Miracle-dz/UniFAR-FTUs huggingface dataset:
SOCIAL CLASS, LIFE CHANCES AND VULNERABILITY TO POVERTY IN SOUTH AFRICA

The wave of upbeat stories on the developing world's emerging middle class has reinvigorated a debate on how social class in general and the middle class in particular ought to be defined and empirically measured. The contribution this paper makes to this literature is both conceptual and empirical. The conceptual contribution consists in proposing a class schema with particular relevance for the emerging and developing country context marked by high economic insecurity. Building on a recently developed framework that defines the middle class in relation to their (in)vulnerability to poverty, in this paper, we propose a multi-layered class model that differentiates five social classes: (i) the chronic poor, characterised by high poverty persistence, (ii) the transient poor, who have above average chances of escaping poverty, (iii) the non-poor but vulnerable, whose basic needs are currently being met but who face above average risks of slipping into poverty, (iv) the middle class, who are in a better position to maintain a non-poor standard of living even in the event of negative shocks, and (v) the elite, whose living standards situate them far above the average. The empirical contribution consists in the application of this conceptual innovation to the South African case using a model of poverty transitions that is fitted to four waves of panel data from the National Income Dynamics Study (NIDS) covering 2008 through to 2014/15. Given the classification derived in this paper, we find that only about 20 per cent of the South African population can be considered as stably middle class. Africans remain underrepresented in the middle class, and race is still one of the strongest predictors of poverty in South Africa. Members of larger, female headed, or rural households face a higher risk of poverty, and are less likely to enter the ranks of the middle class. Having access to stable labour market income, by contrast, is a key determinant for households to achieve economic stability in South Africa. Improving the access to insurance and credit instruments can importantly help households to sustain their living standards over time.
LOAD BALANCER WITH AUTOMATIC LIFTING FORCE COMPENSATION

The most simple and usual way to handle material is to use so called conventional devices like hydraulic jacks, cranes and pulley systems. There are also specific devices, which use counterweights or springs to balance the weight and assist the material handling. For more specific applications a robot manipulator is used. Comparing these two solutions, it is possible to say that the conventional devices have the advantages such as low cost and simple operation. On the other hand, robots provide fast and accurate operation, but they have limitations in the working range. Conventional solutions offer more flexibility. In this context, this work describes the study and the development of a new mechatronic device that can be classified as an intermediate solution, between the conventional device and the robotic solution. The newly proposed solution is based on a mechanism in which: a lever system lifts the load and, instead a counterweight, a gas spring provides the lifting force. This results in a simple mechanism, lightweight, compact in size, easy to operate and low cost. However due to the simplicity, the load is perfectly balanced only in some restricted positions in the working volume, not allowing perfect balancing in all working volume. To solve this problem, a control system is coupled to the lever and gas spring mechanism. The control system is composed by a sensor, that constantly monitors the inclination of the lever, a PLC (Programmable Logic Controller) to execute a control algorithm and an electric motor to correct the gas spring position. Thus, the lifting force is constantly corrected, achieving a perfect balancing at any position. Besides, the control system allows automatic adaptation of the device to work with different weights. The study is developed assuming a specific application: reduce the effort of an electrician to handle a hydraulic saw of around 8 kg weights, during the tree trimming procedure. The prototype is developed, constructed and tested. Tests show the effectiveness of the proposed device.
Composition of a maternal high fat diet rich in satured fats and omega 3 in gestation and lactation for studies with rodents

ABSTRACT Objective To prepare a high fat diet rich in satured fatty acids and supplemented with omega 3 for experimental studies in rodents. Methods Purified industrial ingredients and flaxseed oil as a source of omega 3 at a concentration of 3.5% (v/w) were used in the elaboration of the diets. Centesimal and nutritional compositions, fatty acids profile and dietary intake were evaluated. Serum levels of total protein, albumin, cholesterol and glucose in pregnant rats were verified. The offspring were assessed with regard to body mass and waist circumference. Statistical analysis was performed using the Kolmogorov-Smirnov, Anova One-Way test and Bonferroni post-test. Results High fat and high fat with omega 3 diets presented, respectively, 37% and 36% saturated fat, and the lipid amount was 80% higher than the American Institute of Nutrition 93G control diet. The omega 3 content was 50% higher in the high fat with omega 3 diet. There was no difference in consumption of diet types in weight (grams). The dams that received the High fat diet developed hypercholesterolemia and their High fat offspring exhibited higher body mass on the 1st day of life and increased abdominal circumference on the 30th day of life compared to the control and the high fat with omega 3 offspring. Conclusion The formulated diets with a higher amount of saturated fatty acids meet the nutritional requirements of the gestation and lactation period. The high fat diet with omega 3 was able to attenuate the changes observed in dams and their offspring.
MOTIVATION-THE FACILITATOR FACTOR OF THE INCULTURATION PHENOMENON IN SCHOOL

