Reviewerly/exHarmony huggingface dataset:
Mortality Prediction in ICU Patients Using Machine Learning Models

Effective utilization of limited intensive care unit (ICU) allocations is a challenging task for medical experts to save precious human lives. The prolonged ICU stay of relatively secure patients and patients with low chances of recovery can cause life-threatening effects on patients waiting for ICU accommodation. Machine learning-based techniques for early prediction of mortality can help in this regard. This paper presents two mortality prediction models using the support vector machine (SVM) and linear discriminant analysis. The proposed models use clinical data of the ICU patients for early prediction of mortality. Distribution filtering is performed using the chi-square distribution during pre-processing. A subset of the publicly available Medical Information Mart for Intensive Care (MIMIC-III v1.4) dataset is used for the evaluation of the proposed models. The problem of class imbalance is handled by synthetic minority oversampling technique. The comparison of obtained results is performed with existing SVM and multiple logistic regression models to show the effectiveness of the proposed models. The results of the study can be helpful for clinical experts for better decision making regarding the utilization of ICU allocations.
A new form of initialization vectors in the FMS attack of RC4 in WEP

S. Fluhrer, I. Mantin and A. Shamir (FMS) had put forward a fascinating attack of the key of RC4 in Wired Equivalent Privacy (WEP) protocol on the basis of special initialization vectors (IVs) of pattern (3, 255, v). Inspired by the FMS attack, this paper tries to find new pattern of IVs that can be used for recovering the key of RC4 in WEP. We discovered that IVs of new pattern (v, 257-v, 255) can also be used in the FMS attack in a very similar way. Combining the new pattern with the previous pattern (3, 255, v) could result in a higher proportion of IVs that can be exploited in the FMS attack.
Similar Trademark Detection via Semantic, Phonetic and Visual Similarity Information

Millions of trademarks were registered last year in China, and thousands of applications are submitted daily. A trademark must be unique in the category it belongs to. Therefore, each new trademark application needs to be checked against all the existing ones in its category. A trademark can be a text string (characters, words or phrases), a figure (symbol or design), or both. In this study, we focus on the textual trademark in Chinese, and propose a model for finding similar trademarks for a given one. This neural network model exploits the semantic, phonetic and visual similarities between two textual trademarks. We evaluated our model based on a dataset that were built from the real trademark application data. Our evaluation shows that the proposed model outperforms other approaches.
Research on Security Consensus Algorithm Based on Blockchain

As a distributed shared ledger and database technology, blockchain has the characteristics of decentralization, tamper-proof, openness and transparency. With the continuous development of blockchain technology, the technology has received high attention from government departments, finance, technology and other industries. The key technology of blockchain is the design of consensus algorithm, which affects the overall efficiency and performance of the blockchain system. How to achieve consistency and efficiency among nodes in a simple and efficient blockchain distributed system. This paper divides consensus algorithms into CFT and BFT according to fault tolerance types, and analyzes the basic principles and consensus process of each algorithm in detail. In five aspects of block generation speed, degree of decentralization, security, consistency and resource consumption, the mainstream consensus algorithms in the blockchain are compared and analyzed, the advantages and disadvantages of each consensus algorithm are summarized. Finally, the future blockchain consensus algorithms are summarized and prospected.
Training Machine Learning Models Through Preserved Decentralization

In the era of big data, fast and effective machine learning algorithms are urgently required for large-scale data analysis. Data is usually created from several parts and stored in a geographically distributed manner, which has stimulated research in the field of distributed machine learning. The traditional master-level distributed learning algorithm involves the use of a trusted central server and focuses on the online privacy model. On the contrary, the specific linear learning model and security issues are not well understood in this column. We built a decentralized advanced-Proof-of-Work (aPoW) algorithm specifically for learning a general predictive model over the blockchain. In aPoW, we establish the data privacy of the differential privacy based schemes to protect each party and propose a secure domain against potential Byzantine attacks at a reduced rate. We explored a technical module in newsprint to consider a universal learning model (linear or non-linear) to provide a secure, confidential decentralized machine learning system called deepLearning Chain. Finally, we introduce deepLearning Chain on blockchain through comprehensive experiments, demonstrate its performance and effectiveness.
Match Plan Generation in Web Search with Parameterized Action Reinforcement Learning

