Miracle-dz/UniFAR-FTUs huggingface dataset:
IAES International Journal of Artificial Intelligence (IJ-AI)

Received Sep 5, 2022 Revised Jan 21, 2023 Accepted Mar 10, 2023 In recent times, malware visualization has become very popular for malware classification in cybersecurity. Existing malware features can easily identify known malware that have been already detected, but they cannot identify new and infrequent malwares accurately. Moreover, deep learning algorithms show their power in term of malware classification topic. However, we found the use of imbalanced data; the Malimg database which contains 25 malware families don’t have same or near number of images per class. To address these issues, this paper proposes an effective malware classifier, based on costsensitive deep learning. When performing classification on imbalanced data, some classes get less accuracy than others. Cost-sensitive is meant to solve this issue, however in our case of 25 classes, classical cost-sensitive weights wasn’t effective is giving equal attention to all classes. The proposed approach improves the performance of malware classification, and we demonstrate this improvement using two Convolutional Neural Network models using functional and subclassing programming techniques, based on loss, accuracy, recall and precision.
Abstract geometrical computation: Turing-computing ability and unpredictable accumulations (extended abstract)

geometrical computation: Turing-computing ability and unpredictable accumulations (extended abstract) Jérôme Durand-Lose Mars 2004 Research Report N 2004-09 École Normale Supérieure de Lyon 46 Allée d’Italie, 69364 Lyon Cedex 07, France Téléphone : +33(0)4.72. 72.80. 37 Télécopieur : +33(0)4.72. 72.80. 80 Adresse électronique : lip@ens-lyon.fr Abstract geometrical computation: Turing-computing ability and unpredictable accumulations (extended abstract)geometrical computation: Turing-computing ability and unpredictable accumulations (extended abstract)
Probe conditioning via convolution neural network for scanning probe microscopy automation

We present an automation system for conditioning a scanning probe microscopy (SPM) probe into different states on a Si(111)–(7 × 7) surface at room temperature. Topography images representing multiple surface states and probe condition states divided into 11 categories and trained by a convolution neural network with an accuracy of 87% were used to estimate the effectiveness of the probe with an accuracy of 98%. We demonstrate the responsiveness of the method by experimentally reforming a probe into different conditions defined by preset categories. This system will promote advancements in autonomous SPM experiments at atomic scale and room temperature.
A quantum approach for time series data based on graph and Schrödinger equations methods

The supremacy of quantum approach is able to solve the problems which are not practically feasible on classical machines. It suggests a significant speed up of the simulations and decreases the chance of error rates. This paper introduces a new quantum model for time series data which depends on the appropriate length of intervals. To provide effective solution of this problem, this study suggests a new graph-based quantum approach. This technique is useful in discretization and representation of logical relationships. Then, we divide these logical relations into various groups to obtain efficient results. The proposed model is verified and validated with various approaches. Experimental results signify that the proposed model is more precise than existing competing models.
Prospects for Higgs boson & top quark measurements and applications of digital calorimetry at future linear colliders

Within the next few years a decision must be made by the global community on what type of high energy colliders should be built in the post LHC era. Here we present studies showing what might be achieved if a linear lepton collider such as Compact Linear Collider ( CLIC ) is chosen. Two physics studies are presented showing the precision achievable in the electroweak sector when operating at 1.4 TeV. Firstly the measurement of σ Hν ν × BR(H → W W ∗ ), an integral component for model independent Higgs measurements, is described using the semileptonic decay channel and is shown to yield a statistical precision of 1.3% for 1.5 ab −1 of data. A differential measurement of the top quark forward backward asymmetry is also performed as a probe of the electroweak form factors of the ttX vertex yielding a statistical precision of O(1%) for 1.5 ab −1 of data. Lastly, the potential for using a novel design of a Digital Electromagnetic Calorimeter ( DECAL ) at the International Linear Collider ( ILC ) is studied showing that an energy resolution ofσ E⁄E=16.1 "\%"⁄E⊕0.5 "\%"⁄E⊕0.4"\%" be achieved, similar to what is seen for the standard design choice, when using 30 μm pitch pixels with a 12 μm epitaxial thickness.
Role Of Micronutrient Zinc In Facing Drought Stress And Germination Of Chickpea

