Reviewerly/exHarmony huggingface dataset:
High Capacity HEVC Video Hiding Algorithm Based on EMD Coded PU Partition Modes

Data hiding in videos has been a big concern as their rich redundancy can be used for embedding a lot of secret information. Further, as high efficiency video coding (HEVC) introduces many innovative technologies compared with the previous standard, H.264, it has gradually become the mainstream. Therefore, it is valuable to develop new information hiding algorithms by using novel features of HEVC. A HEVC video data hiding algorithm based on prediction unit (PU) partition modes from inter prediction process is proposed in this paper. Firstly, code units (CUs) in two sizes of 8 × 8 and 16 × 16 are selected for embedding, then the PU partition modes in these CUs are coded by a spatial coding method. After that, two specific hiding algorithms by modifying coded PU partition modes in CUs of 8 × 8 and 16 × 16 are proposed, respectively. Experimental results show that the proposed algorithm has achieved excellent performance with high visual quality, and high embedding capacity and low bitrate increase in both high- and low-resolution videos compressed with different quantization parameters (QPs). Compared with the state-of-the-art work, the proposed algorithm achieves a much higher capacity while keeping quite high visual quality with little increase of bitrate.
A Person-Following Shopping Support Robot Based on Human Pose Skeleton Data and LiDAR Sensor

In this paper, we address the problem of real-time human pose-based robust person tracking for a person following shopping support robot. We achieve this by cropping the target person's body from the image and then apply a color histogram matching algorithm for tracking a unique person. After tracking the person, we used an omnidirectional camera and ultrasonic sensor to find the target person's location and distance from the robot. When the target person is fixed in front of shopping shelves the robot finds the fixed distance between the robot and target person. In this situation our robot finds the target person's body movement orientation using our proposed methodology. According to the body orientation our robot assumes a suitable position so that the target person can easily put his shopping product in the basket. Our proposed system was verified in real time environments and it shows that our robot system is highly effective at following a given target person and provides proper support while shopping the target person.
Untargeted Adversarial Attack via Expanding the Semantic Gap

Recent studies have demonstrated deep neural network-based image classifiers are vulnerable to adversarial examples. Although many existing methods could obtain outstanding attack performance, they often require certain information about the attacked model, e.g., the output category scores. Meanwhile, the optimization-based methods need many steps to generate adversarial examples. In practice, we could obtain the output label but the category scores. Besides, compared to those samples with large semantic category gaps, e.g., Panda and Gibbon, most existing methods are not easy to find adversarial examples on samples with small semantic category gaps, e.g., Tabby Cat and Egyptian Cat. Thus, we propose an untargeted adversarial attack method via expanding the semantic gap, which only relies on the output label. And we use the optimization-based method to generate adversarial examples. On five normally trained models and five state-of-the-art attack methods, extensive experiments show that our method is effective and obtains better attack performance.
Modern Educational Technologies in Professional Education

In the article the essence of modern educational technologies in vocational education is considered, the concept “educational technologies” is revealed. It is noted that modern educational technologies are aimed at the effectiveness of the use of study time, individualization, distance and variability of the educational process, mobility of students regardless of their age and level of education. Currently, there are a number of educational technologies. We examined the most effective and often used in professional education to date: gaming, case studies, design, integrative and information technologies. The article describes the composition of educational technology as a method: design, implementation, evaluation, correction and subsequent reproduction of the teaching and educational process. Based on the experience of using innovative methods, several of their advantages can be singled out. The subject of any pedagogical technology is determined.
Extraction of System States from Natural Language Requirements

In recent years, simulations have proven to be an important means to verify the behavior of complex software systems. The different states of a system are monitored in the simulations and are compared against the requirements specification. So far, system states in natural language requirements cannot be automatically linked to signals from the simulation. However, the manual mapping between requirements and simulation is a time-consuming task. Named-entity Recognition is a sub-task from the field of automated information retrieval and is used to classify parts of natural language texts into categories. In this paper, we use a self-trained Named-entity Recognition model with Bidirectional LSTMs and CNNs to extract states from requirements specifications. We present an almost entirely automated approach and an iterative semi-automated approach to train our model. The automated and iterative approach are compared and discussed with respect to the usual manual extraction. We show that the manual extraction of states in 2,000 requirements takes nine hours. Our automated approach achieves an F1-score of 0.51 with 15 minutes of manual work and the iterative approach achieves an F1-score of 0.62 with 100 minutes of work.
Empirical Analysis of Critical Success Factors for Project Management in Global Software Development

