Miracle-dz/UniFAR-FTUs huggingface dataset:
The Ideal Concept of Corporate Social Responsibility through the Social Welfare Approach

As the legal subject of the company in the course of business undertaken by its managing business has the burden of moral rights and responsibilities, and it is not just for moral responsibility and obligation to shareholders or investors only. However, the company's management also has the responsibility and moral obligation to other parties related to the business operations of a company. The concept of CSR embodies the meaning that the company has a responsibility that includes legal, economic, ethical, and environmental responsibility. The present study used a constructivism paradigm or more precisely Legal Constructivism. This paradigm belongs to a group of non-positivistic paradigms. The constructivism paradigm chose it by considering that the law is a social reality; thus the truth depends on how people interpret. Through the constructivism paradigm, the authors want to study objectively with accurate data, then constructed through legal concepts that are still being tested for their strengths. The specification of this research is descriptive analytical. Ten manufacturing companies in this study admit they have been implementing the CSR program for ten years. Hence the significance of regulation about their CSR is understood. In fact, there has been a change in the paradigm of the implementation of CSR initially only a philanthropic act that is temporary, as government rules and community demands are further evolving into a sustainable CSR implementation paradigm. The manufacturing company argues that by applying CSR programs to communities around the operational areas of the company can enhance the image and caring for the fellow to have a good effect to improve the welfare of the people around the company.
Robust Principal Component Analysis and Fractal Methods to Delineate Mineralization-Related Hydrothermally-Altered Zones from ASTER Data: A Case Study of Dehaj Terrain, Central Iran

DOI: 10.22044/jme.2020. 9619.1876 The Dehaj area, located in the southern part of the Urumieh-Dokhtar magmatic belt, is a well-endowed terrain hosting a number of world-class porphyry copper deposits. These deposits are all hosted in an acidic to intermediate volcano-plutonic sequence greatly affected by various types of the hydrothermal alterations, whether argillic, phyllic or propylitic. Although there are a handful of hitherto-discovered porphyry copper deposits in the area, the geological setting of the area suggests the possibility of finding further deposits. The recognition and delineation of the hydrothermal alterations can pave the way for the discovery of further potential zones that possibly host the porphyry copper deposits. The current work proposes a hybrid methodology applied to the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery by combining the application of dimension reduction and fractal techniques to delineate the hydrothermally-altered zones In order to reduce the dimensionality of multi-band ASTER data, Robust Principal Component Analysis (RPCA) was employed to elicit the traces of hydrothermally-related mineral assemblages including illite, sericite, quartz, kaolinite, epidote, and chlorite. Highlighting the existence of the aforementioned minerals, the extracted components require interpretation, i.e. a boundary is required to constraint the hydrothermally affected zones from the rest of the geological units. In order to tackle such a challenge, the authors introduce the concept of value-pixel fractal technique for the extracted principal components. The Prediction-Area (P-A) plot is used for the validation, which shows that the identified alterations correlate with the mineralization. The results obtained are verified by a geological survey, where a number of samples are collected from the delineated zones. The samples are analyzed by the XRD techniques, finding that this work is successful in classifying the hydrothermally-altered zones. Keywords
Terrorism and Contemporary Religious Cults : Jim Jones

The present study breaks new ground in the field of radical cults as it provides a contemporary insight into three cult leaders; Jim Jones, Shoko Asahara and Abu Bakr Al-Baghdadi. This comparative analytical study will assist national and local government agencies with the identifying the relations between those leaders and their conclusions of terrorism, which in turn, had led to the mass death of cult adherents. Terrorism is a major, and growing, concern in the world today as it relates to security, safety, and the efficient operation of the most important service sectors in any state. The personalities of Jones, Asahara and Al-Baghdadi have made of them sect leaders from three very different religions: Christianity, Buddhism and Islam. Therefore, religion is not the source of radicalism/mass death as the similarities and commonalities exist among those three different groups. Eminently, this study has emphasized on the answers analytically to the research query through academic process supported by refereed articles and real-life occurrences.
The Positive Mediating Impact of Teacher Helping Behaviour on HPWS and Teacher Performance in SHRM

Incorporating the strategic human resource management research with teaching staff views of their performance. In this study, it proposed that the human resource management practices of a high-performance work system enhance teacher performance in the school context. However, the integration of Helping Behaviour as a mediator between HPWS and Teacher performance encourage teachers to engage in cooperative behaviors among their colleagues and the students who are essential in achieving its superior performance. The results based on the data from six education district in Lagos State Nigeria. The 322 teachers significantly embraced the proposed theoretical framework and enliven the effect of a teacher helping behavior on high-performance work system and teacher performance adopting Structural Equation Modelling (SEM) analysis. Results of the analysis revealed that collective teacher Helping Behaviour moderated the link between HPWS and Teacher Performance indirectly. Consequently, HPWS directly influenced the teacher performance.
Smart Grid Security: Attack Modeling from a CPS Perspective

