Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of improved symmetric incremental learning algorithm in glass defect classification

This paper focuses on the application and development of image recognition technology in glass defect detection industry, and studies the basic theory and implementation method of support vector machine. Aiming at the main shortcomings of incremental learning algorithm in current support vector machines, an improved symmetric incremental learning algorithm (S-ISVM) is designed. Support vector machines are selected as training tools and classification tools, and the training samples and test samples are used as prediction samples respectively. The feasibility of the algorithm and the correctness and validity of data processing methods are verified. The research results have broad prospects for development and practical value in the field of glass industry production.
Extremely Low Light Video Enhancement along with No-Reference Video Quality Measurements

Low light video enhancement is a challenge many researchers have been facing. Low illumination is the cause for low light videos. Noise present in the videos adds to the low quality of the low light video. So to improve the quality of videos the noise present in the videos has to be suppressed before enhancement is attempted. This paper addresses Kalman filter to suppress the noise from the extremely low light video. Though entire noise could not be suppressed by this technique a Non local means filter helps to remove the remaining noise after amplification. The Gray World technique is used for amplification. After removing the noise by using NLM filter image enhancement of extremely low light video is carried out.
Naive Regularizers for Low-Resource Neural Machine Translation

Neural machine translation models have little inductive bias, which can be a disadvantage in low-resource scenarios. Neural models have to be trained on large amounts of data and have been shown to perform poorly when only limited data is available. We show that using naive regularization methods, based on sentence length, punctuation and word frequencies, to penalize translations that are very different from the input sentences, consistently improves the translation quality across multiple low-resource languages. We experiment with 12 language pairs, varying the training data size between 17k to 230k sentence pairs. Our best regularizer achieves an average increase of 1.5 BLEU score and 1.0 TER score across all the language pairs. For example, we achieve a BLEU score of 26.70 on the IWSLT15 English–Vietnamese translation task simply by using relative differences in punctuation as a regularizer.
Instructional Rounds and Problem-Solving: An Evaluative Case Study

Instructional Rounds and Problem-Solving: An Evaluative Case Study by Cheryl A. Scalzo MA, Kutztown University, 1999 BS, Kutztown University, 1995 Project Study Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Education Walden University October 2019 Abstract In the target district, instructional rounds (IR), were implemented to reform district-wideIn the target district, instructional rounds (IR), were implemented to reform district-wide educational practices to increase student problem-solving skills over time. This evaluative case study investigated the perceived efficacy of IRs as a model to address student problem-solving. Specifically, the study examined the influences, if any, that the IR process has had on problem-solving, academics, and pedagogy since implementation. The study was framed by Bandura’s social learning theory as it states that behavior is learned from one’s environment through the process of observation. Qualitative data were collected from 86 stakeholders through a district-wide questionnaire, semi-structured interviews with 8 administrators, and a review of IR feedback notes and fundamental instructional practices. Data were analyzed and open coded to identify common themes and assess if there was perceived efficacy of the IR process. Findings showed that participants perceived the implementation of IRs as effective in improving academic, social, and pedagogical processes throughout the district. Participants shared the opinion that IR improved critical thinking among students, though there was no formal measure for this. A white paper was generated to inform the district of these changes, with recommendations for improvement in instructional rounds implementation. The project will promote social change by improving the teaching and learning process for students, teachers, and administrators at the target district. Based on what was reported about IR, continuing to improve the IR process can bring improvements to the teaching and learning process which will support stronger problemsolving, collaboration, and critical thinking among students. Instructional Rounds and Problem-Solving: An Evaluative Case Study by
Short Text Mining: State of the Art and Research Opportunities

With the growing number of connected online users producing a tremendous amount of unstructured short-texts daily, understanding and mining these data becomes very useful for individuals, governments and companies for identifying the public users’ attitudes towards different entities, such as products, services, events, places, organizations and topics. However, analyzing these short-texts using traditional methods becomes a significant challenge due to the shortness and sparsity nature of short-texts. To address such challenges, the literature introduced a broad spectrum of short-texts mining approaches and applications. Hence, this paper provides a comprehensive survey of this spectrum based on a criterion-based research strategy. The different mining techniques and approaches utilized in short-texts were highlighted along with their related issues and challenges. This paper surveyed a total of 1575 research papers published in the refereed conferences and journals in the area of short-texts mining were sur-veyed from 2006 until 2017, from which 187 primary studies were included and analyzed to constitute the source of the present paper. After a careful review of these articles, it is obvious that there are research gaps in other languages than English and Chinese, multi-languages, and in specific domain studies.
Combinatorial optimization. Polyhedra and efficiency.

