Reviewerly/exHarmony huggingface dataset:
Multistage Shallow Pyramid Parsing for Road Scene Understanding Based on Semantic Segmentation

In this paper, we address the task of semantic image segmentation for road scene understanding. Road scene understanding is an important task in the field of computer vision. The main challenge is to develop an ability to recognize whole or partial objects in the road scene for various purposes such as surveillance systems, driving assistance systems, and autonomous vehicles. Here, we propose a novel method for semantic segmentation of road scenes that uses a modified pyramid pooling and multistage parsing to handle detection of small objects. The proposed method achieves excellent results, exceeding the accuracy of 78,61 % of mIoU with 16 fps using a single Titan X 1080 GPU.
Training Gradient Boosting Machines Using Curve-Fitting and Information-Theoretic Features for Causal Direction Detection

Detecting causal relationships between random variables using only matched pairs of noisy observations is a crucial problem in many scientific fields. In this paper the problem is addressed by extracting a number of features for each matched pair using a selection of curve-fitting and information theoretic features. Using these features, we train a pair of Gradient Boosting Machines whose hyperparameters we optimise using stochastic simultaneous optimistic optimisation. The results show that our method is relatively successful, gaining a third place in the 2013 Kaggle’s Causality Challenge. Our method is sound enough to be used in causality detection (or as part of a more comprehensive toolkit), although we believe it might be possible to considerably improve the quality of results by adding more features in the same vein.
Trend Review of Solar Energy Forecasting Technique

The proportion of solar photovoltaic power generation has steadily increased in the power trade market. Solar energy forecast is highly important for the stable trade of volatile solar energy in the existing power trade market, and it is necessary to identify accurately any forecast error according to the forecast lead time. This paper analyzes the latest study trend in solar energy forecast overseas and presents a consistent comparative assessment by adopting a single statistical variable (nRMSE) for forecast errors according to lead time and forecast technology.
The Challenge in Application of Cryptocurrency as Commodity in Indonesia

Cryptocurrency is a variety of digital currencies and is an asset used as a medium of exchange.This currency uses cryptography and how to make it safe.The system used will create and analyze algorithms and protocols.This prevents no information being changed or interrupted while connected by a third party.Context of digital money, no underlying and unpublished authority.Underlying is the asset used as the basis of the transaction.Based on the international standard of The Accounting and Auditing Organization for Islamic Financial Institutions (AAOIFI), a product is so called if gharar is heavy, exists in non-social business transactions, and on selling objects rather than complementary.virtual money is not a currency.For, if looking at the definition, the currency must be accepted by the public and recognized authority.Digital money is accepted in some circles so it is not a currency.Then the sharf provision (payment) does not apply there because it is not a currency.The purpose of this research is to see the potential of Cryptocurrency whether it can be applied into Digital Asset form only, or even in the future will be an Effect or Comudity.This study uses the Literature study method based on several scientific journals and the theoretical basis related to cryptocurrency and the potential of virtual transactions.The results of this study indicate that the cryptocurrency can be categorized as a virtual product commodity in Indonesia that already has Legal Basis and Legislative regulation.It is hoped that research in this regard will have a positive impact on the development of cryptocurrency in Indonesia, so that later regulation can be made about the cryptocurrency transaction model in Indonesia that has legal force and clear regulation to be applied in the cryptocurrency management.
A Compact Probe for EM Side-Channel Attacks on Cryptographic Systems

A shielded loop probe designed for evaluating EM side-channels attacks on cryptographic systems is described. This probe is compact and is sensitive enough to measure extremely weak signals that usually comprise EM side-channels. Furthermore, this probe can greatly suppress the influence of the electric field on its measurements. At its center frequency, the probe has a sensor factor of -0.17 dB S/m and electric field suppression ratio of 30.18 dB.
Multi-objective Genetic Programming based Automatic Modulation Classification

Automatic modulation classification (AMC) plays a crucial role in the cognitive radio networks, to which feature-based (FB) methods are the dominating solutions. However, the original features in FB methods are redundant, leading to the ambiguity of classification. To tackle this problem, this paper proposes a novel multi-objective modulation classification (MOMC) method. To reduce the redundant features, the original multi-features are recombined into a single feature by multiobjective genetic programming (MOGP) algorithm. Two quantitative objectives, the classification error rate and the variance for robustness, are then presented to jointly optimize the algorithm as two fitness functions. Furthermore, the single feature generated by MOGP is classified by logistic regression (LR) with low computational complexity. Simulation results verify the enhanced robustness and classification accuracy performance yielded by our proposed MOMC method compared to the existing classification methods.
MODL: A Modular Ontology Design Library.

