Reviewerly/exHarmony huggingface dataset:
MadPipe: Memory Aware Dynamic Programming Algorithm for Pipelined Model Parallelism

The training phase in Deep Neural Networks (DNNs) is very computationally intensive and is nowadays often performed on parallel computing platforms, ranging from a few GPUs to several thousand GPUs. The strategy of choice for the parallelization of training is the so-called data parallel approach, based on the parallel training of the different inputs (typically images) and the aggregation of network weights with collective communications (AllReduce operation). The scalability of this approach is limited both by the memory available on each node and the networking capacities for collective operations. Recently, a parallel model approach has been proposed (PipeDream, Gpipe), in which the DNN weights are distributed and images are trained in a pipeline/stream manner over the computational nodes. In this paper, we formalize in detail the optimization problem associated with the placement of DNN layers onto computation resources when using pipelined model parallelism, and we derive a dynamic programming based heuristic, MadPipe. We show through extensive simulations based on realistic networks that MadPipe significantly improves the performance of the pipelined parallel model approach compared to PipeDream.
Review of data scraping and data mining research

Abstract In recent years, with the advent of the era of big data, the importance of data has also become more prominent. This article introduces the characteristics of today’s Internet data from the background of big data, and the main method of data scraping-crawlers. In addition, the concepts of data mining, as well as hotspots and trends are briefly introduced. Highlights the importance of data scraping and data mining in today’s Internet field.
Cost Optimisation for Wheelchair Redesign

Requirements of users in developing countries differ from those of developed countries. This difference can be seen through wheelchair displacement in infrastructures that don’t meet international standards. However, developing countries are obliged to purchase products from developed countries that don’t necessarily meet all user’s requirements. The modification of these requirements will generate disruption on all the supply chain. This paper proposes a model for optimising the cost of requirement modification on the supply chain and seeks to evaluate the introduction of a new requirement on an existing product/process. This model is adapted to the redesign and development of products, such as wheelchairs, satisfying specific Algerian end-user requirements.
Socioeconomic Determinants of Farmers using Social Network in Advancing Agribusiness in Anambra State, Nigeria

The study examined socioeconomic determinants of farmers’ using social network in advancing agribusiness in Anambra State, Nigeria. Specifically, it described the socio-economic characteristics of the respondents, identified various social networks available and level of usage among the respondents, determined the influence of socio-economic factors on net marketing income among the respondents and identified the challenges of social network in advancing agribusiness by the respondents. Multi-stage, purposive and simple random methods were used to select one hundred and sixty respondents for the study. Data were collected from primary source. Primary data were collected using structured questionnaire. Collected data were analyzed by means of descriptive statistics, and multiple regression techniques. From the result, findings on the socioeconomic characteristics showed that farmers are relatively young, energetic, are in computer age and able to use phone and other devices to advance agribusiness in their area and beyond for optimum profit. Farmers are educated, married and there is male dominance in the use of social network in advancing agribusiness in the study area. The result showed that whatsapp, facebook, you tube and messenger were mostly used in advancing agribusiness in the study. Marital status, education, household size, means of delivery and marketing cost influenced net marketing income earned by the respondents. Lack of awareness, poor assessment of social network and costly charge of data when accessing the social network were perceived as the main constraints affecting agribusiness. Measures will be taken by appropriate authorities to reduce the tariff of data in accessing social network. Farmers should be trained more on the use of social network to advance their produce to the world. Network providers should of necessity improve their services to enable social network users to be effective. Keywords: Determinants, Social network, Agribusiness.
Analysis of Sentences for Deaf Children in Primary School

This study aims to find out the ability to compose sentences of deaf children in outstanding schools. This study was conducted using qualitative case study method in the form of interviews and documentation studies of students' learning outcomes, interviews were conducted on 14 elementary school teachers. This research was conducted by two schools, namely in SLB-B YRTRW and SLB YAAT surakarta. The results showed that deaf children have problems in composing sentences and understanding the meaning of sentences. This is due to, 1) the receptive ability of children who are hampered by hearing ability, 2) the interest in learning deaf children in indonesian language is low, and 3) the use of non-standard sign language systems in communication between deaf children in activities outside the classroom. According to the class teacher, most deaf children also think that indonesian language is a difficult and boring lesson. Therefore, teachers need to increase innovation and creativity in the delivery of Indonesian language materials to be more interesting, either by using media or props that are fun and worth using to increase the motivation of learning deaf children and help the understanding of materials composing Indonesian sentences.
A multi-layered bigraphical modelling approach for context-aware systems

