Reviewerly/exHarmony huggingface dataset:
Text- and author-dependent moral foundations classification

Moral Foundations Theory (MFT) has attracted a great deal of attention in Natural Language Processing (NLP) and social media analysis, including both applications that attempt to infer moral values inference from text, or otherwise use moral foundations information to perform another downstream task. In this work, we address the issue of moral foundations inference from text data according to two perspectives, hereby called text- and author-dependent classification, by presenting a number of experiments to compare traditional text classifiers with more recent approaches based on pre-trained language models in both English and Portuguese languages. Results suggest that moral foundations classification relies heavily on lexical information, and that different models may be more suitable to each task, and leave a number of opportunities for further research in the field.
Placement methods of Virtual Machines in servers

In a data center, the amount of data to be stored and processed is increasing more and more, given technological innovation and the diversity of services offered by companies in the cloud. Therefore, there is a need to know how to use server resources in a way that does not waste energy in data centers while optimizing SLAs (Service Level Agreement), and migration of virtual machines (VMs). This paper presents an algorithm named FSS-VM (Fuzzy Soft Set based-Virtual Machine) which consists of placing VMs in physical machines (PMs) taking into consideration the use of CPU, memory, RAM and correlation values. Another algorithm named DRL-VM is presented to make the placement of VMs in servers in an optimal way using heuristics, and taking into consideration software failures, the energy consumed by servers in the data center, and the collocation interference between VMs. As a result, FSS-VM has shown its performance on energy consumption and improvement of the QOS (Quality Of Service) comparing to other methods. The other method which is DRL-VM consists in making the placement of the VMs by choosing the optimal method among dot product, first fit, norm2, $\rho$-greedy, $\eta-$greedy and $\omega$-greedy. Virtual machine placement method is a process that aims to improve the treatment complexity of user’s requests, and optimize energy consumption, both methods need to be enhanced to get better results.
A New Prediction Approach Using FCM and Deep Cascade Elastic Broad Learning for Photovoltaic Power Generation in Short Term

In recent years, photovoltaic power generation prediction has mainly faced some difficulties, such as low prediction accuracy, time-consuming model training, and so on. In order to improve the prediction accuracy of photovoltaic power generation in short term, it is greatly beneficial to optimize power allocating plans and improve economic benefits. Therefore, a new prediction method of Photovoltaic (PV) power generation based on Fuzzy C-Means (FCM) and deep cascade elastic broad learning is proposed in this paper. Firstly, the fuzzy c-means clustering algorithm is used to classify the input factors. Then, the feature mapping nodes of the broad learning model are constructed by the deep cascade method to extract the features of the input factors fully. The elastic net regularization method is employed to constrain the network output weight, measure the influence of the nodes on the weight, extract the more influential nodes, and sparse network structure. A deep cascade elastic broad learning network is established for short-term photovoltaic power prediction. Finally, the effectiveness of the method is verified through the original data. The experimental results verify the model can better predict the actual photovoltaic power generation and has a good application value.
Distributed Actor-Critic Learning Using Emphatic Weightings

In this paper a new Actor-Critic algorithm is proposed for distributed off-policy multi-agent reinforcement learning. It is composed of the Emphatic Temporal Difference ETD <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${\left(\lambda \right)}$</tex> algorithm (at the Critic stage) and a complementary distributed consensus-based algorithm using the exact gradients of a given criterion function (at the Actor stage). It is demonstrated that the algorithm converges weakly to the invariant set of an ordinary differential equation (ODE) characterizing the whole algorithm. Simulation results are presented as an illustration of high efficiency of the proposed algorithm.
Blockchain-based Mechanism Design for Collaborative Mathematical Research

Scientific research, and particularly research in mathematics, is arguably one of the crowning achievements of our collective human intellect. Its creation increasingly requires collaboration between multiple researchers with different and sometimes complementary backgrounds. On the other hand, its verification requires a careful matching between the expertise of reviewers and authors. Unfortunately, errors do happen and sometimes are only corrected many years after they appear in print, if at all. Nevertheless, at least when it comes to mathematical research, computer-verified formal proofs are possible as a final arbiter of mathematical truth, however, they are hard to produce even for relatively simple statements. Hence, such approaches typically lack far behind the current research frontier. In our work we present a novel blockchain-based system to tackle such issues. We discuss how the use of a combination of existing technologies such as decentralized file storage, review systems, smart contracts, non-fungible tokens (NFTs) and proof assistants can speed up and improve the quality and collaboration of mathematical research. As a proof of concept, we also design a mechanism to incentivize collaborative work and credit sharing that encourages provers to share their partial progresses without delay. Simultaneously, our mechanism incentivizes expert reviewers to challenge publicized proofs with formal proofs enabling dispute resolution, if needed.
POLIMI-ITW-S: A large-scale dataset for human activity recognition in the wild

