Reviewerly/exHarmony huggingface dataset:
Learning Representations using Spectral-Biased Random Walks on Graphs

Several state-of-the-art neural graph embedding methods are based on short random walks (stochastic processes) because of their ease of computation, simplicity in capturing complex local graph properties, scalability, and interpretibility. In this work, we are interested in studying how much a probabilistic bias in this stochastic process affects the quality of the nodes picked by the process. In particular, our biased walk, with a certain probability, favors movement towards nodes whose neighborhoods bear a structural resemblance to the current node's neighborhood. We succinctly capture this neighborhood as a probability measure based on the spectrum of the node's neighborhood subgraph represented as a normalized laplacian matrix. We propose the use of a paragraph vector model with a novel Wasserstein regularization term. We empirically evaluate our approach against several state-of-the-art node embedding techniques on a wide variety of real-world datasets and demonstrate that our proposed method significantly improves upon existing methods on both link prediction and node classification tasks.
UAV Path Planning in Search and Rescue Operations

Unmanned aerial vehicle (UAV) search operations usually involve two consequent steps. The first one is the area of interest observation and data capturing with the aid of optoelectronic cameras, and the second one is the data transmission to the flight control center. Very often the position of the observed object is rather far from the area covered by a communication network formed by stationary general use base stations or a group of mobile base stations temporarily deployed for the search mission. This, as well as the constraints caused by surface features, may prevent the immediate transmission of the collected data. Therefore, after capturing the data, the UAV must find an appropriate position to perform a successful transfer of the information flow. Moreover, in case of manifold search mission, the UAV must be ready for an unplanned task correction, and therefore a landing and commencing a new flight are not acceptable. In this paper, UAV search mission planning is considered as a path optimization problem. The optimization goal is to set up the best conditions for data capturing and transmission under velocity and timing mission constraints. The optimization parameters are the UAV path and the data transmission plan; thus, the optimization problem is naturally decomposed into the internal problem of data transmission optimization and the external surveillance path planning. The first one is solved in an explicit way for a given path, and for the second one, the numerical solution in the deterministic case is found with the aid of the maximum principle. For the stochastic case, the approach is also outlined. The findings are illustrated by simulation results.
Non-Steady Trading Day Detection Based on Stock Index Time-Series Information

Outlier detection in a data set is very important in performing proper data mining. In this paper, we propose a method for efficiently detecting outliers by performing cluster analysis using the DS algorithm improved from the k-means algorithm. This method is simpler to detect outliers than traditional methods, and these detected outliers can quantitatively indicate “the degree of outlier”. Using this method, we detect abnormal trading days from OHLCs for S&P500 and FTSA, which are typical and world-wide stock indexes, from the beginning of 2005 to the end of 2015. They are defined as non-steady trading days, and the conditions for becoming the non-steady markets are mined as new knowledge. Applying the mined knowledge to OHLCs from the beginning of 2016 to the end of 2018, we can predict the non-steady trading days during that period. By verifying the predicted content, we show the fact that the appropriate knowledge has been successfully mined and show the effectiveness of the outlier detection method proposed in this paper. Furthermore, we mutually reference and comparatively analyze the results of applying this method to multiple stock indexes. This analyzes possible to visualize when and where social and economic impacts occur and how they propagate through the earth. This is one of the new applications using this method.
SoundFactory

The proliferation of smart connected devices using digital assistants activated by voice commands (e.g., Apple Siri, Google Assistant, Amazon Alexa, etc.) is raising the interest in algorithms to localize and recognize audio sources. Among the others, deep neural networks (DNNs) are seen as a promising approach to accomplish such task. Unlike other approaches, DNNs can categorize received events, thus discriminating between events of interests and not even in presence of noise. Despite their advantages, DNNs require large datasets to be trained. Thus, tools for generating datasets are of great value, being able to accelerate the development of advanced learning models.
Optimum Design on In-wheel Motor of New Energy Vehicles based on Improved Artificial Bee Colony Algorithm

In order to improve the power density of the in-wheel motor and reduce its cost of materials. A multi-objective optimization method of in-wheel motor for electric vehicles (EV) is proposed based on an improved artificial colony algorithm. The new improved artificial colony algorithm is used to implement motor optimizing design with the geometry size and material parameters of motor as variables and the quality, cost and power consumption of the motor as the optimization goal. The results show that compared with conventional artificial colony algorithm, the convergence speed and global search ability of improved artificial colony algorithm is better and the quality, cost and power loss of optimized motor is relatively reduced, and the efficiency is improved.
An Augmented Reality Based Modular Platform for Solar Energy Education

