Reviewerly/exHarmony huggingface dataset:
Recommendations for Attractive Hairstyles

People change their hairstyles to make their appearance attractive, however it is difficult to determine which hairstyles are attractive. In this study, we aim to recommend a hairstyle that improves the attractiveness for an input face using attractiveness evaluation and image generation by deep learning. In the experiment, we first learned the attractiveness and obtained results similar to human intuition. Second, the hairstyle of the input image was changed using two methods: hairstyle attribute conversion and face swapping. Finally, a comparison experiment was performed by subjectively evaluating the input image and the image obtained by the proposed method. As a result, the proposed method was able to generate images with high evaluation value.
FPGA-Accelerated compression of integer vectors

An efficient compression of integer vectors is critical in dictionary-encoded column stores like SAP HANA to keep more data in the limited and precious main memory. Past research focused on lightweight compression techniques that trade low latency of data accesses for lower compression ratios. Consequently, only few columns in a wide table benefit from light-weight and effective compression schemes like run-length encoding, prefix compression or sparse encoding. Besides bit-packing, other columns remained uncompressed, which clearly misses opportunities for a better compression ratio for many columns. Furthermore, the main executor for compression was the CPU as compression involves heavy data transfer. Especially when used with co-processors, the data transfer overhead wipes out performance gains from co-processor usage.
Flush+Time: A High Accuracy and High Resolution Cache Attack On ARM-FPGA Embedded SoC

Flush based cache attacks have become a practical threat to data privacy and information security due to their advantages such as high accuracy and resolution. However, their accuracy and resolution still has room for improvement. In addition, although most of the attacks have been demonstrated on x86 processors, few of them have been executed on ARM devices. We propose a high accuracy, high resolution flush based cache attack, Flush+Time. This technique solves two important challenges for cache attacks on ARM: how to flush cache lines and how to achieve precise timing. Experiments show that Flush+Time increases accuracy from 95.1% of Flush+Reload, the most powerful general cache attack so far, to 99.3%. Flush+Time has a 30.5% higher resolution than Flush+Reload, but its execution time is only 0.59 times that of Spectre.
Bridging the hydrodynamic Drude model and local transformation optics theory

The recent ability of plasmonic nanostructures to probe subnanometer and even atomic scales demands theories that can account for the nonlocal dynamics of the electron gas. The hydrodynamic Drude model (HDM) captures much of the microscopic dynamics of the quantum mechanical effects when additional boundary conditions are considered. Here, we revisit the HDM under the Madelung formalism to reexpress its coupled system of equations as a single nonlinear Schr\"odinger equation in order to have a natural quantum mechanical description of plasmonics. Specifically, we study the response of two overlapping nanowires with this formalism. We ensure that an proposed frame concurs with classical electrodynamics when the local response approximation holds in the plasmonic system by finding the correction needed.
A New Hybrid Document Clustering for PRF-Based Automatic Query Expansion Approach for Effective IR

Automatic query expansion (AQE) is an effective measure to improve information retrieval performance by including additional terms in a user query. The pseudo relevance feedback (PRF) method employed for AQE so far has suffered from a major problem of query drift. Therefore, keeping it in view, a new hybrid document clustering for PRF based AQE approach is proposed in the present article. In this, Fuzzy logic and Particle Swarm Optimization (PSO) are used to construct document clusters. Further, a new and effective hybrid PSO and Fuzzy logic-based term weighting approach is followed to find more suitable additional query terms using a weighted score of four IR evidences which is considered maximized. Moreover, a combined semantic filtering method along with query terms re-weighting algorithms are also used to remove noisy or irrelevant terms semantically. The performance of the presented approaches in this article is tested and compared with other approaches on three benchmark data sets. The comparative analysis of all the tested approaches illustrates the superior performance of the proposed approach.
GenVibe

Research about vibrotactile patterns is traditionally conducted with patterns handcrafted by experts which are then subsequently evaluated in general user studies. The current empirical approach to designing vibrotactile patterns mostly utilizes expert decisions and is notably not adapted to individual differences in the perception of vibration. This work describes GenVibe: a novel approach to designing vibrotactile patterns by examining the automatic generation of personal patterns. GenVibe adjusts patterns to the perception of an individual through the utilization of interactive generative models. An algorithm is described and tested with a dummy smartphone made from off-the-shelf electronic components. Afterward, a user study with 11 participants evaluates the outcome of GenVibe. Results show a significant increase in accuracy from 73.6% to 84.0% and a higher confidence ratings by the users.
K-Anonymization-Based Temporal Attack Risk Detection Using Machine Learning Paradigms

