Reviewerly/exHarmony huggingface dataset:
Evolutionary Feature Scaling in K-Nearest Neighbors Based on Label Dispersion Minimization

K-Nearest Neighbors (KNN) has remained one of the most popular methods for supervised machine learning tasks. However, its performance often depends on the characteristics of the dataset and on appropriate feature scaling. In this paper, we explore characteristics of a dataset that make it suitable for being used within KNN. As part of this, two new measures for dataset dispersion, called mean neighborhood target standard deviation (MNTSD), and mean neighborhood target entropy (MNTE) are formulated to determine the expeced performance while using KNN regressors and classifiers, respectively. It is empirically demonstrated that these measures of dispersion can be indicative of the performance of KNN regression and classification. This idea is further used to learn feature weights that help improve the accuracy of KNN classification and regression. For this, it is argued that the MNTSD and MNTE, when used to learn feature weights, cannot be optimized using gradient-based optimization methods and we develop optimization strategies based on metaheuristic methods, namely genetic algorithms and particle swarm optimization. The feature-weighting method is tried in both regression and classification contexts on publicly available datasets, and the performance is compared to KNN without feature weighting. The results indicate that the performance of KNN with appropriate feature weighting leads to better performance.
A Decentralized, Privacy-preserving and Crowdsourcing-based Approach to Medical Research

Access to data at large scales expedites the progress of research in medical fields. Nevertheless, accessibility to patients' data faces significant challenges on regulatory, organizational and technical levels. In light of this, we present a novel approach based on the crowdsourcing paradigm to solve this data scarcity problem. Utilizing the infrastructure that blockchain provides, our decentralized platform enables researchers to solicit contributions to their well-defined research study from a large crowd of volunteers. Furthermore, to overcome the challenge of breach of privacy and mutual trust, we employed the cryptographic primitive of Zero-knowledge Argument of Knowledge (zk-SNARK). This not only allows participants to make contributions without exposing their privacy-sensitive health data, but also provides a means for a distributed network of users to verify the validity of the contributions in an efficient manner. Finally, since without an incentive mechanism in place, the crowdsourcing platform would be rendered ineffective, we incorporated smart contracts to ensure a fair reciprocal exchange of data for reward between patients and researchers.
Organization of Enterprise Digital Infrastructure

Effective transformation of an enterprise management system being in condition of organization's information management infrastructure digitalization is implemented through the project approach and such a form of inter-institutional interaction as IT outsourcing taking into account strategic targeting of outsource projects. Widening the practice of the use of information communication technologies, networking of enterprise management in terms of integration their business-processes with the use the common information communication infrastructure is the basis for transition to technologies of the fourth industrial revolution. On the strong base tasks of digitalization have entered to the production organization plans and obtained additional relevance in the connection with launching of national programs defining a vector of long-term scientific and technological as well as economic development. Cooperation with suppliers, industrial partners and clients, as well as distribution of information into the company are carried out within the general collaboration of processes. In the paper are considered the main stages of production organization in condition of digital transformation. It is shown a connection between the use of automatically controlled means in the organization of drawing production with the assessment of measures of efficiency if these means are used. It is mentioned that forming of digital infrastructure is built on the base of interrelation of automated enterprise elements and specialists affecting within the product life cycle implementation. Scenarios for formation of collaborative production which is the result of globalization organizational and technical interactions of many enterprises are considered.
A TCM Question and Answer System Based on Medical Records Knowledge Graph

In recent years, the combination of knowledge map and question and answer system are more and more applied in professional fields. In this paper, we firstly constructed a knowledge graph of traditional Chinese medical records which contains four entities: herbs, prescriptions, diseases, symptoms and their relationships; Secondly, we use Node2vec algorithm to represent the graph; Finally, we match questions and answers based on KNN. Experiment result shows that with the output of five prescriptions, our system's accuracy reaches 93.4%, which achieves good results.
Behavior Recognition Based on Category Subspace in Crowded Videos

