Reviewerly/exHarmony huggingface dataset:
ViTag: Automatic Video Tagging Using Segmentation and Conceptual Inference

Massive increase in multimedia data has created a need for effective organization strategy. The multimedia collection is organized based on attributes such as domain, index-terms, content description, owners, etc. Typically, index-term is a prominent attribute for effective video retrieval systems. In this paper, we present a new approach of automatic video tagging referred to as ViTag. Our analysis relies upon various image similarity metrics to automatically extract key-frames. For each key-frame, raw tags are generated by performing reverse image tagging. The final step analyzes raw tags in order to discover hidden semantic information. On a dataset of 103 videos belonging to 13 domains derived from various YouTube categories, we are able to generate tags with 65.51% accuracy. We also rank the generated tags based upon the number of proper nouns present in it. The geometric mean of Reciprocal Rank estimated over the entire collection has been found to be 0.873.
Maxout into MDLSTM for Offline Arabic Handwriting Recognition

Research on Arabic handwriting recognition has been seriously challenged due to its cursive appearance, the variety of writers and the diversity of styles. In fact, motivated by a series of success cases in computer vision, we try to explore the Maxout units in Multidirectional neural networks for the offline task. Therefore, in this work, we model an Arabic handwritten word with a deep MDLSTM-based system. This architecture can directly work on raw input images since it enables us to model the script variations on both axes of the image due to recurrence over them. However, several problems, such as the vanishing gradient, can affect the training of this recognition system. To overcome this problem, we should integrate Maxout units into MDLSTM system in order to enhance it and improve its performance. In this context, different integration modes are carried out to draw out the best topology. Proposed systems are evaluated on a large database IFN/ENIT. According to the experimental results and compared to the baseline system, the best tested architecture reduced the label error rate by 6.86%.
On the Initialization of Long Short-Term Memory Networks

Weight initialization is important for faster convergence and stability of deep neural networks training. In this paper, a robust initialization method is developed to address the training instability in long short-term memory (LSTM) networks. It is based on a normalized random initialization of the network weights that aims at preserving the variance of the network input and output in the same range. The method is applied to standard LSTMs for univariate time series regression and to LSTMs robust to missing values for multivariate disease progression modeling. The results show that in all cases, the proposed initialization method outperforms the state-of-the-art initialization techniques in terms of training convergence and generalization performance of the obtained solution.
A novel collaborative requirement prioritization approach to handle priority vagueness and inter-relationships

One of the challenging tasks of requirement engineering is to choose which among the set of requirements are more advantageous than others and ought to be viewed as first for execution. Years of research has established it is vital to capture, analyze and prioritize requirements, which to a great extent relies on acknowledgment acceptance of stakeholder’s requirements, concerns and criteria’s. Proposed method provides interactive support for eliciting stakeholders and developer’s initial ranking decision in collaborative manner. Intuitionistic Fuzzy Approach (IFS) is used to support stakeholders view; whereas analysis of inter relationships, requirement slicing and backtracking are used to support developers view using weighted page rank algorithm. Finally a ranking is generated in a collaborative manner to support requirement prioritization. The feasibility of the approach is illustrated through an experimental proof of concept by comparing results of proposed approach with state of art, Analytic Hierarchy Process (AHP) and Interactive Genetic Algorithm (IGA) prioritization techniques. Results of experimentation conclude that presented approach is capable of producing accurate and comparable results by handling technical constraints of dependency, tradeoffs of collaboration and scope of inclusion of initial multi-criteria preferences expressed as priority values to support reliability and robustness to errors.
Enhanced LSTM with Batch Normalization

Recurrent neural networks (RNNs) are powerful models for sequence learning. However, the training of RNNs is complicated because the internal covariate shift problem, where the input distribution at each iteration changes during the training as the parameters have been updated. Although some work has applied batch normalization (BN) to alleviate this problem in long short-term memory (LSTM), unfortunately, BN has not been applied to the update of the LSTM cell. In this paper, to tackle the internal covariate shift problem of LSTM, we introduce a method to successfully integrate BN into the update of the LSTM cell. Experimental results on two benchmark data sets, i.e. MNIST and Fashion-MNIST, show that the proposed method, enhanced LSTM with BN (eLSTM-BN), has achieved a faster convergence than LSTM and its variants, while obtained higher classification accuracy on sequence learning tasks.
BERT Based Hierarchical Sequence Classification for Context-Aware Microblog Sentiment Analysis