Global software development (GSD) is a well-recognized business model to achieve competitiveness in the current global market. However this model has its own challenges due to the complex nature of its development process. Project management is an area of concern in software development and it has a strong influence in the GSD environment. Mostly, companies embrace GSD without prior knowledge of its issues and as a result fail in the management of its process and outcomes of the project. Major reason behind this failure is poor project management practices that do not fit into the GSD context. This research work explores significance of project management and critical success factors (CSF's) in GSD in order to improve processes and facilitate software companies in implementing this developmental model successfully.
Effective Algorithms for Finding the Remainder of Multi-Digit Numbers

In this paper, the theoretical foundations of the developed methods for finding the remains of multi-digit numbers and the Mersenne number in a binary number system are given, the use of which allows reducing the time complexity. The numerical experiments of the time characteristics of program implementations of the proposed approaches and the classical method are given. The scheme of the algorithm for finding the remainder of the multi-digit number and the multi-digit Mersenne number is given.
Area scanning method for 3D surface profilometry based on an adaptive confocal microscope

Conventional confocal 3D microscopes suffer from a slow measurement speed due to its requirement of scanning in three directions. Although lateral slit scanning has been proven to perform similarly to single point scanning, a direct area confocal scanning microscope is commonly considered impossible as the system is reduced into a wide-field microscope and loses its depth discerning capability. In this article, a direct area scanning method is proposed for 3D surface profilometry based on a tilted focal field. To demonstrate the underlying principle, theoretical analysis is conducted with simulation result, showing that depth discerning capability can be maintained in area scanning when the tilting angle is specifically chosen according to the numerical aperture of the system. An adaptive experimental setup is constructed based on a programmable light source and a programmable array microscope with chromatic axial encoding. Measurement result of a test target using the proposed method is comparable to the result of conventional array scanning. Although the axial uncertainty is increased by a factor of approximately 2.5, the direct area scanning method is more than 300 times faster than the conventional array scanning mode of the same setup.
Supporting Reuse of Smart Contracts through Service Orientation and Assisted Development

This paper focuses on two of the key challenges a developer encounters when reusing smart contracts: finding actionable information about existing smart contracts (descriptors) and writing the necessary integration logic to invoke selected contracts and implement missing functions. As for the former issue, the paper proposes a smart contract description format that allows the developer to search for publicly available contracts, understand which features a contract exposes and how to invoke them, according to a service-oriented approach. For the latter, the paper implements a simple, model-driven development environment consisting in a visual programming editor that provides a set of modeling constructs encoding specific, reuse-oriented code patterns. The approach is implemented and demonstrated in the context of the blockchain platform Ethereum and its programming language Solidity. The results obtained show that the proposed approach can be beneficial in the development of composite smart contracts and generic blockchain applications.
Interpretable Partitioned Embedding for Intelligent Multi-item Fashion Outfit Composition

Intelligent fashion outfit composition has become more popular in recent years. Some deep-learning-based approaches reveal competitive composition. However, the uninterpretable characteristic makes such a deep-learning-based approach fail to meet the businesses’, designers’, and consumers’ urges to comprehend the importance of different attributes in an outfit composition. To realize interpretable and intelligent multi-item fashion outfit compositions, we propose a partitioned embedding network to learn interpretable embeddings from clothing items. The network contains two vital components: attribute partition module and partition adversarial module. In the attribute partition module, multiple attribute labels are adopted to ensure that different parts of the overall embedding correspond to different attributes. In the partition adversarial module, adversarial operations are adopted to achieve the independence of different parts. With the interpretable and partitioned embedding, we then construct an outfit-composition graph and an attribute matching map. Extensive experiments demonstrate that (1) the partitioned embedding have unmingled parts that correspond to different attributes and (2) outfits recommended by our model are more desirable in comparison with the existing methods.
A Generalized Heterogeneous Type-2 Fuzzy Classifier and Its Industrial Application

Recently, evolving fuzzy systems have been proved to be effective in dealing with real-time data streams. However, their fixed structures are not flexible enough to address the structural variations triggered by the changing operating conditions or system states in complex industrial environments. A novel generalized heterogeneous interval type-2 (IT2) fuzzy classifier, named as GHIT2Class, is proposed in this paper, which is built upon a multivariable IT2 fuzzy neural network. To fully reflect the industrial data characteristics of uncertainty, this paper proposes an approach of constructing the uncertainty footprint with ellipsoidal rotation. A rule pruning method based on error and incentive intensity dynamic adjustment mechanism is reported in the process of modeling, and a corresponding rule recall mechanism is designed to avoid rules of catastrophic forgetting. In addition, the simultaneous update of the upper and lower bounds of IT2 fuzzy consequent parameters is designed to relieve the computing overhead of the fuzzy systems. The performance of the proposed GHIT2Class is experimentally validated by a number of synthetic datasets and industry study cases by using state-of-the-art comparative classifiers, where the proposed approach outperforms the others in achieving the best tradeoff between accuracy and simplicity.
Face Recognition Using Eigenfaces, Geometrical PCA Approximation and Neural Networks

