Reviewerly/exHarmony huggingface dataset:
A Framework for Monitoring Patient’s Vital Signs with Internet of Things and Blockchain Technology

In this prevailing situation of automation, private data related to the public needs to be stored for all businesses and transactions. Most often, the private data of the patient’s name, mobile number, diseases, laboratory report, and treatment undergoing is breached by intruders. Automation in the healthcare sector can be made in a highly secured and less complicated manner by integrating patient’s health records and health insurance agencies by adopting blockchain technology. In this article, a real-time patient health monitoring system has been built and implemented on an IoT platform which can allow healthcare agencies like hospitals and doctors to monitor critical data of the patient in real-time and respond to the needs of the patient accordingly. The proposed system will prove to be beneficial for patients as well as the doctors and nurses and will lead to the implementation of an intelligent system which can automate various overviews being conducted by healthcare agencies adopting blockchain technology.
Contrastive counterfactual visual explanations with overdetermination

Abstract A novel explainable AI method called CLEAR Image is introduced in this paper. CLEAR Image is based on the view that a satisfactory explanation should be contrastive, counterfactual and measurable. CLEAR Image seeks to explain an image’s classification probability by contrasting the image with a representative contrast image, such as an auto-generated image obtained via adversarial learning. This produces a salient segmentation and a way of using image perturbations to calculate each segment’s importance. CLEAR Image then uses regression to determine a causal equation describing a classifier’s local input–output behaviour. Counterfactuals are also identified that are supported by the causal equation. Finally, CLEAR Image measures the fidelity of its explanation against the classifier. CLEAR Image was successfully applied to a medical imaging case study where it outperformed methods such as Grad-CAM and LIME by an average of 27% using a novel pointing game metric. CLEAR Image also identifies cases of causal overdetermination, where there are multiple segments in an image that are sufficient individually to cause the classification probability to be close to one.
Curriculum, caring for the Earth, and planetary responsibility

This text is a simultaneously personal and political commentary on those who inhabit the border between worlds, such as those now at war in a viral assemblage. Starting from a general intention of shifting curricular responses away from instrumental and technical solutions toward cultivating the ability to act and think in times of uncertainty, the argument developed here is that the need to respond to the Covid-19 crisis involves repositioning curriculum and responsibility as caring for the Earth. The article creates a dialogue between cosmoecological alliances of different onto-epistemological practices and formulations that expand the ethics of care for other-than-humans. The central issue is to defend reimagining the relationship between curriculum and subjectivity within interdependent stories on the planet. We do this in order to develop a sort of vaccine to prevent curricular imagination from becoming captive to the geometric coordinates of the economization of life.
DEFAULT: Cipher-Level Resistance Against Differential Fault Attack

Differential Fault Analysis (DFA) is a well-known cryptanalytic technique that exploits faulty outputs of an encryption device. Despite its popularity and similarity with the classical Differential Attack (DA), a thorough analysis explaining DFA from a designer’s point of view is missing in the literature. To the best of our knowledge, no DFA immune cipher at an algorithmic level is proposed. Further, all known DFA countermeasures somehow depend on the device/protocol or on the implementation such as duplicate and compare. As all of these are outside the scope of the cipher designer, we focus on designing a cipher which can protect from DFA on its own. We present the first concept of cipher-level DFA resistance which does not rely on any device/protocol-related assumption, nor does it depend on any form of duplication. Our construction is simple, software/hardware-friendly, and DFA security scales up with the state size. It can be plugged before and/or after (almost) any symmetric key cipher and will ensure a non-trivial search complexity against DFA. One key component in our DFA protection layer is SBoxes with linear structures. Such SBoxes have never been used in cipher design as they generally perform poorly against differential attacks. We argue that they in fact represent an interesting trade-off between good cryptographic properties and DFA resistance. As a proof of concept, we construct a DFA protecting layer, named DEFAULT-LAYER, as well as a full-fledged block cipher, DEFAULT. Our solutions compare favourably to the state of the art, offering advantages over the sophisticated duplication-based solutions like CRAFT or infective countermeasures.
A Recommendation Specific Human Activity Recognition Dataset with Mobile Device’s Sensor Data

Human activity recognition is a challenging field that grabbed considerable research attention in the last decade. Two types of models can be used for such predictions, those which use visual data and those which use data from inertial sensors. To improve the classification algorithms in the sensor category, a new dataset has been created, targeting more realistic activities, during which the user may be more prompt to receive and act upon a recommendation. Contrary to previous similar datasets, which were collected with the device in the user's pockets or strapped to their waist, the introduced dataset presents activities during which the user is looking on the screen, and thus most likely interacts with the device. The dataset from an initial sample of 31 participants was gathered using a mobile application that prompted users to do 10 different activities following specific guidelines. Finally, towards evaluating the resulting data, a brief classification benchmarking was performed with two other datasets (i.e., WISDM and Actitracker datasets) by employing a Convolutional Neural Network model. The results acquired demonstrate a promising performance of the model tested, as well as a high quality of the dataset created, which is available online on Zenodo.
Classification of credit card holders based on random forest algorithm