Reading a book is also kind of better solution when you have no enough money or time to get your own adventure. This is one of the reasons we show the combinatorial optimization polyhedra and efficiency as your friend in spending the time. For more representative collections, this book not only offers it's strategically book resource. It can be a good friend, really good friend with much knowledge.
Reinforcement Learning for Real-Time Pricing and Scheduling Control in EV Charging Stations

This article proposes a reinforcement-learning (RL) approach for optimizing charging scheduling and pricing strategies that maximize the system objective of a public electric vehicle (EV) charging station. The proposed algorithm is “online” in the sense that the charging and pricing decisions made at each time depend only on the observation of past events, and is “model-free” in the sense that the algorithm does not rely on any assumed stochastic models of uncertain events. To cope with the challenge arising from the time-varying continuous state and action spaces in the RL problem, we first show that it suffices to optimize the total charging rates to fulfill the charging requests before departure times. Then, we propose a feature-based linear function approximator for the state–value function to further enhance the efficiency and generalization ability of the proposed algorithm. Through numerical simulations with real-world data, we show that the proposed RL algorithm achieves on average 138.5% higher charging-station profit than representative benchmark algorithms.
Resource Allocation Techniques for Non-Orthogonal Multiple Access Systems

Non-orthogonal multiple access (NOMA) has been proposed as a viable multiple access (MA) technique to meet the demanding requirements in fifth-Generation (5G) and beyond wireless networks. Unlike conventional orthogonal multiple access (OMA) techniques, NOMA simultaneously sends signals to multiple users in the same resource block (RB) in time and frequency domains using power-domain superposition coding (SC) at transmitter. Therefore, NOMA has the potential capabilities to serve a large number of devices while significantly improving spectrum efficiency (SE) compared to the conventional MA techniques, which supports massive connectivity of Internet-of-Things (IoT) networks. To introduce additional degrees of freedom, and hence facilitate implementing NOMA in ultra-dense networks, NOMA has been integrated with different key technologies including multiple antenna techniques and conventional OMA techniques. In particular, the combination between multiple-input single-output (MISO) and NOMA, referred to as MISONOMA, is firstly considered in this thesis. In which, different beamforming designs have been proposed for MISO-NOMA system, including global energy efficiency maximization (GEE-Max) design and EE fairness-based designs. In addition, different multi-performance metrics have been also considered in the designs including GEE-SE design and fairness-sum rate design. Due to non-convexity of the formulated optimization problems, different convex relaxation and approximation techniques have been exploited throughout the thesis to approximate the original non-convex problems with convex problems. The performance of the proposed designs has been evaluated through drawing comparisons with that of the existing beamforming designs in the literature. Secondly, the combination of NOMA with OMA scheme has been investigated, particularly, energy harvesting (EH) capabilities of time division multiple access (TDMA) and NOMA system has been considered. In this hybrid TDMA-NOMA system, simultaneous wireless information and power transfer (SWIPT) technique is integrated such that user has the capability to harvest energy and decode information, simultaneously. Simulation results show that EH capabilities of the TDMA-NOMA system outperform that of the conventional TDMA system.
Comparing and Recommending Conferences

This paper first introduces techniques for comparing conferences that use familiar similarity measures and a new measure based on co-authorship communities. Then, it focuses on two families of techniques for conference recommendation, the first one based on the similarity measures and the second on the idea of finding the most related authors in the co-authorship network. The experiments suggest that the best performing techniques are: the technique for comparing conferences that uses the new similarity measure based on co-authorship communities; and the conference recommendation technique that explores the most related authors in the co-authorship network.
Performance improvement of optical high-speed short-range communication systems utilizing bandwidth extension and advanced digital signal processing

