Reviewerly/exHarmony huggingface dataset:
Two-Stage Learning Brain Storm Optimizer

Brain storm optimizer (BSO), a new swarm intelligence paradigm inspired from the human brainstorming process, have received a surge of attentions. However, the original BSO easily suffers from the premature convergence due to its ineffective solution generation operation. In this paper, a two-stage learning strategy is proposed to accelerate the efficiency of the solution generation operation in BSO, thereby enhancing the convergence speed as well as the diversity of population. At the first stage, a learning automaton strategy is conducted to select an appropriate learning exemplar to guide the updating of each solution (i.e., idea). This strategy learns from the feedback information from the environment to enhance the exploration and exploitation. At the second stage, a comprehensive learning strategy is used to generate a set of directional learning exemplars, using utilize useful search experiences during the search. The experimental results on a set of CEC2017 benchmarks validate the effectiveness of the proposed strategy. Then, the resultant algorithm called ACLBSO is applied to resolving the quantitative association rule mining problem. Simulation results show that ACLBSO is a satisfactory optimizer to deal with the complex association rule mining problems.
An Overview of Recent Developments in Convolutional Neural Network (CNN) Based Face Detector

Face detection is a fundamental and extensively studied problem in computer vision. It is the first and primary step for many applications which include face recognition/verification, face tracking, facial behavior analysis, and many other applications. A major challenge for face detector is to detect faces in unconstrained conditions (also called in-the-wild) such as variations in pose, illumination, scale, expressions, makeup, and occlusion. Recently, accuracy and performance of face detectors have improved tremendously because of the use of Convolutional Neural Network (CNN). This survey paper focuses on recent advancements in face detection techniques based on Convolution Neural Network and categorization of CNN face detector. Paper concludes by identifying future directions of the field.
Sharing Open Data in Agriculture

The main focus of the study was to explore the practices of open data sharing in the agricultural sector, including establishing the research outputs concerning open data in agriculture. The study adopted a desktop research methodology based on literature review and bibliographic data from WoS database. Bibliometric indicators discussed include yearly productivity, most prolific authors, and enhanced countries. Study findings revealed that research activity in the field of agriculture and open access is very low. There were 36 OA articles and only 6 publications had an open data badge. Most researchers do not yet embrace the need to openly publish their data set despite the availability of numerous open data repositories. Unfortunately, most African countries are still lagging behind in management of agricultural open data. The study therefore recommends that researchers should publish their research data sets as OA. African countries need to put more efforts in establishing open data repositories and implementing the necessary policies to facilitate OA.
Elderly fall detection system device based on blood pressure abnormality

Some of elderly people prefer to live independently and hence, choose to stay away from their children and relatives. Bad incidents could happen during the time for instance the elderly might be severely injured due to falling down. However, nobody else would notice and this may cause the victim to receive delayed emergency help. In serious cases, victims were found dead only after few days by the neighbours. To mitigate this issue, a fall detection system device for elderly based on their blood pressure is developed and presented in this paper. The developed device detects fall incident through accelerometer and pulse sensor reading as attached to the elderly body. If a fall incident occurs based on blood pressure abnormality, the device will promptly send an alert message to victim's emergency contact. This innovative development would alert people regarding bad incident happened on their love ones and thus, prompt emergency assistance can be provided.
Zero-Shot Learning Based on Salient Region and Enhanced Semantics

Zero-shot learning (ZSL) refer to recognizing the new class without training samples. Traditionally, the projection function learned from visual features to semantic features is used for object recognition. However, few works will focus on accurate feature representation of recognition objects. The human designed semantics are not discriminative and sufficient to recognize different and new classes. In this paper, we propose to use the image reconstruction to extract enhanced semantics (ES) on salient region of image. The salient region of image is encoded corresponding to predefined attributes and ES features. And then decoded to original image of salient region. The Lifted structure feature embedding (LSFE) is applied to make the extended features more discriminative. Softmax is used for classification thus makes ES features more accurate. Experiments on two benchmark datasets AwA2 and CUB, demonstrate the effectiveness of the proposed approach.
Comparison of Artificial Intelligence Algorithms for ELINT Signals Classification

Ability to automatically identify objects of interest and to automatically classify their signals belongs to essential functionalities of electronic intelligence systems. Objects identification results and signals classification results are conditioned by accurate measurement and technical analysis of objects signal parameters. Classification algorithm based on Feedforward Neural Network, and classification algorithms based on Support-Vector Machine, with three types of Kernel Function, are tested and compared in this paper as the first stage of objects identification in electronic intelligence systems.
A novel quality inspection method for components with small diameter and deep aperture

