Reviewerly/exHarmony huggingface dataset:
Recent Trends in IoT Healthcare-based Blockchain Solutions

This review revolves around the idea of inculcating and amalgamating all the possible forte of healthcare that can be governed and handled by Blockchain Technology (BCT). Relating back to all the archaic systems involved in the healthcare sector, the entire process was quite slow and many a time leads to a significant amount of delay in any process, be it report of the patient, tracking data from the smart watch, ruling out appointment from a doctor, medicine prescription and many more. BCT in fusion with the Internet of Things (IoT), leads to generating a completely revolutionary and robust system that expands its range from very minute detail to a completely new horizon. As predicted by 2022, more than 18 billion devices will be available across the world; thus, managing this data that is related to healthcare, devising BCT for same can yield some exponential results in advancement in health care sector. This entire healthcare data is quite vulnerable to all the cyber-attacks and all the collected valuable information may be at a huge risk; hence by deploying BCT, security can be greatly increased. BCT is nothing but an immutable time stamp series of records, just like an array working; when even one of the blocks is initialized with any value of information, that piece of information will remain there forever. This review attempts to group together all such various fields that can be brought under a single umbrella covering the range of fields that can be governed by BCT in accordance with IoT for the development of health care sector.
Consistent Training and Decoding For End-to-end Speech Recognition Using Lattice-free MMI

Recently, End-to-End (E2E) frameworks have achieved remarkable results on various Automatic Speech Recognition (ASR) tasks. However, Lattice-Free Maximum Mutual Information (LF-MMI), as one of the discriminative training criteria that show superior performance in hybrid ASR systems, is rarely adopted in E2E ASR frameworks. In this work, we propose a novel approach to integrate LF-MMI criterion into E2E ASR frameworks in both training and decoding stages. The proposed approach shows its effectiveness on two of the most widely used E2E frameworks including Attention-Based Encoder-Decoders (AEDs) and Neural Transducers (NTs). Experiments suggest that the introduction of the LF-MMI criterion consistently leads to significant performance improvements on various datasets and different E2E ASR frameworks. The best of our models achieves competitive CER of 4.1\% / 4.4\% on Aishell-1 dev/test set; we also achieve significant error reduction on Aishell-2 and Librispeech datasets over strong baselines.
Empathy and exclusion in the design process

Designers are now taught that empathy with users is crucial to technology design. We offer a warning that this dictum and its implementation, despite admirable intentions, can promote exclusion in design: Empathy will not bring the desired benefit to the design process if it is naively construed and understood as a feminine trait, if shortcuts are used to allegedly take the effort out of the empathic process, or if the social situation in which empathy is taking place is not considered. We show that these issues are closely coupled in design practices. Using personas—fictitious descriptions of people used to make users visible in the design process—as an example, we argue that the danger of reifying gendered assumptions might be inherent in those methods and tools in human-computer interaction research that are supposed to enable and strengthen empathy.
Integer Subspace Differential Privacy

We propose new differential privacy solutions for when external \emph{invariants} and \emph{integer} constraints are simultaneously enforced on the data product. These requirements arise in real world applications of private data curation, including the public release of the 2020 U.S. Decennial Census. They pose a great challenge to the production of provably private data products with adequate statistical usability. We propose \emph{integer subspace differential privacy} to rigorously articulate the privacy guarantee when data products maintain both the invariants and integer characteristics, and demonstrate the composition and post-processing properties of our proposal. To address the challenge of sampling from a potentially highly restricted discrete space, we devise a pair of unbiased additive mechanisms, the generalized Laplace and the generalized Gaussian mechanisms, by solving the Diophantine equations as defined by the constraints. The proposed mechanisms have good accuracy, with errors exhibiting sub-exponential and sub-Gaussian tail probabilities respectively. To implement our proposal, we design an MCMC algorithm and supply empirical convergence assessment using estimated upper bounds on the total variation distance via $L$-lag coupling. We demonstrate the efficacy of our proposal with applications to a synthetic problem with intersecting invariants, a sensitive contingency table with known margins, and the 2010 Census county-level demonstration data with mandated fixed state population totals.
A Study on the Improvement of Crime Victim Protection in Military Police

