Reviewerly/exHarmony huggingface dataset:
Testing Machine Learning Algorithms for Balanced Data Usage

With the increased application of machine learning (ML) algorithms to decision-making processes, the question of fairness of such algorithms came into the focus. Fairness testing aims at checking whether a classifier as "learned" by an ML algorithm on some training data is biased in the sense of discriminating against some of the attributes (e.g. gender or age). Fairness testing thus targets the prediction phase in ML, not the learning phase. In this paper, we investigate fairness for the learning phase. Our definition of fairness is based on the idea that the learner should treat all data in the training set equally, disregarding issues like names or orderings of features or orderings of data instances. We term this property balanced data usage. We consequently develop a (metamorphic) testing approach called TiLe for checking balanced data usage. TiLe is applied on 14 ML classifiers taken from the scikit-learn library using 4 artificial and 9 real-world data sets for training, finding 12 of the classifiers to be unbalanced.
Apply rich psychological terms in AI with care

There is much to be gained from interdisciplinary efforts to tackle complex psychological notions such as 'theory of mind'. However, careful and consistent communication is essential when comparing artificial and biological intelligence, say Henry Shevlin and Marta Halina.
Improved Zhang neural network with finite-time convergence for time-varying linear system of equations solving

This paper proposes an improved Zhang neural network (IZNN) for time-varying linear system of equations solving. Such a neural network is activated by an array of continuous sign-bi-power function. Theoretical analysis is provided to show the desired finite-time convergence property of the proposed IZNN. As compared to Zhang neural network activated by an array of discontinuous signum-function, the solution synthesized by the proposed neural network can converge to theoretical solution, while the solution synthesized by the latter oscillates to some extent around the equilibrium point. Moreover, the remarkable finite-time convergence of the proposed IZNN model is corroborated by a simulative example. Simulation results also demonstrate that the proposed neural network is more suitable in engineering applications than Zhang neural network activated by the array of discontinuous signum-function.
Classifying Text-Based Emotions Using Logistic Regression

Emotion detection textual content is getting popular among individuals and business companies to analyze user emotional reaction on the products they use. In this work, emotion detection from textual content is performed by using supervised learning-based Logistic Regression classifier. ISEAR dataset is used to taring the classifier, while testing dataset is used to evaluate the prediction capability of the classifier for emotion classification. The prior works used rule-based techniques, supported by lexical resources. However, limited coverage of emotional clues, was the major issue, which resulted in poor performance of system. The proposed work overcomes this limitation by proposing supervised learning technique using Logistic Regression classifier. The results obtained are encouraging and show that the proposed system performed better than the similar methods.
Recognizing F-Formations in the Open World

A key skill for social robots in the wild will be to understand the structure and dynamics of conversational groups in order to fluidly participate in them. Social scientists have long studied the rich complexity underlying such focused encounters, or F-formations. However, current state-of-the-art algorithms that robots might use to recognize F-formations are highly heuristic and quite brittle. In this report, we explore a data-driven approach to detect F-formations from sets of tracked human positions and orientations, trained and evaluated on two openly available human-only datasets and a small human-robot dataset that we collected. We also discuss the potential for further computational characterization of F-formations beyond simply detecting their occurrence.
Fluxonic Processing of Photonic Synapse Events

Much of the information processing performed by a biological neuron occurs in the dendritic tree.For artificial neural systems using light for communication, it is advantageous to convert signals to the electronic domain at synaptic terminals, so dendritic computation can be performed with electrical circuits.Here, we present circuits based on Josephson junctions and mutual inductors that act as dendrites, processing signals from synapses receiving single-photon communication events with superconducting detectors.We show simulations of circuits performing basic temporal filtering, logical operations, and nonlinear transfer functions.We further show how the synaptic signal from a single photon can fan out locally in the electronic domain to enable the dendrites of the receiving neuron to process a photonic synapse event or pulse train in multiple different ways simultaneously.Such a technique makes efficient use of photons, energy, space, and information.
A pseudo-3D ball lattice artifact and method for evaluating the metrological performance of structured-light 3D scanners

