Miracle-dz/UniFAR-FTUs huggingface dataset:
The Dynamic Properties of Economic Preferences

The time-stability of preferences is a crucial and ubiquitous assumption in economics, yet to date there is no method to test its validity. Based on a model of the dynamics of individual preferences, I develop a simple method to test this assumption. Time-persistance in preferences is captured via an autoregressive parameter that accounts for observable characteristics and is unattenuated by measurement error, which forms the basis of the test. The method also estimates the variance of persistent shocks to latent preferences, which measures unobserved heterogeneity, and preference measurement error. I illustrate the use of this method by testing the stability of risk aversion and patience using micro-level data, and find that patience is time-stable but risk aversion is not. However, change very slowly over time. This method provides researchers with a simple tool to properly test the assumption on preference stability, and to measure the degree of preference changes due to observable and unobservable factors.
An automated ECG classification system based on a neuro-fuzzy system

The 12-lead electrocardiogram (ECG), as well as the patient history, plays an important role in the early diagnosis of acute myocardial infarction (AMI). In this paper, a hybrid neuro-fuzzy approach to the diagnostic classification of 12-lead ECGs is presented. The architecture used is a combination of fuzzy logic and neural network theory. For ECG diagnosis, the system benefits from the reasoning capabilities of fuzzy logic as well as the learning ability of neural networks. This hybrid system consists of two phases: (1) Use fuzzy logic to establish the diagnosis system in the form of symbolic IF-THEN rules based on expert cardiac knowledge; (2) Through a training process, use a backpropagation network to automatically adjust the parameters of the system. A total of 124 ECGs from patients with or without acute myocardial infarction have been studied and eight diagnostic classes have been taken into account regarding the different locations of AMI. Sensitivity, specificity, partial and total accuracy are used for evaluation of the system. After the training process, the neuro-fuzzy system correctly identified 89.4% of the patients with AMI and 95.0% of the patients without AMI. The results confirmed that AMI can be diagnosed with reasonable accuracy. While we recognize that the diagnosis of AMI varies according to clinical circumstances, the hybrid system has the potential for automatic classification of AMI.
The Impact of Person-Organization Fit on Talent Management: A Structure-Aware Convolutional Neural Network Approach

Person-Organization fit (P-O fit) refers to the compatibility between employees and their organizations. The study of P-O fit is important for enhancing proactive talent management. While considerable efforts have been made in this direction, it still lacks a quantitative and holistic way for measuring P-O fit and its impact on talent management. To this end, in this paper, we propose a novel data-driven neural network approach for dynamically modeling the compatibility in P-O fit and its meaningful relationships with two critical issues in talent management, namely talent turnover and job performance. Specifically, inspired by the practical management scenarios, we first creatively design an Organizational Structure-aware Convolutional Neural Network (OSCN) for hierarchically extracting organization-aware compatibility features for measuring P-O fit. Then, to capture the dynamic nature of P-O fit and its consequent impact, we further exploit an adapted Recurrent Neural Network with attention mechanism to model the temporal information of P-O fit. Finally, we compare our approach with a number of state-of-the-art baseline methods on real-world talent data. Experimental results clearly demonstrate the effectiveness in terms of turnover prediction and job performance prediction. Moreover, we also show some interesting indicators of talent management through the visualization of network layers.
Adversarial Attention Modeling for Multi-dimensional Emotion Regression

In this paper, we propose a neural network-based approach, namely Adversarial Attention Network, to the task of multi-dimensional emotion regression, which automatically rates multiple emotion dimension scores for an input text. Especially, to determine which words are valuable for a particular emotion dimension, an attention layer is trained to weight the words in an input sequence. Furthermore, adversarial training is employed between two attention layers to learn better word weights via a discriminator. In particular, a shared attention layer is incorporated to learn public word weights between two emotion dimensions. Empirical evaluation on the EMOBANK corpus shows that our approach achieves notable improvements in r-values on both EMOBANK Reader’s and Writer’s multi-dimensional emotion regression tasks in all domains over the state-of-the-art baselines.
Exploring Sequence-to-Sequence Learning in Aspect Term Extraction

Aspect term extraction (ATE) aims at identifying all aspect terms in a sentence and is usually modeled as a sequence labeling problem. However, sequence labeling based methods cannot make full use of the overall meaning of the whole sentence and have the limitation in processing dependencies between labels. To tackle these problems, we first explore to formalize ATE as a sequence-to-sequence (Seq2Seq) learning task where the source sequence and target sequence are composed of words and labels respectively. At the same time, to make Seq2Seq learning suit to ATE where labels correspond to words one by one, we design the gated unit networks to incorporate corresponding word representation into the decoder, and position-aware attention to pay more attention to the adjacent words of a target word. The experimental results on two datasets show that Seq2Seq learning is effective in ATE accompanied with our proposed gated unit networks and position-aware attention mechanism.
THE EVALUATIONS OF THE COLLEGE STUDENTS' PERCEPTIONS ON DISTANCE EDUCATION FROM THE POINT OF THE TECHNICAL AND EDUCATIONAL FACTORS

