Miracle-dz/UniFAR-FTUs huggingface dataset:
Linear Ordering in the SAT Encoding of the All-Different Constraint over Bit-Vectors

A novel eager encoding of the ALLDIFFERENT constraint over bit-vectors is presented in this short paper. It is based on 1-to-1 mapping of the input bit-vectors to a linearly ordered set of auxiliary bit-vectors. Experiments with four SAT solvers showed that the new encoding could be solved order of magnitudes faster than the standard encoding in a hard unsatisfiable case.
on Q & A Forum of Online Learning

The forums have become the most general ways for people to study and communicate nowadays, and both the level of users’ interactions and the efficiency of solving problems will influence the effect of users’ learning on the forums. In this paper, in order to solve the problems that how to find users who have similar tastes and interests rapidly and how to get a quick answer when users have agent questions, a framework has been proposed to apply the recommendation technology to the forums. For the empirical research, the data from an authentic forum is used, and finally we find that this recommendation technology can do improve the forum’s service efficiency and it is not only useful for reality uses of forum system but also provide research references on how to improve the use efficiency of form
A COMPARISON STUDY OF PERMEABLE AND IMPERMEABLE SEED COATS OF LEGUME SEED CROPS REVEALS THE PERMEABILITY RELATED STRUCTURE DIFFERENCE

Impermeability of Legume seeds is commonly related to physicaldormancy, which is useful for seed preservation but problematical for the soybean food industry. The primary aim of this study was tocharacterize the structure difference between permeable and impermeable seed coats, revealing thewater-controlling structures and demonstrating the water uptake process. Threemeasurements of water uptakefor four Legume species were carried out. The general anatomy of micropyle and seed coat were observed by scanning electron microscopy (SEM). Dye tracking was carried out to investigate the pathway of water movement during imbibition. The mature seed coat of four species contained three layers of cells. SEM observationsrevealed that the impermeable coat differs from the permeable one. Layer thickness has no direct relationship to the permeability of Legume seeds. The micropyle opening could be observed in permeable seed coats. Osteosclereids could impede the uptake of water; the layer of the osteosclereid near the embryo is hydrophilic and distal sideis hydrophobic. The present research provided a varietyof information related to the permeability of Legume seeds. The coat structures of permeable and impermeable micropyles are different in several aspects. A novel finding is that the outer surface of the osteosclereid layer is essential to permeability.
Evaluation Science

Culturally and politically science is under attack. The core consequence of perceiving and asserting evaluation as science is that it enhances our credibility and effectiveness in supporting the importance of science in our world and brings us together with other scientists to make common cause in supporting and advocating for science. Other consequences include communicating our role more clearly and credibly to those who value science. Viewing evaluation as science may affect how we are viewed, treated, and positioned in academia, government, and by funders and users of evaluation. The ramifications of evaluation science for evaluation’s status as a profession, branch of applied social science, discipline, and transdiscipline are reviewed. The conclusion offers implications, caveats, and cautions regarding the identity and practice of evaluation science.
IoT based Temperature and Humidity Controlling using Arduino and Raspberry Pi

Internet of Things (IoT) plays a pivotal part in our mundane daily life by controlling electronic devices using networks. The controlling is done by minutely observing the important parameters which generate vital pieces of information concerning the functioning of these electronic devices. Simultaneously, this information will transmit these vital statistics from the transmitting device as well as save the same on the cloud to access by the applications and supplementary procedures to use them. This scrutiny associates the outcomes of the environmental observances like the humidity and temperature measurements using sensors. The gathered information could be profitably used to produce actions like distantly dominant cooling, heating devices, or long term statistics, which will be useful to control the same. The detected data are uploaded to the cloud storage through network and associate using android application. The system employs Arduino UNO with Raspberry Pi, HTU 211D sensor device, and an ESP8266 Wi-Fi module. The experimental results show the live temperature and humidity of the surroundings and the soil moisture of any plant using Arduino UNO with Raspberry Pi. Raspberry Pi is mainly used here for checking the temperature and humidity through the HTU 211D sensor element. The sensors are used for measuring the temperatures from the surroundings, storing displayed information with different devices. Here, the ESP8266 Wi-Fi module has been used for data
Extended Complexity Results for the Production Leveling Problem

This work takes up the recently introduced Production Leveling Problem and provides extended complexity results. The problem is a multi-objective constrained combinatorial optimization problem that aims assign orders to production periods such that the load between production periods is balanced while at the same time minimizing the number of priority inversions. As the problem is NP-hard, there is currently no algorithm known which could solve arbitrary instances in polynomial time. We contribute a new variant of the problem called the Fixed-Order Production Leveling Problem which works on a restricted set of instances and has the priorities as hard constraints. We show that these adaptions make the problem tractable by proposing a dynamic programming algorithm and analyzing its complexity.
Using MATLAB to Improve Learning Effectiveness and Quality in an Undergraduate Course on Wireless Communications and Systems

