Reviewerly/exHarmony huggingface dataset:
Updatable Trapdoor SPHFs: Modular Construction of Updatable Zero-Knowledge Arguments and More

Recently, motivated by its increased use in real-world applications, there has been a growing interest on the reduction of trust in the generation of the common reference string (CRS) for zero-knowledge (ZK) proofs. This line of research was initiated by the introduction of subversion non-interactive ZK (NIZK) proofs by Bellare et al. (ASIACRYPT’16). Here, the zero-knowledge property needs to hold even in case of a malicious generation of the CRS. Groth et al. (CRYPTO’18) then introduced the notion of updatable zk-SNARKS, later adopted by Lipmaa (SCN’20) to updatable quasi-adaptive NIZK (QA-NIZK) proofs. In contrast to the subversion setting, in the updatable setting one can achieve stronger soundness guarantees at the cost of reintroducing some trust, resulting in a model in between the fully trusted CRS generation and the subversion setting. It is a promising concept, but all previous updatable constructions are ad-hoc and tailored to particular instances of proof systems. Consequently, it is an interesting question whether it is possible to construct updatable ZK primitives in a more modular way from simpler building blocks.In this work we revisit the notion of trapdoor smooth projective hash functions (TSPHFs) in the light of an updatable CRS. TSPHFs have been introduced by Benhamouda et al. (CRYPTO’13) and can be seen as a special type of a 2-round ZK proof system. In doing so, we first present a framework called lighter TSPHFs (L-TSPHFs). Building upon it, we introduce updatable L-TSPHFs as well as instantiations in bilinear groups. We then show how one can generically construct updatable quasi-adaptive zero-knowledge arguments from updatable L-TSPHFs. Our instantiations are generic and more efficient than existing ones. Finally, we discuss applications of (updatable) L-TSPHFs to efficient (updatable) 2-round ZK arguments as well as updatable password-authenticated key-exchange (uPAKE).
Remote sensing image semantic segmentation method based on improved Deeplabv3+

In recent years, with the continuous development of remote sensing technology and computer vision technology, the semantic segmentation of remote sensing images is of great significance in terms of earth observation, urban planning, military simulation, etc. This paper proposes a remote sensing image semantic segmentation method based on improved Deeplabv3+. Firstly, the backbone network is improved. Xception is selected to replace the traditional ResNe101 as the backbone network for the improved Deeplabv3+, and the network structure is deepened and depth separable. Optimization methods such as product replacement improve the segmentation efficiency; then, in order to improve the feature extraction effect of small targets in remote sensing images, the expansion rate of the cavity convolution in the ASSP module is optimized and adjusted. The experimental results show that the improved Deeplabv3+ algorithm has achieved good segmentation results on the data set, miou reached 91.23%, pixel accuracy reached 93.31%, and F1-score reached 89.2%, which is an increase of 2.4%,1.9% and 2.7% compared with the original Deeplabv3+. At the same time, compared with mainstream U-net and SegNet algorithms, this algorithm also has strong advantages in semantic segmentation of remote sensing images.
Serverless Containers - Rising Viable Approach to Scientific Workflows.

The increasing popularity of the serverless computing approach has led to the emergence of new cloud infrastructures working in Container-as-a-Service (CaaS) model like AWS Fargate, Google Cloud Run, or Azure Container Instances. New infrastructures facilitate an innovative approach to running cloud containers where developers are freed from managing underlying resources. In this paper, we focus on evaluating the capabilities of elastic containers and their usefulness for scientific computing in the scientific workflow paradigm using AWS Fargate and Google Cloud Run infrastructures. For the experimental evaluation of our approach, we extended the HyperFlow engine to support these CaaS platforms, together with adapting four scientific workflows composed of several dozen to hundreds of tasks organized into a dependency graph. Studied applications are used to create cost-performance benchmarks and flow execution plots, delay, elasticity, and scalability measurements. Results show that serverless containers can be successfully utilized for running scientific workflows. Moreover, the results allow for gaining insight into the specific advantages and limits of the studied platforms.
Meta Hyperparameter Optimization with Adversarial Proxy Subsets Sampling

Hyperparameter optimization (HPO), aiming at automatically searching optimal hyperparameter configurations, has attracted increasing attention in the machine learning community. HPO generally suffers from high searching costs when dealing with large-scale real-world datasets since training the model with a certain hyperparameter configuration is time-consuming. Existing works suggest sampling subsets uniformly to represent the full dataset for HPO but ignoring the complex and dynamic distribution in real-world scenarios and the exploration of hyperparameter transfer. To tackle this problem, we propose a novel meta hyperparameter optimization model with an adversarial proxy subsets sampling strategy (Meta-HPO), which can transfer hyperparameters optimized on the sampled proxy subsets to the full dataset and further adapt to the new data in an out-of-sample updating manner. In particular, a perturbation-aware adversarial sampling strategy is designed to select the proxy subsets that significantly influence the model performance. With the searched hyperparameter configurations and corresponding performance scores on the proxy subsets, we propose a meta transfer framework, named "hp-learner'', to build the connection between the distribution of dataset and the optimal hyperparameter configuration. Our Meta-HPO provides a flexible and efficient hyperparameter optimization algorithm. Extensive experiments on real-world datasets validate the advantages of our proposed Meta-HPO model against existing state-of-the-art benchmarks.
Towards a Secure Electronic Health Record System Using Blockchain Technology

