Miracle-dz/UniFAR-FTUs huggingface dataset:
ANALYSIS OF JANUARY EFFECT ON BIG STOCK COMPANIES AND SMALL STOCK COMPANIES AT INDONESIA STOCK EXCHANGE

January Effect is one of market anomaly where the stock returns in January are higher than other months. Some of causes the January Effect are the actions of investor who carry out tax-loss selling and windows dressing. In addition, investors have different views to choose stocks, based on market capitalization dan risk. This study is purposed to find the January Effect in the Indonesia Stock Exchange and January Effect on small company stock is stronger than large company stock. The data is normally distributed using the One-Sample KolmogorovSmirnov test. The test using the OLS method with dummy variable at five percent significance level. By using a sample of 30 large company stocks and 30 small company stocks based on market capitalization during period 2013-2017, the result of this study found a January Effect in the Indonesia Stock Exchange. But the January Effect doesn’t occur on small company stock, except on large company stock during that periode.
Using Multiple Deep Neural Networks Platform to Detect Different Types of Potential Faults in Unmanned Aerial Vehicles

Many researchers developed new algorithms to predict the faults of unmanned aerial vehicles (UAV). These algorithms detect anomalies in the streamed data of the UAV and label them as potential faults. Most of these algorithms consider neither the complex relationships among the UAV variables nor the temporal patterns of the previous instances, which leaves a potential opportunity for new ideas. A new method for analyzing the relationships and the temporal patterns of every two variables to detect the potentially defected sensors. The proposed method depends on a new platform, which is composed of multiple deep neural networks. The method starts by building and training this platform. The training step requires reshaping the dataset into a set of subdatasets. Each new subdataset is used to train one deep neural network. In the testing phase, the method reads new instances of the UAV testing dataset. The output of the algorithm is the predicted potential faults. The proposed approach is evaluated and compared it with other well-known algorithms. The proposed approach showed promising results in predicting different kinds of faults.
UV/H2O2 Degradation and Toxicity Reduction of Textile Azo Dyes: Remazol Black-B, a Case Study

Abstract The treatment of textile dyeing and finishing wastewaters by conventional methods, such as biological, physical and chemical processes or combinations thereof, are in common practice but have unique drawbacks, namely, a number of refractory chemicals in the effluents and difficulty in the complete removal of color. The study described herein is an investigation of an advanced oxidation treatment of a common azo dye, Remazol Black-B, widely used in textile dyeing and finishing operations. The investigation was focused on monitoring the degradation and toxicity reduction of an aqueous solution of the dye up to 30 min of treatment by a UV/H2O2 oxidation system. The dye was completely decolorized and converted to colorless intermediates during this treatment, while only 44 % of the organic carbon was mineralized. The toxicity of the tested dye solution increased during the first few minutes of treatment and decreased rapidly thereafter, ending up in a 68 % reduction after 20 min of treatment. The influence of the initial H2O2 concentration on the rate of dye removal was found to be inhibitory beyond the “effective” H2O2 dose. The correlation was statistically modeled by a non-linear regression technique.
Research on the Quality of Internal Governance and Accounting Information Disclosure

This paper focuses on the effect of ownership structure and management structure on the quality level of accounting information disclosure. Through empirical research methods, this paper applied the Shenzhen stock exchange information disclosure evaluation results as concerns the quality of accounting information disclosure standards, and nine years’ data of 2010-2018 as the research range. The empirical results show that the degree of equity concentration, management shareholding, institutional shareholding and the quality of accounting information disclosure are significantly correlated. The independence of the board of directors, the power characteristics of the management and the characteristics of the managers indicate a correlation with the quality of accounting information disclosure. This research found no significant relationship between the duality of the chairman's duty and the quality of accounting information disclosure. 1 Research background The quality of accounting information disclosure of listed companies has always been an important issue of concern to shareholders, because of the great impact on the effectiveness of the securities market. The domestic research on corporate governance mechanism and information disclosure quality has gradually improved and matured in recent years. This article aims to reveal the actual laws existing in my country's market by analysing a large sample of data. 2 Research hypotheses 2.1 Shareholding structure The shareholding structure reflects the situation and variety of shareholders of the listed company controlling the company, which is closely related to the company's organizational structure and governance system. (1) Equity concentration. The relationship between the concentration of equity and information disclosure is based on the analysis of the principal-agent problem: research of La Porta et.al (1998) [1] on the protection of creditors and the mechanism with shareholders found that the concentration of equity in listed companies and the quality of financial
Preserving Consistency for Liquid Knapsack Voting