Random forest algorithm is one of good means in data mining and information processing. It has been used in many applications such as resource harness, pattern recognition, and so on. In this paper, we investigate the information of credit card users, and try to classify the features of user data based on random forest in terms of decision tree. Experimental results have confirmed the system.
How big data analytics is changing the face of precision medicine in women’s health

A crucial factor in Big Data is to take advantage of available data and use that for discovery, hypothesis generation, and testing the hypothesis. When it comes to chronic diseases, worldwide, women continue to have poorer outcomes than men. The causes of these discrepancies have yet to be fully elucidated. Personalized medicine has transformed the way we identify and manage health-related issues. The big difference between traditional assessment of a situation and personalized medicine is the reliance of the multidimensional data (e.g., demographic, genetics) to make decisions about a treatment for individual patients. Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve health care quality in general. Deep learning models could be used in a wide variety of predictive problems and settings in health care. In this chapter, we describe the role of big data analytics, machine learning, deep learning, and personalized medicine to improve women’s health. Then we describe a summary of use cases from our research group at UCSF School of Medicine and Mayo Clinic Healthcare Delivery and Informatics that shows big-scale advanced data analytics to improve women’s health in dermatology, cardiology, and breast cancer.
Large-scale combinatorial optimization in real-time systems by FPGA-based accelerators for simulated bifurcation

Combinatorial optimization problems are economically valuable but computationally hard to solve. Many practical combinatorial optimizations can be converted to the ground-state search problems of Ising spin models. Simulated bifurcation (SB) is a quantum-inspired algorithm to solve these Ising problems. One of the remarkable features of SB is the high-degree parallelism, providing an opportunity for quickly solving those problems by massively parallel processing. In this article, we review our recent works on the design and implementation of high-performance FPGA-based accelerators for SB and their applications toward innovative real-time systems that make optimal responses to ever-changing situations. An example of such an application is an ultrafast financial transaction machine that detects the most profitable cross-currency arbitrage opportunities at microsecond speeds. Also, we show a scale-out architecture for SB-based Ising machines with all-to-all spin-spin couplings that allows continued scaling of both machine size and computational throughput by connecting multiple chips, rather than scaling up a single chip.
Smartphone Audio Replay Attacks Dataset

Smartphone based biometric applications are increasing exponentially in recent years. The challenges due to presentation attacks in biometrics have emerged to cause a potential vulnerability, limiting the reliability of biometrics for secure applications. In speaker recognition, audio replay attacks have demonstrated a severe threat to automatic speaker verification (ASV) systems. Alongside, the difference in language for enrolment and testing has displayed some impact on speaker recognition. In this direction, we have created a novel audio replay attack dataset for four different languages using smartphones as playback and recording devices. We have collected data in two different scenarios where the attack recording sensor and bona fide sensor are the same and different. The captured dataset is used for testing the vulnerability on both state-of-the-art speaker recognition method and commercial-off-the-shelf (COTS) method from VeriSpeak. The baseline presentation attack detection methods are benchmarked on replay attacks in a cross-smartphone scenario. The results show that the replay attacks indicate a severe threat towards the ASV methods, especially in the cross-smartphone scenario.
Analyzing the Soft Error Reliability of Convolutional Neural Networks on Graphics Processing Unit

Abstract There has been extensive use of Convolutional Neural Networks (CNNs) in safety-critical applications. Presently, GPUs are the most prominent and dominated DNN accelerators to increase the execution speed of CNN models to improve their performance as well as the Latency. However, GPUs are prone to soft errors. These errors can impact the behaviors of the GPU dramatically. Thus, the generated fault may corrupt data values or logic operations and cause errors, such as Silent Data Corruption (SDC). unfortunately, soft errors propagate from the physical level (GPUs) to the application level (CNN model). This paper analyzes the reliability of the AlexNet model to identify which part of the model more vulnerable to the soft error. To achieve this, we injected the AlexNet run on top of NVIDIA’s GPU, using the SASSIFI fault injector as the major evaluator tool. The experiments demonstrate a high reduction from 9.3 % to 0.00% SDCs errors in STORE and 5.0 % to 0.00% SDCs errors in GPR in Im2col. While Add_bias kernel instructions STORE and GPR the errors reduced from 0.8 % to 0.00% and 1.2 % to 0.1% SDCs error respectively.
P‐28: Dynamic Information Fusion System with Anti‐Diffraction Backplane Design for Mobile Application

