Reviewerly/exHarmony huggingface dataset:
FedClassAvg: Local Representation Learning for Personalized Federated Learning on Heterogeneous Neural Networks

Personalized federated learning is aimed at allowing numerous clients to train personalized models while participating in collaborative training in a communication-efficient manner without exchanging private data. However, many personalized federated learning algorithms assume that clients have the same neural network architecture, and those for heterogeneous models remain understudied. In this study, we propose a novel personalized federated learning method called federated classifier averaging (FedClassAvg). Deep neural networks for supervised learning tasks consist of feature extractor and classifier layers. FedClassAvg aggregates classifier weights as an agreement on decision boundaries on feature spaces so that clients with not independently and identically distributed (non-iid) data can learn about scarce labels. In addition, local feature representation learning is applied to stabilize the decision boundaries and improve the local feature extraction capabilities for clients. While the existing methods require the collection of auxiliary data or model weights to generate a counterpart, FedClassAvg only requires clients to communicate with a couple of fully connected layers, which is highly communication-efficient. Moreover, FedClassAvg does not require extra optimization problems such as knowledge transfer, which requires intensive computation overhead. We evaluated FedClassAvg through extensive experiments and demonstrated it outperforms the current state-of-the-art algorithms on heterogeneous personalized federated learning tasks.
Robust particle filter for state estimation in presence of bounded but uncertain parameters based on ellipsoidal set membership approach

Accurate estimation of state variables plays an important role in control, monitoring and optimization. Considering that the uncertainty of parameter may back-propagate to the particle and then influence the state estimation, this paper proposes a robust particle filtering method in the presence of bounded uncertain parameters based on the ellipsoidal set membership filtering (ESMF) approach. This proposed method employs ellipsoidal sets to enclose the parameter uncertainty, and each prior particle and posterior particle are determined according to the ellipsoidal calculation based on the ESMF algorithm: The prior particle is characterized by an ellipsoid derived by the ellipsoidal summation of linear transformation ellipsoid, linearization error ellipsoid and system noise ellipsoid; Each posterior particle is obtained by updating the prior particle through the ellipsoidal intersection of the prior particle ellipsoid and the measurement ellipsoid. As a result, the uncertainty of parameter may be incorporated into the state estimation, and the advantages of both ESMF and particle filter are taken by the proposed method. The efficacy of the proposed method is shown by three simulation examples.
Results of the online survey "Readiness and needs of teachers for the use of digital tools and ICT in quarantine: January-February 2022" analytical report

An analysis of the results of the all-Ukrainian online survey conducted by scientists of the Institute for Digitalisation of Education of the NAES of Ukraine in the period from January 25 to February 25, 2022 is aimed at determining the readiness of educators to use digital tools general secondary education institutions, their needs and educational resources, e-learning tools that are most in demand. The generalized data of results of self-assessment by pedagogical workers of their digital competence are given. Conclusions are provided that will facilitate the use of survey results by education authorities, teacher training institutions and institutions, general secondary education institutions, research institutions and other stakeholders in organizing the necessary activities, the implementation of which will promote the successful use of ICT and distance learning.
Topic Modeling, Sentiment Analysis and Text Summarization for Analyzing News Headlines and Articles

Newspapers and News Websites have become a part and a crucial medium in society. They provide information regarding the events that are happening around and how society is getting influenced by these events. For example, a pandemic like Covid-19 has raised the importance of these mediums. They have been giving detailed news to society on a variety of topics, such as how to detect the strains of the coronavirus, reasons for lockdown along with what are the other restrictions to be followed during the pandemic. They also provided information about the government policies which were built to be taken care of in case of pandemics and so on and they kept updated with the details about the development of the vaccines. Due to this lot of information on Covid-19 is generated. Examining the different topics/themes/issues and the sentiments expressed by different countries will aid in the understanding of the covid-19. This paper discusses the various models which were built to identify the topics, sentiments, and summarization of news headlines and articles regarding Covid-19. The proposed topic model has achieved a Silhouette score of 0.6407036, 0.6645274, 0.6262914, and 0.6234863 for 4 countries like South Korea, Japan, the UK, India on the news articles dataset, and it was found that the United Kingdom was the worst-hit, and it had the largest percentage of negative sentiments. The proposed XlNet sentiment classification model obtained a validation accuracy of 93.75%.
A framework for maintaining sustainable energy use in Bitcoin mining through switching efficient mining hardware

