Reviewerly/exHarmony huggingface dataset:
Experimentally undoing an unknown single-qubit unitary

Undoing a unitary operation, i.e., reversing its action, is the task of canceling the effects of a unitary evolution on a quantum system, and it may be easily achieved when the unitary is known. Given a unitary operation without any specific description, however, it is a hard and challenging task to realize the inverse operation. Recently, a universal quantum circuit has been proposed [Phys. Rev. Lett. 123, 210502 (2019)] to undo an arbitrary unknown $d$-dimensional unitary $U$ by implementing its inverse with a certain probability. In this paper, we report the experimental reversal of three single-qubit unitaries $(d=2)$ by linear optical elements. The experimental results prove the feasibility of the reversing scheme, showing that the average fidelity of inverse unitaries is $F=0.9767\ifmmode\pm\else\textpm\fi{}0.0048$, in close agreement with the theoretical prediction.
Blockchain-Based Infrastructure to enable Trust in IoT environment

Nowadays, with the further development of telecommunication, IoT technology has become a concrete reality of everyday life. However, the widespread adoption of such technologies is sharply limited by Trust concerns for IoT devices, in terms of reliability, traceability, integrity, and privacy of the data. The lack of digital traceability increases the market time for IoT environment adoption. Contemporary Blockchain has been identified as one of the most promising technology to grant data security and reliability. The recent developments of such technologies have led to the possibility to build a shared, distributed, trustful, and autonomous infrastructure system making it hopeful for IoT systems. The constrained resources of low-level devices used in the IoT networks represent a significant hindrance to the integration of Blockchain and IoT technologies. Different kind of light protocols for IoT communication and secure authentication has been proposed to this end; some of this requires the slightest effort for resources integration. In this paper, an audit system for metrological traceability in a distributed measurement system is proposed. The system is based on Blockchain technology and Physical Unclonable Functions (PUF) access security protocol.
Video tracker analysis: a strategy for measuring students communication and collaboration skills

Abstract Students must have communication skills to communicate their ideas and thoughts. The communication skill of students plays a role in physics learning because it can change the learning situation in a better direction. In addition to communication skills, collaboration skills must also be owned by the students in the learning of physics, one of which is used to conduct observations in groups. The purpose of this research is to know the relationship between communication skills and students’ collaboration skills, especially in physics. This research is experimental and the sample was taken by purposive sampling technique. In the results, there is a very high relationship between communication skills and students’ collaboration. The score students’ communication skills was 0.876, indicating that there was a direct relationship between students’ communication and collaboration skills, where students’ communication skills increase with the increasing of collaboration skill. Additionally, learning used of video tracker software created new knowledge and skills.
Recordkeeping and Relationships

We describe an approach for designing information infrastructure that addresses lifelong recordkeeping needs for those caught up in the child protection sector. The challenge is to enable people to exert their rights over information as it manifests and changes through time over generational timescales. We conducted a series of participatory design and prototyping workshops over an 18-month period, with a core group of eight academic and community researchers. Using Recordkeeping Informatics to inform critical, rights-based, and trauma-sensitive systems design, we prototyped a distributed and participatory recordkeeping system that allows those with childhood protection experience to participate in their records. In this paper, we describe approaches we adapted for long-term participatory design in sensitive domains, and discuss the design artefacts we developed to capture the complexity of through-time information system design. We propose a set of design guidelines and discuss their implications for design work and systems.
Data Clustering Method Using Efficient Fuzzifier Values Derivation

The Type-2 fuzzy set (T2 FS) is widely used for efficient control uncertainties, such as noise sensitivity in the fuzzy set. In addition, unsupervised machine learning requires a clustering parameter value in advance, and may affect clustering performance according to prior information such as the number and size of clusters. In this case, the fuzzifier value m to be applied is the most important factor in improving the accuracy of data. Therefore, in this paper, we intend to perform clustering to automatically acquire the determination of m <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> and m <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> values that depended on existing repeated experiments. To this end, in order to increase efficiency on deriving appropriate fuzzifier value, we used the Interval type-2 possibilistic fuzzy C-means (IT2PFCM), clustering method to classify a given pattern. In Efficient IT2PFCM method, used for clustering, we propose an algorithm that derives suitable fuzzifier values for each data. These values also extract information from each data point through the histogram approach and Gaussian Curve Fitting method. Using the extracted information, two adaptive fuzzifier value m <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> and m <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> are determined. Obtained values apply the new lowest and highest membership values. In addition, it is possible to form an appropriate fuzzy area on each cluster by only taking advantage of the characteristics of IT2PFCM, which reduces uncertainty. This doesn't only improve the accuracy of clustering of measured sensor data, but can also be used without additional procedures such as data labeling or the provision of prior information. It is also efficient at monitoring numerous sensors, managing and verifying sensor data collected in real time such as smart cities. Eventually, in this study, the proposed method is to improve IT2PFCM performance on accurate and quick clustering of large amount of complex data such as Internet of Things (IoT).
Mathematical Model For Information Security System Effectiveness Evaluation Against Advanced Persistent Threat Attacks

