Reviewerly/exHarmony huggingface dataset:
Ensemble Feature Selection in Binary Machine Learning Classification: A Novel Application of the Evaluation Based on Distance from Average Solution (EDAS) Method

Combining filters in an ensemble to improve feature selection performance is a growing field in the literature. Current techniques, however, are focused on approaches that suffer from drawbacks such as sensitivity to skewed distribution, among others. To address this gap, this paper investigates the applicability of multiple criteria decision-making in ensemble feature selection. This paper adopts the Evaluation based on Distance from Average Solution (EDAS) method due to its many familiar elements to the feature selection community. An experiment was performed on six datasets and a control group. The paper uses the six datasets as levels of the blocking factor. A negative control group (i.e., no feature selection) was adopted to compare with the proposed algorithm. Results show that the proposed ensemble FS algorithm was able to reduce the dataset without compromising the performance of the classifier. The findings in this study would contribute to the literature in several ways. First, the paper is one of the few works to demonstrate how MCDM can be used in feature selection with promising results. Second, this paper is one of the few works to demonstrate the significance of including datasets as levels of a blocking factor when performing significance testing. Finally, this paper is the first to demonstrate the applicability of EDAS as an ensemble FS algorithm. As such, the findings in this paper could spark the cross-fertilization of feature selection and MCDM.
Tight Maneuvering for Path Planning of Hyper-Redundant Manipulators in Three-Dimensional Environments

An effective path-planning algorithm in three-dimensional (3D) environments based on a geometric approach for redundant/hyper-redundant manipulators are presented in this paper. The method works within confined spaces cluttered with obstacles in real-time. Using potential fields in 3D, a middle path is generated for point robots. Beams are generated tangent to the path points, which constructs a basis for preparing a collision-free path for the manipulator. Then, employing a simply control strategy without interaction between the links, the motion planning is achieved by advancing the end-effector of the manipulator through narrow terrains while keeping each link’s joints on this path until the end-effector reaches the goal. The method is simple, robust and significantly increases maneuvering ability of the manipulator in 3D environments compared to existing methods as illustrated with examples.
Learning to Ask: Conversational Product Search via Representation Learning

Online shopping platforms, such as Amazon and AliExpress, are increasingly prevalent in society, helping customers purchase products conveniently. With recent progress in natural language processing, researchers and practitioners shift their focus from traditional product search to conversational product search. Conversational product search enables user-machine conversations and through them collects explicit user feedback that allows to actively clarify the users’ product preferences. Therefore, prospective research on an intelligent shopping assistant via conversations is indispensable. Existing publications on conversational product search either model conversations independently from users, queries, and products or lead to a vocabulary mismatch. In this work, we propose a new conversational product search model, ConvPS, to assist users in locating desirable items. The model is first trained to jointly learn the semantic representations of user, query, item, and conversation via a unified generative framework. After learning these representations, they are integrated to retrieve the target items in the latent semantic space. Meanwhile, we propose a set of greedy and explore-exploit strategies to learn to ask the user a sequence of high-performance questions for conversations. Our proposed ConvPS model can naturally integrate the representation learning of the user, query, item, and conversation into a unified generative framework, which provides a promising avenue for constructing accurate and robust conversational product search systems that are flexible and adaptive. Experimental results demonstrate that our ConvPS model significantly outperforms state-of-the-art baselines.
Public governance and corporate innovation: evidence from a quasi-natural event in China

Using a unique quasi-natural event, i.e. gangdom and evil forces (hereafter, GEs) elimination in China, we investigate the relationship between the public governance proxied by GEs elimination and corporate innovation based on the data of Chinese listed firms from 2013 to 2021. The results show that the GEs elimination leads to a 0.39% increase in corporate innovation investment. Quantitatively, the increase is approximately equivalent to 4.42 million yuan for a firm of median innovation investment. Furthermore, we find that the GEs elimination increases corporate innovation through two possible channels: relieving financing constraints and intensifying market competition. In addition, we also reveal that the effects of GEs elimination on corporate innovation are stronger in firms located in a better marketization environment and a stronger Intellectual Property Rights (IPR) protection environment. Our main results pass a series of robustness tests and endogeneity issues.
Plane’s Detection in Border Area Using Modern Deep Learning Methods

Border surveillance is the utmost concerns of any country. In order to maintain peace and ensure the safety of the people, borders need to be controlled 24/7. To curb activities such as illegal movement of beings and terrorist attacks, it becomes crucial to use modern smart technology. Deep learning is one of these advanced technologies. It has shown tremendous promise in this field. One of its potentials is object detection. It has achieved auspicious results concerning the finding of an object in images. The objective of this paper is to annotate and localize planes in images detected by cameras in order to supervise the borders of a country. So, a comparative study is represented.
Human Violence Detection Using Deep Learning Techniques