The purpose of the research was to investigate the college students’ opinions about distance education courses. The distance education questionnaire (DEQ) was developed for evaluating the perceptions of the students. The DEQ was conducted on 66 college students enrolled in the compulsory courses. The DEQ consisted of 18 items and two factors covering the technical and educational factors in the light of statistical analyses. When the results of the research were generally evaluated, it could be said that the majority of the students were not pleased with the distance education courses from the point of the technical (connection, accessibility, etc.) and educational (interaction, communication, etc.) factors. Some implications were presented regarding the results.
Glove-Integrated Passive UHF RFID Tags—Fabrication, Testing and Applications

Passive radio-frequency identification (RFID) technology enables versatile energy- and cost-efficient wireless platforms. We present passive ultra-high frequency (UHF) RFID tags integrated into normal work gloves for wearable RFID applications. The tags were designed to be functional throughout the global UHF RFID frequency range. The use environments of work gloves are challenging, for example due to moisture and mechanical stresses, which means the glove-integrated solutions have unique reliability issues to consider. We introduce 3-D printing of a flexible thermoplastic material directly onto the glove and embed electro-textile antennas and RFID microchip components inside the 3-D-printed structure. This manufacturing method solves the above-mentioned reliability challenges and enables the practical use of glove-integrated passive RFID tags. The performance of the fabricated glove-tags is first evaluated on a male test subject in an EMC room and in an office environment. Based on the wireless measurement results, the read ranges of the glove-tags are around 2–3 m throughout the frequency range of 800–1000 MHz. These results meet the requirements of many practical applications of glove-tags. Further, these glove-tags are found to endure extreme moisture and mechanical stresses, which supports their use in versatile use environments. Finally, the developed glove-tags are successfully tested in actual use situations for identification and access control. These results are very promising, especially considering the cost-effectiveness and the easiness of tag integration into different types of gloves.
A transparent service for synchronized replication across loosely-connected file systems

n-DFS/Parrot is a service that provides synchronized file replication across two loosely-connected file systems. It propagates the modification of replicated files and directories from either file system to the other. Parrot guarantees sequential write sharing over a local- or wide-area network: file access under Parrot has exactly the same semantics as if all of the processes on both hosts were executing on a single time-sharing system and sharing a single copy of each file. Parrot implements a read-one write-all replication scheme as a shared library that runs within an application's address space. It preserves the operating system interface between applications and the file system; it requires no modification in the kernel or the applications. <<ETX>>
A Stochastic Geometry Based Approach to Tractable 5G RNPO with a New $H$-LOS Model

We consider a 3D cellular network in which generalized shadowing and radio network planning and optimisation (RNPO) parameters (e.g., antenna height, antenna tilt/azimuth, power biaising…) are incorporated into the cell-selection model. Using tools from stochastic geometry (SG), we derive an equivalent 2D network in which no shadowing and RNPO parameters are considered. Next, we derive coverage probability for a tractable case-study network, and the regimes where coverage probability is maximized in addition to the interference-limited one are investigated. An intermediary result is a closed-form expression generator encompassing the $\mathcal{Q}$ -function based-expression in [1]. Numerical results confirm the accuracy of our approximations.
Self-Attentive, Multi-Context One-Class Classification for Unsupervised Anomaly Detection on Text

There exist few text-specific methods for unsupervised anomaly detection, and for those that do exist, none utilize pre-trained models for distributed vector representations of words. In this paper we introduce a new anomaly detection method—Context Vector Data Description (CVDD)—which builds upon word embedding models to learn multiple sentence representations that capture multiple semantic contexts via the self-attention mechanism. Modeling multiple contexts enables us to perform contextual anomaly detection of sentences and phrases with respect to the multiple themes and concepts present in an unlabeled text corpus. These contexts in combination with the self-attention weights make our method highly interpretable. We demonstrate the effectiveness of CVDD quantitatively as well as qualitatively on the well-known Reuters, 20 Newsgroups, and IMDB Movie Reviews datasets.
Exploring Pre-trained Language Models for Event Extraction and Generation

