Miracle-dz/UniFAR-FTUs huggingface dataset:
Recurrent Translation-Based Network for Top-N Sparse Sequential Recommendation

Fulfilling users’ needs and increasing the retention rate of recommendation systems are challenging. Most users have consumed a few items in most systems. Translation-based model performs well on sparse datasets. However, a user and only single previous item are considered for the user suggestion of next items. Alternatively, recurrent neural network utilizes sequential dependency but performs poorly on sparse datasets. We unify both and propose Recurrent Translation-based Network (RTN). RTN utilizes sequences of users’ consumed items without limiting interactions between items to the most recent one. The results of conducting experiments on real-world datasets show that RTN outperforms other state-of-the-art approaches on sparse datasets.
An improved join‐free snowflake schema for ETL and OLAP of data warehouse

The emergence of big data makes more and more enterprise change data management strategy, from simple data storage to OLAP query analysis; meanwhile, NoSQL‐based data warehouse receive more increasing attention than traditional SQL‐based database. By improving the JFSS model for ETL, this paper proposes the uniform distribution code (UDC), model identification code (MIC), standard dimension code (SDC), and attribute dimensional code (ADC); defines the data storage format of ; and identifies the extraction, transformation, and loading strategies of data warehouse. Several experiments are carried out to analyze single record and range record queries as typical OLAP based on Hadoop database (HBase). The results show the proposed scheme can provide lower overhead than the traditional SQL‐based database while facilitating the scope and flexibility of data warehouse services.
A brain-computer interface based on pupillometry using the rapid serial visual presentation paradigm

Brain-computer interfaces (BCI) use brain signals to interact with external devices (computers, prosthetics). A brain-computer interface can provide a means of communication for severely disabled people. We created a BCI based on the pupillometry method using a paradigm whereby stimuli are presented in rapid temporal succession, this paradigm is known as rapid serial visual presentation (RSVP). The pupil constricts and dilates in response to brightness. Pupil size is not solely dependent on retinal input but is guided by cognitive influences such as attention. It is well documented that attention and the pupil light response interact in such way that the pupil dilates when you attend a dark object, we have coined this phenomenon as the interaction effect. In our first experiment, in which the participant had to pay attention to letters, we investigated under what conditions this interaction effect would manifest itself. Participants were instructed to pay attention to a prespecified letter while a RSVP stream of twenty-six letters was presented to them. Our hypothesis was that the mean pupil size area would differ across RSVP streams with different target brightness. The interaction effect was not found in our first experiment presumably due to the absence of the pupil response associated with attention. In our second experiment we built a BCI which inferred which target the participant was paying attention to in real time. Rather than letters, participants had to select, by paying attention, either a triangle or a diamond. This method is an instance of the method known as two-forced alternative choice (2AFC). The data of the second experiment displayed a strong learning effect and the average perfor- mance of 59.11% was significantly above-chance level. Moreover, our BCI achieved an information- transfer rate of 0.06 bits/min. In conclusion, the RSVP paradigm in combination with the pupil- lometry method does show some potential for creating an effective BCI.
Open-Unmix - A Reference Implementation for Music Source Separation

Music source separation is the task of decomposing music into its constitutive components, e.g., yielding separated stems for the vocals, bass, and drums. Such a separation has many applications ranging from rearranging/repurposing the stems (remixing, repanning, upmixing) to full extraction (karaoke, sample creation, audio restoration). Music separation has a long history of scientific activity as it is known to be a very challenging problem. In recent years, deep learning-based systems-for the first time-yielded high-quality separations that also lead to increased commercial interest. However, until now, no open-source implementation that achieves state-of-the-art results is available. Open-Unmix closes this gap by providing a reference implementation based on deep neural networks. It serves two main purposes. Firstly, to accelerate academic research as Open-Unmix provides implementations for the most popular deep learning frameworks, giving researchers a flexible way to reproduce results. Secondly, we provide a pre-trained model for end users and even artists to try and use source separation. Furthermore, we designed Open-Unmix to be one core component in an open ecosystem on music separation, where we already provide open datasets, software utilities, and open evaluation to foster reproducible research as the basis of future development.
Strategic Information Systems Enabling Strategy-as-practice Under Uncertain Environments

Recent research in the new era of strategic planning (SP) shows its ambivalent capacity for prescriptive rationality as a way for managing the creation of emerging and flexible strategies to deal with environmental turbulence. However, there is limited research on how this capacity can be developed, and even less on strategic planning as enabled by strategic information systems (SIS). Contingency and Practice theory were used to investigate how SIS enables SP effectiveness in practice, under the influence of environmental uncertainty, by applying PLS-PM statistical techniques to a sample of 139 companies. The study confirmed that SIS enhances SP effectiveness under different contingency levels of dynamism, heterogeneity and hostility. Results from post-analysis study show: (a) SIS demonstrated greater predictive power in strategy formulation and control and less emphasis on executing it; (b) SIS presented greater influence on SP effectiveness in environments with high heterogeneity; (c) companies showed greater emphasis on controlling and formulating strategy than executing it. The study contributed to understanding how SIS creates value via strategy-as-practice under environmental turbulence. JEL Classification Codes: L86, D83, L2.
A computational analysis of short sentences based on ensemble similarity model

