Reviewerly/exHarmony huggingface dataset:
Advancing a paradigm shift in evaluation of forensic evidence: The rise of forensic data science

Widespread practice across the majority of branches of forensic science uses analytical methods based on human perception, and interpretive methods based on subjective judgement. These methods are non-transparent and are susceptible to cognitive bias, interpretation is often logically flawed, and forensic-evaluation systems are often not empirically validated. I describe a paradigm shift in which existing methods are replaced by methods based on relevant data, quantitative measurements, and statistical models; methods that are transparent and reproducible, are intrinsically resistant to cognitive bias, use the logically correct framework for interpretation of evidence (the likelihood-ratio framework), and are empirically validated under casework conditions.
Problems with Cosine as a Measure of Embedding Similarity for High Frequency Words

Cosine similarity of contextual embeddings is used in many NLP tasks (e.g., QA, IR, MT) and metrics (e.g., BERTScore).Here, we uncover systematic ways in which word similarities estimated by cosine over BERT embeddings are understated and trace this effect to training data frequency.We find that relative to human judgements, cosine similarity underestimates the similarity of frequent words with other instances of the same word or other words across contexts, even after controlling for polysemy and other factors.We conjecture that this underestimation of similarity for high frequency words is due to differences in the representational geometry of high and low frequency words and provide a formal argument for the two-dimensional case.
High-resolution electricity generation model demonstrates suitability of high-altitude floating solar power

<h2>Summary</h2> This paper develops a meteorological site selection algorithm to quantify the electricity generation potential of floating solar design configurations on alpine water bodies in Switzerland. Using European power market demand patterns, we estimate the technical and economic potential of 82 prospective high-altitude floating solar sites co-located with existing Swiss hydropower. We demonstrate that the amount of solar energy radiating from high-altitude Swiss water bodies could meet total national electricity demand while significantly reducing carbon emissions and addressing seasonal supply/demand deficits. We construct a global map overlaying sites on each continent where high-altitude floating solar could provide low-carbon, land-sparing electricity. Our results present a compelling motivation to develop alpine floating solar installations. However, significant innovations are still needed to couple floating solar with existing hydropower operations or low-cost energy storage. As the industry matures, high-altitude floating solar technology could become a high-value, low-carbon electricity source.
An Efficient Asymmetric Nonlinear Activation Function for Deep Neural Networks

As a key step to endow the neural network with nonlinear factors, the activation function is crucial to the performance of the network. This paper proposes an Efficient Asymmetric Nonlinear Activation Function (EANAF) for deep neural networks. Compared with existing activation functions, the proposed EANAF requires less computational effort, and it is self-regularized, asymmetric and non-monotonic. These desired characteristics facilitate the outstanding performance of the proposed EANAF. To demonstrate the effectiveness of this function in the field of object detection, the proposed activation function is compared with several state-of-the-art activation functions on the typical backbone networks such as ResNet and DSPDarkNet. The experimental results demonstrate the superior performance of the proposed EANAF.
Novel design of cryptosystems for video/audio streaming via dynamic synchronized chaos-based random keys

In this paper, a novel chaos-based cryptosystem is proposed to ensure the communication security of video/audio streaming in the network environment. Firstly, by the proposed synchronization controller for the master and slave chaotic systems, respectively, embedded in the transmitter and receiver, the cryptosystem can generate the synchronized and dynamic chaotic random numbers at the transmitter and receiver simultaneously. Then integrating the chaotic random numbers with SHA3-256 (Secure hash algorithm 3), the design of synchronized dynamic key generators (SDKGs) is completed. Continuously, we can apply the SDKGs to encrypt/decrypt streaming audio/video data. In our design, we introduce the AES CFB (Advanced encryption standard cipher feedback) encryption algorithm with SDKGs to encrypt the video/audio streaming. Then the cipher-text is transmitted to the receiver via the network public channel and it can be fully decrypted with the dynamic random keys synchronously generated at the receiver. A duplex audio/video cryptosystem is realized to illustrate the performance and feasibility of this proposed research. Finally, many tests and comparisons are performed to stress the quality of random sequences generated by proposed SDKGs.
Combined Prediction of Photovoltaic Power Based on Sparrow Search Algorithm Optimized Convolution Long and Short-Term Memory Hybrid Neural Network

