Reviewerly/exHarmony huggingface dataset:
A Blockchain-Based Secured Land Record System Using Hyperledger Fabric

In Bangladesh, scarcity of land and fast population growth is putting a strain on the land-man ratio. The property ownership registration system in Bangladesh is incomplete and insufficient. As a result, various government agencies handle various documents, and bureaucracy flaws enable organized crime. Considering these, we are proposing a method where we have come up with a Blockchain-based solution. We build a Blockchain-based system to ensure that individuals are not deceived under any circumstances. In our system, where land data will be safe and secure, data synchronization and transparency will be available, as well as ease of access, irreversible record management, and rapid and low-cost quick solutions. We designed a modern architecture to digitally store land records using Blockchain-based Hyperledger Fabric to ensure land security. This proposed model has b‘een presented by private Blockchain, taking into account the technological expertise and authority of the people and government. Finally, we compare our proposed architecture with the existing land records management model. Our system provides more security and data privacy than other models and saves both money and time in our daily lives. This will ensure that our system is transparent and acceptable in all directions.
Activity Classification from First-Person Office Videos with Visual Privacy Protection

With the advent of wearable body cameras, human activity classification from First-Person Videos (FPV) has become a topic of increasing importance for various applications, including life-logging, law enforcement, sports, workplace, and health care. One of the challenging aspects of FPV is its exposure to potentially sensitive objects within the user’s field of view. In this work, we developed a visual privacy-aware activity classification system focusing on office videos. We utilized a Mask R-CNN with an Inception-ResNet hybrid as a feature extractor for detecting and later blurring out sensitive objects (e.g., digital screens, human face, paper) from the videos. We incorporate an ensemble of Recurrent Neural Networks (RNNs) with ResNet, ResNeXt, and DenseNet-based feature extractors for activity classification. The proposed system was trained and evaluated on the FPV office video dataset which is a subset of the BON [1] vision dataset for office activity recognition (2021) and includes 18 classes made available through the IEEE Video and Image Processing (VIP) Cup 2019 competition. On the original unprotected FPVs, the proposed activity classifier ensemble reached an accuracy of 85.078% with precision, recall, and F1 scores of 0.88, 0.85, and 0.86, respectively. The performances were slightly degraded on privacy-protected videos, with accuracy, precision, recall, and F1 scores at 73.68%, 0.79, 0.75, and 0.74, respectively.
Exploring the Performance of Traditional Word Embeddings and Contextual Embedding Models for COVID-19 Sentiment Analysis

Sentiment Analysis (SA) is the act of classifying people’s feelings. As the Covid-19 pandemic spreads fast over the world, claiming the lives of millions, a number of countries proclaimed a state of emergency in order to contain its spread. During this period of lockdown, social media platforms played an essential role in disseminating information about the epidemic throughout the world, as people utilized social media to express their emotions. This study makes use of a Twitter dataset including 41157 COVID-19-related tweets. Furthermore, the incorporation of contextual word embeddings - representations of words that integrate semantic and syntactic information from their context - has resulted in significant gains in a wide range of Natural Language Processing (NLP) tasks. Recent contextual models, on the other hand, have excessively high computing costs in many use-cases and are frequently difficult to analyze. In this study, we hypothesize that the performance of contextual word embeddings may be enhanced by integrating static word vectors particularly trained on social media. We demonstrate that an attention-based strategy for combining such word vectors is effective on social media reviews (tweets about COVID-19).
Surveillance System for Real-Time High-Precision Recognition of Criminal Faces From Wild Videos

As violent criminals, such as child sex offenders, tend to have high recidivism rates in modern society, there is a need to prevent such offenders from approaching socially disadvantaged and crimeprone areas, such as schools or childcare centers.Accordingly, national governments and related institutions have installed surveillance cameras and provided additional personnel to manage and monitor them via video surveillance equipment.However, naked-eye monitoring by guards and manual image processing cannot properly evaluate the video captured by surveillance cameras.To address the various problems of conventional systems that simply store and retrieve image data, a system is needed that can actively classify captured images in real-time, in addition to assisting surveillance personnel.Therefore, this paper proposes a video surveillance system based on a composable deep face recognition method.The proposed system detects the faces of criminals in real time from videos captured by a surveillance camera and notifies relevant institutions of the appearance of criminals.For real-time face detection, a down-sampled image forked from the original is used to localize unspecified faces.To improve accuracy and confidence in the recognition task, a scoring method based on face tracking is proposed.The final score combines the recognition confidence and the standard score to determine the embedding distance from the criminal face embedding data.The blind spots of surveillance personnel can be effectively addressed through early detection of criminals approaching crime-prone areas.The contributions of the paper are as follows.The proposed system can process images from surveillance cameras in real-time by using down-sampling.It can effectively identify the identity of criminals by using a face tracking ID unit and minimizes prediction reversal by solving the congested embedding problem in the feature space that may occur when performing identification matching on a large amount of face embedding DBs.Additionally, the reliability of the identification results is complemented by an identification score accumulation method.In this paper, we prototyped the proposed system and experimented with the recognition model, achieving an accuracy of 0.900 and an F-1 score of 0.943.We also experimentally confirmed that the models proposed in other studies have higher performance when using the tracked instance-level face identification method proposed in this paper.It is expected that the proposed system can be used to locate criminals and protect national facilities, and such responses can quickly prevent accidents/incidents.The dataset and code are available at https://github.com/aengoo/focusface
The utilization of silica sand and clay with the addition of sawdust as raw material for manufacturing ceramic membranes to reduce TSS and TDS levels of peat water

