Reviewerly/exHarmony huggingface dataset:
Penerepan Metode Waterfall Dalam Pembuatan Sistem Informasi Akuntansi Persediaan Barang Sekali Pakai Pada Dinas Kesehatan Provinsi Kalimantan Barat

Information technology is now showing many major changes as well as its influence, in the accounting field in particular it can help company management as well as in government, one of the information system activities that can be facilitated is the disposable inventory system. The West Kalimantan Provincial Health Office realized that the recording of inventory of disposable goods was still done manually or not computerized, thus allowing for errors in recording incoming goods, outgoing goods, stock of goods, and making reports. The research method used is data collection, observation, interviews, literature study, system design, analysis of software requirements for system design and software, implementation and unit testing. The design of this website is built using the waterfall model development method and the PHP programming language and utilizes the MySQL database as a database server. The results of this study are the right solution to overcome the problems that exist in the West Kalimantan Provincial Health Office. With a computerized system will be very helpful in supporting all activities so as to produce effective and efficient work.
Penerapan XP Dalam Pemenuhan Kebutuhan Sistem Yang Dinamis Pada Rancang Bangun Sistem Informasi Akuntansi Pendapatan Penjualan

Gathering system requirements is the most important thing in an information system development stage. As an initial stage it also determines the success of the next stage. But often when the system development process takes place there are changing needs so that it can make the system project go round and round at just one stage and the system fails to build. For this reason, an agile approach is used, namely Extreme Programming (XP), which by using XP the dynamic system needs can be better accommodated. One system that has dynamic needs is Sales Revenue SIA at a printing company in Pontianak City. With a small project team, XP is the main choice to be implemented in the system development process. As a result, a Sales Revenue Accounting Information System is obtained which has a user acceptance percentage of 96%.
Pengaruh Impelementasi Sistem Informasi Akuntansi Manufaktur Dalam Zahir Accounting Versi 6

With increasingly fierce competition and business development as well as management's need for accurate accounting information as a basis for decision making, the manual system is no longer effective. However, it cannot be denied that there are still many entrepreneurs who still use manual recording systems due to lack of knowledge about computerized systems. So, the authors observe the accounting data processing process on CV. Muda Teknindo who still uses manual recording where the previous author made observations on the existing system at the lathe workshop and then then applied it to a computerized accounting information system using Zahir Accounting Version 6. The purpose of this study was to implement an Accounting Information System using the Zahir Accounting Version 6 application in beauty salons that still use Microsoft excel in order to achieve accounting reports according to to applicable accounting standards. This research was compiled for approximately one month based on transaction data that occurred in March 2022.&#x0D; Keywords:&#x0D; Accounting Data Processing, Lathe Workshop, Zahir Accounting Verson 6
Vision AI: A Deep Learning-Based Object Recognition System for Visually Impaired People Using TensorFlow and OpenCV

Abstract: Object detection is a challenging task in computer vision that can provide valuable information for visually impaired people. In this paper, we propose a deep learning-based model that detects objects for blind people using frameworks such as TensorFlow and OpenCV. Our system uses a pre-trained model built on YOLO v7 to detect and recognize objects in real-time from images or videos captured by a camera. Other versions of YOLO have also been taken into consideration. However, based on some recent comparisons, YOLOv7 is the fastest and most accurate official YOLO version. It achieves 2% higher accuracy than Cascade-Mask R-CNN models at dramatically increased inference speed (509% faster). The results are then converted to speech using text-to-speech technology and delivered to the user through headphones or a speaker. Our system is intended to be an IoT-based project that can recognize common objects and people. We evaluated our system on several datasets such as Common Object in Context (COCO)- a large-scale labelled dataset containing 1.5 million object images, to demonstrate its accuracy and efficiency. We believe that our system can offer a practical and affordable way for visually impaired people to access visual information and enhance their quality of life.
A Blockchain and Edge-Computing-Based Secure Framework for Government Tender Allocation

Abstract: Block chain innovation is an illustration of such innovation that has been drawing in the consideration of Legislatures across the globe as of late. Upgraded security, further developed detectability, and least expense foundation engage the block chain to infiltrate different spaces. By and large, legislatures discharge tenders to some outsider associations for various tasks. During this interaction, various contenders attempt to snoop the delicate upsides of others to win the delicate. Block chain procedure utilized under different security administration with various model. It is utilized as backend data set model that keeps up with. No. of clients can enlist and make the delicate citation under different division. Administrator will check and give the reaction from the citation result. Administrator or authority check the experience and interaction the board level ability for universally useful.
Real-Time Video Violence Detection Using CNN

