Reviewerly/exHarmony huggingface dataset:
Multimodal 3D Convolutional Neural Networks for Classification of Brain Disease Using Structural MR and FDG-PET Images

The classification and identification of brain diseases with multimodal information have attracted increasing attention in the domain of computer-aided. Compared with traditional method which use single modal feature information, multiple modal information fusion can classify and diagnose brain diseases more comprehensively and accurately in patient subjects. Existing multimodal methods require manual extraction of features or additional personal information, which consumes a lot of manual work. Furthermore, the difference between different modal images along with different manual feature extraction make it difficult for models to learn the optimal solution. In this paper, we propose a multimodal 3D convolutional neural networks framework for classification of brain disease diagnosis using MR images data and PET images data of subjects. We demonstrate the performance of the proposed approach for classification of Alzheimer’s disease (AD) versus mild cognitive impairment (MCI) and normal controls (NC) on the Alzheimer’s Disease National Initiative (ADNI) data set of 3D structural MRI brain scans and FDG-PET images. Experimental results show that the performance of the proposed method for AD vs. NC, MCI vs. NC are 93.55% and 78.92% accuracy respectively. And the accuracy of the results of AD, MCI and NC 3-classification experiments is 68.86%.
An Improved Cubature Information Filtering for Target Tracking in Distributed Wireless Sensor Networks with Measurement Outliers

In distributed wireless sensor networks (DWSN), measurements are always corrupted by outliers or impulsive noise. This situation usually results in poor tracking performance and even target missing when DWSN is used for target tracking. Cubature information filtering (CIF) is based on minimum mean square error (MMSE) criterion, which is not applicable to non-Gaussian noise. Therefore, a new robust CIF (RCIF) is founded based on maximum correntropy criterion (MCC) to improve the robustness of state estimation for the local node. Then, weighted average consensus (WAC) is used for distributed information fusion to enhance the stability of DWSN and the precision of state estimation. Finally, the effectiveness of the proposed WAC based RCIF (WRCIF) is verified by a simulation experiment.
Design and Implementation of Humanoid Robot Behavior Imitation System Based on Skeleton Tracking

This paper builds and implements a robot humanoid motion imitation system, which consists of three parts. Firstly, a Microsoft Kinect 2.0 somatosensory camera is applied to capture human skeleton data. Secondly, using the EWMA (exponentially weighted moving averages, EWMA), we can smooth the acquired skeleton data considering the local and entire process without prior knowledge of noise. And the Holt-Winters second-order EWMA is employed to track the original signal slope and incorporate its trend into the smoothed data. Then, the smoothed data are mapped to the robot joint angles by geometric calculations. Finally, a NAO robot implements motion imitation based on the joint angles. Experimental results demonstrate that the NAO robot can imitate the action of the instructor smoothly and steadily using the proposed motion imitation system.
From preference into decision making

User-system interaction in recommender systems involves three aspects: temporal browsing (viewing recommendation lists and/or searching/filtering), action (performing actions on recommended items, e.g., clicking, consuming) and inaction (neglecting or skipping recommended items). Modern recommenders build machine learning models from recordings of such user interaction with the system, and in doing so they commonly make certain assumptions (e.g., pairwise preference orders, independent or competitive probabilistic choices, etc.). In this paper, we set out to study the effects of these assumptions along three dimensions in eight different single models and three associated hybrid models on a user browsing data set collected from a real-world recommender system application. We further design a novel model based on recurrent neural networks and multi-task learning, inspired by Decision Field Theory, a model of human decision making. We report on precision, recall, and MAP, finding that this new model outperforms the others.
Kinematic modelling and emulation of robot for traversing over the pipeline in the refinery

Robotics always used to have an extraordinary contribution into multiple parts of operations in process industries, the one presently discussed in this research paper deals with the mathematical analysis and real-time replication model of out pipe crawler robot used for thickness measurement in refineries. The aim of this paper is to evaluate and emulate the movement of the robot in a 2-dimensional plane over a black track motif as a pipeline line which infers the effect of the real-time simulation prototype model. Robotic manipulators are more complex than its successors but there is a complete analysis of the kinematics of differential drive robot. The workspace of a wheel based mobile robot is not only limited to the position of end effector but it is a range of all possible positions that the robot makes while moving over or on the path. The analysis presented in this paper is very useful in developing a mathematical model and crafting a prototype of wheelbase mobile robot to traverse over an uneven surface of ferromagnetic pipelines with the help of an array of IR sensors used for estimation of the thickness of pipes.
Quantum LLL with an Application to Mersenne Number Cryptosystems