The article deals with the mathematical model for information security controls optimization and evaluation of the information security systems effectiveness. Distinctive features of APT attacks are given. The generalized efficiency criterion in which both the requirements of the return of security investment maximization and the return on attack minimization are simultaneously met. The generalized reduced gradient method for solving the optimization of the objective function based on formulated efficiency criterion is proposed.
Flat-Design Icon Sets: A Case for Universal Meanings?

Nowadays, the Internet has reached a level of maturity that enables categorizing the main types of websites, the most common actions that the end-user can engage in, and the recurring patterns that characterize interactions. As users have become more accustomed to the shared features of websites and web applications (e.g., sign in, access cart, download file), pictographic icons have been increasingly used to replace text labels. Flat design is among the most recent developments in this abstraction climax. However, removing traits, affordances, and attributes increases the ambiguity of symbols, affects their recognition, and impairs the ability to associate them with their intended meaning. In this paper, we focus on open-source typographic icon sets for the web (e.g., Font Awesome). Specifically, we investigate to what extent flat-design icons have acquired a universal meaning and we analyze human factors and design aspects that play a key role in icon recognition.
Parallelizing pruned landmark labeling

To help compute shortest path distances over large graphs efficiently, 2-hop labeling has emerged as a major tool, with Pruned Landmark Labeling (PPL) as a popular algorithm. This paper demonstrates the first scalable parallel implementation of the PPL algorithm that produces the same results as the sequential algorithm. Based on theoretical analysis, we show how computations on each vertex can be performed in parallel while maintaining correctness, resulting in the Vertex-Centrix PLL (VC-PLL) algorithm. We also show a formulation of this algorithm based on linear algebra and argue why the use of a library based on linear algebra operations will not produce an efficient implementation. Next, we introduce a batched VC-PLL (BVC-PLL) algorithm to reduce the computational inefficiency in VC-PLL. We have carried out a parallel implementation of this method for modern clusters, combining shared memory and distributed memory parallelism, that can efficiently execute on graphs with more than a billion edges. We also demonstrate how BVC-PLL algorithm can be extended to handle directed graphs and weighted graphs and how the version for weighted graphs can benefit from SIMD parallelization.
Abnormal operation status identification in warehousing based on neighborhood information entropy considering mixed-valued attributes

A warehousing system is critical to enterprises, as a connection between supply and demand in a supply chain. However, an abnormal operation status (AS) may appear in actual production operations, especially in a developing warehousing system. In this study, to identify an AS in a warehousing system, mixed-valued attributes are used to describe the warehousing operation status, and an integration method is performed based on neighbourhood information entropy. First, the neighbourhood information system is structured. A distance function and neighbourhood radius are defined for numeric data and categorical data, respectively, to eliminate information loss from transforming different types of attributes. Second, the relative neighbourhood information entropy, abnormal degree, and abnormal factor are gradually defined. Third, an evaluation index is defined to measure the identification accuracy in parameter adjustment for two key parameters: the adjustment parameter for the neighbourhood radius (λ), and the discrimination threshold for AS (μ). Finally, a real case study of AS identification in a manufacturing enterprise is implemented to demonstrate the effectiveness of the proposed method, and the identification results are analysed from the practical point of view.
A robust real-time facial alignment system with facial landmarks detection and rectification for multimedia applications

Face detection often plays the first step in various visual applications. Large variants of facial deformations due to head movements and facial expression make it difficult to identify appropriate face region. In this paper, a robust real-time face alignment system, including facial landmarks detection and face rectification, is proposed. A facial landmarks detection model based on regression tree is utilized in the proposed system. In face rectification framework, 2-D geometrical analysis based on pitch, yaw and roll movements is designed to solve the misalignment problem in face detection. The experiments on the two datasets verify the performance significantly improved by the proposed method in the facial recognition task and outperform than those obtained by other alignment methods. Furthermore, the proposed method can achieve robust recognition results even if the amount of training images is not large.
Multi-camera stereo vision based on weights

