Reviewerly/exHarmony huggingface dataset:
Static-Dynamic Co-teaching for Class-Incremental 3D Object Detection

Deep learning-based approaches have shown remarkable performance in the 3D object detection task. However, they suffer from a catastrophic performance drop on the originally trained classes when incrementally learning new classes without revisiting the old data. This "catastrophic forgetting" phenomenon impedes the deployment of 3D object detection approaches in real-world scenarios, where continuous learning systems are needed. In this paper, we study the unexplored yet important class-incremental 3D object detection problem and present the first solution - SDCoT, a novel static-dynamic co-teaching method. Our SDCoT alleviates the catastrophic forgetting of old classes via a static teacher, which provides pseudo annotations for old classes in the new samples and regularizes the current model by extracting previous knowledge with a distillation loss. At the same time, SDCoT consistently learns the underlying knowledge from new data via a dynamic teacher. We conduct extensive experiments on two benchmark datasets and demonstrate the superior performance of our SDCoT over baseline approaches in several incremental learning scenarios. Our code is available at https://github.com/Na-Z/SDCoT.
Deep Translation Prior: Test-Time Training for Photorealistic Style Transfer

Recent techniques to solve photorealistic style transfer within deep convolutional neural networks (CNNs) generally require intensive training from large-scale datasets, thus having limited applicability and poor generalization ability to unseen images or styles. To overcome this, we propose a novel framework, dubbed Deep Translation Prior (DTP), to accomplish photorealistic style transfer through test-time training on given input image pair with untrained networks, which learns an image pair-specific translation prior and thus yields better performance and generalization. Tailored for such test-time training for style transfer, we present novel network architectures, with two sub-modules of correspondence and generation modules, and loss functions consisting of contrastive content, style, and cycle consistency losses. Our framework does not require offline training phase for style transfer, which has been one of the main challenges in existing methods, but the networks are to be solely learned during test time. Experimental results prove that our framework has a better generalization ability to unseen image pairs and even outperforms the state-of-the-art methods.
RAST: Domain-Robust Dialogue Rewriting as Sequence Tagging

The task of dialogue rewriting aims to reconstruct the latest dialogue utterance by copying the missing content from the dialogue context.Until now, the existing models for this task suffer from the robustness issue, i.e., performances drop dramatically when testing on a different dataset.We address this robustness issue by proposing a novel sequence-taggingbased model so that the search space is significantly reduced, yet the core of this task is still well covered.As a common issue of most tagging models for text generation, the model's outputs may lack fluency.To alleviate this issue, we inject the loss signal from BLEU or GPT-2 under a REINFORCE framework.Experiments show huge improvements of our model over the current state-of-the-art systems when transferring to another dataset.
Insights of JSON Web Token

In almost every organization where user sensitive data is available, security and privacy of the data plays a vital role. As far as computer science is concerned, it is just a game of saving data in unrecognizable format and accessible to authorized person. User sensitive data mainly includes passwords which are required for the sessions but need to be handled and stored safely.As storage of these information is overhead in database, Tokens are generated which handles sessions and also self contains user details. One of such widely used stateless token is Json Web Token. This paper deals with the introduction, working and algorithms of Json web token. Also pros, cons, hacking possibilities, Proper usage and security measures of JWT are discussed.
Teaching Design of Integrated Course of Reading and Writing Based on In-depth Learning in Senior High School — Take Unit 3 Computers as an Example

In-depth learning is a learning method, which can transfer knowledge and solve problems in various complex and real situations through learners' critical understanding and thinking towards learning content. Guided by the subject core competence, the integrated course of English reading and writing should combine autonomous and cooperative learning to promote the perceptual understanding, and then design multi angle writing training such as text structure imitation, plot rewriting and ending continuation, so as to promote the application and transfer of language knowledge to promote students' in-depth learning.
Evaluating changes in gait and activity associated with cognitive impairment using a home‐based technology platform

