Miracle-dz/UniFAR-FTUs huggingface dataset:
Human-Centered Financial Summarization

Automatic summarization has seen a rapid growth in recent years, allowing for efficient handling and processing of the huge amount of documents, which are available on the Web. There is also an increasing growth of domain-specific summarization models which use knowledge of the particular domain terminology e.g. medical reports, scientific publications, financial and company filings etc. Despite the success of these models, the majority of them are isolated from the users, not allowing any kind of useful feedback from them during the summarization process as well as not providing any meaningful explanations about the final predictions to them. In this thesis, we focus on financial summarization and we contribute a new dataset, which consists explicitly of financial newswire articles from economic and business categories via Bloomberg’s website. We also propose a financial human-centered summarization model, utilizing the implementation of an existing state-of-the-art transformer model: PEGASUS. We approach the task of summarization from a human-centered aspect, providing an interpretation analysis of the complex structure of the existing neural model, especially by examining the transparency of a widely used attention mechanism, namely self-attention. The user has an active role in our summarization system by selecting among different diverse hypotheses during inference, improving this way the quality of our collected dataset with human-approved summaries. We significantly improve the performance of the model after the fine-tuning by 10 points in terms of ROUGE-1 score. At the same time, we provide some useful insights about the causative factors which drive the behavior of the model, allowing for better understanding of the final predictions. We combine all the above aspects into a simple humancentered summarization interface. Our analysis sets the ground for the development of sophisticated human-centered summarization techniques, opening the way for prospective research directions.
Joining Interactive Graphics and Procedural Modeling for Precise Free-Form Designs

JIPCAD (Joint-Interactive-Procedural CAD) is a 3D procedural CAD tool used for programmatically creating geometries with a shape description language. Users can interactively modify the scene in a graphical user interface and save modifications back into the corresponding file as reusable code; a reopening of the enhanced file will reproduce the latest graphical state, and the user can continue in either a graphical manner or by textual changes in the . jip file. JIPCAD combines procedural and interactive modeling, making it easier for users to model 2-manifold free-form surfaces of high complexity and inherent regularity. In this report, we will discuss JIPCAD and the development of its shape description language and graphical editing capabilities over the past year. Key contributions include generalized progressive sweeps along arbitrary 3D space curves, dynamic scenes, advanced shape generators for tori, error catching, graphical editing and saving capabilities, and advanced rendering options.
Classification of Cattle Behaviours Using Neck-Mounted Accelerometer-Equipped Collars and Convolutional Neural Networks

Monitoring cattle behaviour is core to the early detection of health and welfare issues and to optimise the fertility of large herds. Accelerometer-based sensor systems that provide activity profiles are now used extensively on commercial farms and have evolved to identify behaviours such as the time spent ruminating and eating at an individual animal level. Acquiring this information at scale is central to informing on-farm management decisions. The paper presents the development of a Convolutional Neural Network (CNN) that classifies cattle behavioural states (‘rumination’, ‘eating’ and ‘other’) using data generated from neck-mounted accelerometer collars. During three farm trials in the United Kingdom (Easter Howgate Farm, Edinburgh, UK), 18 steers were monitored to provide raw acceleration measurements, with ground truth data provided by muzzle-mounted pressure sensor halters. A range of neural network architectures are explored and rigorous hyper-parameter searches are performed to optimise the network. The computational complexity and memory footprint of CNN models are not readily compatible with deployment on low-power processors which are both memory and energy constrained. Thus, progressive reductions of the CNN were executed with minimal loss of performance in order to address the practical implementation challenges, defining the trade-off between model performance versus computation complexity and memory footprint to permit deployment on micro-controller architectures. The proposed methodology achieves a compression of 14.30 compared to the unpruned architecture but is nevertheless able to accurately classify cattle behaviours with an overall F1 score of 0.82 for both FP32 and FP16 precision while achieving a reasonable battery lifetime in excess of 5.7 years.
PART 1 CONCEPTUAL ISSUES OF MARKET STRUCTURE IN AGRICULTURAL COMMODITY VALUE CHAINS