Human activity recognition is attracting increasing research attention. Many activity recognition datasets have been created to support the development and evaluation of new algorithms. Given the lack of datasets collected in real environments (In The Wild) to support human activity recognition in public spaces, we introduce a large-scale video dataset for activity recognition In The Wild: POLIMI-ITW-S. The fully labeled dataset consists of 22,161 RGB video clips (about 46 h) including 37 activity classes performed by 50 K+ subjects in real shopping malls. We evaluated the state-of-the-art models on this dataset and get relatively low accuracy. We release the dataset including the annotations composed by person tracking bounding boxes, 2-D skeleton, and activity labels for research use at: https://airlab.deib.polimi.it/polimi-itw-s-a-shopping-mall-dataset-in-the-wild.
What is Discussed About Software Engineering Ethics on Stack Exchange (Q&amp;A) Websites? A Case Study

Software engineering ethics has been an interest of the software engineering community for over two decades, as evidenced by the creation of the joint ACM/IEEE-CS Soft-ware Engineering Code of Ethics. When reviewing the cur-rent software engineering literature, it was found that Stack Exchange question-and-answer (Q&#x0026;A) websites are particularly good means of gaining a better understanding on a given subject: what topics are addressed within a subject area, the popularity of these topics, and the difficulty of these topics. As such, this paper utilizes Stack Exchange Q&#x0026;A websites to review software engineering ethics and presents a case study that aims to provide a better understanding of the presence of software engineering ethics on three popular Stack Exchange Q&#x0026;A websites, which are Stack Overflow (SO), Software Engineering (SE), and Project Management (PM). The study analyzes over 2,170 posts, using Latent Dirichlet Allocation (LDA), to better understand what ethic-related topics are discussed. Subsequently, the authors analyzed the popularity and difficulty of each identified topic. The study found that users discuss six ethic-related topics, which are as follows: web scraping, software quality, software security, open-source software usage, team and employers, and software billing. Moreover, the results revealed that software security is the most popular and the most difficult topic. This study highlights ethic-related challenges that are faced by Q&#x0026;A website users. Software practitioners, researchers, and educators can utilize the results presented in this study to pursue new avenues when addressing pressing issues related to software engineering ethics.
Applying Semi-Supervised learning on Human Activity Recognition Data

Mobile devices have now become a part of our lives, and due to the sophisticated sensors incorporated in them, their utility to the users is enormous. At the same time the data thus captured can be analyzed for applications ranging from user identification, user authentication to activity monitoring. Machine learning algorithms for supervised classification are restricted by their requirement of large amount of labelled data. In such cases supervised learning may be adopted as it uses labelled as well -semi as unlabeled data to perform the learning tasks and construct better classifiers. In this paper semi supervised learning approach has been applied on the accelerometer data (labelled as well as unlabeled), collected from smart phones, for human activity recognition. The results have been compared with supervised classifiers and are found to be comparable.
Applications of Artificial Intelligence for Auditing and Classification of Incongruent Descriptions in Public Procurement

Context: Despite the advancement of technology, many services and information systems, especially in the public sector, still use unstructured natural language descriptions of products, services, or events, making their classification and analysis difficult. For efficient audits, it is necessary to classify and automatically totalize invoices issued for the purchase of products, considering their unique identification codes. Problem: The codes are not always registered correctly by the suppliers. Furthermore, if the product description is considered an alternative to the code, as aforementioned, this is not a uniform field, having free and variable writing. Solution: This work aimed to identify and characterize the approaches, techniques and intelligent algorithms used to classify incongruous textual descriptions present in the invoices issued. IS theory: General systems theory; Competitive strategy (Porter); Knowledge-based theory of the firm. Method: A systematic mapping was conducted to find the primary studies in the literature and collect evidence for directing future research. Summary of Results: 225 articles were identified, with Scopus and Web of Science being the bases with the most articles. Only 15 articles passed the inclusion and exclusion criteria. Among the approaches used, supervised machine learning stands out, present in 60% of the works. The most widely used techniques were Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), present in 40% of the articles. Contributions and Impacts in the IS area: The research showed that the use of artificial intelligence techniques helped to mitigate the problem of classification and analysis of invoices with incongruous codes and descriptions, which can help in the audit process, investigation, and fight against corruption. Finally, trends and gaps to be explored were also presented.
Virtual Dance Mirror: A Functional Approach to Avatar Representation through Movement in Immersive VR

