Reviewerly/exHarmony huggingface dataset:
Encoder-Recurrent Decoder Network for Single Image Dehazing

This paper develops a deep learning model, called Encoder-Recurrent Decoder Network (ERDN), which recovers the clear image from a degrade hazy image without using the atmospheric scattering model. The proposed model consists of two key components- an encoder and a decoder. The encoder is constructed by a residual efficient spatial pyramid (rESP) module such that it can effectively process hazy images at any resolution to extract relevant features at multiple contextual levels. The decoder has a recurrent module which sequentially aggregates encoded features from high levels to low levels to generate haze-free images. The network is trained end-to-end given pairs of hazy-clear images. Experimental results on the RESIDE-Standard dataset demonstrate that the proposed model achieves a competitive dehazing performance compared to the state-of-the-art methods in term of PSNR and SSIM.
Biomedical CT Image Retrieval Using 3D Local Oriented Zigzag Fused Pattern

In this paper, we introduce a new feature descriptor 3D local oriented zigzag fused pattern (3D-LOZFP) for retrieval of medical CT images. The existing local patterns such as local binary pattern (LBP), local tetra pattern (LTrP) etc. captures the relationship between the reference and its surrounding pixels in a circular fashion in a 2D plane. The proposed descriptor encodes the relation between the reference pixel and its neighboring pixels using three unique 3D zigzag patterns in four different directions in a 3D plane. Therefore a total of 12 effective 3D zigzag patterns are introduced to capture the relationship between the reference and its neighbors in a 3D plane. The 3D plane is constructed by passing the input image through a Gaussian filter bank producing multiple filtered images containing multi-scale information. The feature dimensions are reduced using quantization and a fusion based scheme. The retrieval performance of the proposed descriptor is investigated by conducting experiments on two benchmark CT image datasets and then compared it with several recent techniques. The experimental results in terms of average retrieval precision (ARP) and average retrieval recall (ARR) across two databases validate the retrieval supremacy of the proposed descriptor over other techniques in CT image retrieval.
Commuting Conditional GANS for Multi-Modal Fusion

This paper presents a data driven approach to multi-modal fusion where a hidden latent sub-space between the different modalities is learned. The hidden space is estimated via a bank of Conditional GANs which also commute with each other, leading to an output that lies in a common subspace. Experimental results show improved detection performance compared to existing fusion techniques in ideal as well as noisy sensor condition.
Blockchain-based Access Control Data Distribution System

In the Internet age, data distribution is an important means of information dissemination. However, there are many problems that are difficult to solve in data distribution. Current data distribution needs to rely on third-party distribution platforms, but it is easy to generate centralized rights, which causes data creators to lose their legitimate rights and interests. Moreover, data leakage is easy to occur during data storage and transmission, and data integrity and authenticity cannot be guaranteed. In view of the above problems, a blockchain-based data distribution system is designed. We use the decentralization feature of the blockchain to remove the centralized platform, allowing peers in the blockchain network to directly trade as data creators and data consumers. And the data is tracked using the integrity of the data in the blockchain network. At the same time, the ciphertext-based attribute encryption algorithm and smart contract are used to control the user's access rights. Use cryptography to protect the privacy, security, and verifiability of your data.
Composite-structured-light profilometry using greyscale expansion

In traditional composite-structured-light profilometry, the N-frame carrier gratings and modulated phase-shifting sinusoidal gratings must share a 256-greyscale dynamic range, which is the standard for commercial digital light projectors (DLPs). Additionally, each of the demodulated phase-shift gratings must possess a 43-greyscale dynamic range. This decreases the greyscale dynamic range for each of the demodulated gratings and compresses the phase information. Consequently, measurement accuracy is a challenge, even for incomplete reconstructions. To solve this problem, we propose a new composite-structured-light profilometry using greyscale expansion. Specifically, a composite video is synthesised using three different purposefully designed patterns, each possessing a 256-greyscale dynamic range. These patterns are used as the composite-structured-light to replace the traditional static composite grating. After projecting the repetitive video onto a measured object and controlling the exposure time of the charge-coupled device camera, the deformed composite 766-greyscale pattern can be stably captured. Furthermore, after demodulation, the dynamic range of the deformed demodulated patterns can be expanded up to 229. Numerical and experimental results indicate that the proposed method can be used to divide the single 256-greyscale DLP standard and accurately reconstruct measured objects.
Privacy-Preserving Data Sharing by Integrating Perturbed Distance Matrices

