Miracle-dz/UniFAR-FTUs huggingface dataset:
Market Microstructure Effects of the Transparency of Indian Banks

One of the primary objectives of the banking sector deregulation in India is to introduce an element of market discipline into the regulatory process. For the market’s disciplining mechanism to operate, however, banks must provide transparent disclosures of their operations and risks in a timely fashion and must adopt prudent accounting policies. Transparency, though a costly endeavour for a bank, generates significant benefits at a firm-specific level. Previous empirical research has demonstrated that firms that provide higher quality of disclosures benefit by way of improved market liquidity and reduced cost of capital primarily because of the reduction in the level of information asymmetry among investors. This paper investigates whether enhanced transparency in the case of Indian banks is indeed rewarded with increased market liquidity by way of reduced bid-ask spreads. The paper also examines the market’s reaction to the enhanced disclosure requirements as of March 31, 2000 as required by Reserve Bank of India guidelines. Finally, to verify the market’s reaction to enhanced disclosures based on US GAAP requirements, the paper also examines the markets reaction to the Form 20F filing made by ICICI Ltd. with the Security Exchange Commission. The results of the paper indicate that, in the Indian case, enhanced transparency had no significant impact on the market liquidity of private sector banks. In the case of public sector banks, it is observed that enhanced transparency is associated with reduced market liquidity. In addition, no significant change in the market liquidity was observed with the release of the additional disclosure information as of March 31, 2000 as required by the Reserve Bank of India. Finally, the market reacted favourably to the release of ICICI’s Form 20F disclosure with the Security Exchange Commission containing market risk and segment information as per US GAAP not provided in the Indian annual report. These results suggest that the market’s disciplining mechanism does not seem to work at least in the case of Indian private sector banks because the disclosures are not of adequate quality, or, that investors find the enhanced disclosures relatively opaque. However, the favourable reaction to ICICI’s Form 20F filing indicates that Indian investors do not find the enhanced risk-based disclosures relatively opaque. The results for the private sector banks point to a need to significantly improve the level and credibility of
ABUSIVE DATING BEHAVIOURS AMONG UNDERGRADUATE STUDENTS

Adolescence is the crux of sexuality and a period of development marked by the establishment of intimate relationships with the opposite sex. The occurrences of abusive dating behaviours at this stage of development are on the rise. Although little attention has been placed on this, these behaviours could serve as risk factors for worse maladaptive behaviours later in life and even the manifestation of psychological distress which is not limited only to the victims but as well, the perpetrators. Hence, this study sought to identify the role of self-esteem, empathy, family background and peer influence in predicting the occurrence of abusive dating behaviour. Cross-sectional survey design was employed in the collection of data from a sample of 219 undergraduates of University of Ibadan, consisting of 156 females and 63 males. A self-administered structured questionnaire was used to measure patterns of abusive dating behaviour, self-esteem, empathy and resistance to peer influence while family background was measured as part of the demographic characteristics. Three hypotheses were tested using T-test and multiple regression analytical tools. Results reveal that self-esteem, empathy and peer influence had significant joint influence on abusive dating behaviour (F (3,206) = 8.796, p <. 05; R 2 = . 114). Self-esteem and empathy had significant independent influence on abusive dating behaviour [[(β = -.306, p <.05) and (β = .156, p <.05) respectively] while peer influence had no significant independent influence on abusive dating behaviour (β = -.020, p >.05). This infers that an undergraduate’s self and empathy are major concerns for engaging in abusive dating, peer influence could only complement the influence of these two factors. It was also found that students from monogamous homes and polygamous homes did not differ in their abusive dating behaviour (t (213) = . 519, p>. 05) and that gender differences do not exist in abusive dating behaviour (t (213) = . 500, p>. 05). This also infers that neither experiences
User Aware Rare Sequential Topic Patterns In Document Streams

Textual documents created and distributed on the Internet are ever changing in various forms. Most of existing works are devoted to topic modeling and the evolution of individual topics, while sequential relations of topics in successive documents published by a specific user are ignored. In this paper, in order to characterize and detect personalized and abnormal behaviors of Internet users, we propose Sequential Topic Patterns (STPs) and formulate the problem of mining User-aware Rare Sequential Topic Patterns (URSTPs) in document streams on the Internet. They are rare on the whole but relatively frequent for specific users, so can be applied in many real-life scenarios, such as real-time monitoring on abnormal user behaviors. We present a group of algorithms to solve this innovative mining problem through three phases: preprocessing to extract probabilistic topics and identify sessions for different users, generating all the STP candidates with (expected) support values for each user by pattern-growth, and selecting URSTPs by making user-aware rarity analysis on derived STPs. Experiments on both real (Twitter) and synthetic datasets show that our approach can indeed discover special users and interpretable URSTPs effectively and efficiently, which significantly reflect users? characteristics.
Computers and Chemical Engineering

