Reviewerly/exHarmony huggingface dataset:
LIWC-UD: Classifying Online Slang Terms into LIWC Categories

Linguistic Inquiry and Word Count (LIWC), a popular tool for automated text analysis, relies on an expert-crafted internal dictionary of psychologically relevant words and their corresponding categories. While LIWC’s dictionary covers a significant portion of commonly used words, the continuous evolution of language and the usage of slang in settings such as social media requires fixed resources to be frequently updated in order to stay relevant. In this work we present LIWC-UD, an automatically generated extension to LIWC’s dictionary which includes terms defined in Urban Dictionary. While original LIWC contains 6,547 unique entries, LIWC-UD consists of 141K unique terms automatically categorized into LIWC categories with high confidence using BERT classifier. LIWC-UD covers many additional terms that are commonly used on social media platforms like Twitter. We release LIWC-UD publicly to the community as a supplement to the original LIWC lexicon.
Educational Activities in the Context of the Realities of the Information Society: Problems, Pro-spects

The study aims to analyze key trends in the informatization of the educational space and assess further prospects for its functioning in the context of the development of the information society. The research is based on systematic and comparative analysis and The process of informatization of education in the context of global processes makes it possible to form a particular online (digital) educational space that eliminates national and territorial differences between states, thereby providing open and fast access to innovative educational resources from all over the world. ICT educational resources provide an opportunity to address two of the most pressing challenges: to ensure continuity between all levels of education; to gradually address the “gaps” in digital literacy between the young and old generations, thereby realizing one of the essential values recognized by UNESCO - inclusive and continuing education/education throughout life.
Sports Biology Seminar of Three-dimensional Movement Characteristics of Yoga Standing Based on Image Recognition

With the exponential improvement of the integration capabilities of electronic hardware devices, digital images have become an indispensable information carrier, thus promoting the development of image recognition, detection, or tracking technologies. At present, there is a blank in the field of image recognition measured by the action standard of yoga standing three-dimensional movement, which will bring huge room for researchers to play. Based on the traditional image recognition technology, there are cases where missed detection or false detection cannot be correctly identified and made a correct judgment. In this paper, the ability to capture the action is improved by optimizing the network for the positioning of the stereoscopic action target of the yoga station. The linear discriminant analysis (LDA) after adding optimization is used to reduce the dimensionality of the captured image data, which is beneficial to improving image recognition rate and reducing image loss rate. Through the analysis of the three-dimensional movement of the yoga station, the model based on the improved algorithm is compared with the traditional model, and the final image recognition accuracy is improved compared with the network model before the improvement. The image recognition error rate steadily tends to 5%, and the loss rate is also below 2%. Through the optimized convolutional neural network, this paper can accurately capture the image position, and the recognition rate has also been greatly improved, which can provide a reference for future research in other fields.
Assessing coherence through linguistic connectives: Analysis of speech in patients with schizophrenia-spectrum disorders

Incoherent speech is a core diagnostic symptom of schizophrenia-spectrum disorders (SSD) that can be studied using semantic space models. Since linguistic connectives signal relations between words, they and their surrounding words might represent linguistic loci to detect unusual coherence in speech. Therefore, we investigated whether connectives' measures are useful to assess incoherent speech in SSD.Connectives and their surrounding words were extracted from transcripts of spontaneous speech of 50 SSD-patients and 50 control participants. Using word2vec, two different cosine similarities were calculated: those of connectives and their surrounding words (connectives-related similarity), and those of free-of-connectives words-chunks (non-connectives similarity). Differences between groups in proportion of five types of connectives were assessed using generalized logistic models, and connectives-related similarity was analyzed through non-parametric multivariate analysis of variance. These features were evaluated in classification tasks to differentiate between groups.SSD-patients used less contingency (e.g., because) (p = .008) and multiclass connectives (e.g., as) (p < .001) than control participants. SSD-patients had higher minimum similarity of multiclass (adj-p = .04) and temporality connectives (e.g., after) (adj-p < .001), narrower similarity-range of expansion (e.g., and) (adj-p = .002) and multiclass connectives (adj-p = .04), and lower maximum similarity of expansion connectives (adj-p = .005). Using connectives' features alone, SSD-patients and controls could be distinguished with 85 % accuracy.Our results show that SSD-speech can be distinguished from speech of control participants with high accuracy, based solely on connectives' features. We conclude that including connectives could strengthen computational models to categorize SSD.
Application of Human Posture Recognition Based on the Convolutional Neural Network in Physical Training Guidance