This paper examines from the viewpoint of microeconomic theory and, in particular, from that of the modern theory of industrial organization, issues that arise when agricultural and food markets deviate substantially from the perfectly competitive model. We review some important ways in which terms of trade, profits, and the allocation of resources are affected when markets are monopolistic or oligopolistic at various stages of the vertically related commodity value chain. We also discuss the role of contracts between upstream and downstream producers and how contracts affect the firms’ incentives for investment, innovation, and pricing decisions. Finally, we refer to possible issues raised for vertical markets in the context of competition policy.
Future Research Directions for Improved Service Modeling

: The continuous emergence of new business requirements and technological innovations requires the constant adaptation of an enterprise’s service architecture. Due to the involved complexity, various modeling approaches have been developed for facilitating this task. In this paper we review existing service modeling approaches and derive novel research directions for this area that consider the semantics of the content exchanged via services. In particular, we propose to integrate techniques from machine learning and semantic technologies to automatically analyze, propose and integrate suitable services.
Innate Immunity Sublethal Hyperoxia Impairs Pulmonary

Supplemental oxygen is often required in the treatment of critically ill patients. The impact of hyperoxia on pulmonary host defense is not well-established. We hypothesized that hyperoxia directly impairs pulmonary host defense, beyond effects on alveolar wall barrier function. C57BL/6 mice were kept in an atmosphere of > 95% O 2 for 4 days followed by return to room air. This exposure does not lead to mortality in mice subsequently returned to room air. Mice kept in room air served as controls. Mice were intratracheally inoculated with Klebsiella pneumoniae and followed for survival. Alveolar macrophages (AM) were harvested by bronchoalveolar lavage after 4 days of in vivo hyperoxia for ex vivo experiments. Mortality from pneumonia increased signiﬁcantly in mice exposed to hyperoxia compared with infected mice in room air. Burden of organisms in the lung and dissemination of infection were increased in the hyperoxia group whereas accumulation of inﬂammatory cells in the lung was impaired. Hyperoxia alone had no impact on AM numbers, viability, or ability to phagocytize latex microbeads. However, following in vivo hyperoxia, AM phagocytosis and killing of Gram-negative bacteria and production of TNF- (cid:1) and IL-6 in response to LPS were signiﬁcantly reduced. AM surface expression of Toll-like receptor-4 was signiﬁcantly decreased following in vivo hyperoxia. Thus sublethal hyperoxia increases Gram-negative bacterial pneumonia mortality and has a signiﬁcant adverse effect on AM host defense function. Impaired AM function due to high concentrations of supplemental oxygen may contribute to the high rate of ventilator-associated pneumonia seen in critically ill patients. The Journal of Immunology, 2003, 171: 955–963. (cid:1) sterile normal saline solution, was inoculated using a 26-gauge nee- dle. Uninfected inoculated with the volume of sterile normal saline solution. The incision was closed with 4-0 sur- gical prolene with simple stitches. were allowed to recover from the procedure and were returned to appropriate housing. Mice in the hyperoxia group were returned to hyperoxia to complete 4 days of total exposure. For the survival studies, mice in the hyperoxia were returned to room air after completion of 4 days of and both groups were followed for survival for 14 days. For the survival studies, a dose of 5 (cid:3) 10 2 CFU was used. Previous studies indicated this dose would cause intermediate mortality in control mice. For the CFU counts and inﬂammatory cell accumu- lation studies, a lower dose (4 (cid:3) 10 2 CFU) was used.
USER-LEVEL SENTIMENT ANALYSIS TECHNIQUE IN ONE GO ON SOCIAL NETWORK AND E-COMMERCE

