Reviewerly/exHarmony huggingface dataset:
Smartphone-assisted augmented reality technology for preoperative planning in spine surgery

Objective. To present a virtual three-dimensional model of pathologically altered segments of the patient’s spine and to analyze the results of its application when planning a surgical intervention in the smartphone-assisted augmented reality. Material and Methods. A three-dimensional modeling of the target area of the intended surgical site was performed based on computed tomography data of five patients with various spinal deformities. A smartphone application has been developed that allows displaying a three-dimensional object of the intended surgical site in the form of augmented reality. Results. The created virtual three-dimensional models were successfully used in five cases for preoperative planning and simulation training before surgery, which allowed to see in detail the anatomical features of the spine, the location of vascular structures when contrasting them, and to plan the direction of the screws. The potential of using augmented reality in clinical practice was demonstrated. Conclusion. The advantages of the smartphone-assisted augmented reality technology for preoperative planning in spine surgery are the simplicity of creating a computer model, the possibility for a surgeon to use a three-dimensional model for orientation in complex anatomical zone at any time of surgery, and the reduction in the risk of technical errors.
Estimating causal effects with the neural autoregressive density estimator

Abstract The estimation of causal effects is fundamental in situations where the underlying system will be subject to active interventions. Part of building a causal inference engine is defining how variables relate to each other, that is, defining the functional relationship between variables entailed by the graph conditional dependencies. In this article, we deviate from the common assumption of linear relationships in causal models by making use of neural autoregressive density estimators and use them to estimate causal effects within Pearl’s do-calculus framework. Using synthetic data, we show that the approach can retrieve causal effects from non-linear systems without explicitly modeling the interactions between the variables and include confidence bands using the non-parametric bootstrap. We also explore scenarios that deviate from the ideal causal effect estimation setting such as poor data support or unobserved confounders.
SEMPIR : Sequence Multiple Point of Interest Recommender System for Overland Tourism

Recommender system is a tool that can help human find their preferences from a massive amount of data based. One of the practical implementation is for geogprahical recommender system. In this study, we propose such as geographical recommender system that recommend multiple Point of Interest (POI) in sequence manner. The usage of this method is for overland tourism. Overlan tourisme is one type of tourism that is currently chosen by many travelers or tourists. Tourists who choose the overland method tend to use a long time to visit several destination objects for a long time. The traveler will choose to start from one city and end in another city. Throughout the trip from the city, a traveler will visit several destination objects. Some travelers have problem when they want to plan their itenary or travel plans within in certain constrains such as certain budget. They find it difficult to visit anything and anywhere within the available budget. This research propose some algoritm solution to solve this problem by developing a recommendation system in list of tourist destinations with a constraint on the budget. The proposed method have several stages. One of the important stages is selecting a list of recommended destination object to be visited. This algorithm would produce set or list of recommended tourist in sequence of destination according to the available budget for a certain duration of time.
An End-to-End Network for Single Image Dedusting

Dust storm not only reduces visibility of natural scene, but also affects capture capability of outdoor monitoring equipment. Image dedusting aims to remove dust storm in images and make image scenes clearer. However, most of existing image enhancement-based methods have focused on defogging and deraining, and have paid little attentions to image dedusting. Therefore, research of image dedusting is necessary. In this paper, we presented an end-to-end network for single image dedusting. First, the proposed method uses an encoder module to encode a dusty image into image feature maps. Second, it uses the latest smoothed dilated residual convolution module to alleviate gridding artifacts caused by a widely-used dilated convolution. Third, it adopts an attention-based module to fuse image features at different levels and extract more contextual image information. Finally, it utilizes a decoder module, which decodes the feature maps, to obtain a residue, and adds it to the input dusty image to obtain the final image dedusting result. Experiments demonstrate the effectiveness of the proposed method in image dedusting.
Understand the Importance of Garments’ Identification and Combination to Blind People

Throughout our lives, we are very dependent on the ability to see the “world”. Everyday our eyes capture images that enable us to describe our ideas, our tastes and influence our identity. How we dress up is determined by our personal taste, mainly fed through our own eyesight. Once esthetics are supported by our vision, blind people is limited while performing tasks such as purchasing, selecting and matching outfits. These are considered easy tasks for visually enabled people and difficult tasks for people that cannot see. Therefore, within this scope it has been developed and distributed a survey to all the departments of the Association of the Blind and Amblyopes of Portugal (ACAPO). The survey has two main objectives: to recognize the importance of identifying and combing garments and to assess the needs in the development of clothing identification and combination systems that support blind people.
Chances of Interpretable Transfer Learning for Human Activity Recognition in Warehousing

