Reviewerly/exHarmony huggingface dataset:
A model-driven engineering approach for the service integration of IoT systems

With the development of IoT devices and web services, the objects of the real world are more interconnected, which allows applications to extend their characteristics in different fields, including industrial or home environments, among other possible examples such as health, trade, transport, or agriculture. However, this development highlights the challenge of interoperability, because devices are heterogeneous and use different communication protocols and different data formats. For this reason, we propose a model for point-to-point integration in three-layer IoT applications: (a) hardware, which corresponds to the physical objects (controller, sensor and actuator), (b) communication, which is the bridge that allows the exchange of data between a MQTT queue and REST web services, and (c) integration, which establishes a sequence of transactions to coordinate the components of the system. For this purpose, a metamodel, a graphic editor and a code generator have been developed that allow the developer to design IoT systems formed by heterogeneous components without having in-depth knowledge of every hardware and software platform. In order to validate our proposal, a smart home scenario has been developed, with a series of sensors and actuators that combined show a complex behavior.
Advanced exact synthesis of Clifford+T circuits

Abstract Quantum systems provide a new way of conducting computations based on the so-called qubits. Due to the potential for significant speed-ups, this field received significant research attention in recent years. The Clifford+T library is a very promising and popular gate library for these kinds of computations. Unlike other libraries considered so far, it consists of only a small number of gates for all of which robust, fault-tolerant realizations are known for many technologies that seem to be promising for large-scale quantum computing. As a consequence, (logic) synthesis of Clifford+T quantum circuits became an important research problem. However, previous work in this area has several drawbacks: Corresponding approaches are either only applicable to very small quantum systems or lead to circuits that are far from being optimal. The latter is mainly caused by the fact that current synthesis realizes the desired circuit by a local, i.e., column-wise, consideration of the underlying unitary transformation matrix to be synthesized. In this paper, we analyze the conceptual drawbacks of this approach and propose to overcome them by taking a global view of the matrices and perform a separation of concerns regarding individual synthesis steps. We precisely describe a corresponding algorithm as well as its efficient implementation on top of decision diagrams. Experimental results confirm the resulting benefits and show improvements of up to several orders of magnitudes in costs compared to previous work.
Adversarial Learning With Multi-Modal Attention for Visual Question Answering

Visual question answering (VQA) has been proposed as a challenging task and attracted extensive research attention. It aims to learn a joint representation of the question-image pair for answer inference. Most of the existing methods focus on exploring the multi-modal correlation between the question and image to learn the joint representation. However, the answer-related information is not fully captured by these methods, which results that the learned representation is ineffective to reflect the answer of the question. To tackle this problem, we propose a novel model, i.e., adversarial learning with multi-modal attention (ALMA), for VQA. An adversarial learning-based framework is proposed to learn the joint representation to effectively reflect the answer-related information. Specifically, multi-modal attention with the Siamese similarity learning method is designed to build two embedding generators, i.e., question-image embedding and question-answer embedding. Then, adversarial learning is conducted as an interplay between the two embedding generators and an embedding discriminator. The generators have the purpose of generating two modality-invariant representations for the question-image and question-answer pairs, whereas the embedding discriminator aims to discriminate the two representations. Both the multi-modal attention module and the adversarial networks are integrated into an end-to-end unified framework to infer the answer. Experiments performed on three benchmark data sets confirm the favorable performance of ALMA compared with state-of-the-art approaches.
A Novel Cognitive Computing Technique Using Convolutional Networks for Automating the Criminal Investigation Process in Policing

Criminal Investigation (CI) plays an important role in policing, where police use various traditional techniques to investigate criminal activities such as robbery and assault. However, the techniques should hybrid with the use of artificial intelligence to analyze and determine different crime types for taking actions in real-time. In contrast with the manual process of investigating a large amount of data collected related to a criminal investigation. In this paper, we present a novel Cognitive Computing enabled Convolution Neural Networks (CC-CNN) approach for identifying crime types, such as robbery and assault, collected from unstructured textual data. We develop learning algorithms and provide a cognitive assistant to assist a police investigator in easily understanding crime types. We train and validate the CC-CNN technique on two datasets including handcrafted text-crime dataset and sentiment polarity dataset of negative and positive reviews. The experimental results show that our approach performs at a high level in terms of accuracy, error rate and time processing using both datasets.
Hierarchical Coded Gradient Aggregation for Learning at the Edge