Peat water is abundant in Riau Province. The amount has the potential to be processed into clean water but is constrained by the condition of peat water, which is not good for health. Therefore, it is necessary to process it first so that the community can use it. Ceramic membrane technology is one of the alternative technologies that are easy and inexpensive to use to purify peat water. The quality of peat water can be improved by filtering out contaminants using ceramic membranes, which have porous media. This work combined silica sand, clay, and sawdust with varied compositions to create ceramic membranes. This study aims to: combine clay, sawdust, and silica sand with varied composition to create ceramic membrane; find the ideal composition (optimal ratio) of clay, sawdust, and silica sand; and reduce the TDS and TSS levels of peat water at Ujung Batu Street, Farmer Field, Mandau District. Ceramic membranes were created for 4 h at a burning temperature of 700 °C and had a plate-shaped membrane of 11 cm in diameter and 0.5 cm in height. The composition ratios of the materials in this study are (silica sand: clay: sawdust) M1 = 58%:38%:4%, M2 = 48%:48%:4%, and M3 = 38%:58%:4%. Based on the results of this study, the best ceramic membrane for rejecting TSS is a membrane with a composition of silica sand: clay: sawdust (38%: 58%: 4%) of 26.97%, and the ceramic membrane that produces the highest flux for peat water is the membrane with a composition of 58%:38%:4% of 879 × 10-4 l/m2.hour. The ceramic membrane succeeded in reducing the TDS level of peat water by 10.36% and the TSS level of peat water by 26.97%.
Needs and possibilities of adopting national and global values in school

Summary: The paper points out the needs and possibilities of adopting national and global values in school. The psychological point of view on values and the phases of learning about them is emphasized, as well as on theories that shed light on the process of learning and realizing values. Special emphasis in the work is placed on didactic systems that contribute to quality teaching and that greatly facilitate the work of teachers in the realization of the adoption of national and global values.
COVID-19 and the Data Governance Gap

The COVID-19 pandemic has highlighted many complexities involved in using data and advanced technologies to help resolve public health emergencies. These complexities highlight the need to embrace a broader framework of data governance with three foundational questions: ( a) who decides about data flows, ( b) on what basis, and ( c) with what accountability and oversight. These questions can accommodate the issues that have arisen in the literature regarding new types of data harms. However, these questions also foreground important issues of power, authority, and legitimacy. Data governance can provide an organizing normative framework to address emerging data themes including access to data, collective decision making, data intermediaries, data sovereignty, design and digital infrastructure, regulatory technologies, the rule of law, and social trust and license. The pandemic experience with contact tracing apps, in particular, showed the many unresolved governance challenges.
Early warning model and prevention of regional financial risk integrated into legal system

