Reviewerly/exHarmony huggingface dataset:
Predicted Robustness as QoS for Deep Neural Network Models

The adoption of deep neural network (DNN) model as the integral part of real-world software systems necessitates explicit consideration of their quality-of-service (QoS). It is well-known that DNN models are prone to adversarial attacks, and thus it is vitally important to be aware of how robust a model’s prediction is for a given input instance. A fragile prediction, even with high confidence, is not trustworthy in light of the possibility of adversarial attacks. We propose that DNN models should produce a robustness value as an additional QoS indicator, along with the confidence value, for each prediction they make. Existing approaches for robustness computation are based on adversarial searching, which are usually too expensive to be excised in real time. In this paper, we propose to predict, rather than to compute, the robustness measure for each input instance. Specifically, our approach inspects the output of the neurons of the target model and trains another DNN model to predict the robustness. We focus on convolutional neural network (CNN) models in the current research. Experiments show that our approach is accurate, with only 10%–34% additional errors compared with the offline heavy-weight robustness analysis. It also significantly outperforms some alternative methods. We further validate the effectiveness of the approach when it is applied to detect adversarial attacks and out-of-distribution input. Our approach demonstrates a better performance than, or at least is comparable to, the state-of-the-art techniques.
E-Voting on Blockchain using Solidity Language

Electronically voting process without paper and the ballot box, namely electronic voting (e-voting), required some improvement, especially in terms of security elements. This is against the anonymize properties of the e-voting system. Therefore, a blockchain e-voting system was proposed which have a secure and transparency features to deploy during the Student Representative Council (SRC) election in Universiti Tun Hussein Onn Malaysia (UTHM). Blockchain technology ensured all aspects such as transparency, security, and auditability were achieved without sacrificing the privacy of voters. In this study, e-voting using a blockchain system was developed to detect the fraud that occurs during the voting session using Solidity language. A total of 3 cases were analyzed and a questionnaire was done to evaluate the proposed system. Analysis of respondents gave positive perception with a mean value of 4.5, 4.6, and 4.9, for 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> , 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd,</sup> and 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> Case respectively. To sum up, the proposed system can shorter the time for the voting process and diminishes the expense because of no need to print out the ballot.
Embedding Backdoors as the Facial Features

Deep neural network (DNN) based face recognition systems have been widely applied in various identity authentication scenarios. However, recent studies show that the DNN models are vulnerable to backdoor attacks. An attacker can embed backdoors into the neural network by modifying its internal structure or poisoning the training set. In this way, the attacker can login into the system as the victim, while the normal use of the system by legitimate users will not be affected. However, the backdoors used in existing attacks are visually perceptible (black-frame glasses or purple sunglasses), which will arouse humans' suspicions thus lead to the failure of the attacks. In this paper, we propose a novel backdoor attack method, BHF2 (Backdoor Hidden as Facial Features), where the attacker can embed the backdoors as the inherent facial features. The proposed method can greatly enhance the concealment of the injected backdoor, which makes the backdoor attacks more difficult to be discovered. Besides, the BHF2 method can be launched under the black-box conditions, where the attacker is completely unaware of the target face recognition system. The proposed backdoor attack method can be applied in those rigorous identity authentication scenarios where the users are not allowed to wear any accessories. Experimental results show that the BHF2 method can achieve high attack success rate (up to 100%) on the state-of-the-art face recognition model, DeepID1, while the normal working performance of the system has hardly been affected (the recognition accuracy of the system has only dropped by 0.01% at the lowest).
Optical character recognition based on local invariant features

The Optical Character Reader (OCR) process means the transition from scanned manual or written images to a machine-determined document. The American Standard Code for Information Interchange (ASCII) in cognitive processing uses OCR. The challenge is two primary folds: word segmentation by letters and character recognition. Implement a new approach to include the two functions by Scale-Invariant Transforming Feature (SIFT) descriptors. To compare SIFT descriptors (RootSIFT), devise a new procedure, that offers outstanding results without increasing computation or storage requirements. In order to identify English characters, Artificial Bee Colony (ABC) method suggests that the back propagation neural network for classification of character be utilized. Conducted experiments with more than 10 measures intended for every character and tested the accuracy for numerical numbers, chart letters, small letters and alphanumeric characters. The performance analysis of ABC optimized neural network algorithm has achieved a maximum accuracy of 97.3077% compared with precision recall and f-measure.
Research on Named Entity Recognition of Traditional Chinese Medicine Electronic Medical Records