Pattern-based, modular ontologies have several beneficial properties that lend themselves to FAIR data practices, especially as it pertains to Interoperability and Reusability. However, developing such ontologies has a high upfront cost, e.g. reusing a pattern is predicated upon being aware of its existence in the first place. Thus, to help overcome these barriers, we have developed MODL: a modular ontology design library. MODL is a curated collection of well-documented ontology design patterns, drawn from a wide variety of interdisciplinary use-cases. In this paper we present MODL as a resource, discuss its use, and provide some examples of its contents.
Poster

Proof of Retrievability (PoR) and Provable Data Possession (PDP) schemes have been proposed to ensure the integrity of stored data on untrusted servers. A successful PoR audit ensures, with high probability, that every piece of stored data is recoverable by the server. Most PoR schemes proposed have focused on bandwidth and computation cost, but in some deployment scenarios the size of remote storage can be the most expensive factor. We propose a simple PoR scheme which is a variant on some existing PDP work. Compared to existing audit routines, the server computation cost and bandwidth are higher, but the server storage cost is minimal. Our preliminary work indicates that deploying this scheme may be less costly in commercial cloud settings, depending on the cost structure and frequency of audits.
Enhanced Multiple-Object Tracking Using Delay Processing and Binary-Channel Verification

Tracking objects over time, i.e., identity (ID) consistency, is important when dealing with multiple object tracking (MOT). Especially in complex scenes with occlusion and interaction of objects this is challenging. Significant improvements in single object tracking (SOT) methods have inspired the introduction of SOT to MOT to improve the robustness, that is, maintaining object identities as long as possible, as well as helping alleviate the limitations from imperfect detections. SOT methods are constantly generalized to capture appearance changes of the object, and designed to efficiently distinguish the object from the background. Hence, simply extending SOT to a MOT scenario, which consists of a complex scene with spatially mixed, occluded, and similar objects, will encounter problems in computational efficiency and drifted results. To address this issue, we propose a binary-channel verification model that deeply excavates the potential of SOT in refining the representation while maintaining the identities of the object. In particular, we construct an integrated model that jointly processes the previous information of existing objects and new incoming detections, by using a unified correlation filter through the whole process to maintain consistency. A delay processing strategy consisting of the three parts—attaching, re-initialization, and re-claiming—is proposed to tackle drifted results caused by occlusion. Avoiding the fuzzy appearance features of complex scenes in MOT, this strategy can improve the ability to distinguish specific objects from each other without contaminating the fragile training space of a single object tracker, which is the main cause of the drift results. We demonstrate the effectiveness of our proposed approach on the MOT17 challenge benchmarks. Our approach shows better overall ID consistency performance in comparison with previous works.
Optimasi Cluster Pada Data Stunting: Teknik Evaluasi Cluster Sum of Square Error dan Davies Bouldin Index

The clusters number optimization problem is a problem that still requires continuous research so that the information produced can be a consideration. Cluster evaluation techniques with Sum of Square Error (SSE) and Davies Bouldin Index (DBI) are techniques that can evaluate the number of clusters from a data test. Research with these two techniques utilizes Stunting data from a number of regions in Indonesia. The result is information on stunting data which is formed from the optimal number of clusters where the largest SSE is formed at k = 5 and the smallest DBI is formed at k = 5, with values of 23.403 and 1,178 respectively. Changes in the number of clusters also influence the information produced and DBI is proven to produce optimal number of clusters that contain information with a better pattern because it has a small intra-cluster value. It is expected that the results of this study can show the performance of the two evaluation techniques in producing the optimal number of clusters so that grouping information is in accordance with the expected pattern.
How Emotional and Contextual Annotations Involve in Sensemaking Processes of Foreign Language Social Media Posts

The goal of this paper is to investigate how computational tools to annotate communication can support multilingual sense-making on social media. We conducted a field study of SenseTrans, a browser extension that uses sentiment analysis and named entity extraction techniques to annotate Facebook posts with emotional and contextual information. Interviews with 18 participants who used SenseTrans in their Facebook newsfeed for two weeks suggest that the annotations often supported sensemaking by providing additional information they could use to get a quick gist of the posts or to supplement their own interpretations. Participants varied in the extent to which they were motivated to evaluate the credibility of and form mental models of how the annotations were generated, which shaped how they utilized the annotations for sensemaking. Our findings demonstrate the value of designing to support cross-lingual communication and inform design implications for intelligent tools that support communication and sensemaking.
Dense 3D Displacement and Strain Measurement Framework of Miter Gates Using Computer Vision

