Miracle-dz/UniFAR-FTUs huggingface dataset:
Sales Territory Design and Salesforce Performance in the Detergent Manufacturing Companies: The Actual Kenyan Context

This paper focuses on determining the influence of sales territory design on salesforce performance in the detergent manufacturing companies in Kenya. Detergents play a critical role in our everyday lives by ensuring hygiene at our personal level, in our homes, in schools, institutions, and hospitals. Detergents have gained a lot of prominence since the onset of the Corona Virus (COVID-19) disease in 2019, as it has been proved that they play an important role in curtailing the transmission of the COVID-19 disease. The Salesforce plays an important role in organizations as they are the ones who in most cases interact with customers, provide information about their organization and products, provide prices, demonstrate how products are used, train customers, provide after sales service, and resolve any issues that may arise. The study was anchored on Expectancy Theory, Resource Based Theory, Equity Theory, and Agency Theory. The study adopted positivism philosophy and used a descriptive cross sectional design. The unit of analysis was the sales people in the detergent manufacturing companies in Kenya who are members of Kenya Association of Manufacturers as per list obtained from KAM in December 2018. The study used primary data which was collected by administering semi-structured questionnaires. The data was analyzed using a combination of both descriptive and inferential statics to describe the manifestations of the variables in the data collected, simple regression model was used to test the significance of the influence of sales territory design (independent variable) on the salesforce performance (dependent variable). Fischer distribution test (F-test) was used to test the significance of the independent variable and the overall model. The p-value for the F-statistic was used to determine the robustness of the model. This was done at 95% confidence level (p<0.05). The study found a strong relationship between sales territory design and salesforce performance. This depicts that sales territory design is critical in determining salesforce performance in detergent manufacturing companies in Kenya.
SUSTain: Scalable Unsupervised Scoring for Tensors and its Application to Phenotyping

This paper presents a new method, which we call SUSTain, that extends real-valued matrix and tensor factorizations to data where values are integers. Such data are common when the values correspond to event counts or ordinal measures. The conventional approach is to treat integer data as real, and then apply real-valued factorizations. However, doing so fails to preserve important characteristics of the original data, thereby making it hard to interpret the results. Instead, our approach extracts factor values from integer datasets as scores that are constrained to take values from a small integer set. These scores are easy to interpret: a score of zero indicates no feature contribution and higher scores indicate distinct levels of feature importance. At its core, SUSTain relies on: a) a problem partitioning into integer-constrained subproblems, so that they can be optimally solved in an efficient manner; and b) organizing the order of the subproblems' solution, to promote reuse of shared intermediate results. We propose two variants, SUSTain_M and SUSTain_T, to handle both matrix and tensor inputs, respectively. We evaluate SUSTain against several state-of-the-art baselines on both synthetic and real Electronic Health Record (EHR) datasets. Comparing to those baselines, SUSTain shows either significantly better fit or orders of magnitude speedups that achieve a comparable fit (up to 425× faster). We apply SUSTain to EHR datasets to extract patient phenotypes (i.e., clinically meaningful patient clusters). Furthermore, 87% of them were validated as clinically meaningful phenotypes related to heart failure by a cardiologist.
Religious Language

When are sentences A and B the same belief? Following Quine, observation sentences A and B are the same belief when they share the same stimulus–meaning, similar patterns of assent and dissent by subjects when the sentences are queried in the presence of the same non–linguistic stimuli. As for non–observation sentences we note a suggestion of Karl Schick: apply linguistic stimuli in the form of utterances of the language, and map the connections between sentences in the language in terms of linguistic conditioned–responses to utterances. The mapping will yield a network of relations between non–observation sentences themselves, and between the latter and observation sentences at the ‘periphery’. Thus, each sentence receives its place in the overall criss–crossing of relations in the network of the language. Out of a commitment to the ‘autonomy of meaning’, we can say that when A and B are non–observational, they are the same belief when they occupy similar places in the network of sentences in a given language, or corresponding places in corresponding networks of two languages. (Since we can identify the place of sentences in the language network, and since the present suggestion identifies the sameness of belief with location identity, it turns out that there needn't be indeterminacy of translocation.)
Abstract

This study probes the link between workplace bullying, emotional exhaustion and presenteeism in employees of public and private sector service organizations. Based on affective events theory, this study found that emotional exhaustion mediates the relationship between bullying and employee presenteeism. However, the climate for conflict management (CCM) was found to moderate the aforementioned relationship. Data taken from 2250 employees working in health, education and forest departments support the assumptions that workplace bullying and presenteeism are partially mediated by individual’s perceived emotional exhaustion and that the CCM moderates this mediated association. This mediated moderation model suggests that workplace bullying emotionally exhausts employees, who despite being present at workplace cannot perform to their full potential. On the other hand, organizations that are active in conflict management practices may be able to prevent and reduce the adverse effects of workplace bullying.
Prediction of Ionospheric TEC Based on the NARX Neural Network

