Reviewerly/exHarmony huggingface dataset:
Blockchain Technology Diffusion and Adoption: Tunisian Context Exploration

Blockchain is an emerging technology that has the potential to transform rules of exchange and ways to do business; its diffusion is accelerating and growing vertically (adoption rate) and horizontally (many applications and economic sectors). The paper aims to explore the state of Blockchain technology diffusion and adoption according to Rogers’ Theory of Innovation Diffusion. Hence, we want to identify the current stage of adoption decision process, early and potential adopters’ and determinants of Blockchain technology adoption. Given the novelty nature of the phenomenon, an exploratory study was conducted to understand deeply and answer many questions related to the diffusion of Blockchain technology in the Tunisian context. Results provide insights which can be useful not only for future researches but also for managers and decisions makers to facilitate the adoption of Blockchain given its relevance for such developing economy.
C2BERT: Cross-contrast BERT for Chinese Biomedical Sentence Representation

Pre-trained language models (PLMs), such as BERT, have achieved great success on various natural language processing (NLP) tasks. Nevertheless, we observe that PLM-derived native Chinese biomedical sentence representations are somehow collapsed, which means PLMs induce a non-smooth anisotropic semantic space of Chinese biomedical sentences and most sentences are mapped into a small area and therefore produce high similarity. Such PLM-derived native sentence representations poorly capture semantic meaning of Chinese biomedical sentences.To alleviate the aforementioned collapse issue, we then propose a novel contrastive learning framework, named Cross-contrast BERT (C2 BERT), that advances the state-of-the-art Chinese biomedical sentence embeddings. C2 BERT proposes to derive positive/negative samples from two transformer-based different PLMs; this design decision reflects our philosophy that our goal is to conflate the knowledge stored in different PLMs to produce Chinese biomedical sentence embeddings, rather than introducing new noise. Moreover, without costly further pretraining, C2 BERT exploits contrastive learning as an auxiliary training objective during fine-tuning with supervision from biomedical sentence-related tasks. We demonstrate with extensive experiments that our C2 BERT model is more effective than competitive baselines on diverse Chinese biomedical sentence-related tasks.
Non-homogeneous Haze Removal Through a Multiple Attention Module Architecture

This paper presents a novel attention based architecture to remove non-homogeneous haze. The proposed model is focused on obtaining the most representative characteristics of the image, at each learning cycle, by means of adaptive attention modules coupled with a residual learning convolutional network. The latter is based on the Res2Net model. The proposed architecture is trained with just a few set of images. Its performance is evaluated on a public benchmark—images from the non-homogeneous haze NTIRE 2021 challenge—and compared with state of the art approaches reaching the best result.
Regularizing deep networks with label geometry for accurate object localization on small training datasets

Localization is a critical subtask in object detection, which is closely related to spatial information of objects. Most current detectors simply rely on the fitting ability of deep neural networks to regress towards numerical targets such as coordinates of object boxes. Training deep networks for sufficient fitting ability requires a large number of annotations that are expensive to obtain. In this work, we fully exploit limited annotations by extracting label geometry to improve localization performance on small datasets. We generate distance transform of bounding box edges according to localization labels, with which we supervise intermediate outputs of networks pixel by pixel to reconstruct object geometry for localization. Distance transform is sensitive to box edges and provides geometric gradients flowing into boundaries. We learn such gradients to enhance geometric-aware features through a coupled training with regression, and use it to refine regressed boxes in an evolutionary manner in inference. Extensive experiments are implemented to demonstrate the effectiveness of our method. Our method can be applied in applications that required human-machine interaction, such as the driver-assistance system in autonomous driving, by providing accurate detections to assist humans in making better decisions.
Digital Platforms and its Importance in Enhancing the Educational and Media Role of the National Records and Archives Authority in Oman

The Archives' digital platforms constitute a new way to present their services online and interact with users. The National Records and Archives Authority (NRAA) possesses precious collections of archival and historical documents of great importance for documenting the history, political, economic and social life and cultural aspects of Oman during its history. This study explores the reality of the NRAA's use of digital platforms to enhance its efforts to reach the Omani society and raise awareness of documents and their importance in documenting Omani history. The method used in the study is the content analysis to analyze the content of NRAA's digital platforms and identify its role in enhancing the educational role. The study explores also the educational services provided by the National Archives in the United States, the United Kingdom, and France to guide the researcher in the evaluation of educational content on the digital platforms of NRAA. The researcher finds that despite the diversity of digital platforms used by the NRAA, there are no educational programs, courses, or workshops for students or teachers on how to use documents and benefit from them in studying the topics of curriculum, and no examples of lesson plans are provided to help teachers in building lesson plans for curriculum topics.
Long Passphrases: Potentials and Limits