Liquid Democracy (LD) uses transitive delegations in voting. In its simplest form, it is used for binary decisions, however its promise holds also for more advanced voting settings. Here we consider LD in the context of Participatory Budgeting (PB), which is a direct democracy approach to budgeting, most usually done in municipal budgeting processes. In particular, we study Knapsack Voting, in which PB voters approve projects, such that the sum of costs of projects each voter approves must respect the budget limit. We observe possible inconsistencies, as the cost of voter-approved projects may go over the budget limit after resolving delegations. We offer ways to overcome them by studying the computational complexity of updating as few delegations as possible to arrive— after following all project delegations—to a consistent profile.
Comparison of the two routes of video endoscopic inguinal lymphadenectomy in vulvar cancer: a systematic review and a single-center experience

Background Video endoscopic inguinal lymphadenectomy (VEIL) has fewer complications than open surgery for vulva cancer. No high-level evidence comparing the efficacy and safety of the two routes of VEIL (VEIL-H and VEIL-L) is available. Methods We performed a comprehensive literature search for reports on VEIL for vulva cancer from 2000 to 2020 and reviewed our experience. Data extraction includes patients’ characteristics, perioperative indicators, postoperative complications, and cancer recurrence. Results A total of 11 eligible studies were included in the systematic review. Nine studies included only one route of VEIL, and the other two studies directly compared the two routes of VEIL. The average operation time of the VEIL-L group was 85 (range, 33–180.12) minutes, and that of the VEIL-H group was 112 (range, 35–170.79) minutes. The average blood loss of the VEIL-L group was 9 (range, 5–30) mL, and that of the VEIL-H group was 96 (range, 5.5–214.8) mL. The average number of intraoperatively removed lymph nodes in the VEIL-L group was 9 (range, 7.5–13.2), and that in the VEIL-H group was 14 (range, 9.5–16). No significant difference was observed in the cancer recurrence rate between the two groups. We found similar results in our cohort study. Conclusions The operation time, intraoperative blood loss, and time of drainage of the VEIL-L procedure seemed less than those of the VEIL-H procedure. VEIL-L and VEIL-H were equivalent in the efficacy of lymph node dissection, surgery-related complications, and cancer recurrence rate.
A Survey on Cloud Attack Detection using Machine Learning Techniques

Cloud concepts such as resource sharing, outsourcing, and multi-tenancy create significant challenges to the security community. Also, trusted third party and web technologies based cloud service provisioning arises new security threats in the cloud environment. Cloud security has become a vital research area with new security models, protocols, and policies in recent years. Despite the fact, the existing cloud security research still faces the shortcomings in improving the detection accuracy and detecting the new or unknown attacks in the cloud. To address the constraints above, many security researchers have focused on developing cloud security models with the assistance of the machine learning methods. Machine learning techniques play a significant role in automatically discovering the potential difference between legitimate and malicious data with high accuracy. The deep learning is a branch of machine learning that provides remarkable performance in cloud security issues. This survey provides a comprehensive study of cloud security concerns, traditional security measures, and machine learning-based security solutions in the cloud environment. Initially, it identifies cloud vulnerabilities and presents state-of-the-art methods to control security threats, weaknesses, and attacks. This work also reviews the security solutions developed by machine learning and deep learning techniques for the cloud environment.
Leveraging Social Interactions in Human-Agent Decision-Making

Through social interactions, humans and machines can express their intents, acknowledge each other’s, and coordinate with one another to arrive at a joint decision. These interactions can also help achieve goals beyond just task performance. They can help build trust between interactants, which is crucial for effective collaborations. My work aims to i) develop frameworks for social interaction in human-agent joint decision-making and ii) implement artificial agents that improve joint decisions while considering the social and interpersonal implications of their actions. In this extended abstract, I describe past and current work and propose future directions for my dissertation.
Designing a Teaching Pattern from Students’ Lived Experiences of the Teaching Process in Universities of Iran (Technical and Engineering Disciplines)

Designing a Teaching Pattern from Students' Lived Experiences of the Teaching Process in Universities of Iran (Technical and Engineering Disciplines) Batul Turki Nisiani, Narges Keshtiaray, Farzad Sharifian 1. PhD Student of Planning Curriculum, Department of Educational Sciences, Islamic Azad University, Isfahan (Khorasgan) Branch, Isfahan, Iran. 2. Associate Professor, Department of Educational Sciences, Islamic Azad University, Isfahan (Khorasgan) Branch, Isfahan, Iran. 3. Professor, Department of Educational Sciences, Monash University, Melbourne, Ausralia.
Development of Mechanism for Finger Prosthesis

