Reviewerly/exHarmony huggingface dataset:
Person Re-Identification in a Car Seat: Comparison of Cosine Similarity and Triplet Loss based approaches on Capacitive Proximity Sensing data

Currently there is much research in the area of person identification. Mostly it is based on multi-biometric data. In this paper, we aim to leverage soft biometric properties to achieve person re-identification by using unobtrusive sensors, envisioning assistive environments, which recognize their user and thus automatically personalize and adapt. In practice, a car seat recognizes the person who sits down and greets the person with their own name, enabling various customisation in the car unique to the user, like seat configurations.
Search-based Path Planning for a High Dimensional Manipulator in Cluttered Environments Using Optimization-based Primitives

In this work we tackle the path planning problem for a 21-dimensional snake robot-like manipulator, navigating a cluttered gas turbine for the purposes of inspection. Heuristic search based approaches are effective planning strategies for common manipulation domains. However, their performance on high dimensional systems is heavily reliant on the effectiveness of the action space and the heuristics chosen. The complex nature of our system, reachability constraints, and highly cluttered turbine environment renders naive choices of action spaces and heuristics ineffective. To this extent we have developed i) a methodology for dynamically generating actions based on online optimization that help the robot navigate narrow spaces, ii) a technique for lazily generating these computationally expensive optimization actions to effectively utilize resources, and iii) heuristics that reason about the homotopy classes induced by the blades of the turbine in the robot workspace and a Multi-Heuristic framework which guides the search along the relevant classes. The impact of our contributions is presented through an experimental study in simulation, where the 21 DOF manipulator navigates towards regions of inspection within a turbine.
Vulnerability of Virtual Private Networks to Web Fingerprinting Attack

Virtual private networks (VPN) are used to maintain secrecy of internet usage. They provide end-to-end encrypted traffic to hide the content and destination details from potential eavesdroppers. Recent studies show that 72% of VPN users apply it to access blocked content or hide identity from government. Concerned government departments and other organizations need to analyze the encrypted traffic of VPN to observe whether people are using blocked content or not. Typical traffic analysis fails in this case as traffic is encrypted and the destination IP address is hidden. However, traffic metadata and some packet attributes, like packet size and time, could be considered as fingerprint of any specific web service. In this paper, we analyze five commonly used VPN services, namely: Psiphone, Softether, HotspotShield, OpenVPN, and AviraPhantom. Our goal is to identify which VPN service is vulnerable to Cai et al. fingerprinting attack, and what types of web services are most appropriate to detect using this attack. The results show that Open VPN is more vulnerable to this attack compared to the other VPNs in this study. The efficiency of the web traffic classification through VPN is also estimated for four different web services with Cai et al. Useful recommendations are provided as a result of this study.
Using Deep Structured Semantic Model to Analysis Text Documents in the Building Normative Base

This paper analyzes artificial neural networks that can been used to search for web documents in the electronic database of regulatory documents in the field of construction and building materials. The expediency of using artificial neural networks of the category Deeply Structured Semantic Models to solve the problem of semantic analysis of text documents contained in the regulatory framework of buildings has substantiated. Deeply structured semantic models perform a nonlinear projection to map a query into a common semantic space. After such a mapping, the relevance of each document found on the query has calculated by the cosine of the angles between the vector query model and the vector document model. In addition, the architecture of a deeply structured semantic model uses hidden layers that has designed to resize input vectors. This allows models to manipulate vectors of different sizes. The scheme for identifying different documents on the same issue has proposed. The possibility of applying models and methods of fuzzy mathematics to formalization of texts of building norms and rules and expression of their semantics in the internal language of the Semantic Text Information Analysis System has shown.
Isomorphisms of Galois groups of number fields with restricted ramification

Let $K$ be a number field and $S$ a set of primes of $K$. We write $K_S/K$ for the maximal extension of $K$ unramified outside $S$ and $G_{K,S}$ for its Galois group. In this paper, we answer the following question under some assumptions: "For $i=1,2$, let $K_i$ be a number field, $S_i$ a (sufficiently large) set of primes of $K_i$ and $\sigma :G_{K_1,S_1}\to G_{K_2,S_2}$ an isomorphism. Is $\sigma$ induced by a unique isomorphism between $K_{1,S_1}/K_1$ and $K_{2,S_2}/K_2$?" Here the main assumption is about the Dirichlet density of $S_i$.
Predicting Effects of University Service Quality and Internet Use Motives Towards Study Abroad Students’ Depression

