Miracle-dz/UniFAR-FTUs huggingface dataset:
The Influence of Bid-Ask Spread, Market Value, Variance Return, and Stock Trading Volume on the Stock Holding Period on the SRI-KEHATI Index listed on the Indonesia Stock Exchange in 2018-2022

The increasing awareness of society to play a role in preserving the environment has encouraged more and more investors to choose environmentally friendly stocks. One of the environmentally friendly stock indexes in Indonesia is the SRI-KEHATI Index. This research aims to determine the factors that influence the holding period of SRI-KEHATI Index stocks. Thus, this research examines the influence of bid-ask spread, market value, return variance, and stock trading volume on the stock holding period. The research focuses on the SRI-KEHATI Index listed on the Indonesia Stock Exchange in 2018-2022. The research uses quantitative methods with descriptive and associative approaches. The population in this study was 41 companies. Sampling used a purposive sampling technique with a total sample of 13 companies so 65 observation data were obtained. The data analysis technique uses panel data regression analysis. Partial test results show that the bid-ask spread and return variance do not affect the stock holding period. Market value and stock trading volume positively affect the stock holding period. Simultaneous testing shows that bid-ask-spread, market value, return variance, and stock trading volume impact the stock holding period.
Favoring One Among Equals - Not a Good Idea: Many-to-one Matching for Robust Transformer based Pedestrian Detection

We investigate the reasons for lower performance of transformer based pedestrian detection models compared to convolutional neural network (CNN) based ones. CNN models generate dense pedestrian proposals, refine each proposal individually, and follow it up with non-maximal-suppression (NMS) to generate sparse predictions. In contrast, transformer models select one proposal per ground-truth (GT) pedestrian box and backpropagate positive gradient from them. All other proposals, many of them highly similar to the selected ones, are passed negative gradient. Though this leads to sparse predictions, obviating the need of NMS, the arbitrary selection of one among many similar proposals, hinders effective training, and lower accuracy of pedestrian detection. To mitigate the problem, instead of commonly used Kuhn-Munkres matching algorithm, we propose Min-cost-flow based formulation, and incorporate constraints such as, each ground truth box is matched to atleast one proposal, and many equally good proposals can be matched to a single ground truth box. We propose first transformer based pedestrian detection model incorporating our matching algorithm. Extensive experiments reveal that our approach achieves a miss rate (lower is better) of 3 . 7 / 17 . 4 / 21 . 8 / 8 . 3 / 2 . 0 on Eurocity / TJU-traffic / TJU-campus / Cityperson / Caltech datasets compared to 4 . 7 / 18 . 7 / 24 . 8 / 8 . 5 / 3 . 1 by the current SOTA. Code is available at https://ajayshastry08.github. io/flow_ matcher
The Importance of Region of Origin in Sparkling Wines: An International Analysis of Consumers’ Perception

Consumers’ perception of the region of origin represents a critical issue for building a valuable and sustainable business in the wine sector. The aim of this study is to investigate the role of the region of origin in Italian sparkling wines, comparing consumers’ perception of emerging wines with a designation of origin in regions not traditionally associated with sparkling wine production (Abruzzo and Romagna), with an affirmed brand (Prosecco) and with generic Italian sparkling wine. The three most important markets for Italian sparkling wines (Italy, the USA, and the UK) are considered. The importance of the region of origin is analyzed within a set of wine attributes, including color and sugar content, applying the conjoint analysis technique to simulate realistic market conditions, offering multiple options for wine attributes. There is no cumulative effect of the perception of wines with a Protected Designation of Origin in the three markets since consumers who positively perceive a specific designation do not extend this positive perception to others in comparison with generic sparkling wine. Positive and negative utilities associated with each designation of origin are calculated and used to discriminate consumers according to socio-demographic variables, consumers’ behavior, and attitudes.
Hyena: Balancing Packing, Reuse, and Rotations for Encrypted Inference

