Reviewerly/exHarmony huggingface dataset:
A Static Analysis Approach to Detect Confidentiality Leakage of Database Query Languages

Sensitive information stored in a database may be leaked to an unauthorized user due to improper coding of the database applications. In some cases, the information system is made in such a way that the confidentiality may be bypassed explicitly or implicitly during data processing along with the control structure of the program to the output channels. In this work, a framework is proposed to detect possible confidentiality leakage of the database programs. This framework is designed by the following steps: (i) computing all the possible data-flow path from the database-oriented program dependency graph with respect to the public database attributes, (ii) refining the data-flow path (removing false alarm) using a Condition-Action rule-based approach, and (iii) detecting the confidentiality leakage by checking the satisfiability of the formulae with respect to truth-value assignment based on their security classes.
Research on the Human Vehicle Recognition System Based on Deep Learning Fusion Remove Haze Algorithm

The human vehicle identification system is a key module in the field of automatic driving, but the image quality obtained by the system is poor in haze weather, which makes it difficult for the system to identify the target. In view of the above problems, this paper studies the human vehicle recognition system based on deep learning and fusion of remove haze algorithm. This system is based on the dark channel priori with different resolution to research the image of removed haze, in order to effectively detect the categories and behaviors of moving objects in the image sensing area, introduce artificial intelligence into human vehicle recognition, propose a target detection algorithm based on deep learning and Kalman filtering and Hungarian algorithm. This algorithm can ensure the high-speed feedback of the detected object and improve the detection accuracy at the same time, solve the problem that small and medium targets are easy to be missed in the detection process. Finally, the feasibility of the algorithm is verified by experimental research.
Metal anomalies in till in the Sarvlaxviken area, Lovisa, southern Finland

Geochemical investigations of till is a widely used method in metal exploration as the till commonly inherits the geochemical signature (including the metal contents) of the parent bedrock. In this investigation, over 2000 till samples were collected in the Sarvlaxviken area, southern Finland, where several polymetallic (Cu, Zn, Pb, As, Sn, W and In) veins recently have been discovered in Proterozoic crust along the border between Late Svecofennian granites and the Wiborg Batholith. The bedrock is commonly covered by compact and poorly sorted basal till, formed during the Late Weichselian glaciation event. Several glacial-transported boulders, with high contents of Cu, Zn, As, Sn, Mo and Bi and derived from the local bedrock, have also been discovered on top of the till and provide evidence for concealed mineralisation in the local bedrock under the till cover. The frequent distribution of till in the Sarvlaxviken area provides excellent conditions for the search of such hidden mineralisation by means of systematic till sampling, even if large farm field areas, composed of clay-rich sediments, and seawater-covered areas (Sarvlaxviken bay), had to be avoided in the sampling program. The till samples were collected during university courses and training programs led by the authors and were analysed in a cost-efficient and certified laboratory. Obtained geochemical data were statistically processed by using K-means clustering algorithms which can be used to treat large sets of geochemical data. The results provided anomalies that mainly occur in till with a thickness of <1 m and are considered to be derived from a local bedrock source. The discovered anomalies provide strong evidence for numerous undiscovered veins beneath the till cover.
Intelligent Deep Neural Network integrated with Chaotic Particle Swarm Intelligence based Sentiment Analysis in Big Data Paradigm

In big data paradigm usage of sentiment analysis has rapidly increased its commendable pace in any kind of environment like political, social or present affairs. As it is easy to gather the sentiments of public of entire world are stated through the assistance of social media which is more suitable for sentiment mining. This paper focuses on analyzing the service of airline industries, by applying the fuzzy induced Intelligent deep neural network (IDNN) empowered with the knowledge of chaotic particle swarm optimization, which uses the twitter sentiment analysis on their respective passengers to get their feedback or opinion. This work used the deep learning model with the fuzzy control for fine tuning the weight assignment of the DNN more precisely. The tweets are extracted and they are preprocessed to extract the essential features involved in classification of the sentiments as either positive, negative or neutral. The fuzzy deep neural network is fine-tuned by integrating chaotic particle swarm optimization. The particle swarm behaviour improves the performance of the Fuzzy DNN by avoiding random selection of population to chaotic dynamics. The performance of the developed IDNN is compared with the support vector machine and random forest classifier. The results show that the proposed model well handles with imbalance voluminous dataset in big data paradigm with higher accuracy rate.
Fast Approximate String Search for Wikification