The purpose of this study is to suggest the need to strengthen the military police's investigative power for crimes that occur in military units and the direction of legislative and policy revision of the military police's role in protecting crime victims due to the incident. Recently, problems with the investigative power and jurisdiction of the Korean military police have been continuously raised both inside and outside the military, and there have been considerable internal improvement efforts, but it is pointed out that the system has not yet been sufficiently improved. Under the current law, frequent conflicts or inadequate institutional issues have been revealed in the military police investigation process, and these problems have emerged due to the acceleration of the military's civilization, so it is believed that human rights violations of crime victims will become serious. In particular, military police are in charge of investigating civilians with exceptions to most military personnel or military materials for general crimes, so the investigation procedures must be clearly based on laws and minimize possible abuse, but there are insufficient regulations to support them. In addition, the role of a special judicial police officer is also an increasingly important area, but domestic violence crimes and the protection of crime victims in the military unit have become important issues, and the law has been revised. According to the above-mentioned grounds, the enactment of relevant laws and institutional development necessary for the military police to perform their duties for domestic violence crimes (including dating crimes) and crime victims (including soldiers and military family victims) will fall within the scope of military police's important duties in the future. This study examined various related laws such as the Military Court Act and the Judicial Police Management Act, which are the legal basis for the exercise of the military police's investigative power, and derived matters pointed out by the National Human Rights Commission, the National Assembly, and the media regarding the actual effectiveness of the laws. In addition, in the application of various laws and regulations for investigation, military police and general police were compared and analyzed to analyze various disturbances that may cause problems and improvements to overcome them.
Improving Zero-shot Generalization and Robustness of Multi-modal Models

Multi-modal image-text models such as CLIP and LiT have demonstrated impressive performance on image classification benchmarks and their zero-shot generalization ability is particularly exciting. While the top-5 zero-shot accuracies of these models are very high, the top-1 accuracies are much lower (over 25% gap in some cases). We investigate the reasons for this performance gap and find that many of the failure cases are caused by ambiguity in the text prompts. First, we develop a simple and efficient zero-shot post-hoc method to identify images whose top-1 prediction is likely to be incorrect, by measuring consistency of the predictions w.r.t. multiple prompts and image transformations. We show that our procedure better predicts mistakes, outperforming the popular max logit baseline on selective prediction tasks. Next, we propose a simple and efficient way to improve accuracy on such uncertain images by making use of the WordNet hierarchy; specifically we augment the original class by incorporating its parent and children from the semantic label hierarchy, and plug the augmentation into text prompts. We conduct experiments on both CLIP and LiT models with five different ImageNet-based datasets. For CLIP, our method improves the top-1 accuracy by 17.13% on the uncertain subset and 3.6% on the entire ImageNet validation set. We also show that our method improves across ImageNet shifted datasets, four other datasets, and other model architectures such as LiT. The proposed method is hyperparameter-free, requires no additional model training and can be easily scaled to other large multi-modal architectures. Code is available at https://github.com/gyhandy/Hierarchy-CLIP.
Fine-grained Image Editing by Pixel-wise Guidance Using Diffusion Models

Our goal is to develop fine-grained real-image editing methods suitable for real-world applications. In this paper, we first summarize four requirements for these methods and propose a novel diffusion-based image editing framework with pixel-wise guidance that satisfies these requirements. Specifically, we train pixel-classifiers with a few annotated data and then infer the segmentation map of a target image. Users then manipulate the map to instruct how the image will be edited. We utilize a pre-trained diffusion model to generate edited images aligned with the user's intention with pixel-wise guidance. The effective combination of proposed guidance and other techniques enables highly controllable editing with preserving the outside of the edited area, which results in meeting our requirements. The experimental results demonstrate that our proposal outperforms the GAN-based method for editing quality and speed.
VILA: Improving Structured Content Extraction from Scientific PDFs Using Visual Layout Groups

Accurately extracting structured content from PDFs is a critical first step for NLP over scientific papers. Recent work has improved extraction accuracy by incorporating elementary layout information, e.g., each token's 2D position on the page, into language model pretraining. We introduce new methods that explicitly model VIsual LAyout (VILA) groups, i.e., text lines or text blocks, to further improve performance. In our I-VILA approach, we show that simply inserting special tokens denoting layout group boundaries into model inputs can lead to a 1.9% Macro F1 improvement in token classification. In the H-VILA approach, we show that hierarchical encoding of layout-groups can result in up-to 47% inference time reduction with less than 0.8% Macro F1 loss. Unlike prior layout-aware approaches, our methods do not require expensive additional pretraining, only fine-tuning, which we show can reduce training cost by up to 95%. Experiments are conducted on a newly curated evaluation suite, S2-VLUE, that unifies existing automatically-labeled datasets and includes a new dataset of manual annotations covering diverse papers from 19 scientific disciplines. Pre-trained weights, benchmark datasets, and source code are available at https://github.com/allenai/VILA.
Analysis of agricultural economic development and optimisation measures under the strategy of rural revitalisation