In this work, we present statistical model predictive control with Control Lyapunov-Barrier Functions (CLBF) built using machine learning approaches, and analyze closed-loop stability and safety properties in probability using statistical machine learning theory. A feedforward neural network (FNN) is used to construct the Control Barrier Function, and a generalization error bound can be obtained for this FNN via the Rademacher complexity method. The FNN Control Barrier Function is incorporated in a CLBF-based model predictive controller (MPC), which is used to control a nonlinear process subject to input constraints. The stability and safety properties of the closed-loop system under the sample-and-hold implementation of FNN-CLBF-MPC are evaluated in a statistical sense. We use a chemical process example to demonstrate the relation between various factors of building an FNN model and the generalization error, as well as the probabilities of closed-loop safety and stability for both bounded and unbounded unsafe sets.
This paper is included

Trusted execution environments (TEEs) provide an environment for running workloads in the cloud without having to trust cloud service providers, by offering additional hardware-assisted security guarantees. However, main memory encryption as a key mechanism to protect against system-level at-tackers trying to read the TEE’s content and physical, off-chip attackers, is insufficient. The recent Cipherleaks attacks infer secret data from TEE-protected implementations by analyzing ciphertext patterns exhibited due to deterministic memory encryption. The underlying vulnerability, dubbed the ciphertext side-channel, is neither protected by state-of-the-art counter-measures like constant-time code nor by hardware fixes. Thus, in this paper, we present a software-based, drop-in solution that can harden existing binaries such that they can be safely executed under TEEs vulnerable to ciphertext side-channels, without requiring recompilation. We combine taint tracking with both static and dynamic binary instrumentation to find sensitive memory locations, and mitigate the leakage by masking secret data before it gets written to memory. This way, although the memory encryption remains deterministic, we destroy any secret-dependent patterns in encrypted memory. We show that our proof-of-concept implementation protects various constant-time implementations against ciphertext side-channels with reasonable overhead.
BODY MASS ESTIMATIONS IN LUJANIAN (LATE PLEISTOCENE-EARLY HOLOCENE OF SOUTH AMERICA) MAMMAL MEGAFAUNA

In this paper a data base is initiated, with the body mass estimations for a number of xenarthran and epitherian species of the Lujanian Land Mammal Age (late Pleis- tocene - early Holocene of South America). For doing that, a set of allometric equations was used, which had been previously developed from craniodental and limb bone dimensions in modern mammals. The results were analysed statistically. The dispersion of body mass estimations was remarkable in the totally extinct xenarthran groups. Certain measurements (particularly, the posterior jaw length in glyptodonts and the transverse diameter of the femur in ground sloths) gave spurious predictions relative to non-xenarthran mammals. New equa- tions specifically for xenarthrans should be developed to use these measurements. The dispersion of the epitherian mammals was lower than in the studied xenarthran. It is sug- gested that arithmetic mean should be used in those studies in which large size is the conservative hypothesis, and another statistic (such as the geometric mean, median or mode) in the opposite case. This database is intended to be the starting point for many autecological and synecological studies of this extinct fauna.
Cross comparison of COVID-19 fake news detection machine learning models

The quick advancement of technology in internet communication and social media platforms eased several problems during the COVID-19 outbreak. It was, however, used to spread untruths and misinformation regarding the illness and the immunization. In this study, it is examined whether machine-learning algorithms (Naive Bayesian, Random Forest, Logistic Regression, Decision Tree, and Support Vector Machine, as well as Gradient Boost, Bagging, AdaBoost, Stochastic Gradient Descent, and Multi-layer Perceptron) can automatically classify and point out fake news text about the COVID-19 pandemic posted on social media platforms. The "COVID19-FNIR DATASET" was used to train, test, and fine-tune machine learning models in order to predict the sentiment class of each fake news item on COVID-19. The results were assessed using a variety of evaluation metrics (confusion matrix, classification rate, true positives rate, etc.). The findings collected demonstrate an extremely high level of accuracy when compared to other models.
Poisson Process for Bayesian Optimization