There exist several approaches proposed for building Context-Aware Systems (CAS). However, due to the continually changing environment, the large number of interrelated components, complexity and diversity of application domains make the modelling of context-aware systems a particularly challenging task. To address the increasing complexity of the modelling: i) It is critical to take into account the importance of the environment (operational context); and ii) rely on software engineering concepts such as abstraction and modularity in order to reduce the level of complexity. Also, a context-aware system may require intelligence and autonomy. These naturally lead us to apply intelligent agent-based engineering. This work introduces a formal layered design approach that combines intelligent control of multi-agent systems and bigraph's rigor to model context-aware systems. Bigraphical reactive systems are particularly compelling for their capacity to specify, simultaneously, the physical and logical distribution of system components and their interconnections using two distinct structures; that is: place graph and link graph. While the behaviour and dynamic evolution are expressed using defined reaction rules, and as a last step, the bigraph specifications are coded in the Maude language to allow their execution and the verification of their validity.
Test of Quantumness with Small-Depth Quantum Circuits

Recently Brakerski, Christiano, Mahadev, Vazirani and Vidick (FOCS 2018) have shown how to construct a test of quantumness based on the learning with errors (LWE) assumption: a test that can be solved efficiently by a quantum computer but cannot be solved by a classical polynomial-time computer under the LWE assumption. This test has lead to several cryptographic applications. In particular, it has been applied to producing certifiable randomness from a single untrusted quantum device, self-testing a single quantum device and device-independent quantum key distribution. In this paper, we show that this test of quantumness, and essentially all the above applications, can actually be implemented by a very weak class of quantum circuits: constant-depth quantum circuits combined with logarithmic-depth classical computation. This reveals novel complexity-theoretic properties of this fundamental test of quantumness and gives new concrete evidence of the superiority of small-depth quantum circuits over classical computation.
Cross-cultural UX pedagogy: a China–US partnership

The recent emergence of new undergraduate and graduate design programs with a focus specific to User Experience (UX) offers new opportunities to engage with the complexity of these educational practices.In this paper, we report on a series of ten interviews with students and faculty to describe cross-cultural connections between two UX-focused programs, one in China and one in the United States.Our study includes the perspectives of students who engaged in intercultural UX experiences, as well as the perspectives of the faculty who designed those student experiences through an intercultural partnership.We report on how each program was created, developed, and iterated upon, describing program goals and student experiences across both programs from student and instructor perspectives.We demonstrate the complexity of UX educational experiences on an international scale, concluding with opportunities for intercultural engagement and the potential for links among education, profession, culture, and pedagogy.
Long-Range Augmented Reality with Dynamic Occlusion Rendering

Proper occlusion based rendering is very important to achieve realism in all indoor and outdoor Augmented Reality (AR) applications. This paper addresses the problem of fast and accurate dynamic occlusion reasoning by real objects in the scene for large scale outdoor AR applications. Conceptually, proper occlusion reasoning requires an estimate of depth for every point in augmented scene which is technically hard to achieve for outdoor scenarios, especially in the presence of moving objects. We propose a method to detect and automatically infer the depth for real objects in the scene without explicit detailed scene modeling and depth sensing (e.g. without using sensors such as 3D-LiDAR). Specifically, we employ instance segmentation of color image data to detect real dynamic objects in the scene and use either a top-down terrain elevation model or deep learning based monocular depth estimation model to infer their metric distance from the camera for proper occlusion reasoning in real time. The realized solution is implemented in a low latency real-time framework for video-see-though AR and is directly extendable to optical-see-through AR. We minimize latency in depth reasoning and occlusion rendering by doing semantic object tracking and prediction in video frames.
Rolling the Dice on CMC in the Time of Covid-19

nication. These describe -respectively -the speed of interaction, the different possible ways of communicating, to which degree multiple communications may be maintained at once, the extent of preparation possible before communicating, and the ability to re-access the communication later . Salient for this paper is the extrapolation that virtual tabletops -providing the affordances of both video chat and written chat -have the potential to be equal to face-to-face communication in terms of immediacy of feedback and symbol variety and superior to that medium in terms of parallelism, rehearsability, and reprocessability .
The importance of e-learning and e-learning resources in individualized learning

