Miracle-dz/UniFAR-FTUs huggingface dataset:
Sentimental Analysis Challenges in Persian Language

The rapid growth in data on the internet requires a data mining process to reach a decision support insight. Persian language has strong potential for deep research in any aspect of natural language processing especially sentimental analysis approach. Thou-sands of websites and blogs updates and modifies by Persian users around the world that contains millions of Persian context. This range of application requires a comprehensive structured framework to extract beneficial information for helping enterprises to enhance their business and initiate customer-centric management process by producing effective recommender systems . Sentimental analysis is an intelligent approach for extracting useful information from huge amounts of data to help an enterprise for smart management process. In this road, machine learning and deep learning techniques will become very helpful but there are number of challenges which are face to them. This paper tried to present and assort most important challenges of sentimental analysis in Persian language. This language is an Indo-European language which spoken by over 110 million person around the world and is official language in Iran, Tajikistan and Afghanistan. It’s also widely used in Uzbekistan, Pakistan and Turkish by order.
Cascaded F\"orster Resonance Energy Transfer and Role of the Relay Dye

The effect of introducing a relay dye on the energy transfer efficiency in a new donor-acceptor system has been studied. The values of the critical transfer distance, the reduced concentration, and the energy transfer efficiency of the cascaded system (consisting of three dyes- donor-relay dye-acceptor) are compared with that of the two dye system. Experimentally, it has been observed that the presence of a relay dye increases the transfer efficiency from the donor to the acceptor.
An Algorithmic Framework for Approximating Maximin Share Allocation of Chores

We consider the problem of fairly dividing m indivisible chores among n agents. The fairness measure we consider here is the maximin share. The previous best known result is that there always exists a 4/3-approximation maximin share allocation[3]. With our algorithm, we can always find a 11/9-approximation maximin share allocation for any instance. We also discuss how to improve the efficiency of the algorithm and its connection to the job scheduling problem. The full paper can be found at https://arxiv.org/abs/1907.04505.
Partially Observable Planning and Learning for Systems with Non-Uniform Dynamics

We propose a neural network architecture, called TransNet, that combines planning and model learning for solving Partially Observable Markov Decision Processes (POMDPs) with non-uniform system dynamics. The past decade has seen a substantial advancement in solving POMDP problems. However, constructing a suitable POMDP model remains difficult. Recently, neural network architectures have been proposed to alleviate the difficulty in acquiring such models. Although the results are promising, existing architectures restrict the type of system dynamics that can be learned --that is, system dynamics must be the same in all parts of the state space. TransNet relaxes such a restriction. Key to this relaxation is a novel neural network module that classifies the state space into classes and then learns the system dynamics of the different classes. TransNet uses this module together with the overall architecture of QMDP-Net[1] to allow solving POMDPs that have more expressive dynamic models, while maintaining efficient data requirement. Its evaluation on typical benchmarks in robot navigation with initially unknown system and environment models indicates that TransNet substantially out-performs the quality of the generated policies and learning efficiency of the state-of-the-art method QMDP-Net.
{Combining Algorithm-based Fault Tolerance and Checkpointing for Iterative Solvers}

Several recent papers have introduced a periodic verification mechanism to detect silent errors in iterative solvers. Chen [PPoPP'13, pp. 167--176] has shown how to combine such a verification mechanism (a stability test checking the orthogonality of two vectors and recomputing the residual) with checkpointing: the idea is to verify every $d$ iterations, and to checkpoint every $c \times d$ iterations. When a silent error is detected by the verification mechanism, one can rollback to and re-execute from the last checkpoint. In this paper, we also propose to combine checkpointing and verification, but we use ABFT rather than stability tests. ABFT can be used for error detection, but also for error detection and correction, allowing a forward recovery (and no rollback nor re-execution) when a single error is detected. We present a performance model to compute the performance of all schemes, and we instantiate it using the Conjugate Gradient algorithm. Finally, we validate our new approach through a set of simulations.
Do Design Metrics Capture Developers Perception of Quality? An Empirical Study on Self-Affirmed Refactoring Activities

