Reviewerly/exHarmony huggingface dataset:
Understanding the Effect of Counterfactual Explanations on Trust and Reliance on AI for Human-AI Collaborative Clinical Decision Making

Artificial intelligence (AI) is increasingly being considered to assist human decision-making in high-stake domains (e.g. health). However, researchers have discussed an issue that humans can over-rely on wrong suggestions of the AI model instead of achieving human AI complementary performance. In this work, we utilized salient feature explanations along with what-if, counterfactual explanations to make humans review AI suggestions more analytically to reduce overreliance on AI and explored the effect of these explanations on trust and reliance on AI during clinical decision-making. We conducted an experiment with seven therapists and ten laypersons on the task of assessing post-stroke survivors' quality of motion, and analyzed their performance, agreement level on the task, and reliance on AI without and with two types of AI explanations. Our results showed that the AI model with both salient features and counterfactual explanations assisted therapists and laypersons to improve their performance and agreement level on the task when `right' AI outputs are presented. While both therapists and laypersons over-relied on `wrong' AI outputs, counterfactual explanations assisted both therapists and laypersons to reduce their over-reliance on `wrong' AI outputs by 21\% compared to salient feature explanations. Specifically, laypersons had higher performance degrades by 18.0 f1-score with salient feature explanations and 14.0 f1-score with counterfactual explanations than therapists with performance degrades of 8.6 and 2.8 f1-scores respectively. Our work discusses the potential of counterfactual explanations to better estimate the accuracy of an AI model and reduce over-reliance on `wrong' AI outputs and implications for improving human-AI collaborative decision-making.
The Baseline/Elaboration Model for the Constructional Meaning of Chinese “V1OV2de” Construction

Chinese “V1OV2de” construction has its special syntactic features. The traditional grammar of formal linguistics finds it difficult to analyse this atypical and marginal construction, but the theory of baseline/elaboration in Cognitive Grammar can provide a solution for it. The paper studies the constructional meaning of “V1OV2de” construction based on the theory of baseline/elaboration model. It draws a conclusion that “V1OV2de” construction has two constructional meanings: “cause-effect relationship” and “non-cause-effect relationship”. In addition, it also discusses the reasons why the verb appears for the second time in “V1OV2de” construction. The constructional meanings of “V1OV2de” construction are generated because of the two same verbs.
Analisis dan Implementasi Knowledge Management System pada Bagian Administrasi PT HASIL KARYA TAMBANG Menggunakan Metode KMSLC DAN Analisis PIECES

The problem that often occurs at PT.Hasil Karya Tambang, especially in the administration section, is the availability of information in each branch office that is not qualified in solving problems that are often raised by customers so that it takes longer to handle them. With the Knowledge Management System, all problems in the field of information availability for administration can be overcome, where later the Knowledge Management System will be Web-oriented, which can be accessed easily, and can quickly provide solutions for customers who make complaints at various branch office of PT Karya Karya Tambang.&#x0D; And with this Knowledge Management System, the company can better analyze what problems are most often experienced and can fix them.&#x0D; &#x0D;
Perancangan Website Sistem Informasi Penunjuk Arah Wifi id Wilayah Semarang

Wifi@id internet is a public service of technology-based Wifi / Hotspot provided by TELKOM Group. WiFi@id wifi is a promising business for the company because of its CDMA business declined previously lived. Problems that occur at this time is the lack of information between users with id being wifi locations. It causes the user to access the wifi en manual are based on the location, while the location of wifi id contained in the shared location. Design website information systems and route search nearby directions wifi @ id is located. This website system enables users to find the location nearest wifi id with the position where the user is located. This mapping information design using a model of the development of the SDLC (system development life cycle), namely the development of a model system in stages starting from the analysis, the initial design, through implementation phase object-oriented programming is UML (unified modeling language), the data collection techniques namely observation and interviews. Conclusion of this website information system that can manage data wifi, managing user data, and know the location of the user's location.
Diffusion-based Visual Counterfactual Explanations -- Towards Systematic Quantitative Evaluation