This paper presents a novel reference artifact and procedure for assessing the metrological performance of structured-light 3D scanners, also known as fringe projection systems. Despite the increasing popularity of these scanners, there is still no international standard aiming at providing a guideline for how to evaluate their metrological performance. The users of the scanners, especially for part inspection, need instructions for comparing the accuracy of different systems in measuring specific parameters, as well as acceptance tests to verify whether a particular scanner can be used to inspect the part in question. As the structured-light scanners are area-scanning systems, they necessitate testing over their entire measurement volume. In order to enable this, we propose a method based on a pseudo-3D artifact calibrated by a contact probe on a coordinate measuring machine (CMM). The proposed artifact consists of a 2D ball plate that is mounted on top of spacers of different heights using kinematic couplings that ensure repeatable positioning of the ball plate with respect to the base in order to create a 3D lattice once measured. The artifact provides reference values for the distance between the centers of any pair of precision balls, as well as the reference for size and form, in order to benchmark the scanner measurements. Using the reference data, the proposed procedure enables acceptance tests and a comprehensive insight into the errors of the scanner in measuring distance, size, and form at different positions within its entire scanning volume.
Single-Sample Face Recognition Based on Feature Expansion

Face recognition (FR) with a single sample per person (SSPP) is one of the most challenging problems in computer vision.In this scenario, it is difficult to predict facial variation such as pose, illumination, and disguise due to the lack of enough training samples.Therefore, the development of the FR system with only a small number of training samples is hindered.To address this problem, this paper proposes a scheme combined transfer learning and sample expansion in feature space.First, it uses transfer learning by training a deep convolutional neural network on a common multi-sample face dataset and then applies the well-trained model to a target data set.Second, it proposes a sample expansion method in feature space called k class feature transfer (KCFT) to enrich intra-class variation information for a single-sample face feature.Compared with other expanding sample methods in the image domain, this method of expanding the samples in the feature domain is novel and easy to implement.Third, it trains a softmax classifier with expanded face features.The experimental results on ORL, FERET, and LFW face databases demonstrate the effectiveness and robustness of the proposed method for various facial variations.
MultiTL-KELM: A multi-task learning algorithm for multi-step-ahead time series prediction

Time series prediction for higher future horizons is of great importance and has increasingly aroused interest among both scholars and practitioners. Compared to one-step-ahead prediction, multi-step-ahead prediction encounters higher dose of uncertainty arising from various facets, including accumulation of errors and lack of information. Many existing studies draw attention to the former issue, while relatively overlook the latter one. Inspired by this discovery, a new multi-task learning algorithm, called the MultiTL-KELM algorithm for short, is proposed for multi-step-ahead time series prediction in this work, where the long-ago data is utilized to provide more information for the current prediction task. The time-varying quality of time-series data usually gives rise to a wide variability between data over long time span, making it difficult to ensure the assumption of identical distribution. How to make the most of, rather than discard the abundant old data, and transfer more useful knowledge to current prediction is one of the main concerns of our proposed MultiTL-KELM algorithm. Besides, unlike typical iterated or direct strategies, MultiTL-KELM regards predictions of different horizons as different tasks. Knowledge from one task can benefit others, enabling it to explore the relatedness among horizons. Based upon its design scheme, MultiTL-KELM alleviates the accumulation error problem of iterated strategy and the time consuming of direct strategies. The proposed MultiTL-KELM algorithm has been compared with several other state-of-the-art algorithms, and its effectiveness has been numerically confirmed by the experiments we conducted on four synthetic and two real-world benchmark time series datasets.
The craftivists: Pushing for affective, materially informed pedagogy

Abstract The idea of making as a form of activism or, as we refer to it in this paper, craftivism , underpins our ambition to transform pedagogical environments into spaces of possibility through sensory and affective making practices. A craftivist agenda pushes for open teaching and learning with materials so that students can inhabit a what if space which ruptures institutional time and space. Beginning with a theorization of disruptive making and dissensus and then moving on to foreground a materialist stance on making, the paper illustrates how students experience materialities across two very different classroom environments and how these experiences led to greater presence, voice and agency for students. There is an underlying focus in the paper on embodied responses and affective flows. After theorizing craftivism, there are three sections that explore making, teaching and feeling through craftivist, postdigital methods.
A Study on Join Operations in MongoDB Preserving Collections Data Models for Future Internet Applications

Presently, we are observing an explosion of data that need to be stored and processed over the Internet, and characterized by large volume, velocity and variety. For this reason, software developers have begun to look at NoSQL solutions for data storage. However, operations that are trivial in traditional Relational DataBase Management Systems (DBMSs) can become very complex in NoSQL DBMSs. This is the case of the join operation to establish a connection between two or more DB structures, whose construct is not explicitly available in many NoSQL databases. As a consequence, the data model has to be changed or a set of operations have to be performed to address particular queries on data. Thus, open questions are: how do NoSQL solutions work when they have to perform join operations on data that are not natively supported? What is the quality of NoSQL solutions in such cases? In this paper, we deal with such issues specifically considering one of the major NoSQL document oriented DB available on the market: MongoDB. In particular, we discuss an approach to perform join operations at application layer in MongoDB that allows us to preserve data models. We analyse performance of the proposes approach discussing the introduced overhead in comparison with SQL-like DBs.
Interactive Narrative in a Mobile Health Behavioral Intervention (Tumaini): Theoretical Grounding and Structure of a Smartphone Game to Prevent HIV Among Young Africans