Human activity recognition evolves around classifying and analyzing workers’ actions quantitatively using convolutional neural networks on the time-series data provided by inertial measurement units and motion capture systems. However, this requires expensive training datasets since each warehouse scenario has slightly different settings and activities of interest. Here, transfer learning promises to shift the knowledge a deep learning method gained on existing reference data to new target data. We benchmark interpretable and non-interpretable transfer learning for human activity recognition on the LARa order-picking dataset with AndyLab and RealDisp as domain-related and domain-foreign reference datasets. We find that interpretable transfer learning via the recently proposed probabilistic rule stacking learner, which does not require any labeled data on the target dataset, is possible if the labels are sufficiently semantically related. The success depends on the proximity of the reference and target domains and labels. Non-interpretable transfer learning via fine-tuning can be applied even if there is a major domain-shift between the datasets and reduces the amount of labeled data required on the target dataset.
Are Transformers a Modern Version of ELIZA? Observations on French Object Verb Agreement

Many recent works have demonstrated that unsupervised sentence representations of neural networks encode syntactic information by observing that neural language models are able to predict the agreement between a verb and its subject.We take a critical look at this line of research by showing that it is possible to achieve high accuracy on this agreement task with simple surface heuristics, indicating a possible flaw in our assessment of neural networks' syntactic ability.Our fine-grained analyses of results on the long-range French objectverb agreement show that contrary to LSTMs, Transformers are able to capture a non-trivial amount of grammatical structure.
On Random-Oracle-Free Top-Level Secure Certificateless Signature Schemes

Abstract Certificateless public key cryptography (CL-PKC) overcomes the difficulties of the certificate managements in traditional public key infrastructure (PKI) and the key escrow problem in ID-Based public key cryptography (ID-PKC), concurrently. In 2018, Tseng et al. proposed a certificateless signature (CLS) scheme and claimed that their proposal is the first scheme which satisfies the security against the level-3 KGC (according to Girault’s three categorizations of the honesty level of a trusted third party (TTP) which is proposed in 1991), in the standard model. However, we will show that unfortunately their scheme is even vulnerable against a malicious KGC. Afterwards, we will improve their scheme to be robust against the proposed attack. Finally, we will propose a CLS scheme secure against the level-3 KGC in the standard model, based on Yuan and Wang’s CLS scheme. We will show that our proposal not only satisfies the level-3 security as well as the basic security requirements of a CLS scheme in the standard model, but also is more efficient than the previous works in the sense of computation and communication costs.
Nosype: A Novel Nose-tip Tracking-based Text Entry System for Smartphone Users with Clinical Disabilities for Touch-based Typing

Smartphones are ubiquitous nowadays, be it for setting a reminder, quick messaging, or an email reply, which requires typing through a soft-keyboard. However, people with medical issues like dactylitis, sarcopenia, joint pains might feel difficulty in typing, using the conventional approach. Existing gaze or voice-based approaches do not work well without commercial trackers or in noisy environments. In this paper, we develop a novel technique called Nosype, a contact-free text entry system for such users. Nosype’s core functionality lies in nose-tip tracking and projection and allows the users to draw alphanumeric characters in the air for constructing a text. With 11 users with different clinical conditions, on a lab-scale, we observe that Nosype can help in typing with an average typing error rate of 6.9% and a typing-speed of 6.31 words/minute. A large-scale usability study with 60 participants, including 10 participants having clinical disabilities, shows an average usability score of 77.708.
Planning-integrated Policy for Efficient Reinforcement Learning in Sparse-reward Environments

Model-free reinforcement learning algorithms can learn an optimal policy from experience without requiring prior knowledge. However, model-free agents require vast amounts of samples, particularly in sparse reward environments where most states contain zero rewards. We developed a model-based approach to tackle the high sample complexity problem in sparse reward settings with continuous actions. A trained world model is queried by a particle swarm optimization (PSO) planner and employed as the action selection mechanism, hence taking the role of the actor in an actor-critic architecture. Parameters of the PSO regulate the agent's exploration rate. We show that the planner aids the agent to discover rewards even in regions with zero value gradient. Our simple planning integrated policy architecture learns more efficiently with fewer samples than continuous model-free algorithms.
Coins, Covid, Keynes and K-Shaped Recovery