To address the problem of strong uncertainty in the high proportion of new energy output, an improved convolutional long- and short-term memory (CLSTM) hybrid neural network is proposed for PV power combination prediction. Firstly, considering the large impact of weather changes on PV power output, a fluctuation feature identification model is used to classify historical PV power series samples into slow weather change type and severe weather change type. Secondly, taking into account the multimodal characteristics of PV power output, an improved variational modal decomposition technique is used to adaptively determine the number of modal components, K, and decompose the two types of samples. Regarding the existence of the low-frequency steady state component and the high-frequency fluctuation component of PV power output, the high-frequency component is used to train the long- and short-term memory (LSTM) model and the low-frequency component is used to train the convolutional neural network (CNN) model. The improved sparrow search algorithm (SSA) is used to optimize the parameters of the LSTM and CNN models during the training process. Finally, the predicted component values of each model are superimposed and reconstructed to obtain PV power prediction values. The actual operation data of a PV plant in northern China were used for comparison and validation, and the experiments showed that the accuracy of the prediction results, based on the improved SSA to optimize the parameters of the CLSTM hybrid neural network for predicting PV output, was significantly better than that of the BP, CNN, LSTM single neural network prediction results, and of the prediction accuracy of the unoptimized CLSTM hybrid neural network. At the same time, compared with the above single neural network and unoptimized hybrid prediction model, the proposed method converged faster and was more adaptable to weather changes.
MultiJAF: Multi-modal joint entity alignment framework for multi-modal knowledge graph

Entity Alignment (EA) is a crucial task in knowledge fusion, which aims to link entities with the same real-world identity from different Knowledge Graphs (KGs). Existing methods have achieved satisfactory performance, however, they mainly focus on single modal KG, which is difficult to be effectively applied to multi-modal scenes. In this paper, we propose a Multi-modal Joint entity Alignment Framework (MultiJAF), which can effectively utilize the knowledge of various modalities. Concretely, we first learn the embeddings of different modalities, i.e., structure, attribute and image modalities. Next, we adopt an attention-based multi-modal fusion network to integrate these embeddings and use obtained joint embeddings to compute a joint embedding-based similarity matrix SJ. Moreover, we design a Numerical Process Module (NPM) to infer a similarity matrix SN according to the numerical information of entities. In the end, we utilize a simple late fusion method to ensemble two similarity matrices for the final alignment. In addition, to reduce the cost of labeling data, we propose a novel NPM-based unsupervised multi-modal EA method. Experimental results on two real-world datasets demonstrate the effectiveness of our proposed MultiJAF.
Robust Random Forest-Based All-Relevant Feature Ranks for Trustworthy AI

Feature selection is a fundamental challenge in machine learning. For instance in bioinformatics, it is essential when one wishes to detect biomarkers. Tree-based methods are predominantly used for this purpose. In this paper, we study the stability of the feature selection methods BORUTA, VITA, and RRF (regularized random forest). In particular, we investigate the feature ranking instability of the associated stochastic algorithms. For stabilization of the feature ranks, we propose to compute consensus values from multiple feature selection runs, applying rank aggregation techniques. Our results show that these consolidated features are more accurate and robust, which helps to make practical machine learning applications more trustworthy.
Searching for PETs: Using Distributional and Sentiment-Based Methods to Find Potentially Euphemistic Terms

This paper presents a linguistically driven proof of concept for finding potentially euphemistic terms, or PETs.Acknowledging that PETs tend to be commonly used expressions for a certain range of sensitive topics, we make use of distributional similarities to select and filter phrase candidates from a sentence and rank them using a set of simple sentiment-based metrics.We present the results of our approach tested on a corpus of sentences containing euphemisms, demonstrating its efficacy for detecting single and multi-word PETs from a broad range of topics.We also discuss future potential for sentiment-based methods on this task.
Solar Energy Potential on Surfaces with Various Inclination Modes in Saudi Arabia: Performance of an Isotropic and an Anisotropic Model

The present work investigated the performance of an isotropic (Liu–Jordan, L–J) and an anisotropic (Hay) model in assessing the solar energy potential of Saudi Arabia. Three types of solar collectors were considered: with southward fixed-tilt (mode (i)), with fixed-tilt tracking the Sun (mode (ii)), and with varying-tilt tracking the Sun (mode (iii)). This was the first time such a study was conducted for Saudi Arabia. The average annual difference between anisotropic (Hay) and isotropic (L–J) estimates is least ≈38 kWhm−2 year−1 over Saudi Arabia for mode (i), and therefore, the L–J model can be used effectively. In modes (ii) and (iii), the difference is greater (≈197 and ≈226 kWhm−2 year−1, respectively). It is, then, up to the solar energy engineer to decide which model is to be used, but it is recommended that the Hay model be utilised for mode-(iii) solar collectors. These results fill a research gap about the suitability of models in practice. An interesting feature for the ratio of the annual mean solar energy yield of Hay over L–J as function of the latitude, φ, and the ground albedo, ρr, is the formation of a “well” for 29° ≤ φ ≤ 31° and 1.15 ≤ ρr ≤ 1.
Automatic Expert Selection for Multi-Scenario and Multi-Task Search