In microblog sentiment analysis task, most of the existing algorithms treat each microblog isolatedly. However, in many cases, the sentiments of microblogs can be ambiguous and context-dependent, such as microblogs in an ironic tone or non-sentimental contents conveying certain emotional tendency. In this paper, we consider the context-aware sentiment analysis as a sequence classification task, and propose a Bidirectional Encoder Representation from Transformers (BERT) based hierarchical sequence classification model. Our proposed model extends BERT pre-trained model, which is powerful of dependency learning and semantic information extracting, with Bidirectional Long Short Term Memory (BiLSTM) and Conditional Random Field (CRF) layers. Fine-tuning such a model on the sequence classification task enables the model to jointly consider the representation with the contextual information and the transition between adjacent microblogs. Experimental evaluations on a public context-aware dataset show that the proposed model can outperform other reported methods by a large margin.
Analysis of the Success of Student Monitoring Information System Implementation Using DeLone And McLean Model

Abstract The Primary School implements the Student Monitoring Information System, which is used for communication between the teacher and parents of students regarding student learning. This study analyzes the successful implementation of the Student Monitoring Information System and what factors need to be improved. The method used in the research is the DeLone and McLean Information System Success Model. The results of this study indicate the percentage of successful implementation of the Student Monitoring Information System, including assessment of system quality, information quality, service quality, usage, user satisfaction and net benefits. Overall, the implementation of the Student Monitoring Information System was declared successful, with 90% of respondents agree strongly, 6% of respondents agree, and 4% quite agree with the implementation of the Student Monitoring Information System.
Analysis of Controllability, Observability and Stabilization for a Class of Systems Described by Takagi-Sugeno Fuzzy Models by means of Fuzzy Pole Assignment

The present work is concerned to solve the problem of nonlinear robotic control systems on the basis of Takagi- Sugeno (T-S) fuzzy models. In addition, controllability and observability studies for T-S fuzzy model systems are considered for Single-Input Single-Output (SISO) and Multiple-Input Multiple-Output (MIMO) systems. In particular, the robust pole assignment approach is extended to the fuzzy field. This approach is used to design fuzzy stabilizers and fuzzy observers directly on the overall T-S fuzzy system. The latter one is the representation of the nonlinear robotic system, which is linearized around certain operation regions. The suggested method is considered for either SISO or MIMO systems. Furthermore, the method allows inducing an arbitrary behavior into the fuzzy plant in a relatively easy way. Two examples are used to verify the effectiveness of the proposed approach. The results are compared with the well-known Parallel Distributed Compensation (PDC) method, which is designed on the basis of Linear Matrix Inequalities (LMIs).
Self-Adversarial Learning with Comparative Discrimination for Text Generation

Conventional Generative Adversarial Networks (GANs) for text generation tend to have issues of reward sparsity and mode collapse that affect the quality and diversity of generated samples. To address the issues, we propose a novel self-adversarial learning (SAL) paradigm for improving GANs' performance in text generation. In contrast to standard GANs that use a binary classifier as its discriminator to predict whether a sample is real or generated, SAL employs a comparative discriminator which is a pairwise classifier for comparing the text quality between a pair of samples. During training, SAL rewards the generator when its currently generated sentence is found to be better than its previously generated samples. This self-improvement reward mechanism allows the model to receive credits more easily and avoid collapsing towards the limited number of real samples, which not only helps alleviate the reward sparsity issue but also reduces the risk of mode collapse. Experiments on text generation benchmark datasets show that our proposed approach substantially improves both the quality and the diversity, and yields more stable performance compared to the previous GANs for text generation.
Subpixel Line Localization With Normalized Sums of Gradients and Location Linking With Straightness and Omni-Directionality

This paper presents a method to localize line locations with subpixel accuracy and a method to link the locations based on a linking distance.This paper first proposes a subpixel line localization method based on normalized sums of gradients (NSG) calculated by dividing pixel sum of gradients by the sum of gradient lengths within the pixel neighborhood.The proposed NSG method is compared with current stateof-the art based on a Taylor series approximation of intensity surface and the normal vector derived from the Hessian matrix.Comparative experiments for subpixel line localization methods were performed with simulated and natural images and confirmed the proposed subpixel localization method provided superior accuracy and faster localization under most combinations of varying line width and noise strengths than the state-of-the art localization method.The proposed linking method was also designed to have more straightness and omni-directionality than a current state-of-the art method.Experimental comparison of linking methods confirmed the proposed linking method provided superior linking quality than current stateof-the art.
A social trust and preference segmentation-based matrix factorization recommendation algorithm