The present study proposes an analysis of motivation, in the context of its development within the fundamental institution that produces, manages and disseminates knowledge: namely, school. Within the (de facto) present context we wanted to reflect on the importance of the motivation in implementing the socio-cultural factors of the reference background (school), these playing an essential role in shaping and structuring the individual in terms of psycho-behavioral and with ideological value, resulting in the process of enculturation. In the circumscribed area of pedagogy, there is a conceptual approach, as a reflection of shades of the terms: acculturation, enculturation, inculturation, highlighting commonalities or differences as opposing to the approached situations, offering useful coordinates for thorough analysis of the interaction processes. A conclusion and also an objective, finally emerge by finding strategies, methods and pedagogical techniques, to reduce current problems, that lead to a higher demotivation, which are to be validated by the results obtained in schools,
A study on Consumers' Brand Awareness, Consumption Experience, Service Quality and Repurchase Intention in the Hot Spring Resorts

The main purpose of this study is to analyze consumers' perceptions of hot spring hotel brands, their consumption experience, the quality of their experience, and the intention to repurchase. This research adopts the investigation method of the questionnaire, with 420 consumers in Taiwan hot spring area in Tai'an as the research object, the effective questionnaire through statistical analysis, this study found that different demographic variables will affect consumers' Hot Springs Hotel "brand awareness", "consumer experience", "service quality" and "repurchase intentions". The "repurchase intention" of hot spring hotels in Tai'an has a positive correlation with "brand awareness", "consumption experience" and "service quality". According to the results of the study, this study puts forward specific suggestions for hotel management in hot spring area.
Efficient Search for Multi-Scale Time Delay Correlations in Big Time Series Data

Very large time series are increasingly available from an ever wider range of IoT-enabled sensors deployed in different environments. Significant insights and values can be obtained from these time series through performing cross-domain analyses, one of which is analyzing time delay temporal correlations across different datasets. Most existing works in this area are either limited in the type of detected relations, e.g., linear relations alone, only working with a fixed temporal scale, or not considering time delay between time series. This paper presents our Time delaY COrrelation Search (TYCOS) approach which provides a powerful and robust solution with the following features: (1) TYCOS is based on the concept of mutual information (MI) from information theory, giving it a strong theoretical foundation to detect all types of relations including non-linear ones, (2) TYCOS is able to discover time delay correlations at multiple temporal scales, (3) TYCOS works in an efficient, bottom-up fashion, pruning non-interesting time intervals from the search by employing a novel MI-based theory, and (4) TYCOS is designed to efficiently minimize computational redundancy. A comprehensive experimental evaluation using synthetic and real-world datasets from the energy and smart city domains shows that TYCOS is able to find significant time delay correlations across different time intervals among big time series. The performance evaluation shows that TYCOS can scale to large datasets, and achieve an average speedup of 2 to 3 orders of magnitude compared to the baselines by using the proposed optimizations.
Identifying Physical Traits with Body-worn Accelerometers: A Preliminary Investigation

This paper describes our preliminary investigation of the end user physical characteristics (e.g. gender, dominant hand, and skill at sport) that can be successfully estimated solely from bodyworn accelerometers. For this purpose we use the huge quantities of data, which include 14880 labeled activities (e.g., walking and running) obtained from 61 subjects. We try to estimate various kinds of characteristics based on our simple idea ‘When the activity sensor data of two users are similar, the physical characteristics of the two users may also be similar. ’ We consider that estimating the end user’s physical characteristics will enable us to realize new kinds of applications that automatically recommend information/services to an end user according to her estimated physical characteristics such as gender and weight.
Deep Unsupervised Binary Coding Networks for Multivariate Time Series Retrieval