With the development of smart grid technologies and the fast adoption of household IoT devices in recent years, new threats, attacks, and security challenges arise. While a large number of vulnerabilities, threats, attacks and controls have been discussed in the literature, there lacks an abstract and generalizable framework that can be used to model the cyber-physical interactions of attacks and guide the design of defense mechanisms. In this paper, we propose a new modeling approach for security attacks in smart grids and IoT devices using a Cyber-Physical Systems (CPS) perspective. The model considers both the cyber and physical aspects of the core components of the smart grid system and the household IoT devices, as well as the interactions between the components. In particular, our model recognizes the two parallel attack channels via the cyber world and the physical world, and identifies the potential crossing routes between these two attack channels. We further discuss all possible attack surfaces, attack objectives, and attack paths in this newly proposed model. As case studies, we examine from the perspective of this new model three representative attacks proposed in the literature. The analysis demonstrates the applicability of the model, for instance, to assist the design of detection and defense mechanisms against smart grid cyber-attacks.
APPLICATION OF ACC-DEAMINASE CONTAINING PGPR IMPROVES SUNFLOWER YIELD UNDER NATURAL SALINITY STRESS

Salinity increase ethylene level in root rhizosphere. Plant growth promoting rhizobacteria having ACC deaminase have ability to mitigate the adverse effect of enhanced level of ethylene. Two field experiments were carried out at two different sites during 2013. The site1 had ECe 9.42 dS m and SAR 20.75 (mmolc L), while site2 had ECe 7.51 dS m and SAR 16.25 (mmolc L). Four PGPR strains (KS 44, KS 7, KS 41, KS 42) having ACC deaminase activity were selected for this study. Randomized completes block design (RCBD) was used and hybrid sunflower seeds (SMH-0917) were inoculated with these PGPR strains. The crop was harvested at maturity and data were recorded about plant height, shoot dry weight, head dry weight, number of seed head, 1000 seed weight, seed weight head and seed yield kg ha. The data were statistically analyzed by using soft ware statistic 8.1. The average increase in grain yield (site 1 + site 2) was 110.07, 108.89, 49.09, 65.77 and 69.70% over control due to inoculation with KS 44, KS 7, KS 41, KS 42 and KS mix treatments respectively. On the basis of statistical analysis, the bacterial strains KS 44 and KS 7, were declared as most promising strains on both sites. Hence these two strains (KS 44 and KS 7) could be used to mitigate negative impact of salinity stress on sunflower.
Scotch: Generating FPGA-Accelerators for Sketching at Line Rate

Sketching algorithms are a powerful tool for single-pass data summarization. Their numerous applications include approximate query processing, machine learning, and large-scale network monitoring. In the presence of high-bandwidth interconnects or in-memory data, the throughput of summary maintenance over input data becomes the bottleneck. While FPGAs have shown admirable throughput and energy-efficiency for data processing tasks, developing FPGA accelerators requires a sophisticated hardware design and expensive manual tuning by an expert. We propose Scotch, a novel system for accelerating sketch maintenance using FPGAs. Scotch provides a domain-specific language for the user-friendly, high-level definition of a broad class of sketching algorithms. A code generator performs the heavy-lifting of hardware description, while an auto-tuning algorithm tunes the summary size. Our evaluation shows that FPGA accelerators generated by Scotch outperform CPUand GPU-based sketching by up to two orders of magnitude in terms of throughput and up to a factor of five in terms of energy efficiency. PVLDB Reference Format: Martin Kiefer, Ilias Poulakis, Sebastian Breß, and Volker Markl. Scotch: Generating FPGA-Accelerators for Sketching at Line Rate. PVLDB, 14(3):
TOC removal in biological filters

Laboratory-scale biologically active sand filters were used to evaluate the effect of natural organic matter (NOM) source and empty bed contact time (EBCT) on the removal of total organic carbon (TOC) from coagulated and ozonated NOM solutions. Ozonation at dosages in the range of 2.0 to 4.0 mg ozone/mg TOC were used to enhance biodegradation of the organic matter. TOC removal in the biofilters ranged from 16 to 33 percent of the biofilter influent concentration. TOC removal was significantly affected by the source of the organic carbon but was independent of EBCT in the range of 4 to 20 min. In the context of an entire water treatment plan, including coagulation and biofiltration, the contribution of biofiltration to TOC removal was relatively small compared with total removals across the entire treatment train. In one biofiltration experiment, however, removal of the biodegradable fraction of TOC was 100 percent, suggesting that biofilters may be effective in reducing subsequent regrowth in distribution systems.
Learn what you can't learn: Regularized Ensembles for Transductive Out-of-distribution Detection

