Reviewerly/exHarmony huggingface dataset:
Text2shape Deep Retrieval Model: Generating Initial Cases for Mechanical Part Redesign under the Context of Case-Based Reasoning

Retrieving the similar solutions from the historical case base for new design requirements is the first step in mechanical part redesign under the context of case-based reasoning. However, the manual retrieving method has the problem of low efficiency when the case base is large. Additionally, it is difficult for simple reasoning algorithms (e.g., rule-based reasoning, decision tree) to cover all the features in complicated design solutions. In this regard, a text2shape deep retrieval model is established in order to support text description-based mechanical part shapes retrieval, where the texts are for describing the structural features of the target mechanical parts. More specifically, feature engineering is applied to identify the key structural features of the target mechanical parts. Based on the identified key structural features, a training set of 1000 samples was constructed, where each sample consisted of a paragraph of text description of a group of structural features and the corresponding 3D shape of the structural features. RNN and 3D CNN algorithms were customized to build the text2shape deep retrieval model. Orthogonal experiments were used for modeling turning. Eventually, the highest accuracy of the model was 0.98; therefore, the model can be effective for retrieving initial cases for mechanical part redesign.
A bunch of sessions: a propositions-as-sessions interpretation of bunched implications in channel-based concurrency

The emergence of propositions-as-sessions, a Curry-Howard correspondence between propositions of Linear Logic and session types for concurrent processes, has settled the logical foundations of message-passing concurrency. Central to this approach is the resource consumption paradigm heralded by Linear Logic. In this paper, we investigate a new point in the design space of session type systems for message-passing concurrent programs. We identify O’Hearn and Pym’s Logic of Bunched Implications (BI) as a fruitful basis for an interpretation of the logic as a concurrent programming language. This leads to a treatment of non-linear resources that is radically different from existing approaches based on Linear Logic. We introduce a new π-calculus with sessions, called πBI; its most salient feature is a construct called spawn, which expresses new forms of sharing that are induced by structural principles in BI. We illustrate the expressiveness of πBI and lay out its fundamental theory: type preservation, deadlock-freedom, and weak normalization results for well-typed processes; an operationally sound and complete typed encoding of an affine λ-calculus; and a non-interference result for access of resources.
Differentially Private XGBoost Algorithm for Traceability of Rice Varieties

Privacy protection in agricultural traceability has received more and more attention. Most of the existing methods only protect the original data information from the perspective of cryptography and ignore the availability of the protected information. In fact, after data is processed by cryptography, blockchain, and other technologies, it cannot be directly used for machine learning model training. Therefore, differential privacy has great potential value for privacy protection in agricultural traceability, which can enable data to participate in classification tasks under privacy protection. In this paper, we propose an integrated algorithm for agricultural traceability called Differentially Private XGBoost (DP-XGB), which can protect the privacy of the original data during the training process and obtain high model utility under the condition of a small sample size. We inject Gaussian noise into the gradient operator and Hesse operator of the original XGBoost and give the calculation method of the resulting privacy budget. Experiments show that our method can effectively obtain differential privacy guarantees and achieves very high classification accuracy when the noise is small.
IMPACT OF MEDIA TECHNOLOGIES ON DIGITAL EDUCATIONAL CONTENT IN MEDIA SECTOR

The rapid development of media technologies has tremendously influenced all spheres of human life. In media education, they are widely used for both organization of future media industry workers training, and its content. Being progressive, the media sector makes an active use of the technological capacity to enhance the education efficiency and quality. At the same time, those processes create a tough competitive environment. In the context of the distance learning prevailing in the last few years, higher educational establishments face the emergence of numerous alternative educational internet initiates at the market that may pose a certain threat for the traditional higher education in the future. It is partially driven by reorientation of media industry’s economic models, including a fundamental revision of approaches to monetization and the popularity of the non-commercial independent journalism with its growing employment opportunities. They all urge journalists gain new skills and pursue knowledge from other spheres often unrelated to journalism. On top of that, changing communication channels and higher technological literacy level of young people shape the need to adopt content formats to new realities. Seeking to assert their traditional dominance in the media education market, universities should revisit the content of training curricula and more efficiently use technological tools in the educational process. The current study that is based on the survey of future media workers conducted during 2020-2021 (20 months) made it possible to identify the content formats preferred by students and assess the potential and risks of using them in the future. Described practices of using media technologies by universities allow recommend those of them that could be most efficiently integrated in the educational process. For example, the development of communication channels enabling integration of educational courses and clusters into a single interdisciplinary system both at the level of individual educational units and at the university level seems promising. It would make university curricula significantly more adaptable and competitive in the face of new challenges at the media market and streamline limited resources in the existing circumstances.
A comprehensive review of significant learning for anomalous transaction detection using a machine learning method in a decentralized blockchain network