—Deep neural networks are widely used in a range of commercial services. Many of these services are hosted on the cloud, requiring users to send their personal data to the cloud. This, in turn, exposes the user’s private and sensitive data to several third parties. To address this problem, Homomorphic Encryption (HE) has been introduced, where the user encrypts their data before sending it to the cloud; the cloud performs operations on encrypted data and returns a ciphertext that the user must then decrypt. While this approach keeps user data private, it demands orders of magnitude more computation and data movement. It is, therefore, imperative to design hardware/software techniques to lower the overheads when executing AI services under Homomorphic Encryption schemes. In this paper, we consider a range of HE implementations for AI inference and address the key bottlenecks in state-of-the-art frameworks. We start by making the case for a hybrid HE and Multi-Party Computation (MPC) scheme that is more practical than pure Fully HE. This paper introduces new techniques at various levels: (i) we introduce new data packing techniques that result in lower data movement, (ii) we introduce new dataflows that increase reuse and reduce other costly HE operations (rotations, key switching, NTT conversion), (iii) we evaluate Hyena on a balanced pipelined architecture that efficiently handles the above primitives. The resulting framework, Hyena (new packing + dataflow), achieves better performance and energy than several packing baselines. Compared to the widely used Channel-packing, Hyena is 38 × faster and achieves 162 × lower energy consumption, with an overall ResNet20 inference end-to-end latency of 11 . 4 ms , using a 163 mm 2 accelerator dissipating 16 . 75 W .
Recurrent Semi-supervised Classiﬁcation and Constrained Adversarial Generation with Motion Capture Data

We explore recurrent encoder multi-decoder neural network architectures for semi-supervised sequence classiﬁcation and reconstruction. We ﬁnd that the use of multiple reconstruction modules helps models generalize in a classiﬁ-cation task when only a small amount of labeled data is available, which is often the case in practice. Such models provide useful high-level representations of motions allowing clustering, searching and faster labeling of new sequences. We also propose a new, realistic partitioning of a well-known, high quality motion-capture dataset for better evaluations. We further explore a novel formulation for future-predicting decoders based on conditional recurrent generative adversarial networks, for which we propose both soft and hard constraints for transition generation derived from desired physical properties of synthesized future movements and desired animation goals. We ﬁnd that using such constraints allow to stabilize the training of recurrent adversarial architectures for animation generation.
EFFECTIVENESS OF 24 WEEKS OF WALKING WITH RESTRICTED BLOOD FLOW ON MUSCLE ACTIVATION AND STRENGTH IN OVERWEIGHT ELDERLY WOMEN WITH OSTEOPOROSIS: A RANDOMIZED CLINICAL TRIAL

The present study aimed to analyze the effect of 24 weeks of AT with RFS on muscle activation and strength in overweight elderly women with osteopenia/osteoporosis. Thirty elderly women [66 (5) years] were randomly assigned to one of three interventions: CAM (moderate-intensity walking), CAM+RFS (low-intensity walking with RFS), or RFS (RFS alone). Muscle activation (EMGS) and knee flexor and extensor strength were measured, respectively, by surface electromyography and dynamometry, pre-intervention and after 12 and 24 weeks. Knee extensor EMGs and knee flexor strength increased similarly across all interventions at 24 weeks (p<0.05). A moderate effect size (ES, Cohen’s d) was observed in knee extensor EMGs (ES=0.65) for CAM+RFS compared to CAM. The ESs for knee flexor strength were −0.86 and −0.69, respectively for RFS and CAM+RFS compared to CAM. Therefore, interventions based on the RFS method showed similar strength gains, however, elderly women can achieve long-term neuromuscular gains over 24 week
Prospective mathematics teachers’ reflections on their strategies for solving a simple combination problem