Blockchain has emerged as a key technology for ensuring reliability and security in several application areas primarily including the healthcare sector. Blockchain is a distributed ledger where blocks of transactions are connected in a chain using the cryptographic hash function of the previous block, making it an append-only structure. Any modification on any of the blocks will generate different hash values in other blocks in a cascading manner and different link relations. In this way the blockchain achieves immutability and security. Blockchain also involves consensus mechanisms in order to ensure synchronization among blocks and agreement between existing nodes to add a new transaction in the chain. Healthcare data is highly sensitive in nature, which consists of private information related to the diagnosis and treatment of patients. In traditional approach these data are stored by each hospital separately where sharing data is very time consuming. Also, health data is very limited to access due to the risk of disclosure of sensitive information. In order to address these issues, the objective of this paper is to integrate blockchain technology with healthcare records/data in order to provide better sharing of data without the fear of data tampering or security breach.
Application of Data Stream Pattern Evolution Analysis in Energy Control System

Abstract Aiming at the data stream obtained in the energy control system of a cigarette factory, a data stream pattern analysis method is proposed which provides support for anomaly detection and other applications by detecting and tracking the pattern and extracting the evolution between the patterns. In this paper, the concept and definition of pattern tracking method for data streams are proposed, as well as the measurement criteria of pattern similarity. On this basis, the paper introduces in detail how to generate and cluster the hypercube grids, store the grid, generate the pattern and track the pattern on the real-time data stream. The paper also defines and describes the dynamic process of the generating, retreating, mutating, dividing and merging of the data stream pattern. The algorithm in this paper is applied to the real data stream collected in the energy control system of Ningbo cigarette factory, identifying and analyzing various feature of the data stream pattern, which can effectively describe the physical changes of the energy system.
Word Embeddings for Romanian Language and Their Use for Synonyms Detection

The aim of this paper is to present some results on word embeddings for the Romanian language, based on the word2vec method. More concretely, we generate word embeddings of different lengths, and using different preprocessing and training techniques. The embeddings are general purpose, and we use the Romanian language version of Wikipedia as corpus. We also evaluate the computational resources needed for the task. The embeddings are validated by performing some experiments on synonyms detection, using a new dataset created for this purpose. The code and the dataset are made publicly available. The results indicate that these types of embeddings can be used with the summarization approaches.
Estimating the Effective Topics of Articles and Journals Abstract Using LDA and K-Means Clustering Algorithm

Analyzing journals and articles abstract text or documents using topic modeling and text clustering becomes modern solutions for the increasing number of text documents. Topic modeling and text clustering are both intensely involved tasks that can benefit one another. Text clustering and topic modeling algorithms are used to maintain massive amounts of text documents. In this study, we have used LDA, K-means cluster, and also lexical database WordNet for keyphrases extraction in our text documents. K-means cluster and LDA algorithms achieve the most reliable performance for keyphrases extraction in our text documents. This study will help the researcher to make searching string based on journals and articles by avoiding misunderstandings.
Latency-Constrained Spatial-Temporal Aggregated Architecture Search for Video Deraining

Existing deep learning-based video deraining techniques have achieved remarkable processes. However, there exist some fundamental issues including plentiful engineering experiences for architecture design and slow hardware-insensitive inference speed. To settle these issues, we develop a highly efficient spatial-temporal aggregated video deraining architecture, derived from the architecture search procedure under a newly-defined flexible search space and latency-constrained search strategy. To be specific, we establish an inter-frame aggregation module to fully integrate temporal correlation according to a set division perspective. Subsequently, we construct an intra-frame enhancement module to eliminate the residual rain streaks by introducing rain kernels that characterize the rain locations. A flexible search space for defining architectures of these two modules is built to avert the demand for expensive engineering skills. Further, we design a latency-constrained differentiable search strategy to automatically discover a hardware-sensitive high-efficient video deraining architecture. Extensive experiments demonstrate that our method can obtain best performance against other state-of-the-art methods.
The Two Ostrom Problem