Multivariate time series data are becoming increasingly ubiquitous in varies real-world applications such as smart city, power plant monitoring, wearable devices, etc. Given the current time series segment, how to retrieve similar segments within the historical data in an efficient and effective manner is becoming increasingly important. As it can facilitate underlying applications such as system status identification, anomaly detection, etc. Despite the fact that various binary coding techniques can be applied to this task, few of them are specially designed for multivariate time series data in an unsupervised setting. To this end, we present Deep Unsupervised Binary Coding Networks (DUBCNs) to perform multivariate time series retrieval. DUBCNs employ the Long Short-Term Memory (LSTM) encoder-decoder framework to capture the temporal dynamics within the input segment and consist of three key components, i.e., a temporal encoding mechanism to capture the temporal order of different segments within a mini-batch, a clustering loss on the hidden feature space to capture the hidden feature structure, and an adversarial loss based upon Generative Adversarial Networks (GANs) to enhance the generalization capability of the generated binary codes. Thoroughly empirical studies on three public datasets demonstrated that the proposed DUBCNs can outperform state-of-the-art unsupervised binary coding techniques.
Symbolic Execution Based Automated Static Bug Detection for Eclipse CDT

Software vulnerabilities may be exploited for intruding into a system by an attacker. One approach to mitigation is to automatically analyze software source code in order to find and remove software bugs before release. A method for contextsensitive static bug detection is symbolic execution. This article presents an SMT-constrained static symbolic execution engine with sound path merging. The engine is used by checkers for memory access violation, infinite loops, and atomicity violations. Context information provided by the engine is shared by the different checkers. Further checkers can easily be connected. The engine integrates as plug-in extension into Eclipse CDT and uses CDT’s parser, AST visitor and CFG builder, as well as Eclipse’s GUI and marker framework for bug reporting. The presented approach is evaluated with test cases from the Juliet test suite for C/C++. The evaluation shows a significant speed-up by path merging already for the small Juliet programs. The speed-up depends on the number of decision nodes with more than one satisfiable branch and increases for larger programs. Keywords–Static analysis; Symbolic execution.
Reactive Probabilistic Programming

Reactive programming allows a user to model the flow of a program for event-driven streams of data without explicitly stating its control flow. However, many domains are inherently uncertain and observations (the data) might ooze with noise. Probabilistic programming has emerged as the go-to technique to handle such uncertain domains. Therefore, we introduce the concept of reactive probabilistic programming, which allows to unify the methods of reactive programming and probabilistic programming. Herewith, we broaden the scope of probabilistic programming to event-driven data.
Learning stable mutation in (1, λ)ES evolutionary strategy

In this paper the concept of two-dimensional discrete spectral measure is introduced in the context of its application to real-valued evolutionary strategy (1, λ)ES. The notion of discrete spectral measure makes it possible to uniquely define a class of multivariate heavy-tailed distributions, that have received more and more attention of evolutionary optimization community, recently. In particular, an adaptation procedure known from the class of estimation of distribution algorithms (EDAs) is proposed. The effectiveness of the evolutionary strategy is tested by means of a set of popular benchmark functions.
Predicting Performance and Potential Difficulties of University Student using Classification: Survey Paper

Education is one of the sectors that established for the reason of fruitful production of the student and society in the world. While doing their education student faces different factor that affects the academic performance. The performance of the student is one of the crucial aspects of every educational institute. Knowing performance of the student has significant benefit for the student as well as the Educational institute. Educational data mining is the use of data mining tools related to education. Data. Different data mining tools used to predict the performance of the student using the available data set based on the dominant attribute. With the help of this educational data mining techniques, an academic performance of the student and different factor affect that lead the student to the failure and success identified. Classification, clustering, and prediction all aid to analyze the performance of the student. The result of the performance of algorithm is vary based on the different condition. Data set size, missing value and attribute selection determines which data mining tools perform best from all the other.
Characteristics of plant communities, population features, and edaphic conditions of Arnica montana L. populations in pine forests of mid-Eastern Europe

Mountain arnica, Arnica montana L. , an herbaceous plant species critically endangered in Europe, is the source of raw material, which is abundant in its secondary metabolites. During the field investigation conducted in Augustow and Knyszyn forests (Poland) and in Grodno Forest (Belarus), the plant species composition and population characteristics were measured. Additionally, to evaluate the edaphic conditions of arnica populations, soil samples were taken and analyzed. The sandy and very nutrient-poor soils are characterized by strong acidity and a very low concentration of macro- and microelements. The analyzed characteristics of the studied populations indicate a good status of populations located in Grodno Forest. However, the very small number of individuals and the very small proportion of flowering individuals in the populations in Augustow Forest and Knyszyn Forest indicate the need for active protective actions. Calamagrostis arundinacea can play the role of a competitor; therefore, during planning active protection, individuals of this species should be eliminated, and particular attention should be paid to the frequency and coverage of this plant species and the plant height of the herb layer. The dependence between population characteristics, especially the proportion of flowering stems and the concentration of available phosphorus, may indicate the effect of the concentration of this macroelement on flowering and, in consequence, provide a greater chance for the generative propagation of this plant species.
Patent Citation Recommendation Based on Topic Model Expansion