The increasing availability of smartphones, including in low-income countries, offers an unprecedented opportunity to reach individuals with innovative health promotion interventions. Electronic games delivered via smartphone offer promising avenues for sexual health promotion and HIV prevention, especially for young people. By giving players real agency in a virtual and safe environment, well-designed games can provide a level of experiential learning unparalleled by many other behavioral interventions. The design of effective games for health relies on multidisciplinary insight and expertise. However, relatively few studies discuss the theoretical understanding underlying their intervention. Making explicit the theoretical grounding of a game-based intervention allows articulation of assumptions and strategies, anticipation of outcomes, and evaluation of impacts (including intermediate effects), thereby increasing understanding of pathways to change, with a view to contributing to the development of more effective games. It also helps strengthen the credibility and improve the accountability of games for health. We present the multidisciplinary theoretical framework-integrating intervention design, mediators, and behavioral outcomes-and the structure of an HIV prevention game for young African adolescents that has shown promise in a randomized pilot study in Western Kenya. The central component of Tumaini (hope for the future in Kiswahili) is an interactive role-playing narrative in which the player makes choices for characters that determine how their paths unfold. In addition, a series of mini-games reinforce skills, and the "My Story" component links the game world to the player's own life and goals, and a reward system motivates continued play. With its "choose-your-own-adventure" format, Tumaini is intended to be replayed so that players can experience the consequences resulting from different choices made in the role-playing narrative. Grounded in theories of narrative and applied communication and in social behavioral theories, especially Social Cognitive Theory, Tumaini is designed to help young adolescents acquire the information, skills, and motivation they need to avoid and reduce sexual risks. We close by situating Tumaini within discussion of the theory and practice of using interactive narrative in health promotion, with a view to furthering theoretical elaboration.
Efficiently Masking Binomial Sampling at Arbitrary Orders for Lattice-Based Crypto

With the rising popularity of lattice-based cryptography, the Learning with Errors (LWE) problem has emerged as a fundamental core of numerous encryption and key exchange schemes. Many LWE-based schemes have in common that they require sampling from a discrete Gaussian distribution which comes with a number of challenges for the practical instantiation of those schemes. One of these is the inclusion of countermeasures against a physical side-channel adversary. While several works discuss the protection of samplers against timing leaks, only few publications explore resistance against other side-channels, e.g., power. The most recent example of a protected binomial sampler (as used in key encapsulation mechanisms to sufficiently approximate Gaussian distributions) from CHES 2018 is restricted to a first-order adversary and cannot be easily extended to higher protection orders. In this work, we present the first protected binomial sampler which provides provable security against a side-channel adversary at arbitrary orders. Our construction relies on a new conversion between Boolean and arithmetic (B2A) masking schemes for prime moduli which outperforms previous algorithms significantly for the relevant parameters, and is paired with a new masked bitsliced sampler allowing secure and efficient sampling even at larger protection orders. Since our proposed solution supports arbitrary moduli, it can be utilized in a large variety of lattice-based constructions, like NewHope, LIMA, Saber, Kyber, HILA5, or Ding Key Exchange.
Particle Filtering With Soft State Constraints for Target Tracking

In practice, additional knowledge about the target to be tracked, other than its fundamental dynamics, can often be modeled as a set of soft constraints and utilized in a filtering process to improve the tracking performance. This paper develops a general approach to the modeling of soft inequality constraints, and investigates particle filtering (PF) with soft state constraints for target tracking. We develop two PF algorithms with soft inequality constraints, i.e., a sequential-importance-resampling particle filter and an auxiliary sampling mechanism. The latter probabilistically selects the candidate particles from the soft inequality constraints of the state variables so that they are more likely to comply with the soft constraints. The performances of the proposed algorithms are evaluated using Monte Carlo simulations in a target tracking scenario.
Blind Denoising of Mixed Gaussian-impulse Noise by Single CNN

The removal of mixed noise is a stiff problem since the distribution of the noise cannot be predicted accurately. The most common mixed noise is the combination of Additive White Gaussian Noise (AWGN) and Impulse Noise (IN). Many methods first attempt to remove IN but it might collapse the texture of the image. In this paper, we propose a new learning-based method using convolutional neural network (CNN) for removing mixed gaussian-impulse noise. Since our denoising network can remove various level of mixed noise, neither the preprocessing for removing IN nor noise-level estimation is necessary.
Basic Cyber Hygiene: Does It Work?