Immersive Virtual Reality (VR) technologies offer new possibilities for studying embodied interaction with different sets of constraints and affordances for action-taking while using one’s physical body. In this study, we designed and prototyped a VR dance experience, Virtual Dance Mirror, where a dancer’s bodily movements are reflected on a 3D avatar model using a motion-capture suit. We investigated the novel possibilities for avatar design based on the expression of movements available for dancers in VR environment. After a preliminary briefing session, we conducted a user-study with five dancers with semi-structured interviews. Our findings support HCI literature on virtual body design to facilitate collaboration and non-verbal communication between VR users.
Screen culture ontology: towards the problem statement

We regard the screen phenomenon as a formative principle of modern culture. We have modeled the cultural concepts sequence representing the evolution of screen ontology: water – mirror – surface – shield – glass – portal – implant. The article covers (1) the issues of developing an integrated methodological approach to the interpretation of the formative, transmitting, and sense-making roles of the screen in modern culture; (2) systematic conceptual prognostications of screen transformational influence on cognition, corporeality, the aesthetic worldview of a modern man; (3) establishment of the “screen culture man” anthropological project formation in the contemporary world. A screen as a cultural phenomenon evolves and acquires expanded functions, revealing its own modes, the list of which is open-ended. Despite the fact that the formation of a screen phenomenon has not been completed, it is already obvious today that it is moving in the direction of a series of transformations of a screen-means into a screen-substance, a screen-object into a screen-subject, which inevitably levels out the value of a person looking at this screen, whose transformation occurs in the opposite direction to the screen.
Unsupervised blind image quality assessment based on joint structure and natural scene statistics features

Compared with the widely used supervised blind image quality assessment (BIQA) models, unsupervised BIQA models require little prior knowledge for calculating the objective quality scores of distorted images. In this paper, we propose an unsupervised BIQA method that aims to achieve both good performance and generalization capability with low computational complexity. Carefully selected and extensive structure and natural scene statistics (NSS) features can better represent image quality. First, we employ phase congruency (PC) and finely selected gradient magnitude map and Laplacian of Gaussian response (GM-LOG) features to represent image structure information. Second, we calculate the local mean-subtracted and contrast-normalized (MSCN) coefficients and the Karhunen–Loéve transform (KLT) coefficients to represent the naturalness of the distorted images. Last, multivariate Gaussian (MVG) model with joint features extracted from both the pristine images and the distorted images is adopted to calculate the objective image quality. Extensive experiments conducted on nine IQA databases demonstrate that the proposed method achieves better performance than the state-of-the-art BIQA methods.
Performers’ Use of Space and Body in Movement Interaction with A Movement-based Digital Musical Instrument

Movement-based musical interfaces support performers’ music and movement expressions by drawing from expertise and creative practices of both disciplines. In this work, we qualitatively and quantitatively analyze the movement interaction of participants with Bodyharp, a movement-based musical instrument. This wearable instrument offers musical affordances that allow performers to extend beyond small gestural spaces. Its wearable design encourages the performers to move while creating music and to express while using their bodies. Data was collected from twenty participants’ interactions, reflections, and compositions with Bodyharp. Video recordings of the experiment were annotated and qualitatively analyzed to reveal which performed gestures directly contribute to sound production and modification and which gestures accompany these musical actions. For a subset of participants, Musical Gestures Toolbox was used to further quantify the gestures. Using the Laban Movement Analysis framework, we observed participants’ use of space and body in their interaction with a movement-based musical instrument and how their backgrounds in music or movement (based on participants’ self-reported experiences) influenced the interaction. Our results offer design practices for creating new interactions at the intersection of music and dance.
Perspectives Of Classical Methods Of Face Recognition

Through this paper, we have discussed the perspectives of classical facial recognition techniques which have provided good results, especially the two methods eigenfaces and fisherfaces. Firsthand, we have chosen to evaluate the performance of the two models by a very recognized database in the field which is AT&T. Thus, for the training of the model, we have used 60 percent of the images, while the rest will be consecrated for the testing. Then, we have evaluated the influence of the number of images trained per person, and the total number of eigenvectors on the enhancement of recognition rate. On the second hand, we presented another method based on deep learning which is the VGG-16 model, improved by the integration of SVM architecture for classification and PCA module for dimension reduction, this model was trained by the benchmark in the domain: labeled faces in the wild (LFW), which gives us an accuracy rate that exceeds 98%.
A Hybrid Online and Offline Approach to Teaching Spoken English Based on Modern Educational Technology