The rapid development of Internet along with the wide use of social media applications produce huge volume of unstructured data in short text form such as tweets, text snippets and instant messages. This form of data rarely contains repeated word. It presents challenge in sentences similarity analysis as the standard text similarity models merely rely on the number of word occurrence, often resulting unreliable similarity value. Besides, the use of abbreviation, acronyms, slang, smiley, jargon, symbol or non-standard short form also contributes to the difficulty in similarity analysis. Thus, an extended ensemble similarity model approach is proposed. An experimental study has been conducted using datasets of English short sentences. The findings are very encouraging in improving the similarity value for short sentences.
Association of Angiotensinogen Gene M235T and T174M Polymorphisms with Coronary Heart Disease in Rostov Population

Objectives: In this study, we examined the relationship between the AGT gene M235T and T174M polymorphisms and CHD risk in Rostov population. Material and Methods: We studied two groups of patients with clinical and coronary angiography evidence of CHD and ageand gender-matched controls, respectively. AGT gene M235T and T174M polymorphisms were analyzed by polymerase chain reaction. Results: The frequency distribution of AGT (M235T) genotypes among patients and controls (M235T: 28.5% and 16.2%; 235T: 22.4% and 7.3%, respectively) was statistically different (P = 0.03 and 0.001, respectively). CHD odds ratio associated with M235T heterozygotes and 235T homozygotes were 2.63 (1.1-6.1) and 4.88 (1.7-13.8), respectively. Conclusion: This study demonstrates the contribution of AGT gene M235T polymorphism, but not the AGT (T174M), to the presence of CHD risk in Rostov population. Genetic association studies involving very large sample size are needed to provide conclusive evidence on the effects of the AGT gene and other genes within the RAS system on risk of CHD.
Mining Negative Frequent regular Itemsets from Data Streams

Many Application in modern days requires capturing continuous data generated from remote sensors. Devices etc. and the data flows in streams, and the data is processed, and the processed results are temporarily stored. The original data is seldom stored. A lot amount of s Knowledge hidden in the data flowing in streams. Data patterns hidden in the data when mined will discover the hidden knowledge. Most of the research focussed on ming frequent items and positive associations. The regularity of occurrence of items is also important in addition to the frequency of occurrence of the itemsets. While positive associations are good, negative associations also revel very interesting findings which will help in taking important decisions. In this approach, novel algorithms, along with its implementation presented that will help ming regular, frequent, and negative itemsets from the data streams.
Analysis and Simulation of Bio-Inspired Intelligent Salp Swarm MPPT Method for the PV Systems under Partial Shaded Conditions

This paper proposes a new bio-inspired intelligence maximum power point tracking (MPPT) algorithm to abstract the high power from the photovoltaic (PV) array systems with the high tracking efficiency under partial shading conditions (PSC). The algorithm is based on the swarming behavior of salp, and this method locates global peak (GP) with the high convergence speed and high efficiency. The proposed salp swarm (SS) algorithm reduces the computational time than the conventional MPPT algorithms such as whale optimization (WO) algorithm, and perturb and observation (PO) algorithm discussed in the literature. The modeling and simulation is done with Matlab software and validated under PSCs. The simulation result demonstrates the effectiveness of SS algorithm with high tracking efficiency and the high convergence to GP.
A Review of the DCP-DN Pavement Design Method for Low Volume Sealed Roads: Development and Applications

Widespread implementation of the DCP-DN design method for low volume roads has been promoted internationally over the past decade or so. The method has progressed from a simple determination of the in situ CBR investigation based on DCP-CBR correlations with respective cover requirements to a more sophisticated method using the DCP penetration data directly and omitting any need to use correlations with the CBR. This paper summarises the development of the method, and some of its advantages and compares the design structures with other recognised and widely implemented designs.
Deep Learning-Based MIMO-NOMA With Imperfect SIC Decoding

Nonorthogonal multiple access (NOMA) and multiple-input multiple-output (MIMO) are two key enablers for 5G systems. In this article, considering the practical issue that successive interference cancellation (SIC) decoding is imperfect in the real-world NOMA system, we propose a novel scheme for the downlink of the MIMO-NOMA system based on deep learning. In this scheme, both precoding and SIC decoding of the MIMO-NOMA system are jointly optimized (or learned) in the sense of minimizing total mean square error of the users’ signals. To this end, we construct the precoder and SIC decoders using deep neural networks such that the transmitted signals intended to multiple users can be properly precoded at the transmitter based on the superposition coding technique and the received signals are accurately decodable at the users by the SIC decoding. Numerical results demonstrate the effectiveness and superior performance of the proposed scheme.
The Story after Immigrants’ Ambitious Educational Choices: Real Improvement or Back to Square One?

