Reviewerly/exHarmony huggingface dataset:
Person Re-Identification With Character-Illustration-Style Image and Normal Photo

Retrieving the given objects hidden amidst the gallery set is important for public safety and decision-making. Heterogeneous pedestrian retrieval (person re-identification) aims to retrieve the same person images from different modality set for identification. To address this problem, we contribute a new character-illustration-style image and normal photo pedestrian re-identification dataset (CINPID), which is collected on campus. The CINPID dataset includes two modalities, i.e., normal photos captured by one camera and character-illustration-style images drawn by the painter. To handle the problem of pedestrian retrieval with character-illustration-style images and normal photos, we propose a semi-coupled mapping and discriminant dictionary learning (SMD <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> L), which can learn a semi-coupled mapping matrix and dictionary pair from heterogeneous samples. With the learned semi-coupled mapping matrix, the differences between heterogeneous data can be reduced to some extent. Experimental results on the new CINPID dataset show that our approach outperforms the compared competing methods.
Lessons from the field: What researchers learned from evaluating ICT platforms for rural development and education

The field of information and communication technology for development is a field constantly changing as new ICT tools emerge and new knowledge gained by field researchers while performing their duties. The research problem: The field ICT field is littered with examples of failed projects because field researchers did not know the best way to carry out their work. The paper is about knowledge imparted by six monitoring and evaluation field researchers after working for almost eight years, from 2010 to 2018, in ICT platforms projects. These platforms were deployed across South Africa’s remote rural areas. The work followed interpretivism as its philosophy and was underpinned by qualitative research methods. Written projects reports, face-to-face interviews and questionnaires were used to collect data and also to triangulate the findings. The participatory evaluation formed the basis for the complete understanding of the finding. (i) Planning; (ii) Deployment; and iii) Usage were found to be critical elements for a successful implementation of ICT platform. Although well planned, numerous lessons were still learned for the benefit of future projects.
Note on the Reliability of Biological vs. Artificial Neural Networks

Various types of neural networks are currently widely used in diverse technical applications, not least because neural networks are known to be able to “generalize.” The latter property raises expectations that they should be able to handle unexpected situations with similar success than humans. Using fundamental examples, we show that in situations for which they have not been trained, artificial approaches tend to run into substantial problems, which highlights a deficit in comparisons to human abilities. For this problem–which seems to have obtained little attention so far–we provide a first analysis, based on simple examples, which exhibits some key features responsible for the difference between human and artificial intelligence.
Multi-Scale Attention Network for Image Cropping

Automatic image cropping is a completely practical but challenging task which aims to improve the aesthetic quality of an image by removing irrelevant areas. Most previous image cropping methods ignored compositional relationships among different regions of a given image. Global compositional relationships are extremely important for cropping models to decide whether to reserve a certain object of an input image. In this work, we propose a multi-scale attention network (MSANet) to address this issue. We employ three plug-and-play attention modules to catch the context on three different scales. The multi-scale attention (MSA) module ensures that our model perceives objects of different sizes and preserve needed areas. Moreover, we design a border-reserved grid anchor based formulation to better handle the situations where the subjects are at the edge of input images. The cosine similarity loss function is also utilized to acquire stable results. Extensive quantitative and qualitative experimental results show that our model is well aware of the compositional relationships of images. Compared to existing works, our multi-scale attention network achieves state-of-the-art performance with less time and lighter weights.
GraphCGAN: Convolutional Graph Neural Network with Generative Adversarial Networks

Graph convolutional networks (GCN) achieved superior performances in graph-based semi-supervised learning (SSL) tasks. Generative adversarial networks (GAN) also show the ability to increase the performance in SSL. However, there is still no good way to combine the GAN and GCN in graph-based SSL tasks. In this work, we present GraphCGAN, a novel framework to incorporate adversarial learning with convolution-based graph neural networks, to operate on graph-structured data. In GraphCGAN, we show that generator can generate topology structure and features of fake nodes jointly and boost the performance of convolution-based graph neural networks classifier. In a number of experiments on benchmark datasets, we show that the proposed GraphCGAN outperforms the baseline methods by a significant margin.
Catalog of Question & Answering Online Services built on Embeddings Platform

