Reviewerly/exHarmony huggingface dataset:
The Convergence of Struggles! Reusability Assessment of Inner‐Source Components for Product Lines

ABSTRACT Inner source is establishing open source‐like collaborations within an organization. Product Line Engineering (PLE) is the approach for engineering a related product portfolio in an efficient manner, taking advantage of products’ similarities while managing their differences. These two well‐documented approaches propose smart techniques for reuse, but they use different terminology. A language characteristic is to be polysemic and polymorphic. Indeed, PLE and inner source do not use the same words to refer to equivalent concepts. This could badly affect project performance when evolving in a multi‐domain context. What if there was a way to better integrate PLE, inner source, modeling, data management, hardware and software engineering, and integration, verification, validation, and qualification (IVVQ) through the similarity concept? This paper shows it is possible to build a common way to assess the components (also called building blocks) contributing to a product line, thanks to a process to determine the component maturity levels using the similarity approach. After detailing the commonalities between the inner source and PLE domains, we present the Inner Sourcing Process Maturity Level (ISPML) as a key engineering practice. Why is it important for engineering? If engineers reuse components defined by their engineering assets, it is important to have a formalized, common way to do this across the company to integrate reusable multi‐domain assets with a certain confidence level. This paper introduces a simple method for multi‐domain organizations to better determine whether sharing an engineering asset is favorable or not.
Robustness and Diversity Seeking Data-Free Knowledge Distillation

Knowledge distillation (KD) has enabled remarkable progress in model compression and knowledge transfer. However, KD requires a large volume of original data or their representation statistics that are not usually available in practice. Data-free KD has recently been proposed to resolve this problem, wherein teacher and student models are fed by a synthetic sample generator trained from the teacher. Nonetheless, existing data-free KD methods rely on fine-tuning of weights to balance multiple losses, and ignore the diversity of generated samples, resulting in limited accuracy and robustness. To overcome this challenge, we propose robustness and diversity seeking data-free KD (RDSKD) in this paper. The generator loss function is crafted to produce samples with high authenticity, class diversity, and inter-sample diversity. Without real data, the objectives of seeking high sample authenticity and class diversity often conflict with each other, causing frequent loss fluctuations. We mitigate this by exponentially penalizing loss increments. With MNIST, CIFAR-10, and SVHN datasets, our experiments show that RDSKD achieves higher accuracy with more robustness over different hyperparameter settings, compared to other data-free KD methods such as DAFL, MSKD, ZSKD, and DeepInversion.
MACHINE TRANSLATION: TEACHING AND LEARNING ISSUES

ABSTRACT Considering the boost in technological development, and that machine translation has been widely used by both industry and academia, the main goal of this paper is to describe possibilities for the incorporation of a basic machine translation module in a Translation program at university level. The didactic proposal herein presented aims at providing Translation students ways of identifying general output results different modern machine translation systems have in common. Through a task-based teaching approach, students may assess machine translation strengths and weaknesses in order to make better use of this technology.
Hidden Interfaces for Ambient Computing: Interacting with High-brightness Visuals through Everyday Materials

Consumer electronics are increasingly using traditional materials to allow technology to better blend into everyday environments. Specifically, transmissive materials enable emissive displays to disappear when turned off, and appear when turned on. However, covering displays with textile meshes, veneer, one-way mirrors, or translucent plastics greatly limit the display brightness of typical graphical displays.
Technoinnovation as an essential element of training for engineers

Abstract The paradox of the modern labor market, when there is a shortage of engineering and technical personnel in the presence of a sufficient number of graduates of technical universities, is associated with a deformation of demand: only those specialists are in demand, which are either economically unprofitable or impossible to replace with technical devices, since so far artificial intelligence is not capable of creative creating innovations. The problem is that there are disproportionately few well-prepared schoolchildren among applicants to technical universities, and among university graduates there are many who have chosen a career outside the STEM sphere. There is no shortage of engineers, there is a shortage of highly qualified creative specialists. To train such specialists, it is necessary to increase the inflow of the most talented and creative students to technical universities, and then keep them in the STEM sphere. To test the hypothesis that a good way of attracting young people to the choice of an engineering career is to endow students with successful experience of technological invention, courses “Techno-Startup” for schoolchildren and “Technoinnovation” for students of technical universities were developed. The objectives of these courses are: to give students the experience of creating techno-innovation, to increase the motivation for a career in STEM among the most prepared students, to form critical thinking.
Instance Segmentation of Traffic Scene Based on YOLACT

