Reviewerly/exHarmony huggingface dataset:
Optimisation of the Execution Time Using Hadoop-Based Parallel Machine Learning on Computing Clusters

Due to its growing popularity, the discipline of machine learning has been able to take advantage of the large amount of data that is now available. Whilst data set sizes increase, so does algorithm execution time. Data and processing capacity may be distributed across a large number of computer nodes using cluster computing frameworks, allowing algorithms to run in a reasonable length of time even for very huge data sets. On the basis of MapReduce-K-means (MR-KMeans)-based distributed document clustering, MapReduce PSO-K-means (MR-PKMeans), and a hybrid-distributed document clustering method, three alternative approaches to document clustering are proposed in this work (MR-hybrid). Semantically related document clusters with excellent quality and speed are developed after comprehensive examinations. In order to improve clustering quality and speed up the localised clustering solution, the MapReduce-K-means-distributed (MR-K-means) document clustering approach is implemented in the Hadoop framework using an efficient similarity metric.
Credible Internet of Intelligence Toward Future Multidimensional Connection

In the multidimensional future connection, users&#x0027; demands for high data rate, ubiquitous accessibility, and low latency have fueled the continuous advancement of artificial intelligence applications, especially edge intelligence. Meanwhile, the methodology of edge intelligence has been widely researched. However, there are some challenges remaining to be resolved, including the divisible feature of deep networks and the credibility of edge intelligence nodes. In this article, we consider a credible Internet of Intelligence (IoI) framework, with the help of deep networks segmentation and aggregation, as well as blockchain. Specifically, deep networks are separated into several sub-models to be trained by numerous edge trainers. Mean-while, smart contract in the blockchain records and calculates the credibility of each edge trainer. According to the credibility, the trained sub-models would be adopted or abandoned. In order to show the effectiveness of the IoI system, we employ it to solve the problem of future multidimensional connection. Experimental results reveal the superior performance of the proposed scheme.
IoMT Tsukamoto Type-2 Fuzzy Expert System in Medical Diagnostic

The exact monitoring of disease is an extremely tedious job for a medical expert and the technocrats involved in it for technological support for the diagnosis system. To overcome such situations, we developed an Internet of Medical Things (IoMT) based Tsukamoto type-2 fuzzy inference system (TT2FIS) that can easily deal with the diagnosis and prediction aspects of medical field. In the proposed system, we developed a type-2 Tsukamoto fuzzy inference system that involves the symptoms of the patient as input factor and medical equipment as output factors for the outcomes. The aim of this work is to show the utility of type-2 fuzzy sets in the diagnostic system for the Tuberculosis (TB) disease. Numerical computation is also been carried out for the illustration of applicability of the proposed method. The validation of the proposed inference for the propose IoMT model is also given. Results and conclusion of the proposed work are also been discussed in this work.
Comparison of Visual Question Answering Datasets for Improving Their Reading Capabilities

Answering a question related to a given image is called as visual question answering (VQA).The state-of-the-art VQA models require a convolutional neural network (CNN) for image feature extraction and a family of recurrent neural networks (LSTM, GRU etc.,) to extract the question features. Further, attention-based methods are applied on extracted image and question features to focus on salient image regions and important question word. Both the feature vectors are then fused to obtain the fused vector which is fed to the answering module to predict the answer. These models do not take care of the text present in images which can play a crucial role in answer generation and better image understanding. In this paper, a detailed discussion about the VQA datasets such as TextVQA, ST-VQA, and OCR-VQA-200 K which mainly contains text in images is performed. Also, the issues and challenges that may encounter in building such datasets is presented. These datasets will definitely help in building VQA models that have the capability to read the text present in images and generate answers related to the texts.
Embodied VQA

It is a long-standing goal for scientists to develop robots that can perceive, communicate with humans in natural language and complete commands as requested. Several sub-tasks are proposed to achieve this goal in sequential manner, e.g. Vision-and-Language Navigation requires the intelligent agent to follow detailed instructions with visual perception, Remote object localizationRemote object localization gives the agent shorter and more abstract instructions, Embodied QA expects the agent to actively explore the environment and respond to inquiries, Interactive QA hopes the agent actively interact with a virtual environment to get responses of inquiries. In this chapter, we first briefly introduce some mainstream simulators, datasets and evaluation criteria that benchmark applied in this field, such as MatterPort3D, iGibison and Habitat et al. Subsequently, we describe the motivation, methodology and key performance of several methods corresponding to each sub-tasks.
Enhanced Machine Learning Sketches for Network Measurements