Abstract: In order to effectively enforce the law and keep cities secure, monitoring technologies that detect violent events are becoming increasingly important. In computer vision, the practice of action recognition has gained popularity. In the field of computer vision, action recognition has gained popularity. The action recognition group, however, has mainly concentrated on straightforward activities like clapping, walking, jogging, etc. Comparatively little study has been done on identifying specific occurrences that have immediate practical applications, like fighting or violent behaviors in general. The responsiveness, precision, and flexibility of violent event detectors are indicators of their effectiveness across a range of video sources. This capacity might be helpful in specific video surveillance situations. Several research focused on violence identification with an emphasis on speed, accuracy, or both while ignoring the generalizability of various video source types. In this paper, a deeplearning-based real-time violence detector has been proposed. CNN serves as an extractor of spatial features in the suggested model. Here, a convolutional neural network (CNN) architecture called MobileNet V2 is utilized to extract frame-level information from a video, and LSTM, which focuses on the three factors (overall generality, accuracy, and quick reaction) as a temporal relation learning approach.
Dark Store E-Commerce Website using Sentiment Analysis Prediction

Abstract: The Dark Store is a physical store that has been closed and turned into a fulfilment centre. These distribution points are not open to the public, which allows more space for inventory and the ability to fill orders quickly and accurately. Dark Stores has the facility to provide customers with innumerable and expanding resources availability and provides options such as of buying online products, the very same-day delivery or even in instance of hours, or in-store pickup. However, this concept is not new.
Opinion and Suggestion Mining on Customer Reviews

Abstract: Opinion and suggestion mining on customer reviews is a system providing both organizations and customers with valuable insights into the quality of products and services. The system operates on two perspectives, the customer, and the organization. For new customers, the system analyses reviews from previous customers to help them determine whether the product or service offered by the organization is worth purchasing. For organizations, the system performs suggestion mining on negative reviews to provide actionable feedback for improvements. Additionally, the system provides organizations with the percentage of customers who have liked or disliked their products or services, offering valuable insights into customer satisfaction. Overall, this project offers a valuable tool for organizations looking to improve their offerings and for customers seeking reliable information to inform their purchasing decisions
Ways of seeing design research: A polyphonic speculation

We present six speculative designs that all explore the challenge of representing the broad corpus of Design Research in the form of an interactive data repository. We describe the development of the ideas, identify common themes, and highlight two related challenges: (i) The challenge of reflecting the diversity of Design Research in a repository; (ii) The challenge of capturing context(s) during the Design Research process. We argue that these challenges constitute a ‘causality dilemma’ that is inhibiting the Design Research movement. We offer insights into potential responses to the dilemma, signpost opportunities for future work and reflect on the value of ‘polyphonic speculation’ – dialogue between design researchers speculating through design on a common topic – as a design tool for probing complex challenges.
Factory fieldwork informing the design research process

Engaging with manufacturing operators in design research poses a myriad of challenges ranging from ethics to logistics.We describe a process of qualitative, mixed-methods, non-participant fieldwork user research involving 10 garment factory workers created to inform the design of technology to support operators' physical and mental health.The resulting data were subjected to thematic analysis which identified themes that informed not only the design of technology, but also the co-design process itself.In this paper, we explore the latter as a contribution to design research methods, discussing the implications of analysing user research data towards informing design researchers' behaviour in their interactions with research participants and how these might help collect ecologically valid data.
Prospective films: When a strategic vision meets the diversity of human experience

Through the medium of corporate vision videos, foresight has become a vehicle for companies to communicate on their vision of the future. These films are of strategic importance and should be both watched and designed with awareness of such purposes. Mobilized on an action research project in the mobility sector, our design team accompanied a company towards defining and formalizing their vision of the future mass transportation system. A filmic analysis served as a basis for recommendations, which helped us story-boarding a prospective film meant to spotlight the diversity of human experience rather than conveying a standardized perspective of user interaction. This exploratory paper accounts for this analytical and generative study and proposes a four-category typology of corporate vision videos.
Theory instruments: Helping designers see the invisible

