Reviewerly/exHarmony huggingface dataset:
Computation-Efficient Three-Party Encrypted Key Exchange for Telecare Medicine Information Systems

A three-party encrypted key exchange (3PEKE) protocol for telecare medicine information systems (TMISs) enables two communicating parties, such as patients, doctors, nurses and health visitors, sharing a long-lived secret only with a trusted third party- Medical Center Server (MCS) to exchange confidential and authenticated Electronic Medical Records (EMRs) and Electronic Health Records (EHRs) with another party over an insecure network. Recently, Lee et al. presented an improved 3PEKE protocol to solve the weaknesses of previous protocols. However, this study states that Lee et al.'s improved 3PEKE protocol still has some security faults such that their protocol cannot execute correctly and fails to resist password guessing attacks. This study also develops an enhanced protocol which is based on Lee et al.'s improved 3PEKE protocol. Additionally, the enhanced protocol protects the user's password by using a one-time key shared with the MCS, eliminates the redundant computations, and rearranges the messages. Compared with related protocols, the enhanced protocol not only has higher security, but also increases efficiency in computation and transmission.
General Secret Sharing Schemes Using Hierarchical Threshold Scheme

We propose two secret sharing schemes realizing general access structures, which are based on unauthorized subsets. In the proposed schemes, shares are generated by Tassa's (k,n)-hierarchical threshold scheme instead of Shamir's (k,n)-threshold scheme. Consequently, the proposed schemes can reduce the number of shares distributed to each participant.
Application of AHP Analysis to Increase Employee Career Paths in Decision Support Systems

Abstract Employee career paths are one way for companies to improve employee performance in educating their students. The problem of manager in decision making uses the selection of methods and assessment processes subjectively so that the process is not in accordance with the goals of the career path. Therefore, an Analytical Hierarchy Process (AHP) method is needed to help make decisions. In this study, using the criteria of Planning, Teaching, Evaluating and Learning. Ranking results obtained that Learning criteria are very important criteria with calculation results of 0.602 when compared to the other three criteria. The Decision Support System (DSS) application provides the final results obtained from testing the calculation that the alternative Employee C is the best employee with a calculation of 0.227 compared to the other alternatives. The DSS application using the AHP method has a data accuracy rate of 86.67% and can be used as a support for manager decisions to make recommendations for increasing employee career paths.
Kinetic and thermodynamic studies of sorption of lead and cadmium from aqueous solution by &lt;i&gt;Moringa oleifera&lt;/i&gt; pod wastes

Equilibrium sorption of Pb(II) and Cd(II) from aqueous solution by Moringa oleifera pods waste was conducted under batch conditions. Effects of operating variables such as initial concentration, contact time, adsorbent dose, pH and temperature on the sorption of these heavy metals have been studied. The optimum pH for the adsorption was found to be 5.0 and the adsorption process was found to be exothermic for both metal ions. The competitive adsorption characteristics of the combinations of binary mixtures of aqueous metal ion species, Pb2+ (aq) and Cd2+ (aq), on the adsorbent were also investigated. The Langmuir, Freundlich, Temkin and Dubinin Radushkevich adsorption isotherm models were used to analyse the equilibrium data. Kinetics data evaluation showed that the adsorption followed a pseudo-second order model. A solution of 0.05 M HCl has been found to be sufficient for the regeneration of spent adsorbent with up to 90% recovery for the two metal ions.
Unsupervised Domain Adaptation for Sentimental Classification by Word Embeddings on the Lower Layer of BERT

Bidirectional Encoder Representations from Transformers (BERT) is a stacked (12 or 24) model of multi-head attention applied in the transformers. The multi-head attention of each layer outputs a word embedded expression sequence corresponding to the input word sequence. When BERT is applied to the feature base, the output is the word-embedded expression column of the highest layer used in each task. On the other hand, in domain adaptation, projecting the data of each region onto the common subspace of the source and target domains is an effective approach. When constructing a feature vector on a common subspace from a word-embedded representation output of the BERT, the most significant layer depends on the task learning task assigned to BERT, so is not necessarily more significant than the word-embedded representation of a lower layer. Layers are suboptimal for regional adaptation. Here we confirm this concept on unsupervised domain adaptation of an emotion analysis.
Privc: Privacy Preserving Verifiable Computation