The number of students participating in study abroad programs has been increasing for the past decades. In Taiwan, the rising number of incoming study abroad students have also helped in augmenting the oversupply of higher education. However, it is reported that within study abroad situations culture shock does happens. In the current study, it is assumed that the internet has already created a highly connected society. Hence, study abroad depression should be minimized. In addition, with the current drive for the advancement of service quality within higher education, these perceived positive interactions between the school and students should be able to further lessen study abroad related difficulties. With the trend of knowledge management within university institutional research, the collection and understanding of data with regards to study abroad students is quite important for future policy recommendations. To further understand these assumptions, information from a total of 665 study abroad students in Taiwan are surveyed. Service quality (SERVQUAL), the Study Abroad Internet Use Motives Survey (IUM), and the Center for Epidemiologic Studies Depression Scale (CES-D) were used to collect the information. Results show that IUM factors online habits and online facilitation, and the SERVQUAL factors tangibles and reliability are able to predict study abroad students’ depression. In essence, for study abroad to become successful universities should make use of such tools and provide the appropriate adjustments within their service provisions.
Interaction of Human Cognitive Mechanisms and “Computational Intelligence” in Systems that Support Teaching Mathematics

The article presents an analysis of a number of projects in which the authors of the article participated. All projects are related to computer support for the productive activities of students in the process of studying mathematics. All analyzed systems can be considered as systems that make human cognitive mechanisms interact with machine “computational intelligence”. Based on the analysis of projects, various ways of coordinating the user’s mental activity with the pseudo-intellectual behavior of systems, which provide an intelligent dialogue in the process of solving mathematical problems, are identified. It is shown that the support of productive activity of students, including research and constructive activity, requires non-standard computer systems, and that the separation of intellectual operations between a learning system and a person supports the productive components of the learning process and initiates the development of new pedagogical approaches.
An Analysis of the Cognitive Process and Similarities of Complex Problem Solving Discussions

The concept of complex problem solving (CPS) as a new cognitive scientific area was initially introduced by Doner 30 years ago. It was not until recently that the verdicts of CPS studies were gradually being applied in business training . This research aims to convert lab findings into light-weighted cognitive-oriented guidelines for CPS. To achieve this purpose, we investigated the pattern of the underlying cognitive process in CPS discussions. This paper proposes a method that compares cognitive similarities based on the Kolmogorov–Smirnov test. Instead of comparing the semantic similarity, this method compares the proportion of conversation content. It serves the purpose of examining the similarities of thinking processes. From the correlation between the subjective problem conflict level and cognitive similarities, we found that a discussion process best practice can help build a more comprehensive problem understanding.
NerdBug: automated bug detection in neural networks

Despite the exponential growth of deep learning software during the last decade, there is a lack of tools to test and debug issues in deep learning programs. Current static analysis tools do not address challenges specific to deep learning as observed by past research on bugs specific to this area. Existing deep learning bug detection tools focus on specific issues like shape mismatches. In this paper, we present a vision for an abstraction-based approach to detect deep learning bugs and the plan to evaluate our approach. The motivation behind the abstraction-based approach is to be able to build an intermediate version of the neural network that can be analyzed in development time to provide live feedback programmers are used to with other kind of bugs.
On Generation of Cycles, Chains and Graphs of Pairing-Friendly Elliptic Curves

We study the problem of generation of cycles, chains and graphs of pairing-friendly elliptic curves using in succinct non-interactive arguments for knowledge protocols in blockchain. The task to build a “stick” for existing MNT753 cycle is reduced to the factorization problem for big numbers. Together with graphs of pairing friendly elliptic curves we consider auxiliary graphs of their orders (primes or irreducible polynomials) associated to vertices and embedding degrees to edges. Numerical experiments allow us to conjecture that (except of MNT case): 1) for any fixed embedding degrees there exist only finite number of such cycles and, hence, there are no families of such cycles; 2) chains of prime order are very rare; we suppose that there are no polynomial families of such chains. It is hard to find a family of pairing friendly elliptic curves with the base field order q(x) such that ζ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> ∈ Q[x]/(q(x)) for k > 6. From other hand our examples show that we can apply Brezing-Weng construction with k=6 and D=3 iteratively to obtain chains of length 3-4. We build 1) a family of 1-chains with embedding degrees 8 and 7, where all orders are given by cyclotomic polynomials; 2) a combination of MNT cycle and near-MNT curve.
Evaluating the Use of Visual Prompts in Online Meeting Applications for Kindergarteners