The electronic medical record (EMR) is a patient's individual medical record written by health care providers to describe the medical activities of patients. Named entity recognition (NER) of EMR is helpful to extract important information from a large number of unstructured texts, which lays a foundation for medical data mining and application. The named entity of Traditional Chinese Medicine (TCM) is more complex and its length is uncertain. In order to explore the effective method of named entity recognition of TCM EMR, after comparing the existing entity recognition methods and models, this paper selects three models, BiLSTM-CRF, lattice LSTM-CRF and BERT, to recognize the symptom entities in EMR, and carries out comparative experiments. After the real EMR data was manually labeled, three models were used to train, and the precision, recall and F1 value were used to evaluate the recognition effect of the model. The experimental results show that BERT model has the best recognition effect about TCM EMR, the precision is 89.94%, the recall is 88.27%, and the F1 value is 89.10%.
Highly Area-Efficient Implementation of Modular Multiplication for Elliptic Curve Cryptography

The core operation of public-key cryptosystem e.g. elliptic curve cryptography (ECC) is the modular multiplication. It is the heavy computational block and the most costly cryptographic operation. Area-Efficient hardware architecture of 256-bit modified interleaved modular multiplication (IMM) is represented in this research. The novelty of this work is the device area minimization with keeping computational time as minimum as possible i.e., 2.09 μs for ECC with Koblitz Curve. In this research, IMM is implemented using a fewer number of resources such as 421 slices, 514 FF pairs, 522 registers, 1770 LUTs, and 1463 LUT-FF pairs. This hardware implementation provides a maximum frequency of 122.883 MHz and area-time (AT) product 0.879 and throughput rate 122.49 Mbps on Virtex-7 FPGA technology which is better than the other related recent works. The proposed design saves approximately 61.75 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> to 93.16% slice LUTs, 95.76% to 133.69% LUT-FF pairs, and 103.8% to 168.65% occupied slices on the Virtex-7 FPGA for the 256-bit prime field. This proposed hardware implementation design also keeps less AT product which is the most crucial parameter for ECC operation. To our best knowledge, this design provides better performance than the recently available designs for IMM for ECC operation.
Combating the Spread of Coronavirus by Modeling Fomites with Depth Cameras

Coronavirus is thought to spread through close contact from person to person. While it is believed that the primary means of spread is by inhaling respiratory droplets or aersols, it may also be spread by touching inanimate objects such as doorknobs and handrails that have the virus on it ("fomites''). The Centers for Disease Control and Prevention (CDC) therefore recommends individuals maintain "social distance'' of more than six feet between one another. It further notes that an individual may be infected by touching a fomite and then touching their own mouth, nose or possibly their eyes. We propose the use of computer vision techniques to combat the spread of coronavirus by sounding an audible alarm when an individual touches their own face, or when multiple individuals come within six feet of one another or shake hands. We further propose using depth cameras to track where people touch parts of their physical environment throughout the day, and a simple model of disease spread among potential fomites. Projection mapping techniques can be used to display likely fomites in realtime, while headworn augmented reality systems can be used by custodial staff to perform more effective cleaning of surfaces. Such techniques may find application in particularly vulnerable settings such as schools, long-term care facilities and physician offices.
WITHDRAWN: Blockchain based data trading ecosystem

Data, a key asset in our data-driven economy, has fuelled the emergence of a new data trading industry. However, there is variety of limitations in conventional data trading platforms thanks to the existence of dishonest buyer/data broker. To mitigate these limitations, the importance posit of a data processing–as–a-service model, which complements Conventional data hosting/exchange-as-a-service model. Specifically, a secure data trading ecosystem has introduced and present a new blockchain based data trading ecosystem (hereafter referred to as SDTE). In the ecosystem, both data broker and buyer aren't ready to obtain access to the seller’s data, as they're only getting access to the analysis findings that they require. In other words reducing the challenge to secure the data processing. Also build a security model to research the info trading market, and describe a replacement set of trading protocols for the whole data trading market. To demonstrate utility, the implementation of proposed secure data trading platform (SDTP) on Ethereum & Intel Software Guard Extensions (SGX) and perform an in-depth analysis.
Multi-stage Tag Guidance Network in Video Caption

Recently, video caption plays an important role in computer vision tasks. We participate in Pre-training for Video Captioning Challenge which aims to produce at least one sentence for each challenge video based on the pretraining models. In this work, we propose a tag guidance module to learn a representation which can better build the interaction in cross-modal between visual content and textual sentences. First, we utilize three types of features extraction networks to fully capture the information of 2D, 3D and object information. Second, to prevent overfitting and time issues, the entire process of training is divided into two stages. The first stage trains all data, and the second stage introduces a random dropout. Furthermore, we train a CNN-based network to pick out the best candidate results. In summary, we were ranked third place in Pre-training for Video Captioning Challenge which proved the effectiveness of our model.
Specializing the network for scatter-gather workloads