Traditional approaches to the task of ACE event extraction usually depend on manually annotated data, which is often laborious to create and limited in size. Therefore, in addition to the difficulty of event extraction itself, insufficient training data hinders the learning process as well. To promote event extraction, we first propose an event extraction model to overcome the roles overlap problem by separating the argument prediction in terms of roles. Moreover, to address the problem of insufficient training data, we propose a method to automatically generate labeled data by editing prototypes and screen out generated samples by ranking the quality. Experiments on the ACE2005 dataset demonstrate that our extraction model can surpass most existing extraction methods. Besides, incorporating our generation method exhibits further significant improvement. It obtains new state-of-the-art results on the event extraction task, including pushing the F1 score of trigger classification to 81.1%, and the F1 score of argument classification to 58.9%.
Transmit Antenna Selection in Secure MIMO Systems Over $\alpha-\mu$ Fading Channels

This paper investigates the secrecy performance of multiple-input multiple-output systems under generalized $\alpha -\mu $ fading conditions. To this end, we focus on two distinct scenarios: 1) the transmitter has knowledge of the channel state information (CSI) of the eavesdropper channel and 2) the transmitter is not aware of the CSI of the wiretap link. By considering transmit antenna selection in the underlying system, we develop closed-form analytical expressions for the lower bound of the secrecy outage probability (SOP) and the probability of the strictly positive secrecy capacity. Furthermore, two novel approaches are proposed to derive an analytical expression of the average secrecy capacity (ASC). First, the ASC is expressed as a function of the average capacity of the desired link and an interaction term regarded as the ASC loss. Second, the ASC is expressed in terms of the average capacities of the desired and eavesdropper links, plus an interaction term that can be regarded as some sort of ASC gain due to the statistical independence between the desired and eavesdropper links. In addition, asymptotic studies of the SOP and the ASC at high signal-to-noise ratio are carried out, which precisely reveal the secrecy diversity and array gains.
Corpus Creation and Analysis for Named Entity Recognition in Telugu-English Code-Mixed Social Media Data

Named Entity Recognition(NER) is one of the important tasks in Natural Language Processing(NLP) and also is a subtask of Information Extraction. In this paper we present our work on NER in Telugu-English code-mixed social media data. Code-Mixing, a progeny of multilingualism is a way in which multilingual people express themselves on social media by using linguistics units from different languages within a sentence or speech context. Entity Extraction from social media data such as tweets(twitter) is in general difficult due to its informal nature, code-mixed data further complicates the problem due to its informal, unstructured and incomplete information. We present a Telugu-English code-mixed corpus with the corresponding named entity tags. The named entities used to tag data are Person(‘Per’), Organization(‘Org’) and Location(‘Loc’). We experimented with the machine learning models Conditional Random Fields(CRFs), Decision Trees and BiLSTMs on our corpus which resulted in a F1-score of 0.96, 0.94 and 0.95 respectively.
Modeling the interplay of information seeking and information sharing

Purpose The purpose of this paper is to contribute to the creation of a holistic picture of information behavior by examining the connections between information seeking and sharing. Design/methodology/approach Conceptual analysis is used to focus on the ways in which the researchers have modeled the interplay of information seeking and sharing. The study draws on conceptual analysis of 27 key studies examining the above issue, with a focus on the scrutiny of six major models for information behavior. Findings Researchers have employed three main approaches to model the relationships between information seeking and sharing. The indirect approach conceptualizes information seeking and sharing as discrete activities connected by an intermediating factor, for example, information need. The sequential approach assumes that information seeking precedes information sharing. From the viewpoint of the interactive approach, information seeking and sharing appear as mutually related activities shaping each other iteratively or in a cyclical manner. The interactive approach provides the most sophisticated research perspective on the relationships of information seeking and sharing and contributes to holistic understanding of human information behavior. Research limitations/implications As the study focuses on information seeking and sharing, no attention is devoted to other activities constitutive of information behavior, for example, information use. Originality/value The study pioneers by providing an in-depth analysis of the connections of information seeking and information sharing.
Weighted Sparse Bayesian Learning (WSBL) for Basis Selection in Linear Underdetermined Systems