One important task related to patent citation is to recommend suitable citations for a given patent. Patent citation recommendation is difficult due to legal term usage and different skill level in using keyword search among the users. In this paper, we proposed topic model based technique for term expansion to improve the performance of patent citation recommendation. In order to avoid ambiguity from keyword search, we form a query as a document instead of using keywords. Experiment was performed to compare the similarity among Cosine, Jaccard, and KL-Divergence after finishing topic model expansion approach. The framework we proposed is implemented and evaluated its performance using Mean Average Precision (MAP). And the evaluation results show that our proposed approach outperforms the baseline approach.
Extracting Descriptive Life Profiles from Mobile GPS Data

The pervasiveness of mobile location-based devices that record position, time, and environmental properties has opened new prospects for human mobility research. In this paper, we present an approach to identify user types and location profiles using footprints from anonymous mobile GPS data. The results show promising potential for mining large-scale mobile phone trajectory data to solve real-time urban problems and better understand the complexity of individuals in the urban system.
Differential Indistinguishability: Cryptography with Imperfect Randomness

Cryptographic protocols are commonly designed and their security proven under the assumption that the protocol parties have access to perfect (uniform) randomness. Physical randomness sources deployed in practical implementations of these protocols often fall short in meeting this assumption, but instead provide only a steady stream of bits with certain high entropy. Trying to ground cryptographic protocols on such imperfect, weaker sources of randomness has thus far mostly given rise to a multitude of impossibility results, including the impossibility to construct provably secure encryption, commitments, secret sharing, and zero-knowledge proofs based solely on a weak source. More generally, indistinguishability-based properties break down for such weak sources. In this work, we show that the loss of security induced by using a weak source can be meaningfully quantified if the source is bounded, e.g., for the well-studied SanthaVazirani (SV) sources. The quantification relies on a novel relaxation of indistinguishability by a quantitative parameter. We call the resulting notion differential indistinguishability in order to reflect its structural similarity to differential privacy. More concretely, we prove that indistinguishability with uniform randomness implies differential indistinguishability with weak randomness. We show that if the amount of weak randomness is limited (e.g., by using it only to seed a PRG), all cryptographic primitives and protocols still achieve differential indistinguishability.
Mediating Role of Environmental Commitment between Green Organizational Identity and Green Innovation Performance

Integrating environmental goals in a company‟s mission and strategies not only improves the company's image as a socially responsible organization but also help in gaining a leading position of first mover advantage. In today's era, due to the increasing pressures towards sustainability and pro-environment policies, organizations as well as governments are making efforts in this direction. The purpose of this study is to find out the role of organizations in following green practices such as green organizational identity and green innovation performance. The current study investigates the association between green organizational identity, environmental commitment, and green innovation performance. Moreover, we also investigate how environmental commitment mediates the relationship between green organizational identity and green innovation performance. Data from manufacturing organizations is taken, these are the companies registered with WWF Green office project. Following a quantitative approach, the hypotheses developed in the light of literature are tested using structural equation modeling (SEM). Result indicates that a complementary partial mediation exists between green organizational identity, environmental commitment, and green innovation performance. Moreover, this study also aims to help in comprehending to what extent organizations are focusing on the environment and how much they value both the environment and making their operations green.
Achievable DoF Regions of Three-User MIMO Broadcast Channel With Delayed CSIT

For the two-user multiple-input multiple-output (MIMO) broadcast channel with delayed channel state information at the transmitter (CSIT) and arbitrary antenna configurations, all the degrees-of-freedom (DoF) regions are obtained. However, for the three-user MIMO broadcast channel with delayed CSIT and arbitrary antenna configurations, the DoF region of order-2 messages is still unclear and only a partial achievable DoF region of order-1 messages is obtained, where the order-2 messages and order-1 messages are desired by two receivers and one receiver, respectively. In this paper, for the three-user MIMO broadcast channel with delayed CSIT and arbitrary antenna configurations, we first design transmission schemes for order-2 messages and order-1 messages. Next, we propose to analyze the achievable DoF region of transmission scheme by transformation approach. In particular, we transform the decoding condition of transmission scheme w.r. t. phase duration into the achievable DoF region w.r. t. achievable DoF, through achievable DoF tuple expression connecting phase duration and achievable DoF. As a result, the DoF region of order-2 messages is characterized and an achievable DoF region of order-1 messages is completely expressed. Besides, for order-1 messages, we derive the sufficient condition, under which the proposed achievable DoF region is the DoF region.