In order to improve the laws and regulations of the financial system, in the construction of laws and regulations, the traditional financial risk Early Warning (EW) model is optimized. The financial prevention and control measures with legal protection are implemented to warn the financial risks, which plays an important role in the construction of the rule of law in the Financial Market (FM) and the establishment of financial risk prevention and control laws and regulations. This paper combines the deep learning model and the Markov regime Switching Vector Auto Regression (MS-VAR) model and constructs a regional financial risk EW model from the following aspects: macroeconomic operation EW indicators, regional economic risk EW indicators, regional financial institution risk EW indicators. The model is empirically researched and analyzed. The results show that the fluctuation trend of the macroeconomic pressure index in the time series is relatively large, and the overall fluctuation of the regional economic pressure index is small, and fluctuates around 0 in most periods. After the financial crisis, local governments stepped up their supervision of non-performing corporate and household loans. From 2011 to 2018, the non-performing loan ratio began to decline, and the overall fluctuation of the regional financial comprehensive stress index was small, fluctuating around 0. Due to the lack of legal regulation, from the perspective of the regional economy, the risk level is more likely to change from low risk to moderate risk, while the risk status is less likely to change from high risk to moderate risk. From the perspective of regional financial institutions, the probabilities of maintaining low risk and moderate risk are 0.98 and 0.97, respectively, which is stronger than maintaining the stability of high risk. From the perspective of the state transition of the regional financial risk composite index, the probability of maintaining low risk and high risk is 0.97 and 0.93, which is higher than maintaining the stability of medium risk. The Deep Learning (DL) regional financial risk EW MS-VAR model has strong risk prediction ability. The model can better analyze the conversion probability of regional financial risk EW index and has better risk EW ability. This paper enhances the role of legal systems in financial risk prevention and control. The regional financial risk EW model incorporating financial legal indicators can better describe the regional financial risk level, and the EW results are basically consistent with the actual situation. In order to effectively prevent financial risks and ensure the safety of the financial system, it is recommended that the government improve local debt management, improve financial regulations and systems, and improve the legislative level of financial legal supervision.
The stable and unstable oscillations of a rod attached to many springs

Abstract The oscillation equations of a multi-spring system connected to a rod are developed. Oscillations can be stable or unstable depending on the distribution of the spring constant and the number of springs. The symmetrical spring distribution with respect to the rod center tends to stabilize. The system becomes more stable as the number of springs increases. The equations of motion are identified to satisfy the Mathieu’s equation. The system could be thought of as a rough imitation of the crazy bamboo attraction, a traditional attraction by old civilization that is frequently associated with the mystical phenomenon. One spring represents one performer. The behavior of the performer’s force (opposite to the direction of the bamboo movement and providing a stronger force as the displacement of the bamboo increases) is similar to that of the spring force.
Differentiable learning of rules with constants in knowledge graph

Knowledge reasoning helps overcome the incompleteness of knowledge graphs (KGs) and has significantly contributed to the development of large KGs. Rule mining, one of the key tasks of knowledge reasoning, studies the problem of learning interpretable inference patterns over KGs. Existing rule mining methods mainly focus on learning rules that consist of different relations and variables, restricting the form of rules to be closed path. While rules could be diverse and in order to enrich the forms of rules, we argue that constants should also be considered in the rule mining process. We propose an Elegant Differentiable rUle learning with Constant mEthod (EduCe).1 We propose a constant operator and dynamic weight mechanism, which choose the constants that should be added and decrease the number of parameters, respectively. The model could mine diverse and accurate rules in an efficient way with these modules. The experimental results on several knowledge graph completion benchmarks show that EduCe achieves state-of-the-art link prediction results among differentiable rule mining methods and successfully learns diverse and high-quality rules.
Encrypted video identification - Normalization and activation function perspective

Video identification in encrypted network traffic has become a challenge for researchers in the past few years. However, the accuracy of the identification depends on various factors. This paper highlight the impact of data normalization techniques and the activation function of the hidden and dense layer on accuracy. For this purpose, we tried different normalization techniques for data normalization. Upon selecting the best data normalization technique, we first modified the activation functions of hidden layers. After selecting the most suitable activation function for hidden layers, we modified the activation function of the dense layer. The results show that the Min-Max data normalization technique, the ReLU function on hidden layers, and the Soft-max activation function on dense layer is the most suitable combination for video identification in network traffic. The configuration achieves an accuracy of 96.38%.
Blockchain based Secure Data Management for Healthcare Applications

Blockchain technology is poised to transform the data storage and data interchange models. A blockchain is a distributed ledger of transactions stored across a network of nodes. This decentralized model provides immutability and traceability of the records and is known as Distributed Ledger Technology (DLT). In the implemented healthcare system, we used a decentralized blockchain based peer to peer architecture ensuring data security, availability, and reliability. We leverage the Substrate framework, part of Polkadot ecosystem for building blockchains. Once the patient logs into their profile, they are able to view their medical history along with any doctor’s prescriptions and laboratory reports. When the doctor accesses a patient’s profile, they can make new entries to the medical records and prescriptions. In addition, we show that a tamper-proof record of the healthcare assets can be securely maintained, and accessible to the authorized entities.
A novel font color and compression text steganography technique