Abstract As deep learning shines in the field of machine vision, this article uses the YOLACT instance segmentation algorithm to solve the detection and segmentation of pedestrians, vehicles and other objects in traffic scenes. In this paper, the backbone in the YOLACT algorithm is changed from the original resnet50 to the lightweight resnet18 to ensure that the algorithm can be deployed on Jetson-AGX-Xavier, and the processing speed can reach 9.4 frames per second, and the map can reach 81%. In NVIDIA 2070 super, the inference speed on the graphics card can reach 40 frames per second.
Locally Testable Privacy-Preserving Contact Tracing Protocol Without Exposing Secret Seed

Digital contact tracing using a smartphone has become an important component in managing the spread of contagious diseases. However, there are privacy concerns when using the contact tracing apps. Because the apps require personal information to trace people who have been in contact with an infected person.In this paper, we propose a privacy enhanced contact tracing protocol. We provide a hybrid protocol which protects the infected user's privacy. In the proposed protocol, non-infected users cannot track the location of the infected patient. Even the central authority cannot identify users who had contacted with the infected person.
The Translation Problem

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Digital technology in the humanities and public life in the context of justice

The article explores some methodological issues and the role of digital technologies in the humanities, education system, and public life. The relevance of the research topic is confirmed by the rapid development of digital resources and technologies in public life, including humanities and education. The positive role of digital technologies for the development of humanities and education is emphasized; they are characterized by interdisciplinarity and open up new possibilities for research. It is argued that the process of digitalization is a reflection of the new historical realities of social life, including science and education, a reflection of the new needs of society in the research environment. And materialistic understanding of the phenomena of social life as the most important method of research, comprehension, and evaluation of social processes is used as the main method of research. It is argued that digital technologies in the humanities and education have serious negative consequences. This manifests itself in a potential decrease in intellectual activity, analytical abilities, and physical development of a person. Digital technologies are capable of taking a person into another reality, disrupting the process of traditional socialization, live communication. Such processes have a negative impact on his personal development, which contradicts the criterion of justice and social progress, which are expressed in the comprehensive development of the individual. It is argued that even the most advanced digital technology cannot replace the moral and psychological moments of live teacher-student communication.
Turning a Messy Room into a Fully Immersive VR Playground

In this study, to enable a VR experience with an HMD even in a space with obstacles, we developed a real-time construction system of a reality-based VR space that does not impair the atmosphere of the virtual world. In addition, we aim to construct a VR space that is easier to recognize its structure by classifying "objects that are boundaries of space" and "ordinary obstacles" using a deep learning network and superimposing virtual objects corresponding to each type of real object. We implemented a real-time construction system for a VR space that reflects the distribution of objects in a real space using two depth cameras mounted on HMD, and created guidelines and applications for using the proposed system.
Re-Imagining Performance Reviews: Automated Dashboards for Continuous Visibility of Engineers’ Performance

Traditional methods for performance appraisal are not suitable for agile fast-paced software companies. This has been a realization in the software industry since the early adoption of agile methodologies. Nonetheless, software companies are still struggling to find a practical solution that fits the highly dynamic nature of their teams. In particular, high-growth companies, scaleups, need to be creative at how they approach performance appraisals. In this talk, we introduce automated review dashboards to support a seamless appraisal process and continuous visibility of software engineers' performance. The proposed dashboards leverage structure from existing growth frameworks while automating the aggregation of relevant qualitative and quantitative performance metrics. We reflect on our experience using the dashboards at Robusta, a medium-sized software scaleup company. The dashboards enabled a team of four managers to provide timely feedback to 56 engineers with a base for continuous visibility. We explore the design of the dashboards, the customizable metrics and the overall review experience from the perspectives of different stakeholders. We conclude with the lessons learned and practical advice for scaleups facing the same challenge.
Comparison of Feature Selection and Classification for Human Activity and Fall Recognition using Smartphone Sensors

Human activity and fall recognition have been applied into many applications, especially, daily activity and healthcare systems. The recognition performance is up to the features used in the classification. Applying all features might degrade the performance since irrelevant features are used. In this paper, we study and compare four filter methods of feature selection: Chi square, mutual information, ANOVA, and Pearson's coefficient. Six supervised classifiers are used to evaluate the classification performance according to the sets of features obtained from these methods. The dataset UniMiB SHAR, which consists of nine basic daily activities and eight types of falls, is investigated in the experiment. As a result, among all of the considered feature-selection methods and classifiers, the XGBoost incorporating with the mutual information method gives the best performance whose accuracy, precision, recall, and F1 scores are 0.9122, 0.8727, 0.8629, and 0.8640, respectively.
Research on anti-Financial fraud Technology based on Machine learning