Background. Refactoring is a critical task in software maintenance and is generally performed to enforce the best design and implementation practices or to cope with design defects. Several studies attempted to detect refactoring activities through mining software repositories allowing to collect, analyze and get actionable data-driven insights about refactoring practices within software projects. Aim. We aim at identifying, among the various quality models presented in the literature, the ones that are more in-line with the developer's vision of quality optimization, when they explicitly mention that they are refactoring to improve them. Method. We extract a large corpus of design-related refactoring activities that are applied and documented by developers during their daily changes from 3,795 curated open source Java projects. In particular, we extract a large-scale corpus of structural metrics and anti-pattern enhancement changes, from which we identify 1,245 quality improvement commits with their corresponding refactoring operations, as perceived by software engineers. Thereafter, we empirically analyze the impact of these refactoring operations on a set of common state-of-the-art design quality metrics. Results. The statistical analysis of the obtained results shows that (i) a few state-of-the-art metrics are more popular than others; and (ii) some metrics are being more emphasized than others. Conclusions. We verify that there are a variety of structural metrics that can represent the internal quality attributes with different degrees of improvement and degradation of software quality. Most of the metrics that are mapped to the main quality attributes do capture developer intentions of quality improvement reported in the commit messages.
A Survey of Reconfigurable Intelligent Surfaces: Towards 6G Wireless Communication Networks with Massive MIMO 2.0

Reconfigurable intelligent surfaces (RISs) tune wireless environments to increase spectrum and energy efficiencies. In view of much recent attention to the RIS concept as a promising technology for 6G wireless communications, we present a survey of RISs in this paper. Specifically, we categorize recent research studies of RISs as follows. For RIS-aided communications, the summary includes capacity/data rate analyses, power/spectral optimizations, channel estimation, deep learning-based design, and reliability analysis. Then we review RISs implementations as well as the use of RISs in secure communications, terminal-positioning, and other novel applications. We further identify future research directions for RISs, with an envision of the RIS technology playing a critical role in 6G communication networks similar to that of massive MIMO in 5G networks. As a timely summary of RISs, our work will be of interest to both researchers and practitioners working on RISs for 6G networks.
Specialized Decision Surface and Disentangled Feature for Weakly-Supervised Polyphonic Sound Event Detection

In this article, a special decision surface for the weakly-supervised sound event detection (SED) and a disentangled feature (DF) for the multi-label problem in polyphonic SED are proposed. We approach SED as a multiple instance learning (MIL) problem and utilize a neural network framework with a pooling module to solve it. General MIL approaches include two kinds: the instance-level approaches and embedding-level approaches. We present a method of generating instance-level probabilities for the embedding level approaches which tend to perform better than the instance-level approaches in terms of bag-level classification but can not provide instance-level probabilities in current approaches. Moreover, we further propose a specialized decision surface (SDS) for the embedding-level attention pooling. We analyze and explained why an embedding-level attention module with SDS is better than other typical pooling modules from the perspective of the high-level feature space. As for the problem of the unbalanced dataset and the co-occurrence of multiple categories in the polyphonic event detection task, we propose a DF to reduce interference among categories, which optimizes the high-level feature space by disentangling it based on class-wise identifiable information and obtaining multiple different subspaces. Experiments on the dataset of DCASE 2018 Task 4 show that the proposed SDS and DF significantly improve the detection performance of the embedding-level MIL approach with an attention pooling module and outperform the first place system in the challenge by $\mathbf {6.6}$ percentage points.
Do urban African dwellers pay a premium for food quality and , if so , howmuch ? An investigation of the Malian fonio grain market

Very little data is available concerning the valuation of quality on existing food markets in SubSaharan Africa. Using data collected from a survey of fonio (a cereal) retail markets conducted in Bamako, Mali, Africa and a hedonic price Partial Least Square regression method, this study shows that most quality attributes declared as important by consumers have a specific hedonic price that is different from zero. This is interpreted as a result of the efficient performance of so-called traditional informal markets. Among the valued attributes, some are physical (colour, degree of milling) and thus relatively easy to assess, while the assessment of others (category or country of origin) are more complicated for both parties to the transaction. These attributes, however, also have specific hedonic prices. Informal norms and a certain amount of trust are thus present in these markets and should not be underestimated. The inclusion of buyer characteristics in the model is justified by the differences in bargaining power, which are essentially linked with different levels of experience buying or using the product. We showed that women were getting better prices for the same product quality. The premiums paid for quality varied from 1 to 14% of the price. These estimates have confirmed the other few estimates done in African food markets.
Decomposing the relationship between international bond markets

The correlations between major asset classes are of concern and interest to monetary authorities and financial regulators alike – the potential for a worldwide decline in consumption which might result from a dramatic fall in equity market wealth could have serious implications for both the health of financial institutions and that of the real economy. This concern is reflected in the academic literature, where an increasing number of researchers have tried to understand the characteristics of these linkages and the nature of the processes by which information flows between markets. Perhaps the main catalyst for this concern in recent years was the equity market crash of October 1987. This event more than any other highlighted the high levels of correlation between national equity markets at times of extreme market stress.
Advantages and Disadvantages of Resting State Functional Connectivity Magnetic Resonance Imaging for Clinical Applications