The paper presents a novel method for fast approximate string search based on neural distance metrics embeddings. Our research is focused primarily on applying the proposed method for entity retrieval in the Wikification process, which is similar to edit distance-based similarity search on the typical dictionary. The proposed method has been compared with symmetric delete spelling correction algorithm and proven to be more efficient for longer stings and higher distance values, which is a typical case in the Wikification task.
An Effective Ship Detection Method Based on RefineDet in SAR Images

Ship detection in synthetic aperture radar (SAR) imagery is a fundamental and challenging task. In view of the poor detection performance of traditional methods, we present an effective ship detection method based on RefineDet, which consists of three main parts, namely, an anchor refinement module, an object detection module and a transfer connection block. The proposed method can use the loss function to train uniformly. Experimental results on SSDD dataset illustrate that the proposed method obtains 98.4% AP, whose performance is significantly better than other methods, such as Faster R-CNN, SSD, RetinaNet, YOLOv2, and YOLOv3.
Swarm-based Algorithms Using Chaos for Task Scheduling in Cloud

A dispersed computing standard that assists the users is cloud computing. In this model, users pay as much as use. Cloud servers try to achieve high performance, and one of the main factors is optimal scheduling. Several metaheuristic techniques are used to solve the scheduling problem, which is an NP-hard problem. In this paper, for task scheduling in the cloud, we use Particle Swarm Optimization (PSO), Firefly Algorithm (FA), Bat Algorithm (BA), and Grasshopper Optimization Algorithm (GOA), which are swarm-based algorithms. All of these algorithms have one or more parameters that can be updated adaptively. We update these parameters using Chaos and compare their performance. The experimental results indicate that the improved GOA can optimize task scheduling problem by effective utilization of available resources.
Unscented Kalman Filter and Gauss-Hermite Kalman Filter for Range-Bearing Target Tracking

Radar systems are used for estimating the position and velocity of aircraft and ships from range and bearing measurements. In this article, we consider two motion models for a civilian aircraft: a constant velocity model and a coordinated turn model. Both kinematics models are written in state-space terms, where the velocity perturbations are modeled by a white noise acceleration model. In order to estimate the aircraft states given noisy measurements obtained from sensor outputs, we follow a Bayesian statistical approach that calculates the estimators of the unknown states. In our simulation, we recreate an air traffic control scenario and implement two nonlinear filtering algorithms in order to perform target tracking of the aircraft. The nonlinear Bayesian-based filters for this target tracking problem are the unscented Kalman filter and the Gauss-Hermite Kalman filter. Finally, the performance of both nonlinear filters is evaluated with a performance metric, root mean squared error, in Monte-Carlo runs.
Greedy Layer Pruning: Decreasing Inference Time of Transformer Models

Fine-tuning transformer models after unsupervised pre-training reaches a very high per-formance on many diﬀerent NLP tasks. Unfortunately, transformers suﬀer from longinference times which greatly increases costs in production. One possible solution isto use knowledge distillation, which solves this problem by transferring informationfrom large teacher models to smaller student models. Knowledge distillation main-tains high performance and reaches high compression rates, nevertheless, the size ofthe student model is ﬁxed after pre-training and can not be changed individually for agiven downstream task and use-case to reach a desired performance/speedup ratio. An-other solution to reduce the size of models in a much more ﬁne-grained and compu-tationally cheaper fashion is to prune layers after the pre-training. The price to pay isthat the performance of layer-wise pruning algorithms is not on par with state-of-the-artknowledge distillation methods. In this paper, greedy layer pruning (GLP) is introducedto (1) outperform current state-of-the-art for layer-wise pruning, (2) close the perfor-mance gap when compared to knowledge distillation, while (3) providing a methodto adapt the model size dynamically to reach a desired performance/speedup trade-oﬀ without the need of additional pre-training phases. Our surce code is available onhttps://github.com/deepopinion/greedy-layer-pruning.
Protecting Medical Images by Using Fused Cryptographic Technique with Fog Computing

Medical Service providers are moving towards Cloud Computing as information sharing is a prevalent stage for any medical organization. With the enhancement of innovation, a titanic measure of information is creating with time. Cloud Computing gives a tremendous information stockpiling limit with the adaptability of getting to it without the time and place limitations with virtualized assets. To access information from any topographical area, the medical services industry is moving towards cloud processing. Tremendous expansion in clinical pictures presents a major test for medical services suppliers as they need to oversee, process and share these data with low cost. Medical images show restraint's computerized records, which incorporate touchy data of patients. Apart from all the capable assistance given by cloud processing, security is an essential worry for different associations. To address the security issue, a few cryptographic strategies carried out by analysts around the world. In this paper, an efficient fused Cryptographic technique which is a combination of Elliptic Curve Cryptography and DNA Cryptography with Fog computing facility is discussed.
Agricultural Value Added, Food and Nutrition Security in West Africa: Realizing the SDG 2