Data processing and distributed querying workloads often involve a "scatter-gather" or "partition-aggregate" architectural pattern, whereby one application queries hundreds or even thousands of workers. Network communication is often a bottleneck in this pattern, especially when the compute task at each worker is small, such as for Web queries and interactive analytics. The network bottleneck can result in low throughput, high CPU utilization, and cause job completion time to increase by orders of magnitude. To overcome these inefficiencies, we explore hardware-offload of the scatter-gather primitive, whereby a smart NIC takes on the responsibility of sending out queries and collecting responses. We show that this approach not only virtually eliminates CPU usage, but with suitable scheduling of responses, it also speeds up scatter by allowing parallel queries, and gather by preventing throughput collapse due to excessive congestion. Besides response scheduling, we use a careful design at the NIC to limit FPGA resource usage: our approach uses about 25% of on-chip logic and 33% of on-chip memory on a mid-sized FPGA, leaving enough room for implementing other functions on the smart NIC.
Impacts of Multiple Solutions on the Lackadaisical Quantum Walk Search Algorithm

The lackadaisical quantum walk is a graph search algorithm for 2D grids whose vertices have a self-loop of weight l. Since the technique depends considerably on this l, research efforts have been estimating the optimal value for different scenarios, including 2D grids with multiple solutions. However, specifically two previous works have used different stopping conditions for the simulations. Here, firstly, we show that these stopping conditions are not interchangeable. After doing such a pending investigation to define the stopping condition properly, we analyze the impacts of multiple solutions on the final results achieved by the technique, which is the main contribution of this work. In doing so, we demonstrate that the success probability is inversely proportional to the density of vertices marked as solutions and directly proportional to the relative distance between solutions. These relations presented here are guaranteed only for high values of the input parameters because, from different points of view, we show that a disturbed transition range exists in the small values.
Vehicle Detection in UAV Aerial Images Based on Improved YOLOv3

Vehicle detection in UAV aerial images with complex scenes is a challenging task in intelligent transportation systems, as the sizes of vehicles in the images change with the flight height of UAV. When the UAV is far from the ground, the vehicle object become a small object, which makes it difficult to be detected. This paper presents an improved YOLOv3 model with deeper feature extraction network and four different scale detection layers to detect vehicles in aerial images accurately and robustly. When the high-resolution image of UAV aerial is zoomed to 608×608 as input, the detection speed of improved YOLOv3 is equivalent to original YOLOv3, and the recall rate and AP are significantly increased by 9%, 11% respectively, while the detection precision reaches 97.09%.
A De-raining semantic segmentation network for real-time foreground segmentation

Few researches have been proposed specifically for real-time semantic segmentation in rainy environments. However, the demand in this area is huge and it is challenging for lightweight networks. Therefore, this paper proposes a lightweight network which is specially designed for the foreground segmentation in rainy environments, named De-raining Semantic Segmentation Network (DRSNet). By analyzing the characteristics of raindrops, the MultiScaleSE Block is targetedly designed to encode the input image, it uses multi-scale dilated convolutions to increase the receptive field, and SE attention mechanism to learn the weights of each channels. To combine semantic information between different encoder and decoder layers, it is proposed to use Asymmetric Skip, that is, the higher semantic layer of encoder employs bilinear interpolation and the output passes through pointwise convolution, then added element-wise to the lower semantic layer of the decoder. According to the control experiments, the performances of MultiScaleSE Block and Asymmetric Skip compared with SEResNet18 and Symmetric Skip respectively are improved to a certain degree on the Foreground Accuracy index. The parameters and the floating point of operations (FLOPs) of DRSNet are only 0.54M and 0.20GFLOPs separately. The state-of-the-art results and real-time performances are achieved on both the UESTC all-day Scenery add rain (UAS-add-rain) and the Baidu People Segmentation add rain (BPS-add-rain) benchmarks with the input sizes of 192*128, 384*256 and 768*512. The speed of DRSNet exceeds all the networks within 1GFLOPs, and Foreground Accuracy index is also the best among the similar magnitude networks on both benchmarks.
Intellectual, Emotional, and Spiritual Approaches to the Fitzmaurice Voicework Focus Line