Machine learning models are often used in practice if they achieve good generalization results on in-distribution (ID) holdout data. When employed in the wild, they should also be able to detect samples they cannot predict well. We show that current out-of-distribution (OOD) detection algorithms for neural networks produce unsatisfactory results in a variety of OOD detection scenarios, e.g. when OOD data consists of unseen classes or corrupted measurements. This paper studies how such "hard" OOD scenarios can benefit from adjusting the detection method after observing a batch of the test data. This transductive setting is relevant when the advantage of even a slightly delayed OOD detection outweighs the financial cost for additional tuning. We propose a novel method that uses an artificial labeling scheme for the test data and regularization to obtain ensembles of models that produce contradictory predictions only on the OOD samples in a test batch. We show via comprehensive experiments that our approach is indeed able to significantly outperform both inductive and transductive baselines on difficult OOD detection scenarios, such as unseen classes on CIFAR-10/CIFAR-100, severe corruptions(CIFAR-C), and strong covariate shift (ImageNet vs ObjectNet).
MOLTR: Multiple Object Localization, Tracking and Reconstruction From Monocular RGB Videos

Semantic aware reconstruction is more advantageous than geometric-only reconstruction for future robotic and AR/VR applications because it represents not only where things are, but also what things are. Object-centric mapping is a task to build an object-level reconstruction where objects are separate and meaningful entities that convey both geometry and semantic information. In this letter, we present MOLTR, a solution to object-centric mapping using only monocular image sequences and camera poses. It is able to localize, track and reconstruct multiple rigid objects in an online fashion when a RGB camera captures a video of the surrounding. Given a new RGB frame, MOLTR firstly applies a monocular 3D detector to localize objects of interest and extract their shape codes that represent the object shape in a learnt embedding space. Detections are then merged to existing objects in the map after data association. Motion state (i.e., kinematics and the motion status) of each object is tracked by a multiple model Bayesian filter and object shape is progressively refined by fusing multiple shape code. We evaluate localization, tracking and reconstruction on benchmarking datasets for indoor and outdoor scenes, and show superior performance over previous approaches.
Tensor Composition Net for Visual Relationship Prediction

We present a novel Tensor Composition Network (TCN) to predict visual relationships in images. Visual Relationships in subject-predicate-object form provide a more powerful query modality than simple image tags. However Visual Relationship Prediction (VRP) also provides a more challenging test of image understanding than conventional image tagging, and is difficult to learn due to a large label-space and incomplete annotation. The key idea of our TCN is to exploit the low rank property of the visual relationship tensor, so as to leverage correlations within and across objects and relationships, and make a structured prediction of all objects and their relations in an image. To show the effectiveness of our method, we first empirically compare our model with multi-label classification alternatives on VRP, and show that our model outperforms state-of-the-art MLIC methods. We then show that, thanks to our tensor (de)composition layer, our model can predict visual relationships which have not been seen in training dataset. We finally show our TCN's image-level visual relationship prediction provides a simple and efficient mechanism for relation-based image retrieval.
Deep learning methods for SAR image despeckling: trends and perspectives

Synthetic aperture radar (SAR) images are affected by a spatially-correlated and signal-dependent noise called speckle, which is very severe and may hinder image exploitation. Despeckling is an important task that aims at removing such noise, so as to improve the accuracy of all downstream image processing tasks. The first despeckling methods date back to the 1970's, and several model-based algorithms have been developed in the subsequent years. The field has received growing attention, sparkled by the availability of powerful deep learning models that have yielded excellent performance for inverse problems in image processing. This paper surveys the literature on deep learning methods applied to SAR despeckling, covering both the supervised and the more recent self-supervised approaches. We provide a critical analysis of existing methods with the objective to recognize the most promising research lines, to identify the factors that have limited the success of deep models, and to propose ways forward in an attempt to fully exploit the potential of deep learning for SAR despeckling.
Universality for random permutations and some other groups

We present some Markovian approaches to prove universality results for some functions on the symmetric group. Some of those statistics are already studied in [Kammoun, 2018, 2020] but not the general case. We prove, in particular, that the number of occurrences of a vincular patterns satisfies a CLT for conjugation invariant random permutations with few cycles and we improve the results already known for the longest increasing subsequence. The second approach is a suggestion of a generalization to other random permutations and other sets having a similar structure than the symmetric group.
Variational quantum algorithms for trace distance and fidelity estimation