Latest methods for visual counterfactual explanations (VCE) harness the power of deep generative models to synthesize new examples of high-dimensional images of impressive quality. However, it is currently difficult to compare the performance of these VCE methods as the evaluation procedures largely vary and often boil down to visual inspection of individual examples and small scale user studies. In this work, we propose a framework for systematic, quantitative evaluation of the VCE methods and a minimal set of metrics to be used. We use this framework to explore the effects of certain crucial design choices in the latest diffusion-based generative models for VCEs of natural image classification (ImageNet). We conduct a battery of ablation-like experiments, generating thousands of VCEs for a suite of classifiers of various complexity, accuracy and robustness. Our findings suggest multiple directions for future advancements and improvements of VCE methods. By sharing our methodology and our approach to tackle the computational challenges of such a study on a limited hardware setup (including the complete code base), we offer a valuable guidance for researchers in the field fostering consistency and transparency in the assessment of counterfactual explanations.
SPK UNTUK MENENTUKAN SANKSI PESERTA DIDIK DENGAN METODE AHP DAN SAW DI SMKN 2 KURIPAN

State Vocational High School (SMK) 2 Kuripan, is located on the border of two districts, namely West Lombok and Central Lombok with a fairly large number of students where they come from various regions with varied backgrounds and characters and it is very possible that this will happen. violations by students. Violations that often occur are indiscipline, students are late for class, truant, jumping fences, smoking, fighting and so on. For this reason, it is deemed necessary to have a system used to assist in making decisions in determining sanctions for such violations. This study aims to design a system that can help facilitate BK teachers in determining sanctions to students using the AHP (Analytic Hierarchy Process) and SAW (Simple Additive Weight) methods. In designing the system, it has several assessment criteria with weighted points and priorities for each alternative. Every violation committed by students will be processed quickly accompanied by sanctions and predetermined actions. This process can assist BK teachers in determining sanctions for students and making reports to the waka of students and the principal of SMKN 2 Kuripan.
Similarity-based Secure Deduplication for IIoT Cloud Management System

With the development of the Industrial Internet of Things (IIoT), the scale of IIoT data is rapidly increasing, bringing significant challenges to existing data management systems. To tackle this issue, we propose a similarity-based secure deduplication for IIoT cloud management system, which can effectively balance the security and availability of IIoT data and minimize the storage cost. Concretely, we propose a similarity-based secure deduplication algorithm for IIoT (IIoT-SBSD) by designing a similarity-preserving tag (IIoT-Simhash). This algorithm can perform similarity deduplication over ciphertexts, thus reducing storage space while ensuring data security. Besides, we construct a parallelizable edge-based deduplication framework in which similarity comparison and deduplication operations are performed directly by edge nodes, significantly alleviating the transmission pressure. Additionally, we propose similarity-based proofs of ownership, S-PoWs, to mitigate the impact of data deduplication on the user's access to the IIoT data. Experimental results show that our method significantly reduces storage space and transmission bandwidth without increasing the computation burden.
Pandera: Going Beyond Pandas Data Validation

Data quality remains a core concern for practitioners in machine learning, data science, and data engineering, and many specialized packages have emerged to fulfill the need of validating and monitoring data and models. However, as the open source community creates new data processing frameworks - notably, new highly performant entrants such as Polars - existing data quality frameworks need to catch up to support them, and in some cases, the Python community more broadly creates new data validation libraries for these new data frameworks. This paper outlines pandera's motivation and challenges that took it from being a pandas-only data validation framework niels\_bantilan-proc-scipy-2020 to one that is extensible to other non-pandas-compliant dataframe-like libraries. It also provides an informative case study of the technical and organizational challenges associated with expanding the scope of a library beyond its original boundaries.
PRO-CREATIVE FUNCTION OF PRODUCTIVE IMAGINATION IN KANT’S FIRST CRITIQUE. DISCUSSION REMARK ON THE BOOK OF SAULIUS GENIUSAS “PHENOMENOLOGY OF PRODUCTIVE IMAGINATION: EMBODIMENT, LANGUAGE, SUBJECTIVITY” (Ibidem-Verlag, Stuttgart, 2021. ISBN-13: 978-3-8382-1552-5)