Abstract In order to better promote economic development, my country has successively issued many preferential policies, which have created more and better opportunities for economic development and the improvement of comprehensive national strength. My country’s agricultural development has more development momentum and long-term planning goals. This paper firstly sorts out the practical and theoretical significance of implementing the rural revitalisation strategy. Based on doing a good job in agricultural economic management, when conducting cointegration analysis, the stationarity of the time series is tested, and an error correction model (ECM) is introduced as a supplement. It lays foundation for the analysis of optimisation measures; secondly, the ADF test is carried out for the constructed ECM, and the entropy method and the analytic hierarchy process are used to calculate various indicators. Finally, according to the numerical results obtained, the agricultural economy under the rural revitalisation strategy is completed. The analysis of development and optimisation measures can fundamentally solve the limitations of the concept of rural development, break through the shackles of traditional development models, explore a path more suitable for the development of new rural areas and new agricultural economy and fundamentally improve the quality of production and life of farmers.
ONLINE SKILL TEST PLATFORM

Information communication and technology are the most important skills for 21 st -century learning and help promote other skills, including life and career skills and learning and innovation skills.This kind of learning allows the learner to connect as a learning network without barriers or borders.The growth of online education has taken our education system to another level.Now anyone can learn from anywhere, anytime as per convenience.In different platforms, questions link are shared with a submission time.Although, learners are taking up unfair means to clear the test provided online in which students usually search up the topic, use different means to get the answers, and get good marks.Hence, teachers cannot get an idea of who is good in the class and who needs extra attention.So, our idea is to make such a platform where the teacher will be taking the test just like our offline classes.In this platform, the teacher will be discussing every question after the students submit the answer in a time duration which will also be proctored and at the same time, the teacher will get the top performer and their submission time.This way we can assure minimal malpractice and identify the students who need more explanation for the questions.This will clear their doubts and the teacher understands the actual performance ratio.
Research on Skeleton Plate Flatness Detection Method Based on Laser Sensor

The flatness of the seismic isolation support skeleton plate determines the seismic isolation performance of the seismic isolation support, and in view of the limitations of the traditional measurement method in the engineering field, the use of laser sensor and computer-related processing technology is proposed to solve the problem of flatness measurement of the seismic isolation support skeleton plate. The laser sensor measurement can obtain the data of the skeleton plate plane, and use the relevant tools to calculate the difference between the maximum offset value and the minimum offset value from each measurement point to the ideal plane to obtain the flatness value. The experimental results show that the flatness measurement method of seismic isolation support skeleton plate based on laser sensor can quickly and accurately complete the measurement of skeleton plate flatness, with low requirements for engineering application environment and good measurement stability.
Towards a treatment of register phenomena in HPSG

In this paper, we deal with register-driven variation from a probabilistic perspective, as proposed in Schäfer, Bildhauer, Pankratz, Müller (2022). We compare two approaches to analyse this variation within HPSG. On the one hand, we consider a multiple-grammar approach and combine it with the architecture proposed in the CoreGram project Müller (2015) - discussing its advantages and disadvantages. On the other hand, we take into account a single-grammar approach and argue that it appears to be superior due to its computational efficiency and cognitive plausibility.
DRAINAGE STUDY OF WAREHOUSE DEVELOPMENT PT. ANEKA INDUSTRI, SIDOARJO DISTRICT

The PT. Aneka Industri warehouse is located on a level with a slope of approximately 0-1%. Survey results from PT. Aneka Industri Warehouse in Tambaksawah Village, Waru District, Sidoarjo Regency, showed a height of -0.20m, slightly above the flood level of -0.10m. The area is likely to flood due to the overflow of Afvoer Bunung, which flows out from other areas (runoff water) during heavy rains. In addition, the storage area is a dense industrial area, and the chance of rainwater infiltration and loss is small. According to the analysis, the flood discharge of Afvoer Buntung during the 25-year return period is 193,305 m3/s. Meanwhile, the cross-sectional capacity of Afvoer Buntung is 25.707 m3/s, so the cross-section of Afvoer Buntung at the storage site of PT. Aneka Industri is flooded. It rains for t = 30 minutes, total volume = 297.57 m3.
Trotter Errors and the Emergence of Chaos in Quantum Simulation

