Reviewerly/exHarmony huggingface dataset:
BSOT: Bandwidth-saving oblivious transfer protocol with confidential computing

Abstract Oblivious transfer is a private preserving algorithm for querying sensitive information without known by data owner. The disadvantage of this scheme is that the data owner needs to send amount of redundant data to puzzle the intended information, which will cause the waste of bandwidth. This paper introduces BSOT, a bandwidth-saving oblivious transfer protocol which uses query agent in confidential computing environment in the data source side to reduce bandwidth consumption. In this paper BSOT protocol is defined in CBOR format. Compared to the traditional OT method, this approach could push all the redundant data flow inside the data source instead of transferring by network, and could also restrict the private data from unnecessary leakage.
ArchiveSafe LT: Secure Long-term Archiving System

Every year the amount of digitally stored sensitive information increases significantly. Information such as governmental and legal documents, health, and tax records are required to be securely archived for decades to comply with various laws and regulations. Since cryptographic schemes based on single computational assumptions are not guaranteed to stay secure for such long periods, current state-of-the-art systems providing long-term confidentiality and integrity rely on information-theoretic techniques, such as multi-server secret sharing and commitments. These systems achieve the desired results; however, establishing private channels for secret sharing is costly and requires a complex setup. In this paper, we present ArchiveSafe LT, a framework for archiving systems aiming to provide long-term confidentiality and integrity. The framework relies on multiple computationally-secure schemes using robust combiners, with a design that plans for agility and evolution of cryptographic schemes. ArchiveSafe LT is efficient and suitable for practical adoption as it eliminates the need for private channels compared to its counterparts. We present the ArchiveSafe LT framework structure and its security analysis using an automatic prover. We specify two ArchiveSafe LT-based system designs, which handle different adversarial storage providers. We experimentally evaluate a prototype built based on one of the designs to show the system’s efficiency compared to information-theoretic systems.
Hayyakum (حيَّاكم) – COVID-19 Vaccine Digital Certificate: A Blockchain Approach

As a consequence of the global pandemic, many restrictions and rules were enforced. One predicament was the travel restrictions and requirements put into place with regard to vaccinations. Countries worldwide now require people to be vaccinated upon entry. The process of validating vaccine doses requires lots of paperwork and is inefficient. Blockchain is an uprising technology that is secure and fast at carrying out transactions. We propose implementing vaccine dose verifications between countries through vaccine certificates using Blockchain as an effective solution. The need for a common shared database, avoiding a trusted third party to administrate the network, having several countries involved, ensuring privacy and security, and accountability logs make Blockchain needed in this scenario. Digital vaccine certificates are very sensitive information that must be kept private and secure but accessible to several entities. Blockchain ensures the aforementioned requirements are met while preserving the integrity of the VDCs. This paper describes blockchain technology and its application in digital vaccine certificates.
Mixed Stage Partial Network and Background Data Augmentation for Surveillance Object Detection

State-of-the-art (SoTA) object detection models and their accuracy have been improved by a large margin via CNNs (Convolutional Neural Networks); however, these models still perform poorly for small road objects. Moreover, the SoTA models are mainly trained on public benchmark datasets such as MS COCO, which include more complicated backgrounds and thus make them robust for object detection. However, for surveillance or road videos, their monotone backgrounds make these SoTA detectors background-over-fitted. In applications such as autonomous driving or traffic flow estimation, the background-over-fitting problem will increase various challenges and lead to accuracy degradation in object detection. One novelty of this paper is to propose an MBA (Mixed Background Augmentation) method to improve detection accuracy without adding new labeling efforts and any pre-training processes. During the inference stage, only one input image is needed for vehicle detection without involving background subtraction. Another novelty of this paper is the design of an efficient MSP (Mixed Stage Partial) network to detect objects more accurately and efficiently from surveillance videos. Extensive experiments on KITTI and UA-DETRAC benchmarks show that the proposed method achieves the SoTA results for highly accurate and efficient vehicle detection. The detection accuracy is improved from 78.53% to 83.59% with 25.7 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$fps$ </tex-math></inline-formula> on the UA-DETRAC data set. The implementation code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/pingyang1117/MSPNet</uri> .
Oral Expression in Higher Level Students from the Virtual Modality