Estimating the difference between quantum data is crucial in quantum computing. However, as typical characterizations of quantum data similarity, the trace distance and quantum fidelity are believed to be exponentially-hard to evaluate in general. In this work, we introduce hybrid quantum–classical algorithms for these two distance measures on near-term quantum devices where no assumption of input state is required. First, we introduce the variational trace distance estimation (VTDE) algorithm. We in particular provide the technique to extract the desired spectrum information of any Hermitian matrix by local measurement. A novel variational algorithm for trace distance estimation is then derived from this technique, with the assistance of a single ancillary qubit. Notably, VTDE could avoid the barren plateau issue with logarithmic depth circuits due to a local cost function. Second, we introduce the variational fidelity estimation algorithm. We combine Uhlmann’s theorem and the freedom in purification to translate the estimation task into an optimization problem over a unitary on an ancillary system with fixed purified inputs. We then provide a purification subroutine to complete the translation. Both algorithms are verified by numerical simulations and experimental implementations, exhibiting high accuracy for randomly generated mixed states.
AI Driven Knowledge Extraction from Clinical Practice Guidelines: Turning Research into Practice

Background and Objectives: Clinical Practice Guidelines (CPGs) represent the foremost methodology for sharing state-of-the-art research findings in the healthcare domain with medical practitioners to limit practice variations, reduce clinical cost, improve the quality of care, and provide evidence based treatment. However, extracting relevant knowledge from the plethora of CPGs is not feasible for already burdened healthcare professionals, leading to large gaps between clinical findings and real practices. It is therefore imperative that state-of-the-art Computing research, especially machine learning is used to provide artificial intelligence based solution for extracting the knowledge from CPGs and reducing the gap between healthcare research/guidelines and practice. Methods: This research presents a novel methodology for knowledge extraction from CPGs to reduce the gap and turn the latest research findings into clinical practice. First, our system classifies the CPG sentences into four classes such as condition-action, condition-consequences, action, and not-applicable based on the information presented in a sentence. We use deep learning with state-of-the-art word embedding, improved word vectors technique in classification process. Second, it identifies qualifier terms in the classified sentences, which assist in recognizing the condition and action phrases in a sentence. Finally, the condition and action phrase are processed and transformed into plain rule If Condition(s) Then Action format. Results: We evaluate the methodology on three different domains guidelines including Hypertension, Rhinosinusitis, and Asthma. The deep learning model classifies the CPG sentences with an accuracy of 95%. While rule extraction was validated by user-centric approach, which achieved a Jaccard coefficient of 0.6, 0.7, and 0.4 with three human experts extracted rules, respectively.
Modeling, Control and Human-In-The-Loop Stability Analysis of an Elastic Quadrotor

This paper introduces an analytical framework for the derivation of hybrid equations of motion of a flexible quadrotor. This approach helps obtain rigid and elastic equations of motion simultaneously, in a decoupled form, which facilitates the controller design. A delay-dependent stability condition is obtained for the overall system dynamics, including the operator with reaction time delay, the adaptive controller and the flexible quadrotor dynamics. Two different adaptive controllers are implemented to address the uncertainties. It is demonstrated via simulations that the flexible arm tip oscillations are mitigated when a closed loop reference model adaptive controller is used, compared to a conventional model reference adaptive controller.
Cavitation of a spherical body under mechanical and self-gravitational forces

In this paper, we look for minimizers of the energy functional for isotropic compressible elasticity taking into consideration the effect of a gravitational field induced by the body itself. We consider two types of problems: the displacement problem in which the outer boundary of the body is subjected to a Dirichlet-type boundary condition, and the one with zero traction on the boundary but with an internal pressure function. For a spherically symmetric body occupying the unit ball $\mathcal {B}\in \mathbb {R}^3$ , the minimization is done within the class of radially symmetric deformations. We give conditions for the existence of such minimizers, for satisfaction of the Euler–Lagrange equations, and show that for large displacements or large internal pressures, the minimizer must develop a cavity at the centre. We discuss a numerical scheme for approximating the minimizers for the displacement problem, together with some simulations that show the dependence of the cavity radius and minimum energy on the displacement and mass density of the body.
Clustered colouring of graph classes with bounded treedepth or pathwidth

The "clustered chromatic number" of a class of graphs is the minimum integer $k$ such that for some integer $c$ every graph in the class is $k$-colourable with monochromatic components of size at most $c$. We determine the clustered chromatic number of any minor-closed class with bounded treedepth, and prove a best possible upper bound on the clustered chromatic number of any minor-closed class with bounded pathwidth. As a consequence, we determine the fractional clustered chromatic number of every minor-closed class.