Abstract A recommendation can inspire potential demands of users and make e-commerce platforms more intelligent and is essential for e-commerce enterprises’ sustainable development. The traditional social recommendation algorithm ignores the following fact: the preferences of users with trust relationships are not necessarily similar, and the consideration of user preference similarity should be limited to specific areas. To solve these problems mentioned above, we propose a social trust and preference segmentation-based matrix factorization (SPMF) recommendation algorithm. Experimental results based on the Ciao and Epinions datasets show that the accuracy of the SPMF algorithm is significantly superior to that of some state-of-the-art recommendation algorithms. The SPMF algorithm is a better recommendation algorithm based on distinguishing the difference of trust relations and preference domain, which can support commercial activities such as product marketing.
FPGA-Accelerated Hash Join Operation for Relational Databases

Equi-join operations are fundamental in database management. Hash join algorithms are often used to reduce the computational complexity of join operations; however, their performance is influenced by imperfect data distributions caused by hash collisions. To resolve these collisions, this brief designs a hardware-accelerated hash join architecture. This architecture aims to break the trade-off between complex hash functions, which is computationally expensive, and hash join performance. First, two hash functions are employed to distribute data as evenly as possible, and then multiple entries of the collision list are provided; finally, a small number of the static random-access memory (SRAM) of the field-programmable gate arrays (FPGAs) are used to conduct the join operation in parallel. The proposed method yields high-throughput and is resource-efficient, as it does not require reprocessing of data; further, it improves hash table utilization. The results of implementing this architecture on a Xilinx Zynq FPGA platform indicate an accelerated throughput that is a minimum of 4.2× that of previous hardware-accelerated hash join methods.
Towards Verified Robustness under Text Deletion Interventions

Neural networks are widely used in Natural Language Processing, yet despite their empirical successes, their behaviour is brittle: they are both over-sensitive to small input changes, and under-sensitive to deletions of large fractions of input text. This paper aims to tackle under-sensitivity in the context of natural language inference by ensuring that models do not become more confident in their predictions as arbitrary subsets of words from the input text are deleted. We develop a novel technique for formal verification of this specification for models based on the popular decomposable attention mechanism by employing the efficient yet effective interval bound propagation (IBP) approach. Using this method we can efficiently prove, given a model, whether a particular sample is free from the under-sensitivity problem. We compare different training methods to address under-sensitivity, and compare metrics to measure it. In our experiments on the SNLI and MNLI datasets, we observe that IBP training leads to a significantly improved verified accuracy. On the SNLI test set, we can verify 18.4% of samples, a substantial improvement over only 2.8% using standard training.
The use of automated procedures by older adults with high arithmetic skills during addition problem solving.

In contrast to other cognitive abilities, arithmetic skills are known to be preserved in healthy elderly adults. In fact, they would even outperform young adults because they more often retrieve arithmetic facts from long-term memory. Nevertheless, we suggest here that the superiority of older over younger adults could also stem from the use of more efficient automated and unconscious counting procedures. We tested 35 older participants using the sign priming paradigm and selected the 18 most efficient ones, aged from 60 to 77. Sign priming is interpreted as the indicator of the preactivation of an abstract procedure as soon as the arithmetic sign is presented. We showed that expert elderly arithmeticians behaved exactly as 26 young participants presenting the same level of arithmetic proficiency. More precisely, we showed that presenting the “+” sign 150 ms before the operands speeds up the solving process compared to a situation wherein the problem is classically presented in its whole on the screen. Only tie problems and problems involving 0 were not subjected to these priming effects, and we concluded that only these problems were solved by retrieval, either of the answer for tie problems or of a rule for + 0 problems. These results could provide new insights for the conception of training programs aiming at preserving older individuals’ arithmetical skills and, in a longer-term perspective, at maintaining their financial autonomy, which is decisive for keeping them in charge of their daily life. (PsycInfo Database Record (c) 2022 APA, all rights reserved)
Fast Nonparametric Estimation of Class Proportions in the Positive-Unlabeled Classification Setting

Estimating class proportions has emerged as an important direction in positive-unlabeled learning. Well-estimated class priors are key to accurate approximation of posterior distributions and are necessary for the recovery of true classification performance. While significant progress has been made in the past decade, there remains a need for accurate strategies that scale to big data. Motivated by this need, we propose an intuitive and fast nonparametric algorithm to estimate class proportions. Unlike any of the previous methods, our algorithm uses a sampling strategy to repeatedly (1) draw an example from the set of positives, (2) record the minimum distance to any of the unlabeled examples, and (3) remove the nearest unlabeled example. We show that the point of sharp increase in the recorded distances corresponds to the desired proportion of positives in the unlabeled set and train a deep neural network to identify that point. Our distance-based algorithm is evaluated on forty datasets and compared to all currently available methods. We provide evidence that this new approach results in the most accurate performance and can be readily used on large datasets.
Unsupervised Learning: Deep Learning