Multi-scenario learning (MSL) enables a service provider to cater for users' fine-grained demands by separating services for different user sectors, e.g., by user's geographical region. Under each scenario there is a need to optimize multiple task-specific targets e.g., click through rate and conversion rate, known as multi-task learning (MTL). Recent solutions for MSL and MTL are mostly based on the multi-gate mixture-of-experts (MMoE) architecture. MMoE structure is typically static and its design requires domain-specific knowledge, making it less effective in handling both MSL and MTL. In this paper, we propose a novel Automatic Expert Selection framework for Multi-scenario and Multi-task search, named AESM2. AESM2 integrates both MSL and MTL into a unified framework with an automatic structure learning. Specifically, AESM2 stacks multi-task layers over multi-scenario layers. This hierarchical design enables us to flexibly establish intrinsic connections between different scenarios, and at the same time also supports high-level feature extraction for different tasks. At each multi-scenario/multi-task layer, a novel expert selection algorithm is proposed to automatically identify scenario-/task-specific and shared experts for each input. Experiments over two real-world large-scale datasets demonstrate the effectiveness of AESM2 over a battery of strong baselines. Online A/B test also shows substantial performance gain on multiple metrics. Currently, AESM2 has been deployed online for serving major traffic.
Blockchain-Based IoT-Enabled System for Secure and Efficient Logistics Management in the Era of IR 4.0

As the global logistics business expands as a result of the industrial 4.0 revolution, logistics operations continue to evolve as new technologies such as IoT, cloud, and big data are deployed. These IoT devices improve the logistics function by boosting real-time product tracking, improved data collection, smart storage of logistics data, etc. Some of these new technologies present an avenue for cyberattacks on these logistics systems due to their centralized database structure. Logistics operations entail the exchange of private consumer information (name, address, phone number, and bank account information) as well as product information amongst logistics stakeholders (manufacturers, suppliers, transporters, and customers). And the engagement of so many logistics stakeholders creates privacy and security issues for the private information because customer information, as well as product details, are transferred and shared across different logistical stakeholders during the logistics process. It faces unwanted access, which could lead to fraud or the creation of counterfeit products by a bad actor in the system. All of these challenges are significant because logistics data integrity is important for customer satisfaction. The deployment of Blockchain innovation will address these challenges with the application of its special feature such as immutability, efficient cryptography, and distributed decentralized storage system. In this paper, we highlighted the technology that enables smart logistics and reviewed smart logistics, Blockchain, and IoT in logistics; we present the significance of integrating Blockchain and IoT in logistics. We proposed a Blockchain-based IoT-enabled system framework for secure and efficient logistics management where logistics data can be captured with the use of IoT sensors, and we also designed and describe the sequence diagram for secured communication between the logistics stakeholder through a smart contract. In conclusion, Blockchain can provide security to logistics data and enhance operational efficiency with its key features.
Blockchain-Based Decentralized Cloud Solutions for Data Transfer

Cloud computing has increased its service area and user experience above traditional platforms through virtualization and resource integration, resulting in substantial economic and societal advantages. Cloud computing is experiencing a significant security and trust dilemma, requiring a trust-enabled transaction environment. The typical cloud trust model is centralized, resulting in high maintenance costs, network congestion, and even single-point failure. Also, due to a lack of openness and traceability, trust rating findings are not universally acknowledged. "Blockchain is a novel, decentralised computing system. Its unique operational principles and record traceability assure the transaction data's integrity, undeniability, and security. So, blockchain is ideal for building a distributed and decentralised trust infrastructure. This study addresses the difficulty of transferring data and related permission policies from the cloud to the distributed file systems (DFS). Our aims include moving the data files from the cloud to the distributed file system and developing a cloud policy. This study addresses the difficulty of transferring data and related permission policies from the cloud to the DFS. In DFS, no node is given the privilege, and storage of all the data is dependent on content-addressing. The data files are moved from Amazon S3 buckets to the interplanetary file system (IPFS). In DFS, no node is given the privilege, and storage of all the data is dependent on content-addressing.
Propagating quantum microwaves: towards applications in communication and sensing

Abstract The field of propagating quantum microwaves is a relatively new area of research that is receiving increased attention due to its promising technological applications, both in communication and sensing. While formally similar to quantum optics, some key elements required by the aim of having a controllable quantum microwave interface are still on an early stage of development. Here, we argue where and why a fully operative toolbox for propagating quantum microwaves will be needed, pointing to novel directions of research along the way: from microwave quantum key distribution to quantum radar, bath-system learning, or direct dark matter detection. The article therefore functions both as a review of the state-of-the-art, and as an illustration of the wide reach of applications the future of quantum microwaves will open.
Improved Technique for Preserving Privacy while Mining Real Time Big Data