Blockchain technology revolutionized the financial system with the emergence of cryptocurrencies. Bitcoin as the most used cryptocurrency has been particularly questioned in the literature for its sustainability. Very few studies explore the future of sustainability from the perspective of both miners' and technology makers' continual profitability. Our study evaluates the hardware used for Bitcoin mining from a sustainability analysis perspective; therefore, changes in the difficulty level in the block algorithm and mining hardware's thermal power usage are considered to determine the profitability and the exergetic efficiency of the mining process. The consistent decay in exergetic efficiency curves of several GPU and ASIC-based technology hardware suggest that the hardware can be utilized up to the levels where the efficiency values hit zero. Simultaneously, the diminishing profit levels over the course of changing Bitcoin prices make the mining hardware unsustainable signaling an end to its life cycle and switching the hardware with more efficient ones. Our findings shed light on the future of the sustainability of Bitcoin mining in terms of securing the profitability of the business by determining the exergetic efficiency of the hardware with a completely new perspective in the literature.
Developing a novel approach for missing data imputation of solar radiation: A hybrid differential evolution algorithm based eXtreme gradient boosting model

Having sufficient and qualified datasets is of paramount importance in terms of understanding the internal dynamics of the nature-related phenomenon. Given the necessity to maintain the completeness of the datasets, this study introduced a novel technique containing the implementation of machine learning algorithms and a meta-heuristic optimization algorithm for imputing the gaps encountered in measurements of solar radiation which is one of the crucial meteorological variables in terms of not only climate dynamics but also energy technologies. To accomplish this aim, four different gap sizes, i.e., 5 %, 10 %, 20 %, and 30 %, have synthetically been constituted and the applicability of the extreme gradient boosting (XGBoost) configured by the differential evolution (DE) was examined for each gap size. The corresponding model was benchmarked with conventional interpolation techniques (i.e., linear and spline optimizations) and other widely applied ML algorithms (i.e., random forest and multivariate adaptive regression splines). A multi-perspective input selection strategy was considered to model the missing values based on correlation coefficients under three scenarios encompassing a total of 14 different models. The results revealed that the XGBoost-DE model generated with the solar radiation measurements of neighboring stations was found as the best-performed model in all gap sizes, i.e., 5 % (NSE: 0.950; KGE: 0.967), 10 % (NSE:0.934; KGE: 0.962), and 30 % (NSE: 0.939; KGE: 0.957), but 20 % which the highest accuracy was obtained with the RF (NSE: 0.944; KGE: 0.966). On the other hand, the interpolation techniques had the lowest accuracies among their counterparts in imputation attempts with respect to all gap size alternatives.
Binary Black Widow Optimization Algorithm for Feature Selection Problems

In this research work, we study the ability of a nature-inspired algorithm called the Black Widow Optimization (BWO) algorithm to solve feature selection (FS) problems. We use the BWO as a base algorithm and propose a new algorithm called the Binary Black Widow Optimization (BBWO) algorithm to solve FS problems. The evaluation method used in the algorithm is the wrapper method, designed to keep a degree of balance between two objectives: (i) minimize the number of selected features, (ii) maintain a high level of accuracy. We use the k-nearest-neighbor (KNN) machine learning algorithm in the learning stage to evaluate the accuracy of the solutions generated by the BBWO. This study has two main contributions: (a) applying the BBWO algorithm to solve FS problems efficiently, and (b) test results. The performance of the BBWO is tested on twenty-eight UCI benchmark datasets and the test results were compared with six well-known FS algorithms (namely, the BPSO, BMVO, BGWO, BMFO, BWOA, and BBAT algorithms). The test results show that the BBWO is as good as, or even better in some cases than the FS algorithms compared against. The obtained results can be used as new a benchmark and provide new insights about existing FS solutions.
Security of Truncated Permutation Without Initial Value

Indifferentiability is a powerful notion in cryptography. If a construction is proven to be indifferentiable from an ideal object, it can under certain assumptions instantiate that ideal object in higher-level constructions. Indifferentiability is a particularly useful model for cryptographic hash functions, and myriad results are known proving that a hash function behaves like a random oracle under the assumption that the underlying primitive (typically a compression function, a block cipher, or a permutation) is random. Recently, advances have been made in proving indifferentiability of one-way functions with fixed input length. One such example is truncation of a permutation. If one evaluates a random permutation on an input value concatenated with a fixed initial value, and truncates the output, one obtains a construction that is indifferentiable from a random function up to a certain bound (Dodis et al., FSE 2009; Choi et al., ASIACRYPT 2019). Security of this construction, however, is in part determined by the length of the initial value; omission of this fixed value yields an insecure construction. In this paper, we reconsider truncation of a permutation, and prove that the construction is indifferentiable from a random oracle, even if this fixed initial value is replaced by a randomized value. This randomized value may be the same for different evaluations of the construction, or freshly generated, up to the discretion of the adversary. The security level is the same as that of truncation with fixed initial value, up to collisions in the randomized value. We show that our construction has immediate implications in the context of parallel variable-length digest generation. In detail, we describe Cascade-MGF, that operates on top of any cryptographic hash function and uses the hash function output as randomized initial value in truncation. We demonstrate that Cascade-MGF compares favorably over earlier parallel variable-length digest generation constructions, namely Counter-MGF and Chained-MGF, in almost all settings.
Horizontal Racewalking Using Radical Isogenies