Effective prediction of ionospheric total electron content (TEC) is very important for Global Navigation Satellite System (GNSS) positioning and other related applications. This paper proposes an ionospheric TEC prediction method using the nonlinear autoregressive with exogenous input (NARX) neural network, which uses previous TEC data and external time parameter inputs to establish a TEC prediction model. During the years of different solar activities, 12 datasets of 3 stations with different latitudes are used for experiments. Each dataset uses the first 120 days for training and the next 20 days for testing. For each test dataset, a sliding window strategy is adopted in the prediction process, wherein the TEC of future 2 days are predicted by the true TEC values of the previous 2 days. The results show that in the year with active solar activity (2011), the TEC prediction with the NARX network can improve the accuracy by 32.3% and 43.5%, compared with the autoregressive integrated moving average (ARIMA) model and the 2-day predicted TEC product, named C2PG. While in the year with calm solar activity (2017), the prediction accuracy can be improved by 20.7% and 22.7%.
Dictionary of Modern Slovene: Problems and Solutions

An important building block of a state-of-the-art digital Slovene language dictionary is its technological framework, which is briefly presented in this paper. We view the dictionary as a multi-tier architecture with a presentation tier, a middle application tier (a back-end application system with a component for semi-automatic data extraction), and a data tier. In their natural form, the language data are multidimensional. In a printed dictionary, there is just the presentation tier, and thus many relations contained in the underlying data are difficult to access or are even lost. By contrast, in electronic dictionaries there are no such restrictions. We can preserve the data in all its complexity and present it in various ways, since there is a distinction between the data and their presentation. This separation is the key factor in integrating various data sources (different corpora and external databases) into a unified database. Various users or programs can query different parts of the database based on their interests, and the presentation tier displays or returns the data at different levels of granularity. For each tier, we present the structure and review some of the technological considerations, which guarantee good extensibility, reliability, and adaptability of the final solution.
Feasibility Study on Wastewater Treatment Using Chemically Enhanced Primary Treatment

A techno-economic analysis was performed to investigate wastewater treatment feasibility using chemically enhanced primary treatment (CEPT) compared to conventional primary treatment (PT). An experimental study was conducted to investigate the performance of CEPT at optimum conditions, and experimental results were used to feed the techno-economical study with required input data. The wastewater treatment was focused on reducing BOD5, COD, and TSS. The comparison between CEPT and PT was concerned with removal efficiency and process economics. CEPT process has shown better efficiency compared to PT process. The experimental work indicated that ferric chloride is the optimum coagulant agent since it is highly efficient and available at a low cost. The optimum coagulant experimental results showed that the turbidity removal efficiency was 82%, COD removal 84%, BOD removal 68.1%, and SS removal 85%. The techno-economic study was performed to investigate the feasibility of CEPT. The techno-economic evaluation indicated that CEPT is a cost-effective and technically viable process for wastewater treatment. The techno-economic evaluation indicated that CEPT is a cost-effective and technically viable process for wastewater treatment as the operating cost can be reduced by 66% compared to PT.
Intensity quantification of public opinion and emotion analysis on climate change

Human-related activities are primarily accountable for climate change resulting in natural disasters. Therefore, it has become essential to analyze and evaluate public awareness of climate change globally. With the prevalence of social networks like Twitter, sentiment classification has been recognized as a powerful tool to determine public opinion and concern on such ecological issues. Therefore, this study proposes a framework to classify the tweets containing public opinion towards climate change using Bi-directional Long Short-Term Memory (Bi-LSTM) Networks. The proposed framework quantified the intensity of the public opinion classified by the Bi-LSTM model to measure the strength of the public concern towards climate change and validated it using three case studies: Earth Day, Delhi Air Pollution, and Australian Bushfires. The intensity values of the public sentiments concerning these events were obtained as 98.50%, 96.57%, and 98.33%, respectively. The proposed work was further augmented with a lexicon-based emotion analyzer to categorize the emotions associated with the tweets into positive, negative, neutral, and mixed to substantiate the results. This framework can be utilized before enforcing the policy decisions on the general public in any domain.
Intelligent Rural Tourism Environmental Suitability Evaluation System Based on a Wireless Sensor Network