Blockchain technology has changed the global trading of assets.A blockchain can be viewed as a connected ledger managed by a distributed peer-topeer (P2P) network.Blockchain offers distinctive characteristics such as transactional privacy, the immutability of data, transparency and cryptographic, among others.These features paved the door for blockchain to develop numerous technology solution, including voting applications [1,2], internet of things (IoT) [3,4], and supply chain management (SCM) [5,6], among others.The increasing desire for technological advancements stimulated the development of BT.
Gaussian processes based extended target tracking in polar coordinates with input uncertainty

Abstract Gaussian processes (GP) based extended target tracking (ETT) technique has attracted the attention of scientists since it can accurately estimate the kinematic states as well as the contour states of irregular-shape targets. Most traditional GP methods consider the ETT problem with a linear measurement model in Cartesian coordinates and ignore the input uncertainty of GP for simplicity. In many applications, however, measurements are generated with high-resolution sensors in polar coordinates. More importantly, the ignorance of input uncertainty in GP will exacerbate the tracking performance in these cases. In order to track an irregular-shape extended target in polar coordinates with input uncertainty, an improved GP-based probabilistic data association (IGP-PDA) algorithm is developed that includes the following enhancements: Firstly, a nonlinear measurement model is used and the unbiased converted measurement technique is invoked for the ETT problem. Secondly, the analytical form of statistical properties of the input uncertainty due to measurements noise and predicted error is derived. Thirdly, an IGP-PDA taking into account measurement origin uncertainty as well as input uncertainty is proposed, and three sub-optimal implementations are provided. Last, the posterior Cramer-Rao lower bound of ETT with input uncertainty is derived. Simulation results verify the effectiveness of the proposed method.
Forms of Care in Human–Nature–Technology Environments

With the ongoing environmental disruptions, designers are increasingly interested in exploring nature–technology entanglements that create sustainable and collaborative futures. Although largely unarticulated, these emerging design inquiries are motivated by care for nature, which indirectly depends on cultural and social human practices. Drawing on a broad set of works on care from feminist theory, science and technology studies, and human–computer interaction, this article introduces a care framework that focuses on revealing tensions in the interrelationship between humans and nonhumans. The framework is used to examine an initial study in which five participants engaged with a speculative design probe, specifically a combination of a device and a plant, envisioning a scenario in which plants generate electricity. We reveal how forms of care manifest differently in a human–plant–technology dynamic and identify tensions, such as plants being considered utilities, proxies, or humans.
Review of The Rise of AI: Implications and Applications of Artificial Intelligence in Academic Libraries

Review of Hervieux, S. &amp; Wheatley, A. (Eds.). (200). The Rise of AI: Implications and Applications of Artificial Intelligence in Academic Libraries. Chicago, IL: Association of College and Research Libraries.
Recognizing Stationary and Locomotion Activities using LSTM-XGB with Smartphone Sensors

Nowadays, stationary and locomotion activity recognition, also known as SLAR, is becoming increasingly important in a variety of domains, such as indoor localization, fitness activity tracking, and elderly care. Currently used methods typically involve handcrafted feature extraction, a process that is both difficult and requires specialized knowledge, and results can still be subpar. We proposed a deep learning technique for SLAR called LSTM-XGB that uses data from inertial sensors in smartphones to reduce the effort required for feature development and selection. The proposed LSTM-XGB consists of multiple stacked LSTM layers to automatically learn the temporal features of the input, followed by XGBoost for label prediction in the final layer. The results showed that the proposed LSTM-XGB technique, which automatically extracts features, outperforms conventional machine learning that requires manual feature extraction. We also showed that sensor data from three sensors (accelerometer, linear acceleration, and gyroscope) can be combined. This achieved higher accuracy than other combinations or single sensors.
Building datasets for automated conversational systems designed for use-cases

