Miracle-dz/UniFAR-FTUs huggingface dataset:
THE SURFACE EMG ACTIVITY ANALYSIS BETWEEN BADMINTON SMASH AND JUMP SMASH

Badminton smash is one of the most powerful techniques among all the racket sports. It may divide into smash and jump smash. The purpose of this study was to analyze the surface EMG activity of upper extremities between smash and jump smash by eight Taiwan elite badminton players. We used two digital video cameras to obtain the 3D kinematics data of shuttlecock, and measured the surface EMG signals of seven upper limb muscles. The results showed that there was no significant difference between the two smashes in initial shuttle velocity. Though the movements of the two smashes were similar, there were significant difference between the two smashes in the sequence of the surface EMG activity of the upper limb and the mean IEMG amplitude in a few muscles. We found that the jump smash exerted the higher EMG activity than smash in the phase before contact point.
PERSONALITY CHARACTERISTICS OF PRE/POST-STRIKE AIR TRAFFIC CONTROL APPLICANTS

The 16 Personality Factors (16PF) test has been routinely administered to personnel applying for Air Traffic Control Specialists (ATCS) positions within the Federal Aviation Administration for more than 3 decades. This study was designed to assess the relationship between personality characteristics of a group of post-strike applicants (1984) with data gathered in the late 1960s and 1970s. Additionally, the comparisons provide a baseline with which to assess the characteristics of the new controllers who will start to enter the workforce as the post-strike workforce begins to retire following the year 2000. Outcomes were consistent with previous findings, in revealing that female and male ATCS applicants are brighter than the average individual. When compared with the general population norm, the applicant groups are less anxious, report higher self-discipline, and are more emotionally stable. They are also more self-reliant and assertive. These characteristics appear to be ideally suited for applicants to an occupation that requires quick decision-making and calm, thoughtful responses during emergencies.
Validation of an Acoustic Head Simulator for the Evaluation of Personal Hearing Protection Devices

Abstract : A study was carried out to assess the validity of using an acoustic test fixture (ATF) to assess the sound reduction afforded by personal hearing protection devices. The ATF under review consists of a head simulator, cast in silicone rubber and covered with artificial skin. Cavities milled out on either side allow for the insertion of snap−in ear modules that incorporate a mechanical reproduction of human aural tissues. The ear canal is terminated by Zwislocki coupler and Etymotic ER−11 measurement microphone and models the impedance of the human ear. The experiment was carried out in a semi−reverberant chamber. On two separate occasions scheduled four months apart, the right ear of the ATF was fitted with an earmuff (E−A−R 3000), an earplug (E−A−R Classic foam plug), and the muff and plug in combination. The non test ear was fitted with the plug. The stimulus was a continuous pink noise, presented free−field at a level of 86 dB SPL using a bank of low, middle and high frequency speakers. Monaural sound level measurements were made under each of these conditions, as well as with the test ear unoccluded. These were replicated three times within session with two different settings of the measurement microphone, diffuse−field equalization and flat frequency response. Measurements were recorded and subsequently subjected to a third octave band analysis from 12.5 Hz to 20 kHz. The results showed that the measurements were highly stable within and across sessions. Differences due to the setting of the measurement microphone were in line with the manufacturer's specifications.
A closed-loop driver/vehicle directional dynamics predictor

The highway safety related to vehicle operation on the road is a complex function of dynamic interactions between the vehicle, the driver and the environment. The safety dynamics of a heavy vehicle thus relates to not only its directional stability and control limits, but also the control performance limits of the driver. In view of their lower stability limits, the directional dynamics of articulated freight vehicles have been extensively investigated assuming either negligible contributions due to driver or perfect adaptability of the driver to the vehicle motion. In this dissertation, a number of analytical models of varying complexities are developed to study the lateral, yaw and roll directional performance of an articulated vehicle. Nonlinear analytical models to estimate the cornering properties of tire are derived using Magic Formula and neural networks. Neural network techniques are also applied to derive a nonlinear vehicle model. Based upon a comparative study of response characteristics of various models, it is concluded that a vehicle model based upon yaw-plane dynamics with limited roll degree-of-freedom of the sprung mass can effectively predict the directional response. Parameter sensitivity analyses are performed to identify design parameters that affect the directional dynamics of the combination most significantly. System identification techniques are applied to derive vehicle parameters known to be uncertain. Different analytical models of the human driver are formulated to identify the most effective feedback motion variables and to study the contributions due to driver's interactions with the vehicle. The reported data attained from different field and driving simulator studies are reviewed to identify a range of driver's control parameters, and the performance limits of the drivers. A comprehensive closed-loop driver-articulated vehicle model is formulated, incorporating driver's preview, prediction and compensation abilities, and various motion cues arising from the vehicle's directional motion. The analytical model is analyzed to study the control demands imposed on the driver as functions of selected operational and environmental factors. The control performance limits of three different drivers with varying skill levels are formulated and coupled with the vehicle model. The coupled model is analyzed to identify optimal vehicle design parameters, such that the resulting design can be best adapted to the driver's skill. The results show that the proposed vehicle design that can be adapted for the driver yields considerable performance benefits in view of directional dynamics performance and thus the associated highway safety.
Response to \Slid Pairs in Salsa20 and Trivium"