Nowadays prosthetic fingers provide not only a cosmetic solution to the problem, but mainly they provide function. 3D printing is an ideal technology for the manufacturing of low-cost prostheses, especially the upper limb prosthetics. The advantages offered by this technology make it possible to increase the level of creativity, personalization and at the same time provide space for new variants of control mechanisms regarding movement efficiency. The aim of the paper is to present variant design of a finger prosthesis manufactured by 3D printing, which have been developed and tested at the Department of Biomedical Engineering and Measurement, Faculty of Mechanical Engineering, Technical University of Kosice. Those variants can flex in specified areas and copy the function of a real finger as much as possible. To ensure flexion in the joints, and at the same time achieve sufficient gripping force is a very current problem. This article offers the possibility of solving this problem, and gives evaluation of individual print variants. The results are very useful in the hand prostheses designing process and provide suitable economical solutions without reduction to the effectiveness of the prosthesis.
CAD-CAM endodontic posts: literature review

In the actual times, with so many rehabilitation techniques and endodontic posts in the market, there are many doubts about which one is the best, the most cost-effective, the one that has more resistance among other questions. This work aimed to compare the intraradicular restorative treatments that involve the use of prefabricated posts and posts made with CAD-CAM, through a literature review in order to reinforce these procedures. Articles and other literary sources with interrelation to CAD-CAM and endodontic posts were surveyed through search in Pubmed, Scielo, Scopus, Lilacs and Google Scholar databases. The keywords: endodontic posts, CAD-CAM, prefabricated posts, customizable posts, fiberglass, carbon fiber were used. More than 100 articles were found, and after reading the abstract, 50 were selected. After reading, 26 were selected for the review. After reading, analyzing and interpreting the scientific studies, it was possible to verify that the CAD-CAM endodontic posts have satisfactory results, although there are few specific studies on them compared with the metallic cast posts and prefabricated posts.
Parallel Curriculum Experience Replay in Distributed Reinforcement Learning

Distributed training architectures have been shown to be effective to improve the performance of reinforcement learning algorithms. However, their performances are still poor for problems with sparse rewards, e.g., the scoring task with or without goalkeeper for robots in RoboCup soccer. It is challenging to solve these tasks in reinforcement learning, especially for those that require combining high-level actions with flexible control. To address these challenges, we introduce a distributed training framework with parallel curriculum experience replay that can collect different experiences in parallel and then automatically identify the difficulty of these subtasks. Experiments on the domain of simulated RoboCup soccer show that, the approach is effective and outperforms existing reinforcement learning methods
PROCESSES OF LONG-TERM WORK DISABILITY AND SOCIOECONOMIC DISPARITIES

Due to population ageing and the increasing dependency ratio, maintaining a sufficient workforce has become an important national policy target in Finland. As the population shrinks, prolonging working careers becomes one of the key factors in sustaining a sufficient workforce. Work disability, especially when it is long-term, is one of the main reasons for leaving the labour market early. Previous studies have identified many risk factors associated with long-term work disability, of which one of the most important is low socioeconomic status. However, to date very little research has been conducted on the mechanisms underlying the risk of long-term work disability and the associated socioeconomic disparities. This study examines the process leading to the development of a long-term work disability, and the socioeconomic differences in this process. This dissertation consists of four studies that examine two aspects of long-term work disability: the transition to sickness absence and disability retirement. Further, they examine the socioeconomic disparities that relate to these transitions arising from family background, sickness absence development and psychosocial work environment. The studies use longitudinal register-based datasets on the Finnish or Swedish population over a fifteen years period from 1999–2014, with national representative samples. The first article is based on a 10 % population sample of Finnish residents and uses a discrete-time event history model to examine the association between educational attainment and family background on disability retirement in young adulthood. The second and third articles use a 70 % population sample of Finnish residents linked to data on sickness benefits and disability retirement. These articles use a Cox proportional hazards model to study the role of sickness absence length and diagnosis as predictors of disability retirement in different occupational classes among women and men. The fourth article is based on a full population data of Swedish residents linked with data from labour market surveys and sickness insurance. The article examines the relationship between psychosocial work environment, or more precisely, the occupation-specific job demands and job control in occupations, and the risk of accumulating of sickness absence and disability pension days over time using group-based trajectory analysis and multinomial regression analysis.
A Collaborative Platform for Identifying Context-Specific Values

Value alignment is a crucial aspect of ethical multiagent systems. An important step toward value alignment is identifying values specific to an application context. However, identifying context-specific values is complex and cognitively demanding. To support this process, we develop a methodology and a collaborative web platform that employs AI techniques. We describe this platform, highlighting its intuitive design and implementation
Mechanics-aware deformation of yarn pattern geometry

Triangle mesh-based simulations are able to produce satisfying animations of knitted and woven cloth; however, they lack the rich geometric detail of yarn-level simulations. Naive texturing approaches do not consider yarn-level physics, while full yarn-level simulations may become prohibitively expensive for large garments. We propose a method to animate yarn-level cloth geometry on top of an underlying deforming mesh in a mechanics-aware fashion. Using triangle strains to interpolate precomputed yarn geometry, we are able to reproduce effects such as knit loops tightening under stretching. In combination with precomputed mesh animation or real-time mesh simulation, our method is able to animate yarn-level cloth in real-time at large scales.
Improved Loss Function for Defect Detection of Mobile Phone Screen