Collecting large amounts of data is beneficial in machine learning to generate models that are less biased. There are many cases in which pieces of similar data are distributed among organizations, and it is difficult to integrate these data owing to issues involving privacy and cost. Integrating these distributed data without delivering the original data leads to the concept of data collaboration, which combines data held by different organizations in a secure manner. We propose a method in which a distance matrix of the original data obtained using common data among organizations is shared to learn neighbor information of the original data. Specifically, the proposed method robustly integrates distributed data, which is of as good quality as connected raw data, in cases where the amount of data in each organization is small and the data bias is large. In addition, the proposed method is applicable to data contaminated by noise. To demonstrate the effectiveness of the proposed method, we performed a classification task on open biological data divided into several pieces and found that the classification results for divided data were as precise as when all data were available. Finally, we show that the robustness of the method against noise improves the anonymity of the original data as a by-product.
Ant Colony System Based Ant Adaptive For Search of the Fastest Route of Tourism Object Jember, East Java

Abstract The fastest route search is a problem to find routes having a relatively small or empty number of congestion or density so that the required travel time is faster to get to a location. One of them is the route to the tourist attraction in Jember regency, East Java has many relatively solid lines. Some researchers have done a lot of research on finding the shortest path using Ant Colony System (ACS) method. However, the ACS method has a weakness, where more ants pass through a path, the clearer the footprint but the volume of ants passing through the path is also longer. Therefore in this research use ant adaptive on ACS method to find the fastest path to the tourist location in Jember Regency. Due to the increasing number of densities, it takes longer to get to the food source. In this study, ant adaptive for ACS method is used to determine the optimal number of ants in searching for the fastest path to the tourist attraction in Jember District, East Java. Various trials are done to prevent the search process of resolving the already traced solution space to find the fastest path. The fastest path does not have to have the shortest path but has the fastest time with the paths having relatively little or no density at all. The results showed that ant adaptive for ACS method developed in this research is able to find the optimal number of ants in determining the fastest paths to the tourist object so that the search result of the fastest route of tourist attraction in Jember Regency in accordance with the reality of the passable path and has a density relatively small or almost nonexistent. The success rate was conducted on 8 tourist objects in Jember with 98 paths tested using road data in Jember regency, East Java with the fastest path solution influenced by the closer to the optimum distance, the more number of ants influenced.
Wheelchair control through eye blinking and IoT platform

Decision-making considering commands coming from eye blinking, to give mobility to a wheelchair, is not a simple task, bad decisions can end up in moving a person in a wrong direction, which will give more difficulties instead of solutions. In the actual study a microcontroller with embedded software and hardware for IoT is used, this device can manage multiple sensors as inputs and multiple actuators as outputs. The raspberry Pi 3 was selected because it is single-board computer with wireless LAN and Bluetooth Low Energy (BLE) on board. The developed system discriminates an involuntary blinking from a low motion voluntary blinking and take a decision to move forward a model wheelchair. The position and given commands are sent to an IoT platform to save the wheelchair movement data.
Policy Sharing Using Aggregation Trees for ${Q}$ -Learning in a Continuous State and Action Spaces

<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning is a generic approach that uses a finite discrete state and an action domain to estimate action values using tabular or function approximation methods. An intelligent agent eventually learns policies from continuous sensory inputs and encodes these environmental inputs onto a discrete state space. The application of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning in a continuous state/action domain is the subject of many studies. This paper uses a tree structure to approximate a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -function using in a continuous state domain. The agent selects a discretized action with a maximum <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -value and this discretized action is then extended to a continuous action using an action bias function. Reinforcement learning is difficult for a single agent when the state space is huge. This proposed architecture is also applied to a multiagent system, wherein an individual agent transfers its useful <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -values to other agents to accelerate the learning process. Policy is shared between agents by grafting the branches of trees in which <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -values are stored to other trees. The results for simulation show that the proposed architecture performs better than tabular <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning and significantly accelerates the learning process because all agents use the sharing mechanisms to cooperate with each other.
White Box vs. Black Box Modeling: On the Performance of Deep Learning, Random Forests, and Symbolic Regression in Solving Regression Problems