The paper \Slid Pairs in Salsa20 and Trivium" by Priemuth-Schmid and Biryukov states various \attacks" on Salsa20 and Trivium. The paper claims that \Salsa20 does not have 256-bit security," that its \attacks" demonstrate a \certicational weakness" in Salsa20, that \it is crucial for the security of Salsa20 that nonces are chosen at random," that the \attacks" can be \exploited in certain scenarios," etc. These claims are entirely without merit. The \attacks" on Salsa20 are vastly more expensive than the standard brute-force attacks discussed in the original Salsa20 documentation. (I haven’t looked at the \attacks" on Trivium.) Specically, the best \attack" in the paper receives ciphertexts from 2 191
Redesigning the Academic Role- The Emerging Dimensions of Knowledge Workers

The role of teachers is undergoing a paradigm shift in the present education scenario. During earlier days, teachers played the role of tutors and were highly subject oriented. They were not expected to do anything other than mere teaching. They were considered as instructors. With advancement in technology the role of the teachers became as that of a guide and the teachers were leading the students. In the present era of knowledge explosion, the teachers have to be facilitators. They learn along with the students. The role of the teachers has thus undergone a paradigm shift from that of instructors to leaders to learners. This paper makes an earnest attempt to study the role of teachers working for Government and Private Arts & Science Colleges and Engineering Colleges. An attempt has been made to study the association between the different parameters for evaluating the role of teachers working under various streams and different sectors.
Cheatgrass

Introduced into the United States in packing materials, ship ballast and likely as a contaminant of crop seed, cheatgrass was first found in the United States near Denver, Colorado, in the late 1800s (Whitson et al. 1991). In the late 1800’s and early 1900’s, it spread explosively in the ready-made seedbeds prepared by the trampling livestock hooves of overstocked rangelands. Disturbance associated with homesteading and cultivation of winter wheat also accelerated its spread and establishment. By the 1930’s, cheatgrass was becoming the dominant grass over vast areas of the Pacific Northwest and the Intermountain West regions and the “worst” western range weed.
Error-Sensitive Evaluation for Ordinal Target Variables

Product reviews and satisfaction surveys seek customer feedback in the form of ranked scales. In these settings, widely used evaluation metrics including F1 and accuracy ignore the rank in the responses (e.g., ‘very likely’ is closer to ‘likely’ than ‘not at all’). In this paper, we hypothesize that the order of class values is important for evaluating classiﬁers on ordinal target variables and should not be disregarded. To test this hypothesis, we compared Multi-class Classi-ﬁcation (MC) and Ordinal Regression (OR) by applying OR and MC to benchmark tasks involving ordinal target variables us-ing the same underlying model architecture. Experimental results show that while MC outperformed OR for some datasets in accuracy and F1, OR is signiﬁcantly better than MC for minimizing the error between prediction and target for all benchmarks, as revealed by error-sensitive metrics, e.g. mean-squared error (MSE) and Spearman correlation. Our ﬁndings motivate the need to establish consistent, error-sensitive metrics for evaluating benchmarks with ordinal target variables, and we hope that it stimulates interest in exploring alternative losses for ordinal problems.
“It’s our fault!”: Insights Into Users’ Understanding and Interaction With an Explanatory Collaborative Dialog System

Human-AI collaboration, a long standing goal in AI, refers to a partnership where a human and artificial intelligence work together towards a shared goal. Collaborative dialog allows human-AI teams to communicate and leverage strengths from both partners. To design collaborative dialog systems, it is important to understand what mental models users form about their AI-dialog partners, however, how users perceive these systems is not fully understood. In this study, we designed a novel, collaborative, communication-based puzzle game and explanatory dialog system. We created a public corpus from 117 conversations and post-surveys and used this to analyze what mental models users formed. Key takeaways include: Even when users were not engaged in the game, they perceived the AI-dialog partner as intelligent and likeable, implying they saw it as a partner separate from the game. This was further supported by users often overestimating the system’s abilities and projecting human-like attributes which led to miscommunications. We conclude that creating shared mental models between users and AI systems is important to achieving successful dialogs. We propose that our insights on mental models and miscommunication, the game, and our corpus provide useful tools for designing collaborative dialog systems.
Assessing the Generalization Capacity of Pre-trained Language Models through Japanese Adversarial Natural Language Inference