In this work we analyze the impact of translating the well-known LLL algorithm for lattice reduction into the quantum setting. We present the first (to the best of our knowledge) quantum circuit representation of a lattice reduction algorithm in the form of explicit quantum circuits implementing the textbook LLL algorithm. Our analysis identifies a set of challenges arising from constructing reversible lattice reduction as well as solutions to these challenges. We give a detailed resource estimate with the Toffoli gate count and the number of logical qubits as complexity metrics. As an application of the previous, we attack Mersenne number cryptosystems by Groverizing an attack due to Beunardeau et al. that uses LLL as a subprocedure. While Grover's quantum algorithm promises a quadratic speedup over exhaustive search given access to a oracle that distinguishes solutions from non-solutions, we show that in our case, realizing the oracle comes at the cost of a large number of qubits. When an adversary translates the attack by Beunardeau et al. into the quantum setting, the overhead of the quantum LLL circuit may be as large as $$ 2^{52} $$ qubits for the text-book implementation and $$ 2^{33} $$ for a floating-point variant.
Frame Rate Up-Conversion Based on Edge Information

This paper proposes a Frame Rate Up-Conversion method based on edge information which synthesize edge information with the motion estimation and motion compensation. The proposed method uses variable block motion estimation for accuracy motion vector field, and improves the image quality via edge information. Experimental results show that the proposed method can efficiently remain the image edge information and improve the viewing experience in the subjective evaluation and gives a better PSNR and SSIM in the objective evaluation; meanwhile, it provides a low computational time.
Deflationism About Logic

Logical consequence is typically construed as a metalinguistic relation between (sets of) sentences. Deflationism is an account of logic that challenges this orthodoxy. In Williamson’s recent presentation of deflationism, logic’s primary concern is with universal generalizations over absolutely everything. As well as an interesting account of logic in its own right, deflationism has also been recruited to decide between competing logics in resolving semantic paradoxes. This paper defends deflationism from its most important challenge to date, due to Ole Hjortland. It then presents two new problems for the view. Hjortland’s objection is that deflationism cannot discriminate between distinct logics. I show that his example of classical logic and supervaluationism depends on equivocating about whether the language includes a “definitely” operator. Moreover, I prove a result that blocks this line of objection no matter the choice of logics. I end by criticizing deflationism on two fronts. First, it cannot do the work it has been recruited to perform. That is, it cannot help adjudicate between competing logics. This is because a theory of logic cannot be as easily separated from a theory of truth as its proponents claim. Second, deflationism currently has no adequate answer to the following challenge: what does a sentence’s universal generalization have to do with its logical truth? I argue that the most promising, stipulative response on behalf of the deflationist amounts to an unwarranted change of subject.
Optimization and Comparison of Multiple Game Algorithms in Amazon Chess

Amazon Chess is a research project in the field of machine games. The first step is more than 2,000 moves. Because of its complicated start, it is very suitable for studying the algorithm related to machine game. This article is in the Amazon Chess environment to study the Minimax algorithm, Alpha-Beta algorithm and its optimization, PVS and MTD (f) algorithm, and the experiment comparing the advantages and disadvantages of various algorithms in efficiency. Experiments show that the use of historical heuristic and transposition table technology can greatly improve the search efficiency, and among the above algorithms, MTD(f) algorithm has the highest efficiency, and its search time and number of nodes are lower than other algorithms.
SmartLobby

In this work, we present the SmartLobby, an intelligent environment system integrated into the lobby of a research institute. The SmartLobby is running 24/7, i.e. it can be used any time by anyone without any preparations. The goal of the system is to conduct research in the domain of human machine cooperation. One important first step towards this goal is a detailed human state modeling and estimation with head-eye-tracking as key component. The SmartLobby mainly integrates state-of-the-art algorithms that enable a thorough analysis of human behavior and state. These algorithms constitute the fundamental basis for the development of higher level system components. Here, we present our system with its various hardware and software components. Thereby, we focus on the head-eye-tracking as a key component to continuously observe persons using the system and customize content shown to them. The results of a multi-week lasting experiment demonstrate the effectiveness of the system.
Group Coach for Co-located Collaboration

Collaboration is an important 21st century skill; it can take place in a remote or co-located setting. Co-located collaboration (CC) gives rise to subtle human interactions that can be described with multimodal indicators like gaze, speech and social skills. In this demo paper, we first give a brief overview of related work that has identified indicators during CC. Then, we look briefly at the feedback mechanisms that have been designed based on these indicators to facilitate CC. Using these theoretical insights, we design a prototype to give automated real-time feedback to facilitate CC taking the help of the most abundant modality during CC i.e., audio cues.
Non-separability Effects in Cognitive Semantic Retrieving