This paper considers two distinct but interrelated historiographic problems that influence our perceptions of Elinor Ostrom’s contributions to economics. The first relates to the difficulties women face in dual-career partnerships which often impact their ability to establish independent academic careers and gain fair recognition for contributions to shared research programs. How Ostrom negotiated these challenges highlights the many and often hidden constraints professional women face. The second problem is how to gauge or weight the work of the Ostrom who was a multidisciplinary scholar of the commons with that of the Ostrom who was the first woman to win a Nobel Prize. Exploring her trajectory in public choice over a half century makes apparent the remarkable force a Nobel Prize exerts on post-hoc appraisals of a career. What is different in the case of Ostrom is that as ‘the first woman to win the prize,’ the award has also served to obscure many of the gender-biased experiences and constraints that Ostrom shared with other women of her generation.
Analysis of the Structure of Ceramic Materials of Clay, Mount Kelud Volcanic Ash and Sea Water

Abstract Based on the durability of ancient roman buildings that are 2000 years old and are always exposed to sea waves. In which there is a mixed reaction of volcanic rock with seawater. Indonesia has many active volcanoes, this is because Indonesia is located at the confluence of the Eurasian and Indo-Australian tectonic plates. One of the mountains formed in the area where the plates meet is still active, the mountain is Mount Kelud. Mount Kelud is a volcano in the province of East Java, Indonesia, which is classified as active. The existence of volcanic ash resulting from the eruption of Mount Kelud is quite potential as a ceramic material. So the purpose of this research is to make a ceramic construction made from clay, volcanic ash from Mount Kelud and sea water by the method of die pressing and sintering at a temperature of 1000°C. So it can be known how the physical properties, mechanics and especially the crystal structure formed from ceramic samples. The results of the research that has been carried out are that the highest density value is in the composition of a mixture of 50% volcanic ash and 50% seawater, the highest compressive strength value is also found in the composition of a mixture of 50% volcanic ash and 50% seawater, and from the XRD results, the structure The crystal formed is hexagonal with the phase formed is SiO2.
Full-Threshold Actively-Secure Multiparty Arithmetic Circuit Garbling

In this work, we show how to garble arithmetic circuits with full active security in the general multiparty setting, secure in the full-threshold setting (that is, when only one party is assumed honest). Our solution allows interfacing Boolean garbled circuits with arithmetic garbled circuits. Previous works in the arithmetic circuit domain focused on the two-party setting, or on semi-honest security and assuming an honest majority – notably, the work of Ben-Efraim (Asiacrypt 2018) in the semi-honest, honest majority security model, which we adapt and extend. As an additional contribution, we improve on Ben-Efraim's selector gate. A selector gate is a gate that given two arithmetic inputs and one binary input, outputs one of the arithmetic inputs, based on the value of the selection bit input. Our new construction for the selector gate reduces the communication cost to almost half of that of Ben-Efraim's gate. This result applies both to the semi-honest and to the active security model.
Face Recognition using CNN and Tracking Locations using Image Metadata

Abstract In This Paper Face Recognition has gain the more attention in the society of network multimedia for accessing information. Emerging Areas like network security, content indexing and extraction, and video compression benefits from face recognition technic because “people” are the center of attention in a lot of video footages. Face detection is advanced research area in the computer science vision. The research under this area is expanding in others areas like psychology. Localization of human face will be first and the initial stage as a study of face detection. In this paper the usage of Convolutional Neural Network (CNN) algorithm in detecting the criminal face and also the process of getting the location from image.
Entity Summarization: State of the Art and Future Challenges

The increasing availability of semantic data has substantially enhanced Web applications. Semantic data such as RDF data is commonly represented as entity-property-value triples. The magnitude of semantic data, in particular the large number of triples describing an entity, could overload users with excessive amounts of information. This has motivated fruitful research on automated generation of summaries for entity descriptions to satisfy users' information needs efficiently and effectively. We focus on this prominent topic of entity summarization, and our research objective is to present the first comprehensive survey of entity summarization research. Rather than separately reviewing each method, our contributions include (1) identifying and classifying technical features of existing methods to form a high-level overview, (2) identifying and classifying frameworks for combining multiple technical features adopted by existing methods, (3) collecting known benchmarks for intrinsic evaluation and efforts for extrinsic evaluation, and (4) suggesting research directions for future work. By investigating the literature, we synthesized two hierarchies of techniques. The first hierarchy categories generic technical features into several perspectives: frequency and cen-trality, informativeness, and diversity and coverage. In the second hierarchy we present domain-specific and task-specific technical features, including the use of domain knowledge, context awareness, and personalization. Our review demonstrated that existing methods are mainly unsupervised and they combine multiple technical features using various frameworks: random surfer models, similarity-based grouping, MMR-like re-ranking, or combinatorial optimization. We also found a few deep learning based methods in recent research. Current evaluation results and our case study showed that the problem of entity summarization is still far from being solved. Based on the limitations of existing methods revealed in the review, we identified several future directions: the use of semantics, human factors, machine and deep learning, non-extractive methods, and interactive methods.
Preliminary Results of Automatic P-Wave Regional Earthquake Arrival Time Picking using Machine Learning with Kurtosis and Skewness as The Input Parameters