This paper investigates the framework of vision-based dense 3D displacement and strain measurement using a digital camera. The framework has three components: (i) Estimation of 3D displacement and strain from images before and after deformation (water-fill event), (ii) evaluation of the expected accuracy of the measurement, and (iii) selection of the measurement setting with the highest expected accuracy. The framework first estimates the full-field optical flow between the images before and after water-fill event, and project the flow to the finite element (FE) model to estimate the 3D displacement and strain. Then, the expected displacement/strain estimation accuracy is evaluated at each node/element of the FE model. Finally, methods and measurement settings with the highest expected accuracy are selected to achieve the best results from the field measurement. Physics-based graphics model (PBGM) is used effectively to simulate the vision-based measurements in a photo-realistic environment and evaluate the performance of different methods and measurement settings by comparing the estimated values with the ground-truth values. The framework investigated in this paper can be used to analyze and optimize the performance of the measurement with different camera placement and post-processing steps prior to the field test.
Online Debiasing for Adaptively Collected High-dimensional Data.

Adaptive collection of data is commonplace in applications throughout scienceand engineering. From the point of view of statistical inference however,adaptive data collection induces memory and correlation in the sample, andposes significant challenge. We consider the high-dimensional linear regression, where the sample iscollected adaptively, and the sample size $n$ can be smaller than $p$, thenumber of covariates. In this setting, there are two distinct sources of bias:the first due to regularization imposed for consistent estimation, e.g. usingthe LASSO, and the second due to adaptivity in collecting the sample. Wepropose \emph{`online debiasing'}, a general procedure for estimators such asthe LASSO, which addresses both sources of bias. In two concrete contexts $(i)$batched data collection and $(ii)$ time series analysis, we demonstrate thatonline debiasing optimally debiases the LASSO estimate when the underlyingparameter $\theta_0$ has sparsity of order $o(\sqrt{n}/\log p)$. In thisregime, the debiased estimator can be used to compute $p$-values and confidenceintervals of optimal size.
Pumping Life: Embodied Virtual Companion for Enhancing Immersive Experience with Multisensory Feedback

With the advance of virtual reality (VR) head-mounted display, the appearance of the virtual companion can be more realistic and full of vitality, such as breathing and facial expression. However, the users cannot interact physically with the companions due to they do not have a physical body. In this work, our goal is to enable the virtual companion with multisensory feedback in the VR, which allows the users to play with the virtual companion physically in the immersive environment. We present Pumping Life, a dynamic flow system for enhancing the virtual companion with multisensory feedback, which utilizes water pumps and heater to provide shape deformation and thermal feedback. In this work, to show the interactive gameplay with our system, we deploy the system into a teddy bear and design a VR role-playing game. In this game, the player needs to collaborate with the teddy bear to complete the mission, which would perceive the vitality and expression of the teddy bear with multiple tactile sensations.
Classification of Nuclei in Colon Cancer Images using Ensemble of Deep Learned Features

The classification of the cell nuclei in histopathological cancerous tissue images is a difficult problem due to the inhomogeneity of the cellular structures. In this study, ensemble learning and deep learning methods are used to reduce the difficulty of this problem. On the deep learning side, the features of histopathological images were extracted using convolution layers of convolutional neural networks. It has been suggested that these extracted features will be reduced with feature reduction methods and modeled with conventional machine learning algorithms. In order to get better results, the decisions of the learners were combined and a second proposition was made with ensemble learning. The proposed method in this study showed more robust results compared to the results of other studies in the literature. With this proposed approach, it is aimed to make more accurate analysis of cancer cell types and histopathological images.
Evaluating CNN interpretability on sketch classification

While deep neural networks (DNNs) have been shown to outperform humans on many vision tasks, their intransparent decision making process inhibits wide-spread uptake, especially in high-risk scenarios. The BagNet architecture was designed to learn visual features that are easier to explain than the feature representation of other convolutional neural networks (CNNs). Previous experiments with BagNet were focused on natural images providing rich texture and color information. In this paper, we investigate the performance and interpretability of BagNet on a data set of human sketches, i.e., a data set with limited color and no texture information. We also introduce a heatmap interpretability score (HI score) to quantify model interpretability and present a user study to examine BagNet interpretability from user perspective. Our results show that BagNet is by far the most interpretable CNN architecture in our experiment setup based on the HI score.
Development of the interacting agents behavior scenario in the cyber security system