The use of online meeting applications for educational purpose, has increased during pandemic. These meeting applications mostly use textual prompts as a way of communicating with the user, which might not be suitable for kindergarteners. To enhance the usability of meeting applications, Visual Prompts and visual cues are suggested to apply in this research for attaining better user experience, and the efficacy and efficiency is proved by the experiments and results of this study. A task-based user experiment was designed for kindergarten children, aged from three to seven, where they were asked to complete certain tasks based on three different Visual Prompt designs in the online meeting application interface. The Visual Prompts were designed using Unity software for Zoom application platform. The results were analyzed for task success rate and task completion time using SPSS statistical analysis software. The results showed high t-values of success rate (t = 5.093) and task completion time (t = 10.093), which indicates that the technical scaffolding, in the form of Visual Prompts, is more helpful to communicate the information for a given task. Moreover, it was concluded that the use of Visual Prompts enhanced the learnability of an online meeting application for young children, whereas the textual prompts lagged in performance due to underdeveloped language skills of the same age group.
Analytical View on Non-invasive Measurement of Moving Charge by Various Topologies of Wannier Qubit

Detection of moving charge in free space is presented in the framework of single electron CMOS devices. It opens the perspective for construction of new type detectors for beam diagnostic in accelerators. General phenomenological model of noise acting on position based qubit implemented in semiconductor quantum dots is given in the framework of simplistic tight-binding model. At first linear position-based qubit also known as Wannier qubit is considered with the situation of being excited by external movement of charged particle in its proximity. Analytical formulas describing the change of qubit state are derived analytically. In the next steps the roton semiconductor qubit representing closed loop of coupled quantum dots is given and the effect of external moving charge on the change of qubit state is determined by analytical formulas. Roton qubit physical state can be controlled by external magnetic and electric field what is the extension of controlling mechanism in comparison with linear Wannier qubit, when we have N quantum dots aligned.
Quantum algorithms for structured prediction

We introduce two quantum algorithms for solving structured prediction problems. We first show that a stochastic gradient descent that uses the quantum minimum finding algorithm and takes its probabilistic failure into account solves the structured prediction problem with a runtime that scales with the square root of the size of the label space, and in $$\tilde{O}\left (1/\epsilon \right )$$ with respect to the precision, 𝜖, of the solution. Motivated by robust inference techniques in machine learning, we then introduce another quantum algorithm that solves a smooth approximation of the structured prediction problem with a similar quantum speedup in the size of the label space and a similar scaling in the precision parameter. In doing so, we analyze a variant of stochastic gradient descent for convex optimization in the presence of an additive error in the calculation of the gradients, and show that its convergence rate does not deteriorate if the additive errors are of the order $$O(\sqrt{\epsilon})$$ . This algorithm uses quantum Gibbs sampling at temperature Ω(𝜖) as a subroutine. Based on these theoretical observations, we propose a method for using quantum Gibbs samplers to combine feedforward neural networks with probabilistic graphical models for quantum machine learning. Our numerical results using Monte Carlo simulations on an image tagging task demonstrate the benefit of the approach.
Research on Innovative Application Mode of Human-Computer Interaction Design in Data Journalism

In the context of intelligent communication, with the advancement of artificial intelligence technology, technologies such as voice interaction, image recognition, gesture recognition, and brain-computer interface have been further developed and used, and interactive data news is also considered to be the development of data news trends and directions. However, the current human-computer interaction technology used in data journalism is relatively simple. And it still has some problems. In the future, interactive visualization needs to improve its interactive operation and immersive experience. At the same time, it needs to update data in real time and achieve advanced interaction, encourage crowdsourcing production, and combine VR technology to give users a stronger sense of "presence".
An Investigation into the Impact and Predictability of Emotional Polarity on the Virality of Twitter Tweets

In this study, the likelihood of tweet-sentiment influencing people's opinion and motivating people to re-tweet is examined using data pertaining to three different fields; political, entertainment and financial. This study carried out additional investigation into the impact of Sentiment Compound Polarity and Favourite Count on people's opinions. Metrics of retweets and users' favourite count are used for the development of a predictive model to determine the likelihood of an individual tweet being retweeted. Public datasets were used for this study, focusing on three diverse topics including the 2017 demonetization in India dataset with 14,940 observations, the 2016 US election dataset with 397,629 observations and the 2018 American Music Awards dataset with 27,556 observations. Findings demonstrate that tweet sentiment plays an important role in shaping people's views and thereby inspiring them to retweet a tweet. The resulting predictive model may be used to determine the likelihood of a tweet being retweeted. The outcomes have numerous applications in domains such as advertising and marketing, political, social, commercial and charitable organisations.
Hotcount: A High-Precision Traffic Statistics for Multi-Tenants