Due to the nature of Radio Frequency (RF) signals and their transmission channel characteristics, there are two essential elements for undergraduate students in mastering knowledge in wireless communications: a strong mathematical background and hands-on exposure to RF experiments. Mathematical skills include the complex representation of signals, random variables and stochastic processes, spectral representation and estimation techniques, and so forth. A strong mathematical background helps students better understand RF signal properties and transmission channels characteristics using abstract thinking that are hard to perceive otherwise. Unfortunately, most undergraduate students do not necessarily possess sufficient mathematics to thoroughly understand wireless communication systems. In this case, practical experiences greatly enhance students’ understanding of wireless communication theories. Typically, experiential exploration for students is in the form of laboratory experiments. In an effort to improve teaching and learning effectiveness, and quality of communications courses, Using MATLAB to Improve Learning Effectiveness and Quality in an Undergraduate Course on Wireless Communications and Systems
Psychotherapists ’ interest in using the Furhat social robot for clinical training

Recently, we created and demonstrated a prototype of such a simulated environment (Johansson, Skantze, & Jönsson, 2017). The practice environment uses the Furhat social robot (Moubayed, Beskow, Skantze, & Granström, 2012) and the open-source toolkit IrisTK (Skantze & Al Moubayed, 2012) to implement simulated patients in a dialog system. A goal of our research has been to develop means for training, where it would be possible to practice psychotherapeutic methods on virtual patients in a similar way to that of medical training simulators. While there exist studies on using social robots as part of therapy (e.g., Feil-Seifer & Mataric, 2008), there seem to be hardly any research conducted on using social robots as patients for training purposes. We believe this could open up for a completely new approach to training within psychiatry and clinical psychology.
Chapter 2 Direct Liquid Injection Chemical Vapor Deposition

Thin film technology, based on different chemical and physical methods, enabled miniaturization, co-integration, and amelioration of the performance of the devices. Chemical vapor deposition (CVD) systems ensure high productivity and demonstrate excellent film uniformity (up to 12 inch wafers) and repeatability with high throughput for a variety of different films of oxides, nitrides, metals, chalcogenides, etc. In the last two decades, direct liquid injection (DLI)-CVD enabling the usage of solid and liquid precursors has proven to be one of the most versatile CVD process to meet industrial requirements. In this chapter, the requirements to the precursors suitable for DLI-CVD, different classes of available precursors, and models used to describe the evaporation are overviewed. Then, different liquid delivery devices used in DLI-CVD such as capillary tubes, syringes, aerosol delivery systems, and valves are reviewed in detail.
A Faster Algorithms for Dynamic Algebraic Queries in Basic RSMs with Constant

Interprocedural analysis is at the heart of numerous applications in programming languages, such as alias analysis, constant propagation, etc. Recursive state machines (RSMs) are standard models for interprocedural analysis. We consider a general framework with RSMs where the transitions are labeled from a semiring, and path properties are algebraic with semiring operations. RSMs with algebraic path properties can model interprocedural dataflow analysis problems, the shortest path problem, the most probable path problem, etc. The traditional algorithms for interprocedural analysis focus on path properties where the starting point is fixed as the entry point of a specific method. In this work, we consider possible multiple queries as required in many applications such as in alias analysis. The study of multiple queries allows us to bring in an important algorithmic distinction between the resource usage of the one-time preprocessing vs for each individual query. The second aspect that we consider is that the control flow graphs for most programs have constant treewidth.
THE POWER OF COINTEGRATION TEST

A cointegration test statistic based upon estimation of an error cor· rection model can be approximately normally distributed when no coin­ tegration is present. By contrast, the equivalent Dickey-Fuller statistic applied to residuals from a static relationship has a non-standard asymp­ totic distribution. When cointegration exists, the error-correction test generally is more powerful than the Dickey-Fuller test. These differences arise because the latter imposes a possibly invalid common factor restric­ tion. The issue is general and has ramifications for system-based cointe­ gration tests. Monte Carlo analysis and an empirical study of U.K. money demand demonstrate the differences in power.
An English to Xitsonga statistical machine translation system for the government domain