Abstract The world’s average annual fatality rate from human violence is 7.9 per 10,000 people. Most of this human violence takes place in an isolated area or of sudden. The information delay here is a major impediment in stopping these acts. To thrive on this issue, the detection technique is used in this study. Detecting moving objects from CCTV is one of the most effective computer vision algorithms. CCTV cameras are now in every streets which are extremely helpful in solving cases. Some techniques of deep learning are used as computer vision to predict and detect the action, properties from video. In real-time police reach violent destinations and start checking CCTV cameras, and investigate to proceed further. This study is deliberately designed to detect violent acts from CCTV cameras. The Inception – v3 and Yolo – v5 models detect the violent act, the number of persons involved, and also the weapons used in the situation. The study consists of these deep learning models, which are used to form a video detection system. This model can be used in real-time as an application programming interface (API) or software. The study results showed the proposed model achieves an accuracy of 74%.
Detection of Anaemia using Image Processing Techniques from microscopy blood smear images

Abstract The human blood includes Red blood Corpuscles (RBC), White Blood Corpuscles (WBC), platelets and plasma. The status of one’s health is determined by a complete blood count, therefore segmentation and identification of blood cells are critical. A Complete Blood Count (CBC) is a test that counts all of the cells in the body to assess a person’s health. The RBC and WBC count are vital in diagnosing disorders such as anaemia, leukaemia, tissue damage, and so forth. This paper focuses mainly on RBC counting and the detection of abnormality, anaemia based on the count of RBCs from a peripheral blood smear using digital image processing techniques. Anemia is an indicator, and the most important one at that, for many other diseases. Therefore, basic screening of anemia is very important, especially in regions prone to poverty. Malnutrition due to poverty is the major cause for anemia. The paper presents an algorithm to automatically count the RBCs present in the blood of a person. The count of RBC, in 1 microlitre if blood is considered and it is observed how the count varies in normal blood smears and the anemic blood smears. In remote places, where a lot of people are to be screened, using cell counters and hemocytometer is not feasible. A faster method of counting is one of major demand. Therefore, to reduce the computation time, an algorithm in digital image processing is developed to compute the number of red blood cells. Although anemia is a vast subject and there are various different characteristics to consider, this is a humble approach to automate the counting of the RBCs which would be useful for future research purposes.
A hexagon-based method for polygon generalization using morphological operators

Numerous methods based on square rasters have been proposed for polygon generalization. However, these methods ignore the inconsistent distance measurement among neighborhoods of squares, which may result in an imbalanced generalization in different directions. As an alternative raster, a hexagon has consistent connectivity and isotropic neighborhoods. This study proposed a hexagon-based method for polygon generalization using morphological operators. First, we defined three generalization operators: aggregation, elimination, and line simplification, based on hexagonal morphological operations. We then used corrective operations with selection, skeleton, and exaggeration to detect, classify, and correct the unreasonably reduced narrow parts of the polygons. To assess the effectiveness of the proposed method, we conducted experiments comparing the hexagonal raster to square raster and vector data. Unlike vector-based methods in which various algorithms simplified either areal objects or exterior boundaries, the hexagon-based method performed both simplifications simultaneously. Compared to the square-based method, the results of the hexagon-based method were more balanced in all neighborhood directions, matched better with the original polygons, and had smoother simplified boundaries. Moreover, it performed with shorter running time than the square-based method, where the minimal time difference was less than 1 min, and the maximal time difference reached more than 50 mins.
Research on Digital Marketing Intelligence Innovation of Guangzhou’s Time-Honored Brands in the Era of Big Data

Guangzhou’s time-honored brands are the image of Guangzhou City, the historical legacy of the cultural emblems of the millennium commercial capital, the bearer of the memory of “old Guangdong” over generations, and the legends inscribed in the vast era of history. It not only carries the rise and fall of the city, but also documents the awakening, rejuvenation, and development of these “gold-lettered signboards.” In this age of big data, it is not only the expectation of the older generation, but also the responsibility of the new generation to “polish” the gold-lettered signboards of time-honored brands. “Time-honored brands” have accumulated rich and priceless knowledge from their ancestors in the vast era of business history and have profound cultural legacy. The time-honored brands that have been handed down not only allow the new generation to experience and identify with the unbounded knowledge of ancient people, but also provide economic value that can ensure the survival of future generations. However, in the market tide, the development of time-honored brands is not easy; some have even vanished. Therefore, it is imperative to take action to safeguard the development of time-honored brands, helping them to revitalize and shine once again. This paper discusses the difficulties in the development of Guangzhou’s time-honored brands and provides relevant suggestions for digital intelligence marketing of these “brands.”
WristConduct: Biometric User Authentication Using Bone Conduction at the Wrist

