Reviewerly/exHarmony huggingface dataset:
The Inductive Bias of Quantum Kernels

It has been hypothesized that quantum computers may lend themselves well to applications in machine learning. In the present work, we analyze function classes defined via quantum kernels. Quantum computers offer the possibility to efficiently compute inner products of exponentially large density operators that are classically hard to compute. However, having an exponentially large feature space renders the problem of generalization hard. Furthermore, being able to evaluate inner products in high dimensional spaces efficiently by itself does not guarantee a quantum advantage, as already classically tractable kernels can correspond to high- or infinite-dimensional reproducing kernel Hilbert spaces (RKHS). We analyze the spectral properties of quantum kernels and find that we can expect an advantage if their RKHS is low dimensional and contains functions that are hard to compute classically. If the target function is known to lie in this class, this implies a quantum advantage, as the quantum computer can encode this inductive bias, whereas there is no classically efficient way to constrain the function class in the same way. However, we show that finding suitable quantum kernels is not easy because the kernel evaluation might require exponentially many measurements. In conclusion, our message is a somewhat sobering one: we conjecture that quantum machine learning models can offer speed-ups only if we manage to encode knowledge about the problem at hand into quantum circuits, while encoding the same bias into a classical model would be hard. These situations may plausibly occur when learning on data generated by a quantum process, however, they appear to be harder to come by for classical datasets.
Accuracy enhancement in face recognition using 1D-PCA &amp; 2D-PCA based on multilevel reverse-biorthogonal wavelet transform with KNN classifier

Abstract Face recognition is a physical biometric technique that allows computers to recognize human faces. Currently, face recognition is widely used in the authentication security system. Accuracy improvement in face recognition can be made by doing image decomposition using multi-level reverse biorthogonal wavelets. Composed image features will be extracted using 1D-PCA and 2D-PCA. Face classification was carried out by using the K-Nearest Neighbor algorithm. Furthermore, the face recognition accuracy results using and without the wavelet decomposition will be compared. This research used 565 images in which 400 face images are obtained from the AT&amp;T database, and 400 face images are obtained from the YALE database. This study revealed that the highest accuracy in face recognition using AT&amp;T is98.75% which obtained by 60% training images and 40% testing images, while in face recognition using YALE highest accuracy is obtained on face recognition using 90% training images and 90% testing images with 100% accuracy.
“Gangbangers are gangbangers, hustlers are hustlers”

Criminologists have become increasingly interested in how social media affects inter-gang dynamics. The posturing and provocation characteristic of gang members’ social media interactions, colloquially known as “internet banging,” can sometimes result in physical violence and even homicide. To date, existing research has examined this digital bravado as a cooperative effort and has not meaningfully interrogated how social media may have complicated internal gang structures and the divergent roles gang members may occupy within their respective groups. Drawing from ethnographic research in a Canadian “ghetto” – Toronto’s Regent Park – this chapter will analyze how social media has affected a gang’s internal social organization, specifically as it relates to expectations surrounding “internet banging.” It will outline how gang-involved men who traditionally occupied the role of “hustler” and refrained from collective violence felt mounting pressure to participate in the “internet banging” of their “gangbanger” peers. It discusses how the new gang milieu of social media has augmented the perceived expectation for gang members to participate in violent posturing, thereby pushing even those less inclined to “internet bang” to assume the inherent and potentially fatal risks of such behaviors. Through this, it highlights some of the contradictions within de-contextualized online performances and draws attention to how situated dynamics, positions, tensions, and intra-gang relationships influence “internet banging.”
Self-directed Robot for Car Driving using Genetic Algorithm

The big issue with a human driving car is traffic, with the current continuous growth in the world population. The second big issue with the growing population is creating huge chaos, which leads to accidents. Every year nearly 1.35 million people lose their lives due to traffic crashes, and 20 to 50 million face serious injuries with some untreatable disability as of their road injury. Over 80% of accidents happen due to driver error. Other issues are the efficiency of the car as we are slowly transforming into the electric car. This paper introduced car with the self-driving feature using Genetic Algorithm to reduce the traffic with route optimization, and by reducing traffic, so that many problems related to driving can be solved. It minimizes the rate of an accident and also maximizes the efficiency of the car.
Dynamic Game Theoretic Neural Optimizer