This article aims to analyze and highlight prospective mathematics teachers' reflections on the strategies used to solve a simple combination problem. We carried out a qualitative study in the context of theoretical classes in an undergraduate course on the theory of problem-solving. To this end, 11 students from Paraná solved a simple combination problem. The results showed that the participants believed the diagram and table strategies were the same. After the discussions, they understood that the strategies have their own representations but did not mention those linked to execution. We conclude that the reflections of the mathematics undergraduates provided an understanding of the representations of the strategies and that they began to value them when working in the classroom.
Effects of Collaborative Strategic Reading (CSR) on EFL Learners’ Reading Comprehension, Reading Motivation, and Metacognitive Awareness

This study focused on investigating the effects of collaborative strategic reading (CSR) on English as a foreign language (EFL) learners’ reading comprehension, second language (L2) reading motivation, and metacognitive awareness. Following a quasi-experimental design, 48 Iranian EFL learners were selected from four intact language institute classes. Two of these classes were considered experimental groups (EGs), and two were regarded as control groups (CGs). Then, all four classes received the three pretests of the study (Reading Comprehension pretest, L2 Reading Motivation questionnaire, and metacognitive awareness questionnaire). Next, the treatment phase of the study began and all the groups received reading comprehension instruction. In the EGs, the teacher conducted the essential elements of cooperative learning through CSR strategies (preview, click & clunk, get the gist, and wrap up). On the other hand, CG learners received traditional reading comprehension classes with no group work. After the treatment, all four groups received the three posttests. The results of three one-way ANCOVAs revealed that CSR was significantly more effective than traditional instruction in improving EFL learners’ reading comprehension. In addition, it was found that CSR was significantly effective in improving EFL learners’ reading motivation and metacognitive awareness. The findings of the study can be utilized by language practitioners, materials, and curriculum developers to consider EFL students’ needs and goals in L2 reading.
EMPOWERMENT LEVEL AMONG RURAL WOMEN OF SINDH: A SOCIOLOGICAL ANALYSIS

Women empowerment is an important factor in the growth of any country. It means equal social standing of women to their male counterparts in any society. However, even today, in the 21st century, women’s empowerment is a global issue and is prevalent in most societies, particularly in patriarchal societies. Since Pakistan is a patriarchal society, women in Pakistan are subjected to marginalization and social exclusion, especially in rural areas. This research work studied the complex nature of socio-economic factors and cultural norms that affect the empowerment level of rural women in Sindh, Pakistan, and what interventions can be implemented to improve their social, economic, psychological conditions, and political participation as well as decision-making power with an aim to achieve the following objectives: i) to study the socio-economic background of rural women; ii) to determine the social empowerment levels of rural women; iii) to determine the psychological empowerment; iv) to determine the economic empowerment levels; and v) to determine the political empowerment levels among the rural women of Sindh, province of Pakistan. The present study was conducted using a quantitative survey methodology with a close ended five-point Likert scale questionnaire focusing rural women of Sindh. The research questionnaire consisted of indicators related to socio-economic status, cultural norms, and empowerment level of rural women. The surveyed data was then analyzed using IBM SPSS software. Results drawn under the study inferred that the rural women of Sindh remain highly unempowered socially, psychologically, economically as well as politically with a weak socio-economic background. The study further recommends strong policymaking along with the introduction of various schemes in support of gender security by provincial and federal governments and strict measures taken for the implementation of policies.
Microstructure and physicochemical properties reveal differences between high 1 moisture buffalo and bovine Mozzarella cheeses

31 Mozzarella cheese is a classical dairy product but most research to date has focused on low 32 moisture products. In this study, the microstructure and physicochemical properties of both 33 laboratory and commercially produced high moisture buffalo Mozzarella cheeses were 34 investigated and compared to high moisture bovine products. Buffalo and bovine Mozzarella 35 cheeses were found to significantly differ in their microstructure, chemical composition, 36 organic acid and proteolytic profiles but had similar hardness and meltability. The buffalo 37 cheeses exhibited a significantly higher ratio of fat to protein and a microstructure containing 38 larger fat patches and a less dense protein network. Liquid chromatography mass spectrometry 39 detected the presence of only  -casein variant A2 and a single  -lactoglobulin variant in buffalo 40 products compared to the presence of both  -casein variants A1 and A2 and  -lactoglobulin 41 variants A and B in bovine cheese. These differences arise from the different milk composition 42 and processing conditions. The differences in microstructure and physicochemical properties 43 observed here offer a new approach to identify the sources of milk used in commercial cheese 44 products. 45 46
White Blood Cell/Mean Platelet Volume Ratio as a Predictor of Long-Term Outcomes but Not Coronary Artery Disease Severity in Non-ST Elevation Myocardial Infarction Patients