Passphrases offer an alternative to traditional passwords which aim to be stronger and more memorable. However, users tend to choose short passphrases with predictable patterns that may reduce the security they offer. To explore the potential of long passphrases, we formulate a set of passphrase policies and guidelines aimed at supporting their creation and use. Through a 39-day user study we analyze the usability and security of passphrases generated using our policies and guidelines. Our analysis indicates these policies lead to reasonable usability and promising security for some use cases, and that there are some common pitfalls in free-form passphrase creation. Our results suggest that our policies can support the use of long passphrases.
Smart Contract with Machine Learning for Multi-objective Optimization in Manufacturing Quality Control

Blockchain can be used to store data safely while ensuring data transparency in quality control of advanced manufacturing. A smart contract running on blockchain can prevent data from being tampered with, along with specifying data transmission rules efficiently and securely. This paper proposes a smart contract system for manufacturing quality control that encompasses machine learning to solve dynamic multi-objective combinatorial optimization problem in production. The proposed system was experimented in various production scenarios. Experimental results showed that the system can effectively restore data through smart contracts when the data were artificially tampered with. In addition, the machine learning algorithm can improve the efficiency of the productions and achieve the combined optimization of multiple objectives.
Implementation of Digital Forensics Face Sketch Recognition using Fusion Based Deep Learning Convolution Neural Network

To identify a drawing from a picture, first recognize the face. Previous techniques are focused on K variations of K simple pieces of art, both of which were extracted from training outcomes. However, owing to the neighbour selection method, which operates on vast patches, these methods necessitate a substantial number of computational resources. Instead of using widely used data-driven approaches, less well-known personalized image-driven models are used, which will speed up processes while ensuring equal or better results. The ridge regression techniques were used for picture patch preparation. Top-of-the-range estimators initially discover these regressors have. Similarly, photographs with higher frequencies will illuminate photographs with lower frequencies. To adjust for low-passed performance, the broad average was used. Extensive analysis backs up fusion.
TAK-ML: Applying Machine Learning at the Tactical Edge

The “Every Soldier is a Sensor” (ES2) concept employs warfighters' proximity to unfolding events in order to provide better situational awareness and decision-making capabilities. However, today's ES2 practices put the burden of data collection on warfighters themselves, and the burden of interpretation (across potentially many inputs) on commanders. This leads to a situation where data collection is limited by the capacity of the warfighter (who is busy executing their core objectives), and data fusion, interpretation, and analysis are limited by the cognitive constraints of the human commanders and analysts interpreting the potentially massive amounts of data. The TAK-ML framework transitions these burdens to machines, allowing collection, fusion, and learning to operate at machine speed and scale. To accomplish this, TAK-ML takes recent advancements in mobile device capabilities and machine learning techniques and applies them to the Tactical Assault Kit (TAK) ecosystem, e.g., ATAK mobile devices and TAK servers, to facilitate the easy application of ML to real mission sets. This paper describes the TAK-ML framework which supports data collection, model building, and model execution/employment in tactical environments, as well as a set of initial applications of this framework. The framework and applications are described and evaluated, showing the capabilities available, the ease of use of the system, and initial insights into the efficacy of the resulting models and applications.
A Domain Knowledge-Guided Lightweight Approach for Security Bug Reports Prediction

Security bug report (SBR) prediction has been increasingly investigated for eliminating security attack risks of software products. However, there is still much room for improving the performance of automatic SBR prediction. This work is inspired by the work of two recent studies proposed by Peters et al. and Wu et al., which are focused on SBR prediction and both published on the top tier journal TSE (Transactions on Software Engineering). The goal of this work is to improve the effectiveness of supervised machine learning-based SBR prediction with the help of software security domain knowledge. It first extracts software security domain knowledge from CWE (Common Weakness Enumeration) and CVE (Common Vulnerabilities Exposure), which are authoritative sources of software vulnerability. After that, the matrix of bug reports is generated based on the roots of security domain keywords. Large-scale experiments are conducted on a set of trustworthy datasets cleaned by Wu et al. The results show our domain knowledge-guided approach could improve the effectiveness of SBR prediction by 25% in terms of F1-score on average.
Environmental Adaptive Privacy Preserving Contact Tracing System for Respiratory Infectious Diseases