The human face exhibits a high level of complexity when it is regarded as a multidimensional visual model, leading to face recognition systems that require difficult and extensive computations for coding and decoding the face images. A well-established approach in this regard is based on using principle component analysis (PCA) for both feature extraction and face recognition, known as the eigenface approach. This technique, despite a good recognition rate, suffers from the disadvantage of high computation cost due to the complexity of the PCA algorithm. In this paper, we use a geometrical approximated PCA (gaPCA) algorithm for computing the eigenfaces for three different datasets. The face recognition task is performed using a similarity score based on the inverse Euclidean distance for the first two datasets and using a nerual network in the third case. All the results are compared to the case where standard PCA is used. These accuracy results show that gaPCA represents a viable alternative to the classical statistical approach for computing the principal components.
A Novel Two-Step Fall Detection Method Using Smartphone Sensors

A smartphone-based fall detection system has two major advantages over a traditional fall detection system that comes as a separate device: (1) the phone can automatically send messages to or call the emergency contact person when a fall is detected and (2) a user does not need to carry an extra device. This paper presents a novel two-step fall detection method which uses data extracted from smartphone sensors to detect falls. A fall can happen in many ways. A person can fall while he/she is walking, jogging, sitting, or even sleeping. Patterns of all falls are not the same. It is important to identify the type of falls to precisely distinguish it from non-falls (normal activities). Hence, our method first identifies the correct type of falls by performing multi-class classification. In the second step, this method produces a binary decision based on the multiclass prediction. We collected data from 10 users to evaluate our proposed fall detection method. Each user performed five normal activities-namely, walking, jogging, standing, sitting, lying, and also fell after performing each activity. We performed experiments with five common smartphone sensors: accelerometer, gyroscope, magnetometer, gravity, and linear acceleration. We tested five machine learning classifiers-namely, Support Vector Machine, K-Nearest Neighbor, Decision Tree, Random Forest, and Naive Bayes. Our two-step fall detection method achieved the maximum accuracy of 95.65% and the maximum area under ROC curve (AUC) of 0.93, both with the gyroscope sensor and Support Vector Machine classifier.
A probabilistic explanation of Pearson's correlation

Abstract Pearson's product‐moment correlation can be explained by means of the probability of concordant pairs of observations on two continuous variables (ie, the order of the observations from two randomly selected individuals is the same between the two variables). The probability of concordant pairs features relatively easy computation and offers an alternative perspective on Pearson's correlations.
State-of-the-art review on Twitter Sentiment Analysis

In the last few years, Twitter becomes the most popular platform for individuals to share their experiences and viewpoints towards different products and services. Therefore, it attracts a lot of researchers to use it as a body for sentiment analysis and opinion mining research studies. Most of the previous research studies in this area have been using the traditional machine learning-based and lexicon-based approaches more compared to the deep learning approach to classify the emotional states of English tweets. Also, there is a shortage of research studies that categorize the opinion orientations of tweets in other languages such as Arabic. Recently, deep learning approach has achieved remarkable results over the traditional machine learning algorithms in analyzing a massive amount of data as the case with social networks data. In this research study, we seek to discuss the state-of-the-art of sentiment analysis methodologies used to classify tweets' sentiment orientation and challenges that need to be addressed. Also, this paper provides an overview of deep learning approach and question if this approach can be adopted to improve the classification accuracy of sentiment analysis for both English and Arabic tweets.
Distant Supervision for Relation Extraction via Piecewise Attention and Bag-Level Contextual Inference

Distant supervision (DS) has become an efficient approach for relation extraction (RE) to alleviate the lack of labeled examples in supervised learning. In this paper, we propose a novel neural RE model that combines a bidirectional gated recurrent unit model with a form of hierarchical attention that is better suited to RE. We demonstrate that an additional attention mechanism called piecewise attention, which builds itself upon segment level representations, significantly enhances the performance of the distantly supervised relation extraction task. Our piecewise attention mechanism not only captures crucial segments in each sentence but also reflects the direction of relations between two entities. Furthermore, we propose a contextual inference method that can infer the most likely positive examples of an entity pair in bags with very limited contextual information. In addition, we provide an annotated dataset without false positive examples based on the Riedel testing dataset, and report on the actual performance of several RE models. The experimental results show that our proposed methods outperform the previous state-of-the-art baselines on both original and annotated datasets for the distantly supervised RE task.
Discovering Spatial High Utility Itemsets in Spatiotemporal Databases