To achieve good result quality and short query response time, search engines use specific match plans on Inverted Index to help retrieve a small set of relevant documents from billions of web pages. A match plan is composed of a sequence of match rules, which contain discrete match rule types and continuous stopping quotas. Currently, match plans are manually designed by experts according to their several years’ experience, which encounters difficulty in dealing with heterogeneous queries and varying data distribution. In this work, we formulate the match plan generation as a Partially Observable Markov Decision Process (POMDP) with a parameterized action space, and propose a novel reinforcement learning algorithm Parameterized Action Soft Actor-Critic (PASAC) to effectively enhance the exploration in both spaces. In our scene, we also discover a skew prioritizing issue of the original Prioritized Experience Replay (PER) and introduce Stratified Prioritized Experience Replay (SPER) to address it. We are the first group to generalize this task for all queries as a learning problem with zero prior knowledge and successfully apply deep reinforcement learning in the real web search environment. Our approach greatly outperforms the well-designed production match plans by over 70% reduction of index block accesses with the quality of documents almost unchanged, and 9% reduction of query response time even with model inference cost. Our method also beats the baselines on some open-source benchmarks1.
Mapping XML to RDF: An algorithm based on element classification and aggregation

Abstract Many data in Web are exchanged and stored in XML. RDF achieves rapid response and reasoning by aggregating a large amount of knowledge. It has been widely used in intelligent field as a way of data organization. Building RDF based on XML is the focus of this article. This paper analyzes the tree structure of XML, divides the XML elements into three sub-models according to the structure, then aggregates and classifies XML elements, and abstracts the aggregates into three abstract structure models, and finally proposes related mapping rules. Based on the optimization of existing methods, this paper proposes an XML-to-RDF mapping algorithm based on element classification and aggregation, which is reflected in three aspects: optimized mapping rules to ensure the completeness and accuracy of data content and semantics; proposed general algorithms for different types of XML; identified equivalent elements of XML and solved the problem of RDF data redundancy caused by such equivalent elements.
Credit Card Fraud Detection by Combining Synthetic Minority Oversampling and Edited Nearest Neighbours

Nowadays, humans depend on AI in many fields such as infrastructure, medicine, banking etc., thus we need deeper understanding on how AI-based infrastructures can be further improved to make our lives easier, more secure and more comfortable. Over the last few years Cybercrime has been increasing significantly in payment transactions and particularly credit cards. Therefore AI’s functionality must be improved and better accuracy in detecting fraudulent transactions has yet to be achieved as Artificial Intelligence Fraud Detection (AIFD) pal as well as a shared API which can engage in delivering secure communication between banks and customers. This paper proposes an AIFD based on an advanced deep neural network. The network combines hybrid Synthetic Minority Oversampling and Edited Nearest Neighbours techniques that allows us to improve the detection accuracy on an European credit card data benchmark to 98%.
Non-classicality created by quantum channels with indefinite causal order

Non-classicality of output states of quantum channels with indefinite causal order is studied, where each channel maps any state to a fixed one. The causal indefiniteness means a coherent superposition of the order of which channel first interacts with a quantum system. Non-classicality of a single-mode photon system is investigated in terms of the non-classical depth and the Mandel Q-parameter. It is shown that the thermalizing channels with indefinite causal order can create non-classicality even when an input state is classical.
Person Following from a Nonholonomic Mobile Robot with Ultimately Bounded Tracking Error

In robotics, "person following" depicts the servoing of the relative situation of a robot w.r.t. a moving person. This property may be hard to achieve, especially when the estimation of the person ego-motion is weak (e.g., due to limited prior knowledge or computational resources). This paper introduces a nonholomic mobile robot controller, which ensures an intuitive and safe behavior through an insightful robot-centered problem statement. Under realistic bounded-error readings of hidden constant person velocities, ultimate boundedness of the state vector norm can be ensured in the neighborhood of its equilibrium.
Breaking the Specification: PDF Certification

The Portable Document Format (PDF) is the de-facto standard for document exchange. The PDF specification defines two different types of digital signatures to guarantee the authenticity and integrity of documents: approval signatures and certification signatures. Approval signatures testify one specific state of the PDF document. Their security has been investigated at CCS’19. Certification signatures are more powerful and flexible. They cover more complex workflows, such as signing contracts by multiple parties. To achieve this goal, users can make specific changes to a signed document without invalidating the signature.This paper presents the first comprehensive security evaluation on certification signatures in PDFs. We describe two novel attack classes – Evil Annotation and Sneaky Signature attacks which abuse flaws in the current PDF specification. Both attack classes allow an attacker to significantly alter a certified document’s visible content without raising any warnings. Our practical evaluation shows that an attacker could change the visible content in 15 of 26 viewer applications by using Evil Annotation attacks and in 8 applications using Sneaky Signature by using PDF specification compliant exploits. We improved both attacks’ stealthiness with applications’ implementation issues and found only two applications secure to all attacks. On top, we show how to gain high privileged JavaScript execution in Adobe.We responsibly disclosed these issues and supported the vendors to fix the vulnerabilities. We also propose concrete countermeasures and improvements to the current specification to fix the issues.
Trajectory planning with obstacle avoidance for a concrete pump using harmonic potentials