We propose Weighted SBL (WSBL) for sparse signal recovery, inspired by the Sparse Bayesian Learning (SBL) method. Unlike SBL, where all hyperparameter priors follow Gamma distributions with identical parameters, in WSBL, the hyperparameters are Gamma distributed with distinct parameters. These parameters, guided by some known weights, give more importance to some hyperparameters over others, thus introducing more degrees of freedom to the problem and leading to better recovery performance. The weights can be determined based on a low-resolution estimate of the sparse vector, for example, an estimate obtained via a method that does not encourage sparsity. The choice of the MUSIC estimate as weight is analyzed. Unlike in SBL, the WSBL hyperparameters are upper bounded; this makes it easy to select a threshold to separate zero from non-zero elements in the recovered sparse vector, which makes the iterative recovery process converge faster. Theoretical analysis based on variational approximation theory and also simulation results demonstrate that WSBL results in substantial improvement in terms of probability of detection and probability of false alarm, especially in the low signal to noise ratio regime, as compared to existing approaches, such as SBL, Sparse Bayesian Support knowledge (BSN), and Multiple response Sparse Bayesian Learning (MSBL). The performance of WSBL is evaluated for Direction of Arrival (DOA) estimation in colocated Multiple Input Multiple Output (MIMO) radar.
Scalable Global Alignment Graph Kernel Using Random Features: From Node Embedding to Graph Embedding

Graph kernels are widely used for measuring the similarity between graphs. Many existing graph kernels, which focus on local patterns within graphs rather than their global properties, suffer from significant structure information loss when representing graphs. Some recent global graph kernels, which utilizes the alignment of geometric node embeddings of graphs, yield state-of-the-art performance. However, these graph kernels are not necessarily positive-definite. More importantly, computing the graph kernel matrix will have at least quadratic time complexity in terms of the number and the size of the graphs. In this paper, we propose a new family of global alignment graph kernels, which take into account the global properties of graphs by using geometric node embeddings and an associated node transportation based on earth mover's distance. Compared to existing global kernels, the proposed kernel is positive-definite. Our graph kernel is obtained by defining a distribution over random graphs, which can naturally yield random feature approximations. The random feature approximations lead to our graph embeddings, which is named as "random graph embeddings" (RGE). In particular, RGE is shown to achieve (quasi-)linear scalability with respect to the number and the size of the graphs. The experimental results on nine benchmark datasets demonstrate that RGE outperforms or matches twelve state-of-the-art graph classification algorithms.
$\mathsf{PrivateLink}$ : Privacy-Preserving Integration and Sharing of Datasets

In privacy-enhancing technology, it has been inevitably challenging to strike a reasonable balance between privacy, efficiency, and usability (utility). To this, we propose a highly practical solution for the privacy-preserving integration and sharing of datasets among a group of participants. At the heart of our solution is a new interactive protocol, <inline-formula> <tex-math notation="LaTeX">$\mathsf{PrivateLink}$ </tex-math></inline-formula>. Through <inline-formula> <tex-math notation="LaTeX">$\mathsf{PrivateLink}$ </tex-math></inline-formula>, each participant is able to randomize his/her dataset via an independent and untrusted third party, such that the resulting dataset can be merged with other randomized datasets contributed by other participants in a privacy-preserving manner. Our approach does not require key sharing among participants in order to integrate different datasets. This, in turn, leads to a user-friendly and scalable solution. Moreover, the correctness of a randomized dataset returned by the third party can be securely verified by the participant. We further demonstrate <inline-formula> <tex-math notation="LaTeX">$\mathsf{PrivateLink}$ </tex-math></inline-formula>’s general utilities: using it to construct a structure-preserving data integration protocol. This is particularly useful for private, fine-grained integration of network traffic data. We state the security of our protocols under the well-established real-ideal simulation paradigm and demonstrate practicality by a prototype implementation on: 1) healthcare datasets and 2) DNS and NetFlow datasets.
Legitimating Negative Aspects in Corporate Social Responsibility Reporting: Evidence From China

Research problem: This study investigates the way in which large Chinese firms communicated occupational fatalities in corporate social responsibility (CSR) reports. Research questions: 1. Did the sample firms disclose information about workplace fatalities in their CSR reports? 2. What communicative strategies were used in the disclosure for the purpose of self-legitimation? 3. How were these strategies manifested linguistically and rhetorically? Literature review: The study is based on legitimacy theory, which suggests that when reporting bad news, firms may use communicative strategies to maintain or restore organizational legitimacy. Previous studies of negative CSR disclosures focus more on information selection and omission than on information presentation. A lack of consideration of actual organizational performance in some studies also makes it less feasible to account for strategies that firms use to misrepresent reality. Methodology: The study compared CSR reports issued by Fortune 500 Chinese firms with the firms’ reports of fatal occupational incidents to see whether the incidents were reported faithfully. An integrated analytical framework of legitimation strategies, developed from previous studies of legitimation in organizational communication, was applied to the analysis. Results and conclusions: Most firms disclosed their fatality incidents. Legitimation strategies—in particular, positive performance evaluations and corrective actions—were used by the firms to de-emphasize or minimize the bad news. This study calls for greater attention from CSR monitors and professionals to information presentation as an important indicator of report quality. The findings are limited to one type of CSR disclosure and to the firms that were examined.