After years of vigorous development, the scale of rural tourism industry has gradually expanded throughout the country, and it has become an important means of coordinating urban and rural areas to drive farmers’ incomes and get rich. Nowadays, the level of informatization is constantly improving, the construction of smart cities is constantly advancing, the personalized demand of tourists is increasingly strong, especially the rural tourist spots scattered in the vast countryside which have a great spatial breadth, and the traditional management method can no longer meet the needs of the source objects whose main travel mode is self-driving. Therefore, only when rural tourism develops in the direction of intelligence can it successfully realize the transformation and upgrading of rural tourism industry. This paper takes a rural tourism resort in a certain area as the research object and builds an intelligent rural tourism environmental suitability evaluation system with three criterion levels: natural geographic conditions, traffic environmental conditions, and tourism spatial conditions. The weight of each indicator layer is obtained through the analytic hierarchy process, and the reclassified raster data of indicators is weighted and superimposed on the ArcGIS operating platform to obtain the evaluation result of the environmental suitability of smart rural tourism. According to the results of suitability evaluation, the most suitable area is selected as the potential area of the intelligent rural tourism environment in a certain area. According to the natural and cultural tourism resource conditions in the block, the potential block is divided into functions. A comprehensive comparison of the suitability spatial differentiation map of the smart rural tourism environment in a certain area with the suitability spatial differentiation map of each subsystem can more clearly find that the suitability score of the economic development subsystem and the basic support subsystem is a county-level village in a certain area. Among them, the suitability of the rural economic development subsystem is similar to the distribution of the suitability of the smart rural tourism environment in a certain area; that is, it is positively correlated with the suitability of the smart rural tourism environment. Therefore, improving the level of rural economic development and increasing investment in the construction of a smart rural tourism environment play an important role in improving the level of public services and infrastructure coverage.
Synthesizing waveform sequence-to-sequence to augment training data for sequence-to-sequence speech recognition

: Sequence-to-sequence (seq2seq) automatic speech recognition (ASR) recently achieves state-of-the-art performance with fast decoding and a simple architecture. On the other hand, it requires a large amount of training data and cannot use text-only data for training. In our previous work, we proposed a method for applying text data to seq2seq ASR training by leveraging text-to-speech (TTS). However, we observe the log Mel-scale ﬁlterbank (lmfb) features produced by Tacotron 2-based model are blurry, particularly on the time dimension. This problem is mitigated by introducing the WaveNet vocoder to generate speech of better quality or spectrogram of better time-resolution. This makes it possible to train waveform-input end-to-end ASR. Here we use CNN ﬁlters and apply a masking method similar to SpecAugment. We compare the waveform-input model with two kinds of lmfb-input models: (1) lmfb features are directly generated by TTS, and (2) lmfb features are converted from the waveform generated by TTS. Experimental evaluations show the combination of waveform-output TTS and the waveform-input end-to-end ASR model outperforms the lmfb-input models in two domain adaptation settings.
Ritualism as a Form of Academic Malfunctioning: Iranian Higher Education as a Case Study

Today, the Iranian higher education (IHE) exhibits a paradox. Despite the rapid growth of the higher education system and pervasion of the university institution in Iran, and notwithstanding the increasing number of higher education institutions/students/graduates, and research projects, and the improved position of Iranian universities in international ranking systems, there are concerns about the inefficiency of the university system. It seems that the university in Iran has deviated from its institutional functions and has become afflicted with anomie and malfunctioning and, as Merton (1938) puts it, presents a kind of ritualism. Accordingly, this paper reports on a qualitative study of ritualism in Iranian higher education. The central finding is that ritualism is significantly present in IHE, and especially in research. This ritualism is manifested in 3 domains of causes, indicators and consequences. The findings indicate that indicators of ritualism in IHE: such as quantitativism, certificationism, scientific fashion, and symbolism have resulted in false branding, academic corruption, and system inefficiency. These phenomena have taken root in the universities, albeit influenced by the external environment. Overall, a certain group of factors is suggested as leading to academic ritualism: lack of academic independence, commercialization, international norms (language and ranking systems), and institutional norms (reward systems, hierarchy based on nonacademic discourse, and symbolic violence). Such a pattern amounts to a serious malfunctioning of the higher education system. Seyed Hedayat Davarpanah *
A Basic Characterisation Study of Bioplastics via Gelatinization of Corn Starch