Reckless monetary policy, especially in the wake of a pandemic, amplifies the gap between the extreme ends of the income distribution, thus exacerbating the long term effects of income inequality and loss of human capital. Attempts of federal stimulus bills fall short in timing and size, including directing spending towards those most in need. We propose a general smart contract protocol that distributes funds to targeted individuals with programmatic spending enforceability, alleviating the K-Shape recovery that current monetary policy is creating and turn it into the desired V-Shape. Utilizing incentive structures, our model directs spending to help stimulate the economies of targeted communities and struggling businesses. Smart contracts remove the current inefficiencies in the political trust and permission-based solution and allow for more transparency, verification, and incentives to help one’s community in times of need. Such a system allows for a more positive and direct relationship between those with funds and those who need funds.
Analyzing the Security of OTP 2FA in the Face of Malicious Terminals

One Time Password (OTP) is the most prevalent 2FA method among users and service providers worldwide. It is imperative to assess this 2FA scheme's security from multiple perspectives, considering its ubiquitous presence in the user's day-to-day activities. In this work, we assess the security of seven commercially deployed OTP-2FA schemes against malware in the terminal attack model without compromising any 2FA device or authentication services. To implement this attack scenario, we develop a combination of attack modules that will capture password and OTP in different ways during the user's login attempt. At the same time, it would originate a fresh concurrent hidden session from within the terminal or remotely to get possession to the user account without compromising the service or network or any external device. We examine implemented attack against seven different popular public services, which mostly use two variants of OTP-2FA and observed that almost all of them are vulnerable to this attack. Here, the threat model is practical as the attack components can be installed in the user's terminal without any root/administrator privilege. Moreover, the attack modules require a small number of resources to run. The whole procedure would run from the background that makes the attack very hidden in nature and attain low detectability after examining against prominent anti-malware programs that indicate a real-world threat. Our findings after the analysis of the OTP-2FA schemes indicate that an adversary who can install malware on the user's terminal can defeat almost all popular and widely used OTP-2FA schemes, which are vital security components of online accounts and secure financial transactions. The result also points out that the OTP-2FA scheme does not add extra security on top of the password in the presence of the malicious program in the terminal.
Non-Markovian dynamics of the driven spin-boson model

The non-Markovian dynamics of the driven spin-boson model at zero and finite temperature is investigated theoretically. Based on a unitary transformation and convolution theorem, the analytical expression of population difference in Laplace space is obtained. This method provides a tool to help us to understand the interference between driving and dissipation within the non-Markovian frame. We study the transient and the steady-state non-Markovian dynamics of population difference P(t) in the case of different temperature, driving and spin–bath coupling (SBC) strength. It has been shown that it is necessary to use non-Markovian methods when SBC is strong. At zero temperature, the curve of steady-state amplitude Plt versus Rabi driving amplitude Ω is N-like shaped. While at finite temperature, the curve becomes Λ-like shaped. These curves come from the interference between driving and dissipation. Also, from the interference pattern above, we know that large coherent oscillations can be obtained by optimizing the parameters: Rabi driving amplitude, temperature and SBC strength.
Bridging Semantic Web and Machine Learning: First Results of a Systematic Mapping Study

Both symbolic and subsymbolic AI research have seen a recent surge driven by innovative approaches, such as neural networks and knowledge graphs. Further opportunities lie in the combined use of these two paradigms in ways that benefit from their complementary strengths. Accordingly, there is much research at the confluence of these two research areas and a number of efforts were already made to survey and analyze the resulting research area. However, to our knowledge, none of these surveys rely on methodologies that aim to capture an evidence-based characterization of the area while at the same time being reproducible. To fill in this gap, in this paper we report on our ongoing work to apply a systematic mapping study methodology to better characterise systems in this area. Given the breadth of the area, we scope the study to focus on systems that combine semantic web technologies and machine learning, which we call SWeML Systems. While the study is still ongoing, we hereby report on its design and the first results obtained.
The real E-learning with virtual reality (VR) technology

In 2020, the world is facing a major crisis caused by the COVID-19 pandemic. This pandemic has had a profound impact on the global economic, political, cultural and social situation and reversed the lives of all people. Many schools have to suspend operations, education services in the traditional model cannot be deployed, and public health is given the top priority of a country. Faced with such a practical situation, governments of countries all over urgent need to take measures to stabilize the country. In the field of education, universities make the transition from traditional teaching models to online learning models, although there are still many obstacles to practical modules due to the unreal interactive method. Virtual reality (VR) technology is a newly applied way that creates excitement for users, increases the efficiency of interaction between learners and teachers, allow more learners to participate in accessing the environment in 3D space built graphically. Virtual reality technology completely immerses learners in a virtual situation, eliminating all external distractions and focusing on learning activities. By the method of synthesis, statistics, analysis and comparison, in this research, we mention the limitations in the online teaching model. Thus, proposing the use of VR technology in online teaching to innovate the way of interaction between teachers and learners to improve learning efficiency.
Urdu text in natural scene images: a new dataset and preliminary text detection