— Although a straightforward machine translation system can be trained with relatively little effort additional processing can often make a significant difference in the quality of the translated output. This article discusses several different sets of experiments aimed at improving the output of the English-Xitsonga machine translation system. These experiments include data cleaning, adding extra linguistic information to the training data and using placeholders to decrease data sparsity. All experiments were evaluated using the same evaluation set to ensure comparable results. Some of the methods attempted show promising results and it would be worthwhile to apply the same methods to other South African language pairs.
Phase-Controlled Capacitor Circuit and Efficiency-Based Tuning Method for Wireless Power Transfer

Abstract: In order to solve the detuning phenomenon caused by the influence of load variation, coupling state and other factors in the wireless power transfer system, the phase-controlled capacitor circuit and corresponding tuning strategy are proposed. The simulation and experimental results show that the resonant wireless power transfer system using the phase-controlled capacitor circuit and the tuning strategy can effectively solve the detuning problem and improve the output power and transfer efficiency.
Utilizing Feature Selection in Identifying Predicting Factors of Student Retention

Student retention is an important issue faced by Philippine higher education institutions. It is a key concern that needs to be addressed for the reason that the knowledge they gain can contribute to the economic and community development of the country aside from financial stability and employability. University databases contain substantial information that can be queried for knowledge discovery that will aid the retention of students. This work aims to analyze factors associated with student’s success among first-year students through feature selection. This is a critical step prior to modelling in data mining, as a way to reduce computational process and improve prediction performance. In this work, filter methods are applied on datasets queried from university database. To demonstrate the applicability of this method as a pre-processing step prior to data modelling, predictive model is built using the selected dominant features. The accuracy result jumps to 92.09%. Also, through feature selection technique, it was revealed that postadmission variables are the dominant predictors. Recognizing these factors, the university could improve their intervention programs to help students retain and succeed. This only shows that doing feature selection is an important step that should be done prior to designing any predictive model. Keywords—Educational data mining; feature selection; data preprocessing; knowledge discovery; student retention
Exploring chance in NCAA basketball Albrecht Zimmermann

There seems to be an upper limit to predicting the outcome of matches in (semi-)professional sports. Recent work has proposed that this is due to chance and attempts have been made to simulate the distribution of win percentages to identify the most likely proportion of matches decided by chance. We argue that the approach that has been chosen so far makes some simplifying assumptions that cause its result to be of limited practical value. Instead, we propose to use clustering of statistical team profiles and observed scheduling information to derive limits on the predictive accuracy for particular seasons, which can be used to assess the performance of predictive models on those seasons. We show that the resulting simulated distributions are much closer to the observed distributions and give higher assessments of chance and tighter limits on predictive accuracy.
Deep CNN-based Features for Hand-Drawn Sketch Recognition via Transfer Learning Approach

Image-based object recognition is a well-studied topic in the field of computer vision. Features extraction for hand-drawn sketch recognition and retrieval become increasingly popular among the computer vision researchers. Increasing use of touchscreens and portable devices raised the challenge for computer vision community to access the sketches more efficiently and effectively. In this article, a novel deep convolutional neural network-based (DCNN) framework for hand-drawn sketch recognition, which is composed of three wellknown pre-trained DCNN architectures in the context of transfer learning with global average pooling (GAP) strategy is proposed. First, an augmented-variants of natural images was generated and sum-up with TU-Berlin sketch images to all its corresponding 250 sketch object categories. Second, the features maps were extracted by three asymmetry DCNN architectures namely, Visual Geometric Group Network (VGGNet), Residual Networks (ResNet) and Inception-v3 from input images. Finally, the distinct features maps were concatenated and the features reductions were carried out under GAP layer. The resulting feature vector was fed into the softmax classifier for sketch classification results. The performance of proposed framework is comprehensively evaluated on augmented-variants TU-Berlin sketch dataset for sketch classification and retrieval task. Experimental outcomes reveal that the proposed framework brings substantial improvements over the state-of-the-art methods for sketch classification and retrieval. Keywords—Deep convolutional neural network; sketch recognition; transfer learning; global average pooling
Curvularia linata as bioherbicide for management of Xanthium strumarium L., an abnoxious weed - A Critical Evualation

Xanthium strumarium L. is an exotic plant responsible for several agricultural, environmental and health problems in India. Due to non-acceptability of conventional methods of control, the possibilities of its management through an indigenous strain of Curvularia lunata had been explored. In the present study, the organism was recovered in pure culture from the infected/infested parts of the weed, seedling and detached leaf bioassays were employed to evaluate the effect of different epidemiological factors on efficacy of mycoherbicidal agent. Significant effect on herbicidal potential of C. lunata was recorded when subjected to different conditions. Maximum efficacy of the pathogen was observed at 30°C, 80% RH, 2-3 leaves stage when sprayed @ of 4.5 x 105 Sp. /ml. The present study experimentally emphasizes on the development of Curvularia lunata as a mycoherbicide in the management of the weed, Xanthium strumarium L.
Exploring functional specialization within cerebro-cerebellar circuits for reading