The application of sports game video analysis in athlete training and competition analysis feedback has attracted extensive attention, but the traditional sports human body posture estimation method has a large error between the athlete's human body posture estimation results and the actual results in the complex environment and the athlete's body parts are blocked. Therefore, this study proposes a convolutional neural network for athlete pose estimation in sports game video. Based on the improved model, multiscale model, and large perception model, a superimposed hourglass network is constructed, and the gradient disappearance problem of the convolutional neural network is solved using intermediate supervision. The experimental results show that the athlete pose estimation model based on the convolutional neural network can improve the accuracy of athlete pose estimation and reduce the negative impact of occlusion environment on athlete pose estimation to a certain extent. In addition, compared with other athletes' standing posture estimation methods, the model has competitive advantages and high accuracy under widely used standard conditions.
Sensor and Actuator Electronic System for Active Hand Pose Sensing

We designed, developed and validated a bone conduction-based hand pose sensing wireless and wearable system, capable of distinguishing different hand gestures and enabling the evaluation of the pose angles of the fingers. The system can be used as a valuable alternative to sensory gloves, image processing or electromyographic signal analysis. BCHS has the advantage of significantly reducing the size of the recognition system, not requiring a confined environment as with camera systems, having no blind spots, and being minimally invasive to the user. The test results demonstrated the feasibility of the proposed measurement techniques and encourage further activities for the development of a complete system by embedding the post-processing tasks into the wearable device.
Types and frequency of whole slide imaging scan failures in a clinical high throughput digital pathology scanning laboratory

Digital workflow transformation continues to sweep throughout a diversity of pathology departments spanning the globe following catalyzation of whole slide imaging (WSI) adoption by the SARS-CoV-2 (COVID-19) pandemic. The utility of WSI for a litany of use cases including primary diagnosis has been emphasized during this period, with WSI scanning devices gaining the approval of healthcare regulatory bodies and practitioners alike for clinical applications following extensive validatory efforts. As successful validation for WSI is predicated upon pathologist diagnostic interpretability of digital images with high glass slide concordance, departmental adoption of WSI is tantamount to the reliability of such images often predicated upon quality assessment notwithstanding image interpretability but extending to quality of practice following WSI adoption. Metrics of importance within this context include failure rates inclusive of different scanning errors that result in poor image quality and the potential such errors may incur upon departmental turnaround time (TAT). We sought to evaluate the impact of WSI implementation through retrospective evaluation of scan failure frequency in archival versus newly prepared slides, types of scanning error, and impact upon TAT following commencement of live WSI operation in May 2017 until the present period within a fully digitized high-volume academic institution. A 1.19% scan failure incidence rate was recorded during this period, with re-scanning requested and successfully executed for 1.19% of cases during the reported period of January 2019 until present. No significant impact upon TAT was deduced, suggesting an outcome which may be encouraging for departments considering digital workflow adoption.
Interactive Voice Response Service to Improve High School Students Covid-19 Literacy in Burkina Faso: A Usability Study

Mobile technology is widely used in healthcare. However, designers and developers in many cases have focused on developing solutions that are often tailored to highly literate people. While the advent of the pandemic has called for people to seek and use Covid-19 related information to adapt their behaviors, it is relatively difficult for low literate to get easily access to health information through digital technologies. In this study, we present a Mobile based Interactive Voice Response service designed particularly for low-literate people which provides validated Covid-19 related health information in local African languages. We conducted a field study, among high school students, through a usability study to assess users' perception. The service received an excellent numerical usability score of 78.75.
The perception on mobile-based interactive learning media use in archiving course completion

&lt;span lang="EN-US"&gt;This research analyzed the discriminant of different categories of successfully passing and remedial students based on the perception on mobile-based interactive archiving media. The research method employed quantitative approach with questionnaire instrument. The questionnaire was distributed to students attending Archiving class using interactive archiving media having been developed by the author previously. Data analysis involved prerequisite test analysis including validity and reliability tests. Data was then analyzed using discriminant analysis model with SPSS version 23 application software. The result of research showed that: i) There is a significant difference of perception on interactive media between successfully passing and remedial students; ii) Media originality variable is the one determining the significance of difference; and iii) Discrimination model has high accuracy, so that this research can be used for further research and development with similar theme and variable.&lt;/span&gt;
Invariance Through Latent Alignment