Text detection in natural scene images for content analysis is an interesting task. The research community has seen some great developments for English/Mandarin text detection. However, Urdu text extraction in natural scene images is a task not well addressed. In this work, firstly, a new dataset is introduced for Urdu text in natural scene images. The dataset comprises of 500 standalone images acquired from real scenes. Secondly, the channel enhanced Maximally Stable Extremal Region (MSER) method is applied to extract Urdu text regions as candidates in an image. Two-stage filtering mechanism is applied to eliminate non-candidate regions. In the first stage, text and noise are classified based on their geometric properties. In the second stage, a support vector machine classifier is trained to discard non-text candidate regions. After this, text candidate regions are linked using centroid-based vertical and horizontal distances. Text lines are further analyzed by a different classifier based on HOG features to remove non-text regions. Extensive experimentation is performed on the locally developed dataset to evaluate the performance. The experimental results show good performance on test set images. The dataset will be made available for research use. To the best of our knowledge, the work is the first of its kind for the Urdu language and would provide a good dataset for free research use and serve as a baseline performance on the task of Urdu text extraction.
k , l -Anonymity in Wheel-Related Social Graphs Measured on the Base of k -Metric Antidimension

For the study and valuation of social graphs, which affect an extensive range of applications such as community decision-making support and recommender systems, it is highly recommended to sustain the resistance of a social graph <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>G</mi> </math> to active attacks. In this regard, a novel privacy measure, called the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mfenced open="(" close=")" separators="|"> <mrow> <mi>k</mi> <mo>,</mo> <mi>l</mi> </mrow> </mfenced> </math> -anonymity, is used since the last few years on the base of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5"> <mi>k</mi> </math> -metric antidimension of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M6"> <mi>G</mi> </math> in which <math xmlns="http://www.w3.org/1998/Math/MathML" id="M7"> <mi>l</mi> </math> is the maximum number of attacker nodes defining the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M8"> <mi>k</mi> </math> -metric antidimension of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M9"> <mi>G</mi> </math> for the smallest positive integer <math xmlns="http://www.w3.org/1998/Math/MathML" id="M10"> <mi>k</mi> </math> . The <math xmlns="http://www.w3.org/1998/Math/MathML" id="M11"> <mi>k</mi> </math> -metric antidimension of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M12"> <mi>G</mi> </math> is the smallest number of attacker nodes less than or equal to <math xmlns="http://www.w3.org/1998/Math/MathML" id="M13"> <mi>l</mi> </math> such that other <math xmlns="http://www.w3.org/1998/Math/MathML" id="M14"> <mi>k</mi> </math> nodes in <math xmlns="http://www.w3.org/1998/Math/MathML" id="M15"> <mi>G</mi> </math> cannot be uniquely identified by the attacker nodes. In this paper, we consider four families of wheel-related social graphs, namely, Jahangir graphs, helm graphs, flower graphs, and sunflower graphs. By determining their <math xmlns="http://www.w3.org/1998/Math/MathML" id="M16"> <mi>k</mi> </math> -metric antidimension, we prove that each social graph of these families is the maximum degree metric antidimensional, where the degree of a vertex is the number of vertices linked with that vertex.
Research on the Realistic Dilemma and Optimized Path of Education Governance Modernization from the Perspective of Artificial Intelligence

Supported by Artificial Intelligence centered on heuristic search, machine learning, and expert systems, modern education governance presents the typical characteristics of governance process transparency, subject diversification and system intelligence. However, from the perspective of Artificial Intelligence, education governance modernization is not only stuck in the “structural” absence of multiple governance subjects, but also trapped in the “initiative” shortage of vertical one-way organizational structure and the “data” block of modern education governance paradigm. Thus, the application of Artificial Intelligence in intelligently mining data and information, autonomously adapting to complex educational situations, and systematically optimizing governance decisionmaking behavior is limited. This paper explores the optimized path of education governance from three dimensions of concept cultivation, subject training and technology innovation, rationally discusses the “interactive” application of Artificial Intelligence and education governance modernization, so as to promote the deep integration of artificial intelligence and education governance, and improve education governance modernization as well as talent cultivation in the new era.