The objective of this study was to determine the relationship between the virtual modality from a remote teaching context with oral expression in students of a private institution of higher education in Lima, a relationship supported based on the theory of connectivity. For this purpose, basic research was carried out, with a quantitative approach, correlational level and non-experimental design. The population consisted of 240 students and a sample of 118 students from Administration and Business careers. For the collection of data related to oral expression, a rubric was developed; while, for the virtual modality variable from a remote teaching context, an adapted survey was applied, whose reliability, through Cronbach's Alpha, was 0.949. On the other hand, these instruments were validated through expert judgment, who gave their agreement. Thus, from the analysis of the data collected and the application of the hypothesis test, it was shown that there is a positive relationship between the virtual modality from a remote teaching context and oral expression.
Simulation study of an improved firefly algorithm for solving multi-dimensional complex function optimization

To address the shortcomings of the traditional firefly algorithm in global optimization seeking, such as low solution accuracy, unstable convergence and slow optimization speed, a new evolutionary model of firefly algorithm based on the improved Chebyshev chaos mapping is proposed. Firstly, the population distribution is initialised with the improved Chebyshev chaos mapping to improve the population diversity. Secondly, the non-linear dynamic adaptive inertia weights are introduced to regulate the balance between convergence speed and local optimality seeking ability. Then, the boundary variation strategy is introduced to solve the boundary crossing problem to avoid falling into local optimum and continue to improve the population diversity. Finally, simulation experiments are conducted under six benchmark test functions to compare with the traditional firefly algorithm. The experimental results show that the improved algorithm has higher solution accuracy and faster convergence speed.
Secure Distributed Matrix Multiplication Under Arbitrary Collusion Pattern

We study the secure distributed matrix multiplication (SDMM) problem under arbitrary collusion pattern. In the one-sided SDMM problem, where only one matrix of the matrix multiplication needs to be kept secure, we propose an achievable scheme that attains the optimal normalized download cost. The optimal scheme distributes a different number of encoded copies to each server, and the servers that collude more with others are given fewer encoded copies. The converse result is proved using Shearer’s lemma. In the two-sided SDMM problem under arbitrary collusion pattern, where the user would want to keep both matrices of the matrix multiplication secure, we provide an achievable scheme whose key parameters, including the method with which the random matrices are appended, the number of random matrices appended, the number of encoded copies generated, the number of encoded copies distributed to each server, are given by the proposed algorithm. We also demonstrate, via numerical results, the performance of the proposed scheme in terms of normalized upload-download cost trade-off, and show that it is much better than the current known scheme devised for the homogeneous collusion pattern.
Optimization of the energy lost in the current limitation mode of the In grid-connected photovoltaic systems: a case study of the PV installation of the Mohammed 6 polytechnic campus of Rabat

In the framework of the reduction of the electric bill as well as the optimization of the carbon footprint of the Mohammed 6 Polytechnic University, we opted for the use of green energy and in particular the photovoltaic installed on the roof with self-consumption by injecting the produced energy in the network, but the injection of the excess of the produced energy is not yet authorized in Morocco, for that the on-grid inverter functions in limitation mode to limit the injection of a excess of the produced energy. The objective of this study is to analyze the amount of energy lost in the limitation, and to propose intelligent solutions based on IOT to exploit the totality of the energy produced by using thermal storage.
Approach to text data clustering based on molecular chemical reactions in agricultural and food industries

Abstract We propose an algorithm for clustering short text fragments such as lists of ingredients in food products using the k-means algorithm in combination with the molecular chemical reactions algorithm. The solution obtained with k-means is used as an initial molecular structure solution to optimize chemical reactions in combination with k-means by generating new solutions using the four main types of chemical reactions. The computational experiment showed the comparative efficiency of new algorithm, taking into account numerical metrics: accuracy, precision, recall, and F-measure.
A feature extraction method for XML documents based on PCA

Abstract XML document stores the information of the new power system source load interaction. It has the characteristics of self-description, extensibility, structure, and content, which makes it widely used. Improving the method of extracting elements from XML documents is very helpful in solving the problem of distributed object operation measurement in the power grid. To classify or analyze XML documents better, based on the theoretical analysis of principal component analysis and the study of the text representation model, this paper proposes two methods: vectorization representation of XML documents and further feature extraction. The experiment result shows that the method of all path feature extraction for XML document can represent the main feature of XML document effectively, and is an important work for latter handling XML documents of power grid efficiently
Transformer-based radio modulation mode recognition