With increasingly complex technologies entering the daily lives of people, human-technology relations haves become more difficult to observe and make sense of.In this paper we discuss experiments with designing 'Theory Instruments' that allow designers to collaboratively play with multiple theoretical perspectives on field research material.We argue that the physical, tangible nature of these instruments helps to convey and experience the richness of theoretical frameworks and can make designers and researchers aware of easily overlooked aspects in ethnographic observations.We unfold the case of 'charging electric vehicles' across theories of Product Ecology, Rites of Passage, Exchange & Reciprocity, and Actor Network Theory.
Supporting mutual imaginations through human-material interactions: A toolkit as form of access

We introduce Mutual Imagination as a desirable outcome of Human-Material Interactions, proposing implications for the design of a toolkit that supports such activities.We aim to support Human-Material Interaction in an active, physical way, negotiating between learning foundational knowledge and supporting creative expression and imagination.The focus is set on process rather than finished objects, encouraging abstract reflection and the formulation of Mutual Imaginations emerging from a hands-on making activity.We report on the findings of a first case study utilizing the technology of dye-sensitized solar cells (DSSC), exemplarily probing the interconnection of knowledge, reflection, and making towards toolkits that can support Human-Material Interactions wide open to Imaginations.This paper describes the process of developing a toolkit through a practice-led research process.Based on this process we extract implications for the design of toolkits that aim at opening a space for imaginations through Human-Material Interaction.We start by introducing relevant areas of research we base our investigations on: Human-Material Interaction, Imagination, and Toolkits as Form of Access and follow with introducing DSSC.We describe the practice of designing a preliminary toolkit and holding workshops.Data collected during the design process as well as throughout the workshops are the basis for the analysis to formulate implications for the design of a toolkit for imaginations.
Breast Cancer Using Machine Learning

Abstract: Women die from breast cancer, which is an abstract concept. Breast cancer is the most important problem. The most frequent cancer in women diagnosed globally has now surpassed lung cancer in prevalence. early detection that aids in cancer prevention. If breast cancer is to have a very high survival rate, it must be found in its earliest stages. The efficient machine learning method is utilized to categorize the data. Methods are employed in the medical field to aid in diagnosis and decisionmaking. This study used the Wilcoxon breast cancer dataset to do data visualization and compare various machine learning methods, including the Support Vector Machine (SVM), Decision Trees, Naive Bayes (NB), K Nearest Neighbours (K-NN), Adaboost, Xgboost, and Random Forest. The primary goal is to assess the data's accuracy in terms of each algorithm's efficiency and effectiveness in terms of accuracy, precision, sensitivity, and specificity. Our goal is to use machine learning to detect things quickly, effectively, and precisely. The experimental findings had the lowest error rate and the best accuracy (98.24%).
Business Support System for Local Stores

Abstract: Business support system for local store, promotes the purchase of products that are available in local store. During the pandemic customers became more habituated towards e-commerce sites than local stores. This web application gives an opportunity to local stores that can publish and sell their products through online platform and connects the registered local store to people which contains local store details. It boosts and helps the local store to expand their distribution and also increase their sales. This web application enable connection between the people and local store in efficient manner. Motive is to inspire, encourage and enable the remaining vast majority of small business to step into the online world.
Moving by doing: Gaining sight of and grasping movement

Although physical activity plays an important role in our lives, it has mainly been addressed from a health perspective.Our research explores a partly blank space by making visible movements present in alternative contexts, in our case -the context of designing and crafting with reclaimed materials.Our research contributes to finding and making visible movements that otherwise go unnoticed and to seeing how they can support to expand the notion of what physical activity is and where it can be found.As part of our result, we propose an explorative method directed to the design research community when in the phase of analyzing and making visible movement in empirical data.
Experimental Analysis on Thermal Efficiency and Heat Loss of the Evacuated Tube Collector with Variable Solar Radition and Wind Speed

Abstract: The experimental study was performed on a solar water heater- type evacuated tube collector and results were drawn for efficiency and overall heat transfer coefficient along with net heat loss. In this research, the paper calculation was done on the basis of varying solar radiation and wind speed, average values of five readings were taken to get a better output. Maximum and minimum values were analyzed for which the performance of the system would be optimum. The maximum efficiency was found to be 64% at maximum radiation of 750W/m2 and wind speed of 2m/s, also as solar radiation inversely varies with X factor so at minimum radiation that is 250W/m2 it was found to be maximum at different wind speed. It can be seen that the overall heat transfer coefficient and net heat loss increases with increasing solar radiation and wind speed so, overall heat transfer coefficient (Ue) was found minimum at radiation of 250 W/m2 is 0.5039 at 2m/s and maximum at radiation of 750W/m2 that is 0.55 at 4m/s and Net heat loss (Q) values were 20.65 minimum and 28.25 maximum for corresponding solar radiation and wind speed.
Imitation versus Innovation: What children can do that large language and language-and-vision models cannot (yet)?