This paper investigates a novel quality inspection method for the components with small diameter and deep aperture. A method for extracting the minimum circumscribed rectangle of the target is proposed, which would provide additional shape and angle information and avoid unexpected overlaps and background disturbance. In order to detect the low contrast targets in complex background, a novel method based on Faster R-CNN with several improvements is adopted. In the framework, a five-dimensional (5-D) vector is designed to represent the minimum circumscribed rectangle followed by an improved anchor selection mechanism in the region proposal network. Furthermore, an improved POI pooling process is employed to extract feature maps. Comparison experiments fully demonstrate the superiority of the proposed inspection method over existing methods. Meanwhile, successful inspection results on challenging real-world images prove that the system is of practical significance to industrial applications.
Graduality in Data Sciences: Gradual Patterns

Graduality is a key concept when dealing with imprecision and uncertainty. There would thus have been numerous manners to deal with graduality. This chapter however focuses on gradual patterns of the form of “the more/less \(A_1,\) \(\ldots \), the more/less \(A_n\)”. The study of such patterns has emerged very early with the study of rule-based systems but at this time they were provided by experts. The automatic extraction of gradual patterns from databases has then benefited from the progress of data mining (in the sense of pattern mining) and has then generated works from several teams.
KGWD: Knowledge Graph Based Wide &amp; Deep Framework for Recommendation

Knowledge Graph (KG) contains rich real-world auxiliary information, which can be leveraged to improve the performance of recommender systems. Nevertheless, existing recommender systems usually sample and aggregate neighbor entities and relations that link to target items to enrich the representations of items or users, whereas ignoring combinatorial features among different neighbor entities and relations. To resolve the problem mentioned above, we propose an end-to-end Knowledge Graph based Wide & Deep (KGWD) framework to leverage combinatorial features effectively. At the wide level, KGWD introduces a novel Triplet Compressed Interaction Network (TriCIN) to generate high-order combinatorial features among different triplets associated with the target item automatically. At the deep level, KGWD discovers users’ potential long-distance preferences by mining multi-hop neighbor information over the KG. We conduct experiments on three real-world datasets, i.e., Yelp2018, Last-FM, and Amazon-book, to evaluate the performance of KGWD. Experimental results demonstrate that KGWD outperforms state-of-the-art schemes significantly. Specifically, in all three datasets, KGWD improves the F1-score by more than 5% over the state-of-the-art.
Software Engineering Taxonomy Reviews

There is a lack of clarity when discussing taxonomies especially in the field of software engineering. The doubt encourages the writing of software engineering taxonomy paper with the aim of clarifying the applied terminologies. The taxonomic hierarchy plays a major role in regulating knowledge based concepts. Taxonomy is a hierarchical structure based on internal data to classify information. Taxonomy itself has a lot of branches. Software engineering taxonomies are defined based on the evaluation of interrelations terms in document collections. Mapping software engineering needs to be done to analyze data in taxonomic development.
Serendipity, abduction and metaphor: The figures of thought in scientific findings

The notion of serendipity, approached from the standpoint of the sciences of discourse, proves to be more than a discovery merely due to the chance of circumstances external to the subject which, to its great surprise, finds something revealing. The speaking and thinking subject is strongly involved in the happy finding, although he is placed in a very particular position. Serendipity is also a situation that maintains a special relation with certain forms of reasoning which are inseparable from the forms of expression of discourse. In this sense, metaphor is constitutive of these forms of reasoning, especially in abductive reasoning, one of which a possible logic form is the enthymeme.
Activity-value approach in personal life adaptations of students in departmental universities

To implement the tasks of successful personal adaptation of cadets studying in departmental universities, it is necessary to apply modern pedagogical approaches. The authors of the article suggest that teachers use an activity-value approach that provides professional motivation to master the chosen profession, the formation of a system of personal values, and the development of abilities.The article reveals the features of using the activity-value approach in training cadets. It contains empirical data from the research of the authors of the article. It is concluded that the implementation of the activity-value approach provides personal adaptation of future specialists in the innovative educational environment of a departmental University.
Rapid Ensemble Averaging of Orientation without Individual Item Encoding