Black box machine learning techniques are methods that produce models which are functions of the inputs and produce outputs, where the internal functioning of the model is either hidden or too complicated to be analyzed. White box modeling, on the contrary, produces models whose structure is not hidden, but can be analyzed in detail. In this paper we analyze the performance of several modern black box as well as white box machine learning methods. We use them for solving several regression and classification problems, namely a set of benchmark problems of the PBML test suite, a medical data set, and a proteomics data set. Test results show that there is no method that is clearly better than the others on the benchmark data sets, on the medical data set symbolic regression is able to find the best classifiers, and on the proteomics data set the black box modeling methods clearly find better prediction models.
KRDOH: Kurdish Offline Handwritten Text Database

For the Kurdish hand-written studies on text recognition, a comprehensive Kurdish text database is an important resource. Since the Kurdish typewritten script has no such database. Therefore, in this article, we report the comprehensive Kurdish offline (KRDOH) databases, written up by 1076 volunteers of various ages, genders and levels of education in a total of 4304 manuscripts compromising of 4 pages were contributed by each writer in the database. The segments of this dataset have been eradicated from Kurdish poetry. The form was scanned with a resolution of 200, 300, and 600 dpi. The manuscript characters of 4304 are comprising of 17466 lines. The database types were reformed in 70%, 15%, and 15%, respectively for instruction, inspection, and verification. This allows scientists to compare and use the database. We have also defined the handling procedures and evaluate the use of this dataset for several purposes. For research on different related handwritten issues, including text recognition, writer ID and verification, form assessment, preprocessing, segmentation, and free access to this database by researchers around the world.
Love, Hate Thy Neighbour? Or Just Don’t Care Much about Them: A Sentiment Analysis of China-Related Posts and Comments on Reddit.Com

Faced with the explosion of information about many other countries in the world on social media, the audience of a certain country may form different emotions, judgements and thoughts of such information, which are worthy of scrutiny. In this article, our aim is to examine Western users’ opinions towards China-related posts on Reddit.com through sentiment analysis of the comments following such posts. Using a statistical method, we collected and analysed the comments of 384 posts on China on Reddit.com, which were distributed in 36 subreddits. As the finding shows, the number of neutral comments exceeds the number of either positive comments or negative comments on China-related posts on Reddit.com. The results indicate that although most of the opinions on Reddit.com about China are neutral or irrelevant, they are largely neglected by the users or viewers who very much prefer negative or positive comments. Therefore, the perceptions on these opinions could be skewed due to this effect. In addition, for the citizens of any country, except for those who intentionally try to guide the direction of discussion on a topic, it is very unlikely for them to be really concerned or care about the events and incidents happening in another remote country.
Linking Legal and Domain-specific Requirements in a Context-based Adaptive Personalized Learning Environment

In the digital age, privacy is an asset that is worth being protected. It gives users the opportunity to decide what he or she wants to reveal to others about themselves. Although privacy is legally protected, e.g. by the General Data Protection Regulation (GDPR) when processing personal data, users often have no freedom to decide which data is collected and processed for what purpose and to what extent. Usually, systems only support an all-or-nothing approach. To give users back a more fine-grained control over their privacy, it is necessary to selectively approve the data for related functionalities. This addresses the requirements to data minimization. A consent of processing personal data should consider personal preferences and must be withdrawn at any time. The consequences of a consent or a withdrawal should be explained to users. In this paper we present an approach to address the above challenges. Our approach enables us to support I) specific and in-situ explanations of data processing to the users on request and II) in-situ opportunities to make fine-grained decisions about the data usage. For that, we use a context-based adaptive learning environment and a specific domain model. This domain model is used by domain experts that are capable of concerning content, legal and technical requirements equally and that know what is relevant for specific learning resp. collaboration situations. They define related content, legal and technical policies that must be considered at runtime. We illustrated how we use our approach to give users back fine-grained control over their privacy and data, by enabling them to selectively approve the data for related functionalities at hand.
Exploring Hierarchical Concepts: Theoretical and Application Comparisons

