Multi-class Categorization of Reasons behind Mental Disturbance in Long Texts

Motivated with recent advances in inferring users' mental state in social media posts, we identify and formulate the problem of finding causal indicators behind mental illness in self-reported text. In the past, we witness the presence of rule-based studies for causal explanation analysis on curated Facebook data. The investigation on transformer-based model for multi-class causal categorization in Reddit posts point to a problem of using long-text which contains as many as 4000 words. Developing end-to-end transformer-based models subject to the limitation of maximum-length in a given instance. To handle this problem, we use Longformer and deploy its encoding on transformer-based classifier. The experimental results show that Longformer achieves new state-of-the-art results on M-CAMS, a publicly available dataset with 62\% F1-score. Cause-specific analysis and ablation study prove the effectiveness of Longformer. We believe our work facilitates causal analysis of depression and suicide risk on social media data, and shows potential for application on other mental health conditions

Impact of employee engagement activities and organizational culture on job satisfaction on employees

Purpose- The two main factors that affect how satisfied employees are at work are Organizational culture and employee engagement. Employee engagement is a popular concept for inspiring employees. It concerns workers' good attitudes regarding the company. These initiatives foster a positive company culture that increases workers' job satisfaction. The influence of three fundamental components—employee engagement, organizational culture, and job satisfaction—and how they relate to one another are examined in this study. Design/methodology/approach- 84 employees of different organizations of Bhopal were considered who responded through google form – questionnaire. An analysis of the responses was done using a structural equation model. Findings – All of the hypotheses were statistically supported. The result says employee engagement activities create a good organizational work culture providing more satisfaction to employees

Analysing QBER and secure key rate under various losses for satellite based free space QKD

Quantum Key Distribution is a key distribution method that uses the qubits to safely distribute one-time use encryption keys between two or more authorised participants in a way that ensures the identification of any eavesdropper. In this paper, we have done a comparison between the BB84 and B92 protocols and BBM92 and E91 entanglement based protocols for satellite based uplink and downlink in low Earth orbit. The expressions for the quantum bit error rate and the keyrate are given for all four protocols. The results indicate that, when compared to the B92 protocol, the BB84 protocol guarantees the distribution of a higher secure keyrate for a specific distance. Similarly, it is observed that BBM92 ensures higher keyrate in comparison with E91 protocol

Constitutional Perspective Of ‘Right To Vote’ In India: A Critical Study

The right to vote is one of the major foundational roots of democracy which is the legal right and human right in India. The people representatives are elected by vote of people in India. In this regard, several judgments delivered by the hon’ble Indian Judiciary to prevent the abuse of right to vote in India. But still many controversial issues are existent under some exceptions. The research article deals with the constitutional perspective of right to vote in India.  &nbsp

Explainable Causal Analysis of Mental Health on Social Media Data

With recent developments in Social Computing, Natural Language Processing and Clinical Psychology, the social NLP research community addresses the challenge of automation in mental illness on social media. A recent extension to the problem of multi-class classification of mental health issues is to identify the cause behind the user's intention. However, multi-class causal categorization for mental health issues on social media has a major challenge of wrong prediction due to the overlapping problem of causal explanations. There are two possible mitigation techniques to solve this problem: (i) Inconsistency among causal explanations/ inappropriate human-annotated inferences in the dataset, (ii) in-depth analysis of arguments and stances in self-reported text using discourse analysis. In this research work, we hypothesise that if there exists the inconsistency among F1 scores of different classes, there must be inconsistency among corresponding causal explanations as well. In this task, we fine tune the classifiers and find explanations for multi-class causal categorization of mental illness on social media with LIME and Integrated Gradient (IG) methods. We test our methods with CAMS dataset and validate with annotated interpretations. A key contribution of this research work is to find the reason behind inconsistency in accuracy of multi-class causal categorization. The effectiveness of our methods is evident with the results obtained having category-wise average scores of $81.29 \%$ and $0.906$ using cosine similarity and word mover's distance, respectively

Effectiveness Of Innovative Approach Of Ergon® IASTM Technique In Musculoskeletal Disorder: A Systemic Review

Introduction: One of the most popular IASTM methods, the Ergon® IASTM Technique, combines both passive and active soft tissue manipulation with specific clinical tools in order to treat soft tissue constraints and enhance tissue flexibility, joint range of motion, and patient functionality. There is some evidence to suggest that the approach may help with fascia mobilization, the breakdown and absorption of scar tissue, and enhanced tissue repair. There are currently no systematic reviews that have looked precisely at how Ergon® IASTM affects musculoskeletal disorders. Methods: An overview analysis was done. The databases and search engines used to compile this data include PubMed, Google Scholar, and Research Portal. On the reference list of the included studies, a manual search was conducted. Individually or in combination, the search phrases included instrument, assisted, augmented, soft-tissue, mobilization, Ergon, and technique. Studies were chosen based on inclusion criteria evaluated using the PEDro Rating Scale. The potential of bias was examined within 20 research. Results: It is found that implementation of Ergon® IASTM leads to positive improvement in various musculoskeletal pain. Conclusions: This summary of the systematic review provides the comprehensive systematic synthesis of evidence regarding the impact of Ergon IASTM technique on reducing symptoms of musculoskeletal pain. The findings from this review provide supervision to clinicians and researchers for evidence-based selection of ERGON® IASTM for treating various condition

Data Augmentation for Mental Health Classification on Social Media

The mental disorder of online users is determined using social media posts. The major challenge in this domain is to avail the ethical clearance for using the user generated text on social media platforms. Academic re searchers identified the problem of insufficient and unlabeled data for mental health classification. To handle this issue, we have studied the effect of data augmentation techniques on domain specific user generated text for mental health classification. Among the existing well established data augmentation techniques, we have identified Easy Data Augmentation (EDA), conditional BERT, and Back Translation (BT) as the potential techniques for generating additional text to improve the performance of classifiers. Further, three different classifiers Random Forest (RF), Support Vector Machine (SVM) and Logistic Regression (LR) are employed for analyzing the impact of data augmentation on two publicly available social media datasets. The experiments mental results show significant improvements in classifiers performance when trained on the augmented data.Comment: 1