High-speed short-range optical links are a key technology for data center applications. Today, they enable the transmission of huge amounts of information in a cost and energy efficient way and are typically based on simple optics and on-off keying modulation. In order to meet the world-wide increasing demand on transmission capacity for these optical links, new approaches are required to improve the currently achievable capacity. In this thesis two strategies are discussed. The first one utilizes a bandwidth extension concept based on analog mixing to increase the signal bandwidth beyond the limits of today’s signal generators. The second one investigates the application of linear and nonlinear pre-equalization for vertical cavity surface emitting lasers (VCSEL) based links. The bandwidth extension concept is experimentally investigated for a high-speed optical link based on intensity modulation (IM) and direct detection (DD). The achieved transmission rates are well beyond 100 Gb/s showing the feasibility of such an approach. The thesis further includes a detailed investigation of the relevant components, the suitability of different modulation formats and the use of digital signal processing (DSP) to compensate for the system impairments such as inter-symbol interference and in-phase and quadrature (I/Q) imbalance. Furthermore, the demonstration of a wideband digital-analog converter based on this concept is shown. DSP will be one major aspect for future high-speed short-range optical links, enabled by the ever decreasing cost of application-specific integrated circuits (ASIC). Optical links using VCSELs, with their typically non-ideal characteristics, benefit especially from enhanced equalization schemes, which enable an error free transmission at higher symbol rates and the use of higher order modulation formats. Linear and nonlinear pre-equalization are two options to achieve this goal. In this work an optimized linear pre-equalization approach is utilized to demonstrate record high transmission rates for a VCSEL-based IM/DD link. Furthermore, the influence of using a nonlinear pre-equalizer on the system performance is investigated and compared to pure linear pre-equalization.
Food Waste Protein Sequence Analysis using Clustering and Classification Techniques

The rapid development of data mining techniques takes place in 1980. A large number of newly invented technologies came into the computer field such as satellites, high storage of data medium, etc. This improvement paved the way for a destination in one place for the huge collected data. There were many methods followed earlier for the analysis of data but they failed to prove efficiency in higher gathered data and also in noisy data compared to data mining. This paper clearly describes about the various biological data mining techniques and their inter relations for prediction with the basic concepts of clustering, classification and alignment techniques. This provides the characteristics of protein sequence analysis in motif finding.
A Context Model for Code and API Recommendation Systems based on Programming Onsite Data

Code and application programming interface (API) recommendation systems are important guarantees for efficient and accurate code reuse to improve the efficiency of software development. Context data plays a key role in code and API recommendation. A large amount of programming onsite data has been generated, but existing code and API recommendation systems rarely consider the context based on programming onsite data, which leads to low efficiency and poor accuracy of code and API recommendation. In this paper, we proposed a context model for code and API recommendation systems. Our context model is based on programming onsite data collected during programming. It includes four aspects: developer, project, time, and environment. Developer data is labeled data abstracted from information according to developers' programming habits and abilities, project data is information about the project, time data is information about temporal aspects of developers interacting with the project, and environment data is all environment elements used by developers during programming. Then, we collected programming onsite data in three ways: explicit collection, implicit collection, and reasoning. Lastly, we built the context model using a coarse-grained abstract model for recommendation. Our context model retains the key information in the code while eliminating redundant information that may affect the accuracy of the recommend task, and it can theoretically improve the efficiency and accuracy of recommendation.
Compilation of a multilingual parallel corpus

The author has already conducted a fair amount of research into how to compile a multilingual parallel corpus and has experimented with a few compilation methods. Furthermore, his findings on the characteristics of these methods and those of corpora created by them have been published. In this paper, the author proposes a new method based on the extraction of data from Japanese−English bilingual newspaper articles and broadcast media news reports published on the WWW.
Estimating chain length for time delays in dynamical systems using profile likelihood

MOTIVATION Apparent time delays in partly observed, biochemical reaction networks can be modelled by lumping a more complex reaction into a series of linear reactions often referred to as the linear chain trick. Since most delays in biochemical reactions are no true, hard delays but a consequence of complex unobserved processes, this approach often more closely represents the true system compared with delay differential equations. In this paper, we address the question of how to select the optimal number of additional equations, i.e. the chain length (CL). RESULTS We derive a criterion based on parameter identifiability to infer CLs and compare this method to choosing the model with a CL that leads to the best fit in a maximum likelihood sense, which corresponds to optimizing the Bayesian information criterion. We evaluate performance with simulated data as well as with measured biological data for a model of JAK2/STAT5 signalling and access the influence of different model structures and data characteristics. Our analysis revealed that the proposed method features a superior performance when applied to biological models and data compared with choosing the model that maximizes the likelihood. AVAILABILITY AND IMPLEMENTATION Models and data used for simulations are available at https://github.com/Data2Dynamics/d2d and http://jeti.uni-freiburg.de/PNAS_Swameye_Data. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Selecting Representative Critical Paths for Sensor Placement Provides Early FPGA Aging Information