: According to extensive surveys the zinc (Zn) content of soils, is generally in the range of 10-300 ppm. Certainly Zn, because of its concentration, can be considered as a trace element in soil. It occurs most frequently in the lithosphere as the mineral ZnS (sphalerite). Zn appears to be scattered throughout the mineral fraction of soils. The critical Zn level found in youngest expanded leaves for 90% maximum yield was 16 mg kg -1 dry biomass. Zn-efficient Excalibur had greater net Zn uptake rates as compared to Zn-inefficient variety but they were not different at the 6-week harvest. Zinc-deficient plants had greater net uptake rates of Cu, Mn, B, P, and K but a reduced uptake rate of Fe. It is concluded that higher seed Zn content acted similar to a starter-fertilizer effect. Despite the importance of zinc as a micronutrient for plant growth, there have been relatively few studies of the mechanism of zinc uptake. It is clear from a recent review of the literature that there is currently little agreement on how zinc crosses the plasma-lemma of plant cells. The questions which have not been satisfactorily answered are whether zinc enters via ion channels or via a divalent-cation carrier. Zinc functions as an enzyme activator in carbohydrate metabolism and protein formation. Deficiency symptoms usually appear first on relatively young leaves early in growing season. In broadleaf plants, zinc deficiency results in a shortening of internodes and a decrease in leaf size. Snap bean develops a yellowing between the leaf veins. However, it is very difficult to distinguish between zinc and manganese deficiencies in this crop.
Comparing Cross-Section and Time-Series Factor Models

We use the cross-section regression approach of Fama and MacBeth (1973) to construct cross-section factors corresponding to the time-series factors of Fama and French (2015). Time-series models that use only cross-section factors provide better descriptions of average returns than time-series models that use time-series factors. This is true when we impose constant factor loadings and when we use time-varying loadings that are natural for time-series factors and time-varying loadings that are natural for cross-section factors. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.
Mahler’s Das Lied von der Erde and “Chinese Element”

Gustav Mahler was one of the most predominant composers and conductors between the late 19th century and the early 20th century, Das Lied von der Erde is one of the most representative music works in his later years. Creation background, Taoist thinking, application of Tang dynasty poems, and integration of pentatonic mode and Western music system are discussed in the article, which centers analysis of Chinese elements in Das Lied von der Erde and elaborates that Das Lied von der Erde is a great orchestral work with wisdom and poetry. It is the result of civilizational exchange and mutual learning and it has high artistic value.
Under what Conditions is a Plural Represented as More than One? Two Methods for Testing

The current paper reviews the findings from a series of psycholinguistic experiments using two different methods designed to probe the conceptual representation of plural expressions. Experiments using a novel number interference paradigm suggested that singular indefinite NPs within distributed predicates are mentally represented as multiple entities (Patson & Warren 2010), but that distributed predicates are conceptually represented as multiple events only in the presence of strong cues to plurality (Patson & Warren 2015). These findings show that the number interference paradigm can be used to probe when comprehenders mentally represent multiple events and entities. A second set of experiments used a picture-judgment task to begin to understand the nature of the plural representations created by comprehenders. These studies suggest that the conceptual representations of plural definite descriptions are no more similar to pictures of small sets of items than they are to pictures of singletons (Patson et al. 2014). These findings and others contribute to a more nuanced view of how language comprehenders compute and represent number. Importantly, these findings introduce two experimental paradigms useful for probing plural representations. 1 Processing and Representing Plurals: Methods
Estimation methods for the shadow economy. A systematic literature review

Estimating the shadow economy is by no means a simple task. The hidden nature of the phenomenon makes it difficult to measure. In the literature, there are various methods used in estimating the shadow economy, each of which has its strengths and weaknesses. This paper aims to be a detailed literature review of the different types of methods existing at the current time. The review can be a good reference in deciding which of the methods is best to serve one’s purpose when measuring the underground economy. Analyzing the advantages and shortcomings of each method, as well as the methodology used, can lead to a rational decision regarding the estimation of the shadow economy. Reviewing the literature, we identified three main groups of methods for estimating the shadow economy: 1) Direct methods; 2) Indirect methods; and 3) Model approach. Over the years, there has been an increasing interest in developing estimation models for the shadow economy, but, to date, there is no universally accepted definition for the concept, nor a universally accepted type of model used. However, the most frequently used method to estimate the shadow economy, in recent years has been the model approach (MIMIC), despite its limitations. When deciding which model to use to estimate the shadow economy, one has to consider the definition of the concept (what kinds of activities will be included), the availability of data necessary, and the methodology used. Our paper is useful both for academical purposes but also for policy makers in order to find the best tool of measuring the shadow economy around the world countries.
Identification and quantification ofmesoscale eddies in a global ocean simulation