Phenomena are usually multidimensional and their complexity cannot be directly explored via observable variables. For this reason, a hierarchical structure of nested latent concepts representing different levels of abstraction of the phenomenon under study may be considered. In this paper, we provide a comparison between a procedure based on hierarchical clustering methods and a novelty model recently proposed, called Ultrametric Correlation Matrix (UCM) model. The latter aims at reconstructing the data correlation matrix via an ultrametric correlation matrix and supplies a parsimonious representation of multidimensional phenomena through a partition of the observable variables defining a reduced number of latent concepts. Moreover, the UCM model highlights two main features related to concepts: the correlation among concepts and the internal consistency of a concept. The performances of the UCM model and the procedure based on hierarchical clustering methods are illustrated by an application to the Holzinger data set which represents a real demonstration of a hierarchical factorial structure. The evaluation of the different methodological approaches—the UCM model and the procedure based on hierarchical clustering methods—is provided in terms of classification of variables and goodness of fit, other than of their suitability to analyse bottom-up latent structures of variables.
Modeling Asymmetric Exchanges Between Clusters

A nonhierarchical clustering model is proposed here which jointly fits the symmetric and skew-symmetric components of an asymmetric pairwise dissimilarity matrix. Two similar clustering structures are defined depending on two (generally different) partitions of the objects: a "complete" partition fitting the symmetries (where all objects belong to some cluster) and an "incomplete" partition fitting the skew-symmetries, where only a subset of objects is assigned to some cluster, while the remaining ones may remain non-assigned. The exchanges between clusters are accounted for by the model which is formalized in a least squares framework and an appropriate Alternating Least Squares algorithm is provided to fit the model to illustrative real data.
Toward Detecting IoT Device Traffic in Transit Networks

Internet-of-Things (IoT) is playing a more and more important role after its growing up, besides it covers traditional equipment to general household objects. IoT manufacturers are scrambling to keep us with this growing demand. Unfortunately, these devices generally are not equipped with proper security protections, and therefore create vulnerabilities in networks. For defending against IoT security threats, we should understand how many and what kinds of IoT devices are deployed. In this paper, we introduce three heuristic algorithms to identify IoT devices in network traffic: server IPs, names in traffic, and device information in TLS certificates. The first two methods detect IoT devices by server IPs and names, then make device-name-to-IP-address-server-name mapping, use the mapping run by the manufacturer of devices. The third method gets TLS certificate information by scanning. We evaluate these algorithms with two network resolutions for server matching: exact matching and the prefix network matching (/24 prefixes matching for IPv4 addresses, and /56 prefixes matching for IPv6 addresses). Applying the exact matching to passively collected traffic in a transit network, we detected 3 out of 17 devices types from 48 hours of traffic data. From these results, we find devices reported in the original measurements in the US. We also noticed a significant increase between 15:00 - 23:00 for Amazon-FireTV. With the prefix network matching, we can also detect 5 out of 17 devices types.
The Metagraph Multiagent System Based on the Semantic Complex Event Processing

In this paper, the semantic complex event processing based on the metagraph approach and its usage in metagraph multiagent system are discussed. The metagraph model is a kind of complex graph model with emergence. The key element of the metagraph model is the metavertex, which makes the definition of metagraph holonic – a metavertex may include a number of lower-level elements and in turn, may be included in a number of higher-level elements. For metagraph transformation, the metagraph agents are proposed. The distinguishing feature of the metagraph agent is its homoiconicity, which means that it can be a data structure for itself. This is due to the fact that the metagraph agent may be represented as a set of metagraph fragments. Thus, the metagraph agent can change the structure of itself or the structure of the other metagraph agents. Changing the structure of a metagraph agent is a form of agent learning. The metagraph multiagent system based on semantic complex event processing using metagraph description of semantic complex event.
A novel approach to face recognition using freeman chain code and nearest neighbor classifier

This paper proposes a novel approach to face recognition using freeman chain code as a feature extractor for face representation, and the nearest neighbor classifier for face matching. The face description stage starts with resizing, and then dividing the face image into non-overlapping sub-regions, then a set of chains (words) are extracted from each region, and assigned later into the nearest word in a Dictionary of Visual Words (DoVW). As a result, each patch is represented by a histogram of visual words. Finally, the histograms are assembled into one to describe the face image. Unlike the most of the existing methods, which require a mask around the treated pixel, our methodology depends on directional changes from the starting pixel, which allow us to obtain information on the local and also the more global structures. The face matching is performed by using the nearest neighbor classifier with Hellinger, Cosine, or Chi-square as the distances measure between histograms. Experimental results show which metrics perform well and demonstrate the efficiency of the proposed approach in terms of recognition rate compared to the other face recognition methods.