Crowd behavior refers to a collective behavior composed of two or more individuals who influence, interact, and depend on each other for a specific goal. Compared with an ordinary crowd behavior, the probability of a dangerous crowd behavior is much smaller. Video-based crowd behavior recognition can be categorized as one multi-label classification task, which is characterized by complex scenes and imbalanced samples. Aimed at tackling problems of imbalanced samples and multi-label task, a classification method of associative subspace is proposed. For a single category (called main category) with fewer samples, this paper generates a special subspace wherein it is relatively easy to distinguish these samples by association with other categories. A classifier that can weaken the main category and strengthen relationship between the main category and other categories is designed in the subspace. Therefore, the main category can contribute to reducing dependence on the number of samples with the above-mentioned classifier in the corresponding subspace. In order to make full use of the relevant information concerning categories, multi-label information is further injected into spatio-temporal features of video action representation. Experiments on a challenging WWW dataset show that both the proposed subspace method and multi-label information fusion mechanism are efficient.
Partial Key Attack Given MSBs of CRT-RSA Private Keys

The CRT-RSA cryptosystem is the most widely adopted RSA variant in digital applications. It exploits the properties of the Chinese remainder theorem (CRT) to elegantly reduce the size of the private keys. This significantly increases the efficiency of the RSA decryption algorithm. Nevertheless, an attack on RSA may also be applied to this RSA variant. One of the attacks is called partially known private key attack, that relies on the assumption that the adversary has knowledge of partial bits regarding RSA private keys. In this paper, we mount this type of attack on CRT-RSA. By using partial most significant bits (MSBs) of one of the RSA primes, p or q and its corresponding private exponent, d, we obtain an RSA intermediate. The intermediate is derived from p−1 and RSA public key, e. The analytical and novel reason on the success of our attack is that once the adversary has obtained the parameters: approximation of private exponent d˜p, approximation of p, p˜ and the public exponent e where d˜p,p˜,e=Nα/2 where 0&lt;α≤1/4 such that |dp−d˜p|,|p−p˜|&lt;N1−α2 and has determined the largest prime of p−1e, it will enable the adversary to factor the RSA modulus N=pq. Although the parameter space to find the prime factor is large, we show that one can adjust its “success appetite” by applying prime-counting function properties. By comparing our method with contemporary partial key attacks on CRT-RSA, upon determining a suitable predetermined “success appetite” value, we found out that our method required fewer bits of the private keys in order to factor N.
Supporting child–group interactions with hands-off museum exhibit

Technology can be used to augment visitors’ experience with a museum exhibit and provide museum visitors with means of interacting with otherwise hands-off exhibits. However, introducing technology to support interactivity has the likelihood of usurping users’ attention from the exhibit or diminishing interactions with other visitors. Our approach to this problem involves supporting hands-on interactions for groups visiting a pollinator garden—a science museum exhibit containing fragile species where touching is discouraged. Through interviews with museum experts and docents, we elicited design goals that we then enfolded into an Android application that leverages visual recognition to support interactions with the exhibit. We tested our application in-situ with 65 children in three groups and subsequently propose and describe design approaches that uses the ethos of scaling to support different number of users and leveling to varied cognitive levels. We conclude by recommending reusable designs guidelines to support interactions within other evolving and hands-off contexts.
An Insight into Applications of Internet of Things Security from a Blockchain Perspective

The Internet of Things (IoT) has grown wildly in the last years but the security of the generated data has not improved at the same proportion as the number of devices connected to the Internet has multiplied, and this is very worrying because privacy is one of the most important topics when a private IoT application is developed. As the growing number of IoT application is taking place more strategies for data security is required to deal with different scenarios of the wide and vast type of IoT applications. An investigation in order to find proper solutions to common IoT applications that have been challenged for improvements in data security is the goal of this paper. Through blockchain technology that offers security naturally without any expensive investment, the data security that is required by IoT devices become reachable. This paper presents a discussion in order to clarify how the security on Internet of Things applications can be enhanced using blockchain providing a correlation between the capabilities of both technologies.
Endpoint Temperature Prediction of Molten Steel in VD Furnace Based on AdaBoost.RT-ELM