Verifiable computation in cloud setup with a privacy-preserving feature is an interesting research problem which can find application in monitoring system such as healthcare application. For example, a public cloud service provider can facilitate computation service on patients data or symptoms based on doctors prescription (e.g., prediction function) without letting the cloud server know on anything about patient's data and the patient can verify the computed response. We present a privacy-preserving verifiable computation (PriVC) scheme in which the server can authenticate a user in a privacy-preserving manner, and compute on encrypted data of the user stored in the public cloud. The server provides the proof of computation which can be verified by the user. The PriVC preserves the privacy of the user and ensures undeniability of the service offered as well as the service consumed. The PriVC scheme uses homomorphic encryption for user data encryption and a private polynomial function for computation on encrypted data. We show that the PriVC scheme is secure under indistinguishability against chosen function attack (IND-CFA), and the proof of computation is unforgeable in the standard model.
Trajectory Planning in Time Augmented C-space Using RRT with Sampling Point Including Velocity Determined by Initial Conditions

The introduction of collaborative robots is expected as one of the solutions for the labor shortage. To implement the collaborative robot in an industrial line, real-time motion planning is necessary to determine a robot's trajectory in order to consider the moving workers. In this paper, we propose a real-time trajectory planning method for co-worker robot based on the RRT in configuration-time space. Motion planning in configuration-time space is effective to avoid moving obstacles while considering robot dynamics and target reach time. In order to consider the dynamics of a robot in a time augmented configuration space, it is necessary to smoothly connect the robot velocities. The proposed method set the velocity of the sampling points depending on the initial condition in configuration-time space. In the simulation, we found that the proposed method has a higher success rate than the method without considering the velocity of the sampling points. Experiments using PaDY that we have developed show the effectiveness of the proposed method in an environment with two moving workers.
An improved quantum-behaved gravitional search algorithm for high-dimensional multi-modal optimization

A quantum-behaved gravitional search algorithm based on levy flight (LQ-GSA) is proposed on the basis of analyzing the mechanism of quantum-behaved gravitional search algorithm (QGSA), aiming at solving high dimensional multimodal optimization problems. Firstly, an adaptive dynamic adjustment strategy is proposed for the unique parameter of QGSA-contraction and expansion coefficient (CE), so as to maintain the diversity of population evolution. Secondly, levy flight strategy is introduced in the process of particle location update to expand the search range of particles and enhance the ability of particles to jump out of local optimal. Finally, through the optimization experiment results of six standard test functions in different dimensions, it is shown that LQ-GSA is significantly better than other comparison algorithms in terms of convergence accuracy, convergence speed and stability. With the increase of dimension, the advantages become more prominent, the algorithm shows better performance in solving multi-dimensional and multi-modal optimization problems.
Rethinking Authorization Management of Web-APIs

Service providers typically utilize Web APIs to enable the sharing of tenant data and resources with numerous third party web, cloud, and mobile applications. Security mechanisms such as OAuth 2.0 and API keys are commonly applied to manage authorization aspects of such integrations. However, these mechanisms impose functional and security drawbacks both for service providers and their users due to their static design, coarse and context insensitive capabilities, and weak interoperability. Implementing secure, feature-rich, and flexible data sharing services still poses a challenge that many providers face in the process of opening their interfaces to the public.To address these issues, we design the framework that allows pluggable and transparent externalization of authorization functionality for service providers and flexibility in defining and managing security aspects of resource sharing with third parties for their users. Our solution applies a holistic perspective that considers service descriptions, data fragments, security policies, as well as system interactions and states as an integrated space dynamically exposed and collaboratively accessed by agents residing across organizational boundaries.In this work we present design aspects of our contribution and illustrate its practical implementation by analyzing case scenario involving resource sharing of a popular service.
Google Scholar, Web of Science, and Scopus: which is best for me?

Being able to find, assess and place new research within a field of knowledge, is integral to any research project. For social scientists this process is increasingly likely to take place on Google Scholar, closely followed by traditional scholarly databases. In this post, Alberto Martin-Martin, Enrique Orduna-Malea , Mike Thelwall, Emilio Delgado-Lopez-Cozar, analyse the relative coverage of the three main research databases, Google Scholar, Web of Science and Scopus, finding significant divergences in the social sciences and humanities and suggest that researchers face a trade-off when using different databases: between more comprehensive, but disorderly systems and orderly, but limited systems.
STUDIES ON PRACTICAL CRYPTOGRAPHIC SECURITY ANALYSIS FOR BLOCK CIPHERS WITH RANDOM SUBSTITUTIONS