When a Legal Researcher runs a natural language question query through the system, they expect a relevant, concise answer or answers to the question and not entire document hits that happen to have the keywords from their question. An answer to the question is typically found within a passage or paragraph of a document and not the entire document. Answer passages have greater value when they contain citable authority and answer the key legal points of their question. Further, good answers to a question can originate from diverse methodologies, namely, semantic search, lexical search and curated answers.In this work, we briefly examine the high-level architecture of the Question & Answering system that powers the Lexis+ Answers. We will take a closer look at the online components that make query engagement decisions, then orchestrate calls to multiple other components like query intent recognition, embeddings and keyword search and retrieval components and comprehension-based rankers. We will see how aggregating these answers from various sources improves the likelihood of having the right answer to the particular question within the answer set. Some questions benefit from lexical searches, some other questions benefit from semantic searches and still other questions benefit from old fashioned human curated answers. We will demonstrate how this architecture was achieved with various microservices that allow modularized, autonomous and rapid development with integration to Machine Learning systems. Each microservice can be easily and independently deployed, maintained and scaled as demand dictates.
THE EFFECTS OF WORD SQUARE MODEL ON SOCIAL STUDIES CONTENT LEARNING OUTCOMES OF 4th GRADE STUDENTS ELEMENTARY SCHOOL

This study aims to determine the effect of the Word Square learning model on the learning outcomes of the fourth grade students of SDN cluster II of Lingsar district in the 2020/2021 academic year. The type of research used is experimental research with a nonequivalent control group design research design type quasi experiment. The population in the study were students of class IV SD Gugus II, Lingsar District, totaling 108 students, while the sample was grade IV SD Gugus II SD Lingsar District, totaling 85 students. Data collection methods used in this study were documentation and questionnaires, while the instruments used were social studies learning outcomes documents and Word Square model questionnaires. The data analysis technique used to determine the effect of the Word Square model on learning outcomes is the normality test, linearity test, and simple regression test. After the questionnaire results and learning outcomes were analyzed using the normality and linearity test, the data were normally distributed and had a linear relationship. After going through the prerequisite test, then a simple regression test was carried out with the results obtained by the sig value. of 0.000 &lt;0.05 or using a comparison of the value of tcount (3.974)&gt; t table (1.988) which means that Ha is accepted and H0 is rejected so that there is an effect of the Word Square learning model on the learning outcomes of social studies content for fourth grade students of SDN cluster II, Lingsar sub-district, 2020/2021 school year
Pelatihan Model Pembelajaran Berbasis Tutor Balibolae Guna Percepatan Pemberantasan Buta Aksara

Abstract . The aim of the community service is to provide training in Balibolae tutor-based learning models to accelerate illiteracy eradication. The methods used are 1) coordination with the village head of Panaikang, 2) Forming learning citizen groups, 3) Recruiting BalibolaE tutors from neighboring learning residents, 4) Tutor training balibolaE, 5) Delivery of balibolaE tutor training materials, 6) Providing balibolaE tutor training, 7) BalibolaE tutor training closing activities. The results of the activity: (a) The implementation of BalibolaE tutor-based learning model training has been able to improve the performance of the reading, writing and counting skills of learning citizens in Panaikang Village, Pangkep Regency (b) BalibolaE tutor-based accelerated learning model objectives, objectives, activity systematics and time allocation, place and character of tutor participants, tutor requirements, tutor learning approaches, tutor teaching methods, teaching methods and learning materials.
The Effect of Online Learning during Covid19 on Students‘ Performance on Reading Skill

Abstract— This study was to know the effect of online learning during covid19 pandemic on students’ performance on reading skill. The data was taken by google form questionnaire for online survey and  multiple choice and essay test for reading skill. This research used mix method. The sample was 35 students Informatics Engineering Computer Science Faculty. The result showed that the average score on reading multiple choice was 55.14 or considered low. The average score on reading essay test was 71.57 or considered middle.
The Roles of Digital Exhibition in Enhancing Immersive Experience and Purchase Intention

Museums in modern society serve to a broader public than their early predecessors. In response to such transition, many art museums now open digital exhibitions to provide immersive experience and maximize user interaction. This paper focuses on two such features – animated image and storytelling description – and their effect on museum visitors’ immersive experience, which in turn influences willingness-to-pay price premium (WTP). Our results indicate that animated images and storytelling description have both individual and interaction effects on immersive experience as well as WTP. This paper contributes to both the IS literature and practice by providing a systematic understanding of how digital exhibition features enhance museum visitors’ immersive experience and purchase intention.
LibGuides: ACRL, IPEDS, and National Surveys: Archived Report Documents

Support guides and tutorials for PALNI libraries completing the ACRL, IPEDS, and other national surveys.
‘Un mare di parole’ tra Assoli e Udunda Indina