This study examined how food security will be achieved in ECOWAS through agricultural value added. The study made use of panel data sourced from the World Bank World Development Indicators (WDI) and the Food and Agricultural Organisation (FAO) databases; it employed the fixed effects econometric technique. Results from the analysis showed that 1% increase in agricultural production, agriculture employment, agricultural valued added, political stability and absence of violence as well as access to electricity all things being equal, will bring about 14.2, 56.9, 1.9, 1.5 and 42.5% increase in food security. Thus, the study recommended that the governments of the West African countries should ensure that there is adequate security in the economies so as to encourage people to practice agriculture.
Low Illumination Image Enhancement Based on Image Fusion

In this paper, a new simple and effective fusion-based method for enhancement of low illumination images is proposed. Firstly, gamma correction is used to obtain the first image for fusion, and Contrast Limited Adaptive Histogram Equalization (CLAHE) is used to obtain the other. Secondly, we define two new weight maps and blend them by multi-scale fusion. Experimental results show our method has better effects both objective and subjective assessment.
Implications of E- Learning in Education: An Analysis

Computer technology and multimedia have long been used in classes in the field of education. It was usually used by faculties of engineering and science because of its benefits. A series of study conducted have analyzed that using digital methodology of teaching can make a class much more interesting compared to the traditional methodology. There are so many learners with so many diversities and needs, e-learning could be one of the innovative techniques to impart education to them. It is not to be considered that only students need to study through e-learning, rather the teachers too need this technology to impart education which sums up that e-learning is important technique for the students as well as teachers. Sometimes when a teacher needs to take a class through distance mode, he or she could easily conduct classes through digital classroom. Therefore, the paper would be an attempt to start with an introduction of the concept of multimedia, and how multimedia technology could be implemented to impart digital education to university students.
Reflections on a Decade of <i>Journal of Chemical &amp; Engineering Data</i> Editorial Service

ADVERTISEMENT RETURN TO ISSUEEditorialNEXTReflections on a Decade of Journal of Chemical & Engineering Data Editorial ServicePaul M. MathiasPaul M. MathiasFluor Corporation, Aliso Viejo, California 92698, United StatesMore by Paul M. Mathiashttp://orcid.org/0000-0001-5781-9525Cite this: J. Chem. Eng. Data 2021, 66, 6, 2333–2334Publication Date (Web):June 10, 2021Publication History Published online10 June 2021Published inissue 10 June 2021https://doi.org/10.1021/acs.jced.1c00351Copyright © Published 2021 by American Chemical SocietyRIGHTS & PERMISSIONSArticle Views316Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (554 KB) Get e-AlertsSUBJECTS:Vapor liquid equilibrium,Thermodynamics,Chemical engineering and industrial chemistry,Quality management,Theoretical and computational chemistry Get e-Alerts
Context Driven Network with Bayes for Weakly Supervised Temporal Action Localization

Weakly supervised temporal action localization (WTAL) aims to detect action instances from untrimmed videos. It may cause two problems, namely action incompleteness and background disturbance, due to only video-level class labels given. In this paper, we propose a context driven network with Bayes to alleviate the two problems, in which an attention module is used to predict coarse probability for each snippet, and then a Bayesian refinement module is designed to refine the coarse results by capturing the relationship between context snippets. Finally, the coarse and refined probabilities are combined as the inputs of the classifier for training. Quantitative and qualitative studies on two benchmark datasets, i.e., THUMOS’14 and ActivityNet 1.2, demonstrate that the proposed approach exceeds state-of-the-art methods.
Dynamic and Incremental Update of Mined Association Rules Against Changes in Dataset

Association rule mining (ARM) in data mining provides quality association rules based on support and confidence measures. These rules are interpreted by domain experts for making well-informed decisions. However, there is an issue with ARM when the dataset is subjected to changes from time to time. Discovering rules by reinventing wheel, scanning entire dataset every time in other words, consumes more memory, processing power, and time. This is still an open problem due to proliferation of different data structures being used for extracting frequent item sets. We proposed an algorithm for update of mined association rules when dataset changes occur. The algorithm is known as FIN_INCRE which exploits the preorder coded tree used by FIN algorithm for fast item set mining. The proposed algorithm outperforms the traditional approach as it mines association rules incrementally and dynamically updates mined association rules.
Color Image Watermarking Technique Using Principal Component in RDWT Domain