Changing fraud mode and the hidden risk of fraud is the characteristics of P2P network credit. Personal behavior in Internet basic characteristic information and personal data in building personal portrait technique based on large data model has potential value. When the advantage is obvious and credit fraud identification network model have a very good solution efficiency. In this paper, combined with the combination classification algorithm of machine learning, the fraud of network credit platform in Internet finance is deeply analyzed and studied.
Intensifying constructions in second language acquisition

This study analyzes the acquisition of Dutch intensifying constructions by French-speaking learners in Belgium. Additionally, it compares learners enrolled in Content and Language Integrated Learning (CLIL) programs with learners following traditional second language instruction. Within the framework of Diasystematic Construction Grammar, we study the potential impact of CLIL on the acquisition of Dutch intensification by conducting an in-depth constructional analysis at three different levels of schematization. The results of our study indicate that a beneficial CLIL effect is apparent throughout the different levels of abstraction. Moreover, the results allow us to unveil specific reorganizational processes that occur in the diasystem of French-speaking learners of Dutch, such as the overgeneralization of particular schematic patterns and the inaccurate tagging of specific intensifiers.
Critical Urbanism and the Digitally Augmented City: Envisioning Urban Commons as Civic Assemblages in the Age of Networked Translocalism, Multi-Associative Transduction and Counterhegemonic Transculturalism

Abstract This essay discusses problems affecting the contemporary of the urban commons by addressing the emerging spatialization of the relational infrastructures of urban communities due to the recent transformations of the technological framework. It sheds light on three key macrophenomena that are changing the nature of these institutions, translocalism, transduction and transculturalism , and drafts design strategies to foster their agency for the affirmation of equality and pluralism while guaranteeing social inclusiveness, cohesiveness, pluralism and differentiation. It answers the research question on how designers and planners can respond to the effects of these disrupting phenomena that redefine their roles, mission and instruments. It proposes a design framework for new commons operating in the reality–virtuality continuum. We envisage them as constituted by three intertwined components: a) a set of enabling relations , which constitute relational machines that establish dynamic, robust and efficacious connections between distributed and mobile actants engaged in commoning actions; b) a concrete infrastructure , which includes the constitutive elements that come together in meta-stable and variable context-specific platforms supporting the relational machines; c) a set of constitutive agents , which includes counterhegemonic pluralistic narratives of possible futures that foster social formations to subvert the existing abstract (i.e., postpolitical, conflict-free and antidifferential) spaces. Our proposal contributes to the discourse on the spatialization of radical democracy ideas, foregrounding the role of design in liberating the creative collective power of co-create dialogical utopian realms. These narratives through fabulation, allegory and parody devise possible translocal, transductive and transcultural spatialites for commoning and differentiation to foster the collective appropriation of the new ordinary .
What and How? Jointly Forecasting Human Action and Pose

Forecasting human actions and motion trajectories address the problem of predicting what a person is going to do next and how they will perform it. This is crucial in a wide range of applications, such as assisted living and future co-robotic settings. We propose to simultaneously learn actions and action-related human motion dynamics while existing works perform them independently. This paper presents a method to jointly forecast categories of human action and skeletal joint pose, allowing the two tasks to reinforce each other. As a result, our system can predict future actions and the motion trajectories that will result. To achieve this, we define a task of joint action classification and pose regression. We employ a sequence to sequence encoder-decoder model combined with multi-task learning to forecast future actions and poses progressively before the action happens. Experimental results on two public datasets, IkeaDB and OAD, demonstrate the effectiveness of the proposed method.
The Effects of Initial-Terminal Position on Pointing Task for Touch-Screen Tablet

Previous studies have used the Fitts’ Law to predict the performance of pointing task on touch screens. It was found that moving distance, target width, and the direction of motion would affect the task performance. The present research aimed to investigate the impact of the initial and terminal positions (ITPs) on pointing performance in two Exps. ITPs were divided into two categories: center of a screen and outer of a screen (center ↔ outer) pointing, and outer of a screen to outer of a screen (outer→outer) pointing. In Exp 1, 30 participants performed the center ↔ outer pointing tasks with 8 directions. The results showed that the outer → center movement was significantly faster than the center → outer at 45°, 90°, and 180°. In Exp 2, 30 participants performed the outer → outer pointing tasks with eight directions. The current study revealed that the ITPs influenced the performance of the pointing task, possibly due to human biomechanical characteristics associated with different movements.
Research on Commodity Image Classification Based on Local Oppugnant Color Vector Angle Pattern