Much discussion about large language models and language-and-vision models has focused on whether these models are intelligent agents. We present an alternative perspective. We argue that these artificial intelligence models are cultural technologies that enhance cultural transmission in the modern world, and are efficient imitation engines. We explore what AI models can tell us about imitation and innovation by evaluating their capacity to design new tools and discover novel causal structures, and contrast their responses with those of human children. Our work serves as a first step in determining which particular representations and competences, as well as which kinds of knowledge or skill, can be derived from particular learning techniques and data. Critically, our findings suggest that machines may need more than large scale language and images to achieve what a child can do.
Hausdorff Distance Matching with Adaptive Query Denoising for Rotated Detection Transformer

The Detection Transformer (DETR) has emerged as a pivotal role in object detection tasks, setting new performance benchmarks due to its end-to-end design and scalability. Despite its advancements, the application of DETR in detecting rotated objects has demonstrated suboptimal performance relative to established oriented object detectors. Our analysis identifies a key limitation: the L1 cost used in Hungarian Matching leads to duplicate predictions due to the square-like problem in oriented object detection, thereby obstructing the training process of the detector. We introduce a Hausdorff distance-based cost for Hungarian matching, which more accurately quantifies the discrepancy between predictions and ground truths. Moreover, we note that a static denoising approach hampers the training of rotated DETR, particularly when the detector's predictions surpass the quality of noised ground truths. We propose an adaptive query denoising technique, employing Hungarian matching to selectively filter out superfluous noised queries that no longer contribute to model improvement. Our proposed modifications to DETR have resulted in superior performance, surpassing previous rotated DETR models and other alternatives. This is evidenced by our model's state-of-the-art achievements in benchmarks such as DOTA-v1.0/v1.5/v2.0, and DIOR-R.
Self-information Domain-based Neural CSI Compression with Feature Coupling

Deep learning (DL)-based channel state information (CSI) feedback methods compressed the CSI matrix by exploiting its delay and angle features straightforwardly, while the measure in terms of information contained in the CSI matrix has rarely been considered. Based on this observation, we introduce self-information as an informative CSI representation from the perspective of information theory, which reflects the amount of information of the original CSI matrix in an explicit way. Then, a novel DL-based network is proposed for temporal CSI compression in the self-information domain, namely SD-CsiNet. The proposed SD-CsiNet projects the raw CSI onto a self-information matrix in the newly-defined self-information domain, extracts both temporal and spatial features of the self-information matrix, and then couples these two features for effective compression. Experimental results verify the effectiveness of the proposed SD-CsiNet by exploiting the self-information of CSI. Particularly for compression ratios 1/8 and 1/16, the SD-CsiNet respectively achieves 7.17 dB and 3.68 dB performance gains compared to state-of-the-art methods.
Using Language Models to Detect Alarming Student Responses

This article details the advances made to a system that uses artificial intelligence to identify alarming student responses. This system is built into our assessment platform to assess whether a student's response indicates they are a threat to themselves or others. Such responses may include details concerning threats of violence, severe depression, suicide risks, and descriptions of abuse. Driven by advances in natural language processing, the latest model is a fine-tuned language model trained on a large corpus consisting of student responses and supplementary texts. We demonstrate that the use of a language model delivers a substantial improvement in accuracy over the previous iterations of this system.
Morpheus: Automated Safety Verification of Data-dependent Parser Combinator Programs

Parser combinators are a well-known mechanism used for the compositional construction of parsers, and have shown to be particularly useful in writing parsers for rich grammars with data-dependencies and global state. Verifying applications written using them, however, has proven to be challenging in large part because of the inherently effectful nature of the parsers being composed and the difficulty in reasoning about the arbitrarily rich data-dependent semantic actions that can be associated with parsing actions. In this paper, we address these challenges by defining a parser combinator framework called Morpheus equipped with abstractions for defining composable effects tailored for parsing and semantic actions and a rich specification language used to define safety properties over the constituent parsers comprising a program. Even though its abstractions yield many of the same expressivity benefits as other parser combinator systems, Morpheus is carefully engineered to yield a substantially more tractable automated verification pathway. We demonstrate its utility in verifying a number of realistic, challenging parsing applications, including several cases that involve non-trivial data-dependent relations.