Reviewerly/exHarmony huggingface dataset:
Research on the design of accounting information system based on computer network

The development of modern technology has made many enterprises affected by the network. Of course, the accounting profession has also suffered changes, so the accounting information system has set strict requirements on itself. In the intelligent accounting information system, even if the non-automatic accounting system can already achieve intelligent use, but with the construction of accounting, there is no way to practice in many aspects. As a result, the relationship between enterprises and external social groups is not close. When defects arise, they do not think of other ways to solve them, resulting in the value of information technology itself not being brought into play. Because of this, an in-depth discussion of the construction of an accounting information system will help the reform of enterprise accounting. Sui Chunlei proposed that big data technology can improve the relevance and accuracy of management enterprises, integrate information technology into enterprises, apply to accounting management information, and gradually improve its management requirements, while achieving the extensiveness and timeliness of information strengthening. Wu Zhongsheng clearly mentioned the development process of accounting informatization and planned the development prospects of accounting informatization. On the basis of this system, the computer system designed the accounting information system for it. The construction of computer network accounting information system is to sort out the collection and application of big data and data, in order to better realize the correlation between the network and reality, on the basis of the traditional accounting information system, is conducive to better development, will be the function of the data, the module of the system, the comparative results of the test to analyze separately, the purpose is to explore the computer accounting information system to study, the result will be the need to consider the node, convenient computer network on the accounting information system design and research.
Extail: a Kinetic Inconspicuous Wareable Hair Extension Device

Wearable devices that present the wearer’s information have been designed to stand out when worn, making it difficult to conceal their wear. Therefore, we have been working on developing and evaluating a dynamic expression of hair to realize the presentation of the wearer’s information in an inconspicuous wearable device. In the precedents of hair interaction, the hairstyles and expressions to which the technique can be applied are limited. In this paper, we focus on the output mechanism and present Extail, a hair extension type device with a control mechanism that moves bundled hair like a tail. The results of a questionnaire survey on the correspondence between the movement of hair bundles and emotional expression were generally consistent with the results of the evaluation of tail devices in related studies.
FormSense: A Fabrication Method to Support Shape Exploration of Interactive Prototypes

When exploring the shape of interactive objects, existing prototyping methods can conflict with the iterative process. In this paper, we present FormSense: a simple, fast, and modifiable fabrication method to support the exploration of shape when prototyping interactive objects. FormSense enables touch and pressure sensing through a multi-layer coating approach and a custom touch sensor built from commodity electronic components. We use FormSense to create four interactive prototypes of diverse geometries and materials.
Shadow Play using Ultrasound Levitated Props

Shadow play is a traditional culture to communicate narratives. However, its inheritance is in danger, and traditional methods have limitations in their expression. We propose a novel system to perform a shadow play by levitating props using an ultrasound speaker array. Our system computes an ideal position of levitating props to create a shadow of the desired image. Shadow play will be performed by displaying a sequence of images as shadows. Since the performance is automated, our work makes shadow play accessible to people for generations. Also, our system allows 6 DoF and floating movement of props, which expands the limit of expression. Through this system, we aim to enhance shadow plays informatically and aesthetically.
Improving 3D-Editing Workflows via Acoustic Levitation

We outline how to improve common 3D-editing workflows such as modeling or character animation by utilizing an acoustic levitation kit as an interactive 3D display. Our proposed system allows users to directly interact with models in 3D space and perform multi-point gestures to manipulate them. Editing of complex 3D objects can be enabled by combining the 3D display with an LCD, projector or HMD to display additional context.
Self-Supervised Approach for Few-shot Hand Gesture Recognition

Data-driven machine learning approaches have become increasingly used in human-computer interaction (HCI) tasks. However, compared with traditional machine learning tasks, for which large datasets are available and maintained, each HCI project needs to collect new datasets because HCI systems usually propose new sensing or use cases. Such datasets tend to be lacking in amount and lead to low performance or place a burden on participants in user studies. In this paper, taking hand gesture recognition using wrist-worn devices as a typical HCI task, I propose a self-supervised approach that achieves high performance with little burden on the user. The experimental results showed that hand gesture recognition was achieved with a very small number of labeled training samples (five samples with 95% accuracy for 5 gestures and 10 samples with 95% accuracy for 10 gestures). The results support the story that when the user wants to design 5 new gestures, he/she can activate the feature in less than 2 minutes. I discuss the potential of this self-supervised framework for the HCI community.
Top-Levi: Multi-User Interactive System Using Acoustic Levitation