Network monitoring and management require accurate statistics of a variety of flow-level metrics such as flow sizes, top- <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> flows, and number of flows. Arguably, the current best technique to measure these metrics is sketches. While a significant amount of work has already been done on sketching techniques, there is still a lot of room for improvement because the accuracy of existing sketches varies with changing characteristics of network traffic. In this paper, we propose the idea of using machine learning to improve the accuracy of sketches, and propose a <i>generic machine learning framework</i> to reduce the dependence of accuracy of sketches on network traffic characteristics. We further present three case studies, where we applied our machine learning framework on sketches for measuring three flow-level network metrics, namely flow sizes, top- <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> flows, and number of flows. We implemented and extensively evaluated this framework for these three metrics using both real-world and synthetic traffic traces. To the best of our knowledge, this is the first work that uses machine learning to reduce the dependence of sketching techniques on the characteristics of network traffic. We have released all our traces and implementation codes at Github.
Tools For Collecting and Analyzing A Corporal of Texts From English Textbooks

English Abstract: The article deals with the creation of a linguistic corpus of the English textbooks. The purpose of this study is to consider the basic principles of building a corpus of ranked texts of English textbooks. The corpus of texts is a computer collection of the texts representing written or oral speech of various functional styles and types. Our linguistic corpus consists of texts selected from school textbooks of Russian publishers included in the federal list. Each text is located inside a separate document with an assigned code, including: language, subject, grade, year of study, information about the authors of the textbook, text number, genre of the text, number of word usage and year of publication of the textbook. To date, the volume of the corpus of English textbooks is 890 texts and 980 400 word usage.Gratitude: The work was carried out at the expense of the Strategic Academic Leadership Program of Kazan (Volga Region).
Machine Learning of a Pair of Charged Electrically Particles Inside a Closed Volume: Electrical Oscillations as Memory and Learning of System

In this paper the problem of two charged particles inside a frustum is faced through the principles of Machine Learning compacted by the criteria of Tom Mitchell. In essence, the relevant equations from the classical electrodynamics are presented. Once the power is derived, then the systematic errors that might be intrinsic are implemented. Thus, these errors drives the evolution of system inside the frustum in both: experience and learning. In the scenario of electrical oscillations because the repulsion forces, the errors would have oscillatory behavior, fact that is favourable to the system in the sense that acquires memory and improves its learning of measurements done in the past.
Foreground-Background Collaboration Network for Salient Object Detection

Saliency object detection has become an active topic in both computer vision and multimedia fields. Though much remarkable progress has been achieved, it is still challenging to predicted saliency maps correctly in complex scenes due to low contrast or huge similarity between salient object and background region. Previous methods only use pixels from foreground objects. As a difference, background pixels are used for salience detection in this research. Specifically, this paper introduce a foreground-background features separation module with the attention mechanism to extract this pair of complementary features. And a spatial feature refinement module was designed to learn the spatial refinement feature. Useing the spatial feature of the low-layer to enhance the foreground and background features, and combine them to generate a final saliency map. Extensive experiments performed on five benchmark datasets demonstrate that the proposed method can achieve satisfactory results on different evaluation metrics compared to other state-of-the-art salient object detection approaches.
Egocentric Human-Object Interaction Detection Exploiting Synthetic Data

We consider the problem of detecting Egocentric Human-Object Interactions (EHOIs) in industrial contexts. Since collecting and labeling large amounts of real images is challenging, we propose a pipeline and a tool to generate photo-realistic synthetic First Person Vision (FPV) images automatically labeled for EHOI detection in a specific industrial scenario. To tackle the problem of EHOI detection, we propose a method that detects the hands, the objects in the scene, and determines which objects are currently involved in an interaction. We compare the performance of our method with a set of state-of-the-art baselines. Results show that using a synthetic dataset improves the performance of an EHOI detection system, especially when few real data are available. To encourage research on this topic, we publicly release the proposed dataset at the following url: https://iplab.dmi.unict.it/EHOI_SYNTH/ .
MPKAlloc: Efficient Heap Meta-data Integrity Through Hardware Memory Protection Keys