Traffic statistics in large-scale data streams play an important role in the network community, and can be used for congestion control, anomaly detection, heavy hitter detection, etc. However, in the context of multi-tenancy, accomplishing the traffic statistics task of multiple tenants with limited resources (CPU, memory, etc) has become one of the major challenges. To solve the challenges above, this paper proposes a multi-tenant-oriented traffic statistics structure, named Hotcount, which can grantee the dynamic allocation of resources in multi-tenancy scenarios. At the same time, Hotcount is also able to realize multi-tenant traffic statistics task, and further improve the accuracy of the traffic statistics task. Hotcount separates the cold and hot flows based on statistics. It uses the hot/cold part to record the hot flow size with high precision and cold flow size with lower precision. With extensive experiment, we proved that the processing speed of Hotcount is similar to that of the original classic algorithm. Meanwhile, it greatly improves the accuracy of traffic statistics tasks. In the per-flow size statistics task, the accuracy is improved by 6.7 to 49.5 times than the original algorithm. In the heavy hitter detection task, the accuracy is improved by 11.3 times to 2065.6 times than the original algorithm even with memory-size constraints.
Takagi-Sugeno Modeling for missing value imputations based on RReliefF Iterative Learning

The presence of missing values in incomplete dataset increases the difficulty of data mining. This paper, therefore, proposes a Takagi-Sugeno fuzzy model based on RReliefF Iterative Learning (TS-RIL) method for missing value imputations. Since the regression relationship between attributes is usually different in different subsets, the premise part of this method divides input space into several fuzzy subsets based on the Fuzzy C-Means clustering with partial distance strategy (FCM-PDS). To further improve the fineness of the model, the RReliefF algorithm for each fuzzy subset is used to select the relevant features of model input. Focusing on the incomplete model input, this method adopts the iterative learning method and regards missing values as variables, so that missing values, model structure, and parameters can be learned dynamically until the iteration converges. The imputation will be completed with the accompany of the end of the iteration process. Experiments on UCI datasets are conducted to demonstrate the effectiveness of the proposed method.
Designing an Abnormal Posture Detection System to Prevent Accidents During Meal Assistance for Older Adults: A User-Centered Design Approach

This paper proposes a user-centered design (UCD) method and demonstrates the assistive device design, incorporating user involvement and the risk management approach throughout the design process, in a Japanese day-care facility for older adults. The study focused on the lunchtime problem, where there is a limited number of nursing staff for meal assistance and supervision of users to ensure their safety. An abnormal posture notification system that met the technical/functional and user requirements was successfully designed by conducting participant observations and using human error-proofing principles. This approach enabled timely identification of proper solutions in the care facility and areas for improvement, and thus the discovery of users and use-cases that were not initially envisioned.
Linear-dependent multi-interpretation neuro-encoded expression programming

Neuro-Encoded Expression Programming (NEEP) implements the continuous coding for the discrete solution through recurrent neural networks (RNNs), and smooths sharpness of the discrete coding. However, the insertion model generating linear coding in NEEP breaks the coherence of linear coding of RNNs, because the resulting symbols tend to be cluttered when RNNs learn the incoherent sequence relationships. Meanwhile, the redundancy phenomenon that different RNNs generate the same code results in that lots of solutions with the same performance exist in the search space, and causes the decrease for search efficiency. To address these problems, the linear-dependent multi-interpretation NEEP(LM-NEEP) is proposed in this research. LM-NEEP tackles the incoherence problem by employing a linear dependence strategy, and the multi-interpretation strategy is adopted to deal with the redundancy problem in search space. The capability of LM-NEEP is estimated on several symbolic regression problems. The experimental results display that the LM-NEEP significantly outperforms NEEP and some classical genetic programming methods.
Hybrid single-photon, coherent, and vacuum states interference in the six-ports Mach-Zehnder interferometer

Abstract We present a theoretical study of the interferences between hybrid single-photon state, coherent state, and vacuum state in the designed medium of a six-ports Mach-Zehnder Interferometer (6p-MZI). The hybrid three different quantum states were simultaneously injected into the input ports of the interferometer. The derivation result demonstrated that the output state showed the statistical blend states among a substituted single-photon state, coherent state, and vacuum state. Interestingly, our calculation results attested that the maximum probability of detected photons at the individual interferometer output ports were significantly dependents on the amplitude of the inputted coherent state.
Patient Managed Patient Health Record Based on Blockchain Technology

Patients would like to be at the center of the entire information exchange process between medical entities and gain control over their data and further medical decisions. The use of blockchain technology may create this possibility. The work describes the most important aspects of the blockchain technology and digital medical records. The challenges faced by architects of PHR systems based on blockchain are presented. Examples of theoretical and existing solutions are examined and their weaknesses are quoted. We propose a new solutions to this issue. Our main goal is to preserve the characteristics of blockchain without robbing it of its most important advantages.