BayesianOptimization(BO) is a sample-efficient black-box optimizer, and extensive methods have been proposed to build the absolute function response of the black-box function through a probabilistic surrogate model, including Tree-structured Parzen Estimator (TPE), random forest (SMAC), and Gaussian process (GP). However, few methods have been explored to estimate the relative rankings of candidates, which can be more robust to noise and have better practicality than absolute function responses, especially when the function responses are intractable but preferences can be acquired. To this end, we propose a novel ranking-based surrogate model based on the Poisson process and introduce an efficient BO framework, namely Poisson Process Bayesian Optimization (PoPBO). Two tailored acquisition functions are further derived from classic LCB and EI to accommodate it. Compared to the classic GP-BO method, our PoPBO has lower computation costs and better robustness to noise, which is verified by abundant experiments. The results on both simulated and real-world benchmarks, including hyperparameter optimization (HPO) and neural architecture search (NAS), show the effectiveness of PoPBO.
I MPACT OF IMPERFECT CHANNEL ESTIMATION AND ANTENNA CORRELATION ON QUANTISED MASSIVE MIMO SYSTEMS

This paper examines the uplink performance of large-constellation multi-user massive MIMO systems with low-resolution analogue-to-digital converters (ADCs) in the presence of channel correlation and imperfect channel state information (CSI). The base station (BS) employs a large number of antennas for multiplexing and demultiplexing co-channel users with each antenna element having a dedicated radio frequency (RF) chain and two low-resolution ADCs. While such ADCs cause data loss due to coarse quantization, the large number of antennas can be exploited not only to alleviate such a problem but also to make it possible to utilise large-constellation modulation schemes. The results provide an insight into the trade-off between various performance metrics and the number of quantization bits under a wide range of realistic conditions. It will be shown that 1-bit quantization provides sufﬁcient resolution with 100 BS antennas to communicate with 10 UEs using QPSK, but the number of quantization bits must be increased for larger constellations particularly to overcome CSI mismatch and channel correlation. The results also consider the trade-off between average mutual information and power consumption of the low-resolution ADCs. It will be shown that 16QAM with 2-bit quantization may provide a good compromise between energy efﬁciency and average mutual information.
Metadata of the chapter that will be visualized online

Recently there has been an increased use of microarray technology as a tool to determine the presence of functional genes in a population of hard to culture communities (e.g., soil, extreme environments). A functional gene array (FGA), or GeoChip, uses probes to screen for specific functional genes vital in biological systems such as nitrogen and carbon cycling, and has even been expanded to include aquatic conditions. However, the time delay from when the sample is taken from the ocean to evaluating the test results back in the lab still posed a problem. The Environmental Sample Processor (ESP) minimizes this time difference by housing a robotic system placed in the ocean for a long period of time that can collect a small sample, concentrate the DNA, run a microarray, and take a picture of the array before sending the data ashore to be evaluated by a researcher. The included protocol and reagents list goes through both lab microarray procedures as well as the procedures list for the ESP, which briefly mentions deployment and data acquisition. The protocols described here should advance applications in microbial oceanography using robotic instrumentation.
The Impacts of Non-Macroeconomic Indicators on the Exports of Goods and Services the Case of in OECD Countries *

: Research studies regarding micro-and macro-level variables have been faced regularly in the literature taking the factors that affect the export performances of countries into consideration, Nonetheless, the objective of this study was to explicate the impacts of non-macroeconomic variables such as trade freedom index, R&D expenditures, the number of patent entitlements, and the number of newly established companies on exports of goods and services by utilizing the pertinent data compiled from 36 OECD countries throughout the period 2006 - 2020 via the GMM (Generalized Moments Method) analysis method. The findings revealed that trade freedom, the number of newly established companies, and the number of patent entitlements had positive impacts on the share of exports of goods and services in the GDP, whereas R&D expenditures had a negative impact on the share of exports in the GDP
Telepresence Systems for Large Interactive Spaces

The need to analyze and manipulate large datasets and perform complex tasks with computers has increased interest in wall-sized displays and virtual reality systems. As such environments become more common, the need to support telepresence across these large interactive spaces becomes critical for remote collaboration. In this workshop paper, we present our past work on remote collaboration, telepresence and large interactive spaces as well as our ongoing projects to support telepresence in such spaces for face-to-face and side-by-side collaboration.
Natural Language Processing for intent classification: an application to the classification of emotions and dialog act

This paper forcuses on the implementation of a Natural Language Processing (NLP) model that performs contextual emotion classification on a dialogue dataset sourced from the popular TV show ”Friends”. The primary goal is to account for the context of each sentence in the conversations through a self-attention mechanism. To achieve this objec-tive, the authors propose constructing a hierarchical attention network that mimics the structure of a corpus of dialogues, which has a hierarchical structure between the conversation, the sentences within it, and the words in each sentence. The aim is to capture a dual context, namely, that the importance of words varies depending on the sentence or conversation, and not every sentence in a conversation carries equal importance in the context of the dialogue. Therefore, the meaning and, consequently, the label of an utterance will vary depending on its location within the conversation.
Robust Sound Event Classiﬁcation using Deep Neural Networks

