Reviewerly/exHarmony huggingface dataset:
ProtoNet: Learning from Web Data with Memory

Learning from web data has attracted lots of research interest in recent years. However, crawled web images usually have two types of noises, label noise and background noise, which induce extra difficulties in utilizing them effectively. Most existing methods either rely on human supervision or ignore the background noise. In this paper, we propose the novel ProtoNet, which is capable of handling these two types of noises together, without the supervision of clean images in the training stage. Particularly, we use a memory module to identify the representative and discriminative prototypes for each category. Then, we remove noisy images and noisy region proposals from the web dataset with the aid of the memory module. Our approach is efficient and can be easily integrated into arbitrary CNN model. Extensive experiments on four benchmark datasets demonstrate the effectiveness of our method.
Bisindo information system as potential daily sign language learning

Bisindo or Bahasa Isyarat Indonesia is one of sign language methods in Indonesia, besides SIBI or Sistem Isyarat Bahasa Indonesia. Generally, Bisindo is used by deaf people in their daily communication. Deaf people tend to have barriers to communicate with others. Yet, in the educational realm, some of them still have high spirit to continue their education until university. So, they must improve their abilities or competences in communicating with others and try to get information from around the world, for example using sophisticated technology with sign language. Information system of sign language learning is important to help deaf and heard people who want to understand the deaf conversation meaning. Bisindo information system is in website form that is built by PHP, MySQL, and sublime text 3. Based on the black box method, the Bisindo information system can run well. The results of the trial to the user describe that the maximum percentage for each level of testing is above 50% except for testing of conformity with the needs of deaf only at maximum of 42%. The Bisindo information system still has weakness, but it already gives benefits especially for deaf people in Indonesia.
Fermi at SemEval-2019 Task 8: An elementary but effective approach to Question Discernment in Community QA Forums

Online Community Question Answering Forums (cQA) have gained massive popularity within recent years.The rise in users for such forums have led to the increase in the need for automated evaluation for question comprehension and fact evaluation of the answers provided by various participants in the forum.Our team, Fermi, participated in sub-task A of Task 8 at SemEval 2019 -which tackles the first problem in the pipeline of factual evaluation in cQA forums, i.e., deciding whether a posed question asks for a factual information, an opinion/advice or is just socializing.This information is highly useful in segregating factual questions from non-factual ones which highly helps in organizing the questions into useful categories and trims down the problem space for the next task in the pipeline for fact evaluation among the available answers.Our system uses the embeddings obtained from Universal Sentence Encoder combined with XGBoost for the classification subtask A. We also evaluate other combinations of embeddings and off-the-shelf machine learning algorithms to demonstrate the efficacy of the various representations and their combinations.Our results across the evaluation test set gave an accuracy of 84% and received the first position in the final standings judged by the organizers.
Efficient Computation of Fitness Function for Evolutionary Clustering

Evolutionary algorithms (EAs) are random search heuristics which can solve various optimization problems. There are plenty of papers describing different approaches developed to apply evolutionary algorithms to the clustering problem, although none of them addressed the problem of fitness function computation. In clustering, many clustering validity indices exist that are designed to evaluate quality of resulting points partition. It is hard to use them as a fitness function due to their computational complexity. In this paper, we propose an efficient method for iterative computation of clustering validity indices which makes application of the EAs to this problem much more appropriate than it was before.
Application of kohonen neural networks to search for regions of interest in the detection and recognition of objects

One of the most effective ways to improve accuracy and speed of recognition algorithms is to preliminary distinguish the regions of interest in the analyzed images. We studied a possibility of application of self-organizing maps and a Kohonen neural network for detection of regions of interest at a radar or satellite image of underlying surface. There is a high probability of finding an object of interest for further analysis in the found regions of interest. The definition of region of interest is necessary most of all to automate and speed up the process of search and recognition of objects of interest. The relevance is due to the increasing number of satellites. The study presents the process of modeling, analysis and comparison of the results of application of these methods for determination of regions of interest in recognition of images of aircraft against the background of underlying surface. It also describes the process of preliminary processing of input data. The study presents a general approach to construction and training of the Kohonen self-organizing map and neural network. Application of Kohonen maps and neural network makes it possible to decrease an amount of data analyzed by 15–100 times. It speeds up the process of detection and recognition of an object of interest. Application of the above algorithm reduces significantly the required number of training images for a convolutional network, which performs the final recognition. The reduction of a training sample occurs because the size of parts of an input image supplied to the convolutional network is bounded with the scale of an image and it is equal to the size of the largest detected object. Kohonen neural network showed itself more efficient in relation to this task, since it places cluster centers on the underlying surface rarely due to independence of weight of neurons on neighboring centers. These technical solutions could be used in the analysis of visual data from satellites, aircraft, and unmanned cars, in medicine, robotics, etc.
The dynamic-selfsimilar lattice as the vibroisolation systems