This article presents the importance of e-learning and e-learning resources in individualized education, the experience of foreign countries in the use of e-learning resources in the process of individualization of education, in particular, the experience of countries such as USA, UK, Finland, Australia. In addition, e-learning should include new e-technologies in the process of transferring knowledge and skills, including all methods and tools of education provided by computers and the Internet, web-based e-learning processes. units, computer training, learning through virtual environments and multimedia content, access to text, animation, video and audio streams, e - learning distance learning, distance learning, classroom and extracurricular activities to cover different forms, to work together with different interactive objects, to rely on the electronic environment in modern school teaching and management.
Detecting Adversarial Examples by Measuring their StressResponse

Machine learning (ML) and deep neural networks (DNNs)have achieved great success in a variety of application domains,however, despite significant effort to make these networks robust,they remain vulnerable to adversarial attacks in which input thatis perceptually indistinguishable from natural data can beerroneously classified with high prediction confidence. Works ondefending against adversarial examples can be broadly classified ascorrecting or detecting, which aim, respectively at negating theeffects of the attack and correctly classifying the input, ordetecting and rejecting the input as adversarial. In this work, anew approach for detecting adversarial examples is proposed. Theapproach takes advantage of the robustness of natural images tonoise. As noise is added to a natural image, the predictionprobability of its true class drops, but the drop is not sudden orprecipitous. The same seems to not hold for adversarial examples.In other word, the stress response profile for natural images seemsdifferent from that of adversarial examples, which could bedetected by their stress response profile. An evaluation of thisapproach for detecting adversarial examples is performed on theMNIST, CIFAR-10 and ImageNet datasets. Experimental data shows thatthis approach is effective at detecting some adversarial exampleson small scaled simple content images and with little sacrifice onbenign accuracy.
Data Expression and Protection of Intellectual Property Education Resources Based on Machine Learning

With the improvement and growth of instructional informatisation, the contradiction between the open supply of academic resources, information expression, and mental property safety is turning into greater acute. Remedying the relationship between the two is very necessary for the overall performance of records expression of academic assets and the advent of true surroundings for mental property protection. The safety of mental property rights is to shield the rights and pursuits of know-how owners, defend the strength of information producers to produce knowledge, and defend the supply of academic sources sharing. The data expression and protection of intellectual property education resources based on machine learning is a kind of protection tool for the intellectual property of education resources developed using the characteristics of automation, real-time monitoring, and growth of machine learning. It can prevent web crawlers from harming e-commerce websites, prevent them from stealing the intellectual property of e-commerce websites, and analyse web crawlers that visit websites to prevent important website data from being stolen by them. From this point of view, based on the relationship between the fact expression of instructional sources and the safety of mental property rights, this paper advocates to promote the records expression and safety of mental property rights of academic sources from a couple of perspectives.
Impact of HEC Digital Library on Collection Management in University Libraries of Pakistan

This study is an attempt to exam the impact of HEC Digital Library on collection management activities in the university libraries of Pakistan through literature review. Collection management is an umbrella term which contain different segment including collection development, financial issues, and intellectual property rights. The study indicates that HEC Digital Library has placed a good impact on the collection development, selection and acquisition of library materials, financial issues, copyright and related issues and collaborative programs.  The study further found that HEC Digital Library has placed a radical impact on the collection management of university libraries in Pakistan.
Diffusion Priors In Variational Autoencoders.

Among likelihood-based approaches for deep generative modelling, variational autoencoders (VAEs) offer scalable amortized posterior inference and fast sampling. However, VAEs are also more and more outperformed by competing models such as normalizing flows (NFs), deep-energy models, or the new denoising diffusion probabilistic models (DDPMs). In this preliminary work, we improve VAEs by demonstrating how DDPMs can be used for modelling the prior distribution of the latent variables. The diffusion prior model improves upon Gaussian priors of classical VAEs and is competitive with NF-based priors. Finally, we hypothesize that hierarchical VAEs could similarly benefit from the enhanced capacity of diffusion priors.
ANALISIS VARIASI BAHASA DALAM BENTUK ALIH KODE PADA MAHASISWA PRODI PENDIDIKAN BAHASA INDONESIA, UNIVERSITAS DHARMAS INDONESIA