The connection between training deep neural networks (DNNs) and optimal control theory (OCT) has attracted considerable attention as a principled tool of algorithmic design. Despite few attempts being made, they have been limited to architectures where the layer propagation resembles a Markovian dynamical system. This casts doubts on their flexibility to modern networks that heavily rely on non-Markovian dependencies between layers (e.g. skip connections in residual networks). In this work, we propose a novel dynamic game perspective by viewing each layer as a player in a dynamic game characterized by the DNN itself. Through this lens, different classes of optimizers can be seen as matching different types of Nash equilibria, depending on the implicit information structure of each (p)layer. The resulting method, called Dynamic Game Theoretic Neural Optimizer (DGNOpt), not only generalizes OCT-inspired optimizers to richer network class; it also motivates a new training principle by solving a multi-player cooperative game. DGNOpt shows convergence improvements over existing methods on image classification datasets with residual and inception networks. Our work marries strengths from both OCT and game theory, paving ways to new algorithmic opportunities from robust optimal control and bandit-based optimization.
Synthetic Data -- Anonymisation Groundhog Day

Synthetic data has been advertised as a silver-bullet solution to privacy-preserving data publishing that addresses the shortcomings of traditional anonymisation techniques. The promise is that synthetic data drawn from generative models preserves the statistical properties of the original dataset but, at the same time, provides perfect protection against privacy attacks. In this work, we present the first quantitative evaluation of the privacy gain of synthetic data publishing and compare it to that of previous anonymisation techniques. Our evaluation of a wide range of state-of-the-art generative models demonstrates that synthetic data either does not prevent inference attacks or does not retain data utility. In other words, we empirically show that synthetic data suffers from the same limitations as traditional anonymisation techniques. Furthermore, we find that, in contrast to traditional anonymisation, the privacy-utility tradeoff of synthetic data publishing is hard to predict. Because it is impossible to predict what signals a synthetic dataset will preserve and what information will be lost, synthetic data leads to a highly variable privacy gain and unpredictable utility loss. In summary, we find that synthetic data is far from the holy grail of privacy-preserving data publishing.
OPTIMIZATION OF K-NEAREST NEIGHBOUR TO CATEGORIZE INDONESIAN'S NEWS ARTICLES

Text classification is the process of grouping documents based on similarity in categories. Some of the obstacles in doing text classification are many words appeared in the text, and some words come up with infrequent frequency (sparse words). The way to solve this problem is to conduct the feature selection process. There are several filter-based feature selection methods; some are Chi-Square, Information Gain, Genetic Algorithm, and Particle Swarm Optimization (PSO). Aghdam's research shows that PSO is the best among those methods. This study examined PSO to optimize the k-Nearest Neighbour (k-NN) algorithm's performance in categorizing news articles. k-NN is an algorithm that is simple and easy to implement. If we use the appropriate features, then the k-NN will be a reliable algorithm. PSO algorithm is used to select keywords (term features), and it is continued with classifying the documents using k-NN. The testing process consists of three stages. The stages are tuning the parameter of k-NN, the parameter of PSO, and measuring the testing performance. The parameter tuning process aims to determine the number of neighbours used in k-NN and optimize the PSO particles. Otherwise, the performance testing compares the performance of k-NN with and without using PSO. The optimal number of neighbours is 9, with the number of particles is 50. The testing showed that using the k-NN with PSO and a 50% reduction in terms. The results 20 per cent better accuracy than k-NN without PSO. Although the PSO's process did not always find the optimal conditions, the k-NN method can produce better accuracy. In this way, the k-NN method can work better in grouping news articles, especially in Indonesian language news articles
A Standardized Approach to Evaluate Assistive Systems for Manual Assembly Tasks in Industry

Various assistive systems for supporting manual assembly tasks in industry have been proposed within the last years. These systems range from simple work instructions on screens, to animated mixed reality and in-situ projected hints directly onto the work-piece. Several design approaches exist for these systems. Commonly, assistive systems are tested and evaluated using abstract use cases with widely available building blocks (e.g. Duplo or Lego). These approaches work to test simple picking and placing tasks, but cannot reproduce the complexity of real assembly, which may include the tightening of screws, adding components from specific directions or attaching cables in a predefined way. Other evaluation approaches make use of realistic work processes from real industrial contexts. However, these evaluations are less controllable and hardly replicable. Our work presents a realistic and easyto-recreate approach to evaluate assistive systems for manual assembly tasks. It comprises a 3D-modelled object that considers several procedures and aspects of manual assembly and, as an abstract assembly object, helps to improve the usability of laboratory developed assistive systems. The model itself is developed to be 3D printed and uses standardized components to make it easily available for research and learning factories around the globe. With this approach, we want to support researchers in comparing and evaluating assistive systems for industrial assembly tasks by providing a realistic and easy-to-use test setup with high internal and external validity.
Real-Time Human Body Tracking System for Posture and Movement Using Skeleton-Based Segmentation