With the evolution of Big data, data owners require the assistance of a third party (e.g.,cloud) to store, analyse the data and obtain information at a lower cost. However, maintaining privacy is a challenge in such scenarios. It may reveal sensitive information. The existing research discusses different techniques to implement privacy in original data using anonymization, randomization, and suppression techniques. But those techniques are not scalable, suffers from information loss, does not support real time data and hence not suitable for privacy preserving big data mining. In this research, a novel approach of two level privacy is proposed using pseudonymization and homomorphic encryption in spark framework. Several simulations are carried out on the collected dataset. Through the results obtained, we observed that execution time is reduced by 50%, privacy is enhanced by 10%. This scheme is suitable for both privacy preserving Big Data publishing and mining.
Improved Binary Grasshopper Optimization Algorithm for Feature Selection Problem

The migration and predation of grasshoppers inspire the grasshopper optimization algorithm (GOA). It can be applied to practical problems. The binary grasshopper optimization algorithm (BGOA) is used for binary problems. To improve the algorithm's exploration capability and the solution's quality, this paper modifies the step size in BGOA. The step size is expanded and three new transfer functions are proposed based on the improvement. To demonstrate the availability of the algorithm, a comparative experiment with BGOA, particle swarm optimization (PSO), and binary gray wolf optimizer (BGWO) is conducted. The improved algorithm is tested on 23 benchmark test functions. Wilcoxon rank-sum and Friedman tests are used to verify the algorithm's validity. The results indicate that the optimized algorithm is significantly more excellent than others in most functions. In the aspect of the application, this paper selects 23 datasets of UCI for feature selection implementation. The improved algorithm yields higher accuracy and fewer features.
Blockchain in Education - The Case of Language Learning

Abstract New technological developments, such as 5G networks, smart and interconnected devices, and the development of the Internet of Things (IoT), lead to a new reality in which the secure flow of data is non-negotiable. In this new reality, blockchain technology can play a crucial role, as it has the ability to provide the necessary background for the safe and inviolable operation of systems. Blockchain is a distributed ledger, in which information and data are stored and verified. These blocks of data do not have to be related to financial transactions but may concern any other type of data that needs to be securely recorded and not changed retrospectively. Although the first application of blockchain technology was about digital currency (Bitcoin), its usefulness is not limited to cryptocurrencies or the field of economics. Blockchain, especially in its last stage v3.0, has many applications in various sectors such as data storage, certification of products and services, government, insurance, health, science, and education. This paper focuses on the current and potential educational applications of blockchain and presents how this technology can be used to solve specific education problems. Nowadays, a variety of blockchain applications concerning the issue and storage of certificates and diplomas, the evaluation of learning outcomes, the support and the academic degree management, the protection of intellectual property, the cooperation between students and their professors, learning accreditation, payment for studies, formation of an academic passport (portfolio) or administration of the educational process are implemented in some HEI and institutions. The paper analyzes the features and advantages of blockchain technology, presents some of the current blockchain applications for education, as well as the benefits and challenges of using blockchain technology in the educational sector. Finally, special reference is made to applications related to the field of language learning.
A Vehicle Trajectory Privacy Preservation Method Based on Caching and Dummy Locations in the Internet of Vehicles

In the internet of vehicles (IoVs), vehicle users should provide location information continuously when they want to acquire continuous location-based services (LBS), which may disclose the vehicle trajectory privacy. To solve the vehicle trajectory privacy leakage problem in the continuous LBS, we propose a vehicle trajectory privacy preservation method based on caching and dummy locations, abbreviated as TPPCD, in IoVs. In the proposed method, when a vehicle user wants to acquire a continuous LBS, the dummy locations-based location privacy preservation method under road constraint is used. Moreover, the cache is deployed at the roadside unit (RSU) to reduce the information interaction between vehicle users covered by the RSU and the LBS server. Two cache update mechanisms, the active cache update mechanism based on data popularity and the passive cache update mechanism based on dummy locations, are designed to protect location privacy and improve the cache hit rate. The performance analysis and simulation results show that the proposed vehicle trajectory privacy preservation method can resist the long-term statistical attack (LSA) and location correlation attack (LCA) from inferring the vehicle trajectory at the LBS server and protect vehicle trajectory privacy effectively. In addition, the proposed cache update mechanisms achieve a high cache hit rate.