Huge amount of personal data is collected by online applications and its protection based on privacy has brought a lot of major challenging issues. Hence, the [Formula: see text]-anonymization with privacy-preserving data publishing has emerged as an active research field. The published data contains personalized information, which may be used for analysis converting it to useful information. In this paper, Quasi identifier (QI) data publishing with data preservation through the [Formula: see text]-anonymization process is proposed. Moreover, the risks such as the temporal attack in the previous release of re-identifying QI information are evaluated using the [Formula: see text]-anonymity model. The development of independent and ensemble classifiers for finding efficient QI’s to avoid temporal attacks is the major objective of this paper. Therefore, the classifiers like Naïve Bayes, Support Vector Machine, and Multilayer Perceptron are used as base classifiers. An ensemble model based on these base classifiers is also used. The experimental results demonstrate that, the proposed classification approach is an effective K-anonymity tool for the enhancement of sequential release.
An Electronic Voting Scheme Based on ElGamal Homomorphic Encryption for Privacy Protection

Abstract Electronic voting is the product of the development of modern network technology, in view of the electronic voting system security issues. This paper puts forward a kind of electronic voting scheme based on ElGamal homomorphism encryption, the scheme using homomorphic encryption strategies to voters vote operations, to protect the identity of voters and will not be exposed, to realize the security of electronic voting, confidentiality, and fairness, eligibility, and implement the Internet security electronic voting.
Fast deep swept volume estimator

Despite decades of research on efficient swept volume computation for robotics, computing the exact swept volume is intractable and approximate swept volume algorithms have been computationally prohibitive for applications such as motion and task planning. In this work, we employ deep neural networks (DNNs) for fast swept volume estimation. Since swept volume is a property of robot kinematics, a DNN can be trained off-line once in a supervised manner and deployed in any environment. The trained DNN is fast during on-line swept volume geometry or size inferences. Results show that DNNs can accurately and rapidly estimate swept volumes caused by rotational, translational, and prismatic joint motions. Sampling-based planners using the learned distance are up to five times more efficient and identify paths with smaller swept volumes on simulated and physical robots. Results also show that swept volume geometry estimation with a DNN is over 98.9% accurate and 1,200 times faster than an octree-based swept volume algorithm.
Continuous Analogical Proportions-Based Classifier

Analogical proportions, often denoted $$A:B\,{::}\,C:D$$ , are statements of the form "A is to B as C is to D" that involve comparisons between items. They are at the basis of an inference mechanism that has been recognized as a suitable tool for classification and has led to a variety of analogical classifiers in the last decade. Given an object D to be classified, the basic idea of such classifiers is to look for triples of examples (A, B, C), in the learning set, that form an analogical proportion with D, on a maximum set of attributes. In the context of classification, objects A, B, C and D are assumed to be represented by vectors of feature values. Analogical inference relies on the fact that if a proportion $$A:B\,{::}\,C:D$$ is valid, one of the four components of the proportion can be computed from the three others. Based on this principle, analogical classifiers have a cubic complexity due to the search for all possible triples in a learning set to make a single prediction. A special case of analogical proportions involving only three items A, B and C are called continuous analogical proportions and are of the form "A is to B as B is to C" (hence denoted $$A:B\,{::}\,B:C$$ ). In this paper, we develop a new classification algorithm based on continuous analogical proportions and applied to numerical features. Focusing on pairs rather than triples, the proposed classifier enables us to compute an unknown midpoint item B given a pair of items (A, C). Experimental results of such classifier show an efficiency close to the previous analogy-based classifier while maintaining a reduced quadratic complexity.
ActGAN: Flexible and Efficient One-shot Face Reenactment

This paper introduces ActGAN – a novel end-to-end generative adversarial network (GAN) for one-shot face reenactment. Given two images, the goal is to transfer the facial expression of the source actor onto a target person in a photo-realistic fashion. While existing methods require target identity to be predefined, we address this problem by introducing a "many-to-many" approach, which allows arbitrary persons both for source and target without additional retraining. To this end, we employ the Feature Pyramid Network (FPN) as a core generator building block – the first application of FPN in face reenactment, producing finer results. We also introduce a solution to preserve a person’s identity between synthesized and target person by adopting the state-of-the-art approach in deep face recognition domain. The architecture readily supports reenactment in different scenarios: "many-to-many", "one-to-one", "one-to-another" in terms of expression accuracy, identity preservation, and overall image quality. We demonstrate that ActGAN achieves competitive performance against recent works concerning visual quality.
Student Practical Training as an Education Factor