In this research, we have developed dynamic information fusion system with an anti‐diffraction backplane design and applicate in the mobile field. On the backplane design, the results indicate the panel with intensity of the diffracted light about 0.2% compared with traditional pixel design by anti‐diffraction design. The transparent micro LED display with anti‐diffraction pixel design also shows clearer background image than traditional transparent display. The dynamic information fusion system integral three main technologies: gaze tracking, object identification, and display coordinate calculation for virtual image mapping. By integrating those technologies to achieve an AR‐based interactive system on anti‐diffraction direct view transparent display. Such a system can provide passengers with clear interactive information of virtual and real integration in the mobile field.
Research on Xi Jinping’s Important Exposition on Information Technology Education

Since the 18th National Congress of the Communist Party of China, Xi Jinping has made important instructions and discussions on information technology education on many occasions, and formed a systematic thoughts on information technology education. Xi Jinping’s exposition on information technology education includes information technology education must adhere to marxism as a guide, pay more attention to the education of children and adolescents, adhere to the fundamental direction of khalid ents, enhance the level of teachers’ modern education and deepening the reform of modern information technology education, quicken the development of vocational education, create a good classroom teaching with modern information technology, uphold the Party’s overall leadership over information technology and education, create a new landscape of cyber security, and build a community of shared future in cyberspace. This paper adopts the method of literature research, through studing a series of important expositions of Xi Jinping on information technology education, it can not only provide important reference for the reform and innovation of information technology education, but also provide important guidance for the correct direction of information technology education.
Estimating Context Aware Human-Object Interaction Using Deep Learning-Based Object Recognition Architectures

In this work, we propose an architecture for predicting plausible person-object interactions based on image visible objects and room recognition. First, the system detects objects in the video using a popular framework named “YOLO” (You Only Look Once) and associates each object with their possible interactions. Then, making use of a convolutional neural network, our algorithm recognizes which is the room that appears in the image and filters possible context aware human-object interactions. The main purpose of this project is helping people with memory failures to perform daily activities. Many people have problems carrying out actions that can be natural for the rest. With the aim to assist them, we are interested in the development of methods which allow remembering them the actions they may have forgotten.
Training Domain-invariant Object Detector Faster with Feature Replay and Slow Learner

In deep learning-based object detection on remote sensing domain, nuisance factors, which affect observed variables while not affecting predictor variables, often matters because they cause domain changes. Previously, nuisance disentangled feature transformation (NDFT) was proposed to build domain-invariant feature extractor with with knowledge of nuisance factors. However, NDFT requires enormous time in a training phase, so it has been impractical. In this paper, we introduce our proposed method, A-NDFT, which is an improvement to NDFT. A-NDFT utilizes two acceleration techniques, feature replay and slow learner. Consequently, on a large-scale UAVDT benchmark, it is shown that our framework can reduce the training time of NDFT from 31 hours to 3 hours while still maintaining the performance. The code will be made publicly available online <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Nonnegative Matrix Factorization based Heterogeneous Graph Embedding Method for Trigger-Action Programming in IoT

Nowadays, users can personalize Internet of Things (IoT) devices/web services via trigger-action programming (TAP). As the number of connected entities grows, the relations of triggers and actions become progressively complex (i.e., the heterogeneity of TAP), which becomes a challenge for existing models to completely preserve the heterogeneous data and semantic information in trigger and action. To address this issue, in this article, we propose IoT nonnegative matrix factorization (IoT-NMF), a NMF-based heterogeneous graph embedding method for TAP. Prior to using IoT-NMF, we map triggers and actions to an IoT heterogeneous information network, from which we can extract three structures that preserve heterogeneous relations in triggers and actions. IoT-NMF can factorize the structures simultaneously for getting low-dimensional representation vectors of the triggers and actions, which can be further utilized in Artificial Intelligence of Things applications (e.g., TAP rule recommendation). Finally, we demonstrate the proposed approach using an if this then that (IFTTT) dataset. The result shows that IoT-NMF outperforms the state-of-the-art approaches.
Analysing GoLang Projects’ Architecture Using Code Metrics and Code Smell

