. Expanding on the work of Bach, we also show how the degree of non-separability enters into the analysis of linear convergence of steepest descent (without needing strong convexity), as well as the adaptive convergence of stochastic gradient descent. When the training data is separable, first-order methods rather curiously have good empirical success, which is not well understood in theory. In the case of separable data, we demonstrate how the degree of separability enters into the analysis of $\ell_2$ steepest descent and stochastic gradient descent for delivering approximate-maximum-margin solutions with associated computational guarantees as well. This suggests that first-order methods can lead to statistically meaningful solutions in the separable case, even though the ML solution does not exist.
Norm-Range Partition: A Univiseral Catalyst for LSH based Maximum Inner Product Search (MIPS)

Recently, locality sensitive hashing (LSH) was shown to be effective for MIPS and several algorithms including $L_2$-ALSH, Sign-ALSH and Simple-LSH have been proposed. In this paper, we introduce the norm-range partition technique, which partitions the original dataset into sub-datasets containing items with similar 2-norms and builds hash index independently for each sub-dataset. We prove that norm-range partition reduces the query processing complexity for all existing LSH based MIPS algorithms under mild conditions. The key to performance improvement is that norm-range partition allows to use smaller normalization factor most sub-datasets. For efficient query processing, we also formulate a unified framework to rank the buckets from the hash indexes of different sub-datasets. Experiments on real datasets show that norm-range partition significantly reduces the number of probed for LSH based MIPS algorithms when achieving the same recall.
Understanding the Biomineralization Role of Magnetite-Interacting Components (MICs) From Magnetotactic Bacteria

Biomineralization is a process that takes place in all domains of life and which usually helps organisms to harden soft tissues by creating inorganic structures that facilitate their biological functions. It was shown that biominerals are under tight biological control via proteins that are involved in nucleation initiation and/or which act as structural skeletons. Magnetotactic bacteria (MTB) use iron biomineralization to create nano-magnetic particles in a specialized organelle, the magnetosome, to align to the geomagnetic field. A specific set of magnetite-associated proteins (MAPs) is involved in regulating magnetite nucleation, size, and shape. These MAPs are all predicted to contain specific 17–22 residue-long sequences involved in magnetite formation. To understand the mechanism of magnetite formation, we focused on three different MAPs, MamC, Mms6 and Mms7, and studied the predicted iron-binding sequences. Using nuclear magnetic resonance (NMR), we differentiated the recognition mode of each MAP based on ion specificity, affinity, and binding residues. The significance of critical residues in each peptide was evaluated by mutation followed by an iron co-precipitation assay. Among the peptides, MamC showed weak ion binding but created the most significant effect in enhancing magnetite particle size, indicating the potency in controlling magnetite particle shape and size. Alternatively, Mms6 and Mms7 had strong binding affinities but less effect in modulating magnetite particle size, representing their major role potentially in initiating nucleation by increasing local metal concentration. Overall, our results explain how different MAPs affect magnetite synthesis, interact with Fe2+ ions and which residues are important for the MAPs functions.
PriSTE: From Location Privacy to Spatiotemporal Event Privacy

Location privacy-preserving mechanisms (LPPMs) have been extensively studied for protecting a user's location at each time point or a sequence of locations with different timestamps (i.e., a trajectory). We argue that existing LPPMs are not capable of protecting the sensitive information in user's spatiotemporal activities, such as "visited hospital in the last week" or "regularly commuting between Address 1 and Address 2 every morning and afternoon" (it is easy to infer that Addresses 1 and 2 may be home and office). To address this problem, we define the spatiotemporal event as a new privacy goal, which can be formalized as Boolean expressions between location and time predicates. We show that the spatiotemporal event is a generalization of a single location or a trajectory which is protected by existing LPPMs, while some types of spatiotemporal event may not be protected by the existing LPPMs. Hence, we formally define -spatiotemporal event privacy which is an indistinguishability-based privacy metric. It turns out that, interestingly, such privacy metric is orthogonal to the existing indistinguishability-based location privacy metric such as Geo-indistinguishability. We also discussed the potential solution to achieve both -spatiotemporal event privacy and Geo-indistinguishability.
To Compress, or Not to Compress: Characterizing Deep Learning Model Compression for Embedded Inference