The results of modeling and analysis of scenarios of the behavior of interacting agents in conditions of cyber conflict are presented. General approaches to the development of a scenario of the behavior of antagonistic agents are presented. The definition of the scenario is given and the factors determining the scenario of behavior are highlighted. The given scenarios are determined by such factors as the ratio of the capabilities of the attacking and the defending sides, the presence or absence of information exchange between security agents, and the time of switching to a new attack vector. The value of the time of switching to a new attack vector is found, at which the interaction is more stable. This indicates that the reaction of the defense side should not be purely reactive, and the “wait and see” strategy is not always the best. Modeling and analysis of the results were carried out in the conditions of information exchange between agents of the protection system and in the absence of such an exchange. The advantages and disadvantages of this behavior are noted. It is shown that when changing the time of switching to a new attack vector, not only the financial indicators of the activity of the participants in cyber conflict change, but also the nature of the interaction. The value of the time of switching to a new attack vector was found, in which the interaction is more stable, which suggests that the reaction of the defense side should not be purely reactive, and the “wait and see” strategy is not always the best. It is shown how the proposed approach can be used to justify the choice of a strategy for agent behavior in security systems, as well as for economic assessments of countermeasures and their deterrent effect on attackers. The proposed scenarios can be considered as a useful tool for assessing investments in the security of the business process circuit by decision makers
Understanding and Measuring Risk due to Uncertainties in IoT

Internet of Things (IoT) is a large hybrid interconnected mixture of many devices and wearables, driven by several technologies such as communication, computing, control, and data management. IoT concept has impregnated almost every industry and society, yet it is a new fertile area of research on many dimensions. One of them is the study of the nature of uncertainty inherent in each thing in IoT, the level of uncertainty created and propagated by the interconnection of large heterogenous things, in order to quantify the risks in social, economic, and technological sectors. In this paper a systematic approach is presented to study the risk factor in using IoT-based applications in the presence of uncertainty and imprecision inherent in it.
Field trip as method

Understanding people's attitudes towards and uses of technology is an essential aspect of a successful design process. Ethnography is a proven method for acquiring this understanding. However, there are challenges to incorporating fieldwork, most notably the time factor, considered by some as the greatest barrier. This is especially true for many technology companies whose turn-around time from concept to design to implementation is accelerated. We propose a solution to the dilemma between acquiring the benefits of fieldwork with the compressed timescales of many technology projects by using focused field trips as a method for gaining rich insights into peoples' uses of and attitudes towards technologies in real-world settings. In a short amount of time (one or two days), field trips sensitize design teams to the priorities of stakeholder groups. We outline a systematic approach to incorporating field trips as a method for developing rich, qualitative insights using rapid qualitative studies.
Detection and Grading of Breast Cancer via Spatial Features in Histopathological Images

In this study, the detection and grading of breast cancer, which is a very common type of cancer, are studied. In feature extraction phase, feature matrices are created by using local binary pattern histograms and Gabor filters on a dataset with 4812 train data. Afterwards, the classification process are carried out using k-nearest neighbor, decision trees and several ensemble classification methods such as bagging, adaboost, random forests. The accuracies of the classifications are measured with 491 test image which are selected from dataset. At the end of the study, success of feature extraction methods and classifiers are compared and the results were presented in tables.
Numerical and experimental studies of water disinfection in UV reactors

Abstract Performance of UV reactors for water disinfection is investigated in this paper. Both experimental and numerical studies are performed on base reactor LP24. Enterobacteria phage MS2 is chosen as the challenge microorganism in the experiments. Experiments are conducted to evaluate the effect of different parameters, i.e. flow rate and UV transmission, on the reactor performance. Simulation is carried out based on the commercial software ANSYS FLUENT with user defined functions (UDFs) implemented. The UDF is programmed to calculate UV dose absorbed by different microorganisms along their flow trajectories. The effect with boundary layer mesh and without boundary layer mesh for LP24 is studied. The results show that the inclusion of boundary layer mesh does not have much effect on the reactor performance in terms of reduction equivalent dose (RED). The numerical results agree well with the experimental measurements, hence validating the numerical model. With this achieved, the numerical model is applied to study other scaled reactors: LP12, LP40, LP60 and LP80. Comparisons show that LP40 has the highest RED and log inactivation among all the reactors while LP80 has the lowest RED and log inactivation.