Top-Levi is a public multi-user interactive system that requires a pair of users to cooperate with an audience around them. Based on acoustic levitation technology, Top-Levi leverages a unique attribute of dynamic physical 3D contents displayed and animated in the air: such systems inherently provide different visual information to users depending on where they are around the device. In Top-Levi, there are two primary users on opposite (left/right) sides of the device, and audience members to the front. Each sees different instructions displayed on a floating cube. Their collective task is to cooperate to move the cube from a start point to a final destination by synchronizing their responses to the instructions.
Using a Dual-Camera Smartphone to Recognize Imperceptible 2D Barcodes Embedded in Videos

Invisible screen-camera communication is promising in that it does not interfere with the video viewing experience. In the imperceptible color vibration method, which displays two colors of the same luminance alternately at high speed for each pixel, embedded information is decoded by taking the difference between distant frames on the time axis. Therefore, the interframe differences of the original video contents affect the decoding performance. In this study, we propose a decoding method which utilizes simultaneously captured images using a dual-camera smartphone with different exposure times. This allows taking the color difference between the frames that are close to each other on the time axis. The feasibility of this approach is demonstrated through several application examples.
TouchVR: A Modality for Instant VR Experience

In near future, we envision instant and ubiquitous access to the VR worlds. However, existing highly portable VR devices usually lack rich and convenient input modality. In response, we introduce TouchVR, a system that enables BoD interaction in instant VR supported by mobile HMDs.
FullPull : A Stretchable UI to Input Pulling Strength on Touch Surfaces

Touch surfaces are used as input interfaces for many devices such as smartphones, tablets, and smartwatches. However, the flexibility of the input surface is low, and the possible input operations are limited to planar ones such as touch and swipe. In contrast, in the field of HCI, there has been much research on increasing the number of input interactions by attaching augmented devices with various physical characteristics to the touch surface. However, most of these interactions are limited to operations where pressure is applied to the input surface. In this study, we propose FullPull, which consists of a rubber tube filled with conductive ink and a suction cup to attach the rubber tube to the surface. FullPull allows users to input pulling depth and strength on the touch surface. We implemented a prototype FullPull device which can be attached to an existing capacitive touch surface and can be pulled by a user. We then evaluated the accuracy of tensile strength estimation of the implemented device. The results showed that the outflow current value when stretched could be classified into four tensile strength levels.
We Uyghurs Have No Say: An Imprisoned Writer Speaks, ILHAMTOHTI / TRANSLATED BY YAXUE CAO / CINDY CARTER / MATTHEW ROBERTSON, London: Verso, 2022, 174 pp., £14.99

Journal of Law and SocietyVolume 49, Issue 4 p. 874-877 BOOK REVIEW We Uyghurs Have No Say: An Imprisoned Writer Speaks, ILHAM TOHTI / TRANSLATED BY YAXUE CAO / CINDY CARTER / MATTHEW ROBERTSON, London: Verso, 2022, 174 pp., £14.99 AMBREENA MANJI, Corresponding Author AMBREENA MANJI [email protected] School of Law and Politics, Cardiff University, Law Building, Museum Avenue, Cardiff, CF10 3AX Wales Email: [email protected]Search for more papers by this author AMBREENA MANJI, Corresponding Author AMBREENA MANJI [email protected] School of Law and Politics, Cardiff University, Law Building, Museum Avenue, Cardiff, CF10 3AX Wales Email: [email protected]Search for more papers by this author First published: 26 October 2022 https://doi.org/10.1111/jols.12388Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Volume49, Issue4December 2022Pages 874-877 RelatedInformation
Improving the performance of text classification models using keyword extraction and data augmentation techniques

Topic modeling aims to identify and categorize topics latent in documents, and is useful for exploring core topics of each document and the characteristics of the topics. However, a problem with interpreting topics this technique is that common terms often appear near the top of multiple topics, making it hard to extract keywords identifying the topics. Another weakness is that this technique can lead to loss of information when synonyms are excluded from keywords, and high performance often depends on the size and quality of data. To improve these problems, we propose a method that utilizes relevance and word embedding techniques for extracting keywords. In addition, we use the EDA(easy data augmentation) techniques to increase the size of the data, and then apply the KoBERT model for boosting performance on text classification tasks. As a result of data analysis, it was possible to grasp the specific characteristics of the topics based on the discriminating keywords. The results also showed that using the augmented data sets, the text classifier model has higher accuracy than the original data sets with a score of 0.94 and 0.85, respectively.
Data Analysis of the Authenticity of the National Anthems and Turkish National Anthem