Despite the success of multilingual pre-trained language models, it remains unclear to what extent these models have human-like generalization capacity across languages. The aim of this study is to investigate the out-of-distribution generalization of pre-trained language models through Natural Language Inference (NLI) in Japanese, the typological properties of which are different from those of English. We introduce a synthetically generated Japanese NLI dataset, called the Japanese Adversarial NLI (JaNLI) dataset, which is inspired by the English HANS dataset and is designed to require understanding of Japanese linguistic phenomena and illuminate the vulnerabilities of models. Through a series of experiments to evaluate the generalization performance of both Japanese and multilingual BERT models, we demonstrate that there is much room to improve current models trained on Japanese NLI tasks. Furthermore, a comparison of human performance and model performance on the different types of garden-path sentences in the JaNLI dataset shows that structural phenomena that ease interpretation of garden-path sentences for human readers do not help models in the same way, highlighting a difference between human readers and the models.
“I’ll be there for you”: The One with Understanding Indirect Answers

Indirect answers are replies to polar questions without the direct use of word cues such as ‘yes’ and ‘no’. Humans are very good at understanding indirect answers, such as ‘I gotta go home sometime’, when asked ‘You wanna crash on the couch? ’. Understanding indirect answers is a challenging problem for dialogue systems. In this paper, we introduce a new English corpus to study the problem of understanding indirect answers. Instead of crowd-sourcing both polar questions and answers, we collect questions and indirect answers from transcripts of a prominent TV series and manually annotate them for answer type. The resulting dataset contains 5,930 question-answer pairs. We release both aggregated and raw human annotations. We present a set of experiments in which we evaluate Convolutional Neural Networks (CNNs) for this task, including a cross-dataset evaluation and experiments with learning from disagreements in annotation. Our results show that the task of interpreting indirect answers remains challenging, yet we obtain encouraging improvements when explicitly modeling human disagreement.
Zero-Shot Clinical Questionnaire Filling From Human-Machine Interactions

In clinical studies, chatbots mimicking doctor-patient interactions are used for collecting information about the patient’s health state. Later, this information needs to be processed and structured for the doctor. One way to organize it is by automatically filling the questionnaires from the human-bot conversation. It would help the doctor to spot the possible issues. Since there is no such dataset available for this task and its collection is costly and sensitive, we explore the capacities of state-of-the-art zero-shot models for question answering, textual inference, and text classification. We provide a detailed analysis of the results and propose further directions for clinical questionnaire filling.
Officer Sabbaticals: Analysis of Extended Leave Options

Abstract : In recent years the Department of Defense (DoD) has placed increasing focus on attracting and maintaining military personnel with desirable skills and capabilities and on creating a more flexible personnel system to prepare the military for the future. To achieve these goals and as part of its developing human resource strategy, DoD is exploring a wide range of personnel management programs. One such program involves the greater rise of extended leaves for its military officers.
Developing a Benchmark for Reducing Data Bias in Authorship Attribution

Authorship attribution is the task of assigning an unknown document to an author from a set of candidates. In the past, studies in this ﬁeld use various evaluation datasets to demonstrate the effectiveness of preprocessing steps, features, and models. However, only a small fraction of works use more than one dataset to prove claims. In this paper, we present a collection of highly diverse authorship attribution datasets, which better generalizes evaluation results from authorship attribution research. Furthermore, we implement a wide variety of previously used machine learning models and show that many approaches show vastly different performances when applied to different datasets. We include pre-trained language models, for the ﬁrst time testing them in this ﬁeld in a systematic way. Finally, we propose a set of aggregated scores to evaluate different aspects of the dataset collection.
A Coarse-to-Fine Labeling Framework for Joint Word Segmentation, POS Tagging, and Constituent Parsing

The most straightforward approach to joint word segmentation (WS), part-of-speech (POS) tagging, and constituent parsing is converting a word-level tree into a char-level tree, which, however, leads to two severe challenges. First, a larger label set (e.g., ≥ 600) and longer inputs both increase computational costs. Second, it is difficult to rule out illegal trees containing conflicting production rules, which is important for reliable model evaluation. If a POS tag (like VV) is above a phrase tag (like VP) in the output tree, it becomes quite complex to decide word boundaries. To deal with both challenges, this work proposes a two-stage coarse-to-fine labeling framework for joint WS-POS-PAR. In the coarse labeling stage, the joint model outputs a bracketed tree, in which each node corresponds to one of four labels (i.e., phrase, subphrase, word, subword). The tree is guaranteed to be legal via constrained CKY decoding. In the fine labeling stage, the model expands each coarse label into a final label (such as VP, VP*, VV, VV*). Experiments on Chinese Penn Treebank 5.1 and 7.0 show that our joint model consistently outperforms the pipeline approach on both settings of w/o and w/ BERT, and achieves new state-of-the-art performance.
Characterization of constitutive activation and inactivation mutants in conserved regions of the eel luteinizing hormone receptor