Real-time tracking of the human body has proven to be a key technology in several areas. In our proposed model, we have initially performed segmentation of bodies from a live feed and further detected and classified all the body parts to create a skeleton which does analysis of the posture and movement of the body. As this model works on a live feed, it has multiple real-time applications which include, but are not restricted to, applications in the field of medical diagnostics, provision of improved safety and security, human–computer interface, etc. There have been multiple instances and various research works as cited later in this paper that has attempted to track the human body. Myriad different solutions have been presented using different sensors attached at joints for estimation of the poses, but these pose estimations systems are quite personalized and have huge setup costs. The system makes a good tradeoff between system simplicity and accuracy and helps to distinctly identify and track human bodies in real time.
New measures in 2021 to increase the quality and reputation of the Critical Review in Environmental Science and Technology (CREST) journal

Since its first publication in 1970, the Critical Reviews in Environmental Science and Technology (CREST) journal has served as an international forum for the publication of comprehensive, critical...
The use of electronic educational resources in professional education: advantages and risks

The problem of using electronic information resources in the system of higher professional education are discussed. Purpose of the research: theoretically substantiate and empirically identify the advantages and risks of using electronic information resources in professional education. Research methods: theoretical analysis of literature, questionnaire survey, method of expert assessments, ranking method, methods of descriptive statistics and qualitative interpretation of data. The analysis of the levels of integration of electronic information resources in the continuous education system is carried out. The advantages of electronic educational resources are revealed in comparison with the traditionally used means and technologies of teaching. The results of an empirical study of the ideas of students and teachers about the advantages and disadvantages of distance learning and electronic information and educational platforms are presented. Ways to reduce the risks of negative impact of electronic information resources on the processes of professional and personal development of specialists are proposed.
Fast Regular and Interval-based Classification, using parSITs

Parallelized Sequential Indexing Tables (parSITs), are classifiers that have been developed for the processing of large volumes of data rapidly.Their base idea is implementing a sequential indexing table structure with parallelization techniques, using a sequence of Lookup Tables in order to build a chain of value combinations.Although the inference (evaluation) method, that it was originally developed for, is very fast, its performance significantly depends on the arbitrary order of the attributes, in multi-class cases, thus, reducing its classification performance.In this work, we introduce a new inference method, that increases the classification performance of the classifier, at the cost of a small increase in computational complexity.
Make-Up Invariant Face Recognition under Uncontrolled Environment

Face Recognition under makeup is a challenging task in the field of biometric. People or criminal identification while having makeup on their faces are important applications. To address this issue, this paper proposes a local descriptor for computing the similarity features for intra-class variation and discriminative features for inter-class similarity. The proposed Min-Max Difference Pattern descriptor is applied over the local patches of the gray scale face image to create the feature vector. A Multi-class Support Vector Machine has been used with 5-fold cross validation for computing recognition accuracy. The proposed descriptor is robust to handle makeup with pose, illumination and expression variation and improved the results. The evaluation of the proposed descriptor has been performed on two standard and challenging datasets-YMU and VMU. The proposed approach outperformed to the descriptor based existing approaches and comparable to some deep learning methods.
LocoProp: Enhancing BackProp via Local Loss Optimization

We study a local loss construction approach for optimizing neural networks. We start by motivating the problem as minimizing a squared loss between the pre-activations of each layer and a local target, plus a regularizer term on the weights. The targets are chosen so that the first gradient descent step on the local objectives recovers vanilla BackProp, while the exact solution to each problem results in a preconditioned gradient update. We improve the local loss construction by forming a Bregman divergence in each layer tailored to the transfer function which keeps the local problem convex w.r.t. the weights. The generalized local problem is again solved iteratively by taking small gradient descent steps on the weights, for which the first step recovers BackProp. We run several ablations and show that our construction consistently improves convergence, reducing the gap between first-order and second-order methods.
A Robust Pose Transformational GAN for Pose Guided Person Image Synthesis