The efficiency of an Artificial Intelligence (AI) based system depends on the richness and accuracy of its training dataset. These datasets are responsible for training the algorithms used by conversational systems, making them more effective and able to respond autonomously to user interactions. When it comes to specific contexts and communicational situations, there are still gaps in building datasets that are shaped to different realities. Therefore, the present study sought to develop a dataset to support the creation and training of an intelligent conversational system that enables autonomous care, via telephone calls, of restaurant, beauty salon and medical clinic's customers. The stages of subjective user experience data collection, preparation and validation are reported.
Checking the Regularity of the Linear Method of Summation Fourier Series

Systems analysis is a powerful tool for studying complex systems of various natures. Periodic signals can be represented by orthogonal systems of functions. Examples of such orthogonal systems are Walsh system, Chebyshev system and others. The system of trigonometric functions of sines and cosines of multiple arguments has become the most widespread. But, as we know, there are examples of continuous functions, the Fourier series of which diverge at some points. Series summation methods make it possible to match a divergent series to some ”sum”. An example of a matrix with convenient elements for checking the necessary and sufficient conditions for the regularity of the rectangular summation method is given.
OCOLOS: Online COde Layout OptimizationS

The processor front-end has become an increasingly important bottleneck in recent years due to growing application code footprints, particularly in data centers. First-level instruction caches and branch prediction engines have not been able to keep up with this code growth, leading to more front-end stalls and lower Instructions Per Cycle (IPC). Profile-guided optimizations performed by compilers represent a promising approach, as they rearrange code to maximize instruction cache locality and branch prediction efficiency along a relatively small number of hot code paths. However, these optimizations require continuous profiling and rebuilding of applications to ensure that the code layout matches the collected profiles. If an application’s code is frequently updated, it becomes challenging to map profiling data from a previous version onto the latest version, leading to ignored profiling data and missed optimization opportunities.In this paper, we propose OCOLOS, the first online code layout optimization system for unmodified applications written in unmanaged languages. OCOLOS allows profile-guided optimization to be performed on a running process, instead of being performed offline and requiring the application to be re-launched. By running online, profile data is always relevant to the current execution and always maps perfectly to the running code. OCOLOS demonstrates how to achieve robust online code replacement in complex multithreaded applications like MySQL and MongoDB, without requiring any application changes. Our experiments show that OCOLOS can accelerate MySQL by up to $1.41 \times $, the Verilator hardware simulator by up to $2.20 \times $, and a build of the Clang compiler by up to $1.14 \times $.
Designing Personal Knowledge Management System to Support Knowledge Sharing in Government Organization: A Case study at the Indonesian Central Statistics Agency

Tacit knowledge such as regulatory understanding, practical skills to support daily work, and best practices are essential to support organizational knowledge. However, some government agencies in Indonesia do not yet have official guidelines and specific tools to manage this personal knowledge. The purpose of this study is to develop a bottom-up personal knowledge management system for the government institutions, namely BPS. The user-centered design (UCD) was used, along with the B-KIDE, to develop a knowledge management system model. The qualitative method was used through semi-structured interviews with 14 employees in the BPS regency/municipality. The evaluation of the prototype using the system usability scale resulted in an average score of 87.5, which means that the designed system is on grade scale B and indicated that the system can be accepted by users.
Barriers in the Implementation of Effective Intelligent Knowledge Management in the Pakistani Organizations

Organization’s performance depends upon the knowledge and intellectual resources including people, their work experiences, ideas etc. Knowledge management play an important role for an organization’s survival and competitive strength. There are different factors which reduce organizational performance by obstructing effective implementation of knowledge management in organizations i.e., organizational barriers, cultural barriers, technical barriers, personal barriers and environmental barriers. Purpose of this study is to find out the leading barrier(s) towards effective knowledge management implementation in Pakistan. For this purpose, questionnaires are used to gather data from the people of some organizations (teaching, medical and oil and gas). Percentage values of these barriers are 94.8, 94.4, 96.4, 96 and 94.4 respectively. Results show that knowledge management implementation is greatly affected by technological barriers and personal barriers and is suppressed by employ’s performance.
Virtual and augmented experience in cultural places: The perspective of integration in the learning process

The objective of this paper is the development and evaluation of mobile applications for learning tours in cultural and archaeological sites, using virtual and augmented reality. Firstly, the theoretical framework of virtual and augmented reality techniques is presented. Then the virtual model of the bridge of Arta is constructed. The model is to be integrated into the unity gaming machine to accompany a virtual navigation application. The application is enriched with multimedia elements and is connected to a corresponding augmented reality application and is evaluated by secondary school students.
Deep Gaussian process with multitask and transfer learning for performance optimization