The COVID-19 pandemic has made the scientific community devise means to implement “contact tracing” mechanisms to mitigate the spread of the infection. The crucial idea is to scan and record close contacts between users using mobile devices, in order to notify persons when their close contact(s) is diagnosed positive. Current contact tracing systems’ false-positive rate is too high to be practical as they do not filter Bluetooth scan results outside range of infection. Furthermore current systems neglect airborne transmission other than droplet transmission. Moreover, the ability granted to service providers of the contact tracing systems to access user data violates user privacy. Finally, attackers can modify, remove or fabricate contact records in their devices, which harms the integrity of the system. In this paper, we propose and develop a new contact tracing system which uses environmental factors to filter out results outside estimated effective transmission distance, and also take airborne transmission into consideration. In addition, we implement a rerandomizable signature scheme with blockchain bulletin board to provide confidentiality and integrity. We also evaluate the performance of our theory by implementing our algorithm on mobile devices.
ShallowNet: An Efficient Lightweight Text Detection Network Based on Instance Count-Aware Supervision Information

Efficient models for text detection have a wide range of applications. However, existing real-time text detection methods mainly focus on the inference speed without considering the number of parameters. In this paper, we propose an efficient, lightweight network called ShallowNet with fewer layers, which significantly reduces the number of parameters and improves the inference speed. Fewer layers usually incur small receptive fields that degrade the performance of large-size text detection. To address such an issue, we utilize dilated convolution with various receptive fields that satisfies the need for different sizes of text detection. Nevertheless, the design of dilated convolution ignores the continuity property of text and results in stickiness and fragment in the text detection tasks. To tackle this challenge, we introduce instance count-aware supervision information that guides the network focus on text instances and preserves the boundaries. The instance count-aware supervision is used as the weight of the loss function. Extensive experiments demonstrate that the proposed method achieves a desirable trade-off between the accuracy, size of the model, and inference speed.
xBCBench: A Benchmarking Tool for Analyzing the Performance of Blockchain Systems

Blockchain is a technical solution to jointly maintain a reliable database in a decentralized and trustless way. Since the birth of blockchain technology, the performance of blockchain has always been a key issue that has attracted much attention and discussion. Therefore, a performance testing tool can help evaluate the performance of different blockchain systems and guide further optimization. This paper proposes xBCBench, a benchmarking tool for analyzing the performance of blockchain systems, which can be used to compare the performance of different blockchain systems. xBCBench evaluates the performance of blockchain systems based on the metrics like throughput, latency, and system resource utilization. We also use xBCBench to analyze four mainstream blockchain systems (i.e., Ethereum, Hyperledger Fabric, Hyperledger Sawtooth and FISCO-BCOS) and propose further optimization suggestions.
Implementation of Reconfigurable CNN-LSTM Accelerator Based on FPGA

Recently, Convolutional Neural Networks-Long Short-Term Memory (CNN-LSTM) hybrid networks have made significant achievements in deep learning applications such as picture descriptions and video detection. In this paper, we design a reconfigurable accelerator, which can support CNN and LSTM. The reconfigurable acceleration unit (PE) includes weight buffer BRAM and DSP Group modules, which are shared in both modes, effectively saving overhead on-chip resources. we use Uniqlo's 2012–2016 daily transaction data as a dataset to train a small-scale CNN-LSTM model. A reconfigurable CNN-LSTM accelerator with the peak throughput of 49.4GOP/s and the energy efficiency ratio of 26.7GOP/W has been implemented on the Virtex-7VC707 FPGA. Compared with CPU, the accelerator achieves about 100 times more speed than the CPU. Compared with GPU, the power consumption of FPGA is only about 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> of GPU. Therefore, our accelerator performance is better than other software platforms.
XTuning: Expert Database Tuning System Based on Reinforcement Learning

Database performance optimization has become a hot issue in recent years. Some works deeply reconstruct the database to achieve specified goals like throughput or latency. The others focus on the database’s configuration knobs with reinforcement learning (RL) to improve the performance without any empirical knowledge. But the exhaustive offline training process costs plenty of time and resources due to the large inefficient configuration knobs combinations with trial-and-error methods. The most time-consuming part of the process is not the RL network training, but the database performance evaluation for acquiring the reward values of target performance like throughput or latency. So we propose an expert database tuning system (XTuning) which contains a correlation knowledge model to remove unnecessary training costs and a multi-instance mechanism (MIM) to support fine-grained tuning for diverse workloads. The models define the importance and correlations among these configuration knobs for the user’s specified target. Then we implement the models as Progressive Expert Knowledge Tuning (PEKT) algorithm with an abstracted architectural optimization integrated into XTuning. Experiments show that XTuning can effectively reduce the training time and achieves extra performance promotion compared with the state-of-the-art tuning methods.
Data Security and Vulnerability Prevention for Cloudlet-Based Medical Data Sharing