The aim of our “discussion remark” is not to present a critical review on the book written by S. Geniusas, a brilliant study notable by its extreme painstakingness, historical sensitivity and terminological accuracy, but rather to delve deeply into the origins of phenomenological understanding of productive imagination, i.e., to turn “back to Kant”, given in Saulius Geniusas’ book (the first chapter) for introductory reason. We proceed from S. Geniusas remark that productive imagination establishes a relation between different abilities, reconciles the antagonism between them and, in this respect, exercises a pro-creative function. We reveal that it is this pro-creative element of productive imagination that brings it closer to time (as indicated by Viktor Molchanov, a prominent Russian phenomenologist, in his study of 1988) and serves as the basis for gaining new knowledge. Imagination acts as a limit for reflection, however, it gets revealed only through reflecting, and, thus, it proves to be connected with a fundamental layer of consciousness, which appears both as an object and as a means of describing reflection, i.e., as time. The convergence, or rather, identification, of time with imagination lies in the very fact that both of them exercise an objective function: time – as a possibility for semantic definition of objectivity, imagination – as a basis for a possibility of any knowledge. Moreover, imagination turns out to be a source of a paradox and, ultimately, the only thing that explains self-cognition.
A New Multimedia Web-Data Mining Approach based on Equivalence Class Evaluation Pipelined to Feature Maps onto Planar Projection

Multimedia information are semi-organized or unstructured information elements whose essential substance is separately or by and large utilized for correspondence. Sight and sound information mining recognizes, arranges, and recovers important highlights from an assort- ment of media to recognize enlightening examples furthermore, connec- tions for information acquisition. Computer Vision (CV)-based systems have been increasingly popular in recent years, owing to the growing number and complexity of datasets. In CV, finding meaningful photos in a huge dataset is a difficult task to solve. Traditional search engines retrieve photos based on text such as captions and metadata, but this strategy can result in a lot of irrelevant output, not to speak the time, effort, and money required to tag this textual data. In this paper, we proposed a pipelined deep learning oriented method- ology framework for multimedia web-data mining based on content ex- tracted feature maps in planner projection as input. Color, texture, form, and other high-level properties of images are represented as numerical feature vectors. This technique is based on the following computer vision tasks in general i.e., Image segmentation, Image classification, Object de- tection etc. In order to prove the computational efficiency and to validate its statistical behaviour, we have also presented the experimental eval- uation on an standard multimedia dataset. The obtained performance results are then compared with some significant existing approaches in the terms of various statistical measures/parameters.
Doubly even self-orthogonal codes from quasi-symmetric designs

In this paper, we give a construction of doubly even self-orthogonal codes from quasi-symmetric designs. Further, we study orbit matrices of quasi-symmetric designs and give a construction of doubly even self-orthogonal codes from orbit matrices of quasi-symmetric designs of Blokhuis-Haemers type.
Thespian: Multi-Character Text Role-Playing Game Agents

Text-adventure games and text role-playing games are grand challenges for reinforcement learning game playing agents. Text role-playing games are open-ended environments where an agent must faithfully play a particular character. We consider the distinction between characters and actors, where an actor agent has the ability to play multiple characters. We present a framework we call a thespian agent that can learn to emulate multiple characters along with a soft prompt that can be used to direct it as to which character to play at any time. We further describe an attention mechanism that allows the agent to learn new characters that are based on previously learned characters in a few-shot fashion. We show that our agent outperforms the state of the art agent framework in multi-character learning and few-shot learning.
Leveraging Few-Shot Data Augmentation and Waterfall Prompting for Response Generation