In up-to-date information and communication systems (ICS) cryptography is used for ensuring data confidentiality. The symmetric block ciphers (BC) are implemented in different ICS including critical applications. Today theory of analysis and security verification of BC with fixed substitution nodes against linear and differential cryptanalysis (LDC) is developed. There are also BC with substitution nodes defined by round keys. Random substitution nodes improve security of ciphers and complicate its cryptanalysis. But through it all, quantitative assessment is an actual and not simple task as well as the derivation of formulas for practical security verification for BC with random substitution nodes against LDC. In this paper analytical upper bounds of parameters characterized practical security of BC with random substitution nodes against LDC were given. These assessments generalize known analogs on BC with random substitution nodes and give a possibility to verify security improving against LDC. By using the example of BC Kalyna-128, it was shown that the use of random substitution nodes allows improving upper bounds of linear and differential parameters average probabilities in 246 and 290 times respectively. The study is novel as it is one of the few in the cryptology field to calculate analytical upper bounds of BC practical security against LDC methods as well as to show and prove that using random substitutions allows improving upper bounds of linear and differential parameters. The security analysis using quantitative parameters gives possibility to evaluate various BCs or other cryptographic algorithms and their ability to provide necessary and sufficient security level in ICS. A future research study can be directed on improving analytical upper bounds for analyzed LDC in context to practical security against LDC, as well as practical cryptographic security assessment for other BC with random substitutions against LDC and other cryptanalysis methods including quantum cryptanalysis (Shor, Grover, Deutsch-Jozsa algorithms).
Massively parallel Fourier-optics based processor

Performing feature extractions in convolution neural networks for deep-learning tasks is computational expensive in electronics. Fourier optics allows convolutional filtering via dot-product multiplication in the Fourier domain similar to the distributive law in mathematics. Here we experimentally demonstrate convolutional filtering exploiting massive parallelism (10^6 channels, 8-bit at 1kHz) of digital mirror display technology, thus enabling 250 TMAC/s. An FPGA-PCIe board controls the ‘weights’ and handles the data I/O, whereas a high-speed camera detects the inverse-Fourier transformed (2nd lens) data. Gen-1 processes with a total delay (including I/O) of ~1ms, while Gen-2 at 1<sup>-10</sup>ns leveraging integrated photonics at 10GHz and changing the front-end I/O to a joint-transform-correlator (JTC). These processors are suited for image/pattern recognition, super resolution for geolocalization, or real-time processing in autonomous vehicles or military decision making.
Speaking Mouth System for Dumb People Using Hand Gestures

One of the beautiful creation of God is human beings who have been blessed with vision, sound, and speech. But there are some human beings who are deprived of this valuable blessing. Since speech is the major mode of communication which provides a way of exchanging information, perspective and utterance among different people, in both verbal and non-verbal manner, dumb people are mostly affected. In this framework, hand gesture recognition system for dumb people is used for conveying their thoughts to others. These people use sign languages to communicate with the world and people finds it difficult to understand what they are really speaking. So this project uses gestures which are an important part of such people and make use of these gestures to convert it into speech. True communication occurs when others understand one’s message and they can respond in kind. This system smoothens the communication gap between dumb and normal people and provides an effective conversation. This artificially intelligent system make use of keras as platform over which gestures that are captured in real time via Webcam and trained using convolutional neural network (CNN) are converted into text and further to speech output.
Vehicle Detection and Counting the Number of Vehicles for Intelligent Traffic Control Using LabVIEW—An Image Processing Approach

Across the globe traffic lights are used to manage the vehicle movement on road. There have been many developments in this field but yet Static lights are used in most of the cases. With the rising number of vehicles on road there is a significant amount of congestion that leads to delay and many a times due to improper management may cause accidents. This problem thereby leads to a situation where need of a smart system that can efficiently handle traffic congestion is very prominent. This paper discusses about the implementation of various image processing methodology that can be used for detection and counting of vehicles. These methodologies help in counting of vehicles on road or at traffic junctions such that exact density of vehicles on each lane can be determined. The image processing methodologies like edge detection, background subtraction, and pattern matching algorithms has been implemented and tested using LabVIEW and Vision Assistant module. The headlights of the cars appear as a bright blob on captured images. Since the distance between the headlights of an vehicle are constant for a particular type of vehicle it can be used as an criteria for pattern matching and detection of vehicles specially after dusk. The detected vehicles gives the count of number of vehicle at halt on each lanes, depending upon which a suitable lane switching algorithm is implemented, where the most congested lane is most likely to be freed first. The real time monitoring system keeps track of vehicle count on each lane in real time and controls the traffic lights of the lanes such that waiting time can be minimized.
Computing the untruncated signature kernel as the solution of a Goursat problem

Recently there has been an increased interest in the development of kernel methods for learning with sequential data. The truncated signature kernel is a new learning tool designed to handle irregularly sampled, multidimensional data streams. In this article we consider the untruncated signature kernel and show that for paths of bounded variation it is the solution of a Goursat problem. This linear hyperbolic PDE only depends on the increments of the input sequences, doesn't require the explicit computation of signatures and can be solved using any PDE numerical solver; it is a kernel trick for the untruncated signature kernel. In addition, we extend the analysis to the space of geometric rough paths, and establish using classical results from stochastic analysis that the rough version of the untruncated signature kernel solves a rough integral equation analogous to the Goursat problem for the bounded variation case. Finally we empirically demonstrate the effectiveness of this kernel in two data science applications: multivariate time-series classification and dimensionality reduction.
Effects of task complexity and working memory load on eye-tracking indices of cognitive effort in adults and children