GoLang or GO is a statically typed and system-level programming language created by Google.Sarker, Maloy Kanti It is used for programming across large-scale network servers and big distributed systems.Jubaer, Abdullah Al The measurement of the GO projects is rarely addressed in the software engineering research domain, which is needed to assess external attributes such as quality and maintainability.Shohrawardi, Md. Shihab This paper measured two sets of widely used software metrics named Metrics For Object-Oriented Design (MOOD) and metrics proposed by Chidamber and Kemerer (CK metrics) on 5 open-sources GO projects with a range of KLOC (kilo of lines of code) [1] 7 to 601.Das, Tulshi Chandra We measured 5 MOOD and CK metrics accordingly to generate numerical results for visualizing the internal analysis.Siddik, Md Saeed We also demonstrated the presence of two code smells named God struct and Feature envy for GO language. Finally, we have found that the majority of GO projects comprise both of these code smells with a maximum of 27 feature envy have been found which need to be refactored.
The Blockchain Art Simulation (BARTS) and Experiential Exercises

This paper introduces the online Blockchain ART Simulation (BARTS) and a set of experiential online exercises that illustrate hashing concepts for validating blockchain transactions. Our premise is that understanding the role of secure hashing algorithms in the mining process is the primary conceptual impediment for students to gain an accurate picture of the validation process for blockchain transactions. The BARTS simulation helps develop the conceptual foundation for understanding hashing concepts, and it also leads to understanding blockchain-related concepts.
Plants Don't Walk on the Street: Common-Sense Reasoning for Reliable Semantic Segmentation

Data-driven sensor interpretation in autonomous driving can lead to highly implausible predictions as can most of the time be verified with common-sense knowledge. However, learning common knowledge only from data is hard and approaches for knowledge integration are an active research area. We propose to use a partly human-designed, partly learned set of rules to describe relations between objects of a traffic scene on a high level of abstraction. In doing so, we improve and robustify existing deep neural networks consuming low-level sensor information. We present an initial study adapting the well-established Probabilistic Soft Logic (PSL) framework to validate and improve on the problem of semantic segmentation. We describe in detail how we integrate common knowledge into the segmentation pipeline using PSL and verify our approach in a set of experiments demonstrating the increase in robustness against several severe image distortions applied to the A2D2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> autonomous driving data set.
FDO-ABE: A Fully Decentralized Lightweight Access Control Architecture for Mobile Edge Computing

Attribute-based encryption (ABE) is an emergent cryptographic primitive introduced in recent years. Due to its fine-grained access policy, ABE has been widely used in cloud security to build an attribute-based access control architecture. As a promising edge technology, Mobile Edge Computing (MEC), introduced for the Internet of Mobile Things (IoMT), provides the high quality services for the end-users with mobile IoT devices. In MEC, the data is stored and processed isolately and independently in fog nodes or cloudlets located at the edge of the Internet in the vicinity of the mobile users. But in most existing ABE schemes, researchers only consider there is a single data storage in the system. So this makes the existing ABE scheme not applicable for the MEC with distributed micro data storages. To solve this prominent challenge in traditional ABE, in this paper, we propose a fully decentralized outsourced ABE scheme (FDO-ABE) acted as the access control architecture for the MEC. Our FDO-ABE scheme, based on the non-interactive multi-authority ABE (NI-MA-ABE) scheme, not only supports the multi attribute authorities (AAs) in generating the multi attribute-based private keys for user, but also provides the independent and isolated ciphertext storage for the distributed cloudlets or fog nodes. What's more, to offload the computational intensive tasks from mobile devices, we design an efficient outsourced decryption (oDec) algorithm for our FDO-ABE scheme. By using our oDec algorithm, the complex operations in decryption phase can be transmitted from mobile IoT devices to the cloudlets. Summing up, our FDO-ABE scheme is more suitable as an access control model for MEC.
Block-Attentive Subpixel Prediction Networks for Computationally Efficient Image Restoration

Image restoration based on the Deep Convolutional Neural Network (CNN) based image restoration has demonstrated promising results in many sub-tasks, such as image super-resolution, compression artifacts removal, image denoising, and image enhancement. Compared to many CNN-based high-level vision tasks that predict sparse probabilities of each class, the CNN for image restoration requires dense pixel-level predictions with precise intensity-level values. Therefore, a minimum number of spatial pooling (or down-sampling) operations is required to maintain the image details. Therefore, designing a fast or lightweight model for image restoration is a difficult problem and is even more critical when the spatial resolution of an image becomes larger. In this paper, we propose a family of networks called Subpixel Prediction Networks (SPNs) that predict reshaped and spatially down-sampled block-wise tensors instead of raw images with full resolution. Under this scheme, spatial downsampling decreases the restoration performance less while making the network faster. We propose a novel Subpixel Block Attention (SBA) module that re-calibrates blockwise features to diminish blockwise discontinuity to increase the performance further. The experimental results reveal that these networks demonstrate good trade-offs between speed (number of computations) and restoration performance in the three image restoration tasks: image compression artifacts removal, color image denoising, and image enhancement.