A number of security certifications for small- and medium-size enterprises have been proposed, but how effective are these schemes? We evaluated the effectiveness of Cyber Essentials and found that its security controls work well to mitigate threats that exploit vulnerabilities remotely with commodity-level tools.
Nature-inspired cost optimisation for enterprise cloud systems using joint allocation of resources

Resource allocation in cloud being NP-complete lacks a generalised optimal solution. In this work, we propose a nature-inspired ACO-Fuzzy framework based joint resource allocation algorithm for cloud datacenters. The algorithm assigns compute and network resources efficiently to minimize end-user cost. An ACO-based local search heuristic is used to discover the current status of resources and then a Fuzzy-based decision maker finds the most optimal set of compute and network resources minimising the end-user cost. Analytical and experimental evaluations validate the efficiency of joint allocation.Abbreviations: CSP: cloud service provider; VM: virtual machine; VMP: virtual machine placement; NIJA: nature-inspired joint allocation; DM: decision maker; WAN: wireless area network; ACO: ant colony optimisation; SDFF: server defined first-fit; GA: genetic algorithm; PSO: particle swarm optimisation; SA: simulated annealing
New quantum MDS codes with large minimum distance and short length from generalized Reed–Solomon codes

Quantum maximum-distance-separable (MDS) codes are an important class of quantum codes. In this paper we mainly use classical Hermitian self-orthogonal generalized Reed–Solomon codes to construct three classes of new quantum MDS codes. Further, these quantum MDS codes have large minimum distance and short length.
The influence of mobile applications on students’ speaking skill and critical thinking in English language learning

Mobile assisted learning especially in language learning gives both benefits and challenges in optimizing students' outputs toward learning targets. We investigated the influence of mobile applications as learning assistance which can help students improve their speaking skill and critical thinking in English language learning. Convergent parallel mixed method design was used to capture quantitatively and qualitatively the data (test, observation, and interview). The data was taken from 38 students in speaking class at English department of Borneo University Tarakan Indonesia for one semester with 12 meetings (Socratic circle speaking). Mobile applications used were online dictionary, language translator, speaking English, English grammar, speech to text, critical thinking, WhatsApp, Google chrome, YouTube, Weebly, and Gmail. Statistic result for both students' speaking and critical thinking obtained significance value .000 < 0.05 which means that mobile applications used as learning assistance positively optimized students' speaking skill and critical thinking in English language learning. Interview reveals that students were very enthusiastic exploring and practicing mobile learning applications although they got problem with unstable internet connection. Technology in the form of mobile learning applications provides opportunities to both the teacher and the students in improving the quality of teaching and learning. It is applicable for any subject by selecting appropriate mobile learning applications.
A Robust Method for Automatic Panoramic UAV Image Mosaic

This paper introduces a robust method for panoramic unmanned aerial vehicle (UAV) image mosaic. In the traditional automatic panoramic image stitching method (Autostitch), it assumes that the camera rotates about its optical centre and the group of transformations the source images may undergo is a special group of homographies. It is rare to get such ideal data in reality. In particular, remote sensing images obtained by UAV do not satisfy such an ideal situation, where the images may not be on a plane yet and even may suffer from nonrigid changes, leading to poor mosaic results. To overcome the above mentioned challenges, in this paper a nonrigid matching algorithm is introduced to the mosaic system to generate accurate feature matching on remote sensing images. We also propose a new strategy for bundle adjustment to make the mosaic system suitable for the UAV image panoramic mosaic effect. Experimental results show that our method outperforms the traditional method and some of the latest methods in terms of visual effect.
An adaptive color image steganography method using adjacent pixel value differencing and LSB substitution technique

Steganography is a data-concealing technique that provides a covert communication. This paper proposes a spatial domain color steganography method that uses a neighboring pixel-pair differencing concept to enhance the embedding capacity. The proposed method considers the distortion tolerance capacity of each color plane and the difference value between two neighboring pixels while deciding the number of bits that can be concealed in that pixel-pair. Each plane is divided into some disjoint blocks of size 3 × 3 pixels. The method forms eight pairs of pixels in one block using the eight-neighboring pixel concept, which enables the method to utilize all the edge pixels in all probable directions. It also uses the XOR encoding technique before the embedding process. This step provides an additional layer of security to the secret information. The simulation results show that the proposed method is able to achieve a high embedding capacity with good visual quality as per the calculation of imperceptibility metrics. The robustness of the method is tested by RS steganalysis and pixel difference histogram analysis. Further, a comparison between the proposed method and the existing methods on the basis of embedding capacity and stego image quality is shown.