This paper aims to reconstruct the two plays Assoli (1977) and Udunda Indina (1980), emblematic productions by Leo de Berardinis and Perla Peragallo, staged during the most daring phase of their research. Both characterized by an obsessive taste for the manipulation of idioms and for the shredding of meanings, the two theatrical performances – alongside the diagnostic and cathartic function of the word (ironically vilified among wordplays and puns) – bring to light the experimental potential of a very distinctive “teatro di parola”. These verbal phenomena, in terms of scenical praxis, reflect the intimate deconstructive project pursued, in those same years, not only by the two actors theirselves, but also by the wider framework of the so-called Italian “Nuovo Teatro”. The analysis is conducted on the basis of archival sources, direct testimonies, press clippings and, from a methodological point of view, by adopting studies of an interdisciplinary nature: both theatrical and linguistic.
LSTM based Online Public Opinion Rumors Recognition Method

This paper tries to judge the true and fake information of a large number of public opinions in the network, keep the truth, remove the fake, and provide reference for the government public opinion workers to judge rumors.data such as rumor and non-rumor topics, replies and other data of microblog were collected as data sets, and then programmed with paddle fluid API, and the recurrent neural network model was configured.The data set was used for model training, and finally the model analysis and detection were carried out.Through LSTM model training and data analysis, rumor events in public opinion can be digitized, and fake information feature set in text can be mined, so as to make better rumor recognition and make public opinion workers better control rumors.
The Implementation of Task Based Language Teaching in Teaching Reading to The Eleventh Grade at SMAN 7 Banjarmasin

This research is about The Implementation of Task Based Language Teaching in Teaching Reading to The Eleventh Grade at SMAN 7 Banjarmasin. The objectives of this research are to find out how the implementation of task based language teaching in teaching reading to the eleventh grade at SMAN 7 Banjarmasin and students’ responses towards the implementation of task based language teaching in teaching reading. In this research, the researcher used descriptive qualitative method. The technique of collecting data were observation, interview, and documentation. The subject of this research is a teacher who uses the task based language teaching in teaching reading and the students of eleventh grade there are MIPA 2 and MIPA 3 at SMAN 7 Banjarmasin. The results showed that from five times observation, the researcher is very like of English subject matter, judging from the attitude and response of students is very good when learning. First teacher explain theory about the material, then she divides 3 students in one group, first person retell thesis, second person about argument, third person about recommendation, they will present their topic in front of the class. Teacher select the task/ take the material from curriculum 13 which is the material taught can browsing from news paper or from news in television. Teacher develop the material by speaking performance (retell the text). Teacher uses slide as a media in the learning process which uses English language completely. In observation technique, the teacher sometimes convey the material using English language about 50% and sometimes review the material before. At the same time, the teacher often summarizes course content covered direct and student’s preparation for next class. Students’ responses towards the implementation of task based language teaching in teaching reading are good, they feel suitable and comfortable learning reading with the approach used by the teacher. In addition, teachers’ explanation is very clear for students, eventhough they had difficulties in the vocabulary and grammar. Teacher roles in TBLT those are: selector and sequencer of tasks, preparing learners for tasks, acts as a language adviser, and acts as chaiperson. And learner roles in TBLT those are: as group participant, as monitor, as risk-taker, and innovator.
The Origins of Agile and Iterative Methods

Agile methods and iterative and incremental development (IID) have attracted significant attention in recent years. They are widely applied in many industries quite dissimilar to their origins in manufacturing and information system development. Agile methods and IID are part of a rich stream of research and practice that can be traced back to the 1930’s, but much of this history has been lost in recent rhetoric about these methods. The purpose of this paper is to consolidate the many streams of research and practice that have contributed to agile and IID forms of project management. This paper presents a systematic literature review connecting the fragmented streams of academic and applied literature that have historically contributed to the development of agile methods and IID, allowing a deeper view of past recent iterations of how these methods are commonly represented. We argue that although the roots of the currently popular approaches can be traced back to 1930s’, these have been mostly disassociated from present agile and IID practices due to a combination of barriers to transfer of knowledge such as divergent use of language and terminology between fields, attention decay, and the current industry-led narrative. Mapping and clarifying these historical links provides a useful perspective on contemporary project management practice and opens further possibilities for deeper research into agile project management methods.
Learning Common and Transferable Feature Representations for Multi-Modal Data.