In recent years, renewable energy consumption has been rapidly increasing instead of fossil energy sources for countless reasons. Installation or maintenance of the renewable energy power plants are very important in terms of energy quality, installation and maintain personnel's safety. The same manner and in with the by same methods in each power plant installation the applying same standard is very important. Unfortunately, enough number personnel could not have been trained to meet this service as parallel with the rapid increased need for renewable energy. One of the areas with qualified personnel shortage is solar energy systems. It is seen in the literature that the use of virtual reality in education has become widespread. The use of augmented reality in engineering education increases the quality of education and shortens the learning process time. It is inevitable that sufficient number of personnel will be trained in this field in a short time. This training method will enable the personnel to follow the same standard and same process steps to be trained in a shorter time and safely. In this study, an augmented reality has been applied in the training of the personnel in the installation of the solar energy system. From the results obtained, it was concluded that it can be used as training material in certificate programs with vocational and technical school students doing solar energy education.
Mobile Devices and Mobile Apps Use among Indonesian College Students

Abstract This article adds more insights into the previous study about the use of mobile apps on smartphones and tablets among Indonesian college students based on a survey of undergraduate students at one private university in Indonesia. Data analysis includes two-sample tests of proportion and multiple correspondence analysis (MCA). The findings confirm social media as not only the most frequently used app category, but also with the highest level of engagement. While most students use their mobile device mostly for social media, it is regular and heavy use of educational apps that are more closely tied to spending more than five hours per day on mobile device whereas more frequent mobile gaming is associated to more hours spent on mobile device. Students from IT major were found to be an anomaly among other STEMM disciplines. Not only they are more likely to spend more hours on their mobile device, but also, they tend to be more socially active, at least digitally and not necessarily physically in daily life. The findings in this study give a better understanding of how Indonesian students use their mobile device that was already uncovered in the previous study by answering some of the remaining questions.
Distance-guided local search

We present several techniques that use distances between candidate solutions to achieve intensification in Local Search (LS) algorithms. An important drawback of classical LS is that after visiting a very high-quality solution the search process can “forget about it” and continue towards very different areas. We propose a method that works on top of a given LS to equip it with a form of memory so as to record the highest-quality visited areas (spheres). More exactly, this new method uses distances between candidate solutions to perform a coarse–grained recording of the LS trajectory, i.e., it records a number of discovered spheres. The (centers of the) spheres are kept sorted in a priority queue in which new centers are continually inserted as in insertion-sort algorithms. After thoroughly investigating a sphere, the proposed method resumes the search from the first best sphere center in the priority queue. The resulting LS trajectory is no longer a continuous path, but a tree-like structure, with closed branches (already investigated spheres) and open branches (as-yet-unexplored spheres). We also explore several other techniques relying on distances, e.g., in Section 2.3, we show how to use distance information to prevent the search from looping indefinitely on large (quasi-)plateaus. Experiments on three problems based on different encodings (partitions, vectors and permutations) confirm the intensification potential of the proposed ideas.
Neural Architecture Search with Reinforce and Masked Attention Autoregressive Density Estimators.

Neural Architecture Search has become a focus of the Machine Learning community. Techniques span Bayesian optimization with Gaussian priors, evolutionary learning, reinforcement learning based on policy gradient, Q-learning, and Monte-Carlo tree search. In this paper, we present a reinforcement learning algorithm based on policy gradient that uses an attention-based autoregressive model to design the policy network. We demonstrate how performance can be further improved by training an ensemble of policy networks with shared parameters, each network conditioned on a different autoregressive factorization order. On the NASBench-101 search space, it outperforms most algorithms in the literature, including random search. In particular, it outperforms RL methods based on policy gradients that use alternate architectures to specify the policy network, underscoring the importance of using masked attention in this setting. We have adhered to guidelines listed in Lindauer& Hutter (2019) while designing experiments and reporting results.
Automatic Compression Ratio Allocation for Pruning Convolutional Neural Networks

Convolutional neural networks (CNNs) have demonstrated significant performance improvement in many application scenarios. However, the high computational complexity and model size have limited its application on the mobile and embedded devices. Various approaches have been proposed to compress CNNs. Filter pruning is widely considered as a promising solution, which can significantly speed up the inference and reduce memory consumption. To this end, most approaches tend to prune filters by manually allocating compression ratio, which highly relies on individual expertise and not friendly to non-professional users. In this paper, we propose an Automatic Compression Ratio Allocation (ACRA) scheme based on binary search algorithm to prune convolutional neural networks. Specifically, ACRA provides two strategies for allocating compression ratio automatically. First, uniform pruning strategy allocates the same compression ratio to each layer, which is obtained by binary search based on target FLOPs reduction of the whole networks. Second, sensitivity-based pruning strategy allocates appropriate compression ratio to each layer based on the sensitivity to accuracy. Experimental results from VGG11 and VGG-16, demonstrate that our scheme can reduce FLOPs significantly while maintaining a high accuracy level. Specifically, for the VGG16 on CIFAR-10 dataset, we reduce 29.18% FLOPs with only 1.24% accuracy decrease.
Multi-frame super resolution via deep plug-and-play CNN regularization