Background White blood cell count (WBC) and mean platelet volume (MPV) have been shown to be hematologic parameters of prognostic significance in acute coronary syndrome. We sought to determine the relationship between the WBC/MPV ratio (WMR), coronary artery disease (CAD) complexity, and long-term clinical outcomes in patients with non-ST elevation myocardial infarction (NSTEMI). Hypothesis WMR has a relationship with the complexity of CAD and long-term clinical outcomes in NSTEMI patients. Methods A total of 289 NSTEMI patients who underwent coronary angiography were divided into two groups according to the median WMR (>970 or ≤970). CAD complexity was assessed with the SYNTAX score. Results The WMR was not associated with the synergy between percutaneous coronary intervention (PCI) with Taxus and cardiac surgery (SYNTAX) score on multivariate linear regression analysis (ß=0.08, 95% CI = -0.76-2.21, p = 0.14). However, it was of prognostic significance on Kaplan-Meier survival analysis in overall patients (log-rank p = 0.03) and in patients with a SYNTAX Score <22 (log-rank p = 0.01). Follow-up data showed that major adverse cardiac events (MACE) (p = 0.02), death (p < 0.001), non-fatal MI (p = 0.03), ischemia-driven revascularization (p = 0.03), and heart failure (p = 0.04) were more frequent in the high WMR group. After adjustment for age, sex, eGFR, troponin levels, and the Global Registry of Acute Coronary Events (GRACE) score in Cox regression models, the association of high WMR with the cumulative incidence of MACE was preserved (overall patients (HR=1.85, 95% CI 1.1-3.12, p=0.02) and patients with a SYNTAX score <22 (HR=2.06, 95% CI 1.15-3.67, p=0.01). Conclusion The WMR was not related to CAD complexity, but it was associated with long-term clinical outcomes in patients with NSTEMI who underwent coronary angiography.
Prediction of abrasive wear and surface hardness of printed parts by SLA technology

In the present study, a prediction of hardness deterioration and abrasive wear was performed through a neural network using artificial intelligence on a material printed in SLA. This article aims to predict the mechanical properties, wear resistance and surface hardness of parts manufactured by SLA stereolithography printing. A full factorial DOE was used to associate the peculiar parameters (print orientation, cure time, layer height) to perform experiments. The mechanical properties were evaluated according to ASTM regulations, with the objective of obtaining feeding data and validation of the predictions of the Taber Wear Index and hardness using an artificial neural network. The experimental results are in good agreement with the measured data with satisfactory prediction errors with a mean square error (MSE) of 0.01 corresponding to abrasive wear using the clear resin and a mean absolute error (MSE) of 0.09 with an R2 of 0.756, the prediction with the neural network with a mean square error (MSE) of 2.47 corresponding to abrasive wear using the tough resin and a mean absolute error (MSE) of 14.3 with an R2 of 0.97. It was shown that the accuracy of the prediction is reasonable, and the network has the potential to be improved if the experimental database for training the network could be expanded. Therefore, wear and hardness mechanical properties can be predicted appropriately with an ANN.
Technological Advances and Market Developments of Solid-State Batteries: A Review