The vibroisolation properties of the lattices with dynamic-selfsimilar structures are investigated. The concept of dynamic-selfsimilarity (dynamic fractal) is introduced. It consists in the similarity of the elastic-inertial parameters of the forming cells (in contrast to the geometric scaling). It was shown that such structures are equivalent in frequency to the periodic structure with additional fixation, but the nature of the wave propagation in them is significantly different. The elastic waves propagation in dynamically self-similar lattices is investigated. It is shown that in such lattices there is a decrease in the level of a harmonic signal in the all frequency ranges, and in the non-pass band the signal attenuation is significantly higher than in the lattices with a periodic structure.
The Effect of Internal Environmental Factors on Operational Excellence of Manufacturing Industry: A Pilot Study

This paper examines internal environmental factors that influence the operational excellence of SMEs manufacturing sector in Malaysia.The objective of this paper is to measure the reliability and validity of internal environmental factors and its construct.A pilot study was conducted among 30 SMEs company in Terengganu which represented by the managerial level who are closely linked to the manufacturing operations.This study adopted a survey method by using simple random sampling method for the data collection.The reliability and validity of the instrument were examined through content validity, face validity and reliability based on expert assessment and the data was analyzed using the statistical software SPSS version 22.The result shows that the survey instrument is reliable and valid.Therefore, this study expected will help the Malaysian SMEs manufacturing sector in determining the effect of internal environmental factors on operational excellence.
Gradient Noise Convolution (GNC): Smoothing Loss Function for Distributed Large-Batch SGD.

Large-batch stochastic gradient descent (SGD) is widely used for training in distributed deep learning because of its training-time efficiency, however, extremely large-batch SGD leads to poor generalization and easily converges to sharp minima, which prevents naive large-scale data-parallel SGD (DP-SGD) from converging to good minima. To overcome this difficulty, we propose gradient noise convolution (GNC), which effectively smooths sharper minima of the loss function. For DP-SGD, GNC utilizes so-called gradient noise, which is induced by stochastic gradient variation and convolved to the loss function as a smoothing effect. GNC computation can be performed by simply computing the stochastic gradient on each parallel worker and merging them, and is therefore extremely easy to implement. Due to convolving with the gradient noise, which tends to spread along a sharper direction of the loss function, GNC can effectively smooth sharp minima and achieve better generalization, whereas isotropic random noise cannot. We empirically show this effect by comparing GNC with isotropic random noise, and show that it achieves state-of-the-art generalization performance for large-scale deep neural network optimization.
Proposals for Extending the Russian Biobanks Functions to Protect Genomic Information

Genomic research are currently of key importance in the development of medicine. In order to carry out such activities, it is necessary not only to generate new genetic objects, but also to accumulate samples in the form of various biomaterials. Such collections in recent years are the pride of large biobanks, which spend the maximum amount of effort to save samples for subsequent research or application in practice. However, the functioning of biobanks has another side of the coin: the accumulation of certain information entails an increased level of responsibility for the collected data. In modern Russian conditions, the issues of information leakage from such institutions taking place in foreign countries are still irrelevant. In many ways, the reason for this is only the beginning of the normative legal regulation of the relevant social relations, which are only beginning to appear on the territory of our state. Therefore, there is every reason to believe that such problems will arise and it is important to solve them. Some proposals for such activities are set out in this paper.
High Order Volumetric Directional Pattern for Video-Based Face Recognition

Describing the dynamic textures has attracted growing attention in the field of computer vision and pattern recognition. In this paper, a novel approach for recognizing dynamic textures, namely, high order volumetric directional pattern (HOVDP), is proposed. It is an extension of the volumetric directional pattern (VDP) which extracts and fuses the temporal information (dynamic features) from three consecutive frames. HOVDP combines the movement and appearance features together considering the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:msup><mml:mrow><mml:mi>n</mml:mi></mml:mrow><mml:mrow><mml:mi>t</mml:mi><mml:mi>h</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:math> order volumetric directional variation patterns of all neighboring pixels from three consecutive frames. In experiments with two challenging video face databases, YouTube Celebrities and Honda/UCSD, HOVDP clearly outperformed a set of state-of-the-art approaches.
PERIODS OF DUCCI SEQUENCES AND ODD SOLUTIONS TO A PELLIAN EQUATION

A Ducci sequence is a sequence of integer $n$ -tuples generated by iterating the map $$\begin{eqnarray}D:(a_{1},a_{2},\ldots ,a_{n})\mapsto (|a_{1}-a_{2}|,|a_{2}-a_{3}|,\ldots ,|a_{n}-a_{1}|).\end{eqnarray}$$ Such a sequence is eventually periodic and we denote by $P(n)$ the maximal period of such sequences for given $n$ . We prove a new upper bound in the case where $n$ is a power of a prime $p\equiv 5\hspace{0.6em}({\rm mod}\hspace{0.2em}8)$ for which $2$ is a primitive root and the Pellian equation $x^{2}-py^{2}=-4$ has no solutions in odd integers $x$ and $y$ .
Benefits and Pitfalls of the Exponential Mechanism with Applications to Hilbert Spaces and Functional PCA