Protection of multimedia contents and false positive problem (FPP) are challenging issues for the researchers. The digital watermark is a scheme that resolves such issues by inserting watermark. To mitigate the same, a secure color watermarking scheme using singular value decomposition (SVD) in the redundant discrete wavelet (RDWT) domain is presented. Arnold transform (AT) is performed on the watermark image to enhance the security. The RDWT transform is computed over the cover image, and high frequency (HH) sub-band is considered for hiding the scrambled watermark into the principal component (PC). The experimental results validate that the proposed scheme is robust and imperceptible.
Analysis of Adversarial Attacks on Face Verification Systems

Recently, studies on generating adversarial examples on the Face Verification (FV) systems are one of the active research areas. The aim of the attack models is to deceive the FV system by adding the least amount of perturbation to maintain high structural similarity. In contradiction to the defense methods that try to protect the FV systems from attacks regardless of the amount of perturbation. In this paper, we present an analysis of the state-of-the-art adversarial attacks on FV systems, to determine the best value of the amount of perturbation to be added to the probe face images that maintain high structure similarity. Empirical experiment results validate the effectiveness of the amount of perturbation with structural similarity on the Labeled Faces in the Wild (LFW) benchmark dataset on attack setting. We show that increasing the amount of perturbation does not affect on the attack success rate, but it does have a negative effect on the structural similarity which decreased from 54% to 26% in FGSM and from 98% to 70% in PGD.
Research on the Comparison between Human Computer Translation Based on Neural Network Algorithm and Traditional Translation

Abstract With the improvement of computer processing ability, we have been able to apply a variety of algorithms to deal with problems in life. Traditional translation is a kind of artificial translation or cat translation, which is a simple translation of vocabulary and grammar. With the deepening of international trade, traditional translation can’t meet the needs of people, which needs to improve the level of human-computer translation. With the rapid development of machine learning, neural network algorithm and deep learning algorithm have been gradually applied to machine translation, which has made machines process text, language and other information like human beings. In recent years, neural network algorithm has brought a broader prospect to natural language processing, which can deal with text semantics more naturally. Traditional translation is difficult to process information semantically, which is a way of relying on artificial information. With the implementation of neural network algorithm, human-computer translation system can improve the speed of language translation, which can better improve the ability of autonomous learning. Firstly, this paper analyzes the related concepts. Then, this paper analyzes some neural network algorithms. Finally, this paper analyzes the differences between man-machine translation and traditional translation.
Development of automatic spam detection application based on modular programming

Abstract This article discusses developing an application for automatic spam detection based on modular programming. In general website development, certain development features cannot be used on other similar websites. We use modular programming to solve this problem. The problem is how to develop an automatic spam detection application based on modular programming. We used the software prototyping method for application development. The results of this study are obtained an automatic spam detection website application based on modular programming.
Chatbots as a Game Changer in E-recruitment: An Analysis of Adaptation of Chatbots

Recruitment chatbots with artificial intelligence mediate communication between jobseekers and recruiters. Chatbots are the dream assistant to the busy recruiter, and chatbots are trying to make sure it takes away monotonous and tedious task. We have come to rely on a key contact role in an effort to improve organizational and technical transparency. While efficient for design and development, centralizing this role poses challenges to transparency in this kind of sustained maintenance. Interaction with chatbots seems natural and human like. It can be tailored according to the organization’s requirements (Shawar and Atwell, in LDV-Forum 2007 22(1):29–49, 2017 ( Shawar BA, Atwell E (2017) chatbots: are they really useful? LDV-Forum 2007 22(1):29-49)). Big IT companies could not ignore these developments. So, the Big Four, Apple, Microsoft, Amazon, and Google, have taken the issue seriously by investing in the creation of their own chatbot. The paper's key aim is to evaluate the impact of chatbots on the e-recruitment process and to analyze chatbots’ role in improving communication between organizations and candidates. The study also tries to assess how chatbots speed up hiring process and it plays an important role in the early stages of hiring tunnel. Various statistical tools such as confirmatory factor analysis, correlation, and regression were used to test the hypothesis of the study through SPSS-20.