We combine Deep Gaussian Processes with multitask and transfer learning for the performance modeling and optimization of HPC applications. Deep Gaussian processes merge the uncertainty quantification advantage of Gaussian Processes with the predictive power of deep learning. Multitask and transfer learning allow for improved learning efficiency when several similar tasks are to be learned simultaneously and when previous learned models are sought to help in the learning of new tasks, respectively. A comparison with state-of-the-art autotuners shows the advantage of our approach on two application problems.
Site-Wide HPC Data Center Demand Response

As many electricity markets are trending towards greater renewable energy generation, there will be an increased need for electrical grids to cooperatively balance electricity supply and demand. Data centers are one large consumer of electricity on a global scale, and they are well-suited to act as a grid load stabilizer via performing “demand response.” Prior investigations in this space have demonstrated how data centers can continue to meet their users' quality of service (QoS) needs by modeling relationships between cluster job queues, server power properties, and application performance. While server power is a major factor in data center power consumption, other components such as cooling systems contribute a non-nealiaible amount of electricity demand. This work proposes using a simple site-wide (i.e., including all components of the data center) power model on top of QoS-aware demand response solutions to achieve the QoS benefits of those solutions while improving the cost-saving opportunities in demand response. We demonstrate 1.3x cost savings compared to QoS-aware demand response policies that do not utilize site-wide power models, and show similar savings in cases of severely under-predicted site-wide power consumption if 1.5x relaxed QoS constraints are allowed.
The principle of prediction of complex time-dependent nonlinear problems using RNN

An approach based on recurrent neural networks (RNN s) is applied to verify the possibility of using surrogate models for the prediction of dynamic nonlinear problems. Modeling complex time dependencies is currently still a challenge, when the structure of the neural network needs to be adapted to the dynamics of the problem. In this paper, the possibility of using prediction in space-time problems is illustrated by the possibility of using it to predict the course of the current in a simple RL circuit that is powered by a voltage source.
Investigating the Overall Experience of Wearable Robots during Prototype-Stage Testing

Wearable robots (WRs) might interact with humans in a similar manner to teammates to accomplish specific tasks together. However, the available data on WR user experience (UX) studies are limited, especially during the prototyping phase. Therefore, this study aims to examine the overall experience of WRs during the prototyping phase based on an exploratory research model. This theoretical model considered usability, hedonic quality, and attitude toward using WRs as key factors in explaining and predicting overall experience. To test the hypotheses inherent in the research model, quantitative empirical research was conducted and the data were analyzed by partial least squares structural equation modeling (PLS-SEM). The results from the PLS-SEM analysis revealed the significance level of correlations between the latent variables in the research model. The exploratory research model was able to explain up to 53.2% of the variance in the overall experience of using WRs, indicating medium predictive power. This research develops a new quantitative empirical research model that can be used to explain and predict the overall experience of interactive products such as WRs. Meanwhile, the model is needed during WR testing in the prototype phase.
Physical computing for materials acceleration platforms

A ''technology lottery'' describes a research idea or technology succeeding over others because it is suited to the available software and hardware, not necessarily because it is superior to alternative directions--examples abound, from the synergies of deep learning and GPUs to the disconnect of urban design and autonomous vehicles. The nascent field of Self-Driving Laboratories (SDL), particularly those implemented as Materials Acceleration Platforms (MAPs), is at risk of an analogous pitfall: the next logical step for building MAPs is to take existing lab equipment and workflows and mix in some AI and automation. In this whitepaper, we argue that the same simulation and AI tools that will accelerate the search for new materials, as part of the MAPs research program, also make possible the design of fundamentally new computing mediums. We need not be constrained by existing biases in science, mechatronics, and general-purpose computing, but rather we can pursue new vectors of engineering physics with advances in cyber-physical learning and closed-loop, self-optimizing systems. Here we outline a simulation-based MAP program to design computers that use physics itself to solve optimization problems. Such systems mitigate the hardware-software-substrate-user information losses present in every other class of MAPs and they perfect alignment between computing problems and computing mediums eliminating any technology lottery. We offer concrete steps toward early ''Physical Computing (PC) -MAP'' advances and the longer term cyber-physical R&D which we expect to introduce a new era of innovative collaboration between materials researchers and computer scientists.