The endpoint temperature of the molten steel in the VD (Vacuum Degassing) furnace is an important parameter determining the quality of the finished steel products. Based on the in-depth analysis of the vacuum refining process of the VD furnace, combined with the field data, the effective preprocessing of the data was completed. Then the NNG (Non-Negative Garrote) variable selection algorithm is used to determine the input variables. An integrated ELM (Extreme Learning Machine) molten steel endpoint temperature modeling method based on AdaBoost.RT is proposed. Experimental simulation results show that the AdaBoost.RT-ELM prediction model has significantly improved prediction accuracy than a single ELM.
Evaluating Effectiveness of Adversarial Examples on State of Art License Plate Recognition Models

Deep learning takes advantage of large datasets and computationally efficient training algorithms to outperform other approaches at various machine learning tasks. However, DNNs are vulnerable to adversarial examples due to imperfections in the training phase. In this work, we generate some adversarial examples to test their effectiveness against the state-of-the-art License Plate Recognition (LPR) models.
Imprecise Deep Forest for Partial Label Learning

In partial label (PL) learning, each instance corresponds to a set of candidate labels, among which only one is valid.The objective of PL learning is to obtain a multi-class classifier from the training instances.Because the true label of a PL training instance is hidden in the candidate label set and inaccessible to the learning algorithm, the training process of the classifier is significantly challenging.This study proposes a novel deep learning method for PL learning based on the improved error-correcting output codes (ECOC) algorithm and deep forest (DF) framework.For the ECOC algorithm, we extract the prior knowledge of the candidate label sets from the PL training set to optimize the generation of its coding matrix, where different binary training sets can be derived from the PL training set based on the dichotomy corresponding to each column code.For the DF framework, this improved ECOC algorithm is embedded as a unit in its cascade structure; moreover, an imprecise evaluation method is designed to determine the growth of the cascade of the DF.The effectiveness of the proposed method is verified by conducting several experiments on artificial and real-world PL datasets.
HeapExpo: Pinpointing Promoted Pointers to Prevent Use-After-Free Vulnerabilities

Use-after-free (UAF) vulnerabilities, in which dangling pointers remain after memory is released, remain a persistent problem for applications written in C and C++. In order to protect legacy code, prior work has attempted to track pointer propagation and invalidate dangling pointers at deallocation time, but this work has gaps in coverage, as it lacks support for tracking program variables promoted to CPU registers. Moreover, we find that these gaps can significantly hamper detection of UAF bugs: in a preliminary study with OSS-Fuzz, we found that more than half of the UAFs in real-world programs we examined (10/19) could not be detected by prior systems due to register promotion. In this paper, we introduce HeapExpo, a new system that fills this gap in coverage by parsimoniously identifying potential dangling pointer variables that may be lifted into registers by the compiler and marking them as volatile. In our experiments, we find that HeapExpo effectively detects UAFs missed by other systems with an overhead of 35% on the majority of SPEC CPU2006 and 66% when including two benchmarks that have high amounts of pointer propagation.
Learning to generate structured queries from natural language with indirect supervision

Generating structured query language (SQL) from natural language is an emerging research topic. This paper presents a new learning paradigm from indirect supervision of the answers to natural language questions, instead of SQL queries. This paradigm facilitates the acquisition of training data due to the abundant resources of question-answer pairs for various domains in the Internet, and expels the difficult SQL annotation job. An end-to-end neural model integrating with reinforcement learning is proposed to learn SQL generation policy within the answer-driven learning paradigm. The model is evaluated on datasets of different domains, including movie and academic publication. Experimental results show that our model outperforms the baseline models.
Cryptosystem Based on the Elliptic Curve With a High Degree of Resistance to Damage on the Encrypted Images

In this work, a novel symmetric cryptosystem for image encryption is presented. The Symmetric Cryptosystem of the Elliptic Curve (SCEC) can resist damage to encrypted figures, up to 40% of the original figure. SCEC uses chaos to generate an 8×8 S-box with high nonlinearity to avoid the linear attack. A random permutation is used before starting encryption, making the cryptosystem more robust. For testing, damage according to four types of noise was applied to the encrypted images: additive, multiplicative, Gaussian, or occlusion. The median filter was applied to correct the damage in encrypted images, improving its sharpness, and a new measure, the Similarity Parameter (SP), is proposed to evaluate the difference between the original image and the decrypted image with damage. Several parameters and tests were applied to evaluate the performance of SCEC, from the encryption quality to the resistance to the differential attack. Experimental results indicate that SCEC has high-quality cryptographic properties, very much similar to the corresponding values of AES but with the addition of a high protection to noise damage on the encrypted images.
SJSON: A succinct representation for JSON documents