Changes in mobility are associated with cognitive decline in older adults. Mobility is frequently assessed in the clinic setting at episodic intervals. Passive sensors within a home-based technology platform allow for unobtrusive collection of mobility and gait information over an extended time period. This method of high-frequency data collection could be sensitive to early changes in mobility associated with cognitive decline.We present data for a cohort of 32 participants living alone and enrolled in the Collaborative Aging Research Using Technology (CART) Initiative aging study with a pervasive sensing and computing system deployed in their homes. Sixteen individuals with a Clinical Dementia Rating (CDR) scale score of 0.5 were matched by age, sex and education to 16 participants with a CDR of 0; 80% lived in low-income senior apartments; 6% were non-white. Mobility and gait data is derived from a gait line, consisting of four ceiling mounted, field-of-view restricted passive infrared motion sensors placed 61 cm apart in a line, in each home. The number of walking events, defined as all four sensors being triggered, was analyzed between the two groups.Participants with a CDR score of 0 had a mean age of 72.1 years and a mean MoCA score of 26.1 (range 21 - 30). Participants with a CDR score of 0.5 had a mean age of 72.2 and a mean MoCA score of 22.1 (range 15 - 27). Fifty-six percent of participants in each group were female. Preliminary data from the gait line from a combined 1400 days for CDR 0 participants and a combined 1250 days for CDR 0.5 participants is presented here. The mean number of daily walking events detected by the gait line was significantly greater for CDR 0 participants (101.4) compared to CDR 0.5 participants (92.8, p=0.03).Ambient sensors are able to collect longitudinal data on gait and activity levels in individuals with a technology platform deployed within their home. This unobtrusive remote mobility assessment methodology identifies individuals experiencing cognitive impairment. Further evaluation of other gait metrics and home-based activity patterns is ongoing to explore their association with changes in cognition.
Analysis of Leaf Litter as Potential Renewable Energy Source for Cooking

This work explored the potential of leaf litter as biofuel for eco-stoves in the form of shredded and pelleted fuels.Kerai Payung leaves (Filicium decipiens) was used as the fuel.The pelleted fuel was formed by mixing shredded leaves, starch, and water.An eco-cookstove that has an approximately 660 cm 3 volume capacity chamber for solid fuels was used in this work.The stove has a fan attached that supplies an updraft forced vortex flow.The heat release rates of the fuels were measured by using standard cone calorimetry (ISO 5660).It was found that the fuel shape and internal transport processes in fuel beds had a strong influence on the combustion characteristics and the energy released.Peak heat release rates for shredded leaves were lower than the pelleted fuels with a shorter burning period.The results show that leaf litter has the potential as a reliable energy source.
An Empirical Analysis of using Blockchain Tech-nology with Internet of Things and its Application

Blockchain technology uses the cryptographic technique to create expanding list of data records called blocks. Along with transaction and timestamp data, each block holds a hash value obtained using cryptographic technique. Blockchain gains importance for its decentralized data transaction and authorization without the need for third-party intervention. Although, it is mostly used in Finance sector these days, due to its inherent ability to protect data it can be applied to every field of computation especially in fields where data transaction is voluminous. Internet of Things (IoT) is one such area where it involves collection, transfer and processing of real time data from objects, humans and sensors to automate various tasks. Hence, this paper reviews the blockchain technology, and how it can be coupled with IoT to overcome the privacy and security issues. This paper first systematically introduces the concept of blockchain technology, its applications along with the need for IoT devices and its implementation. Finally, it discusses the blockchain based IoT (BIoT) its architecture, advantages, challenges in implementation
Language Models are Few-Shot Butlers

Pretrained language models demonstrate strong performance in most NLP tasks when fine-tuned on small task-specific datasets.Hence, these autoregressive models constitute ideal agents to operate in text-based environments where language understanding and generative capabilities are essential.Nonetheless, collecting expert demonstrations in such environments is a time-consuming endeavour.We introduce a two-stage procedure to learn from a small set of demonstrations and further improve by interacting with an environment.We show that language models fine-tuned with only 1.2% of the expert demonstrations and a simple reinforcement learning algorithm achieve a 51% absolute improvement in success rate over existing methods in the ALFWorld environment.Goal: Rinse the egg to put it in the microwave.Obs: Looking quickly around you, you see a cabinet, a garbagecan, a coffeemachine, [...], a stoveburner, a sinkbasin and a microwave.Action: go to sinkbasin Obs: You arrive at sinkbasin.You see a butterknife, a potato, a spoon and a tomato.Action: go to garbagecan Obs: You arrive at garbagecan.You see an egg.Action: take egg from garbagecan Obs: You pick up the egg from the garbagecan.Action: go to sinkbasin Obs: You arrive at sinkbasin.You see a butterknife, a potato, a spoon and a tomato.Action: clean egg
Examining the Skill in Writing Descriptive Text Among Indonesian Learners of English: The Effects of Task-Based Language Teaching (TBLT)