The next set of unsupervised learning techniques take the unsupervised process into neural networks.
NEUROEVOLUTIONARY APPROACH TO CONTROL OF COMPLEX MULTICOORDINATE INTERRELATED PLANTS

This paper presents the developed by the authors step-by-step neuroevolution based approach to designing control systems for complex multicoordinate interrelated plants (MIP). The proposed approach allows us to build the structure of the automatic control system (ACS) for the MIP on the basis of the single complex neural controller (NC) with multiple inputs and outputs as well as to implement the effective training of its multilayer neural network (NN) by means of the evolutionary based algorithm, taking into account the mutual influence of all variables of the MIP in an optimal way. In order to study and validate the efficiency of the presented approach the design of the ACS for the spatial motion of caterpillar mobile robot (MR) able to move on inclined and vertical ferromagnetic surfaces is carried out in this work. The developed ACS based on the NC with optimal structure allows us to achieve high quality indicators of spatial motion control, taking into account the mutual influence of control channels of the MR’s speed and angle that confirms the high efficiency of the proposed approach.
The Application of Support Vector Machine (SVM) on the Sentiment Analysis of Internet Posts

At present, in the mainstream sentiment analysis methods represented by Support Vector Machine, the vocabulary and the latent semantic information contained in the text cannot be well considered, and sentiment analysis of text is overly dependent on the statistics of sentiment words. In this paper, a Fisher kernel function based on Probabilistic Latent Semantic Analysis is proposed for sentiment analysis by Support Vector Machine. The Fisher kernel function based on the model is derived by the Probabilistic Latent Semantic Analysis model. By means of this method, latent semantic information containing the probability features can be used as the classification features, the effect of classification for support vector machine can be improved, and the problem of not considering the latent semantic features in text sentiment analysis is solved. The results show that compared with the comparison method, the effect of the method proposed in this paper is obviously improved.
A multi-color based features from facial images for automatic ethnicity identification model

&lt;span style="font-size: 9pt; font-family: 'Times New Roman', serif;"&gt;Ethnicity identification for demographic information has been studied for soft biometric analysis, and it is essential for human identification and verification. Ethnicity identification remains popular and receives attention in a recent year especially in automatic demographic information. Unfortunately, ethnicity identification technique using color-based feature mostly failed to determine the ethnicity classes accurately due to low properties of features in color-based. Thus, this paper purposely analyses the accuracy of the color-based ethnicity identification model from various color spaces. The proposed model involved several phases such as skin color feature extraction, feature selection, and classification. In the feature extraction process, a dynamic skin color detection is adapted to extract the skin color information from the face candidate. The multi-color feature was formed from the descriptive statistical model. Feature selection technique applied to reduce the feature space dimensionality. Finally, the proposed ethnicity identification was tested using several classification algorithms. From the experimental result, we achieved a better result in multi-color feature compared to individual color space model under Random Forest algorithm.&lt;/span&gt;
MicroGuard: Securing Bare-Metal Microcontrollers against Code-Reuse Attacks

Bare-metal microcontrollers are a family of Internet of Things (IoT) devices which are increasingly deployed in critical industrial environments. Similar to other IoT devices, bare-metal microcontrollers are vulnerable to memory corruption and code-reuse attacks. We propose MicroGuard, a novel mitigation method based on component-level sandboxing and automated code randomization to securely encapsulate application components in isolated environments. We implemented MicroGuard and evaluated its efficacy and efficiency with a real-world benchmark against different types of attacks. As our evaluation shows, MicroGuard provides better security than ACES, current state-of-the-art protection framework for bare-metal microcontrollers, with a comparable performance overhead.
The multinomial logistic regression model for predicting the discharge status after liver transplantation: estimation and diagnostics analysis

The multinomial logistic regression model (MLRM) can be interpreted as a natural extension of the binomial model with logit link function to situations where the response variable can have three or more possible outcomes. In addition, when the categories of the response variable are nominal, the MLRM can be expressed in terms of two or more logistic models and analyzed in both frequentist and Bayesian approaches. However, few discussions about post modeling in categorical data models are found in the literature, and they mainly use Bayesian inference. The objective of this work is to present classic and Bayesian diagnostic measures for categorical data models. These measures are applied to a dataset (status) of patients undergoing kidney transplantation.