To create next-generation health services, sharing personal health data is essential. A large number of consumers need to group health data into data sets to recognize preemptive and personalized medicine which can be analyzed for the trends of wellness and diseases. Consequently, through cloud adaptability, a new framework of human services is developed in this paper. The cloudlet segments incorporate security protection, data sharing, and interference area. In this paper, another arrangement of medical services is built by utilizing cloud adaptability. The cloudlet capacities incorporate security of protection, information sharing, and interruption discovery. During data collection, a method for encrypting data from user corpses collected through wearable devices by the Number Theory Research Unit (NTRU) is used. These data are energy-efficiently transmitted to the nearby cloud. Second, a new model of confidence which helps users to select reliable cloud data partners is introduced. The trust model additionally assists patients with imparting their infections. Third, appropriate protection of the medical data of users stored in the hospital remote cloud are divided into three parts. A new collaborative intrusion detection method (IDS) is developed to protect the prosperity system against malice attacks, in light of the lattice for cloud, which can successfully stay away from assaults by the distant medical care huge information cloud.
The Development of a Head-up Display (HUD) App on the Android Mobile Operating System

The head-up display (HUD) is a premium feature found mostly in expensive cars. The method how it works is really simple, some information has to be projected on the windscreen of the car, so the driver won’t have to look down on the gauges, and he can keep his eyes always on the road. A head-up display (HUD) is useful due to the fact that it can project the speed of the car on the windscreen and this way the driver can avoid speeding tickets. Also the projection on the windscreen it’s small and transparent, so it won’t bother the driver, which can watch the road, not the gauges. The projection method is straight forward, due to the fact that almost any object is reflected on a car’s windscreen, and a light up display, of a mobile phone, on low lighting conditions, it’s reflected even more. The goal of this work is to create a head-up display (HUD) using an Android mobile phone. This way this expensive feature for cars can be used in any car, by the use of a mobile phone only. The work presents the development process of an app, on a mobile phone, which can be used to project speed and optionally other informations on the windscreen of a car. The main concern of creating such an app is to figure out the mirroring of the mobile phone’s screen, to see an image which is not rotated, on the windscreen of a car.
Robust Augmentations for Small Object Detection of Aerial Images

Object detection is one of the most fundamental but important computer vision tasks. However, small object detection remains an unsolved challenge due to insufficient detailed appearances and additional noises. Meanwhile, aerial images and intelligent transportation systems are under the restriction of difficulties such as dense object arrangement, a large number of small objects, and different perspectives, compared with natural images. To deal with these problems, an adversarial-like data augmentation training is proposed in this paper to narrow the data gap between remote sensing images and natural ones. The difficulty of the remote sensing object detection is verified and analyzed firstly by the classic single-stage anchor-based detector RetinaNet. Then, the multi-scale and data augmentations are introduced to alleviate the mismatch between general detector training and aerial images based on the anchor-free state-of-the-art (SOTA) model FCOS. Experiments on the remote sensing dataset, NWPU VHR10, demonstrate the quasi-antagonism data augmentation method improves the both anchor-based and anchor-free SOTA detectors for natural images with significant margins and shows the effectiveness on aerial images, especially small objects.
An Implementation of Fake News Prevention by Blockchain and Entropy-based Incentive Mechanism

Fake news is undoubtedly a significant threat to democratic countries nowadays because existing technologies can quickly and massively produce fake videos, articles, or social media messages based on the rapid development of artificial intelligence and deep learning. Therefore, human assistance is critical if current automatic fake new identification technologies desire to improve accuracy. Given this situation, prior research has proposed to add a quorum, a group of appraisers trusted by users to verify the authenticity of the information, to the fake news prevention systems. This paper proposes a stake-based incentive mechanism to diminish the negative effect of malicious behaviors on a quorum-based fake news prevention system. Moreover, we use Hyperledger Fabric, Schnorr signatures, and human appraisers to implement a practical prototype of a quorum-based fake news prevention system. Then we conduct necessary case analyses and experiments to realize how dishonest participants, crash failures, and scale impact our system. The outcomes of the case analyses and experiments show that our mechanisms are feasible and provide an analytical basis for developing fake news prevention systems.
Efficient Permutation Protocol for MPC in the Head

The MPC-in-the-head construction (Ishai et al., STOC’07) gives zero-knowledge proofs from secure multiparty computation (MPC) protocols. This paper presents an efficient MPC protocol for permuting a vector of values, making use of the relaxed communication model that can be handled by the MPC-in-the-head transformation. Our construction allows more efficient ZK proofs for relations expressed in the Random Access Machine (RAM) model. We benchmark our construction and compare it against other reasonable constructions of permutations under the MPC-in-the-head transformation and conclude that it significantly improves on efficiency and the range of applicability.