Miracle-dz/UniFAR-FTUs huggingface dataset:
Cost-effective Cross-lingual Document Classification

This article addresses the question of how to deal with text categorization when the set of documents to be classified belong to different languages. The figures we provide demonstrate that cross-lingual classification where a classifier is trained using one language and tested against another is possible and feasible provided we translate a small number of words: the most relevant terms for class profiling. The experiments we report, demonstrate that the translation of these most relevant words proves to be a cost-effective approach to cross-lingual classification.
The performance of a momentum investment strategy during bull and bear periods on the FTSE/JSE Africa Top 40 Index

This paper studies the effects of bull and bear market states on the profitability of a momentum investment strategy. That is, a strategy that buys past winners and sells past losers is simulated over the period 3 July 2002 to 8 August 2012 and its profitability is reviewed in light of bull and bear sub-periods. Such an investment strategy has been shown to yield abnormal returns in several markets around the world, including the South African stock market. By doing so, these studies challenge the efficient market hypothesis, a central and widely accepted hypothesis within traditional portfolio theory. There are many theories that have been used to explain why abnormal profits are achievable using a momentum investment strategy. By determining the effects of bull and bear market states on the profitability of a momentum investment strategy, this paper provides some insight into which theories, if any, are most relevant to the South African stock market context. It is found that on average, a momentum portfolio yields abnormal returns over the full sample period, with the chief driver of these returns being the winner component of the portfolio. When broken into bull and bear sub-periods, it was found that a momentum investment strategy only yields abnormal returns during a bull period, whilst these abnormal returns became negative during a bear period. These results are consistent with one efficient market hypothesis explanation and two behavioural models presented in past studies. The results indicate that the market may be efficient and that changes in macroeconomic risk are the cause of momentum profits. However, insofar as the macroeconomic risk explanation is inaccurate, these results support the behavioural models of Daniel, Hirshleifer, and Subrahmanyam (1998); and Hong and Stein (1999). Both these models predict that momentum returns will be strongest during bull periods.
Bank Lending Strategy in The Stock Market

Margin trading is stocks trading using loan leverage from a financial institution; in here we call a bank. It is beneficial for improving liquidity in the market and creating smooth price formation of the stock. However, margin trading has a tendency to escalate the price. As the price of the stocks increases drastically, the authority then raises the minimum margin or minimum collateral to mitigate the risk. Theoretically it will slow down the price movement. Unfortunately, the market reaction result is different. Most of them sell their stocks, so the price then collapses. It was what happen in US great depression in 1929 and lost decade in Japan 1990. For bank or lender, increasing margin requirements is an effective tool to mitigate the risk. However, good investors usually also being filtered out by it, as margin requirement only keep investors who have good working capital but, unfortunately, are incompetent to predict good performance investor. A new method to predict investor bankruptcy in margin trading for the banking industry is successfully developed. In comparison to the margin requirement that screens investors merely by their capital and collateral, the scope of credit scoring can be extended to include the character, capacity and condition of the trader. Some artificial intelligences to create credit scoring for granting player loan proposals are implemented by comparing multiple discriminant analysis, neural network, decision tree and support vector machine performance then choose the most suitable one. The impact of credit scoring on price movement then is studied to control bubble price movement. Our method can predict investor as bankrupt, surviving and profitable. By classifying the investor into three groups, the bank can manage their loan absorption to profitable and surviving investor. A new bank strategy for taming the bubble is developed using three methods; these are AI credit scoring, bubble detection and loan adjustment. The loan is delivered base on investor prediction. When the reserve’s money is running out or bubble condition is detected, bank adjusts the loan parameter so called financing frame. As loan is restricted, the price movement then slightly decreases. After bubble condition or run-out reserve money disappear. The bank can relax the loan. Various bank’s reserve strategies also are analyzed to find their influence to the price movement. We performed some simulations to compare a smart bank with a non-smart bank and with static and dynamic reserves. A non-smart bank with a static reserve strategy and dynamic reserve
Influence of Rigid Contact Lens Base Curve Radius on Tear Pump Efficiency

The effects of rigid contact lens base curve radius changes on tear pump efficiency were determined by measuring oxygen uptake on six with-the-rule corneas (with toricities between 0.37 and 1.00 D) under three conditions: (1) normal open eye, (2) after 5 min of static (without blinking) wear of polymethyl methacrylate (non-gas permeable) contact lenses, and (3) after 5 min of dynamic (with blinking once every 5 s) wear of the same lenses. The difference between the oxygen uptake rates measured under static and dynamic conditions provides an index of tear pump efficiency. Each subject wore a series of five lenses: fitted on K and 0.05 or 0.10 mm steeper and flatter than the flattest corneal meridian. The overall diameter of all lenses was 8.8 mm, the optic zone was 7.4 mm, the axial edge lift was 0.09 mm, and all other parameters were constant. The contact lens fitted on K was associated with the greatest central corneal oxygen debt under static conditions, whereas the same lens also provided for the best tear exchange with the blink. In addition, better tear exchange was found to be associated with larger palpebral aperture sizes.
Global Propagation of Singularities for Time Dependent Hamilton-Jacobi Equations