Memory corruption exploits continue to plague high profile applications such as web browsers, high performance servers, and mobile devices. Modern defenses for these targets have rendered classic attack vectors that execute shellcode directly on the stack impotent and obsolete. Instead, modern exploits frequently corrupt the data structures found in a program’s memory allocator in order to take control of running processes. These attacks against the heap are much harder to defend against versus classic stack-based buffer overflows because they often rely on an allocator acting on corrupted data in order to take control of a process. In this work, we introduce MPKAlloc, a memory allocator that utilizes memory protection keys (MPKs) found in recent Intel CPUs to effectively isolate heap meta-data from adversaries. We present our prototype implementation of MPKAlloc which hardens the tcmalloc and PartitionAlloc memory allocators used by the popular Chrome web browser. MPKAlloc protects each page containing heap meta-data with a key that provides an allocator exclusive access to the page. Effectively, MPKAlloc thwarts an adversary’s ability to access or corrupt heap meta-data at the hardware level. We embed the MPKAlloc defense in the open-source Chromium web browser, and demonstrate MPKAlloc stopping realistic attack vectors. Furthermore, we evaluate the performance overhead of Chromium configured with MPKAlloc on the top 50 web sites contained in the Alexa site ranking. Our evaluation shows that MPKAlloc introduces a geometric mean of 1.71% performance overhead (2.44% on average) when browsing the most popular web sites, in exchange for a significant increase in security against heap meta-data exploitation.
Extraction of Consumer Emotion Using Diary Data on Purchasing Behavior

In recent years, supermarkets and other retail stores have become increasingly competitive in their marketing as consumer behavior has diversified. In Japanese supermarkets, one of the major marketing strategies is event marketing. To measure differentiation among companies in the future, it is necessary to recommend products that are suitable for the needs of individual consumers. One of the methods used to understand consumer needs is Natural Language Processing using customers’ voices or review posts. By using Natural Language Processing, it is possible to estimate emotions that lead to consumers’ needs and decision-making. In this study, we focus on consumers’ emotions and aim to estimate complex emotions using diary data on purchasing behavior about dietary habits. Specifically, we created a new Japanese emotional expression dictionary by adding documents of the Japanese version of Wikipedia to the existing Japanese emotional expression dictionary. We then estimated the emotion of each text from the diary data using the dictionary. Furthermore, we conducted a questionnaire survey using subjects for the estimated emotions and evaluated the accuracy of the results.
Analysis of Technical Factors in Interactive Media Arts, with a Focus on Prix Ars Electronica Award Winners

Interactive media art combines technical elements such as two-way communication, a hallmark of interactive media art, convergence expressed through complex media, and dynamic information transferability, which uses the network structure to propagate information dynamically. The technical elements of the winning works in the Prix Ars Electronica International Art Competitions “Interactive Art+” category were examined and analyzed, and it was identified that fusion with art is diversifying as technology advances. “Architecture of Radio,” by Richard Vijgen, reminds us that our digital lives are surrounded by physical signal systems that are not visible in the real world but in virtual space. Mary Flanagan’s “Help Me Know the Truth” claims that the surveillance system’s definition of a dangerous person reflects people’s unconscious bias. Lauren Lee McCarthy’s work “Someone” acknowledges the presence of innovative home services in a private space through the communication process between participants and visitors and the view that data and real-time filmed images are shared. Interactive media art fields that use these technological elements to converge art and technology are expected to accelerate as computer programming technology, multimedia technology, Internet and information communication technology, and digital content production.
Object Size Prediction from Hand Movement Using a Single RGB Sensor

Human intention prediction is essential, among others, for safe and fluent human-robot collaboration. Prediction of the intention from human movement offers the advantage of a context-free paradigm. Naturally, such robot capabilities require high spatial and temporal resolution. While certain sensing devices, such as the motion capture systems, satisfy these requirements, in many applications visual sensors might be preferable. However, the information regarding human movement from RGB-D cameras is noisy and of lower frequency. In the present work, we study if it is possible to predict human intentions from hand movements observed by a single RGB-D sensor. We collected data from eight participants as they were grasping objects of three different sizes. OpenPose was used to extract the 2D hand and arm joint pixel coordinates that were then processed to extract human movement kinematic information and engineer appropriate features. Traditional machine learning methods were applied to evaluate the usability of our dataset. Overall, the results were significantly above chance level and showed that it is possible to predict the size of the object a human intents to grasp using hand kinematics extracted from a single visual sensor. Finally, it is feasible, under certain conditions, to use this prediction in real-time human-robot collaboration.
Harnessing Distributional Semantics to Build Context-Aware Justifications for Recommender Systems

This paper introduces a methodology to generate review-based natural language justifications supporting personalized suggestions returned by a rec- ommender system. The hallmark of our strategy lies in the fact that natural language justifi- cations are adapted to the different contextual conditions in which the items are consumed. Indeed, our strategy relies on the following intuition: just like the selection of the most suitable item is influenced by the contexts of usage, a justification that supports a recommendation should vary as well. As an example, depending on whether a person is going out with her friends or her family, a justification that supports a restaurant recommendation should include different concepts and aspects. Accordingly, we designed a pipeline based on distributional semantics models to generate a vector space representation of each context. Such a representation, which relies on a term-context matrix, is used to identify the most suitable review excerpts that discuss aspects that are particularly relevant for a certain context. The methodology was validated by means of two user studies, carried out in two different domains (i.e., movies and restaurants). Moreover, we also analyzed whether and how our justifications impact on the perceived transparency of the recommendation process and allow the user to make more informed choices. As shown by the results, our intuitions were supported by the user studies.
Reference Selection for Offline Hybrid Siamese Signature Verification Systems