According to the cerebellar deficit hypothesis [CDH; (Nicolson, Fawcett, & Dean, 2001)], cerebellar dysfunction can place children at risk for developmental dyslexia. In a recent meta-analytic review, Alvarez and Fiez (2018) proposed a neural model for how the cerebellum interfaces with the cerebral reading network (CDHn). This model posits that two regions in the cerebellum, one in lobule HVIIB and one in lobule Crus1, are interconnected with the cerebral cortex to form a dorsal fronto-parietal and a ventral fronto-temporal circuit, respectively. Moreover, the CDHn model asserts that these circuits are functionally specialized, with the dorsal circuit biased towards phonological processing and the ventral circuit biased towards semantic processing. This dissertation employs functional connectivity and neural activation measures obtained with magnetic resonance imaging to empirically test the CDHn model, with an a priori focus on the five regions proposed as constituents of its dorsal and ventral circuits. Resting-state analyses tested for patterns of functional connectivity between the predefined constituents, with a special interest in circuit specialization within an inferior frontal junction region that is part of both circuits. Univariate and multivariate methods were used to characterize the predicted phonological versus semantic task biases between the dorsal and ventral circuit, respectively. Finally, the products of these analyses were used to test whether activation in cerebellar regions VIIB and Crus1 modulate neural representation in the cerebral constituents of the dorsal and ventral circuits, respectively. Conventional connectivity analyses revealed stronger evidence supporting the ventral circuit. However, success in our more unique connectivity approach to find circuit specialization within the frontal region implicated the presence of dissociative dorsal and ventral networks. As predicted, the parietal region successfully characterized a task-bias for phonological processing; no biased effect within the frontal region; and a trend towards a semantic bias within the temporal region. All cerebral regions succeeded in the multivariate task-dissociation. Although the cerebellar regions did not exhibit predicted functional specialization, the univariate analyses did uncover interactions of tasks, and Crus1 engagement was successful in modulating the semantically related properties of the temporal region. Overall, we found encouraging but incomplete support for the recently proposed CDHn model.
Cathepsin K Regulates Adipocyte Differentiation : Possible Involvement of Type I Collagen Degradation

We previously found that cathepsin K (CTSK) played an important role in adipocyte differentiation. However, the underlying molecular mechanism is not clear. Through the time window study, it was observed that CTSK activities were required mainly in the early phases of adipogenic process. At the same time, the expression of type I collagen disappeared. However, type I collagen can still be observed during the whole process when the CTSK inhibitor-E64 was added. The mRNA levels of peroxisome proliferator-activated receptor γ (PPAR-γ) and CCAAT/enhancer binding protein α (C/EBP-α) was also declining. These imply that CTSK may play a role in adipogenesis in early differentiation phases and produce an effect at least partly by degrading type I collagen, which may provides a basis for developing novel therapeutic approaches to treat obesity and the diseases associated with it.
The impacts of absorptive capacity on improving software development : A preliminary study

Because information systems implementations depend on software development exercise, software process improvement (SPI) plays a critical role in the development of information systems. SPI is considered as an organizational learning process; it often needs external know-how and knowwhat that provide useful information and inspiration in implementation ad-hoc software processes. However, no research exists that focuses on a firm’s ability to increase effective external SPI knowledge acquisition and utilization. Based on the dynamic capability theory, this paper attempts to explore how a firm’s dynamic capability significantly affects SPI implementation success. The entire research consists of the exploration of related literature, the definition of and development of hypotheses and the research model, and the empirical investigation with new knowledge inquiry. This paper serves as a preliminary study of the research that focuses on the survey of relevant literature to support the research motivation and the model. Also, in this paper, a theoretical model draft and the hypotheses are developed to guide the following research of exploring and investigating the relationships between absorptive capacity and SPI success.
Temporal Poverty Prediction using Satellite Imagery

In order to effectively alleviate poverty, the measurement and tracking of support initiatives over time is a necessary step for targeting aid efforts and guiding policy decisions. However, obtaining such data is time and labor intensive, so coverage of poverty stricken areas is often sparse or nonexistent. Previous research has shown that a viable alternative for measuring poverty levels can be achieved through remote sensing methods. Specifically, satellite images processed through convolutional neural networks have shown promise in predicting the intensity of nighttime lights, which can then be used to gauge the underlying poverty level. This paper attempts to extend on past work by finding methods for measuring the change in poverty levels across different years using the same type of publicly available data. We are able to verify the original results of predicting poverty at a single point in time. More work is still needed to produce meaningful results in predicting temporal poverty.