This paper discusses our approaches for task-oriented conversational modelling using subjective knowledge, with a particular emphasis on response generation. Our methodology was shaped by an extensive data analysis that evaluated key factors such as response length, sentiment, and dialogue acts present in the provided dataset. We used few-shot learning to augment the data with newly generated subjective knowledge items and present three approaches for DSTC11: (1) task-specific model exploration, (2) incorporation of the most frequent question into all generated responses, and (3) a waterfall prompting technique using a combination of both GPT-3 and ChatGPT.
ManiVault: A Flexible and Extensible Visual Analytics Framework for High-Dimensional Data

Exploration and analysis of high-dimensional data are important tasks in many fields that produce large and complex data, like the financial sector, systems biology, or cultural heritage. Tailor-made visual analytics software is developed for each specific application, limiting their applicability in other fields. However, as diverse as these fields are, their characteristics and requirements for data analysis are conceptually similar. Many applications share abstract tasks and data types and are often constructed with similar building blocks. Developing such applications, even when based mostly on existing building blocks, requires significant engineering efforts. We developed ManiVault, a flexible and extensible open-source visual analytics framework for analyzing high-dimensional data. The primary objective of ManiVault is to facilitate rapid prototyping of visual analytics workflows for visualization software developers and practitioners alike. ManiVault is built using a plugin-based architecture that offers easy extensibility. While our architecture deliberately keeps plugins self-contained, to guarantee maximum flexibility and re-usability, we have designed and implemented a messaging API for tight integration and linking of modules to support common visual analytics design patterns. We provide several visualization and analytics plugins, and ManiVault's API makes the integration of new plugins easy for developers. ManiVault facilitates the distribution of visualization and analysis pipelines and results for practitioners through saving and reproducing complete application states. As such, ManiVault can be used as a communication tool among researchers to discuss workflows and results. A copy of this paper and all supplemental material is available at https://osf.io/9k6jw and source code at https://github.com/ManiVaultStudio.
Prediksi Indeks Harga Konsumen (IHK) Kota Ambon Menggunakan Elman Recurrent Neural Network (ERNN)

Indeks Harga Konsumen (IHK) is an economic indicator that can provide information on developments and changes in the prices of goods and services that are predominantly consumed by the public within a certain period of time. In this study the method to be used is the Elman Recurrent Neural Network (ERNN). The research data uses Ambon City IHK data from 2016 to 2019. The data used as research objects are: Food, Beverages, Cigarettes and Tobacco, Housing, Water, Electricity, Gas and Fuel, Clothing, Health, Education, Recreation, and Sport, Transportation, Communication and Financial Services as input variables. The results of training with 5 hidden layers at a maximum epoch of 100,000 obtained the smallest MAPE value of 1.1773. Then the results of testing using the parameters in the experiment on the number of hidden layer neurons 20 obtained the smallest MAPE value of 0.461823.
Neural Network on Tsunami Waves Prediction Detector Tools Using Tectonic Earthquakes Data

On 26 December 2004, tsunami waves were generated by undersea megathrust earthquakes particularly hit the Banda Aceh-Indonesia, also Thailand, Sri Lanka, India. The effect of tsunami waves can be very damaging to the coastal areas even more to the land around the coast. It is very interesting to study the relation between the magnitude of the undersea earthquakes and the tsunami. Therefore, we construct an early warning system using Neural Network to predict the tsunami using data from Indonesian Meteorology, Climatology, and Geophysical Agency that integrated with a hardware tool. The hardware tools will show the prediction result and send a short message.
Perbandingan Hasil Pengelompokan Menggunakan Metode Self Organizing Maps dan Metode Average Linkage (Studi Kasus: Data PDRB tiap provinsi di Indonesia)