: Sentiment analysis is the process of identifying and categorizing opinions expressed in the text, especially in order to determine whether the writer's attitude towards a particular topic, product, etc. is positive, negative, or neutral. Sentiment analysis is an effective solution to the concentrate on modeling user-generated review and overall rating pairs. The aim is to identify linguistics aspects and aspect-level sentiments from the review data similarly on predict overall sentiments of reviews. However, systems are not very accurate at the level for determining sentiment of separate sentences. To upset the issues in one go underneath at unified framework, we propose a totally unique probabilistic supervised joint side and sentiment model (SJASM). SJASM represents every review documents among the style of the opinion pairs, and would be possibly at a similar time model aspect terms and corresponding opinion words of the review for hidden side and sentiment detection. It conjointly leverages sentimental overall ratings, which comes frequently with on-line reviews, as supervising data, and would be possibly infer the linguistics aspects and aspect-level sentiments that are not only purposeful but collectively predictive of overall sentiments of reviews. Moreover, we collectively develop economical abstract thought methodology for the parameter estimation of SJASM supported folded gibbs sampling. We build the social network web site on its user post with attaching files, on its file topic name match with product name then recommend to user on e-commerce web site.
"Now I'm a bit angry: " Individuals' Awareness, Perception, and Responses to Data Breaches that Affected Them

Despite the prevalence of data breaches, there is a limited understanding of individuals’ awareness, perception, and responses to breaches that affect them. We provide novel insights into this topic through an online study ( n =413) in which we presented participants with up to three data breaches that had exposed their email addresses and other personal information. Overall, 73% of participants were affected by at least one breach, 5.36 breaches on average. Many participants attributed the cause of being affected by a breach to their poor email and security practices; only 14% correctly attributed the cause to external factors such as breached organizations and hackers. Participants were unaware of 74% of displayed breaches and expressed various emotions when learning about them. While some reported intending to take action, most participants believed the breach would not impact them. Our fndings underline the need for user-friendly tools to improve consumers’ resilience against breaches and accountability for breached organizations to provide more proactive post-breach communications and mitigations.
n-Type organic semiconducting polymers: stability limitations, design considerations and applications

This review outlines the design strategies which aim to develop high performing n-type materials in the fields of organic thin film transistors (OTFT), organic electrochemical transistors (OECT) and organic thermoelectrics (OTE). Figures of merit for each application and the limitations in obtaining these are set out, and the challenges with achieving consistent and comparable measurements are addressed. We present a thorough discussion of the limitations of n-type materials, particularly their ambient operational instability, and suggest synthetic methods to overcome these. This instability originates from the oxidation of the negative polaron of the organic semiconductor (OSC) by water and oxygen, the potentials of which commonly fall within the electrochemical window of n-type OSCs, and consequently require a LUMO level deeper than ∼−4 eV for a material with ambient stability. Recent high performing n-type materials are detailed for each application and their design principles are discussed to explain how synthetic modifications can enhance performance. This can be achieved through a number of strategies, including utilising an electron deficient acceptor–acceptor backbone repeat unit motif, introducing electron-withdrawing groups or heteroatoms, rigidification and planarisation of the polymer backbone and through increasing the conjugation length. By studying the fundamental synthetic design principles which have been employed to date, this review highlights a path to the development of promising polymers for n-type OSC applications in the future.
Quantifying Dimensions of the Vowel Space in Patients with Schizophrenia and Controls