The improvement of measurement accuracy has always been a hot topic in visual measurement. The multi-camera stereo vision, which is composed of more than two cameras, provides more image information, stronger interference capability and higher 3D reconstruction accuracy than binocular vision, has been widely used. The imaging quality, camera calibration accuracy and vision system structure parameters of different cameras may be different. However, in the traditional multicamera stereo vision, the contribution of each camera to the reconstruction results is the same, which may lead the reduction of the reconstruction accuracy. In this paper, multi-camera stereo vision based on weights is proposed to reduce the impact of cameras with large errors, eventually, the measurement accuracy is improved. The error characteristics are analyzed comprehensively, and the error model is established to calculate weights, then the weighted least square method is used for 3D reconstruction. The feasibility of the proposed method is verified by the trinocular vision 3D reconstruction experiment. Compared with the traditional 3D reconstruction method based on least square method, the accuracy is improved by about 3%.
Developing an Indigenous Cultural Values Based Emoji Messaging System: A Socio-Technical Systems Innovation Approach

Digital divide remains a major concern among indigenous people despite the rapid increase in mobile phone penetration. Beyond the appalling participatory gaps, the devaluing of humanity in the digital era is seen as a much bigger threat. This paper addresses the emerging challenges that are putting indigenous communities at a great disadvantage. The use of emoji in social media and its prominence as a visual language for delivering emotional expressions, gestures and action in a speedy manner is fast becoming a way of life. The universality of emoji poses a major problem to the sustainability of the indigenous cultural values as it imposes western cultural and social behaviour hegemony on the indigenous society. Interactions with remote rural communities in Borneo over the past twenty years have revealed the devastating effect that such an outside-in communication medium has on their cultural resilience. Diversifying the emoji design in adopting a local cultural flavour alone, as described in current literature, will not address the issue effectively. Without considering the whole socio-technical system at a macro-level, the dangers relating to the aggravated disconnect to their rich traditional way of life cannot be averted. We posit a holistic socio-technical systems innovation approach with the participatory involvement of the indigenous community. In this paper, we demonstrate the modeling of complex systems that embed the socio-cultural context without compromising cultural values.
Autonomous Cumulative Transfer Learning

Autonomous knowledge transfer from a known task to a new one requires discovering task similarities and knowledge generalization without the help of a designer or teacher. How transfer mechanisms in such learning may work is still an open question. Transfer of knowledge makes most sense for learners for whom novelty is regular (other things being equal), as in the physical world. When new information must be unified with existing knowledge over time, a cumulative learning mechanism is required, increasing the breadth, depth, and accuracy of an agent’s knowledge over time, as experience accumulates. Here we address the requirements for what we refer to as autonomous cumulative transfer learning (ACTL) in novel task-environments, including implementation and evaluation criteria, and how it relies on the process of similarity and ampliative reasoning. While the analysis here is theoretical, the fundamental principles of the cumulative learning mechanism in our theory have been implemented and evaluated in a running system described priorly. We present arguments for the theory from an empirical as well as analytical viewpoint.
A novel algorithm for infrared image contrast enhancement based on neutrosophic sets

Due to the narrow thermal window of infrared (IR) image sensor array, the represented image becomes dim and its details become blurred. Different contrast and detail enhancement technologies were deployed to improve image quality. An enhancement algorithm with bilateral filter is a state of art method, which first transforms an infrared image into a base part and a detail part, and then expands the detail part and suppresses the base part to enhancement the contrast of the IR image. However, this method cannot efficiently distinguish the background and objectives of the detail part. As a result, the noise information gets amplified when the detail part is expanded, leading to increase noise and to obstruct the sharpening of the detail part. To solve these problems, a novel enhancement algorithm based on the neutrosophic sets is proposed, which transforms the detail part into the neutrosophic domain. Our method utilises three membership functions of pixels in the neutrosophic domain: T (True), F (False) and I (Indeterminacy), which correspond to objective, background and transitional regions of image, respectively. The proposed algorithm is verified and compared with other existing algorithms. The experiment results show that the proposed algorithm can effectively enhance the contrast and preserve the details of an infrared image.
Type debugging with counter-factual type error messages using an existing type checker