Abstract The traditional method in determining first arrival time of earthquake dataset is time consuming process due to waveform manual inspection. Additional waveform attributes can help determine events detection. One of the widely used attribute is The Short Term Averaging/Long Term Averaging (STA/LTA) which is simply division moving average of waveform amplitude between short time and longer time. Alternatively, waveform attribute can also be built using kurtosis and skewness. The kurtosis attribute is defined as sharpness of data distribution. By definition, the maximum signal should be at or close to the P wave arrival. The skewness is typically used to show normal distribution of the data. The uniqueness of this method is that it has an ability to determine whether the first P wave arrival has positive of negative number. The skewness calculation is similar to kurtosis but it uses the power of 3 instead of 4. With the objective of generating efficient scheme to pick first time arrival, we explore use artificial neural network and a combination of kurtosis and skewness attributes. We use a collection of magnitude events with moment magnitude larger than 3 located close to Moluccas island, Indonesia. We collected all events information from the Indonesian Agency of Meteorology, Climatology and Geophysics. The process is started with manually pick all P wave arrivals using manual inspection. Next, we trained the artificial neural network with attributes numbers as inputs and arrival time we manually picked as the output. In total we used 100 regional events that has clear P wave phases. Then, we validated the results until reaching 0.99 accuracy. In the last step, we tested the once trained procedures on new waveforms contained events. Current result shows an average of 0.4s different between manual pick and trained picked from machine learning. The accuracy can be improved by applying a band pass 0.1-2 Hz filtering with an average of 0.2s.
Automatic Brand Name Translation Based on Hexagonal Pyramid Model

Brand name translation is of great importance for international corporations when their goods enter exotic markets. In this chapter, we investigate the strategies and methods of brand name translation from Western languages to Chinese, and propose a hexagonal pyramid brand name translation model, which provides a comprehensive summary of brand name translation methods and makes the classification of some translated brand names from vague to clear. Based on the model and similarity calculating, an efficient automatic translation method has been proposed to provide help of finding adequate translated words in Chinese. And an experiment has been done by the way of a dedicated program with results of a cluster of recommended Chinese brand words with a good potential to be used.
Toward a Better Text Data Augmentation via Filtering and Transforming Augmented Instances

Thanks to a large amount of high-quality labeled data (instances), deep learning offers significant performance benefits in a variety of tasks. However, instance construction is very time-consuming and laborious, and it is a big challenge for natural language processing (NLP) tasks in many fields. For example, the instances of the question matching dataset CHIP in the medical field are only 2.7% of the general field dataset LCQMC, and its performance is only 79.19% of the general field. Due to the scarcity of instances, people often use methods such as data augmentation, robust learning, and the pre-trained model to alleviate this problem. Text data augmentation and pre-trained models are two of the most commonly used methods to solve this problem in NLP. However, current experiments have shown that the use of general data augmentation techniques may have limited or even negative effects on the pre-trained model. In order to fully understand the reasons for this result, this paper uses three types of data quality assessment methods from two levels of label-independent and label-dependent, and then select, filter and transform the results of the three text data augmentation methods. Our experiments on both generic and specialized (medical) fields have shown that through analysis, selection/filtering, and transformation of augmented instances, the performance of intent understanding and question matching in the pre-trained model can be effectively improved.
Possibilities of Blockchain Technology for Nuclear Security

This chapter summarizes key concepts and stakeholders involved in implementing and maintaining strong security to protect nuclear materials and facilities. It then considers potential blockchain use-cases for the nuclear sector that could build confidence and trust among various stakeholders by tracking and securing data. It also lists criteria necessary for blockchain to be accepted and adopted by the nuclear enterprise.
The Mineralization and Structural Geology of the Porphyry Copper Deposits of Pakistan

The purpose of this review is to shed light on copper deposits found in different regions of Pakistan. The geological attributes of copper deposits have been considered with their tectonic context. The porphyry copper deposits can be traced in Pakistan from the north through Kohistan Island Arc (KIA) up to the south to Chaghi Magmatic Arc (CMA). These deposits are mainly found in and around the Late Tertiary–Early Tertiary Himalayan Belt, Kohistan magmatic arc, Karakorum Block Foreland fold and thrust belt, Ophiolite Thrust belt, Suture zone and Chaghi Magmatic Arc. These deposits in Pakistan are chiefly established in different episodes of tectonic regimes, including subduction processes, oceanic island arc, continental arc, along with Chaman- OrnachNal Fault system and post-collisional settings.