Batteries are essential in modern society as they can power a wide range of devices, from small household appliances to large-scale energy storage systems. Safety concerns with traditional lithium-ion batteries prompted the emergence of new battery technologies, among them solid-state batteries (SSBs), offering enhanced safety, energy density, and lifespan. This paper reviews current state-of-the-art SSB electrolyte and electrode materials, as well as global SSB market trends and key industry players. Solid-state electrolytes used in SSBs include inorganic solid electrolytes, organic solid polymer electrolytes, and solid composite electrolytes. Inorganic options like lithium aluminum titanium phosphate excel in ionic conductivity and thermal stability but exhibit mechanical fragility. Organic alternatives such as polyethylene oxide and polyvinylidene fluoride offer flexibility but possess lower ionic conductivity. Solid composite electrolytes combine the advantages of inorganic and organic materials, enhancing mechanical strength and ionic conductivity. While significant advances have been made for composite electrolytes, challenges remain for synthesis intricacies and material stability. Nuanced selection of these electrolytes is crucial for advancing resilient and high-performance SSBs. Furthermore, while global SSB production capacity is currently below 2 GWh, it is projected to grow with a >118% compound annual growth rate by 2035, when the potential SSB market size will likely exceed 42 billion euros.
Thyroglossal cyst – A retrospective analysis in a tertiary care center

Background: Thyroglossal duct cyst (TGDC), one of the most common congenital neck lesions, is due to the persistence and dilatation of remnants of an epithelial tract, formed during migration of the thyroid in embryogenesis. Aims and Objectives: The objective was to study the location of TGDC in different age and sex groups and variations in presentation. Materials and Methods: A retrospective study was carried out in the Department of ENT at Assam Medical College and Hospital, Dibrugarh from July 2018 to June 2023. The diagnosis was made on the basis of fine-needle aspiration cytology and ultrasonography. Results: A total of 25 cases were analyzed. Majority of them belonged to the age group of 11–20 years. Male: female ratio was 2:3. Nearly 100% of cases had a chief complaint of midline neck swelling that moves with protrusion of the tongue. Most common site of occurrence is the infrahyoid region. Nearly 80% of cases underwent Sistrunk operation. Conclusion: Thyroglossal cyst with its varied presentation is notorious for recurrence. Proper evaluation and excision are the keys toward successful outcomes.
A Machine Learning Approach for the Underwater Investigation of Malicious Activity in a Wireless Sensor Network

Underwater Wireless Sensor Networks (UWSN) have drawn increased interest from researchers in recent years due to advancements in underwater tracking, the implementation of multiple uses, and marine monitoring. Both researchers and business are drawn to the UWSN field. It is also susceptible to hostile attacks and threats because of the harsh undersea surroundings, its own capabilities, and the open acoustic channel. These features make it simple for attackers to intercept information across the source and destination. Many studies have been done to secure UWSN from malicious attacks, but the majority of them rely on a threshold that is determined before deployment or a training set of data. Because there is no proven security foundation, it is difficult and challenging for UWSN to identify harmful attacks. To identify harmful attacks in a UWSN, we support vector machines in this research. SVM produces good results and requires significantly less time to train than neural networks. This study will help the researchers design the routing protocols to cover the known security threats and help industries manufacture the devices to observe these threats and security issues.
A Systematic Analysis of Enhancing Cyber Security Using Deep Learning for Cyber Physical Systems

In this current era, cyber-physical systems (CPSs) have gained concentrated consideration in various fields because of their emergent applications. Though the robust dependence on communication networks creates cyber-physical systems susceptible to deliberated cyber related attacks and detecting these cyber-attacks are the most challenging task. There is the interaction among the components of the cyber and physical worlds, so CPS security needs a distinct approach from past security concerns. Deep learning (DL) distributes better performance than machine learning (ML) due to its layered architecture and the efficient algorithm for extracting prominent information from training data. So, the deep learning models are taken into consideration quickly for detecting cyber-attacks in cyber physical systems. As numerous attack detection methods have been proposed by various authors for enforcing CPS security, this paper reviews and analyzes multiple ways of attack detection presented for CPS using deep learning. We will be putting the excellent potential for detecting cyber-attacks for CPS concerning deep learning modules. The admirable performance is attained partly as highly quality datasets are eagerly obtainable for the use of the public. Moreover, various challenges and research inclinations are also discussed in impending research.