Abstract The wireless communication technology develop rapidly, signal modulation mode technology becomes increasingly important. Therefore, in order to enhance the ability of radio signal modulation mode recognition in the complex electromagnetic environment, a novel radio signal modulation mode recognition method is proposed on account of Transformer network. Firstly, raw data were split with a fixed size window. Then, the segmented data was projected into a vector sequence and fed into the Transformer module to model and mine the relationship between signal waveform and modulation method to achieve identification. Extensive experiments were conducted on the RML2016.10a data set generated by GNU radio in the real system. The experimental results show that the proposed Transformer-B/8×2 achieves optimal results for modulation mode recognition with signal-to-noise from -20dB to 18dB, and recognition accuracy reaches 86.75%, verifying the practicability and validity of the forenamed algorithm.
Citizen science: Realizing a participatory rain data management for flood disasters early warning systems and building disaster awareness from a very young age

Abstract According to official data from the National Disaster Management Agency (BNPB), in 2021, flooding was Indonesia’s most frequent and destructive disaster. Thus, better disaster management, including early warning systems (EWS) and disaster mitigation based on accurate rainfall data, is essential. This paper was written based on preliminary research on the development of citizen science in Wonosobo and Banjarnegara regencies, Central Java, which aims to develop a very early warning system (VEWS) and community-based rainfall data management. Modified rain gauges are installed in elementary schools in the upstream Serayu watershed. Students, in turn, do data recording. This effort ensures continuity of observation, the introduction of science and technology, and increased literacy about floods and landslides and their mitigation. From the initial research, it can be stated that the integration of the citizen science model into compulsory subjects for elementary school children is very prospective for accelerating students’ understanding of subjects related to natural sciences. A citizen science approach that is carried out correctly is able to produce accurate rainfall data continuously. These two things are enabling factors for the effective and efficient implementation of the VEWS flood disaster in the upstream watershed.
Visual Management (VM) supporting collaborative practices in infrastructure engineering design

Abstract Managing the design of complex engineering systems requires an organisational structure and an information system to support collaboration among all stakeholders. Technological developments in information management have the potential to facilitate interactions across physical boundaries, even more during the Covid-19 pandemic. Visual Management (VM) is an information management strategy, as well as a means for communication between individuals, supporting collaborative work. However, there is a lack of effective understanding of how digital VM can support infrastructure engineering design. The adoption of digital collaborative VM in the context addressed is new, under rapid evolution, and there is limited understanding of how the users embrace VM while interacting with it. The aim of the paper is to explore the adoption of VM, focusing on digital whiteboards, to support collaborative practices in design processes. The ongoing investigation is carried out in collaboration with an infrastructure design and consultancy company, and follows the action research approach. The VM effectiveness was investigated by analysing the whiteboards applicability to diverse functions and comparing digital and manual implementations. Initial findings include understanding digital whiteboards as a means for collaboration among individuals with different perceptions to establish a common point of view, as it allows the information to be transferred across time and space, identifies abnormalities, and supports problem-solving. By creating a common ground, it has the potential to support complex and emergent interactions in the collaborative space.
Blockchain-based framework for a smart supply chain management in construction

Abstract The flow of information in construction supply chain stands out for being inefficient and lacking in automation. Some authors have identified the potential of blockchain technology to solve problems related to the lack of transparency, automation, productivity, and efficiency. This study builds a novel blockchain-based information management framework for a typical supply chain of the construction industry. Using design science research as methodological research strategy, this study presents and analyses a blockchain framework and its development processes. The framework performance was evaluated through its potential to generate organizational knowledge and its impact on the current management context. The challenges of the proposed system are the integration of Hyperledger systems, the development of channels and the layout of the supply chain within the blockchain, as well as the addition and control of members within the network. The main contribution of this study is the proposition of a SaaS (Software as a Service) concept platform with great potential to meet the needs and particularities of the Construction sector.
Applications and limitations of portable density meter measurements of Na-Ca-Mg-K-Cl-SO4 brines