People nowadays care more about the source and outcome of English instruction, resulting in an improvement in the quality of English instruction. As a result, the majority of students’ reading and writing levels increase steadily, but their listening ability does not, resulting in an extremely unbalanced development of the “listening, reading, and writing” four skills. The use of language in real-world situations is a reflection of human creativity, and the content and tone of the same speaker’s speech at different times, places, and occasions can vary significantly. Language usage cannot be separated from its context. This study proposes a hybrid online and offline English speaking teaching method based on modern educational technology. The method transforms offline teaching materials into a suitable context to create a rich and diverse online speaking classroom. This new teaching method constructs a teaching platform with online and offline as the primary subjects, replacing the previous single mode of teacher output and passive student input to form an all-round teaching platform distinguished by systematization, standardization, all-round language training, high intensity, accuracy, and fluency in language output. The experimental results demonstrated that our evaluation results are useful for providing feedback on the quality of teaching and promoting learning outcomes.
Deep Learning based Detection, Segmentation and Vision based Pose Estimation of Staircase

Mobile robots can move around in the surrounding. They are used in military, industrial applications, for surveillance tasks, etc. These tasks involve multi-floor navigation through the staircase. For efficient and safe stair climbing, it is necessary for the robot to align itself with the staircase. This paper presents a deep learning based approach for staircase detection, semantic segmentation of stair edges and vision based techniques with statistical operations for pose estimation of the staircase. The objective is to solve the problem of staircase traversing for mobile robots without any prior knowledge of the stair geometry and location by iteratively calculating the pose of the staircase from the robot’s point of view till it gets aligned with the staircase. The experimental results of staircase detection, semantic segmentation and pose estimation algorithms are presented further in the paper.
SCFFNet: Spatial Context Feature Fusion Network for Understanding the Highly Congested Scenes

Accurate counting in dense scenes can effectively prevent the occurrence of abnormal events, which is crucial for flow management, traffic control, and urban safety. In recent years, the application of deep learning technology in counting tasks has significantly improved the performance of models, but it still faces many challenges, including the diversity of target distribution between image and background, the drastic change of target scale, and serious occlusion. To solve these problems, this paper proposes a spatial context feature fusion network, abbreviated as SCFFNet, to understand highly congested scenes and perform accurate counts as well as produce high-quality estimated density maps. SCFFNet first uses rich convolutions with different scales to calculate scale-aware features, adaptively encodes the scale of contextual information needed to accurately estimate density maps, and then calibrates and refuses the fused feature maps through a channel spatial attention-aware module, which improves the model’s ability to suppress background and focus on main features. Finally, the final estimated density map is generated by a dilated convolution module. We conduct experiments on five public crowd datasets, UCF_CC_50, WorldExpo’10, ShanghaiTech, Mall, and Beijing BRT, and the results show that our method achieves lower counting errors than existing state-of-the-art methods. In addition, we extend SCFFNet to count other objects, such as vehicles in the vehicle dataset HBR_YD, and the experimental results show that our proposed method significantly improves the output quality with higher accuracy than previous methods.
Range extension of a scanning confocal chromatic sensor for precise robotic inline 3D measurements

This paper introduces the intermediate range extension concept of a precision 3D measurement module for robotic inline measurements. A scanning confocal chromatic sensor (SCCS) is integrated with a magnetically levitated and actuated measurement platform (MP) for enabling the acquisition of 3D images with sub-micrometer resolution. The MP is capable of tracking a sample surface in the out-of-plane degrees of freedom to actively compensate for relative motion between the SCCS and the sample. Using the MP to position the SCCS at multiple measurement locations, 3D measurements of the surface structure under test are acquired with overlapping regions between neighbouring frames. A 3D image processing and parallel registration algorithm is implemented in order to ensure a robust and precise merging of the individual 3D images. Experimental results demonstrate an extension of the SCCS’ lateral measurement range by a factor 3 to about 500×500µm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> while achieving sub-micrometer resolution in the entire range-extended 3D measurement. Compared to state-of-the-art repositioning with the robot, the measurement accuracy is increased by a factor of 9.