The recent advances in deep neural networks (DNNs) make them attractive for embedded systems. However, it can take a long time for DNNs to make an inference on resource constrained computing devices. Model compression techniques can address the computation issue of deep inference on embedded devices. This technique is highly attractive, as it does not rely on specialized hardware, or computation-offloading that is often infeasible due to privacy concerns or high latency. However, it remains unclear how model compression techniques perform across a wide range of DNNs. To design efficient embedded deep learning solutions, we need to understand their behaviors. This work develops a quantitative approach to characterize model compression techniques on a representative embedded deep learning architecture, the NVIDIA Jetson Tx2. We perform extensive experiments by considering 11 influential neural network architectures from the image classification and the natural language processing domains. We experimentally show that how two mainstream compression techniques, data quantization and pruning, perform on these network architectures and the implications of compression techniques to the model storage size, inference time, energy consumption and performance metrics. We demonstrate that there are opportunities to achieve fast deep inference on embedded systems, but one must carefully choose the compression settings. Our results provide insights on when and how to apply model compression techniques and guidelines for designing efficient embedded deep learning systems.
Expectation Disconfirmation in ENP Information Filtering: An fMRI Experiment

Online forums sponsored by electronic networks of practice (ENPs) have become an important platform for technology-mediated knowledge exchange, yet relatively little is known about how ENP participants filter and evaluate information they encounter on these forums. This study integrates perspectives from expectation disconfirmation theory, prospect theory, and neuroscience research to explore how ENP forum filtering judgments are influenced when expectations formed on the basis of contextual cues are confirmed or disconfirmed by examination of solution quality. We report the results of an fMRI experiment that paired both positive and negative contextual cues with high and low quality solutions on a mock ENP forum interface. Results suggest that evaluation judgments are strongest in conditions where initial contextual cue judgments are confirmed by examination of solution quality. Moreover, our results offer evidence of differences in filtering behaviors and underlying cognitions with respect to expected and unexpected gains and losses.
Towards automated neural design: an open source, distributed neural architecture research framework

NORD (Neural Operations Research & Development) is an open source distributed deep learning architectural research framework, based on PyTorch, MPI and Horovod. It aims to make research of deep architectures easier for experts of different domains, in order to accelerate the process of finding better architectures, as well as study the best architectures generated for different datasets. Although currently under heavy development, the framework aims to allow the easy implementation of different design and optimization method families (optimization algorithms, meta-heuristics, reinforcement learning etc.) as well as the fair comparison between them. Furthermore, due to the computational resources required in order to optimize and evaluate network architectures, it leverage the use of distributed computing, while aiming to minimize the researcher's overhead required to implement it. Moreover, it strives to make the creation of architectures more intuitive, by implementing network descriptors, allowing to separately define the architecture's nodes and connections. In this paper, we present the framework's current state of development, while presenting its basic concepts, providing simple examples as well as their experimental results
Incidence of Long-Term Esophageal Dilation With Various Treatment Approaches in the Older Head and Neck Cancer Population