It is a well-established finding in the literature that immigrants make ambitious educational choices. Once controlling for prior achievement and socioeconomic status, children of immigrants are more likely than natives to switch to the more demanding educational tracks. However, less is known about whether immigrants can actually benefit from these optimistic choices in terms of educational attainment or whether they have a higher risk of dropping out from the more demanding tracks. By focusing on a representative sample of adolescents with and without immigrant background in Germany, this contribution investigates how enrolment and completion rates change over time—from the end of lower secondary education until the end of upper secondary education—and how this affects ethnic inequalities in educational outcomes. When comparing academic completion rates and academic enrolment rates in grade 9, we observe long-term improvements within the immigrant group as a result of immigrants’ ambitious choices. When comparing both outcomes between natives and immigrants, however, ethnic differences in academic completion rates remain comparable to the disparities in enrolment rates as observed in grade 9.
Energy Efficiency of Massive MIMO Downlink WPT With Mixed-ADCs

This letter studies the energy efficiency of massive multi-input multi-output downlink wireless power transfer systems with mixed analog-to-digital converters (ADCs). Taking into account of imperfect channel state information estimation, the closed-form expression for the average amount of harvested energy of the users are derived. Then, considering a realistic energy consumption model, the end-to-end energy efficiency (EE) of the system is studied, and the optimal strategy maximizing the EE is analytically characterized. The findings of the letter suggest that the base station should adopt a binary policy by using either high-resolution ADCs or low-resolution ADCs. Simulation results are provided to corroborate the analytical results.
Integration of statistical and neural network method for data analysis

Integration of Statistical and Neural Network Method for Data Analysis Krishna K Chavali The issues that are to be considered while selecting data analysis methods are data type and expected solution type. Incorrect selection of either can lead to incomplete solution leading user taking an uninformed decision. The software developed for this thesis (SANE Statistical And NEural network data analysis) is aimed at enabling a user to use advanced data analysis techniques to handle a given research issue. An effective navigation system is designed for user to select the analysis method by responding to a set of questions. SANE provides web based data analysis using statistical and neural networks methods. The statistical analysis module has methods for finding a relationship between variables, predicting group membership and finding group differences. The neural net module has back propagation and cascade correlation algorithms. Users can apply different methods on same dataset and compare the results. This software is implemented in ASP.NET2. 0 with backend in SQL2005.
Disaster preparations and relief: The partnership between the American National Red Cross and Amateur Radio

Disaster preparations and relief: The partnership between the American National Red Cross and Amateur Radio Bayley B. Brown The goal of this master’s thesis is to evaluate the current state of the partnership between the American National Red Cross chapters and Amateur Radio Emergency Service (ARES) groups. The alliance between these organizations has historically been outlined by national representatives, the most recent being the 2010 Memorandum of Understanding between the American National Red Cross and ARRL, the national association for Amateur Radio. This partnership was assessed through a nationwide survey of American National Red Cross chapter representatives. Findings suggest that most Red Cross chapters have current partnerships with their local ARES group, and collaboration occurs primarily for disaster preparation and during relief efforts. The information gathered in this project helps raise awareness of the partnership between the Red Cross and ARES groups, familiarizes other organizations of both group's resources, and adds to the theory of collaborative advantage.
A 265- $\mu$ W Fractional- ${N}$ Digital PLL With Seamless Automatic Switching Sub-Sampling/Sampling Feedback Path and Duty-Cycled Frequency-Locked Loop in 65-nm CMOS

This article proposes a fractional-<italic>N</italic> digital phase-locked loop (DPLL) that achieves a 265-<inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> ultra-low-power operation. The proposed switching feedback can seamlessly change the DPLL from sampling operation to sub-sampling operation without disturbing the phase-locked state of the DPLL to reduce the number of building blocks that works at the oscillator frequency, leading to significant power reduction. With the reduced number of high-frequency circuits, scaling the reference frequency is fully used to reduce the power consumption of the DPLL. Together with an out-of-dead-zone detector and a duty-cycled frequency-locked loop running in the background, the switching feedback achieves robust frequency and phase acquisition at start-up and helps the sub-sampling PLL recover when large phase and frequency disturbances occur. A transformer-based stacked-<inline-formula> <tex-math notation="LaTeX">$g_{m}$ </tex-math></inline-formula> oscillator is proposed to minimize the power consumption while providing the sufficient swing to drive the subsequent stages. A truncated constant-slope digital-to-time converter is proposed to improve the power efficiency while retaining good linearity. The proposed fractional-<inline-formula> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula> DPLL consumes only 265 <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> while achieving an integrated jitter of 2.8 ps and a worst case fractional spur of −52 dBc, which corresponds to a figure of merit (FOM) of −237 dB.
Anatomy of a Shale Boom: The Roles of Long-Lived Capital, Leasing, and Productivity Differences