Human observers can accurately report summary statistics of features varying across stimuli within an ensemble. However, the literature on ensemble coding has yet to resolved a crucial question: Is it necessary to encode each individual item in visual short-term memory (VSTM), or can summaries be extracted earlier, during initial processing and iconic memory? To evaluate this distinction, participants performed an orientation averaging task on a display that consisted of 15 bisected circles presented across two 10ms presentations, with a variable ISI between. Participants then recreated what they believed to be the average of the 15 circles by rotating a probe circle. Although the stimulus presentation times were within the parameters of iconic memory displays, and too brief for saccades to individual items, participants gave significantly precise reproductions of central tendency. We then compared the results of the averaging task to two item-specific tasks, temporal integration and segregation, conducted using the same stimuli and within the same experimental session. Results revealed that while the performance of the participants for the item-specific tasks was strongly dependent on the ISI, as previously reported, the averaging task was equally precise across the ISIs. These results suggest that central tendency information can be extracted rapidly in the absence of (and hence, prior to) the entry of items into STM. This ability to quickly process “the forest” rather than the individual “trees” may allow central tendency representations to be extracted regardless of whether individual item information is preserved in VSTM.
CapsNet based on Encoder and Decoder for Object Detection

The recently proposed capsule network (CapsNet) can learn the hierarchy relationships of entity features and realize the equivariance to affine transformations, which makes the capsule architecture more promising for object detection. In this paper, based on capsule architecture, we create the CapsNet-V1 models for object detection. The proposed CapsNetV1 mainly consists of the classification net as encoder to extract multi-class information and the reconstruction net as decoder to obtain masks with multi-object position information. In the experiments, based on the randomly expanded MNIST dataset, we simultaneously evaluate the multi-object classification and reconstruction abilities of the proposed CapsNet. The results indicate that our capsule models can reconstruct the object masks with accurate location information at correct labels, which exactly demonstrates the feasibility of using capsule networks for object detection. Further, our CapsNet can be widely applied to the multi-object detection with simple backgrounds in the industrial production lines.
Introduction to Text Analytics

Data processing regards analysis of various types of data, including numerical data, signals, texts, pictures, videos, etc. This paper focuses on defining and studying various tasks of text analytics following the typical processing pipeline. Sources of textual data are introduced and related challenges are discussed. Along with the process of text analytics, examples are presented to demonstrate how text analytics should be carried out. Finally, potential applications of text analytics are given including sentiment analysis and automatic generation of content.
Actual Causality in Contextual Abduction

Causality is an important part of science and our daily life, e.g., to determine responsibility in legal arguments and causal explanations for observations. This paper presents how actual causality can be modeled by contextual abduction in logic programming. We refer to causal model of Halpern and Pearl, and translate it into a causal abductive framework. Using this framework and by treating the given actual world as the context of abduction, actual causes are computed by abducing explanations consistent to that context. We provide a procedure for computing actual causes by means of contextual abduction in Prolog and experiment with examples from the literature. The result coincides with that computed by the clausal causal theory of Bochman.
A Meta Graph-Based Top-k Similarity Measure for Heterogeneous Information Networks

Studies have demonstrated that real-world data can be modeled as a heterogeneous information network (HIN) composed of multiple types of entities and relationships. Similarity search is a basic operation requiring many problems in HINs. Similarity measures can be used in various applications, including friend recommendation, link prediction, and online advertising. However, most existing similarity measures only consider meta path. Complex semantic meaning cannot be expressed through meta path. In this paper, we study the similarity search problem of complex semantics meaning between two HIN objects. In order to solve the problem, we use meta graphs to express the semantic meaning between objects. The advantage of meta graphs is that it can describe the complex semantic meaning between two HIN objects. And we first define a new meta graph-based relation similarity measure, GraphSim, which is to measure the similarity between objects in HINs, then we propose a similarity search framework based on GraphSim. The experiments with real-world datasets from DBLP demonstrated the effectiveness of our approach.
Interfacing the City

The aim of the paper is to analyse the interface as a cognitive map of the smart city from a media-theoretic perspective. The issue of cognitive mapping in the context of urbanism was articulated in Lynch’s The Image of the City where the notion of “image ability” or constructing a mental map of the spatial and environmental features of the city played the major role in how humans experience the city. Media theory sees the city as an information-processing medium that consists of both physical and digital layers, making the (smart) city a hybrid product made of “atoms and bits”. Following Manovich, the paper argues that there is no necessary link between the digital data and their form, as digital material is a material without qualities which requires a certain form, that is an interface, to be perceived. The paper argues that the interface is 1) a relation between the visible surface layer and the deeper, invisible layer of a medium and 2) Norman’s cognitive artefact which helps humans with information complexity and overload. Applying Haken and Portugali (2003), the paper asserts the information-centric view of the smart city. Lynch claimed that the future form of the city should allow experiencing the city as a whole by constructing a synthetic image. The paper suggests that the only solution to Lynch’s requirement in the age of the smart city is designing an urban interface. Finally, the paper defines the urban interface and offers a brief selection of historical and contemporary examples of urban interfaces.
Service user experiences of risk assessment and management in a low secure service