Abstract Because of the ill-posedness of multi-frame super resolution (MSR), the regularization method plays an important role in the MSR field. Various regularization terms have been proposed to constrain the image to be estimated. However, artifacts also exist in the estimated image due to the artificial tendency in the manually designed prior model. To solve this problem, we propose a novel regularization-based MSR method with learned prior knowledge. By using the variable splitting technique, the fidelity term and regularization term are separated. The fidelity term is associated with an “ <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:msup> <m:mi>L</m:mi> <m:mn>2</m:mn> </m:msup> </m:math> {L^{2}} - <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:msup> <m:mi>L</m:mi> <m:mn>2</m:mn> </m:msup> </m:math> {L^{2}} ” form sub-problem. Meanwhile, the sub-problem respect to regularization term is a denoising problem, which can be solved by denoisers learned from a deep convolutional neural network. Different from the traditional regularization methods which employ hand-crafted image priors, in this paper the image prior model is replaced by learned prior implicitly. The two sub-problems are solved alternately and iteratively. The proposed method cannot only handle complex degradation model, but also use the learned prior knowledge to guide the reconstruction process to avoid the artifacts. Both the quantitative and qualitative results demonstrate that the proposed method gains better quality than the state-of-the-art methods.
Fully Convolutional ASR for Less-Resourced Endangered Languages

The application of deep learning to automatic speech recognition (ASR) has yielded dramatic accuracy increases for languages with abundant training data, but languages with limited training resources have yet to see accuracy improvements on this scale. In this paper, we compare a fully convolutional approach for acoustic modelling in ASR with a variety of established acoustic modeling approaches. We evaluate our method on Seneca, a low-resource endangered language spoken in North America. Our method yields word error rates up to 40% lower than those reported using both standard GMM-HMM approaches and established deep neural methods, with a substantial reduction in training time. These results show particular promise for languages like Seneca that are both endangered and lack extensive documentation.
A Strategic Analysis of Improving the Quality of English Teaching in Colleges and Universities

Under the background of the new era, great changes have taken place in the teaching mode of our country, and all kinds of creative teaching strategies emerge as the times require. In order to construct an efficient classroom and ensure the rational allocation and utilization of teaching resources, teachers must adhere to the central position of students in the process of carrying out classroom teaching practice, clarify the individual differences of students, and fully consider the influencing factors of various details from the point of view of students. In order to better promote the improvement of teaching quality and teaching level, to ensure that every student can learn something, learn to achieve success. However, combined with the relevant practical investigation, it is not difficult to find that long-term examination-oriented education leads to some teaching classics. In the course of carrying out the classroom teaching practice, the teacher who is not under test directly uses the learning of the basic knowledge of the simple theory as the main body, and puts the core energy in the indoctrination and the teaching of the theoretical knowledge, which seriously restricts the individual growth and development of the students, and ignores the individual difference of the students, The result is that the efficiency and quality of the classroom teaching are declining continuously, some of the teaching resources are wasted directly, which is especially obvious in the course of the practice of English teaching in colleges and universities. In this respect, this paper, based on the macroscopic angle, takes the college English teaching as the analysis object, and the relevant strategies and requirements to improve the English teaching quality, with a view to promoting the English teaching in colleges and universities, and provides some reference for the smooth development of teaching practice.
BeCAPTCHA: Bot Detection in Smartphone Interaction using Touchscreen Biometrics and Mobile Sensors.

In this paper we study the suitability of a new generation of CAPTCHA methods based on smartphone interactions. The heterogeneous flow of data generated during the interaction with the smartphones can be used to model human behaviour when interacting with the technology and improve bot detection algorithms. For this, we propose a CAPTCHA method based on the analysis of the information obtained during a single drag and drop task in combination with the accelerometer data. We evaluate the method by generating fake samples synthesized with Generative Adversarial Neural Networks and handcrafted methods. Our results suggest the potential of mobile sensors to characterize the human behaviour and develop a new generation of CAPTCHAs. The experiments are evaluated with HuMIdb (Human Mobile Interaction database), a novel multimodal mobile database collected for this work that comprises 14 mobile sensors acquired from 600 users. HuMIdb is freely available to the research community.
Stress testing the skills gap