This work seeks to further understand the climatic role of mesoscale eddies, which are long-lived ocean vortices that span up to hundreds of kilometers. They are already known to be involved in the transport and redistribution of salt, heat, and nutrients throughout the ocean. However, precisely determining their involvement has proven to be a challenge, as the mechanics of eddies are only partly understood; a standard definition for mesoscale eddies does not exist and, therefore, scientifically meaningful, robust methods for eddy extraction, characterization, tracking and visualization remain a challenge. To address these challenges, this work begins by developing a new method for extracting vortices from geophysical flow data, based on a reference model describing the expected physics and geometry of an idealized vortex. Additionally, a method for tracking eddies through the ocean over time is described. Using these mechanisms, a global descriptive census of eddies is carried out. The census data allow the eddy behavior of both the model and extraction technique to be compared against observational work by others. This census also builds on previous work, by providing a global view of the depth-dependent behavior of mesoscale eddies. Finally, two studies were carried out covering the functional behavior of eddies. In the first, eddies were seeded with a passive tracer, which was allowed to be advected and diffused by the simulation itself. The changes in passive tracer concentration were then studied to derive the characteristic time and length scales on which eddies can transport water. In the second, active tracer transport was decomposed, allowing eddy leakage to be quantified, both instantaneously and in aggregate. This work contributes, on the one hand, to the understanding of the role of mesoscale eddies in the ocean, particularly concerning the scales on which they operate, and on the other, to the problems of identification, extraction, tracking, and visual characterization of vortices in any geophysical flow. The eddy leakage metric in particular could be further developed, either to visualizing vortex characteristics in other domains, or to further quantification of leakage effects with the new metric as a baseline.
Kinetic modelling of dusty plasmas

Modelling of ECR‐heated methane plasmas was done using a particle‐in‐cell code with collisions (PIC MCC) to get a better understanding in the problem of carbon layer production and chemical sputtering in such plasmas, which serve as a model system of divertor plasmas being dominated by carbon sputtering, especially at detached conditions. A fully kinetic model (PICMCC, resolving also the sheath in front of the wall) including all relevant species and their reactions (neutrals, ions, electrons) was developed and applied. The same model was used to study capacitive RF discharges which are also used to create so‐called plasma crystals. Depending on neutral gas pressure different plasma conditions appear (determined by the importance of collisions with neutrals). For low neutral densities rather dynamic plasma profiles and distribution function emerge (stochastic Fermi heating), whereas for high densities quasi‐stationary conditions show up. These are then used for the study of plasma crystals, where the dust particles are introduced as additional species in the model.
Operator splitting for numerical solution of the modified Burgers' equation using finite element method

The aim of this study is to obtain numerical behavior of a one‐dimensional modified Burgers' equation using cubic B‐spline collocation finite element method after splitting the equation with Strang splitting technique. Moreover, the Ext4 and Ext6 methods based on Strang splitting and derived from extrapolation have also been applied to the equation. To observe how good and effective this technique is, we have used the well‐known the error norms L2 and L∞ in the literature and compared them with previous studies. In addition, the von Neumann (Fourier series) method has been applied after the nonlinear term has been linearized to investigate the stability of the method.
Tools4SeedSystems: working towards resilience through root, tuber and banana crops in humanitarian settings

Sweetpotato is an excellent recovery crop for post disaster interventions. It is already an important component of the cropping systems in Africa, south Asia and parts of Latin America and the Caribbean (LAC) because of its robustness and ability to produce under difficult conditions. It will become more important in the face of a changing climate. In some settings, especially in Africa, it is also considered a “women’s crop,” reflecting the relatively strong control women have in decision making in production and marketing. It has short maturity (3-4 months), flexible planting dates, and can produce a harvest with only 2 months rainfall, with 2-3 crop cycles per year. It can be grown in sole stands, intercropped with maize, cassava or tree crops, and is an excellent rotation crop with rice. Varieties with spreading growth habit establish good ground cover to reduce soil erosion and reduce labor requirement for weeding. Early food availability fills the hunger gap before cereal crops can be harvested. Piecemeal harvesting allows for a longer harvesting period. Sweetpotato has high calorie and vitamin A output per unit area/unit time compared to cereal crops. For nutrient enriched varieties, just one small root (125 grams) meets the daily vitamin A needs of a young child and supplies good levels of vitamins C, K, and E, several B vitamins and the minerals manganese and potassium1 [Low and Thiele, 2020]. There is a wide portfolio of varieties to meet preferences of adults for dry mouth feel or soft, easier consumption by young children and elderly. It has a short cooking time: can be eaten uncooked, boiled, roasted as a staple or snack food. Sweetpotato leaves are also widely consumed as vegetable and are an excellent source of lutein, which like betacarotene, prevents eye degeneration. The roots can be chipped and dried for future use. Dual purpose varieties can be used as a fodder crop for pigs, dairy, goats. Triple S (Storage in Sand and Sprouting) technology allow for longer storage of fresh roots for consumption or for sprouting as planting material for then next season. Sweetpotato benefits from a large network of African NARIs conducting modernized, state of the art breeding approaches in close partnership with CIP main breeding hubs (Mozambique and Uganda). In addition to agronomic characteristics, the said breeding efforts are focusing on increasing the crop’s climate resilience and cooking/organoleptic quality.
Sparse Representation-based Classification of Farsi Handwritten Digits Using Fisher Discrimination Criterion and Local Linear Embedding (LLE)