This article proposes a methodology for critical path selection and delay sensor insertion for aging monitoring in field-programmable gate arrays (FPGAs). The aging information can be used to reconfigure FPGAs to achieve better performance or wear leveling. To filter out the paths aging less aggressively, our methodology decides based on both physical-level parameters [e.g., path delay, process variation, temperature, static stress (duty cycle), and dynamic stress (switching activity) as well as aging-relevant parameters (e.g., fan-out, and endpoint physical location)]. After selection of an optimal set of paths, age sensors, introduced in our earlier work, are placed in a distributed fashion throughout the FPGA area to provide aging information of its various parts as they are being used. Accurate aging models for FPGAs are required to determine the contribution of the above-mentioned parameters in aging. In this article, ASICs’ aging models are adapted for FPGAs through measurement-based fitting approach. The experimental results using various benchmarks reveal that our algorithm selects paths with a minimum error.
Talking to a pedagogical agent in a smart TV: modality matching effect in human-TV interaction

ABSTRACT The current study examined how voice control and a virtual agent in a smart TV interplay in influencing users’ evaluation of the TV. In a 2 (input modality: voice control vs. remote controller) X 2(agent type: realistic vs. cartoonlike agent) between-subjects experiment, participants (N = 64) used and evaluated a smart TV in an educational context. Results revealed negative effects of voice control, especially when it accompanied a cartoonlike agent. Those who used the voice control to the cartoonlike agent rated the agentless humanlike, attractive, intelligent and intimate than did those who used the remote controller to the cartoonlike agent. Also, those who used voice control reported that watching the TV was less involving and enjoyable than those who used the remote controller. The negative effects were mitigated, however, when they interacted with a realistic agent. These results suggest the importance of the matching between input modality and visual interface to reduce potential negative effects.
Parking Assignment: Minimizing Parking Expenses and Balancing Parking Demand Among Multiple Parking Lots

Recently, a rapid growth in the number of vehicles on the road has led to an unexpected surge of parking demand. Consequently, finding a parking space has become increasingly difficult and expensive. One of the viable approaches is to utilize both public and private parking lots (PLs) to effectively share the parking spaces. However, when the parking demands are not balanced among PLs, a local congestion problem occurs where some PLs are overloaded, and others are underutilized. Therefore, in this article, we formulate the parking assignment problem with two objectives: 1) minimizing parking expenses and 2) balancing parking demand among multiple PLs. First, we derive a matching solution for minimizing parking expenses. Then, we extend our study by considering both parking expenses and balancing parking demand, formulating this as a mixed-integer linear programming problem. We solve that problem by using an alternating direction method of multipliers (ADMM)-based algorithm that can enable a distributed implementation. Finally, the simulation results show that the matching game approach outperforms the greedy approach by 8.5% in terms of parking utilization, whereas the ADMM-based algorithm produces performance gains up to 27.5% compared with the centralized matching game approach. Furthermore, the ADMM-based proposed algorithm can obtain a near-optimal solution with a fast convergence that does not exceed eight iterations for the network size with 1000 vehicles. Note to Practitioners—The efficiency of the parking assignment is critical to the parking management systems in order to provide the best parking guides. This article investigates the cost minimization problem for parking assignment while balancing parking demand among multiple parking lots (PLs). Previous parking assignment approaches do not jointly investigate the cost of parking and the cost of PL utilization. Therefore, they can fail to the local congestion problem caused by a large number of vehicles driving toward the same PL. In this article, a new method that considers both of minimizing parking expenses and balancing parking demand is proposed. It is obtained by using the alternating direction method of multipliers (ADMM)-based proposal that distributively solves a constrained optimization problem. Based on the experimental results, the ADMM-based algorithm outperforms the matching-based algorithm and the greedy algorithm in terms of the balancing parking demand and reducing parking expenses. The proposed method can be readily implemented in real-world industrial PLs. In the future work where parking assignments for electric vehicles are needed, our proposed mechanism can then be extended to solve the balanced electricity overload multiple charging stations.