As noisy intermediate-scale quantum (NISQ) processors increase in size and complexity, their use as general purpose quantum simulators will rely on algorithms based on the Trotter-Suzuki expansion. We run quantum simulations on a small, highly accurate quantum processor, and show how one can optimize simulation accuracy by balancing algorithmic (Trotter) errors against native errors specific to the quantum hardware at hand. We further study the interplay between native errors, Trotter errors, and the emergence of chaos as seen in measurements of a time averaged fidelity-out-of-time-ordered-correlator
Pojok Baca “LAPAU” Sebagai Upaya Peningkatan Literasi Inklusi Sosial Masyarakat di Nagari Pariangan Kabupaten Tanah Datar

This research was aimed to investigate empirically about Pustaka Lapau in increasing reading habits for the community. This research was conducted in Nagari Tuo Pariangan, Tanah Datar Regency, West Sumatera. Pustaka Lapau was operated in this village since 2018 and has been giving the significant contribution to the people in Pariangan. The purpose of this research is to know the impact of the program of Pustaka Lapau. It is also to know the problems in managing reading corner like Pustaka Lapau. Qualitative method was used to conducting this research. Data was collected through interview and observation to the location. After analyzing the data, the researcher finally concluded the finding of this research. First, Pustaka Lapau is one of the solution for increasing reading habits of the people. Second, Pustaka Lapau gives significant contribution to enlarge people knowledge, science, economic and important information. Third, Pustaka Lapau is one of the best strategies for implementing social inclusion in the community.
Safety Aware Autonomous Path Planning Using Model Predictive Reinforcement Learning for Inland Waterways

In recent years, interest in autonomous shipping in urban waterways has increased significantly due to the trend of keeping cars and trucks out of city centers. Classical approaches such as Frenet frame based planning and potential field navigation often require tuning of many configuration parameters and sometimes even require a different configuration depending on the situation. In this paper, we propose a novel path planning approach based on reinforcement learning called Model Predictive Reinforcement Learning (MPRL). MPRL calculates a series of waypoints for the vessel to follow. The environment is represented as an occupancy grid map, allowing us to deal with any shape of waterway and any number and shape of obstacles. We demonstrate our approach on two scenarios and compare the resulting path with path planning using a Frenet frame and path planning based on a proximal policy optimization (PPO) agent. Our results show that MPRL outperforms both baselines in both test scenarios. The PPO based approach was not able to reach the goal in either scenario while the Frenet frame approach failed in the scenario consisting of a corner with obstacles. MPRL was able to safely (collision free) navigate to the goal in both of the test scenarios.
Cloud Computing Services for Mobile Applications

Mobile applications are now dominating the marketplace, and the ease with which users can install and use the applications has also increased. To solve the problem of back-end infrastructure not being to handle high amounts of traffic generated by worldwide audiences, mobile developers now use cloud computing technologies. There are different options available in the market. In this research paper, we will go through the cloud service providers like AWS, Google Cloud, Oracle Cloud, and Microsoft Azure Cloud and compare their offerings.
Fingerprint Based Voting System

Biometric Finger print devices are used in the Electronic Voting machine for voter verification. We have designed a finger print based voting machine where there is no need for the user to carry their government ID. The voter at the polling booth needs only to place their Finger on the device, thus allowing the acquisition of an on-spot fingerprint from the voter which serves as an identification. This Finger print reader reads the details from the tag. This data is passed onto the controlling unit for the verification. The controller fetches the data from the reader and compares this data with the already existing data stored during the registration of the voters. If the data matches with the pre-stored information of the registered fingerprint, the person is allowed to cast his vote. If not, a warning message is displayed on LCD and the person is barred from polling his vote. The vote casting mechanism is carried out manually. LCD is used to display the related messages, warnings and ensuing results.
All-to-key Attention for Arbitrary Style Transfer

Attention-based arbitrary style transfer studies have shown promising performance in synthesizing vivid local style details. They typically use the all-to-all attention mechanism -- each position of content features is fully matched to all positions of style features. However, all-to-all attention tends to generate distorted style patterns and has quadratic complexity, limiting the effectiveness and efficiency of arbitrary style transfer. In this paper, we propose a novel all-to-key attention mechanism -- each position of content features is matched to stable key positions of style features -- that is more in line with the characteristics of style transfer. Specifically, it integrates two newly proposed attention forms: distributed and progressive attention. Distributed attention assigns attention to key style representations that depict the style distribution of local regions; Progressive attention pays attention from coarse-grained regions to fine-grained key positions. The resultant module, dubbed StyA2K, shows extraordinary performance in preserving the semantic structure and rendering consistent style patterns. Qualitative and quantitative comparisons with state-of-the-art methods demonstrate the superior performance of our approach.