Cognitive effort a subjective phenomenon, generally defined as the amount of sustained mental activity, exerted during a cognitive task. A well-established eye movement indice of cognitive effort is blink rate. Many studies show that in cognitive tasks that involve visual stimuli blink rate decreases as a function of difficulty (Maffei, & Angrilli, 2018). Working memory (WM) is a core cognitive ability and refers to the number of items or schemes that can be simultaneously held and manipulated in mind. While a great deal of studies have explored behavioral correlates of WM load and task complexity, little is known about how these relate to eye movements across development. We implement an eye-tracking paradigm to study effect of complexity and WM load on eye movements from a developmental perspective. 57 healthy adults (23 male., age = 23.25±3.6) and 26 children (10 male, age = 9.53±0.76) participated in the study. Eye-tracking data was recorded with the EyeLink Portable Duo, while participants performed the Colour Matching Task (Arsalidou et al., 2010). During the CMT the participant is shown a picture with multiple colours for 3 s. and gives a response at the following picture: are the colours same or different. CMT has 6 levels of WM load: the number of relevant colors and two levels of task complexity (low and high interference conditions). Analyses of variance showed a significant main effect of age group on blink rate (p < 0.01, F = 9.091, η2 = 0.009) with children making less blinks in all levels of WM load, as well as significant main effect of WM load (p < 0.001, F = 130.5, η2 = 0.021) with blink rate decreasing as WM load increased. No significant effects were observed for task complexity. Results will be discussed in terms of cognitive development and implications to education.
Active Observers in a 3D World: The 3D Same-Different Task

Most past and present research in computer vision involves passively observed data. Humans, however, are active observers outside the lab; they explore, search, select what and how to look. Here, we are investigating active, visual observation in a 3D world. To focus, we ask subjects to decide if two 3D objects are the same or different, with no constraints on how they view those objects. Such 3D unconstrained, active observation seems under-studied. While many studies explore human performance, usually, they use line drawings portrayed in 2D, and no active observer is involved. The ability to compare two objects seems a core visual capability, one we use many times a day. It would also be essential for any robotic vision system whose role it is to be a real assistant at home, manufacturing or medical setting. To explore the 3D 'same-different task', we designed a novel experimental environment and created a set of twelve 3D printed objects with known complexity. The subject is allowed to move around freely in a 4m x 3m controlled environment, outfitted with eye gaze tracker and observed by head trackers. In this environment, two objects are presented at a time at a fixed 3D locations but with a varying 3D pose. We track precise 6D head motion, gaze and record a video of all actions, synchronized at microsecond resolution. Additionally, the subject is interviewed about how the task was approached. Our results show that at least six strategies for solving this task are employed, not always independently. We found that the strategy used is dependent on three variables: object complexity, object orientation, and initial viewpoint. Furthermore, we show that performance improves over time as subjects refine their strategies throughout the study. Since no external feedback is given, an internal feedback mechanism must exist that refines strategies.
Tests for differential Gaussian Bayesian networks based on quadratic inference functions

Hypotheses testing procedures based on quadratic inference functions are proposed to test whether two Gaussian Bayesian networks are differential in structure, strength of associations between nodes, or both. Bootstrap procedures are developed to estimate p-values to quantify the statistical significance of the tests. Operating characteristics of these testing procedures are investigated using synthetic data in simulation experiments. Additionally, the proposed methods are applied to flow cytometry data from a designed experiment, and data of bile acids from an observational study in the Alzheimer’s Disease Neuroimaging Initiative.
Deep Latent-Variable Kernel Learning

Deep kernel learning (DKL) leverages the connection between the Gaussian process (GP) and neural networks (NNs) to build an end-to-end hybrid model. It combines the capability of NN to learn rich representations under massive data and the nonparametric property of GP to achieve automatic regularization that incorporates a tradeoff between model fit and model complexity. However, the deterministic NN encoder may weaken the model regularization of the following GP part, especially on small datasets, due to the free latent representation. We, therefore, present a complete deep latent-variable kernel learning (DLVKL) model wherein the latent variables perform stochastic encoding for regularized representation. We further enhance the DLVKL from two aspects: 1) the expressive variational posterior through neural stochastic differential equation (NSDE) to improve the approximation quality and 2) the hybrid prior taking knowledge from both the SDE prior and the posterior to arrive at a flexible tradeoff. Extensive experiments imply that DLVKL-NSDE performs similar to the well-calibrated GP on small datasets, and shows superiority on large datasets.