In the modern era, data gains lots of importance day by day, and data has become a valuable asset for any organization. Data protection from any unauthorized access gains much importance in this regard. Many techniques have been adopted to secure data for various situations, like encryption, Blockchain and Stenography. Among these, Text steganography is a technique that allows for the concealment of secret messages within the cover text. Various researchers have indicated some common issues of steganography like limited capacity, imperceptibility and robustness. In this article, we proposed a novel text steganography technique that utilizes font color changes after data compression to conceal secret messages. This approach is based on the idea of embedding the hidden message into the RGB font color after compression. This provides an added level of security and makes it more difficult for an attacker to detect the hidden message. We evaluated the proposed technique and found its effectiveness in terms of high embedding capacity, good imperceptibility, and robustness against various steganalysis methods.
User Recognition based on Gait Pattern via Smartwatch Accelerometer in Unrestricted Environment

Wearable gadgets are becoming a major constituent of our society due to a wide range of applications like financial transactions, unlocking automobiles, tracking health and fitness, and many more. Personal data is usually required to manage these services, however, most commercially available wearables devices either lack a user authentication mechanism or employ knowledge-based authentication like passwords, PINs, or pattern locks. In this growing digital age, it is really difficult for an individual to memorize such a huge number of passwords. In this research, we put forward a technique for recognizing users from their walking patterns recorded in real-life settings using a smartwatch accelerometer sensor. Ensemble classification techniques (bagging and boosting) are used for the purpose of user identification using the selected time domain features. Our proposed method attained the highest classification accuracy of 88.35% which transcends the existing user recognition schemes available in the literature.
A Configurable Mixed-Precision Convolution Processing Unit Generator in Chisel

We present a configurable implementation of a convolution processing unit suitable for computing mixed-precision quantized neural networks. The design is implemented as a hardware generator written in Chisel, which is a software framework for writing hardware circuit generators. Our generator is designed to use minimal hardware resources and is very flexible in regards to various aspects of the convolution operation, including: image size, kernel size, image bitwidth, kernel bitwidth, activation function, and more. The processing unit is configurable only at generation time, thus we don’t pay the price of using more general hardware, instead we can tailor it to the problem at hand.
Split-Et-Impera: A Framework for the Design of Distributed Deep Learning Applications

Many recent pattern recognition applications rely on complex distributed architectures in which sensing and computational nodes interact together through a communication network. Deep neural networks (DNNs) play an important role in this scenario, furnishing powerful decision mechanisms, at the price of a high computational effort. Consequently, powerful state-of-the-art DNNs are frequently split over various computational nodes, e.g., a first part stays on an embedded device and the rest on a server. Deciding where to split a DNN is a challenge in itself, making the design of deep learning applications even more complicated. Therefore, we propose Split-Et-Impera, a novel and practical framework that i) determines the set of the best-split points of a neural network based on deep network interpretability principles without performing a tedious try-and-test approach, ii) performs a communication-aware simulation for the rapid evaluation of different neural network rearrangements, and iii) suggests the best match between the quality of service requirements of the application and the performance in terms of accuracy and latency time.
MODEE-LID: Multiobjective Design of Energy-Efficient Hardware Accelerators for Levodopa-Induced Dyskinesia Classifiers

Taking levodopa, a drug used to treat symptoms of Parkinson’s disease, is often connected with severe side effects, known as Levodopa-induced dyskinesia (LID). It can fluctuate in severity throughout the day and thus is difficult to classify during a short period of a physician’s visit. A low-power wearable classifier enabling long-term and continuous LID classification would thus significantly help with LID detection and dosage adjustment. This paper deals with an automated design of energy-efficient hardware accelerators of LID classifiers that can be implemented in wearable devices. The accelerator consists of a feature extractor and a classification circuit co-designed using genetic programming (GP). We also introduce and evaluate a fast and accurate energy consumption estimation method for the target architecture of considered classifiers. In a multiobjective design scenario, GP evolves solutions showing the best trade-offs between accuracy and energy. Compared to the state-of-the-art solutions, the proposed method leads to classifiers showing a comparable accuracy while the energy consumption is reduced by 49 %.
Resilience-Performance Tradeoff Analysis of a Deep Neural Network Accelerator

Nowadays, Deep Neural Networks (DNNs) are one of the most computationally-intensive algorithms because of the (i) huge amount of data to be transferred from/to the memory, and (ii) the huge amount of matrix multiplications to compute. These issues motivate the design of custom DNN hardware accelerators. These accelerators are widely used for low-latency safety-critical applications such as object detection in autonomous cars. Safety-critical applications have to be resilient with respect to hardware faults and Deep Learning (DL) accelerators are subjected to hardware faults that can cause functional failures, potentially leading to catastrophic consequences. Although DNNs possess a certain level of intrinsic resilience, it varies depending on the hardware on which they are run. The intent of the paper is to assess the resilience of a systolic-array-based DNN accelerator in the presence of hardware faults, in order to identify the architectural parameters that may mainly impact the DNN resilience.