Functional Magnetic Resonance Imaging (fMRI) is a powerful non-invasive technique for imaging BOLD (Blood Oxygenation Level Dependent) signal changes that is due to changes in brain hemodynamics responses associated to local neuronal activity to identify activated brain regions [1,2]. This technique allows researchers in laboratory environment to investigate the BOLD signals to determine the activated brain reigns for different stimuli or the processing of various cognitive tasks [3,4]. Furthermore, fMRI can be used in clinical applications to determine the brain abnormalities in population of subjects with neurological disease. Our knowledge about brain functions comes from task-state studies in the presence of external stimuli that the neural activity and subject responses are measured for analysis. However, the brain is very active in resting-state without any stimuli. Recent findings that have been proven very valuable in the clinical area of fMRI applications, involves investigation of brain fluctuations at resting condition and their results demonstrate that spontaneous modulation of the BOLD does not produce randomly. Spontaneous modulations in BOLD signal are structured in spatial patterns that make correlated networks between various brain regions. The analysis of brain fluctuations in BOLD signal usually involves spatial patterns of correlated activity across regions of interests that are known as resting state connectivity networks. After data acquisition and preprocessing [5], there are two important data analysis techniques for studying the resting state functional connectivity: seed-based correlations approaches that are based on calculating the correlation between extracted regions of interests to identify spatial pattern of spontaneous activity and ICA (Independent Component Analyses)-based approaches that use all brain voxels activity to separate brain functional networks that are correlated with spontaneous component of BOLD signal (to account for non-neural noise with considering maximally independent spontaneous BOLD fluctuations) [4,6-8]. Functional connectivity approaches are divided into resting state and task state studies [4,9,10].
Peer Effects in After-School Programs : Experimental Evidence in

This paper provides experimental evidence of the overall impact of an after-school program on students’ outcomes, and of peer effects in that context. Participants were between 10-16 years old and enrolled in public schools in El Salvador. I find that the program reduced reports of bad behavior by 0.17 standard deviations, school absenteeism by 23%, and increased school grades by 0.11-0.13 standard deviations. Changes in highly violent students mainly drove the results. Regarding group composition, I find that mixing students with different propensities for violence was better than segregating them. Moreover, there is an interaction between the group composition and individual baseline propensity to violence; more violent participants have greater effects on their academic outcomes and behavior when they are assigned to heterogeneous groups, supporting the rainbow model of peer effects. Additionally, it finds positive social spillover effects for non-enrolled children exposed to treated students.
Integration and visualization of structured data representing biological networks

The investigation of biological networks for their better understanding and making available for practical use is currently the important task in systems biology. The paper presents an integrated environment BiNArr aimed to perform some data preparation operations as well as visualization of the network data stored in biological databases. We proposed the unified graph representation for the structures extracted from original resources and developed the modules for their visualization and edition. Another important feature is the automatic coding of the resulting graphs in several formats required by different graph mining applications. In order to present some capabilities of the application, the structures from example databases representing metabolic pathways (KEGG) as well as protein-protein interactions (DIP) were used.
Valuation of venture capital backed firms-guess or professional judgment of agency risks and observable firm characteristics ?

Valuing young and innovative companies is a challenging task. Still, determining their economic value is one of the most important issues for investors and entrepreneurs engaged in venture capital financing. We analyze how venture capitalists determine enterprise values of their portfolio firms. We find that the anticipated magnitude of agency risk is a major value driver for young and innovative firms. However, even taking into account agency risks, accounting information and observable firm characteristics remain important value drivers. As a result we provide a valuation framework in which venture capital backed firms may be valued as precise as publicly traded firms. This shows that valuation of venture capital backed firms is not a guess. JEL classification: G24, G32
A Study on Scalable and Untraceable Authentication Protocol of RFID tags