This paper discusses a Bell test analogue known in quantum physics, which allows determining the presence of non-separability features by using semantic search of information and document ranking for articles in Russian. The model of Bell test in semantics is based on hyperspace analogue to language (HAL) algorithm provides to obtain vector representation of words (in Hilbert space) using the dictionary index and considering the word order. We show the existence of certain quantum-like correlations between two words of the user’s query; these correlations cannot be taken into account in the classical probabilistic description. We predict that the contextuality revealed can be regarded as human cognitive level both while writing of certain texts and queries to them.
Designing Authentication and Authorization System with Blockchain

Data is accumulated daily in a society where the Internet has infiltrated. In order to further develop the industry, it is effective to establish a framework that can share and use data resources widely. If the IoT devices and various services trade data without human intervention, they will be able to provide new services that enrich our lives. If an authentication and authorization system that can openly confirm the data owners and give authority to them is constructed, various services, not limited to a single service, can authenticate and authorize the devices, and the data sharing will be promoted. In this paper, we propose a system that uses blockchain as a database for storing credentials and authority information and allows users and services to perform authentication and authorization based on that information.
IGE+: A Framework for Learning Node Embeddings in Interaction Graphs

Node embedding techniques have gained prominence since they produce continuous and low-dimensional features, which are effective for various tasks. Most existing approaches learn node embeddings by exploring the structure of networks and are mainly focused on static non-attributed graphs. However, many real-world applications, such as stock markets and public review websites, involve bipartite graphs with dynamic and attributed edges, called attributed interaction graphs. Different from conventional graph data, attributed interaction graphs involve two kinds of entities (e.g. investors/stocks and users/businesses) and edges of temporal interactions with attributes (e.g. transactions and reviews). In this paper, we study the problem of node embedding in attributed interaction graphs. Learning embeddings in interaction graphs is highly challenging due to the dynamics and heterogeneous attributes of edges. Different from conventional static graphs, in attributed interaction graphs, each edge can have totally different meanings when the interaction is at different times or associated with different attributes. To tackle the above challenges, we introduce the temporal dependency and conditional proximity, which are two fundamental characteristics of interaction graphs. Then, we propose a deep node embedding method called IGE+ (Interaction Graph Embedding+). By preserving these two characteristics, IGE+ is able to produce effective node embeddings in interaction graphs. We evaluate our proposed method and various comparing methods on four real-world datasets. The experimental results prove the effectiveness of the learned embeddings by IGE+ on both node-based and edge-based tasks.
Interest-Forgetting Markov Model for Next-Basket Recommendation

Recommendation systems provide users with ranked items based on individual's preferences. Two types of preferences are commonly used to generate ranking lists: long-term preferences which are relatively stable and short-term preferences which are constantly changeable. But short-term preferences have an important real-time impact on individual's current preferences. In order to predict personalized sequential patterns, the long-term user preferences and the short-term variations in preference need to be jointly considered for both personalization and sequential transitions. In this paper, a IFNR model is proposed to leverage long-term and short-term preferences for Next-Basket recommendation. In IFNR, similarity was used to represent long-term preferences. Personalized Markov model was exploited to mine short-term preferences based on individual's behavior sequences. Personalized Markov transition matrix is generally very sparse, and thus it integrated Interest-Forgetting attribute, social trust relation and item similarity into personalized Markov model. Experimental results are on two real data sets, and show that this approach can improve the quality of recommendations compared with the existed methods.
Anti-quantum generalized signcryption scheme based on multivariate and coding

The development of quantum technology is changing with each passing day. While bringing convenience to us, the security problems it brings cannot be underestimated. By studying the multivariate cryptography and coding cryptography in anti-quantum cryptography, the core mapping of the improved version of Simple Matrix scheme is modified by LRPC code, which combines the MQ problem and the decoding difficulty problem, effectively reduces the ciphertext expansion rate and realizes the flexible choice of LRPC codes. Based on the improvement of the trapdoor, a generalized signcryption scheme against quantum is proposed, which proves to meet the security of IND-CCA2 and the security of EUF-CMA. It can realize the adaptive conversion between signature, encryption, signcryption and normal mode, adapt to more complex network communication, and show strong survivability in the post-quantum era.
Mixed-Reality Aided System for Glioblastoma Resection Surgery using Microsoft HoloLens

Glioblastoma is the most common and primary type of brain cancer. A surgical intervention is the first treatment as it improves the prognosis of the patient. Unfortunately, this type of tumor is aggressive, and difficult to remove completely, which makes the resection surgery more challenging. Magnetic resonance imaging (MRI) is the most used screening technique for brain cancer diagnosis and surgery planning as it provides detailed information about the tumor's location and size. In this paper, we propose a system for the reconstruction of three-dimensional brain models containing a glioblastoma tumors using the Microsoft headset HoloLens. The developed mixed-reality system projects and overlaps the 3D brain model from the MRI images onto the patient's head during the surgery. This system has the potential to simplify the surgery, reduce the surgery high-risk, increase the resection precision, and help remove the tumor tissue as much as possible.
Credit card fraud detection by dynamic incremental semi-supervised fuzzy clustering