Client devices at the edge are generating increasingly large amounts of rich data suitable for learning powerful statistical models. However, privacy concerns and heavy communication load make it infeasible to move the client data to a centralized location for training. In many distributed learning setups, client nodes carry out gradient computations on their local data while the central master server receives the local gradients and aggregates them to take the global model update step. To guarantee robustness against straggling communication links, we consider a hierarchical setup with n <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> clients and n <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">h</sub> reliable helper nodes that are available to aid in gradient aggregation at the master. To achieve resiliency against straggling client-to-helpers links, we propose two approaches leveraging coded redundancy. First is the Aligned Repetition Coding (ARC) that repeats gradient components on the helper links, allowing significant partial aggregations at the helpers, resulting in a helpers-to-master communication load (C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HM</sub> ) of O(n <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">h</sub> ). ARC however results in a client-to-helpers communication load (C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EH</sub> ) of Θ(n <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">h</sub> ), which is prohibitive for client nodes due to limited and costly bandwidth. We thus propose Aligned Minimum Distance Separable Coding (AMC) that achieves optimal C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EH</sub> of Θ(1) for a given resiliency threshold by using MDS code over the gradient components, while achieving a C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HM</sub> of O(n <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> ).
Personalized Image Retrieval with Sparse Graph Representation Learning

Personalization is essential for enhancing the customer experience in retrieval tasks. In this paper, we develop a novel method CA-GCN for personalized image retrieval in the Adobe Stock image system. The proposed method CA-GCN leverages user behavior data in a Graph Convolutional Neural Network (GCN) model to learn user and image embeddings simultaneously. Standard GCN performs poorly on sparse user-image interaction graphs due to the limited knowledge gain from less representative neighbors. To address this challenge, we propose to augment the sparse user-image interaction data by considering the similarities among images. Specifically, we detect clusters of similar images and introduce a set of hidden super-nodes in the graph to represent clusters. We show that such an augmented graph structure can significantly improve the retrieval performance on real-world data collected from Adobe Stock service. In particular, when testing the proposed method on real users' stock image retrieval sessions, we get promoted average click position from 70 to 51.
Using Tree Structure to Mine High Temporal Fuzzy Utility Itemsets

Data mining is a critical technology for extracting valuable knowledge from databases. It has been used in many fields, like retail, finance, biology, etc. In computational intelligence, fuzzy logic has been applied in many intelligent systems widely because it is simple and similar to human inference. Fuzzy utility mining combines utility mining and fuzzy logic for getting linguistic utility knowledge. In this paper, we study a more challenging, complicated, but practical topic called temporal fuzzy utility data mining, which considers the temporal periods in transactions, purchased amounts, item profits, and understandable linguistic terms as important factors. Although an Apriori-based algorithm was proposed previously, its execution was not efficient. We thus use a modified tree structure based on the classical frequent-pattern tree to improve its performance. A tree-based mining algorithm is also proposed to mine temporal fuzzy utility itemsets from quantitative transactional databases. The tree structure is built to keep all temporal fuzzy utility 1-itemsets in a database. All the high temporal fuzzy utility itemsets in a database can be obtained by traversing the tree-based structure. The proposed algorithm gets the final results through two phases. In the first phase, a procedure like FP-Growth is used to find the candidate itemsets. In the second phase, the temporal fuzzy utility database is scanned to decide whether the candidate itemsets are desired. Experimental results show that the proposed algorithm is superior to the existing algorithm for temporal fuzzy utility mining in terms of processing time and used memory.
Deleuze’s &lt;i&gt;Postscript on the Societies of Control&lt;/i&gt; Updated for Big Data and Predictive Analytics

In 1990, Gilles Deleuze published Postscript on the Societies of Control , an introduction to the potentially suffocating reality of the nascent control society. This thirty-year update details how Deleuze’s conception has developed from a broad speculative vision into specific economic mechanisms clustering around personal information, big data, predictive analytics, and marketing. The central claim is that today’s advancing control society coerces without prohibitions, and through incentives that are not grim but enjoyable, even euphoric because they compel individuals to obey their own personal information. The article concludes by delineating two strategies for living that are as unexplored as control society itself because they are revealed and then enabled by the particular method of oppression that is control.
A Blockchain-based Reputation System for Small Satellite Relay Networks