Purpose: Treatments for locoregionally advanced head and neck cancer (LAHNC) negatively impact swallowing function, but the long-term incidence of severe toxicity requiring esophageal dilation is not well-documented in the population. The aim of this study was to compare the incidence of long-term esophageal dilation across varying treatments for LAHNC. Methods and Materials: We identified 5,223 patients with LAHNC diagnosed from 2000 to 2009 in the SEER-Medicare database. We compared the incidence of esophageal dilation for surgery alone vs. surgery plus adjuvant radiotherapy (RT) and chemoradiotherapy (CRT) vs. definitive RT or CRT. Results: The cumulative incidence of esophageal dilation for all sites at 10 years, according to treatment group were as follows: CRT, 14% (95% confidence interval (CI), 12–17%); definitive RT, 13% (95% CI, 10–16%); surgery alone, 5% (95% CI, 3–7%); surgery and CRT, 15% (95% CI, 11–19%); surgery and adjuvant RT: 10% (95% CI, 8–13%). There was no significant difference in the incidence of esophageal dilation between surgery plus adjuvant RT/CRT or definitive RT/CRT (p = 0.37), but the incidence was significantly increased in both groups compared to surgery alone (p = 0.003). On multivariable analysis, chemotherapy was associated with significantly increased incidence of esophageal dilation (HR 2.9, 95% CI 1.5–5.5, p < 0.001) in oropharyngeal cancers. Conclusions: The incidence of esophageal dilation is similar in LAHNC patients undergoing RT with or without surgery. Chemoradiotherapy increases the long-term risk of esophageal dilation events over surgery alone.
Enabling Efficient RDMA-based Synchronous Mirroring of Persistent Memory Transactions

Synchronous Mirroring (SM) is a standard approach to building highly-available and fault-tolerant enterprise storage systems. SM ensures strong data consistency by maintaining multiple exact data replicas and synchronously propagating every update to all of them. Such strong consistency provides fault tolerance guarantees and a simple programming model coveted by enterprise system designers. For current storage devices, SM comes at modest performance overheads. This is because performing both local and remote updates simultaneously is only marginally slower than performing just local updates, due to the relatively slow performance of accesses to storage in today's systems. However, emerging persistent memory and ultra-low-latency network technologies necessitate a careful re-evaluation of the existing SM techniques, as these technologies present fundamentally different latency characteristics compared than their traditional counterparts. In addition to that, existing low-latency network technologies, such as Remote Direct Memory Access (RDMA), provide limited ordering guarantees and do not provide durability guarantees necessary for SM. To evaluate the performance implications of RDMA-based SM, we develop a rigorous testing framework that is based on emulated persistent memory. Our testing framework makes use of two different tools: (i) a configurable microbenchmark and (ii) a modified version of the WHISPER benchmark suite, which comprises a set of common cloud applications. Using this framework, we find that recently proposed RDMA primitives, such as remote commit, provide correctness guarantees, but do not take full advantage of the asynchronous nature of RDMA hardware. To this end, we propose new primitives enabling efficient and correct SM over RDMA, and use these primitives to develop two new techniques delivering high-performance SM of persistent memories.
Biometric Technology for Fighting Fraud in National Health Insurance: Ghana's Experience

The purpose of this study is to understand how developing countries can deploy biometric technology to fight fraud in national health insurance. Information systems research has discussed the use and impact of ICTs in fighting corruption. However, little is known about biometric technology and national health insurance fraud. To fill this knowledge gap, this paper draws on interpretive case study methodology and sociomateriality theory to investigate Ghana’s experience in using biometric technology to fight national health insurance fraud. The findings show that health insurance fraud can be reduced by employing an integrated solution of social and technical systems comprising: 1) online biometric enrollment of members and verification at the point of health service delivery; 2) use of complementary technologies such as eclaims and 3) operational policies such as the use of clinicians in vetting service providers’ claims.
Interests and Agency in AI: The case of image with Inception 3 model

The growth in AI-capabilities and proliferation of AI-enabled artifacts raises questions about unintended consequences of such technologies including the agency problems between intelligent agents and their human principals. This essay demonstrates how the agency theory and the actor-network theory (ANT) offer different, yet complementary views of the issue. Whereas the agency theory is best applied to the mitigation of the agency problem, ANT can be inform our understanding of the heterogeneous goals and interests of IT artifacts. Using an ethnographic mini-case study involving the application of machine learning algorithms to image classification, the essay traces interests inscribed in AI artifacts. The example highlights how interests of sources of training data are inscribed in AI models, and how such interests become apparent when the model is adopted by a user. Implication for future research and practice are discussed.