The US shale boom was enabled by three key factors which are unique to the North American context: (1) private ownership of mineral rights by landowners, (2) a competitive oilfield services sector that owns long-lived capital required to develop resources, and (3) extremely low barriers to entry for heterogeneous E&P firms in markets for mineral rights. These factors are widely cited when prospects for non-US shales are discussed, but economists still have yet to incorporate them into a formal, economic model of a depletable resource to understand how each shapes the evolution of a shale boom.
AUTOMATIC EXAM ATTENDANCE SYSTEM BASED ON ILLUMINATION INVARIANT FACE RECOGNITION

Security systems are used in several ways. In the developing technology, the computer systems are employed to solve different kinds of problems whereas they can be also used for security purposes. Application of the computer-aided security systems are one of the applicable technologies today especially in the crowded places such as entrance gates where high security measure is requested. On the other hand, automatic face recognition is useful in the applications where the recognition of the authorized people should be completed in a limited time. An application, that is the identification of the students for exam security is one of the important issues in universities where crowded exams take place. Unidentified people other than one's own examinations can be defined as problematic in terms of exam assessment. The paper proposes a new automatic class attendance system based on illumination invariant face recognition. System consists of three stages which are the face detection, facial feature extraction and classification. A known method will be employed for face detection part. For the facial feature extraction stage, non-subsampled Contourlet transform is used. The classification is done by the use of a known method which is the correlation coefficient. The system is currently under test and expected to run at acceptable recognition rates to be used in an automatic class attendance system.
How do peer mentors impact college students in undergraduate gateway courses at a large public university

Peer mentoring is a growing trend utilized to improve graduation and retention rates of college students across campuses. It is a strategy being used either outside or inside the classroom to assist students with learning, connecting to resources, and increasing student success in gateway courses. Southwest State University is an example of an institution, initially funded through a Title V grant, using peer mentoring in gateway courses. Peer mentors are placed in high failing gateway courses such as the ones chosen for this study to include Earth and Planetary Science 101, Chemistry 121, and Math 121. Gateway courses are defined as large lecture courses often difficult for students to pass because of class size and subject. Southwest State University includes gateway courses students are required to pass to progress through their intended major. The purpose of this qualitative study was to learn how peer mentors impact student success in gateway courses at one university in the southwest. Course mentors, students, and instructors were interviewed and courses observed to seek understanding of influences peer mentors have in the classroom. Common themes contributing to the effectiveness of peer mentoring in gateway courses were explored to determine how peerto-peer mentoring is influencing these courses and to explore the overall research
Do corporate social responsibility activities enhance customer satisfaction and customer loyalty? Evidence from the Saudi banking sector

Abstract This study examines the influence of corporate social responsibility activities on customer satisfaction and loyalty in the Saudi banking sector. The quantitative method was adopted by conducting a web-based survey via e-mail and social media platforms. A convenience sample of 624 banking customers in Saudi Arabia was withdrawn. Pearson’s Correlation Coefficients were generated, using statistical analyse software (SPSS 20), to test the study hypotheses. The empirical Findings of this study show that corporate social responsibility activities have a strong and positive impact on customer satisfaction and loyalty. It was also found that customer satisfaction is linked positively to customer loyalty. Findings from this paper are of importance for bank managers and directors when developing effective strategies.
A Novel Approach to Component Assembly Inspection Based on Mask R-CNN and Support Vector Machines

Assembly is a very important manufacturing process in the age of Industry 4.0. Aimed at the problems of part identification and assembly inspection in industrial production, this paper proposes a method of assembly inspection based on machine vision and a deep neural network. First, the image acquisition platform is built to collect the part and assembly images. We use the Mask R-CNN model to identify and segment the shape from each part image, and to obtain the part category and position coordinates in the image. Then, according to the image segmentation results, the area, perimeter, circularity, and Hu invariant moment of the contour are extracted to form the feature vector. Finally, the SVM classification model is constructed to identify the assembly defects, with a classification accuracy rate of over 86.5%. The accuracy of the method is verified by constructing an experimental platform. The results show that the method effectively completes the identification of missing and misaligned parts in the assembly, and has good robustness.