Gross domestic regional product (GDRP) is one of important indicators that can be used to determine economic conditions in some areas. This makes GDRP is very interesting to be studied. In this paper we cluster 33 provinces in Indonesia based on GDRP in 2013. We use self organizing and average linkage method of cluster analysis to investigate it. We also compare the results of both methods. We found that Average linkage method has better performance than self organizing method because it has smaller rasio.
Penerapan Jaringan Saraf Tiruan Learning Vector Quantization Untuk Pemetaan Wilayah Berpenduduk Miskin di Provinsi Maluku

Badan Pusat Statistik (BPS) stated that the number of poor people in Indonesia reached 28.01 million people based on data as of March 2016. This figure is around 10.86 percent of the national population. Province of Maluku as the third poor contributor of all provinces in Indonesia reached 27.74 percent. Note that, there are 8 of total 11 districts/cities in Maluku which are determined as underdeveloped regions (Kementerian PDT, 2015), Maluku Barat Daya (MBD) is one of them. Based on data from BPS, in 2014 the percentage of poor people in district of MBD reached 28.33 percent being the second highest district in Maluku after Maluku Tenggara Barat (MTB). It is quite difficult make the poverty level of MBD lower, due to a large number of villages in MBD have some economic access isolations because of geographical conditions. Various programs and policies in social and health have been done to solve this poverty problem, but still could not overcome this problem yet.&#x0D; In this paper we have grouped the districts/cities of Maluku based on poverty factors using Learning Vector Quantization (LVQ) method. The results of this research showed that there are 5 poverty clusters in Maluku. Those are: Cluster 1 consists of Maluku Tenggara Barat, Maluku Utara dan Buru; cluster 2 consists of Maluku Tengah; cluster 3 consists of Kep. Aru, Seram Bagian Barat dan Seram Bagian Timur, cluster 4 consists of Maluku Barat Daya dan Buru Selatan; and cluster 5 consists of Ambon and Tual. Each cluster describes the poverty level with respect to its Partition matrix respectively. The results that we obtained also show that cluster 4 has the highest poverty level.
LHFNet: A Fast and Accurate Object Detector Based on CenterNet

This paper proposes LHFNet, an improved object detection method based on CenterNet, which achieves a better speed-accuracy trade-off. The main contributions of this paper are as follows: We design a lightweight Hourglass network to reduce the number of parameters and computations of CenterNet, thus improving the detection speed. We introduce an intermediate feature map fusion module to enhance the feature extraction capability of Hourglass, which compensates for the possible performance degradation caused by the network simplification. We apply an attention module to the final heat map to improve the image feature representation. We design and optimize a series of loss functions to train LHFNet effectively. Through the research in this paper, we demonstrate that the feature map fusion and the attention module greatly improve the performance of LHFNet. We evaluate our method on the COCO dataset and achieve a high accuracy at a fast speed (Tesla V100), which outperforms other object detection methods in terms of both accuracy and efficiency.
Leaderboards &amp;amp; Prestige Points: How Homegrown Applications Gamify Muslim Hate

This article identifies a shift in the methods of digital repression used by state and non-state actors in Hindu majoritaria India, in the context of the country's deteriorating democracy rankings and looming worries about a possible Muslim genocide. Exploring the advent of hyper nationalistic, domestic social media applications, I argue that the platforms’ features, moderation practises, and regulations legitimise Islamophobia, collective actions against minorities, and surveillance. Based on 18 months of netnography and reflective observations within Kutumb app groups, I argue that the platforms affordances offered by the app ease its use as a covert tool for radicalization and recruiting digital volunteers for the Hindutva cause. My research shows how the app uses game mechanics to reward and "gamify" Islamophobia and different acts of violence against Muslims. This type of social, cultural and political engagement is habituating Islamophobia in every day, ordinary life.
What's New Online

This study aims to gauge Registered Dietitian Nutritionists' familiarity with the Food as Medicine concept
Adaptive quantization scheme for image compression based on human visual contrast sensitivity

Abstract The full text of this preprint has been withdrawn by the authors due to author disagreement with the posting of the preprint. Therefore, the authors do not wish this work to be cited as a reference. Questions should be directed to the corresponding author.