This research is motivated by the language transition that occurs in both formal and informal situations. In this research, the focus of the study is code switching on FKIP UNDHARI students of Indonesian Language Study Program. This study aims to describe the code switching in FKIP students of the Indonesian Dharmas University (UNDHARI) Indonesian Language Education Study Program in communication outside the classroom and to describe the factors that influence code switching in communication outside the classroom. This study uses qualitative methods which are descriptive research and tend to use analysis. The data obtained in this study were obtained from student speech that occurred in the form of code switching in communication, both in formal and informal situations. The technique in collecting data in this study is to use observation, interview, and note-taking techniques. The subjects in this study were students of Indonesian language education courses, faculty of education and education, dharmas university of Indonesia. The results showed that: (1) the form of code switching for Indonesian Language Study Program students. the factors causing code switching for students who speak Minang, because speakers deliberately switch to Indonesian so that the intimacy between the speaker and the interlocutor is more established. The transition is done by speakers consciously. The occurrence of code switching in the conversation is caused by the speaker fator and the interlocutor. Listeners or opponents said opponents. The code switching is included in the type of internal code switching (2) the form of code transfer The presence of a third person. the presence of a third person or another person who does not have the same language background as the language being spoken by the speaker and the interlocutor causes code switching. The presence of a third person determines the language changes and variants that will be used.
Estimation of 3D human pose using prior knowledge

Estimating three-dimensional (3D) human poses from the positions of two-dimensional (2D) joints has shown promising results. However, using 2D joint coordinates as input loses more information than image-based approaches and results in ambiguity. To overcome this problem, we combine bone length and camera parameters with 2D joint coordinates for input. This combination is more discriminative than the 2D joint coordinates in that it can improve the accuracy of the model’s prediction depth and alleviate the ambiguity that comes from projecting 3D coordinates into 2D space. Furthermore, we introduce direction constraints, which can better measure the difference between the ground truth and the output of the proposed model. The experimental results on the Human3.6M show that the method performed better than other state-of-the-art 3D human pose estimation approaches. The code is available at: https://github.com/XTU-PR-LAB/ExtraPose/.
ReaSCAN: Compositional Reasoning in Language Grounding

The ability to compositionally map language to referents, relations, and actions is an essential component of language understanding. The recent gSCAN dataset (Ruis et al. 2020, NeurIPS) is an inspiring attempt to assess the capacity of models to learn this kind of grounding in scenarios involving navigational instructions. However, we show that gSCAN's highly constrained design means that it does not require compositional interpretation and that many details of its instructions and scenarios are not required for task success. To address these limitations, we propose ReaSCAN, a benchmark dataset that builds off gSCAN but requires compositional language interpretation and reasoning about entities and relations. We assess two models on ReaSCAN: a multi-modal baseline and a state-of-the-art graph convolutional neural model. These experiments show that ReaSCAN is substantially harder than gSCAN for both neural architectures. This suggests that ReaSCAN can serve as a valuable benchmark for advancing our understanding of models' compositional generalization and reasoning capabilities.
Research on College English Teaching Model Based on Chaoxing Platform and Dingding Live Broadcast

Under the influence of coVID-19, colleges and universities across the country of PRC have delayed the start of school term. Colleges and universities across the country have responded to the call of the Ministry of Education to suspend classes in classrooms and carried out online teaching based on Internet tools. College English course is an important course to cultivate the comprehensive ability of contemporary college students, and online teaching is of great significance to the smooth implementation of college English teaching during the epidemic period. This paper elaborates on the college English teaching model based on Chaoxing platform and Dingding live broadcast. Taking College English Ⅱ as an example, this paper builds an online learning center with the help of the Chaoxing platform and uses the Dingding platform to carry out online broadcast and evaluation of the teaching effect. After a semester of exploration and practice, the online teaching has acquired good teaching results and achieved the expected teaching objectives.
Compressive strain measurements in porous materials using micro-FE and digital volume correlation

A local Digital Volume Correlation (DVC) based measurement of displacements and strains of synthetic bone samples under an ex-situ compression using the time-lapsed imaging procedure was performed in the present study. Micro Finite Element (µFE) model was used to simulate the compression of synthetic bone samples with experimental-based ( ExBC), and DVC interpolated displacement boundary conditions ( IPBC). The obtained µFE nodal displacement data compared with DVC. A good match of displacement patterns and correlation values of R 2 = 0.85–0.99 and RMSE ≤ 12 µm was observed for the IPBC predicted displacements against DVC displacements. However, the ExBC provided a good correlation of transverse displacements only (U: R 2 = 0.85–0.99 and V: R 2 = 0.77–0.99). The average axial displacement of ExBC matched well with DVC, and a qualitative and quantitative understanding of the axial displacement was possible with ExBC. A moderate agreement of axial strain patterns was observed between DVC and IPBC, even though a good agreement on displacement was observed. The ExBC showed a higher axial strain compared to DVC in all samples. The transverse strains varied between the same extreme values for both boundary conditions and within the DVC range.