The exponential mechanism is a fundamental tool of Differential Privacy (DP) due to its strong privacy guarantees and flexibility. We study its extension to settings with summaries based on infinite dimensional outputs such as with functional data analysis, shape analysis, and nonparametric statistics. We show that one can design the mechanism with respect to a specific base measure over the output space, such as a Guassian process. We provide a positive result that establishes a Central Limit Theorem for the exponential mechanism quite broadly. We also provide an apparent negative result, showing that the magnitude of the noise introduced for privacy is asymptotically non-negligible relative to the statistical estimation error. We develop an \ep-DP mechanism for functional principal component analysis, applicable in separable Hilbert spaces. We demonstrate its performance via simulations and applications to two datasets.
Documentation of Acidic Mining Exploration Drill Cuttings at the Pebble Copper–Gold Mineral Prospect, Southwest Alaska

During exploration drilling of the Pebble copper–gold–molybdenum (Cu–Au–Mo) deposit, drilling wastes were disposed of directly on the landscape or passed through unlined sumps prior to disposal. The ore and host rock are rich in sulfides, which weather to sulfuric acid with consequent metal leaching. Oxidized cuttings were visually evident, and confirmed with laboratory and field testing to have a pH of 2.7–4.3. At these sites, Cu and Mo exceeded or were at the high end of the natural background. With one exception, Cu was in the range of 545 mg/kg to 4865 mg/kg. Dead vegetation was observed at all sites with drill cuttings on the surface. Dead vegetation was also observed on sump soil covers, unrelated to drilling waste. Sites where vegetation had not re-established were from four to thirteen years old. The potential impact to surface and groundwater was not determined. Understanding the source and extent of damage from cuttings could lead to better site management.
Threat Modeling for Cyber Range: An Ontology-Based Approach

Cyber Range has become a very important means to support tasks such as network security technology validation, network weapon testing, training of network attack and defense and network risk assessment. However, Cyber Ranger faces many security threats from internal and external environments. In order to establish an adaptive security protection system, threat modeling is needed to analyze potential threats and provide security solutions. In this paper, we present a novel threat modeling method for Cyber Range. Based on ontology and knowledge graph, our research focuses on the design of threat ontology, knowledge base, and unified description specification. Typical cases are given to demonstrate our approach. This study could serve as groundwork for further Cyber Range researches including security architecture, situation awareness and intelligent decision-making.
The Indirect Convolution Algorithm

Deep learning frameworks commonly implement convolution operators with GEMM-based algorithms. In these algorithms, convolution is implemented on top of matrix-matrix multiplication (GEMM) functions, provided by highly optimized BLAS libraries. Convolutions with 1x1 kernels can be directly represented as a GEMM call, but convolutions with larger kernels require a special memory layout transformation - im2col or im2row - to fit into GEMM interface. The Indirect Convolution algorithm provides the efficiency of the GEMM primitive without the overhead of im2col transformation. In contrast to GEMM-based algorithms, the Indirect Convolution does not reshuffle the data to fit into the GEMM primitive but introduces an indirection buffer - a buffer of pointers to the start of each row of image pixels. This broadens the application of our modified GEMM function to convolutions with arbitrary kernel size, padding, stride, and dilation. The Indirect Convolution algorithm reduces memory overhead proportionally to the number of input channels and outperforms the GEMM-based algorithm by up to 62% on convolution parameters which involve im2col transformations in GEMM-based algorithms. This, however, comes at cost of minor performance reduction on 1x1 stride-1 convolutions.
Tw-StAR at SemEval-2019 Task 5: N-gram embeddings for Hate Speech Detection in Multilingual Tweets

In this paper, we describe our contribution in SemEval-2019: subtask A of task 5 "Multilingual detection of hate speech against immigrants and women in Twitter (HatEval)".We developed two hate speech detection model variants through Tw-StAR framework.While the first model adopted one-hot encoding ngrams to train an NB classifier, the second generated and learned n-gram embeddings within a feedforward neural network.For both models, specific terms, selected via MWT patterns, were tagged in the input data.With two feature types employed, we could investigate the ability of n-gram embeddings to rival one-hot n-grams.Our results showed that in English, n-gram embeddings outperformed one-hot ngrams.However, representing Spanish tweets by one-hot n-grams yielded a slightly better performance compared to that of n-gram embeddings.The official ranking indicated that Tw-StAR ranked 9 th for English and 20 th for Spanish.
Beyond DAGs: Modeling Causal Feedback with Fuzzy Cognitive Maps