In recent years, sparse representation-based methods have performed well in machine vision and image processing. The main challenge in designing a proper classifier to detect Farsi digits is modeling the data subspace and classification based on the presented model. In this paper, we propose a method based on sparse representation. Local Linear Embedding (LLE) to recognize the handwritten Farsi digits. The conducted approach to recognizing Farsi digits in this paper is sparse representation, based on Fisher Discrimination Criterion for linear transformation of the data. In this method, data sets belonging to the same class are grouped together, whereas data sets that belong to different classes are kept apart. LLE is used as a regulator to maintain the local neighborhood of the data. Experimental results of Hoda database test data with 80,000 samples indicate that the proposed method has a higher accuracy than previously presented methods and has achieved the accuracy of 99.36% for 60,000 training samples and 20,000 test data.
A common trajectory recapitulated by urban economies

Is there a general economic pathway recapitulated by individual cities over and over? Identifying such evolution structure, if any, would inform models for the assessment, maintenance, and forecasting of urban sustainability and economic success as a quantitative baseline. This premise seems to contradict the existing body of empirical evidences for path-dependent growth shaping the unique history of individual cities. And yet, recent empirical evidences and theoretical models have amounted to the universal patterns, mostly size-dependent, thereby expressing many of urban quantities as a set of simple scaling laws. Here, we provide a mathematical framework to integrate repeated cross-sectional data, each of which freezes in time dimension, into a frame of reference for longitudinal evolution of individual cities in time. Using data of over 100 millions employment in thousand business categories between 1998 and 2013, we decompose each city's evolution into a pre-factor and relative changes to eliminate national and global effects. In this way, we show the longitudinal dynamics of individual cities recapitulate the observed cross-sectional regularity. Larger cities are not only scaled-up versions of their smaller peers but also of their past. In addition, our model shows that both specialization and diversification are attributed to the distribution of industry's scaling exponents, resulting a critical population of 1.2 million at which a city makes an industrial transition into innovative economies.
PHYTOCHEMICAL STUDY AND ANTIOXIDANT, ANTIBACTERIAL AND ANTIDIABETIC ACTIVITIES OF FLACOURTIA INDICA LEAVES EXTRACTS FROM THE TOGOLESE FLORA

Objective: Faced with the devastating effect of diabetes, the search for new natural antidiabetic molecules is an exploratory avenue. Methods: In this study, the leaves of Flacourtia indica were collected from the Togolese flora, and their antioxidant activity was evaluated using DPPH and FRAP assays, following a phytochemical screening conducted through colorimetric and precipitation reactions. The content of total phenols was determined using Folin-Ciocalteu's reagent and flavonoids by aluminum trichloride. The antibacterial activity of the extracts was evaluated using the method of dilution in agar wells with Muller-Hinton® agar. Additionally, the extracts' antidiabetic effect was evaluated in rats made diabetic by alloxan at a dose of 120 mg/kg. b.w. Results: The results of our work showed the presence of flavonoids, tannins, anthocyanins, and saponins in the extracts. The best polyphenol and flavonoid contents were measured in the hydroethanolic extract and were 186.46±0.308 mg EqAG/g and 464.14±17.043 mgEqQ/g. ES respectively. The hydroethanolic extract has a higher antioxidant power with an IC50 = 110.22 µg/ml. The reducing powers of the ferric ions were 3706±12.124 and 3777±9.238 µmol/ml, respectively for the hydroethanolic and aqueous extracts. The extracts were active against Staphylococcus aureus and are bacteriostatic. The hydroethanolic extract of the leaves of Flacourtia indica significantly reduced (p = 0.0014) blood glucose levels in diabetic rats. Conclusion: This plant can therefore be a potential medicine in the treatment of diabetes.
A Semieager Classifier for Open Relation Extraction

A variety of open relation extraction systems have been developed in the last decade. And deep learning, especially with attention model, has gained much success in the task of relation classification. Nevertheless, there is, yet, no research reported on classifying open relation tuples to our knowledge. In this paper, we propose a novel semieager learning algorithm (SemiE) to tackle the problem of open relation classification. Different from the eager learning approaches (e.g., ANNs) and the lazy learning approaches (e.g., kNN), the SemiE offers the benefits of both categories of learning scheme, with its significantly lower computational cost (O(n)). This algorithm can also be employed in other classification tasks. Additionally, this paper presents an adapted attention model to transform relation phrases into vectors by using word embedding. The experimental results on two benchmark datasets show that our method outperforms the state-of-the-art methods in the task of open relation classification.