Risk assessment and management are core features within forensic services. With the implementation of recovery principles, there has been more investment in adopting collaborative approaches in this process. This paper explores five service user perspectives of collaborative risk assessment and management within a low secure service. Semi-structured interviews were analysed using Interpretative Phenomenological Analysis which produced two superordinate themes: ‘Snakes and Ladders’ and ‘Knowledge is Power’. Although there have been positive experiences of collaborative risk, more work needs to be done to develop collaborative working throughout the risk process. The findings are consistent with the forensic literature. Clinical implications and directions for future research are discussed.
Towards a Context-Sensitive User Interaction Framework for Information Systems

With the rise of mobile devices, users operate applications in a large variety of contexts. In each of these contexts, a user may have different requirements and preferences regarding an application’s user interface. The context describes the current physical and social environment of the user, his activity and locomotion, as well as the current location and time. Hence, different user interfaces may be more suitable in specific contexts than others. At the moment, a user interface is an integral part of an application, consequently also limiting its usefulness in certain contexts as the information is not presented in the best possible way. Therefore, we propose a way to decouple the user interface from a specific form of representation. Through this decoupling, it is possible to dynamically adapt user interfaces to the user’s specific needs in a context, to increase the value of the application for the user. In this paper, we introduce a general framework for the context-dependent adaptation of user interfaces and evaluate it in the specific context of travel information systems. Travel information systems are particularly suited to evaluate such a framework, as they are usually operated in many different contexts – before a trip, during a trip, and after a trip. The adaptation framework transforms between system-oriented messages and user-oriented messages. The user’s context, the output device capabilities, and the user’s preference, all influence the choice of the actual representation for user-oriented messages. We implemented a prototype of the proposed system and conducted an experimental evaluation focusing on scenarios from the domain of travel information systems.
Effective Methodology for Co-Referential Aspect Based Sentiment Analysis of Tourist Reviews

Tourist reviews are the source of data that is going to be used for the travelers around the world to find the hotels for their stay according to their comfort. In this the hotels are ranked over the parameters or aspects considered keeping travelers in mind. This computation of data sets is done with the help of the machine learning algorithms and the neural network. The knowledge processing done over the reviews generates the sentiment score for each hotel with respect to the aspects defined. Here, the explicit , implicit and co-referential aspects are identified by suppressing the noise. This paper proposes the method that can be best used for the detection of the sentiments with the high accuracy.
Ambre: Augmented Metaphor Based Representation System for Electricity

With the rise of the Maker Movement and STEM education, toolkit research for beginners in electronics has become popular. Most of them are focused on the efficient creation and debugging of electrical circuits. Therefore, users were implicitly required to understanding of the nature of electricity and some prerequisites knowledge. In this paper, we propose tangible interface to express electrical properties such as voltage and resistance. This system aims to bring a deep interest and understanding of the nature of electricity to the beginners explicitly. In this research, we discuss the interaction between the laws of electricity and another several physical laws, present an example of a prototype implementation, and describe the results of a preliminary user questionnaire.
FT-BlinGui: A Fuzzy-Based Wearable Device System to Avoid Visually Impaired Collision in Real Time

There are several technological applications and computational techniques for detection and prevention of obstacles that aim at assisting the locomotion of robots, autonomous vehicles, people with visual impairments, unmanned aerial vehicles, detection of anomalies in runways, among others. However, the imprecision and uncertainty in the perception of obstacles in real time are not detected in advance to avoid them. This is due to the dynamism of the environment, where obstacles can present different behavioral scenarios and are constantly changing. To provide a more efficient perception of obstacles in dynamic scenarios, the fuzzy systems embedded in technological applications have been presented as an efficient approach for the detection and prevention of obstacles more effectively and intuitively. In this paper, therefore, we will investigate the applicability of the fuzzy time series for the forecasting collision with obstacles. The time series analyzed was obtained through a wearable device that detects, in real time, obstacles for the visually impaired. Simulation results demonstrate that FT-BlinGui can provide a collision-avoidance alerting system for wearable devices as a result of Fuzzy Time Series forecasting.