Practical training is a basic opportunity for expanding the knowledge base of all future employees, but many employers in the current situation use the opportunity for their own development. The main purpose of the research presented in this paper is to encourage action, to prove the theory and to contribute to the development of knowledge in the field of student practical training. The aim of this paper is to evaluate various factors that influence the development of professional values among students and managers. Employees conducting practical training also miss working hours that may delay the completion of projects. However, in spite of these potential disadvantages, practical training and development give an individual (a student) and organizations as a whole benefit that make cost and time a valuable investment. The return of investment in training and employee development is insignificant in the long run. Continuous professional development is important because it ensures that you are still competent in your profession. It is a continuous process that continues during a professional career. The end result of well-planned continuous professional development is to protect the public, the employer and the professional career of students. Depending on the profession, practical training contributes to better protection and quality of life, environment, sustainability, property and economy. This particularly applies to areas with high risk or areas of specialized practice that are often not practiced individually for each case.
Towards an Understanding of Scaling the Software Robot Implementation

The implementation of software robots is based on the often time-consuming work carried out by the project team, which often leads to higher than expected costs and time delays. This can be made more efficient by scaling the extension of the robot’s functionalities. However, scaling can only take place once one has understood what can be scaled and to what extent. Therefore, based on an empirically illustrated theoretical conceptualization of scaling the software robot implementation, in this chapter we elaborate how scaling can be approached when implementing software robots.
“It’s written<i>niisto</i>but it sounds like<i>knee stew</i>.”

Abstract This paper argues for pragmatism rather than linguistic purity in orthography design for endangered Indigenous languages such as Blackfoot, emphasizing the need to see orthography standardization as a dynamic process rather than a static result. It explores the ongoing lack of community agreement about the best way to write the Blackfoot language and lack of widespread proficiency in the use of its standard orthography, and then describes ways in which this is mitigated in the Blackfoot Language Resources and Digital Dictionary project, a suite of web resources created to support language maintenance and revitalization work. The website uses a combination of relaxed searches, alternative spelling fields, and multimedia content to increase accessibility of the resources for users lacking proficiency in the standard orthography.
An Accurate Prediction of MPG (Miles Per Gallon) using Linear Regression Model of Machine Learning

Considering the growth of the automobile industry coming across since the past two centuries, we are witnessing theincreasing fuel prices and customers being more particularabout the features, the automobile makers are constantlyoptimising their processes to increase fuel efficiency. But what if you could have a reliable estimator for a car’s MPG given some known specifications about the vehicle? Then, you could beat a competitor in the market by both having a more desirable vehicle that is also more efficient, improve the fuel economy and bring more demand and supply to the consumers. We are implementing Machine Learning to design the prediction models and minimise the RMSE(Root Mean Square Error) value in between 1 and 2 of the automobiles made (in MPG) in the past years.
Path Planning of Three Wheeled Omni-Directional Robot Using Bezier Curve Tracing Technique and PID control Algorithm

Nowadays, Drive types are getting popularized in the field of robotics. Holonomic Drive is the concept of existence of motion in every direction. Here, we have controlled the resulting vectors of motion of individual wheels of a three wheel omni robot that are ultimately responsible for a stabilized motion of the robot in any direction without changing the direction faced by the robot. It provides higher efficiency and controllability than non-holonomic drives, when used with odometry and PID control algorithms. Odometry basically is method of acquiring the real time positional data of a system in a world frame. PID control algorithm when applied on any system reduces the transient response and helps to acquire steady state conditions with less settling time.The purpose of this project is to create an algorithm for path planning of Three wheeled Omni Holonomic Drive. Here, we have successfully designed and implemented the algorithm called Bezier curve tracing technique for path planning using built-in encoders of motors and axis control of three wheel omni robot using PID, implemented using IMU(Inertial Measurement Unit).
‘Nested’ Disruptive Technologies for Smart Cities: Effects and Challenges

There has been considerable research on smart cities and on adapting technology for businesses, governments, citizens and other stakeholders. However, scant attention has been paid to determine how disruptive technologies influence smart cities. Through a systemic literature review, this paper provides a point-by-point consideration of ‘nested’ disruptive technologies, namely, the Internet of Things (IoT), autonomous vehicles (AV), artificial general intelligence (AGI) and 5G networks (5G), and the factors affecting their deployment in the context of smart cities. This paper also discusses why these are important disruptive technologies for smart cities and how these technologies influence them. The main challenges of implementing these disruptive technologies have been identified in the literature and are discussed in this paper. This paper also offers suggestions on the implementation of these disruptive technologies for practitioners of smart cities, so that they can consider them while digitalizing smart cities. In addition to discussing research implications, the paper also throws light on theoretical contributions and recommends ways to leverage these disruptive technologies for smart cities.
Learning Adaptable Approach to Classify Sentiment with Incremental Datasets.