Knowledge of brine density and mineral saturation states under different temperatures contributes to understanding Earth systems, including dissolution and crystallization of chemical sediments, mixing water bodies with variable density, and mineral resource development and evolution. However, these interpretations often rely on geochemical measurements with low accuracies at high salinities. Here, we test the utility of density measurments to advance understanding of brine geochemistry. We demonstrate the accuracy and limits of determining a brine's density and halite saturation state at multiple temperatures with rapid and relatively inexpensive portable density meter measurements. Density measurements at different temperatures are used to create samples' equations of state, which are used to extrapolate measurements. This methodology presents multiple opportunities. We use brine samples from around the world to show that density and equation of state data provide semi-quantitative information about sample composition and temperature-sensitive mineral precipitation. Importantly, density measurements can be used to evaluate error-prone geochemical measurements. We illustrate applications with brine samples from the Bonneville Salt Flats, a landscape in Utah, USA, that is valued for mineral resources and recreation opportunities. We developed system-scale equations of state for these saline pan samples and their halite-saturated counterparts. We evaluated methodological limitations with duplication tests and by determining if diurnal fluctuations in salinity and halite saturation in a shallow pool's brine and shallow groundwater in contact with a halite crust were detectable. Average daily changes in halite saturation of shallow pool brine of approximately 3 kg m−3 were clearly detected, while diurnal halite saturation fluctuations in the shallow halite crust brine of 0.4 to 0.7 kg m−3 were near method limits. Because of their accuracy, low cost, and applications, portable density meter measurements of brines are invaluable in studying brine systems.
NLP-Driven Political Analysis of Subreddits

Machine learning has aided in the development of incredibly efficient solutions to today’s complexities. It has virtually pervaded every field, and its potent solutions have substantially assisted humanity’s growth. In this research, our prime objective is not to make the right predictions but to probe the different ways of applying the concepts of machine learning. We aim to comprehend the inner working of the models that aid in decision making. Further, to meet the objectives of this research, we had to analyse a large text corpus. We implemented a fusion of algorithms with high interpretability to derive meaningful information from the data. To this end, we investigated approaches to extract information from political subreddits using embedding-based sentiment analysis and feature analysis of a classification model.
Building up a Categorical Sentiment Dictionary for Tourism Destination Policy Evaluation

Information technologies and the recent pandemic have reshaped the tourism sector. For tourism policy evaluation that is traditionally measured by evaluation methods, such as questionnaire and interview, we developed a dictionary-based sentiment analysis system on tourism destinations using review data generated by tourists. The system is based on the questionnaire developed by the Korean Ministry of Culture, Sports, and Tourism. Through a series computational method as well as human rating processes, sentiment lexicons, such as ‘sentimentally polarizable’ and ‘categorically discriminable,’ are extracted. The extraction procedures of the sentiment lexicons are presented in the study. On the basis of the dictionary, Haeundae beach, a famous tourism destination in Korea, is analyzed. Our result is encouraging with respect to reshaping the tourism policy evaluation with the text mining method.
Efficient Data Hiding Model by Using RDH Algorithm

In day-to-day life, information plays a major role. The value of those information becomes higher. So, we need to protect the information from intruders who try to get the secret information. In protecting those information, data compression deals a lot. Compression techniques are of two types which includes lossy and lossless compression. Steganography is the process which is used to protect the information by encrypting the compressed image with secret data and then extract the secret data from the compressed image by decryption. This paper utilizes an reversible data hiding (RDH) algorithm to boost the efficiency of encryption and decryption of the cover image by implementing the steganographic technique. RDH helps to accomplish the reason for picture content security. The encrypted data has been embedded within the cover image using the least significant bit (LSB). The proposed model has been evaluated based on criteria like savings percentage (SP%), compression time (CT), compression ratio (CR), mean squared error (MSE), and peak signal-to-noise ratio (PSNR). In comparison to previous systems that use the RSA algorithm, this technique has improved performance and system methodology.
Anomaly Detection in Image Sequences Using Weakly Supervised Learning

This paper proposes a weakly supervised deep learning method to create an anomaly detection system that can identify abnormal behavior in surveillance videos. A video anomaly detection system analyzes the data from various surveillance videos to detect the presence of any abnormal activities. We have used the inflated 3D convolutional neural network (I3D) for feature extraction and created a neural network model which scores the videos for the presence of any abnormal behavior by using the multiple instance learning (MIL) technique. We use the UCF-Crime dataset to train and test this model. The proposed method achieves a better area under curve (AUC) value of 82.03 for the receiver operating characteristic (ROC) plot than the value of 77.92 reported by Sultani et al. (IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp 6479–6488, 2018).