The cause of a type error can be very difficult to find for the Hindley-Milner type system. Consequently many solutions have been proposed, but they are hardly used in practice. Here we propose a new solution that provides counter-factual type error messages; these messages state what types specific subexpressions in a program should have (in contrast to the types they actually have) to remove a type error. Such messages are easy-to-understand, because programers are already familiar with them. Furthermore, our solution is easy-to-implement, because it reuses an existing type checker as a subroutine. We transform an ill-typed program into a well-typed program with additional λ-bound variables. The types of these λ-bound variables yield actual and counter-factual type information. That type information plus intended types added as type annotations direct the search of the type debugger.
Digits Recognition of Marathi Handwritten Script using LSTM Neural Network

Penmanship acknowledgement is a famously troublesome issue in Machine Learning. In spite of many years of innovative work, current penmanship acknowledgement frameworks still show imperfect execution in reality applications. Ongoing examinations show extraordinary capability of Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM) for transcribed content acknowledgement. In this paper we assess two methodologies dependent on RNN with LSTM for the acknowledgement of transcribed digits in Marathi Script. The methodology utilizes a Connectionist Temporal Classification yield layer. To test this methodology we have constructed a penmanship acknowledgment framework which takes a written by hand Marathi digit picture as an info and gives a decoded text (digit) as a yield. This methodology shows promising outcomes with 79.637% Digit-level precision on the test dataset for the strategy. The Connectionist Temporal Classification approach reliably beats the other Machine learning approaches as far as speculation and expectation of Complex digits.
Supporting Program Comprehension through Fast Query response in Large-Scale Systems

Software traceability provides support for various engineering activities including Program Comprehension; however, it can be challenging and arduous to complete in large industrial projects. Researchers have proposed automated traceability techniques to create, maintain and leverage trace links. Computationally intensive techniques, such as repository mining and deep learning, have showed the capability to deliver accurate trace links. The objective of achieving trusted, automated tracing techniques at industrial scale has not yet been successfully accomplished due to practical performance challenges. This paper evaluates high-performance solutions for deploying effective, computationally expensive trace-ability algorithms in large scale industrial projects and leverages generated trace links to answer Program Comprehension Queries. We comparatively evaluate four different platforms for supporting industrial-scale tracing solutions, capable of tackling software projects with millions of artifacts. We demonstrate that tracing solutions built using big data frameworks scale well for large projects and that our Spark implementation outperforms relational database, graph database (GraphDB), and plain Java implementations. These findings contradict earlier results which suggested that GraphDB solutions should be adopted for large-scale tracing problems.
Large-Scale Physical Modeling Synthesis, Parallel Computing, and Musical Experimentation: The NESS Project in Practice

Abstract Sound synthesis using physical modeling, emulating systems of a complexity approaching and even exceeding that of real-world acoustic musical instruments, is becoming possible, thanks to recent theoretical developments in musical acoustics and algorithm design. Severe practical difficulties remain, both at the level of the raw computational resources required, and at the level of user control. An approach to the first difficulty is through the use of large-scale parallelization, and results for a variety of physical modeling systems are presented here. Any progress with regard to the second difficulty requires, necessarily, the experience and advice of professional musicians. A basic interface to a parallelized large-scale physical modeling synthesis system is presented here, accompanied by first-hand descriptions of the working methods of five composers, each of whom generated complete multichannel pieces using the system.
Variational Auto-encoders application in wireless Vehicle-to-Everything communications

In this paper, a new technique called Embedded Variational Auto Encoder (EVAE) is proposed for Vehicle-to-Everything (V2X) up-link scenario. An innovative method has been proposed for the accurate prediction of the interference at the receiving end of each user which leads to the enhancement of the end-to-end performance of the V2X. The new algorithm infers noise and fading probabilistic models effect using decentralized Probabilistic Neural Networks (PNNs), while a second centralized PNN has been embedded inside the first group of the PNNs. This single PNN will be used to infer the interference effect on each V2X receiver. The performance of EVAE is compared with the recently proposed neural networks (NN) algorithms based on conventional auto-encoders (AE). Numerical and simulation results for the achievable symbol error rates (SER) have shown a significant improvement particularly in the high SINR regime, compared with the classical systems based on maximum likeli-hood detection.
One-Shot Re-identification using Image Projections in Deep Triplet Convolutional Network

Representation learning of images using deep neural networks have shown great results in classificational tasks. In case of instance recognition, or object re-identification other approaches are used. Siamese architectured convolutional networks were the first approach to learn from semantic distances, and give the similarity of two inputs. Triplet networks apply the triplet loss based on the furthest positive and the closest negative pair. In this paper we present a method to apply multi-directional image projections as an initial transformation to compress image data, whereafter the discriminative ability remains. After performing the training on vehicle images, the model is evaluated by measuring the one-shot classification accuracy.