Currently, space communications networks are attempting to move away from large geostationary (GEO) satellites towards large constellations of small satellites. This trend is observed both in government and commercial communications satellites. By relaying data across multiple constellations/networks, we may be able to reduce end-to-end latencies and reduce burden (mass, power, cost) for all users. However, this is only possible if networks across constellations can establish inter-satellite authentication and trust. The key to this trust is based on demonstrated ability of the relay satellites to meet performance requirements, i.e. “reputation.” In this work, we propose leveraging distributed ledger technologies (i.e. blockchains) to develop a secure, decentralized reputation system for satellite relay networks. This informs a reputation-aware routing protocol and reduces the average data latency across the network. In this paper, we discuss designing the blockchain-based reputation system and routing protocol. We then analyze the resultant network performance with respect to average latency, computational complexity, and storage considerations for a variety of use cases.
In-Loop Filter with Dense Residual Convolutional Neural Network for VVC

In-loop filters are usually used to remove artifacts or further improve coding performance in lossy video compression. Encouraged by the superior performance of deep learning on image restoration, a dense residual convolutional neural network (DRN) based in-loop filter is proposed in this paper for Versatile Video Coding (VVC). The proposed network utilizes residual learning module, dense shortcuts and bottleneck layers, to solve the problems of gradient vanishing, encourage features reuse and save computational resources, respectively. We present the modifications of DRN in terms of network structure, training materials and training process for the trade off between computational complexity and coding efficiency, and valid the effectiveness of the modifications based on the experimental results. Besides, the proposed framework outperforms the VVC reference software with three coding configurations.
Hierarchical and lateral multiple timescales gated recurrent units with pre-trained encoder for long text classification

Text classification, using deep learning techniques, has become a research challenge in natural language processing. Most of the existing deep learning models for text classification face difficulties when the length of the input text increases. Most models work well on shorter text inputs, however, their performance degrades with the increase in the input length. In this work, we introduce a model for text classification that can alleviate this problem. We present the hierarchical and lateral multiple timescales gated recurrent units (HL-MTGRU), in combination with pre-trained encoders to address the long text classification problem. HL-MTGRU can represent multiple temporal scale dependencies for the discrimination task. By combining the slow and fast units of the HL-MTGRU, our model effectively classifies long multi-sentence texts into the desired classes. We also show that the HL-MTGRU structure helps the model to prevent degradation of performance on longer text inputs. We demonstrate that the proposed network with the help of the latest pre-trained encoders for feature extraction outperforms the conventional models on various long text classification benchmark datasets.
Eyeball based Cursor Movement Control

An individual Human computer interference system is being introduced. In olden times, as an input device the mouse and keyboard were used by human computer interference system. Those people who are suffering from certain disease or illness cannot be able to operate computers. The idea of controlling the computers with the eyes will serve a great use for handicapped and disabled person. Also this type of control will eliminate the help required by other person to handle the computer. This measure will be the most useful for the person who is without hands through which they can operate with the help of their eye movements. The movement of the cursor is directly associated with the center of the pupil. Hence our first step would be detecting the center of point pupil. This process of pupil detection is implemented using the Raspberry Pi and OpenCV. The Raspberry Pi has a SD/MMC card slot which is used for placing the SD card. The SD card is boosted with the operating system that is required starting up of Raspberry Pi. The Raspberry PI will get executed once the application program is loaded into it.
Using regional geochemical survey data to trace anomalous samples through geochemical genes: The Tieshanlong tungsten deposit area (Southeastern China) case study

In order to determine and trace geochemical anomalies in soils, a tungsten (W) metallogene was constructed according to the idea of the gold (Au) metallogene previously developed. Based on the outcomes obtained for the Tieshanlong tungsten deposit area in southeast China from both three weathered profiles and a subset of data extracted from a regional geochemical survey database, a value of 40% of similarity between a W metallogene and an ideal ore was set as a criterion to individuate samples with potential W mineralization. Based on their W or Au metallogenes, a value of 70% of similarity was also chosen as the threshold criterion to locate samples with similar genes relative to a parent rocks or its weathered products. Based on the regional geochemical survey data available for the Tieshanlong tungsten deposit area, single elemental anomalies were determined and classified by means of the "seven levels' classification" method. Integrated anomalies were also determined and classified by using similarities (relative to their ideal ore) on W and Au metallogenes. Results indicate that the determined anomaly regions are well spatially consistent with the known deposits and that the metallogenes can be used to trace mineralized soil samples. Furthermore, the location of mineralized samples sourced by (altered) magmatic rock could be well pinpointed on geochemical genes, as well. It was also observed that a value of 80% similarity for a lithogene (a geochemical gene based on a sequence of eleven immobile elements) which, similarly to metallogene, allow to compares samples to a specific parent rock and its weathered products, may be too stringent in the case of sedimentary rocks (even if metamorphosed) due to their relevant geochemical heterogeneity.
Human Emotions Recognition from Thermal Images using Yolo Algorithm