We investigate the properties of the set of singularities of semiconcave solutions of Hamilton-Jacobi equations of the form \begin{equation*} u_t(t,x)+H(\nabla u(t,x))=0, \qquad\text{a.e. }(t,x)\in (0,+\infty)\times\Omega\subset\mathbb{R}^{n+1}\,. \end{equation*} It is well known that the singularities of such solutions propagate locally along generalized characteristics. Special generalized characteristics, satisfying an energy condition, can be constructed, under some assumptions on the structure of the Hamiltonian $H$. In this paper, we provide estimates of the dissipative behavior of the energy along such curves. As an application, we prove that the singularities of any viscosity solution of the above equation cannot vanish in a finite time.
Exteroceptive measurement filtering embedded within an SO(3)-based attitude estimator

A nonlinear deterministic attitude estimator is developed. The estimator evolves directly on the special orthogonal group of rigid-body rotations. Similar estimators in the literature are characterized by constant estimator gains that amplify noise across all frequencies. The proposed method accounts for undesirable noise in exteroceptive measurements by incorporating a strictly positive real (SPR) filter in the estimator structure. By tuning the “embedded” SPR filter over a frequency bandwidth undesirable noise corrupting exteroceptive measurements may be rejected. Rate-gyro bias estimation is also considered. The proposed attitude estimator is shown to asymptotically converge to the desired equilibrium point (i.e., when the estimated attitude and bias are equal to their true counterparts), provided a restriction on the set of initial conditions is satisfied. Moreover, exponential convergence is shown in a certain region about the desired equilibrium point. The proposed method is demonstrated in simulation.
Towards a causal ontology coping with the temporal constraints between causes and effects

The paper describes a causal ontology in which the temporal implications of causation (and, in particular, the temporal constraints it imposes between causes and effects) are coped with in detail. It proposes a classification of causal relations on the basis of the temporal constraints they impose between their causes and effects, and further refines the basic classification for coping also with causation with threshold and with phenomena of production/consumption of stuff. Finally, the paper sketches an application of the causal ontology for developing causal nets used as domain knowledge for natural language interpretation, and briefly addresses also the reasoning techniques required to this purpose.
Seminal Fluid Regulates Accumulation of FOXP3+ Regulatory T Cells in the Preimplantation Mouse Uterus Through Expanding the FOXP3+ Cell Pool and CCL19-Mediated Recruitment1

Regulatory T (Treg) cells facilitate maternal immune tolerance of the semiallogeneic conceptus in early pregnancy, but the origin and regulation of these cells at embryo implantation is unclear. During the preimplantation period, factors in the seminal fluid delivered at coitus cause expansion of a CD4+CD25+ putative Treg cell population in the para-aortic lymph nodes draining the uterus. Using flow cytometry, immunohistochemistry, and real-time quantitative PCR (qPCR) for the signature Treg cell transcription factor FOXP3, we confirmed the identity of the expanded lymph node population as FOXP3+ Treg cells and showed that this is accompanied by a comparable increase in the uterus of FOXP3+ Treg cells and expression of Foxp3 mRNA by Day 3.5 postcoitum. Seminal plasma was necessary for uterine Treg cell accumulation, as mating with seminal vesicle-deficient males failed to elicit an increase in uterine Treg cells. Furthermore seminal fluid induced expression of mRNA encoding the Treg chemokine CCL19 (MIP3beta), which acts through the CCR7 receptor to regulate Treg cell recruitment and retention in peripheral tissues. Glandular and luminal epithelial cells were identified as the major cellular origins of uterine CCL19, and exposure to both seminal plasma and sperm was required for maximum expression. Together, these results indicate that Treg cells accumulate in the uterus prior to embryo implantation and that seminal fluid is a key regulator of the uterine Treg cell population, operating by both increasing the pool of available Treg cells and promoting their CCL19-mediated recruitment from the circulation into the implantation site.
Supply chain management technology: a review of empirical literature and research agenda