The massive amounts of data processed in modern computational systems are becoming a problem of increasing importance. This data is commonly stored directly or indirectly through the use of data exchange languages, such as JSON (JavaScript Object Notation) and XML (eXtensible Markup Language), for human-readable platform-agnostic access. This paper focuses on exploring a set of succinct representations for JSON documents, which we call SJSON, achieving both reduced RAM and disk usage while supporting efficient queries on the documents. The representations we propose are mainly based on the idea that JSON documents can be decomposed into structural part and raw data part. In our method, we emulate the structure of the JSON document as a rooted ordered tree and represent it using succinct data structures, as opposed to the usual pointer-based implementation. Furthermore, the remaining raw data is reorganized into arrays of attributes and values. This deconstruction between structure and data allows for a straightforward connection between a node in the succinct tree and its corresponding name–value pair, dispensing pointers altogether. The proposed scheme is implemented as the SJSON library in C++, and evaluated with respect to a number of metrics, comparing its performance with popular alternative JSON parsers. Empirical results show that the library is able to represent JSON files succinctly while efficiently supporting traversal queries.
Urdu Ligature Recognition System: An Evolutionary Approach

Cursive text recognition of Arabic script-based languages like Urdu is extremely complicated due to its diverse and complex characteristics. Evolutionary approaches like genetic algorithms have been used in the past for various optimization as well as pattern recognition tasks, reporting exceptional results. The proposed Urdu ligature recognition system uses a genetic algorithm for optimization and recognition. Overall the proposed recognition system observes the processes of pre-processing, segmentation, feature extraction, hierarchical clustering, classification rules and genetic algorithm optimization and recognition. The pre-processing stage removes noise from the sentence images, whereas, in segmentation, the sentences are segmented into ligature components. Fifteen features are extracted from each of the segmented ligature images. Intra-feature hierarchical clustering is observed that results in clustered data. Next, classification rules are used for the representation of the clustered data. The genetic algorithm performs an optimization mechanism using multi-level sorting of the clustered data for improving the classification rules used for recognition of Urdu ligatures. Experiments conducted on the benchmark UPTI dataset for the proposed Urdu ligature recognition system yields promising results, achieving a recognition rate of 96.72%.
Siamese Network cooperating with Multi-head Attention for semantic sentence matching

To compare a pair of sentences is a fundamental technology in many NLP tasks. According to the difference between the pair of sentence, we divide semantic sentence matching into two situations: Situation A is that the pair of sentences are worded with a context relationship, Situation B is that two are equal in semantics. Models for Situation A works in Situation B too, so prior deep work mostly model each sentence's representation considering the interaction of the other sentence simultaneously. However, models designed for Situation A bring redundant information for Situation B. In this paper, for sentence pairs with equivalence, we present a deep architecture with comparison-interaction separated to match two sentences, which based on Siamese network for comparison and multi-head attention for interaction information between sentence pairs. Experimental results on the latest Chinese sentence matching datasets outline the effectiveness of our approach.
User Interface of Zakat Information System Redesign using Design Thinking Approach. Case Study: KNEKS

The realization of the amount of zakat recorded in Indonesia is still very small compared to its potential. This happens because the existing information system has not facilitated and optimized the process of recording zakat in Indonesia by the Badan Amil Zakat Nasional (BAZNAS), Lembaga Amil Zakat (LAZ), and Unit Pengumpul Zakat (UPZ). This research focuses on designing the appearance and features of Sistem Informasi BAZNAS (SiMBA) which is currently the focus of the Komite Nasional Ekonomi dan Keuangan Syariah (KNEKS) in advancing zakat management in Indonesia. The Design Thinking approach is chosen because it is an approach that provides solutions to existing problems and is highly oriented towards user needs (Schallmo, 2018). By using the Empathy Map and Customer Journey Map visualization methods, the Usability Testing scenario test method, and the survey method of the Post-Study System Usability Questionnaire (PSSUQ) in the Design Thinking stage, researcher can provide the most suitable recommendation for the User Interface of the information system to meet the users' needs.