This study aims to examine the learners’ skills in writing English descriptive text by employing The Explanatory-Sequential (QUAN-QUAL) research method. The data collection instruments were Writing Tests and Interview. The written tests were given in the form of pre and post-test to reveal the effect of Task-Based Language Teaching (TBLT) on improving the learners’ English writing skills. Interviews were held to confirm factors affecting the students’ skills in writing English descriptive texts. 27 learners of MA Madani Alauddin Pao-Pao, Gowa were purposively selected to participate in this study. The results reveal two things namely: The first, there is a statistically significant difference between mean scores of the experimental group (64.85) and the control group (55.03) in the test result after the treatment (post-test). The value according to Student's t-test confirms it: t = 5,846; p &lt; 0,001. This result supports the effectiveness of employing Task-Based Language Teaching (TBLT) as a method in teaching English writing skills. The second, the interviews indicate that there are 5 determinants affecting the learners’ skills in writing English descriptive texts, i.e. Learners’ Interest, Teacher’s Strategy, Teacher’s Media, Classroom Environment, and Learners’ Learning Strategy. Our results revealed that using TBLT has led to significant increases in the students’ skill in writing descriptive texts. We believe, our findings could have a profound impact on the way English writing is taught around the world. Therefore, English language teachers should provide more opportunities to learn this method to enable them apply it in their English pedagogy.
Communicating with Humans and Robots: A Motion Tracking Data Glove for Enhanced Support of Deafblind

In this work, we discuss the design and development of a communication system for enhanced support of the deafblind.The system is based on an advanced motion tracking Data Glove that allows for high fidelity determination of finger postures with consequent identification of the basic Malossi alphabet signs.A natural, easy-to-master alphabet extension that supports single-hand signing without touch surface sensing is described, and different scenarios for its use are discussed.The focus is on using the extended Malossi alphabet as a communication medium in a Data Glove-based interface for remote messaging and interactive control of mobile robots.This may be of particular interest to the deafblind community, where distant communications and robotized support and services are rising.The designed Data Glove-based communication interface requires minimal adjustments to the Malossi alphabet and can be mastered after a short training period.The natural interaction style supported by the Data Glove and the popularity of the Malossi alphabet among the deafblind should greatly facilitate the wider adoption of the developed interface.
Information Technology for Designing Rule bases of Fuzzy Systems using Ant Colony Optimization

This paper proposes the universal information technology for designing the rule bases (RB) with the formation of optimal consequents for fuzzy systems (FS) of different types on the basis of ant colony optimization (ACO) techniques. The developed ACO-based information technology allows effectively synthesizing rule bases of various dimensions both for the MISO and MIMO fuzzy systems taking into account the particular features of the RB consequents formation in the conditions of insufficient initial information. In order to study and validate the efficiency of the presented information technology the design of the RB for the adaptive fuzzy control system of the ship steering device is carried out in this work. The computer simulations results show that adaptive control system with developed RB provides achievement of high enough quality indicators of rudder angle control. Thus, application of the proposed ACO-based information technology allows designing effective RB with optimal consequents by means of minor computational costs that, in turn, confirms its high efficiency.
Multicopy and Multiserver Provable Data Possession for Cloud-Based IoT

Provable data possession (PDP) is widely considered to be an efficient method in verifying the integrity of remote data. While earlier PDP schemes are generally designed to check the integrity of data copies on a single cloud server (CS), there have been attempts to design multicopy and multiserver PDP (MCMS-PDP) schemes in recent years. However, it is known that MCMS-PDP schemes may be vulnerable to copy-summation attacks or do not support dynamic operations. The former enables a (dishonest) CS to only store a summation of copies for successful verification, while the latter does not allow the data owner to update the stored data. In this article, we propose a new MCMS-PDP scheme based on homomorphic verifiable tags. Specifically, our proposed scheme is designed to check the integrity of all copies in one challenge–response and resist copy-summation attacks. The scheme also supports public verification and block-level dynamic operations, such as modification, insertion, and deletion using the divide-and-conquer table. We then prove the security of our scheme, assuming the intractability of the computational Diffie–Hellman problem, in the random oracle model. We also evaluate the performance of the scheme to demonstrate its efficiency.
Cyber Security Risks And Challenges In Remote Work Under The Covid-19 Pandemic

The COVID-19 pandemic is having a negative impact on corporate cybersecurity in terms of workforce habits and digital skills, awareness of cyber hygiene, and the rapid shift to remote work solutions.It has also reiterated the importance of employer-led risk management practices in line with employment habits.During the COVID-19 pandemic the remote workforce has diversified and increased in numbers, offering employees the opportunity to continue working, the availability of which is also facilitated by the national approach to improving the epidemiological situation, which mandated wider use of remote work for those whose work specifics allow it.The study analyzed the current cyber risks and challenges affected by the COVID-19 pandemic, as well as risk management approaches or security controls.The novelty of research relates to the identification of changes in working patterns and their correlation with applied control measures for the management of cybersecurity risks.Data collection tools such as questionnaire and interviews with experts were used.Research conclusions reflect that the approach of companies and organizations to cyber risk management, providing a form of remote work organization is tailored to the industry, the nature of the information processed, the computer skills of employees, and pre-COVID-19 investments in corporate cybersecurity and digital transformation.Employers and information security managers are likely to continue to pay increased attention to limiting the risks associated with the human factor, which has become more multi-faceted during the COVID-19 pandemic, given the psychoemotional challenges of remote work experienced by a large proportion of workers.
Improved Modular Division Implementation with the Akushsky Core Function