A robot's deployment environment often involves perceptual changes that differ from what it has experienced during training.Standard practices such as data augmentation attempt to bridge this gap by augmenting source images in an effort to extend the support of the training distribution to better cover what the agent might experience at test time.In many cases, however, it is impossible to know test-time distribution-shift a priori, making these schemes infeasible.In this paper, we introduce a general approach, called Invariance through Latent Alignment (ILA), that improves the test-time performance of a visuomotor control policy in deployment environments with unknown perceptual variations.ILA performs unsupervised adaptation at deploymenttime by matching the distribution of latent features on the target domain to the agent's prior experience, without relying on paired data.Although simple, we show that this idea leads to surprising improvements on a variety of challenging adaptation scenarios, including changes in lighting conditions, the content in the scene, and camera poses.We present results on calibrated control benchmarks in simulation-the distractor control suite-and a physical robot under a sim-to-real setup.
Towards Accurate Facial Motion Retargeting with Identity-Consistent and Expression-Exclusive Constraints

We address the problem of facial motion retargeting that aims to transfer facial motion from a 2D face image to 3D characters. Existing methods often formulate this problem as a 3D face reconstruction problem, which estimates the face attributes such as face identity and expression from face images. However, due to the lack of ground-truth labels for both identity and expression, most 3D-face reconstruction-based methods fail to capture the facial identity and expression accurately. As a result, these methods may not achieve promising performance. To address this, we propose an identity-consistent constraint to learn accurate identities by encouraging consistent identity prediction across multiple frames. Based on a more accurate identity, we are able to obtain a more accurate facial expression. Moreover, we further propose an expression-exclusive constraint to improve performance by avoiding the co-occurrence of contradictory expression units (e.g., ``brow lower'' vs. ``brow raise''). Extensive experiments on facial motion retargeting and 3D face reconstruction tasks demonstrate the superiority of the proposed method over existing methods. Our code and supplementary materials are available at https://github.com/deepmo24/CPEM.
The FF Heuristic for Lifted Classical Planning

Heuristics for lifted planning are not yet as informed as the best heuristics for ground planning. Recent work introduced the idea of using Datalog programs to compute the additive heuristic over lifted tasks. Based on this work, we show how to compute the more informed FF heuristic in a lifted manner. We extend the Datalog program with executable annotations that can also be used to define other delete-relaxation heuristics. In our experiments, we show that a planner using the lifted FF implementation produces state-of-the-art results for lifted planners. It also reduces the gap to state-of-the-art ground planners in domains where grounding is feasible.
Unsupervised Editing for Counterfactual Stories

Creating what-if stories requires reasoning about prior statements and possible outcomes of the changed conditions. One can easily generate coherent endings under new conditions, but it would be challenging for current systems to do it with minimal changes to the original story. Therefore, one major challenge is the trade-off between generating a logical story and rewriting with minimal-edits. In this paper, we propose EDUCAT, an editing-based unsupervised approach for counterfactual story rewriting. EDUCAT includes a target position detection strategy based on estimating causal effects of the what-if conditions, which keeps the causal invariant parts of the story. EDUCAT then generates the stories under fluency, coherence and minimal-edits constraints. We also propose a new metric to alleviate the shortcomings of current automatic metrics and better evaluate the trade-off. We evaluate EDUCAT on a public counterfactual story rewriting benchmark. Experiments show that EDUCAT achieves the best trade-off over unsupervised SOTA methods according to both automatic and human evaluation. The resources of EDUCAT are available at: https://github.com/jiangjiechen/EDUCAT.
Tractable Abstract Argumentation via Backdoor-Treewidth

Argumentation frameworks (AFs) are a core formalism in the field of formal argumentation. As most standard computational tasks regarding AFs are hard for the first or second level of the Polynomial Hierarchy, a variety of algorithmic approaches to achieve manageable runtimes have been considered in the past. Among them, the backdoor-approach and the treewidth-approach turned out to yield fixed-parameter tractable fragments. However, many applications yield high parameter values for these methods, often rendering them infeasible in practice. We introduce the backdoor-treewidth approach for abstract argumentation, combining the best of both worlds with a guaranteed parameter value that does not exceed the minimum of the backdoor- and treewidth-parameter. In particular, we formally define backdoor-treewidth and establish fixed-parameter tractability for standard reasoning tasks of abstract argumentation. Moreover, we provide systems to find and exploit backdoors of small width, and conduct systematic experiments evaluating the new parameter.
On Dynamic Bitvector Implementations