The operation of concrete pumps is increasingly supported by assistance systems. They facilitate the complex control task and reduce the risk of accidents. In this paper a trajectory planner for point-to-point motion of a concrete pump is presented. The method is based on harmonic artificial potentials to plan the tool center point motion in the task space and constrained quadratic optimization to convert the task space motion into the configuration space. The algorithm is validated by simulation for a five-link concrete pump.
A bottom-up and top-down human visual attention approach for hyperspectral anomaly detection

Hyperspectral anomaly detection (HAD) is a branch of target detection which tries to locate pixels that are spectrally or spatially different from their background. In this paper, a visual attention approach is developed to leverage HAD. Traditional HAD methods often try to locate anomalous pixels based on spectral information. However, the spatial features of hyperspectral datasets provide valuable information. Here, we aim to fuse spatial and spectral anomaly features based on bottom-up (BU) and top-down (TD) visual attention mechanisms. Owe to the BU attention, spatial features are extracted by mimicking the primary visual cortex neurons functionality. Also, spectral information is obtained throughout a deep neural network that imitating the TD visual attention. The BU and TD approaches' results are then integrated to provide both spectral and spatial information. The key findings of our results demonstrate the proposed method outperforms the six state-of-the-art AD methods based on different evaluation metrics.
Streaming changepoint detection for transition matrices

Abstract Sequentially detecting multiple changepoints in a data stream is a challenging task. Difficulties relate to both computational and statistical aspects, and in the latter, specifying control parameters is a particular problem. Choosing control parameters typically relies on unrealistic assumptions, such as the distributions generating the data, and their parameters, being known. This is implausible in the streaming paradigm, where several changepoints will exist. Further, current literature is mostly concerned with streams of continuous-valued observations, and focuses on detecting a single changepoint. There is a dearth of literature dedicated to detecting multiple changepoints in transition matrices , which arise from a sequence of discrete states. This paper makes the following contributions: a complete framework is developed for adaptively and sequentially estimating a Markov transition matrix in the streaming data setting. A change detection method is then developed, using a novel moment matching technique, which can effectively monitor for multiple changepoints in a transition matrix. This adaptive detection and estimation procedure for transition matrices, referred to as ADEPT-M , is compared to several change detectors on synthetic data streams, and is implemented on two real-world data streams – one consisting of over nine million HTTP web requests, and the other being a well-studied electricity market data set.
Matrix factorization with dual-network collaborative embedding for social recommendation

Combining Matrix Factorization (MF) with Network Embedding (NE) has been a promising solution to social recommender systems. However, in most of the current combined schemes, the user-specific linking proportions learned by NE are fed to the downstream MF, but not reverse, which is sub-optimal as the rating information is not utilized to discover the linking features for users. Furthermore, the existing combined models mainly focus on enhancing the representation learning for users by exploiting user–user network, yet ignore the representation improvement for items. In this paper, we propose a novel social recommendation scheme, called MF with dual-network collaborative embedding (MF-decoding), which jointly optimizes an integrated objective function of MF and NE, in which both MF and NE tasks can be mutually reinforced in a unified learning process. In particular, the explicit user–user network and the implicit item–item network are collaboratively used by MF-decoding to enhance the representation learning for users and items simultaneously. Our encouraging experimental results on three benchmarks validate the superiority of the proposed MF-decoding model over state-of-the-art social recommendation methods.
How to form a software engineering capstone team?