In real-world databases, high utility itemset (HUI) is an important class of regularities. Most previous studies have focused on mining HUIs in transactional databases and did not consider the spatiotemporal characteristics of items. In this study, a more flexible model of spatial HUIs (SHUIs) that exist in spatiotemporal databases is proposed. In a spatiotemporal database (STD), an itemset is said to be an SHUI if its utility is not less than a user-specified minimum utility and the distance between any two of its items is not more than a user-specified maximum distance. Identifying SHUIs is very challenging because the generated itemsets do not satisfy the anti-monotonic property. In this study, we present two novel pruning techniques for reducing computational costs. Moreover, a fast single scan algorithm is presented for effectively evaluating all SHUIs in a STD. Furthermore, two case studies are presented, in which the proposed model is used to identify useful information in traffic congestion data and air pollution data.
An unsupervised approach for learning a Chinese IS-A taxonomy from an unstructured corpus

Taxonomies play an important role in various Natural Language Processing (NLP) tasks (e.g., text classification, information extraction and knowledge inference). However, due to the complexity and flexibility of the Chinese natural language, it is challenging to accurately learn a Chinese IS-A (C-IS-A) taxonomy from an unstructured corpus. In this paper, we propose an unsupervised C-IS-A taxonomy learning approach by analyzing a given unstructured corpus. Our approach uses three main steps to automatically learn a C-IS-A taxonomy. First, our approach extracts high-quality C-IS-A seed relations via semantic iterative pattern-based matching and syntactic methods. Second, our approach utilizes an unsupervised taxonomic semantic-clique-based method to increase the coverage of the C-IS-A taxonomy. As the core component of our approach, we exploit the extracted C-IS-A seed relations to construct taxonomic semantic cliques and use the context of the cliques and multi-concept co-occurrence information to infer potential novel C-IS-A relations. Last, a two-step relation detection strategy is proposed to remove potentially incorrect C-IS-A relations, which can substantially improve the accuracy of the learned taxonomy. We implement our approach on four Chinese unstructured corpora and evaluate it in terms of precision, coverage, time cost and the effects of the subcomponents. The evaluation results demonstrate that our approach is an effective method that outperforms the state-of-the-art compared approaches.
Factors Affecting Development Process in Small Software Companies

The importance of small software companies (SSC) cannot be ignored in the general software industry because SSCs represent up to 95% of all software companies worldwide and contribute significantly to the world economy. However, quality of software is a big challenge especially to SSCs and efforts to improve software processes have mainly targeted big companies. The paper focuses on a systematic literature review (SLR) to identify factors affecting the development process of SSCs with the ultimate focus being on companies from the African continent. The results of the study indicate that factors such as organizational, governance and business environment are commonly cited factors which affect the development process of SSCs.
Individual-dependent feasibility rule for constrained differential evolution

Benefiting from its efficiency and quick convergence, the feasibility rule (FR) is well-known for its ability to solve constrained optimization problems (COPs). However, it is highly criticized for its heavy preference for constraints. Thus, an individual-dependent feasibility rule (IDFR) was designed by alleviating the preference from two aspects. Some information of constraints, which might be nonsignificant, is depressed. By contrast, some promising information of objective function is leveraged. The extent of information is individual-dependent. To further enhance the diversity, a two-phase diversity strategy was developed. Due to their numerous merits, two differential evolution (DE) operators were selected as components of the search algorithm. By the above process, we proposed a constrained DE (i.e., IDFRDE). To the best of our knowledge, we made the first attempt to improve FR from the individual perspective. IDFR is more robust than FR while keeping the same computational time complexity. However, it would converge slower than FR on some easy COPs. Experiments on three widely used benchmarks show that: 1) IDFRDE outperforms or gets similar results comparing with other known algorithms; 2) IDFR is more effective than FR on complex COPs; 3) both, the search algorithm and the diversity strategy are important to IDFRDE.
Project-based learning as an effective method in education

Some trends and vectors of a changing educational process are considered herein. An analysis of the learning process at universities by the example of several techniques is given in the paper. Some issues of the integration of new educational methods into the classical education of students are discussed herein, too. A scheme of the educational process with the use of the effective techniques the of students' project-based learning at universities is discussed. Some examples of the project-based solutions for individual courses are given in this article. An appropriate analysis of the proposed project-based learning technique effectiveness from the point of view of acquiring new competencies and real experience in team work by students is offered in the paper.