We address three main open problems concerning the use of radical isogenies, as presented by Castryck, Decru and Vercauteren at Asiacrypt 2020, in the computation of long chains of isogenies of fixed, small degree between elliptic curves over finite fields. Firstly, we present an interpolation method for finding radical isogeny formulae in a given degree N, which by-passes the need for factoring division polynomials over large function fields. Using this method, we are able to push the range for which we have formulae at our disposal from $$N \le 13$$ to $$N \le 37$$ (where in the range $$18 \le N \le 37$$ we have restricted our attention to prime powers). Secondly, using a combination of known techniques and ad-hoc manipulations, we derive optimized versions of these formulae for $$N \le 19$$ , with some instances performing more than twice as fast as their counterparts from 2020. Thirdly, we solve the problem of understanding the correct choice of radical when walking along the surface between supersingular elliptic curves over $$\mathbb {F}_p$$ with $$p \equiv 7 \bmod 8$$ ; this is non-trivial for even N and was settled for $$N = 2$$ and $$N = 4$$ only, in the latter case by Onuki and Moriya at PKC 2022. We give a conjectural statement for all even N and prove it for $$N \le 14$$ . The speed-ups obtained from these techniques are substantial: using 16-isogenies, the computation of long chains of 2-isogenies over 512-bit prime fields can be accelerated by a factor 3, and the previous implementation of CSIDH using radical isogenies can be sped up by about $$12\%$$ .
Threshold Linearly Homomorphic Encryption on $$\textbf{Z}/2^k\textbf{Z}$$

A threshold public key encryption protocol is a public key system where the private key is distributed among n different servers. It offers high security since no single server is entrusted to perform the decryption in its entirety. It is the core component of many multiparty computation protocols which involves mutually distrusting parties with common goals. It is even more useful when it is homomorphic, which means that public operations on ciphertexts translate to operations on the underlying plaintexts. In particular, Cramer, Damgård and Nielsen at Eurocrypt 2001 provided a new approach to multiparty computation from linearly homomorphic threshold encryption schemes. On the other hand, there has been recent interest in developing multiparty computations modulo $$2^k$$ for a certain integer k, that closely match data manipulated by a CPU. Multiparty computation would therefore benefit from an encryption scheme with such a message space that would support a distributed decryption. In this work, we provide the first threshold linearly homomorphic encryption whose message space is $$\textbf{Z}/2^k\textbf{Z}$$ for any k. It is inspired by Castagnos and Laguillaumie's encryption scheme from RSA 2015, but works with a class group of discriminant whose factorisation is unknown. Its natural structure à la Elgamal makes it possible to distribute the decryption among servers using linear integer secret sharing, allowing any access structure for the decryption policy. Furthermore its efficiency and its flexibility on the choice of the message space make it a good candidate for applications to multiparty computation.
Joint image enhancement learning for marine object detection in natural scene

Marine object detection has received an increasing amount of attention due to its enormous application potential in the field of marine engineering, Remotely Operated Vehicles, and Autonomous Underwater Vehicles. It has made substantial progress in generic object detection with the prevalent trend of deep learning in the past few years. However, marine object detection in natural scenes remains certainly an unsolved problem. The challenges stem from low visibility, small size, serious occlusion, and dense distribution. In this article, we attempt to address the marine object detection problem by presenting a clever joint attention-guided dual-subnet network that can jointly learn both image enhancement and object detection tasks for end-to-end training. JADSNet attains significant performance gains by comprising two subnetworks: an image enhancement subnet and a marine object detection subnet. Essentially, the marine object detection subnet is an extended feature pyramid network with a dual attention-guided module and a multi-term loss function. It takes RetinaNet as a backbone and is responsible for classifying and locating objects. In the image enhancement subnet, feature extraction layers are shared with the marine object detection subnet and a feature enhancement module is used. A multi-term loss function is introduced to reduce false detection and miss detection caused by the mutual occlusion of marine objects. We build a new Marine Object Detection (MOD) dataset that contains more than 25,000 train-val and 3000 test underwater images. The experimental findings demonstrate that our JADSNet realize notable performance and reach 74.41% mAP on the MOD dataset. We also verify that the JADSNet method can be applied to object detection in foggy weather and achieve 49.54% mAP on the foggy dataset.
The impact of load-PV profile resolution on distribution system risk assessment