<h2>Abstract</h2> This research paper answers the question that how shall the students of software engineering undergraduate courses form teams for the capstone projects that can be cohesive too. In this research, 128 criteria for team formation are proposed for building teams for self-managing software engineering capstone projects. A comparison is also conducted to ascertain the level of cohesion among those teams that were formed using the proposed criteria and those that were not formed using the proposed criteria. The criteria were identified through a combination of qualitative questionnaire survey targeted at the graduated students of the past batches of Computer Science degree program and through synthesizing the literature on engineering capstone project teams identified under the guidance of KSAO framework for software engineering students. To check the effectiveness of the criteria, 100 students were asked to form the teams using the proposed criteria and other 100 students formed the teams without the proposed criteria. Those students that had used the proposed criteria for building teams and those that had formed teams without using the proposed criteria were asked to fill the modified Group Environment Questionnaire to ascertain the level of cohesion among the team members. The results were analyzed qualitatively and through descriptive quantification. The results show that the level of cohesion in teams that were formed using the proposed team building criteria was higher. There was a need for team building criteria in the literature on software engineering capstone project teams that conforms to a conceptual, theoretical framework; this gap is now filled through this research. This paper may also serve as a literature review paper for some readers.
Sailfish: Vetting Smart Contract State-Inconsistency Bugs in Seconds

This paper presents SAILFISH, a scalable system for automatically finding state-inconsistency bugs in smart contracts. To make the analysis tractable, we introduce a hybrid approach that includes (i) a light-weight exploration phase that dramatically reduces the number of instructions to analyze, and (ii) a precise refinement phase based on symbolic evaluation guided by our novel value-summary analysis, which generates extra constraints to over-approximate the side effects of whole-program execution, thereby ensuring the precision of the symbolic evaluation. We developed a prototype of SAILFISH and evaluated its ability to detect two state-inconsistency flaws, viz., reentrancy and transaction order dependence (TOD) in Ethereum smart contracts. Further, we present detection rules for other kinds of smart contract flaws that SAILFISH can be extended to detect.Our experiments demonstrate the efficiency of our hybrid approach as well as the benefit of the value summary analysis. In particular, we show that S SAILFISH outperforms five state-of-the-art smart contract analyzers (SECURIFY, MYTHRIL, OYENTE, SEREUM and VANDAL ) in terms of performance, and precision. In total, SAILFISH discovered 47 previously unknown vulnerable smart contracts out of 89,853 smart contracts from ETHERSCAN.
A Modern Approach to MOOC Recommender Systems Using ICT Job Classification

In the field of Information Communication Technology (ICT), the demand for highly skilled professionals is increasing. To keep up with the modernized technology trends and to update the skill set, the best way for an ICT specialist or an undergraduate student is to register in a MOOC platform and follow the appropriate courses. However, given the large variety of MOOC platforms and courses, they may get confused as to which are the most beneficial and time worthy choices. The authors provide research of the users’ requirements, existing MOOC recommender systems and their features and propose a client-server recommender system which enables users to choose an existing MOOC platform, based on ICT jobs and skills. The paper proposes a modern approach that helps IT specialists to plan their future development path based on MOOC recommendations, specific to ICT jobs and relevant to their development needs.
Analysing Political Bias in Social Media

India is the greatest world-wide democracy system having the largest number of citizens as voters. Our country is additionally the place that is known for the most day by day distributions. The media houses have an adequate measure of users in the nation.These media portals are celebrated for their fast reporting but their political biasing is often fairly evident. Social media is a driving force behind the integration of news media and politics. Political news on social media platforms influences people’s perception of political figures. This propagation can be both helpful and harmful to our community. In our research, attempt is made to reveal inclination of news channels getting biased towards a political party, which can mislead the viewers. An objective watcher, knowing the accurate degree of the predisposition, can understand that in many cases terrible news isn’t accounted for and the uplifting news is misrepresented. To decipher every bit of such information available in the public domain can be quite useful in predicting the outcome of an election. The goal of our research is to build an analyzer capable of detecting political bias from twitter tweets of these news channels. With the help of web scraping and data mining techniques, this information can be captured in both structured and unstructured form at an unprecedented scale and analyzed to predictbias.
Using Twitter Data for the Study of Language Change in Low-Resource Languages. A Panel Study of Relative Pronouns in Frisian

This paper investigates the usability of Twitter as a resource for the study of language change in progress in low-resource languages. It is a panel study of a vigorous change in progress, the loss of final t in four relative pronouns ( dy't, dêr't, wêr't, wa't ) in Frisian, a language spoken by ± 450,000 speakers in the north-west of the Netherlands. This paper deals with the issues encountered in retrieving and analyzing tweets in low-resource languages, in the analysis of low-frequency variables, and in gathering background information on Twitterers. In this panel study we were able to identify and track 159 individual Twitterers, whose Frisian (and Dutch) tweets posted in the era 2010–2019 were collected. Nevertheless, a solid analysis of the sociolinguistic factors in this language change in progress was hampered by unequal age distributions among the Twitterers, the fact that the youngest birth cohorts have given up Twitter almost completely after 2014 and that the variables have a low frequency and are unequally spread over Twitterers.