This paper presents an off-line handwritten signature verification system based on the Siamese network, where a hybrid architecture is used. The Residual neural Network (ResNet) is used to realize a powerful feature extraction model such that Writer Independent (WI) features can be effectively learned. A single-layer Siamese Neural Network (NN) is used to realize a Writer Dependent (WD) classifier such that the storage space can be minimized. For the purpose of reducing the impact of the high intraclass variability of the signature and ensuring that the Siamese network can learn more effectively, we propose a method of selecting a reference signature as one of the inputs for the Siamese network. To take full advantage of the reference signature, we modify the conventional contrastive loss function to enhance the accuracy. By using the proposed techniques, the accuracy of the system can be increased by 5.9%. Based on the GPDS signature dataset, the proposed system is able to achieve an accuracy of 94.61% which is better than the accuracy achieved by the current state-of-the-art work.
Ssd Optimization Model Based on Shallow Feature Fusion

Object detection has been an important research branch in the field of computer vision. The single-shot-detection (SSD) is an object detection model based on deep learning, which can achieve a good balance between the detection accuracy and the detection speed, but has the problem of poor recognition accuracy for small objects. To address this limitation , this paper improves the structure of the SSD feature pyramid and upsamples the shallow feature map with small object information and fuses it with the upper feature map, thus enhancing the ability of the shallow feature map to represent detailed information. In this way, not only the overall detection accuracy of the SSD is improved, but also a relatively high detection speed is maintained. The proposed model is verified by experiments on two common datasets, the Pascal VOC and MS COCO datasets. On the PASCAL VOC07+12, MS COCO14, and VOC07+12 + COCO datasets, the improved model achieves the mean average precision values of 80.1% (+3.3% compared with the conventional model), 49.9% (+6.8%), and 82.1% (+3.0%), respectively. Meanwhile, the proposed model can achieve the detection speed of 42.2 frames per second.
Increasing Importance of Analog Data Processing

This paper points out that although DX (Digital Transformation) is getting wide attention these days and it is very good from the standpoint of making the most of the current bit computer, we should remember that most data the Natural World is composed of are analog. And analog data are increasing their importance quickly, because materials are getting softer and softer with the remarkable progress of material engineering. We need need to directly interact with the objects to find out what they are and how we should handle them. And although our traditional engineering approach was Euclidean Space based, with orthonormality and interval-based distance with units as its requirements, the Real World is getting more and more complicated and complex, and the data come to contain a wide variety of information. So, the Euclidean space approach cannot be applied anymore and we need to develop Non-Euclidean Space approach. To cope with this situation, Mahalanobis Distance, which is an ordinal approach combined with pattern is proposed. The basic idea of this approach is to help our Instinct with performance indicator to provide it with the whole picture of the situation and to enable it to make the appropriate decisions on what actions we should take. As this approach is Instinct-based, it can process data with extremely large number of dimensions.
Assessing Cloud Computing Security Threats in Malaysian Organization Using Fuzzy Delphi Method

The growth of cloud computing become one of the fastest technologies adopted in the industry. Nevertheless, cloud computing security issues are still debated and organizations are struggling in safeguarding the security of cloud computing. Moreover, challenges in identifying and evaluating risks in cloud computing are add-on factors. In this study, we identified threats in cloud computing using judgment-based assessment. Twenty experts were selected to assess the likelihood of threats. The fuzzy Delphi method was used to analyze the assessment results and the list of threats was ranked and discussed in the results. The findings show that security risks are highlighted as the top-ranked compared to organizational or non-cloud-related threats.
T01067* Series Fuel Pump Pulp Molded Package Dynamic Drop Simulation

In this paper, combined with the actual situation encountered in the process of product transportation, the finite element analysis software ANSYS/LS-DYNA was used to simulate the dynamic drop process of the buffer packaging structure of T01067* series fuel pump, and the simulation results were analyzed, and a conclusion was drawn. According to the fuel pump weight calculation buffer material thickness, according to the product size and structure design of the pulp molded cushion structure, simulation of static cushioning performance, and dynamic drop simulation, for the subsequent structural optimization cost reduction to provide early warning [,]. Check the simulation production cost, reduce many times a large number of experiments and time, structure is ideal type prediction, find out the ideal optimization total, physical test, further optimization and improvement [].