The residue number system (RNS) is widely used in different areas due to the efficiency of modular addition and multiplication operations. However, non-modular operations, such as sign and division operations, are computationally complex. A fractional representation based on the Chinese remainder theorem is widely used. In some cases, this method gives an incorrect result associated with round-off calculation errors. In this paper, we optimize the division operation in RNS using the Akushsky core function without critical cores. We show that the proposed method reduces the size of the operands by half and does not require additional restrictions on the divisor as in the division algorithm in RNS based on the approximate method.
Applications of AR and VR Technologies in Healthcare Marketing

Applications of AR and VR Technologies in Healthcare Marketing Nishant Renu Abstract The healthcare industry faces a variety of unique challenges when it comes to marketing and communications. The whole industry has grappled with these distinct and specific communications and marketing strategies on several occasions. As a result, new technologies like virtual reality and augmented reality have emerged as problem solvers, dramatically altering the game and the future of healthcare and pharmaceutical communications. This article focuses on the usage of virtual reality (VR) and augmented reality (AR) in healthcare and pharmaceutical marketing and sales. After in-depth qualitative research on primary and secondary sources, this article describes multiple VR and AR technologies applications in healthcare and pharmaceutical marketing and sales. It helps in experiential marketing that allows customers to easily evaluate products and service innovations they are about to buy just as if they had their physical version by themselves. Further, attracting, converting, and retaining customers and educating digitally engaged customers on several aspects of the technology. This study suggests that with the inclusion of VR and AR technology, we can create genuine, authentic, and engaging experiences for marketing and sales in the healthcare and biotech industry in the future. Full Text: PDF DOI: 10.15640/jmm.v9n2a5
Improved image inpainting exemplar-based algorithms by boundary priori-knowledge

Image inpainting plays an important role in restoration of cultural relics, pictures beautification. Criminisi algorithm creates good results in large-area inpainting. However, it does still have some deficiencies such as over-extending. In this paper, two improved algorithms based on prior knowledge of the boundary had been proposed by simulating the idea of manual repairing. An algorithm, by simulating the strategy that the next inpainted pixel will be near to the prior one, named nearer neighbor first algorithm, can void the random bounding of the to-be-inpainted pixle. Another algorithm, by simulating the strategy that the inpainting process, named no-inpainted first algorithm, will be in multiple directions, can void the inpainting process in a single direction. The results reveal that the neighborhood-first algorithm performs better than Criminsi algorithm in repairing the missing structure while the unrepaired-first algorithm performs better than Criminsi algorithm in repairing the missing texture .
Latent-Insensitive Autoencoders for Anomaly Detection

Reconstruction-based approaches to anomaly detection tend to fall short when applied to complex datasets with target classes that possess high inter-class variance. Similar to the idea of self-taught learning used in transfer learning, many domains are rich with similar unlabeled datasets that could be leveraged as a proxy for out-of-distribution samples. In this paper we introduce the latent-insensitive autoencoder (LIS-AE) where unlabeled data from a similar domain are utilized as negative examples to shape the latent layer (bottleneck) of a regular autoencoder such that it is only capable of reconstructing one task. We provide theoretical justification for the proposed training process and loss functions along with an extensive ablation study highlighting important aspects of our model. We test our model in multiple anomaly detection settings presenting quantitative and qualitative analysis showcasing the significant performance improvement of our model for anomaly detection tasks.
A Sentiment-Enhanced Corruption Perception Index

Direct measurement of corruption is difficult due to its hidden nature, and measuring the perceptions of corruption via survey-based methods is often used as an alternative.This paper constructs a new non-survey based perceptions index for 111 countries by applying sentiment analysis to Financial Times articles over 2005-18.This sentiment-enhanced corruption perception index (SECPI) captures not only the frequncy of corruption related articles, but also the articles' sentiment towards corruption.This index, while correlated with existing corruption perception indexes, offers some distinct advantages, including heightened sensitivity to current events (e.g., corruption investigations and elections), availability at a higher frequency, and lower costs to update.The SECPI is negatively correlated with business environment and institutional quality.Increases in the perceived incidence or scope of corruption influences economic agents' behaviors, and thus economic dynamics.We found that when the SECPI is at least one standard deviation above the mean, the growth per capita falls by 0.65 percentage point on average, with more pronounced impacts for emerging market and low income countries.