Acknowledgment of facial expression is a prospective application in emotion analysis. This research is mostly concentrated to examine emotions concealed in the face which includes stress, anxiety or exhilaration that can set off a warm spread across the cheeks. In a similar way, face temperature changes in an individual when he experience different situations. The estimation of these thermal images was performed through its thermal value of pixel, not with its pixel's intensity value. Regions of interest were obtained through image processing methods, which include segmenting out the subject from its background. Despite countless readily available strategies for acknowledgment of human emotions, efficiency, robustness, and accuracy can be enhanced by integrating one modality with various others and they are based on visual facial expressions, thermal face imaging, and anatomical parameters. This paper checks out the strong point and also shortages of the currently offered emotion recognition systems based on faces, face thermal imaging and also physiological criteria.
An empirical study on self-fixed technical debt

Technical Debt (TD) can be paid back either by those that incurred it or by others. We call the former self-fixed TD, and it is particularly effective, as developers are experts in their own code and are best-suited to fix the corresponding TD issues. To what extent is TD self-fixed, which types of TD are more likely to be self-fixed and is the remediation time of self-fixed TD shorter than non-self-fixed TD? This paper attempts to answer these questions. It reports on an empirical study that analyzes the self-fixed issues of five types of TD (i.e., Code, Defect, Design, Documentation and Test), captured via static analysis, in more than 17,000 commits from 20 Python projects of the Apache Software Foundation. The results show that more than two thirds of the issues are self-fixed and that the self-fixing rate is negatively correlated with the number of commits, developers and project size. Furthermore, the survival time of self-fixed issues is generally shorter than non-self-fixed issues. Moreover, the majority of Defect Debt tends to be self-fixed and has a shorter survival time, while Test Debt and Design Debt are likely to be fixed by other developers. These results can benefit both researchers and practitioners by aiding the prioritization of TD remediation activities within development teams, and by informing the development of TD management tools.
Extracting of Patterns Using Mining Methods Over Damped Window

Information mining strategies are utilized broadly in commercial areas for separating data from the database. Information mining comprises of applying Utility Pattern Mining indicates about time utilizing strategies with considering of item sets. Utility Pattern Mining (UPM) is reasonable for significant data which assesses effectively in pattern identification. In this research paper, Hierarchical High Average Utility Pattern Mining (HAUPM) is proposed for e-commerce and retail industries. Unbounded stream data may generate constant outcomes which are needed to be updated based on time factor. Hierarchical High Average Utility Pattern Mining (HAUPM) used to perform operations on unbounded stream information on the database. Upon which state-of-the-art algorithm is performed on information which has a higher impact than more recent information. These data sets give profitable outcomes for retail industry based by making customers buy products which are trending in the market. H-HAUPM is been choose over other techniques is to obtain items that have a high impact based on accuracy in generating itemsets, not consuming more space for usage, scalability and maintaining consistency.
Honeypot Contract Risk Warning on Ethereum Smart Contracts

As Ethereum's smart contracts have boomed, it has become an integral part of the blockchain ecosystem. Unfortunately, some malicious users also find the opportunity to use fraudulent means to profit. A new reported approach is to lure new users or other attackers into the contract in an attempt to make a profit by exposing seemingly obvious flaws in the contract. But in fact, the contract contains a hidden trap that ultimately benefits the creator of the contract. Such contracts are known as honeypot contracts in the blockchain ecosystem. Previous studies proposed two methods to identify such smart contracts by using symbolic execution and contract behaviors. However, these methods either make it difficult to discover new categories or fail to warn users before they lose money. To solve this problem, we propose a machine learning model to detect honeypot contracts based on N-gram features and LightGBM. Extensive experiments show that our proposed model performs well in different conditions.