The problem of credit card fraud detection is approached by a semi-supervised classification task on a data stream.The DISSFCM algorithm is applied, which is based on Dynamic Incremental Semi-Supervised Fuzzy C-Means that processes data grouped in small-size chunks.Experimental results on a real-world dataset of credit card transactions show that DISSFCM has comparable results with some fully-supervised stream data classification methods, also in presence of a high percentage of unlabeled data.
Stable Word-Clouds for Visualising Text-Changes Over Time

Word-clouds are a useful tool for providing overviews over texts, visualising relevant words. Multiple word-clouds can also be used to visualise changes over time in a text. This requires that the words in the individual word-clouds have stable positions, as otherwise it is very difficult so see what changed between two consecutive word-clouds. Existing approaches have used coordinated positioning algorithms, which do not allow for their use in an online, dynamic context. In this paper we present a fast word-cloud algorithm that uses word orthogonality to determine which words can share the same space in the word-clouds combined with a simple, but fast spiral-based layout algorithm. The evaluation shows that the algorithm achieves its goal of creating series of word-clouds fast enough to enable use in an online, dynamic context.
Ubiquitous smart eyewear interactions using implicit sensing and unobtrusive information output

Premature technology, privacy, intrusiveness, power consumption, and user habits are all factors potentially contributing to the lack of social acceptance of smart glasses. After investigating the recent development of commercial smart eyewear and its related research, we propose a design space for ubiquitous smart eyewear interactions while maximising interactivity with minimal obtrusiveness. We focus on implicit and explicit interactions enabled by the combination of miniature sensor technology, low-resolution display and simplistic interaction modalities. Additionally, we are presenting example applications outlining future development directions. Finally, we aim at raising the awareness of designing for ubiquitous eyewear with implicit sensing and unobtrusive information output abilities.
Research on Optimizing Intelligent Test Paper Forming Strategy Based on Improved Genetic Algorithms

Test paper generation is a multi-objective parameter optimization problem under multi-constraints. Aiming at the defects of test paper quality and speed in intelligent test paper generation, the genetic algorithm is improved on the basis of the original genetic algorithm. From the coding method of the test questions, the establishment of weighted objective function to optimize the fitness function, the selection of crossover operator, the setting of mutation, and the combination of optimization strategy and roulette to select the operation, the optimization design is carried out. It achieves good adaptability and can generate test paper quickly and successfully, which improves the efficiency of generating test paper. The simulation results show that, under the same constraints, the algorithm can generate test papers successfully, and improve the success rate and efficiency of generating test papers, and effectively optimize the problems in automatic generating test papers.
An Open Source Dataset and Ontology for Product Footprinting

Product footprint describes the environmental impacts of a product system. To identify such impact, Life Cycle Assessment (LCA) takes into account the entire lifespan and production chain, from material extraction to final disposal or recycling. This requires gathering data from a variety of heterogeneous sources, but current access to those is limited and often expensive. The BONSAI project, instead, aims to build a shared resource where the community can contribute to data generation, validation, and management decisions. In particular, its first goal is to produce an open dataset and an open source toolchain capable of supporting LCA calculations. This will allow the science of lifecycle assessment to perform in a more transparent and more reproducible way, and will foster data integration and sharing. Linked Open Data and semantic technologies are a natural choice for achieving this goal. In this work, we present the first results of this effort ( https://github.com/BONSAMURAIS/ ): (1) the core of a comprehensive ontology for industrial ecology and associated relevant data; and (2) the first steps towards an RDF dataset and associated tools to incorporate several large LCA data sources.
Fitting H-Infinity Filter for Nonlinear Discrete-Time Systems

In this paper, a new strategy referred to as the fitting H-infinity filter (FH∞F) is presented and used for robust estimation of nonlinear systems in the presence of unknown-but-bounded noise. The heart of FH∞F is the fitting transformation (FT), which is a linearization method based on least weighted squares. The FT algorithm is used to obtain the estimated value of the coefficient matrix. Therefore, FH∞F is presented by applying the coefficient matrix to H∞ filter. An experimental system was setup and characterized in order to demonstrate the proposed filtering strategy performance. Monte Carlo simulation results demonstrate that the proposed FH∞F provides improvements in terms of state estimation accuracy and robustness to the system with unknown-but-bounded noise.