Identification of sentiment type is considered as a new business tool for the market and its policies, which involves different learning algorithms. Capability to automatically increase performance from experience and learning through data is the concept behind learning algorithms, which often requires large amount of data to train a model. The aim of this article is to find an adaptable classifier with increasing size of dataset. The experiment starts with splitting a dataset of size 10000 samples into multiple sub-set to check performance of different classifiers using term frequency-inverse document frequency (TF-IDF) and count vectorizer (CV) feature extraction techniques. The classifiers are train with sub-set of dataset and their adaptability performance was analyzed. Support vector machine (SVM) and logistic regression (LR) obtained stable and incremental performance with increase in dataset size using CV. Multinomial naive bayes (MNB) outperformed well with TF-IDF. We also analysed of classifiers with respect to performance, feature extraction techniques and data-set size.
A Novel Intelligent Tutoring System For Learning Programming

The goal of this paper is to propose the concept, structure and implementation of a novel intelligent tutoring system designed for beginners in C language and Python. The system is implemented by adding the functions of code classification, program error repair and personal knowledge tracing to an online programming practice platform. This implementation makes the original platform become more intelligent and help students learn programming better.
Interpretation of Occluded Face Detection Using Convolutional Neural Network

With the rapid development of artificial intelligence in past decades, great attention has been drawn to the field of face detection and recognition. Humans show a high degree of variability in their expressions, poses and appearance. Thus, limitations such as disguised and occluded faces, make it hard to implement high-accuracy face detection in real life. Although several algorithms have been proposed to handle recognition of disguised faces, the interpretation of the possible features that may have impact on the performance of models is hardly mentioned. In this paper, we explored possible features that could distinguish disguised faces compared to original faces, including skin region, luminosity, textures and edges. A Deep Neural Network model was utilised for the comparison between different features using the Disguised Faces in the Wild dataset. Our results show that colour on skin region, luminosity and texture in images could greatly contribute to the performance in detection of disguised faces with a CNN architecture. The results from fusing the individual features significantly outperformed the results when using the whole image, performing 72%, which is considered the state-of-the-art in subject-independent disguised face detection.
Earned Scope Management: Scope Performance Evaluation for Software Projects Considering People and Effort as Resources

Three of the significant constraints on projects are scope (size), cost (money) and time (schedule), and while Earned Value Management (EVM) and Earned Schedule (ES) techniques manage the cost and time constraints, they do not explicitly tackle the scope constraint. Even though scope management is recognized as a critical success factor in software projects, there is a lack of formal techniques in the literature to manage scope. This paper presents the results of the improvements to the Earned Scope Management (ESM) technique developed since 2015 and currently tested in Mexico with real projects.
Sweet Pursuit: User Acceptance and Performance of a Smooth Pursuit controlled Candy Dispensing Machine in a Public Setting

A prototypical smooth pursuit controlled candy dispensing machine was set up in a public area and evaluated regarding performance data, self reported joy of use, learnability, perceived stress and perceived usefulness. 359 sets of user data were collected from visitors ranging from eight to 75 years. The results show an overall high rate of successful interactions (89.8%), indicating no correlation between height, age, gender or the use of corrective glasses or lenses and the ease and success of interaction. Incorrectly entered digits occurred for 36.2% of all participants, with half attributing the error to their own incorrect entry and half reporting the system to have detected a false number. Users reported a generally high joy of use and found the system easy to learn. Users indicated interest to use similar interaction technologies in public settings if privacy requirements such as protection from observers, are met.
Exact Separation of Forbidden-Set Cuts Associated With Redundant Parity Checks of Binary Linear Codes

In recent years, several integer programming (IP) approaches were developed for maximum-likelihood decoding and minimum distance computation for binary linear codes. Two aspects in particular have been demonstrated to improve the performance of IP solvers as well as adaptive linear programming decoders: the dynamic generation of forbidden-set (FS) inequalities, a family of valid cutting planes, and the utilization of so-called redundant parity-checks (RPCs). However, to date, it had remained unclear how to determine whether or not there exists any violated FS inequality w.r.t. any known or unknown parity-check. In this note, we prove NP-hardness of this RPC separation problem. Moreover, we formulate an IP model that combines the search for most violated FS cuts with the generation of RPCs, and report on computational experiments. Empirically, for various instances of the minimum distance problem, it turns out that while utilizing the exact separation IP does not appear to provide a computational advantage, it can apparently be avoided altogether by combining heuristics to generate RPC-based cuts.