Biometric user authentication is an important factor to ensure security and privacy for personal devices. While many devices such as smartphones or laptops can be unlocked based on biometric data, smartwatches or other wrist-worn mobile devices still rely on knowledge-based schemes such as PINs or passwords. In a proof-of-concept study with 24 participants, we show that it is possible to identify individuals using sound waves passing through the wrist bones using a bone conduction speaker and a laryngophone (microphone). We tested support vector machines (SVMs) and artificial neural networks (ANNs) for binary classification. Using ANNs our method shows an authentication accuracy of 98.7%. We discuss the implications of integrating our approach into future devices and contribute with our findings in doing the first step for continuous passive user authentication at the wrist.
Pop-Up Probes

Cultural probes are a popular approach for research settings where researchers cannot observe directly, instead letting participants work on tasks in their own time. The creation of attractive cultural probes can be a time-consuming process requiring a diverse skillset. We suggest the use of pop-up paper elements as a simple, cheap, and effective way to engage participants. We explain how to craft the material based on a case study with Pop-Up Probes used as warm-up exercise for a co-creation session to make participants reflect on their own relationship with objects in their home. Based on this case study we illustrate what information can be attained from the cultural probes and discuss opportunities to transfer the method to other contexts.
Tooling for Developing Data-Driven Applications: Overview and Outlook

Machine Learning systems are, by now, an essential part of the software landscape. From the development perspective this means a paradigmatic shift, which should be reflected in the way we write software. For now, the majority of developers relies on traditional tools for data-driven development, though. To determine how research into tools is catching up, we conducted a systematic literature review, searching for tools dedicated to data-driven development. Of the 1511 search results, we analyzed 76 relevant publications in detail. The diverse sample indicated a strong interest in this topic from different domains, with different approaches and methods. While there are a number of common trends, e.g. the use of visualization, in these tools, only a limited, although increasing, number of these tools has so far been evaluated comprehensively. We therefore summarize trends, strengths and weaknesses in the status quo for data-driven development tools and conclude with a number of potential future directions this field.
Fostering Children's Stewardship of Local Nature Through Game Co-design

In a world of increasing environmental degradation there is an urgency to promote younger generations’ care for nature. However, the process of technology design is often overlooked as a means of enhancing this care and understanding. We explored how the co-design of games with children might support their nature affinity through two case studies of game co-design workshops carried out across schools in Australia and Bhutan. Four key themes highlight the usefulness and challenges of this method: i) expressing care and stewardship; ii) choice of prompts in co-design; iii) experience of play; iv) group dynamics. We discuss how game co-design facilitates children's creative expression by harnessing their expertise through iteration, discussion, and playfulness. This research shifts the focus of environmental games for children from output evaluation to process engagement and in so doing presents insights of use to designers, educators, and children-computer interaction researchers.
Communication of a University Teacher: New Realities and Facets of Transformation

Introduction. The article is devoted to an actual problem - the study of the problems of pedagogical communication in modern higher education, the design and modeling of a modern teacher. Modern realities, state and social orders are changing the requirements for competencies and skills of hard skills and soft skills, predicting future skills.&#x0D; Aim. The purpose of the article is to develop the design of a modern university teacher by analyzing current trends in the transformation of higher education and pedagogical communication.&#x0D; Methodology and methods. The study is based on the integrative-competence paradigm and professional approach. Research methods: analytical review of literary sources and the content of the research, pedagogical modeling and design, included observation, analysis, synthesis, generalization.&#x0D; Results. Based on the analysis of literary sources, current trends in the transformation of higher education and pedagogical communication, the requirements for a modern teacher and problems in pedagogical communication are determined.&#x0D; Scientific novelty lies in the identification and systematization of a set of requirements for a modern teacher, which replenishes scientific knowledge about pedagogical communication.&#x0D; Practical significance. The results of the study can be useful for developing ideas about the formation of professional competence of a university teacher and can be applied by them in the process of self-development, professional development.
DFTMicroagg: a dual-level anonymization algorithm for smart grid data