Background: We analyzed signal transduction of three constitutively activating mutants (designated M410T, L469R, and D590Y) and two inactivating mutants (D383N and Y546F) of the eel luteinizing hormone receptor (LHR), known to be naturally occurring in humans as LHR. The objective of the present study is to assess the functional effects of these mutations in signal transduction. Methods: Site-directed mutant receptors were transiently expressed in CHO-K1 cells and cAMP accumulation, stimulated by recombinant eelLH (rec-eelLH), was measured by homogeneous time-resolved fluorescence (HTRF) assays. Results: The cAMP response in cells expressing the wild-type eel LHR (eLHR-WT) increased in a dose-dependent manner with rec-eelLH ligand stimulation. Cells expressing the activating eelLHR mutants, M410T, L469R, and D590Y, exhibited a 4.0-, 19.1-, and 7.8-fold increase in the basal cAMP response, respectively. However, their maximal responses to agonist were approximately 73, 53, and 92%, respectively, of the maximal response of the LHR-WT. The L469R mutant exhibited a particularly marked increase in cAMP concentration in the absence of ligand. The inactivating mutations did not completely impair signal transduction. The maximal responses of the inactivating mutants, D383N and Y546F, were 32% and 24% of the LHR-WT, respectively. Conclusion: We report here the first characterization of activating and inactivating mutations in eelLHR. These results provide important data on the signal transduction of constitutively active and inactive eelLHR mutants. Keywords: eel LHR, constitutively activating/inactivating mutation
Increasing Sentence-Level Comprehension Through Text Classification of Epistemic Functions

Word embeddings capture semantic meaning of individual words. How to bridge word-level linguistic knowledge with sentence-level language representation is an open problem. This paper examines whether sentence-level representations can be achieved by building a custom sentence database focusing on one aspect of a sentence’s meaning. Our three separate semantic aspects are whether the sentence: (1) communicates a causal relationship, (2) indicates that two things are correlated with each other, and (3) expresses information or knowledge. The three classifiers provide epistemic information about a sentence’s content.
Multi-task Learning in Argument Mining for Persuasive Online Discussions

We utilize multi-task learning to improve argument mining in persuasive online discussions, in which both micro-level and macro-level argumentation must be taken into consideration. Our models learn to identify argument components and the relations between them at the same time. We also tackle the low-precision which arises from imbalanced relation data by experimenting with SMOTE and XGBoost. Our approaches improve over baselines that use the same pre-trained language model but process the argument component task and two relation tasks separately. Furthermore, our results suggest that the tasks to be incorporated into multi-task learning should be taken into consideration as using all relevant tasks does not always lead to the best performance.
Countering the Influence of Essay Length in Neural Essay Scoring

Previous work has shown that automated essay scoring systems, in particular machine learning-based systems, are not capable of assessing the quality of essays, but are relying on essay length, a factor irrelevant to writing proficiency. In this work, we first show that state-of-the-art systems, recent neural essay scoring systems, might be also influenced by the correlation between essay length and scores in a standard dataset. In our evaluation, a very simple neural model shows the state-of-the-art performance on the standard dataset. To consider essay content without taking essay length into account, we introduce a simple neural model assessing the similarity of content between an input essay and essays assigned different scores. This neural model achieves performance comparable to the state of the art on a standard dataset as well as on a second dataset. Our findings suggest that neural essay scoring systems should consider the characteristics of datasets to focus on text quality.
A Pilot Study for BERT Language Modelling and Morphological Analysis for Ancient and Medieval Greek

This paper presents a pilot study to automatic linguistic preprocessing of Ancient and Byzantine Greek, and morphological analysis more specifically. To this end, a novel subword-based BERT language model was trained on the basis of a varied corpus of Modern, Ancient and Post-classical Greek texts. Consequently, the obtained BERT embeddings were incorporated to train a fine-grained Part-of-Speech tagger for Ancient and Byzantine Greek. In addition, a corpus of Greek Epigrams was manually annotated and the resulting gold standard was used to evaluate the performance of the morphological analyser on Byzantine Greek. The experimental results show very good perplexity scores (4.9) for the BERT language model and state-of-the-art performance for the fine-grained Part-of-Speech tagger for in-domain data (treebanks containing a mixture of Classical and Medieval Greek), as well as for the newly created Byzantine Greek gold standard data set. The language models and associated code are made available for use at https://github.com/pranaydeeps/Ancient-Greek-BERT