Cyber security expertise is still not sufficiently distributed, pushing up salaries and causing over-demand and stress among cyber security professionals. Yet cyber security is critical to every organisation. Skills gaps aren't going away on their own. They must be attacked head-on with the appropriate training to bridge the skills gap from within. There's no time to wait and see how things play out before resolving the risks. People are behind the problem and they are also the biggest part of the solution, reckons Melanie Jones at Global Knowledge. Do you feel like there aren't enough hours in the day? Or that quantum physics needs to hurry up and figure out a way for your staff to be in at least two places at once? You're not the only one. Network security has a manpower problem. The annual ‘IT Skills and Salary Report’, a study of 12,200 IT professionals from 159 countries, has found for the fifth year running that cyber security expertise is still not sufficiently distributed within the sector, pushing up salaries and causing over-demand and stress among cyber security professionals. 1
Methodological Aspects of Developing and Managing an Etymological Lexical Resource: Introducing EtymDB 2.0

Diachronic lexical information was mostly used in its natural field, historical linguistics, until recently, when promising but not yet conclusive applications to low resource languages machine translation started extending its usage to NLP. There is therefore a new need for fine-grained, large-coverage and accurate etymological lexical resources. In this paper, we propose a set of guidelines to generate such resources, for each step of the life-cycle of an etymological lexicon: creation, update, evaluation, dissemination, and exploitation. To illustrate the guidelines, we introduce EtymDB 2.0, an etymological database automatically generated from the Wiktionary, which contains 1.8 million lexemes, linked by more than 700,000 fine-grained etymological relations, across 2,536 living and dead languages. We also introduce use cases for which EtymDB 2.0 could represent a key resource, such as phylogenetic tree generation, low resource machine translation and medieval languages study.
Population Control meets Doob's Martingale Theorems: the Noise-free Multimodal Case

We study a test-based population size adaptation (TBPSA) method, inspired from population control, in the noise-free multimodal case. In the noisy setting, TBPSA usually recommends, at the end of the run, the center of the Gaussian as an approximation of the optimum. We show that combined with a more naive recommendation, namely recommending the visited point which had the best fitness value so far, TBPSA is also powerful in the noise-free multimodal context. We demonstrate this experimentally and explore this mechanism theoretically: we prove that TBPSA is able to escape plateaus with probability one in spite of the fact that it can converge to local minima. This leads to an algorithm effective in the multimodal setting without resorting to a random restart from scratch.
Network Fusion for Content Creation with Conditional INNs.

Artificial Intelligence for Content Creation has the potential to reduce the amount of manual content creation work significantly. While automation of laborious work is welcome, it is only useful if it allows users to control aspects of the creative process when desired. Furthermore, widespread adoption of semi-automatic content creation depends on low barriers regarding the expertise, computational budget and time required to obtain results and experiment with new techniques. With state-of-the-art approaches relying on task-specific models, multi-GPU setups and weeks of training time, we must find ways to reuse and recombine them to meet these requirements. Instead of designing and training methods for controllable content creation from scratch, we thus present a method to repurpose powerful, existing models for new tasks, even though they have never been designed for them. We formulate this problem as a translation between expert models, which includes common content creation scenarios, such as text-to-image and image-to-image translation, as a special case. As this translation is ambiguous, we learn a generative model of hidden representations of one expert conditioned on hidden representations of the other expert. Working on the level of hidden representations makes optimal use of the computational effort that went into the training of the expert model to produce these efficient, low-dimensional representations. Experiments demonstrate that our approach can translate from BERT, a state-of-the-art expert for text, to BigGAN, a state-of-the-art expert for images, to enable text-to-image generation, which neither of the experts can perform on its own. Additional experiments show the wide applicability of our approach across different conditional image synthesis tasks and improvements over existing methods for image modifications.
A New Partial 3D Object Indexing and Retrieval Approach Combining 2D slices and Apriori Algorithm

This paper examines the issue of 3D object indexing and retrieval and tries to solve this problem using partial indexing approach.The hypothesis in this context is that similar 3D objects will be composed of similar 2D slices.The proposed partial 3D object indexing and retrieval method is applicable on both complete and incomplete 3D objects, which is based on a similarity measuring between 2D slices of 3D objects.The main idea behind our approach is to extract an initial set of 2D slices corresponding to determined axes, and then use the Apriori algorithm to select the most representative ones, transforming the issue of shapematching between 3D objects into evaluating the similarities between their 2D slices.Experiments on the Princeton Shape Benchmark (PSB) indicate that our approach outperforms evaluated retrieval approaches.