RFID (Radio Frequency IDentification) is recently becoming popular, and plays definitely an important role in moving to ubiquitous society due to deploying its convenience and economic efficiency. Furthermore, RFID nowadays comes into the spotlight as a technology to substitute the barcode system since RFID can solve several problems in the barcode system: (1) to require line of sight for scanning, (2) no read/write capability including limited capacity for encoding information, (3) opportunities of human error, and more problems in [48, 45]. RFID is expected to achieve unlimited economic gain. For example, in case that one billion of tagged items are sold by consumer product manufacturers, then a difference of one cent between tags and the barcode can give ten million dollar economic gain since tags can be attached to any kinds of items [42]. RFID technology, on the other hand, is jeopardized from various attacks and problems preventing widespread RFID deployment: replay attack, spoofing, traceability, desynchronization, scalability, and tag cloning. We focus ourselves on untraceabily and scalability in this thesis. We give the reason why untraceability and scalability is important to deploy RFID widely. To prevent an adversary from tracing tagged item is most important in RFID
PHOTOGRAMMETRIC AUTOMATION : IS IT WORTH ?

This paper focuses on the evaluation of automated image based techniques which are used lately in order to produce three dimensional digital models of cultural heritage objects. This implies a doubt as to how accurate and reliable are the products of these automated algorithms and how efficient they are for providing the necessary 3D material for the virtual environments. The implementation of this innovative approach has gradually become very popular in the field of Cultural Heritage during the last five years. Non-specialists found a way to easily produce 3D reconstructions just by taking a few images and using Structure-from-Motion and Multiview Stereo algorithms implemented by commercial or open source software. However, this fact led to debatable results, as a lot of ambiguities are lurking hidden in the “happiness” of automation. In order to assess the metric performance of these algorithms an innovative metric evaluation strategy has been designed. A very accurately measured test field, set up mainly for calibrating digital cameras, was used as an object of well-known ground truth. It was established that the accuracy of the resulting 3D reconstruction depends on the spatial analysis and the general quality of the original digital images, on the careful selection of the parameters provided by the software, the properties of the object itself and the computing power available.
Perturbed parameter ensembles as a tool for sampling model uncertainties and making climate projections

Uncertainties in the formulation of weather forecasting or climate models are manifested in alternative structural choices (for example, choices of resolution, numerical integration scheme and the basic physical assumptions on which parameterisations of sub-grid scale processes are based), and in the values of poorly constrained parameters controlling the representation of earth system processes or properties within a given model structure. Multi-model ensembles (MMEs), constructed from models available at different centres, have been extensively used in weather and seasonal climate forecasting applications (e.g. Krishnamurti et al., 1999; Palmer et al., 2004; Doblas-Reyes et al., 2009; Johnson and Swinbank, 2009), and for climate change projections (e.g. Meehl et al., 2007). An important strength of the MME approach is that each model undergoes extensive testing, including validation against a wide range of observables to establish its credibility as a tool for weather prediction or climate simulation. Another strength is that the models are constructed from a large pool of alternative components, hence sampling, to some extent, the effects of variations in model structure. The main limitations are that MMEs are rather small (ranging from a handful of models to a maximum of around 20 members), and are not designed to sample modelling uncertainties in a systematic fashion, being assembled on an opportunistic basis. Specifically, it is not clear how to define a space of possible model configurations of which the MME members are a sample. In climate change applications, for example, this creates the need to make substantial assumptions in order to interpret their results in a probabilistic form suitable for risk assessments (Tebaldi and Knutti, 2007).
The “ Sign up and Connect ” eTwinning project : an int ercultural approach to teaching English as a foreign Language

Learning a foreign language does not only involve the mastery of a system of linguistic norms but it also requires intercultural understanding, the competency to see the world in a different perspective. Taking this into consideration, foreign language education has a crucial role to play in fostering the use of English as an intercultural language to interact with people from different cultural backgrounds. The problem is that most European foreign language learners have very few opportunities for ‘real’ intercultural contact. The eTwinning action from the European Union elearning programme represents an opportunity for intercultural contact as it cuts geographical distances through integrating ICT tools in the classroom. The integration of eTwinning appears to be the key to achieve the major goals for a life-long education of the twenty-first century as reported to UNESCO of the International Commission on Education for the Twenty-first Century by Jacques Delors (1996) which is based on four pillars: learning to know, learning to do, learning to live together and learning to be. In this paper we will present the results of a successful curricular integration of an eTwinning project which is being developed as a teaching strategy grounded on intercultural interaction mediated by ICT tools between students and teachers from different countries. As eTwinning project is based on the principles of collaborative and cooperative learning, it is expected to introduce changes in the teaching process and to the design of a learner-oriented curriculum for the acquisition of intercultural communicative competence. In parallel, it will have an impact on the learning process as it will engage learners in learning and using the foreign language to communicate with students from other cultures to accomplish a set of collaborative tasks.