Abstract The introduction of advanced metering infrastructure (AMI) smart meters has given rise to fine-grained electricity usage data at different levels of time granularity. AMI collects high-frequency daily energy consumption data that enables utility companies and data aggregators to perform a rich set of grid operations such as demand response, grid monitoring, load forecasting and many more. However, the privacy concerns associated with daily energy consumption data has been raised. Existing studies on data anonymization for smart grid data focused on the direct application of perturbation algorithms, such as microaggregation, to protect the privacy of consumers. In this paper, we empirically show that reliance on microaggregation alone is not sufficient to protect smart grid data. Therefore, we propose DFTMicroagg algorithm that provides a dual level of perturbation to improve privacy. The algorithm leverages the benefits of discrete Fourier transform (DFT) and microaggregation to provide additional layer of protection. We evaluated our algorithm on two publicly available smart grid datasets with millions of smart meters readings. Experimental results based on clustering analysis using k-Means, classification via k-nearest neighbor (kNN) algorithm and mean hourly energy consumption forecast using Seasonal Auto-Regressive Integrated Moving Average with eXogenous (SARIMAX) factors model further proved the applicability of the proposed method. Our approach provides utility companies with more flexibility to control the level of protection for their published energy data.
Beyond Learning from Next Item

Sequential recommender systems have shown effective suggestions by capturing users' interest drift. There have been two groups of existing sequential models: user- and item-centric models. The user-centric models capture personalized interest drift based on each user's sequential consumption history, but do not explicitly consider whether users' interest in items sustains beyond the training time, i.e., interest sustainability. On the other hand, the item-centric models consider whether users' general interest sustains after the training time, but it is not personalized. In this work, we propose a recommender system taking advantages of the models in both categories. Our proposed model captures personalized interest sustainability, indicating whether each user's interest in items will sustain beyond the training time or not. We first formulate a task that requires to predict which items each user will consume in the recent period of the training time based on users' consumption history. We then propose simple yet effective schemes to augment users' sparse consumption history. Extensive experiments show that the proposed model outperforms 10 baseline models on 11 real-world datasets. The codes are available at https://github.com/dmhyun/PERIS.
UASSR: Unsupervised Arbitrary Scale Super-Resolution Reconstruction of Single Anisotropic 3D Images via Disentangled Representation Learning

Deep learning-based single image super resolution (SISR) algorithms have great potential to recover high-resolution (HR) images from low-resolution (LR) inputs. However, most studies require paired LR and HR images for a supervised training, which are difficult to organize in clinical applications. In this paper, we propose an unsupervised arbitrary scale super-resolution reconstruction (UASSR) method based on disentangled representation learning, eliminating the requirement of paired images for training. Applying our method to applications of generating HR images with high inter-plane resolution from LR images with low inter-plane resolution. At the inference stage, we design a strategy to fuse multiple reconstructed HR images from different views to achieve better super-resolution (SR) result. We conduct experiments on one publicly available dataset including 507 MR volumes of the knee joint and an in-house dataset containing 130 CT volumes of the lower spine. Results from our comprehensive experiments demonstrate superior performance of UASSR over other state-of-the-art methods. A reference implementation of our method can be found at: https://github.com/jialewang1/UASSR .
Confidence estimation for t-SNE embeddings using random forest

Abstract Dimensionality reduction algorithms are commonly used for reducing the dimension of multi-dimensional data to visualize them on a standard display. Although many dimensionality reduction algorithms such as the t-distributed Stochastic Neighborhood Embedding aim to preserve close neighborhoods in low-dimensional space, they might not accomplish that for every sample of the data and eventually produce erroneous representations. In this study, we developed a supervised confidence estimation algorithm for detecting erroneous samples in embeddings. Our algorithm generates a confidence score for each sample in an embedding based on a distance-oriented score and a random forest regressor. We evaluate its performance on both intra- and inter-domain data and compare it with the neighborhood preservation ratio as our baseline. Our results showed that the resulting confidence score provides distinctive information about the correctness of any sample in an embedding compared to the baseline. The source code is available at https://github.com/gsaygili/dimred .
Sharing Fungible Assets Across Polkadot Paraverse

Fungible asset sharing in cross-blockchain technology is becoming more popular and more developed. Blockchain interoperability at the beginning of blockchain technology was practically non-existent. The first blockchains did not have any communication possibilities with the other chains. Crossblockchain asset sharing became possible with the recent rise in popularity of blockchain interoperability. Newly developed blockchains are slowly implementing cross-blockchain sharing capabilities as standard features. This interoperability allows for many new significant use cases, such as in the gig economy or decentralized finances. This paper proposes a cross-blockchain fungible asset sharing system that will be able to transfer assets across specific Polkadot paraverse ecosystem Parachains.