As Catherine Fitzmaurice describes it, "The 'focus line' (as a mental image only) […] extends from the dynamic action at the abdomen down and around the pelvis and up the spine into the head and out of the 'third eye,' so the attention is not on oneself, nor on the vocal tract, but on the point(s) of communication." This article expands upon Fitzmaurice's definition by providing practical approaches to the focus line in a creative context using three approaches: intellectual, emotional, and spiritual. The authors aim to demonstrate how the use of the focus line allows for truthful communication, compassionate connection, and cultural understanding in dynamic performance. In the intellectual approach, participants integrated well-established communication theory into an embodied experience that mirrored key aspects of the focus line. In the emotional approach, participants explored how text is affected by the flow of someone else's focus line. In the spiritual approach, workshop participants explored—through storytelling and devising techniques—how the focus line gets us in touch with our ancestral and cultural roots and histories.
Semantic Web Ontology for Vocational Education Self-Evaluation System

Self-evaluation is one of the most important activities undertaken by vocational high schools to improve internal and external conditions related to the performance and preparation of future work programs. In practice, selfevaluation always requires much data relating to the school. One issue that was released was that the data needed was still scattered on separate systems. Various methods are used to integrate manual and computer-aided, but it is very troublesome and requires a long time. One way to overcome this problem is to develop a self-assessment system using semantic web technology, correctly, ontology from related data. This paper aims to develop VISION, a semantic web-based ontology to improve vocational education self-evaluation systems. The method used is system development, which consists of 5 stages, namely 1) analysis, 2) design, 3) development, 4) implementation and 5) evaluation. This paper focuses on analyzing, designing, and developing a prototype of a self-evaluation system for Vocational Schools based on semantic web ontology. The results show that the concept of web semantic technology can improve the effectiveness and efficiency of self-evaluation in vocational schools in Indonesia.
Enabling Efficient Multi-keyword Search Over Fine-Grained Authorized Healthcare Blockchain System

As a new emerging technology, blockchain is attracting the attention from academic and industry and has been widely exploited to build the large-scale data sharing and management systems, such as healthcare database or bank distributed database system. The health records contain a lot of sensitive information, so putting these health records into blockchain can solve the security and privacy issues while uploading them to an untrustworthy network. In a typical health record management system, there are escalating demands for users including the patients and the doctors to execute multi-keyword search over the huge scale of healthcare records. In the meantime, they can authorize some part of their personal treatments to others according to personalized needs of the patients. In literatures, there is not an existing blockchain solution can satisfy these two requirements at the same time. These issues become prominent since it’s more inconvenient to adjust a blockchain-based system to support efficient multi-keyword search and fine-grained authorization comparing to traditional RDBMS. To overcome the two challenges, we propose a novel multi-keyword searching scheme by establishing a set of Bloom Filters within the health record blockchain system to accelerate the searching process on service provider (SP). Moreover, we reduce the overhead of key derivation by proposing a Healthcare Data Key Derivation Tree (HDKDT) stored locally on the user’s side. Putting our proposed scheme on the medical blockchain can speed up the multi-keyword search [, ] processes and reduce the key storage space to certain extent. At the end of this article, we formally prove the security of the proposed scheme and implement a prototype system to evaluate its performance. The experimental results validate our proposed scheme in this paper is a secure and efficient approach for the health record management scenario.
Creating Emotional Attachment with Assistive Wearables

Wearable technology plays a significant role in improving the quality of life for people, especially individuals who deal with the issue of aging and disability. Despite the advantages, there are some barriers that cause these users to reject or abandon their wearable devices. In the design of these devices, the literature suggests that little attention has been given to the psychological and social needs of the users. However, some researchers are exploring various strategies to design inclusive wearables and minimize the stigma associated with assistive wearables. This paper provides a literature review of the current approaches and frameworks related to wearable technology, with a qualitative focus on the discourse about the design of emotionally engaging and meaningful products. It suggests that there is an opportunity in the area of assistive technology to design inclusive wearables that not only alleviate the stigma attached to the devices, but also lead to the creation of long-term product attachment.
Surface-based computation of the Euler characteristic in the cubical grid

For well-composed (manifold) objects in the 3D cubical grid, the Euler characteristic is equal to half of the Euler characteristic of the object boundary, which in turn is equal to the number of boundary vertices minus the number of boundary faces. We extend this formula to arbitrary objects, not necessarily well-composed, by adjusting the count of boundary cells both for vertex- and for face-adjacency. We prove the correctness of our approach by constructing two well-composed polyhedral complexes homotopy equivalent to the given object with the two adjacencies. The proposed formulas for the computation of the Euler characteristic are simple, easy to implement and efficient. Experiments show that our formulas are faster to evaluate than the volume-based ones on realistic inputs, and are faster than the classical surface-based formulas.