LiDAR sensors are crucial in automotive perception for accurate object detection. However, LiDAR data is hard to interpret for humans and consequently time-consuming to label. Whereas camera data is easy interpretable and thus, comparably simpler to label. Within this work we present a transductive transfer learning approach to transfer the knowledge for the object detection task from images to point cloud data. We propose a multi-modal adversarial Auto Encoder architecture which disentangles uni-modal features into two groups: common (transferable) features, and complementary (modality-specific) features. This disentanglement is based on the hypothesis that a set of common features exist. An important point of our framework is that the disentanglement is learned in an unsupervised manner. Furthermore, the results show that only a small amount of multi-modal data is needed to learn the disentanglement, and thus to transfer the knowledge between modalities. As a result we our experiments show that training with 75% less data of the KITTI objects, the classification accuracy achieved is of 71.75%, only 3.12% less than when using the full data set. The implications of these findings can have great impact in perception pipelines based on LIDAR data.
21세기의 안락의자 탐정: 제프리 디버의 ‘링컨 라임’에 나타나는 기술과 네트워크

This paper aims to study the detective novel in the age of posthumanism, focusing on the formation of network between human and nonhuman elements in the novel. Jeffery Deaver’s Lincoln Rhyme is an armchair detective, as Sherlock Holmes of the 21st century. Rhyme, a quadriplegic detective, has to rely on various machines and computer systems for movement, communication, and criminal investigation. He is a cyborg as a hybrid of human and machine. His body is combined with technology and extended to the outside. Rhyme’s network for detecting crimes is linked to human and nonhuman factors including machines, evidences, and even information, all of which interact as actors. Actor-Network Theory(ANT) places humans and nonhumans on an equal footing, as the products of society by techno-science. As a cyborg, an actor, and a translator of ANT, Rhyme represents an intersection of heterogeneity to blur the dichotomous boundary between human and nonhuman. It provides a new point to reconsider thoughts that we have had about humanity and the relationship between human and nonhuman.
Do Transformers Understand Polynomial Simplification

Recently researchers have demonstrated that Transformers can be trained to learn symbolic tasks such as solving integration and differential equations in an end-to-end fashion. In these setups, for an input symbolic expression, the Transformer predicts the final solution in a single step. Since such tasks may consist of a sequence of logical steps, question remains whether such networks have understood and learnt individual steps to reach the solution. To take a deeper look, we consider the task of polynomial simplification. Polynomials can be written in a simple normal form as a sum of monomials which are ordered in a lexicographic order. For a polynomial which is not necessarily in this normal form, a sequence of simplification steps is applied to reach the fully simplified (i.e., in the normal form) polynomial. For this task, we describe a synthetic Polynomial dataset generation algorithm which generates polynomials with unique proof steps. Then, we conduct an extensive analysis of the Transformer’s abilities to learn the polynomial simplification task along different dimensions.
Sistem Informasi Produksi Barang Pada PT ABC

This study discusses the manufacture of goods production applications in manufacturing companies. Problems that arise include the difficulty of obtaining information relating to the process of production of goods from the purchase of raw materials, the use of raw materials for production, sale of finished goods and information on stock of raw materials for production and finished goods. One of the main objectives of this study is to overcome the deficiencies mentioned above in terms of presenting information and reporting the production process of goods. This research method uses waterfall. The results of research in the form of application of the production of goods provide one alternative solution to the availability of information systems for the production of goods for interested parties
Applied Linguistics-Digital Platforms Whatsapp and Telegram Users Behavioral Tendency

The present research study aimed to deliver an overlook based on the behavioral tendency of the users of WhatsApp and Telegram when applying linguistics into communicative messages. Through a particularized literature framework, the research study has displaced some notable influencer tech-application factors and applied linguistics specificness that are currently characterizing WhatsApp and Telegram Interpersonal Communication backgrounds. The research study was conducted by following suitable research protocols. This prospect involved the revision of specialized materials such as publications and webpages focused on the subject presented. Database from a survey piloted among university students formed part of this research criterium. The survey served to measure public’ level of favoritisms applied linguistics text, voice, and picture WhatsApp and Telegram messages. This study is very useful for scholars interested in Linguistics and Applied Linguistics Studies .
Practice of Autonomous Learning Mode in the Course of “Web Design and Production”

&lt;p&gt;The autonomous learning model is one of the dominant learning models in the era of “Internet + education”. It is sought after by teachers and students in universities. The learning style of college students is shifting from passive to active learning. And the traditional teaching model in the course of “Web Design and Production” is easy to cause the problems of poor learning enthusiasm of students and low teaching effect. According to the characteristics of the course, this article explores how autonomous learning can stimulate students’ learning motivation and improve the learning effect of this course. It also designs a corresponding autonomous learning instructional model to mobilize students’ learning enthusiasm, and to improve the instructional efficiency of the course.&lt;/p&gt;