Due to the excellent feature learning and representation capabilities of deep learning, the method based on deep learning for mobile phone screen defect detection is gradually being applied to industrial detection. Nowadays, the cross-entropy loss commonly used in deep learning only focuses on the differences between different classes with less intra-class differences. It leads to poor discriminantive ability of the model when the similarity between training samples is high. The contrastive loss reduces the intra-class variations and can distinguish between similar objects from different classes. Given the above analysis, we propose a Siamese network for mobile phone screen defect detection (SMSDD) using combined contrastive loss and cross-entropy loss, thereby enhancing the discriminative ability of model. Numerical results show that SMSDD achieves comparable performance.
Atorvastatin prevents the development of diabetic neuropathic nociception by possible involvement of nitrergic system.

AIMS Diabetic neuropathy has been identified as a common complication caused by diabetes. However, its pathophysiological mechanisms are not fully understood yet. Statins, also known as HMG-CoA reductase inhibitors, alleviate the production of cholesterol. Despite this cholesterol-reducing effect of statins, several reports have demonstrated their beneficial properties in neuropathic pain. In this study, we used streptozotocin (STZ)-induced diabetic model to investigate the possible role of nitric oxide (NO) in the antineuropathic-like effect of atorvastatin. METHODS Diabetes was induced by a single injection of STZ. Male rats orally received different doses of atorvastatin for 21 days. To access the neuropathy process, the thermal threshold of rats was assessed using hot plate and tail-flick tests. Moreover, sciatic motor nerve conduction velocity (MNCV) studies were performed. To assess the role of nitric oxide, N(G)-nitro-L-arginine methyl ester (L-NAME), aminoguanidine (AG), and 7-nitroindazole (7NI) were intraperitoneally administered along with some specific doses of atorvastatin. KEY FINDINGS Atorvastatin significantly reduced the hyperalgesia in diabetic rats. L-NAME pretreatment with atorvastatin showed the antihyperalgesic effect, suggesting the possible involvement of the NO pathway in atorvastatin protective action. Furthermore, co-administration of atorvastatin with AG and 7NI resulted in a significant increase in pain threshold in diabetic rats. SIGNIFICANCE Our results reveal that the atorvastatin protective effect on diabetic neuropathy is mediated at least in a part via the nitric oxide system.
A Study on Lower Extremity Malalignment and Its Correlation to Q-Angle in State Level Athletes of Odisha

Introduction: Q-angle is considered to be the most important variable among the lower extremity alignment variables. The present study was undertaken to determine the anatomical factors, those which have impact on the magnitude of Q-angle and to examine sex differences in lower extremity alignment of state level athletes. Methods: 100 state level athletes (50 male and 50 female) of age group 18-27 years were selected purposively from various sports academies of Odisha for study. Five anthropometric variables, i.e. height, weight, Body Mass Index, total leg length, lower leg length; and six lower extremity alignment variables, i.e. tibiofemoral angle, femur anteversion, Q-angle, genu recurvatum, tibial torsion, navicular drop were measured on each subject following standard techniques. Results: The present study depicted that statistically significant differences (p≤0.001) were observed between male and female state level athletes in anthropometric variables, viz., height, weight, right total leg length, left total leg length, right lower leg length, left lower leg length and; in lower extremity alignment variables, viz., right femoral anteversion, left femoral anteversion, right Q-angle, left Q-angle, right tibial torsion, left tibial torsion. However, statistically significant difference (p<0.05) were seen in right tibiofemoral angle, right navicular drop and left navicular drop. Significant positive correlation (p≤0.001) of right Q-angle was noted with right femoral anteversion, right tibial torsion and right navicular drop. Conclusion: It may be concluded that femoral anteversion, tibial torsion and navicular drop have significant impact on the magnitude of Q-angle; and the sex difference may influence the static lower extremity alignments.
PhD position in Computer Vision and Machine Learning Learning the geometric signature of CAD models

on basic level of details. One possibility could be to learn a multimodal style distribution similarly to [4], but adapted to CAD models. Another possibility could be also to learn a distribution of geometric differences between two models, such as the approach proposed in [2] for characterizing shapes only. Another issue to address will be to create meaningful training sets from a small amount of CAD models.  Designing priors for the 3D reconstruction of objects. The candidate will also investigate how to utilize this learned knowledge for reconstructing objects from 3D data measurements with a specific CAD style. In particular, the candidate will propose strategies to convert this knowledge into prior functions that will be integrated into existing reconstruction pipelines such as [5]. Such functions must be able to measure the similarity between a 3D model and a given geometric signature. It will be also interesting to extend the approach to generative models to create objects with new geometric signatures, in particular for applications in design.