Plastic waste is the third-largest waste source in the world, so it raises the world’s human health and environmental concerns. Replacing conventional petroleum plastic with bioplastic is an alternative way to minimise plastic wastes from human life and bioplastic is more environmentally friendly. Therefore, this research study aims to synthesise bioplastic from corn starch via gelatinization and study its characteristics. Different from other studies, in this study, new formulations of bioplastics with different ratios of corn starch to glycerol samples that are 1:0.5, 1:1, 2:1, and 2:2, namely Sets A, B, C, and D, respectively, were studied and compared. From the Fourier Transformation Infrared Spectroscopy analysis, the results show that all produced corn starch-based bioplastic samples had the four major plastic’s functional groups which indicated that they were categorized as polyester. Meanwhile, via thermal property analysis, all bioplastic samples could be thermally decomposed from 34 °C to 504 °C where their weight was reduced from 5 mg to 1 mg. Among the four bioplastic samples (Sets A to D) with different ratios of corn starch to glycerol, it was found that a ratio of corn starch and glycerol that was 1:0.5 (Set A) had more biodegradable characteristics and it had the lowest water holding capacity. From the results, Set A could only hold around 4.27 % of the water that could avoid interaction of water with the contents that were wrapped with. Besides, from the results, Set A could degrade better in soils, and dissolve more in ethanol, acetone, and oils when compared to other samples. Since the bioplastic can degrade naturally by the ethanol produced from bacteria in the soils under anaerobic reactions, thus Set A has the potential application to be used as a fertiliser coating to minimise the fertiliser release rate in regions under heavy rainfall.
On Teacher-Student Semi-Supervised Learning for Chest X-ray Image Classification

The lack of labeled data is one of the main prohibiting issues on the development of deep learning models, as they rely on large labeled datasets in order to achieve high accuracy in complex tasks. Our objective is to evaluate the performance gain of having additional unlabeled data in the training of a deep learning model when working with medical imaging data. We present a semi-supervised learning algorithm that utilizes a teacher-student paradigm in order to leverage unlabeled data in the classification of chest X-ray images. Using our algorithm on the ChestX-ray14 dataset, we manage to achieve a substantial increase in performance when using small labeled datasets. With our method, a model achieves an AUROC of 0.822 with only 2% labeled data and 0.865 with 5% labeled data, while a fully supervised method achieves an AUROC of 0.807 with 5% labeled data and only 0.845 with 10%.
CoLaFUZE: Coverage-Guided and Layout-Aware Fuzzing for Android Drivers

SUMMARY With the commercialization of 5G mobile phones, Android drivers are increasing rapidly to utilize a large quantity of newly emerging feature-rich hardware. Most of these drivers are developed by third-party vendors and lack proper vulnerabilities review, posing a number of new potential risks to security and privacy. However, the complexity and diversity of Android drivers make the traditional analysis methods ine ﬃ cient. For example, the driver-speciﬁc argument formats make traditional syscall fuzzers di ﬃ cult to generate valid inputs, the pointer-heavy code makes static analysis results incomplete, and pointer casting hides the actual type. Triggering code deep in Android drivers remains challenging. We present CoLaFUZE, a coverage-guided and layout-aware fuzzing tool for automatically generating valid inputs and exploring the driver code. Co-LaFUZE employs a kernel module to capture the data copy operation and redirect it to the fuzzing engine, ensuring that the correct size of the required data is transferred to the driver. CoLaFUZE leverages dynamic analysis and symbolic execution to recover the driver interfaces and generates valid inputs for the interfaces. Furthermore, the seed mutation module of CoLaFUZE leverages coverage information to achieve better seed quality and expose bugs deep in the driver. We evaluate CoLaFUZE on 5 modern Android mobile phones from the top vendors, including Google, Xiaomi, Samsung, Sony, and Huawei. The results show that CoLaFUZE can explore more code coverage compared with the state-of-the-art fuzzer, and CoLaFUZE successfully found 11 vulnerabilities in the testing devices.
Chemical Reactivity Descriptors and Molecular Docking Studies of Octyl 6-O-hexanoyl-β-D-glucopyranosides

The present study describes different chemical reactivity predictions of 6-O-hexanoylation of octyl β-D-glucopyranosides prepared from octyl β-D-glucopyranoside (OBG). Also, molecular docking of the OBGs was conducted against SARS-CoV-2 main protease (6LU7), urate oxidase (Aspergillus flavus; 1R51) and glucoamylase (Aspergillus niger; 1KUL). DFT optimization indicated that glucoside 1 and its ester derivatives 2-7 exist in 4C1 conformation with C1 symmetry. Interestingly, the addition of ester group(s) decreased the HOMO-LUMO gap (Δԑ) of glucosides indicating their good chemical reactivities, whereas the other chemical reactivity descriptors indicated their moderate reactive nature. This fact of moderate reactivity was confirmed by their molecular docking with 6LU7, 1R51 and 1KUL. All the esters showed a moderate binding affinity with these three proteins. More importantly, incorporation of the ester group(s) increased binding affinity with 6LU7 and 1R51, whereas decreased with 1KUL as compared to non-ester OBG 1.