The interest in Supply Chain Management (SCM) related technologies continues to garner interest from a variety of research disciplines. However, the varied disciplines and perspectives from which SCM Technology (SCMT) research arises complicates the efforts of those seeking to develop a comprehensive understanding of the subject. This effort provides a multidisciplinary review of the extant literature and assesses the progress of SCMT research. Using a rigorous content analysis-based approach, multiple reviewers identified and categorised 82 empirical SCMT-related articles appearing in highly rated journals from one of three research fields: Operations Management (OM), logistics and Information Systems (IS). Each of the identified articles was categorised according to the methodology, stage of theoretical development and research focus. The progress of SCMT research within each of these areas is discussed and opportunities for the continued development and extension of the body of knowledge are presented.
Adaptive filtering algorithms for stereophonic acoustic echo cancellation

It is likely that stereophonic (and more generally, multichannel) sound pick-up, transmission and diffusion will be implemented in future teleconference systems to provide the users with enhanced quality. Therefore, adequate solutions must be found to solve the problem of stereophonic acoustic echo which will occur in such systems. We explain in this paper the difference between the mono and two-channel systems and the behavior of the two-channel classical adaptive algorithms in comparison with the same algorithms in the mono-channel case. Also, we outline a new NLMS-like algorithm derived from the two-channel RLS algorithm as a first member of a family of improved two-channel adaptive filters.
Verbal Probabilities: An Alternative Approach

Previous studies of verbal probabilities have tried to place expressions like a chance, possible, and certain on 0–1 numerical probability scales. We ask instead, out of a range of outcomes, which outcome a verbal probability suggests. When, for instance, a sample of laptop batteries lasts from 1.5 to 3.5 hours, what is a certain and what is a possible duration? Experiment 1 showed that speakers associate certain with low values and possible with (unlikely) high or maximal values. In Experiment 2, this methodology was applied to several positive and negative verbal probability phrases, showing a preference for high rather than low or middle values in a distribution. Experiment 3 showed that such maxima are not universally described by large numbers. For instance, maximum speed is often described in terms of a small number of time units. What can (possibly) happen is accordingly sometimes described with very low and sometimes with very high values, depending upon focus of interest. Finally, participants in Experiment 4 were given the role of hearers rather than speakers and were asked to infer outcome ranges from verbal probabilities. Hearers appeared to be partly aware of speakers' tendencies to describe outcomes at the top of the range.
Robust Acoustic Speech Emotion Recognition by Ensembles of Classifiers

Automatic speech recognition can fail to a certain extent when confronted with emotionally distorted speech. Great efforts have been spent so far to cope with noise conditions or speaker’s characteristics. Yet, adaptation to the emotional condition of the speaker could help to further improve the overall performance. In this respect we aim at a robust and reliable recognition of the speaker’s emotional state by acoustic features only prior to speech recognition itself. Thereby we can load according emotional speech models. In this work we introduce an optimal feature set for this task selected by Sequential Floating Search Methods. The set comprises high-level prosodic features resembling utterancewise statistic analysis of low-level contours as pitch, higherorder formants, energy, and spectral development. Within classification we apply ensemble classification as Stacking, Bagging, and Boosting.
Requirement of the Caspase-3/CPP32 Protease Cascade for Apoptotic Death following Cytokine Deprivation in Hematopoietic Cells*

Hematopoietic cytokines transduce cell survival signals, which are distinct from the signals necessary for the stimulation of DNA synthesis. Recently, the Ras and phosphatidylinositol 3-kinase pathways have been shown to play important roles in preventing apoptosis in various cell types,e.g. hematopoietic cells and neuronal cells. Withdrawal of cytokine(s), in turn, results in rapid inactivation of these survival pathways and eventually leads to cell death accompanied by the hallmarks of apoptosis. However, the mechanism of cell death caused by cytokine deprivation has not been fully elucidated. In this study, we demonstrate that caspase-3/CPP32, a member of the caspase/interleukin-1β-converting enzyme family, is activated upon interleukin (IL)-3 deprivation in IL-3-dependent cells as well as IL-2 deprivation in IL-2-dependent cells. In addition, poly(ADP-ribose) polymerase, a cellular substrate for the caspase family proteases, was degraded into apoptotic fragments in both cell lines after cytokine removal. Furthermore, inhibition of a caspase family protease by synthetic peptides suppressed apoptotic death. These results indicate that the activation of a caspase-like protease(s) is required for the progression of apoptosis following cytokine deprivation. However, readdition of IL-3 did not restore the proliferative potential of the cells that survived in the presence of the peptide inhibitor after IL-3 depletion. Therefore, cellular commitment to apoptosis appears to precede the activation of a caspase-like protease(s).
Does the brain really outperform Rent’s rule?