Based on the existing commodity image classification research, there is a lack of the use of the most recent color and texture features of the combination of commodity image classification. According to the local oppugnant color vector angle pattern (LOCVAP) feature extraction algorithm and the Deep Believe Network (DBN) classifier, the image classification is simulated and analyzed. The experimental results show that the results of LOCVAP descriptor have a greater improvement than those of other subcategories of feature descriptors.
Blockchain Privacy Data Protection Method Based on HEVC Video Steganography

Blockchain is a shared digital public ledger that cannot be changed once transactions are recorded and verified. Since most blockchain nodes retain copies of the ledger, the blockchain brings the benefits of decentralization while also bringing privacy leakage issues. With the continuous development of blockchain applications, how to protect the privacy data on the blockchain platform is a very worthwhile research question. This paper first divides each blockchain transaction data into two parts: sensitive data and basic data, and then encrypts and hides the sensitive data into the HEVC video to protect the privacy transaction data of the blockchain. The experimental results show that this HEVC video steganography algorithm can effectively increase the privacy data embedding capacity and get good visual quality.
Analysis of Different Fuzzy C-Means Membership Functions for the Design of CAD Tool of Early Breast Cancer Detection

In this paper, analysis of the different membership function for fuzzy C-means based segmentation technique are done to overcome the issue of random initialization of membership matrix, 116 hybrid features set and analysis of various classifier with modified K nearest neighbor (KNN) are proposed for primary breast cancer detection. The steps involved to design CAD tool for breast cancer detection includes preprocessing, segmentation, feature extraction, selection, and classiﬁcation. In pre-processing, manual cropping of original mammograms for removal of background details, and contrast enhancements are used. Contrast Limited Adaptive Histogram Equalization (CLAHE) is used for enhancement of mammographic images. Further, nonlinear complex diffusion based unsharp-masking and crispening method is utilized to highlight the abnormalities of mammogram such as micro calcifications, tumors, etc. Further, 2D DWT is applied for taking approximation coefficient followed by fuzzy C-means segmentation with different types of fuzzy membership functions, which is utilized to segment and extract the region of interest.For feature extraction, 116 hybrid features are extracted from each image such as Geometric based features, Texture based features, Gabor based features and Histogram based features. Next step is Minimum Redundancy and Maximum Relevancy (MRMR) is used as features selection technique for discarding repetitive and irrelevant features. Final step is classification, the modify KNN classifier based on mutual information in place of Euclidean distance is proposed while finding the nearest neighbors. The comparative analysis of the fuzzy C-means segmentation techniques with different types of fuzzy membership methods are presented in terms of RI, GCE, VOI and the various classifier performance are taken in terms of accuracy, precision, sensitivity, specificity.
Distributed Solar PV System for Industrial Applications in Palestine

The paper presents the simulated and field tests of several distributed solar PV systems for industrial applications in Palestine. These systems generate solar power for self-consumption in lighting and air conditioning in a production line of a factory when solar energy is available. They do not feed the excess PV power to the grid. They will be switched to grid power supply when solar energy is not available. These systems started to operate after 2015, and implemented over rooftops in high potential regions in Palestine such as Jerusalem and Ramallah.PVsyst software with meteorological database Metronome 7 was used to make suitable forecast. Field results were compared with the simulated values of production. These results are verified each other and gave satisfied results with respect to >>>>>>>>>>>>>>>>.
Interpreting the Latent Space of GANs via Correlation Analysis for Controllable Concept Manipulation

Generative adversarial nets (GANs) have been successfully applied in many fields like image generation, inpainting, super-resolution, and drug discovery, etc. By now, the inner process of GANs is far from being understood. To get a deeper insight into the intrinsic mechanism of GANs, in this paper, a method for interpreting the latent space of GANs by analyzing the correlation between latent variables and the corresponding semantic contents in generated images is proposed. Unlike previous methods that focus on dissecting models via feature visualization, the emphasis of this work is put on the variables in latent space, i.e. how the latent variables affect the quantitative analysis of generated results. Given a pre-trained GAN model with weights fixed, the latent variables are intervened to analyze their effect on the semantic content in generated images. A set of controlling latent variables can be derived for specific content generation, and the controllable semantic content manipulation is achieved. The proposed method is testified on the datasets Fashion-MNIST and UT Zappos50K, experiment results show its effectiveness.