The speech of patients with schizophrenia has been characterized as being aprosodic, or lacking pitch variation. Recent research on linguistic aspects of schizophrenia has looked at the vowel space to determine if there is some correlation between acoustic aspects of speech and patient status (Compton et al. 2018). Additional research by Hogoboom et al. (submitted) noted that measurements of Euclidean distance (ED), which is the average distance from the center of the vowel space to all vowels produced, and vowel density, which is the proportion of vowels clustered together in the center of the vowel space, were significantly correlated for patients with schizophrenia, but not for controls; this correlation was primarily due to a subset of 13 patients. In addition, they found that ED independently was a weak predictor of patient status, but that both density and ED, when used together, were predictors of patient status. This previous study utilized Prosogram (Mertens 2014), a tool that relies on acoustics to sift through the sound files and identify the vowels, which showed unstable reliability in detecting vowels. Therefore, this research aims to reassess the relationship between the vowel space and patient status by gathering more reliable measurements of the vowels from Hogoboom’s dataset by using the forced aligner FAVE (Rosenfelder et al. 2014). We seek to determine if there is a stronger correlation between vowel space usage and patient status than previously found—one that was previously masked by incomplete vowel measurements. Our current research finds that ED is a strong predictor of patient status (p<0.05). While Hogoboom’s previous work found that ED and density were independently significant, current work finds that those two variables are correlated. These results show that there is a relationship between ED and an individual’s patient status, where patients have lower average ED and controls have higher average ED. Overall, this research clarifies differences in utilization of the vowel space between patients with schizophrenia and controls, which could ultimately be used to create more quantitatively-defined linguistic measurements for diagnosis that are less subject to individual clinical listeners.
CS4860-2020-Full-Lecture 6: PC and iPC are decidable

In this lecture we present important results by Godel, Kleene, and others about the intuitionistic propositional calculus, iPC. Godel shows that iPC is only apparently narrower than classical PC. Kleene notes that the consistency of iPC secures the consistency of PC. Godel showed that there is no truth table decision procedure for iPC. Gentzen showed that there is a decision procedure for both PC and iPC Investigations into logical deduction by Gerhard Gentzen, 1969 [11]. Basically we we can prove that iPC is decidable using the same method that proves PC is decidable. These are important results by some of the best logicians. We draw heavily on Kleene’s book Introduction to Meta-Mathematics , ISHI Press International, 2009 [14].
Novelty Detection and Adaptation: A Domain Agnostic Approach

Novelties are surprises that a system encounters. System must learn about the characteristics, detect, understand, and adapt to novelty in not only the environment but in agents that interact with it. The context, timing, duration, extent, duration of novelty must be considered in agent’s adaption and accommodation. This research contributes towards building AI/ML system that can adapt to fluid novelties in open world. Many real-world problems are stochastic and encounter sudden novelties, which results in highly dynamic environments. Therefore, a robust framework is needed to identify various novelties that can occur, recognize the changes in the underlying environment, and adapt policies to maximize the long-term cumulative rewards. To achieve this, we propose ideas to adopt a change point detection algorithm to detect the changes in the distribution of experiences, and to develop an agent that is capable of recognizing novelties and making informed decisions according to the changes in the underlying environment. These ideas can be adapted in various domains by tuning the agent’s objective function, where it will still capture the changes in the corresponding underlying environment. This research contributes to the SAIL-ON effort [Ted Senator 2019].
Monte Carlo Methods for Index Computation (mod p)

We describe some novel methods to compute the index of any integer rela-tive to a given primitive root of a prime p. Our first method avoids the use of stored tables and apparently requires 0(p ) operations. Our second algorithm, which may be regarded as a method of catching kangaroos, is applicable when the index is known to lie in a certain interval; it requires 0(w ) operations for an interval of width w, but does not have complete certainty of success. It has several possible areas of application, including the factorization of integers.
Comparative Analysis of Various Techniques used for Predicting Student’s Performance

Digitization is transforming all aspects of education. Learner’s interactions with their online and offline learning environment lead to a trail of data that can be used for the purpose of analysis. Learning analytics (LA) and Educational data mining and (EDM) are emerging fields that attempt to develop methods to confront an abundance of data from the educational domain in order to optimize learning and leveraging decisions related to learning, teaching, and educational management. EDM/LA techniques interpret such enormous data and turn it into useful action. It provides insight to teachers to improve teaching, to understand learners, to identify difficulties faced by learners, and to provide meaningful feedback to learners thereby improving the learner’s performance. This paper aims to compare different EDM/LA techniques and to identify their potential strength and weaknesses that are applied in the educational domain to predict the student’s performance.
Demand-Aware Plane Spanners of Bounded Degree