Risk assessment is utilized to evaluate the risk level of the system and guide further operation and planning of utilities. However, to lower the cost of communication and storing, only a coarse resolution profile of load and PV can be collected and used to evaluate the risk level of the system, which may lead to inaccurate assessment. To this end, this paper investigates the impact of load-PV profile resolution on distribution system risk assessment. A super-resolution Generative Adversarial Network with pre- and post-processing processes is proposed to restore the high-frequency component of the load-PV profile. Then, with the generated high-resolution profile, a risk assessment model based on the exterior point method is proposed to evaluate the risk level of the distribution system. Case studies in a modified 33-bus system showed that the proposed super-resolution method could achieve the best performance to restore the peak values and high-frequency component of the load-PV profile compared to the other four methods. In addition, compared to super-resolution Generative Adversarial Networks without the post-processing process, the accuracy of risk assessment can be significantly improved using the proposed super-resolution method, which proves that the post-processing process is necessary.
Optimal Path Planning Through a Sequence of Waypoints

This letter presents a deterministic approach for finding the optimal path through a sequence of spatial waypoints while accounting for vertex or turn costs. A case study is presented where the proposed algorithm is used to determine the optimal path through a sequence of waypoints. This is then compared with the path obtained when considering only two consecutive waypoints at a time. Further, an approximation that uses three waypoints at a time in a staggered manner is described. This approach is shown to be computationally efficient and finds the optimal path even in cases with 2000 waypoints.
ASSBert: Active and semi-supervised bert for smart contract vulnerability detection

With the popularity of blockchain, the amount of smart contracts has increased very fast, and the safety of smart contracts has come to more extensive notice. Recently, machine learning technology has been widely applied in vulnerability detection for smart contracts. However, it implements effective smart contract vulnerability detection still faces a major challenge, that is, there is a problem of insufficient labeled data in the current field. Active learning can label data more efficiently. Nevertheless, classical active learning only uses limited labeled data for model training, contrary to the deep learning of a large amount of data required for model training. Because of the above, we provide a new framework, called ASSBert, that leverages active and semi-supervised bidirectional encoder representation from transformers network, which is dedicated to completing the task of smart contract vulnerability classification with a little amount of labeled code data and a large number of unlabeled code data. In our framework, active learning is responsible for selecting highly uncertain code data from unlabeled sol files and putting them into the training set after manual labeling. Besides, semi-supervised learning is charged to continuously pick a certain number of high-confidence unlabeled code data from unlabeled sol files, and put them into the training dataset behind pseudo-labeling. Intuitively, by combining active learning and semi-supervised learning, we are able to get more valuable data to increase the performance of our detection model. In our experiments, we collect our benchmark dataset included 6 vulnerabilities in about 20829 smart contracts. The result of the experiment demonstrates that our framework is superior to the baseline methods with a little amount of labeled code data and a large number of unlabeled code data.
GIS-based multi-criteria decision making under Silica Saturation Index (SSI) for selecting the best direct use scenarios for geothermal resources in Central and Southern Rift Valley, Kenya

Direct use (DU) of geothermal energy is site-specific and majorly dictated by economics. Even with the availability of high enthalpy geothermal fluids, DU could still be curtailed by fluid pH, salinity, and silica content. Selecting economically viable best direct use (DU) scenarios for a given geothermal resource needs meticulous matching of geothermal resource characteristics to the nearby economic activities and local infrastructure. Silica saturation index (SSI) analysis should also be factored in to avoid silica scaling of DU facilities and reinjection well. There is a lack of a visual framework for incorporating all the necessary criteria for selecting the best DU scenarios, hence the motivation of this research. The current study employed a GIS-based multi-criteria decision making (MCDM) method: a hybrid of the analytical hierarchy process (AHP) method with the weighted aggregated sum product assessment (WASPAS) method. The model analyzed nine utilization scenarios against eleven criteria to select the best DU scenarios for six high-enthalpy quaternary volcanoes in the central and southern Kenyan rift: Menengai, Elementaita, Eburru, Olkaria, Longonot, and Suswa. The best typical DU schemes identified were horticultural greenhouse warming, spa, and crop drying. The model was validated with existing DU case studies to gauge its ability to predict suitable utilization methods. There was a good matching between the model's selection and existing case studies implicating the synergistic merger of GIS-based MCDM methods and SSI as a robust tool for planning and carrying out the feasibility study for DU of geothermal energy.