National anthems are one of the indicators of independence of countries and talk about the sense of independence of countries. In this study, the National Anthem of the Republic of Turkey, Turkish National Anthem, is analyzed by data mining techniques.&#x0D; While analyzing Turkish National Anthem, the anthems of the member states of the Turkic Council with which they have a common language tie, the anthems of the member states of the Organization of Islamic Cooperation, which are thought to have common religious values, and the anthems of the European Union Member States, since Turkey is a European country, were included in the analysis.&#x0D; The analysis includes cluster analysis and association analysis techniques in terms of data mining. In terms of text mining, the similarities of Turkish National Anthem and other national anthems were examined in terms of word size, title size and sentiment analysis. As a result of the analysis, all the country anthems that were compared in one cluster, whose data were calculated as two clusters by cluster analysis, were included, while Turkish National Anthem took place alone in the other cluster. This is an important indicator in revealing the originality of Turkish National Anthem. Again, the analyzes in 3 dimensions under the title of text mining did not reveal a similarity between Turkish National Anthem and the national anthems compared. The results of the association analysis are important in terms of showing the use of common words and words in national anthems together.
Accuracy of Core Biopsy without Image Guidance in the Tissue Diagnosis of Breast Lump

Background: A breast lump is a localized swelling, protuberance, bulge or lump in the breast that feels different from the breast tissue around it or the breast tissue in the same area of the other breast. The aim of this study is to assess the accrurcy/efficacy of the free hand percutatnous core biopsy without image guidance for the palpable radiologically suspicious breast lump.&#x0D; Materials and methods: This is a retrospective cross sectional study based on reviewing the histopathological reports (59) of all patients of suspicious breast lump in a single center (By single surgeon, single pathologist). Study period was from July 2019 to June 2020.&#x0D; Results: The absolute sensitivity of free hand core biopsy for diagnosis of Carmona palpable lesion was 98%and was statically significant (p&lt;.00) and t=43.51. 2% found Carcinoma and other breast diseases.&#x0D; Conclusion: So considering the accuracy of tissue diagnosis of palpable suspicious breast lump is so high that image guidance is unnecessary.&#x0D; IAHS Medical Journal Vol 4(2), December 2021; 28-31
VALIDATION OF POTENCY TEST OF NEW GENERATION JAPANESE ENCEPHALITIS VACCINE IMOJEV BY PLAQUE FORMING UNIT (PFU) METHOD

Implementation of potency test for new generation Japanese Encephalitis vaccine (IMOJEV) is one of the functions of National Institute for Vaccines and Biologicals (NICVB) prior to market release. Potency test of IMOJEV was carried out by using Plaque Forming Unit (PFU) method for both standard and testing samples, the results was in the approved range of manufacturer. The process of validation of potency test for Japanese Encephalitis vaccine IMOJEV was built and implemented by practical validation method with following parameters: accuracy, repeatability, and intermediate precision of the potency method for Japanese encephalitis vaccine&#x0D; Results of study showed that the parameters of accuracy, repeatability and intermediate precision of potency test for Japanese Encephalitis IMOJEV were all qualified and in the approved range of manufacturer: titer of standard sample was about 4.25-5.09 log PFU/0.5 ml and titer of IMOJEV was between 4.0-5.8 log PFU/0.5ml&#x0D; Based on our study conclussion the the potency test for Japanese Encephalitis IMOJEV after validation was qualified for accuracy, repeatability and intermediate precision with coefficient of variation CV ≤ 25% so it is suitable with laboratory condition of NICVB.
FPGA Implementation of Enhanced Throughput Design of AES Architecture using Nikhilam Sutra

The exponential growth of the internet and contemporary communications users have established safety as a fundamental design feature for encrypted transmission. The Enhanced Cryptography Standard is perhaps the most widely used cryptography information security algorithm standard that has been authorized by NIST. This paper proposes a high-throughput design for the AES Algorithm with huge key sizes. AES would be a block cipher that ensures data security by using key lengths of 128,192 and 256-bits. The design concept focuses on a 256-bit key size classification algorithm since a big key size is required to ensure excellent security. Additionally, simultaneous key expansion &amp; encryption/decryption processes would be pipelined to maximize speed. Parallelization of a key expansion module's sub-processes would be used to reduce the critical chain latency. The S-box comprising sub-byte &amp; inverse sub-byte operations has been developed with compound field arithmetic operations to reduce time and area further. The work Increased throughput by 50%, area reduced by 34.32 %, and latency by 20% compared to the old approach with modified nikhilam sutra. Additionally, integrated AES encryption/decryption is planned and implemented on the FPGA Zed board utilizing Verilog HDL in Xilinx Vivado.
Robust Discriminative Non-Negative Matrix Factorization with Maximum Correntropy Criterion