Bitvectors that support rank and select queries are the workhorses of succinct data structures, implementations of which are now widespread, for example, in bioinformatics software. To date, however, most bitvector implementations are static, thus forcing more complex data structures built from them to be static too. In this paper we explore dynamic bitvectors, which, in addition to rank and select queries, also support update operations, specifically: insert, remove, and modify. We first provide several practical optimizations to the recent B-tree based bitvectors of Prezza (Proc. SEA 2017), including the use of buffers at leaves to speed update operations at the cost of a small overhead to query times. We then consider a common use case of succinct data structures, where queries and updates come in separate batches, and examine the efficacy of query support data structures that are fast to construct and speed rank and select queries, but become out of date when update operations are made. Finally, we explore several methods for leaf compression.
Amplitude Spectrum Transformation for Open Compound Domain Adaptive Semantic Segmentation

Open compound domain adaptation (OCDA) has emerged as a practical adaptation setting which considers a single labeled source domain against a compound of multi-modal unlabeled target data in order to generalize better on novel unseen domains. We hypothesize that an improved disentanglement of domain-related and task-related factors of dense intermediate layer features can greatly aid OCDA. Prior-arts attempt this indirectly by employing adversarial domain discriminators on the spatial CNN output. However, we find that latent features derived from the Fourier-based amplitude spectrum of deep CNN features hold a more tractable mapping with domain discrimination. Motivated by this, we propose a novel feature space Amplitude Spectrum Transformation (AST). During adaptation, we employ the AST auto-encoder for two purposes. First, carefully mined source-target instance pairs undergo a simulation of cross-domain feature stylization (AST-Sim) at a particular layer by altering the AST-latent. Second, AST operating at a later layer is tasked to normalize (AST-Norm) the domain content by fixing its latent to a mean prototype. Our simplified adaptation technique is not only clustering-free but also free from complex adversarial alignment. We achieve leading performance against the prior arts on the OCDA scene segmentation benchmarks.
Sarcasm Detection in Arabic Short Text using Deep Learning

Recently, a growing interest among researchers has emerged in discovering ambiguous information in short sarcastic texts in Arabic. Nevertheless, sarcasm is a particularly challenging problem for sentiment analysis algorithms due to its considerable impact on emotions. A short text evaluation can provide important information about a product or service. However, due to the currently small number of sarcastic datasets and their unbalanced nature, it is also important to preprocess data before classification, especially those with dialects. Furthermore, to detect sarcasm, language models must be capable of capturing complicated relationships and ambiguous semantic meanings. In this paper, we attempt to detect sarcasm in Arabic text using a large pre-trained language model (BERT). Therefore, a new dataset for the "iSarcasmEval" shared task is examined in this paper. Preprocessing of the dataset is performed first. Moreover, the data is balanced by applying both Random Swap and Random Deletion, which are both data augmentation techniques. Following that, two transformer-based models, MARBERT and AraBERT, were used to analyze the data. Experimental results reveal that the MARBERT model outperforms the AraBERT model in this dataset.
Offline Handwritten Signature Forgery Verification Using Deep Learning Methods

Offline signature verification is one of the most challenging tasks in biometric authentication. Despite recent advances in this field using image recognition and deep learning, there are many remaining things to be explored. The most recent technique, which is Siamese convolutional neural network, has been used a lot in this field and has achieved great results. This paper presents an architecture that combines the power of Siamese Triplet CNN and a fully-connected neural network for binary classification to automatically verify genuine and forgery signatures even if the forged signature is highly skilled. On the challenging public dataset for signature verification BHSig260, the proposed model can achieve a low False Acceptance Rate = 13.66, which is slightly better than the reference model. Based on this approach, the one-shot learning should make it possible to determine if the input image is genuine or fraudulent just from one base image. Therefore, our model is expected to be extremely suitable for practical problems, such as banking systems or mobile authentication applications, in which the amount of data for each identity is limited in quantity and variety.