—The automatic recognition of sound events by computers is an important aspect of emerging applications such as automated surveillance, machine hearing and auditory scene understanding. Recent advances in machine learning, as well as in computational models of the human auditory system, have contributed to advances in this increasingly popular research ﬁeld. Robust sound event classiﬁcation, the ability to recognise sounds under real-world noisy conditions, is an especially challenging task. Classiﬁcation methods translated from the speech recognition domain, using features such as mel-frequency cepstral coefﬁcients, have been shown to perform reasonably well for the sound event classiﬁcation task, although spectrogram-based or auditory image analysis techniques reportedly achieve superior performance in noise. This paper outlines a sound event classiﬁcation framework that compares auditory image front end features with spectrogram image-based front end features, using support vector machine and deep neural network classiﬁers. Performance is evaluated on a standard robust classiﬁcation task in different levels of corrupting noise, and with several system enhancements, and shown to compare very well with current state-of-the-art classiﬁcation techniques.
The University of Bradford Institutional Repository

Distributed generators (DGs) are a reliable solution to supply economic and reliable electricity to customers. It is the last stage in delivery of electric power which can be defined as an electric power source connected directly to the distribution network or on the customer site. It is necessary to allocate DGs optimally (size, placement and the type) to obtain commercial, technical, environmental and regulatory advantages of power systems. In this context, a comprehensive literature review of uncertainty modeling methods used for modelling uncertain parameters related to renewable DGs as well as methodologies used for the planning and operation of DGs integration into distribution network. The authors strongly recommend this review to researchers, scientists and engineers who are working in this field of research work.
This paper is included in the Proceedings of the 32nd USENIX Security Symposium

Capability-based memory isolation is a promising new architectural primitive. Software can access low-level memory only via capability handles rather than raw pointers, which provides a natural interface to enforce security restrictions. Existing architectural capability designs such as CHERI provide spatial safety, but fail to extend to other memory models that security-sensitive software designs may desire. In this paper, we propose C APSTONE , a more expressive architectural capability design that supports multiple existing memory isolation models in a trustless setup, i.e., without relying on trusted software components. We show how C APSTONE is well-suited for environments where privilege boundaries are fluid (dynamically extensible), memory sharing/delegation are desired both temporally and spatially, and where such needs are to be balanced with availability concerns. C AP - STONE can also be implemented efficiently. We present an implementation sketch and through evaluation show that its overhead is below 50% in common use cases. We also prototype a functional emulator for C APSTONE and use it to demonstrate the runnable implementations of six real-world memory models without trusted software components: three types of enclave-based TEEs, a thread scheduler, a memory allocator, and Rust-style memory safety—all within the interface of C APSTONE .
Effects of Social Inclusionary Status on Self-evaluations: The Moderating Role of Social Anxiety

Social inclusionary status might have diverse effects on self-perception. We conducted an online experiment (N=170) to examine the effects of social inclusionary status and social anxiety on self-evaluation. Our hypothesis was that the social exclusion experience will negatively affect self-evaluations in the competence dimension and social anxiety is expected to moderate this relationship. Participants filled out the Liebowitz Social Anxiety Scale and wrote a short autobiographical essay about an experience from their lives when they felt excluded/rejected (exclusion condition) or included (inclusion condition). Self-evaluation was approached with the two dimensions - warmth and competence-as proposed in the Stereotype Content Model. Results had shown that participants’ warmth judgments about themselves were not affected by neither inclusionary status nor social anxiety. On the other hand, inclusionary status had an effect on self-competence evaluations as a function of participants’ social anxiety levels. Accordingly, remembering an inclusive experience (from their past) was related with higher self-competence only among low social anxiety participants. The positive effect of remembering social inclusion experience was not observed among high social anxiety individuals. In conclusion, the effect of social inclusionary status on self-competence assessments was moderated by social
Habitat segregation and mosaic sympatry of the two species of manta ray in the Indian and Pacific Oceans: Manta alfredi and M. birostris

Habitat use of Manta alfredi and M. birostris was assessed at 20 localities in the Indian and Pacific Oceans based upon 3328 photographic observations. Geographical relationships of the two species were scored as either microallopatry, microparapatry or microsympatry at each locality. Our study revealed a mosaic habitat occupancy by the two species within their macrosympatric range.
[Health Technology Assessment: institutionalization of actions in the Brazilian Ministry of Health].

Os sistemas de saúde de diferentes países vêm sofrendo o impacto provocado por um cenário de elevação de gastos e de restrição de recursos em saúde, além da conseqüente reestruturação dos serviços. Assim, os gestores têm demandado informações consistentes sobre os benefícios das tecnologias e a repercussão financeira sobre a esfera pública, visando a subsidiar a formulação de políticas e a efetiva tomada de decisão.