Generating photorealistic images of human subjects in any unseen pose have crucial applications in generating a complete appearance model of the subject. However, from a computer vision perspective, this task becomes significantly challenging due to the inability of modelling the data distribution conditioned on pose. Existing works use a complicated pose transformation model with various additional features such as foreground segmentation, human body parsing etc. to achieve robustness that leads to computational overhead. In this work, we propose a simple yet effective pose transformation GAN by utilizing the Residual Learning method without any additional feature learning to generate a given human image in any arbitrary pose. Using effective data augmentation techniques and cleverly tuning the model, we achieve robustness in terms of illumination, occlusion, distortion and scale. We present a detailed study, both qualitative and quantitative, to demonstrate the superiority of our model over the existing methods on two large datasets.
Health Cloud—Health Care as a Service

The healthcare sector has moved from computerized procedures and virtual medical treatment since digital technologies entered into the world of health care to more than just Hospital Information Systems (HIS), Electronic Medical Records (EMR). Through the developments of computer technologies, health care in all forms of industries becomes rapidly online, collaboratively, more patient-centered and data-driven. This vast volume of data produced and the numerous healthcare facilities accessible to patients cannot be protected by conventional technical technology in the healthcare sector. Cloud Storage is an increasingly growing phenomenon involving many providers all delivered via a pay-as-you-go format on demand over the Internet. It aims to speed up the delivery of apps and rising costs. Online infrastructure will play a key role in controlling the movement toward digital data development and medical services everywhere possible. Cloud infrastructure will also provide a major difference to reducing the expense of change of health care, maximizing services and generating a new age in healthcare creativity. The report quickly addresses some of the problems that the insurance sector faces with digital technology. This outlines a framework designed to deliver different cloud-based health resources. The article also describes the distribution of a service that is offered within the mentioned framework.
Alternative Design For An Interactive Exhibit Learning In Museums: How Does User Experience Differ Across Different Technologies-VR, Tangible, And Gesture

This paper investigates three types of user interface: VR, Gesture-based, and Tangible. We examine how user experience differs across these different technologies, and explore the factors that underpin the differences. We conduct an empirical study, in which we create three different interactive exhibits that apply these technologies to deliver the same scientific content. The study uses a mixed-method approach to measure two factors. First, user experience is measured accorded to six dimensions of user experience: attractiveness, perspicuity, efficiency, dependability, simulation, and novelty. Second, attention holding power is measured by playing time. The study uses semi-structured interviews to explore the user experience and learning outcomes afforded by each interface. Thirty-one subjects took part the study. The results show that there are significantly different user experiences when using different types of interface. There are differences across five user experience dimensions; only novelty is relatively unchanged. Differences are found primarily between the VR and Gesture, and Tangible and Gesture conditions. No significant difference in holding power between the three types of interface was observed. The statistical analysis of results and interview data from participants suggest six aspects to focus on when choosing an alternative interface for a new interactive exhibit: novelty, user-friendliness, precision of the input device, task and device design, multi-modality of feedback, and quality of text in VR.
Efficient prediction of concentrating solar power plant productivity using data clustering

Concentrating solar power (CSP) plants convert solar energy to electricity and can be deployed with a thermal storage capability to shift electricity generation from time periods with available solar resource to those with high electricity demand or electricity price. Rigorous optimization of plant design and operational strategies can improve the market-competitiveness and commercial viability; however, such optimization may require hundreds of annual performance simulations, each of which can be computationally expensive when including considerations such as optimization of dispatch scheduling, sub-hourly time resolution, and stochastic effects due to uncertain weather or electricity price forecasts. This paper proposes a methodology to reduce the computational burden associated with simulation of electricity yield and revenue for CSP plants over a single- or multi-year period. Data-clustering techniques are employed to select a small number of limited-duration time blocks for simulation that, when appropriately weighted, can reproduce generation and revenue over a single year or within each year of a multi-year period. After selection of appropriate data features and weighting factors defining similarity between time-series profiles, the methodology captured annual revenue within 2.3%, 1.7%, or 1.2% using simulation of 10, 30, or 50 three-day exemplar time blocks, respectively, for each of three single-year location/weather/market scenarios and five plant configurations ranging from low to high solar multiple and storage capacity. When applied to multi-year datasets, the proposed methodology can capture inter-year variability that is unavailable from typical meteorological year (TMY) datasets while simultaneously requiring simulation of less than a single year of data.