Fuzzy cognitive maps (FCMs) model feedback causal relations in interwoven webs of causality and policy variables. FCMs are fuzzy signed directed graphs that allow degrees of causal influence and event occurrence. Such causal models can simulate a wide range of policy scenarios and decision processes. Their directed loops or cycles directly model causal feedback. Their nonlinear dynamics permit forward-chaining inference from input causes and policy options to output effects. Users can add detailed dynamics and feedback links directly to the causal model or infer them with statistical learning laws. Users can fuse or combine FCMs from multiple experts by weighting and adding the underlying fuzzy edge matrices and do so recursively if needed. The combined FCM tends to better represent domain knowledge as the expert sample size increases if the expert sample approximates a random sample. Many causal models use more restrictive directed acyclic graphs (DAGs) and Bayesian probabilities. DAGs do not model causal feedback because they do not contain closed loops. Combining DAGs also tends to produce cycles and thus tends not to produce a new DAG. Combining DAGs tends to produce a FCM. FCM causal influence is also transitive whereas probabilistic causal influence is not transitive in general. Overall: FCMs trade the numerical precision of probabilistic DAGs for pattern prediction, faster and scalable computation, ease of combination, and richer feedback representation. We show how FCMs can apply to problems of public support for insurgency and terrorism and to US-China conflict relations in Graham Allison's Thucydides-trap framework. The appendix gives the textual justification of the Thucydides-trap FCM. It also extends our earlier theorem [Osoba-Kosko2017] to a more general result that shows the transitive and total causal influence that upstream concept nodes exert on downstream nodes.
Recognizing Faults in Software Related Difficult Data

In this paper we have investigated the use of numerous machine learning algorithms, with emphasis on multilayer artificial neural networks in the domain of software source code fault prediction. The main contribution lies in enhancing the data pre-processing step as the partial solution for handling software related difficult data. Before we put the data into an Artificial Neural Network, we are implementing PCA (Principal Component Analysis) and k-means clustering. The data-clustering step improves the quality of the whole dataset. Using the presented approach we were able to obtain 10% increase of accuracy of the fault detection. In order to ensure the most reliable results, we implement 10-fold cross-validation methodology during experiments. We have also evaluated a wide range of hyperparameter setups for the network, and compared the results to the state of the art, cost-sensitive approaches - Random Forest, AdaBoost, RepTrees and GBT.
Perspectives on solar power in dense urban areas – with Copenhagen International School as case study

With the solar power panels as integrated surface elements in new building constructions, solar power will be economic attractive in urban areas, close to the power consumption. In the new dense urban areas, the rooftop areas will be attractive for other purposes, like terraces and green roofs. The solar power panels may instead be placed at the facades of the buildings – this is especially relevant at high geographic latitudes, where the altitude of the sun is low. It may even make sense to mount panels at all facades, better distributing the generation over the day.
Novel forms of “magical” human-computer interaction within the cyber-physical smart workplace: Implications for usability and user experience

The growing use of advanced AI, ambient intelligence, augmented reality (AR), and virtual reality (VR) technologies of the sort found within the emerging cyber-physical smart workplace has been described as enabling new forms of human-computer interaction (HCI) that are "magical" in nature.This study shows that from an anthropological perspective, such a workplace environment can indeed be understood as "magical"; however, that "magicality" is a double-edged sword that can potentially both enhance and damage user experience (UX) for workers and other occupants of such environments.First, by analyzing existing social anthropological and philosophical anthropological accounts of magic, typical elements of magical practice are identified.Using Nielsen's empirical analysis of HCI usability heuristics as a basis, a prospective heuristic evaluation is then carried out for the usability of a generic "magical" environment, in order to identify elements of magical practice that might be expected to enhance or impair user experience when they are required for interaction with the environment.A more specific heuristic usability evaluation is then performed for the "magical" aspects of HCI created by two kinds of constituent technologies that are typical for a cyber-physical smart workplace: those of (a) ambient intelligence and IoT-enabled systems and (b) AR and VR systems.It is shown that the magical aspects of HCI within the emerging cyber-physical smart workplace differ significantly in their potential UX impacts from the magicality involved with earlier forms of computing, and the implications of this fact for the management of future workplaces are identified and discussed.
Leveraging Rule-Based Machine Translation Knowledge for Under-Resourced Neural Machine Translation Models

This publication has emanated from research supported in part by a research grant from ScienceFoundation Ireland (SFI) under Grant NumberSFI/12/RC/2289, co-funded by the European Regional Development Fund, and the Enterprise Ireland (EI) Innovation Partnership Programme undergrant agreement No IP20180729, NURS – NeuralMachine Translation for Under-Resourced Scenarios