Non-negative matrix factorization (NMF) is an effective dimension reduction tool widely used in pattern recognition and computer vision. However, conventional NMF models are neither robust enough, as their objective functions are sensitive to outliers, nor discriminative enough, as they completely ignore the discriminative information in data. In this paper, we proposed a robust discriminative NMF model (RDNMF) for learning an effective discriminative subspace from noisy dataset. In particular, RDNMF approximates observations by their reconstructions in the subspace via maximum correntropy criterion to prohibit outliers from influencing the subspace. To incorporate the discriminative information, RDNMF builds adjacent graphs by using maximum correntropy criterion based robust representation, and regularizes the model by margin maximization criterion. We developed a multiplicative update rule to optimize RDNMF and theoretically proved its convergence. Experimental results on popular datasets verify the effectiveness of RDNMF comparing with conventional NMF models, discriminative NMF models, and robust NMF models.
SENTINET: A DEEP SENTIMENT ANALYSIS NETWORK FOR POLITICAL MEDIA BIAS DETECTION

India has the highest circulation of newspapers in the world, but unfortunately also has high media bias rates and one of the lowest press freedom rankings for democracies. A biased media prevents citizens from receiving information that might be essential to public wellbeing by filtering information through a lens that supports government interests first. Media bias plays an influencing role even at the voting booth as propaganda can skew voter decisions and perceptions of what is true in this era of fake news. It's vital to keep an eye on bias in the news and to provide a platform where people can get unbiased and reliable news. Researchers in sentiment analysis and bias detection have been using various techniques to achieve higher accuracy to detect media bias. This study aims to take a different technical approach to the problem of Indian political media bias detection by developing SentiNet - a graphical processing unit (GPU) accelerated modified convolution neural network (CNN) model consisting of linearly inverted depth-wise separable convolutions capable of classifying news as either ‘unbiased’ or ‘biased’ from twitter data. Because of its simple architecture and lesser number of tuning parameters, it is observed that SentiNet is a good fit in terms of accuracy and loss function and its training time reduces by 50% when using a GPU. From results, Republic TV and BBC emerged as the most biased towards ruling party and Opposition parties respectively. NDTV and News19 emerged unbiased towards ruling party with balanced reporting. India TV has emerged as unbiased towards Opposition parties. From twitter political discourse, it is found that parties discuss themselves or their opposing parties and seldom issues of national interest. The research and the proposed robust model can be extended to other social media and be analysed for a bigger social network.
Machine Learning Prediction of Locomotion Intention from Walking and Gaze Data

In many applications of human–computer interaction, a prediction of the human’s next intended action is highly valuable. To control direction and orientation of the body when walking towards a goal, a walking person relies on visual input obtained by eye and head movements. The analysis of these parameters might allow us to infer the intended goal of the walker. However, such a prediction of human locomotion intentions is a challenging task, since interactions between these parameters are nonlinear and highly dynamic. We employed machine learning models to investigate if walk and gaze data can be used for locomotor prediction. We collected training data for the models in a virtual reality experiment in which 18 participants walked freely through a virtual environment while performing various tasks (walking in a curve, avoiding obstacles and searching for a target). The recorded position, orientation- and eye-tracking data was used to train an LSTM model to predict the future position of the walker on two different time scales, short-term predictions of 50[Formula: see text]ms and long-term predictions of 2.5[Formula: see text]s. The trained LSTM model predicted free walking paths with a mean error of 5.14[Formula: see text]mm for the short-term prediction and 65.73[Formula: see text]cm for the long-term prediction. We then investigated how much the different features (direction and orientation of the head and body and direction of gaze) contributed to the prediction quality. For short-term predictions, position was the most important feature while orientation and gaze did not provide a substantial benefit. In long-term predictions, gaze and orientation of the head and body provided significant contributions. Gaze offered the greatest predictive utility in situations in which participants were walking short distances or in which participants changed their walking speed.