Plane spanners of bounded degree are efficient communication backbones for networks. However, while existing spanners provide attractive guarantees in the worst-case, they are demand-oblivious and may hence be suboptimal under specific traffic demands. This paper thus initiates the study of demand-aware plane spanners of bounded degree, geometric spanners whose topology accounts for the actual communication traffic. We show that demand-awareness can significantly reduce the distance travelled per bit, and present a spanner which exploits topological flexibilities to account for the demand, without losing desirable guarantees of demand-oblivious spanners, namely constant stretch and degree. We complement our analytical results with heuristic improvements and a simulation study exploring the benefits of demand-awareness under realistic traffic traces.
Implications of the CDF tt̄ Forward-Backward Asymmetry for Hard Top Physics

The CDF collaboration has recently reported a large deviation from the standard model of the tt̄ forward-backward asymmetry in the high invariant mass region. We interpret this measurement as coming from new physics at a heavy scale Λ , and perform a model-independent analysis up to O(1/Λ4) . A simple formalism to test and constrain models of new physics is provided. We find that a large asymmetry cannot be accommodated by heavy new physics that does not interfere with the standard model. We show that a smoking gun test for the heavy new physics hypothesis is a significant deviation from the standard model prediction for the tt̄ differential cross section at large invariant mass. At Mtt̄ > 1 TeV the cross section is predicted to be at least twice that of the SM at the Tevatron, and for Mtt̄ > 1.5 TeV at least three times larger than the SM at the LHC.
Relationship of Rainfall and IOD Phenomena by using Spectral Analysis and SARIMA

Rainfall is very essential for life and it has complexity process. To obtain the relationship between rainfall in time-spatial of Aceh region and the Indian Ocean Dipole (IOD) phenomenon, we used spectral-analysis and SARIMA model. Identification results show that the rainfall data monthly in Aceh Barat (ABR), Aceh Besar (ABS), Sabang (S) and Aceh Utara (AU) has a similar pattern. However, due to the sensitivity of the occurrence of the IOD, the ABR region has higher rainfall compared to ABS, S and AU regions. Rainfall in all regions observation has a peak time, namely at the end of the year (October-November-December). It has the negative peak of the IOD event occurred in September-2016 while the positive peak point occurred in October-2019. Overall, the normal phase occurred from January to April-2019. In peak season in ABR and ABS, the rainfall intensity increased. The results showed that the rainfall model in ABR and S has normal distribution, whereas in ABS and AU has nonnormal distribution. Furthermore, the rainfall model in ABR with SARIMA(2,0,2)(0,1,1)6 is the best model compared to model of three other regions with high accuracy forecasting.
Plant Growth Promoting Rhizobacteria (PGPR): as a Potential Biocontrol for Curvularia lunata Invitro

Rhizobacteria is an environmentally friendly and sustainable biocontrol agent, used as an alternative in the management of plant pathogens. Furthermore, their suppression mechanism often varies, as some are capable of producing antibiotic compounds, competing with, and increasing plant growth, although t some forms possess multiple control. This study, therefore, aims to determine the activity of rhizobacterial antibiosis against Curvularia lunata in vitro. The experiment was conducted at the Laboratory of Biological Control, Department of Pests and Plant Diseases, Faculty of Andalas University. In addition, the procedure required using an experimental method, which was carried out in a Completely Randomized Design. The antagonistic rhizobacterial selection of C. lunata was carried out using a dual culture test, on a total of 7 rhizobacteria, and 4 were selected, encompassing Stenotrophomonas malthopilia KJKB5.4, Stenotrophomonas pavanii LMTSA5.4, Bacillus cereus AJ34 and Alcaligenes faecalis AJ14 as potential candidates in the control of C. lunata, suppression effectiveness that was above 50%. In addition, it was also established that all four isolates the propensity to produce chitinase enzymes.