This paper presents a thorough analysis of brain's connectivity from the perspective of the latest interpretation of Rent's rule, and using neurological data. While at first sight the brain might seem to outperform most of the known network topologies, a closer look will reveal that in fact it does not. From another perspective, the brain will seem to be just on the border of the latest range of Rent's rule values, while neurological data will show that it is almost right on the average. We conclude that, among quite a few network topologies, the crossbar (for very small sizes) and the cube connected cycles are highly competitive contenders for future brain-inspired nanoarchitectures.
Plural forms in store networks: a model for store network evolution

Very few papers have been written about plural forms in store networks. But today, many store chains have both franchise and company-owned arrangements. Actually, this has been the case for more than twenty years. After a review of the literature devoted to the choice between franchise and company-owned systems, Bradach's research and his model of plural forms are described. This model was based on a study of five American fast food companies. Research conducted in France in the hotel and catering, bakery, and cosmetics industries, is then described. The advantages and drawbacks of plural forms are defined as they appear in the results of a survey among managers of twenty-one companies managing thirty-five chains. A model of the evolution of store chain organization is then shown. This model takes into account various strategic and managerial considerations met by store chains during their life cycle. Finally, chains and networks are compared, and the results and research perspectives, discussed.
Biology Inspired Image Segmentation using Methods of Artificial Intelligence

In recent years,many efforts have been devoted to image segmentation. Although for a man general image segmentation is considered an easy task, for computers it is still considered to be difficult, computationally intensive and still unresolved task. This work presents an innovative algorithm combining theory of artificial intelligence and knowledge of human eye anatomy. The resulting algorithm has not ambitions to be universal like human brain but can be trained and perform on selected domain. The effectiveness of the algorithm is demonstrated on the selected examples.
Detecting Fraud in Narrative Annual Reports

Annual reports present the activities of a listed company in terms of its operational performance, financial conditions, and social responsibilities. These reports also provide valuable reference for numerous investors, creditors, or other accounting information end-users. However, many annual reports exaggerate enterprise activities to raise investor capital and support from financial institutions, thereby diminishing the usefulness of such reports. Effectively detecting fraud in the annual report of a company is thus of priority concern during an audit. Therefore, this work develops a novel fraud detection method for narrative annual reports to effectively detect fraud in the narrative annual report of a company in order to reduce investment losses and investor- and creditor-related risks, as well as enhance investment decisions. A developmental procedure of fraud detection is designed for narrative annual reports. Fraud detection-related techniques are then developed for narrative annual reports, followed by a demonstration and evaluation of the proposed fraud detection method. Fraud detection-related techniques for narrative annual reports consist mainly of establishing a fraudulent feature term library and clustering fraudulent and non-fraudulent narrative annual reports. Moreover, establishing fraudulent feature term library involves data preprocessing, term-pair combination, and filtering of fraudulent feature terms.
Large Scale Aggregated Sentiment Analytics

In the past years we have witnessed Sentiment Analytics becoming increasingly popular topic in Information Retrieval, which has established itself as a promising direction of research. With the rapid growth of the user-generated content represented in blogs, forums, social networks and micro-blogs, it became a useful tool for social studies, market analysis and reputation management, since it made possible capturing sentiments and opinions at a large scale and with the ever-growing precision. Sentiment Analytics came a long way from product review mining to full-fledged multi-dimensional analysis of social sentiment, exposing people attitude towards any topic aggregated along different dimensions, such as time and demographics. The novelty of our work is that it approaches Sentiment Analytics from the perspective of Data Mining, addressing some important problems which fall out of the scope of Opinion Mining. We develop a framework for Large Scale Aggregated Sentiment Analytics, which allows to capture and quantify important changes in aggregated sentiments or in their dynamics, evaluate demographical aspects of these changes, and explain the underlying events and mechanisms which drive them. The first component of our framework is Contradiction Analysis, which studies diverse opinions and their interaction, and allows tracking the quality of aggregated sentiment or detecting interesting sentiment differences. Targeting large scale applications, we develop a sentiment contradiction measure based on the statistical properties of sentiment and allowing efficient computation from aggregated sentiments. Another important component of our framework addresses the problem of monitoring and explaining temporal sentiment variations. Along this direction, we propose novel time series correlation methods tailored specifically for large scale analysis of sentiments aggregated over users demographics. Our methods help to identify interesting correlation patterns between demographic groups and thus better understand the demographical aspect of sentiment dynamics. We bring another interesting dimension to the problem of sentiment evolution by studying the joint dynamics of sentiments and news, uncovering the importance of news events and assessing their impact on sentiments. We propose a novel and universal way of modeling different media and their dynamics, which aims to describe the information propagation in news- and social media. Finally, we propose and evaluate an updateable method of sentiment aggregation and retrieval, which preserves important properties of aggregated sentiments and also supports scalability and performance requirements of our applications.