Reviewerly/exHarmony huggingface dataset:
Single index fuzzy neural networks using locally weighted polynomial regression

The novel single index fuzzy neural network models are proposed in this paper for general machine learning problems. The proposed models are different from the usual fuzzy neural network models in that the output nodes of the networks are replaced by (nonparametric) single index models. Specifically, instead of pre-specifying the output activation functions as in the usual models, they are re-estimated adaptively during the training process via “Loess” (“LOcal regrESSion”), a powerful (nonparametric) scatterplot smoother. These estimated activation functions are not necessarily the usual sigmoidal or identity functions. It is interesting to find that in many cases the estimated output activation functions are well approximated by simple polynomial or generalized hyperbolic tangent functions. These problem-tailored simple functions can, if necessary, then be used as the actual output activation functions for neural network training and prediction. Particle swarm optimization, a commonly used evolutionary computation technique, is adopted in this study to search the optimal connection weights of the neural networks. The main advantages of the single index fuzzy neural network models are that they are well suited in situations when one lacks the information about the probability distribution of the response and it is not necessary to specify the output activation functions of the neural networks. Simulation results show that the proposed models usually provide better fits than the usual models for the data at hand.
The List is the Process: Reliable Pre-Integration Tracking of Commits on Mailing Lists

A considerable corpus of research on software evolution focuses on mining changes in software repositories, but omits their pre-integration history. We present a novel method for tracking this otherwise invisible evolution of software changes on mailing lists by connecting all early revisions of changes to their final version in repositories. Since artefact modifications on mailing lists are communicated by updates to fragments (i.e., patches) only, identifying semantically similar changes is a non-trivial task that our approach solves in a language-independent way. We evaluate our method on high-profile open source software (OSS) projects like the Linux kernel, and validate its high accuracy using an elaborately created ground truth. Our approach can be used to quantify properties of OSS development processes, which is an essential requirement for using OSS in reliable or safety-critical industrial products, where certifiability and conformance to processes are crucial. The high accuracy of our technique allows, to the best of our knowledge, for the first time to quantitatively determine if an open development process effectively aligns with given formal process requirements.
Zero-Shot Classification Based on Multitask Mixed Attribute Relations and Attribute-Specific Features

Zero-shot classification is a hot topic in computer vision and pattern recognition. Most zero-shot classification methods are based on the intermediate level representation of attributes to achieve knowledge transfer from the training classes to the unseen test classes. Recently, multitask learning (MTL) has been shown as one of state-of-the-art approaches for attribute learning and zero-shot classification. Aiming at the attribute relation learning, features shared by attributes learning and attribute heterogeneity, we propose a zero-shot classification based on multitask mixed attribute relations and attribute-specific features. First, considering the relationship between attribute-attribute and attribute-features, a second-order attribute relation and attribute-specific features learning model is constructed from training samples based on MTL. Second, second-order attribute relation is extended to high-order attribute relation and multiple attribute classifiers are learned. Finally, zero-shot classification is completed based on the maximum posterior probability. Experimental results on AWA and PubFig data sets show that the proposed method can yield more accurate attribute prediction and zero-shot classification compared with several multitask attribute learning methods.
Memory/Disk Operation Aware Lightweight VM Live Migration Across Data-centers with Low Performance Impact

Live virtual machine migration technique allows migrating an entire OS with running applications from one physical host to another, while keeping all services available without interruption. It provides a flexible and powerful way to balance system load, save power and tolerant faults in data centers. Meanwhile, with the stringent requirements of latency, scalability, and availability, an increasing number of applications are deployed across distributed cloud data-centers. However, existing live migration approaches still suffer from long downtime and serious performance degradation in cross data-center scenes due to the mass of dirty retransmission, which limits the ability of cross data-center scheduling. In this paper, we propose a system named Memory/disk operation aware Lightweight VM Live Migration across data-centers with low performance impact (MLLM). It significantly improves the cross data-center migration performance by reducing the amount of dirty data in the migration process. In MLLM, we predict disk read workingset (i.e., more frequently read contents) and memory write workingset (i.e., more frequently write contents) based on the access sequence trace. And then we adjust the migration models and data transfer sequence based on the workingset information. We also present two optimizing methods to filter unused blocks and to de-duplicate data content by a hot data cache, thereby greatly decreasing the amount of data to be transferred. We implement MLLM on the QEMU/KVM platform and conduct several real-world experiments. The experimental results show that our method averagely reduces 67.0% of total migration time and 41.6% service downtime over existing methods.
An Energy-Efficient Algorithm for Classification of Fall Types Using a Wearable Sensor

Objective: To mitigate damage from falls, it is essential to provide medical attention expeditiously. Many previous studies have focused on detecting falls and have shown that falls can be accurately detected at least in a laboratory setting. However, a very few studies have classified the different types of falls. To this end, in this paper, a novel energy-efficient algorithm that can discriminate the five most common fall types was developed for wearable systems. Methods: A wearable system with an inertial measurement unit sensor was first developed. Then, our novel algorithm, temporal signal angle measurement (TSAM), was used to classify the different types of falls at various sampling frequencies, and the results were compared with those from three different machine learning algorithms. Results: The overall performance of the TSAM and that of the machine learning algorithms were similar. However, the TSAM outperformed the machine learning algorithms at frequencies in the range of 10-20 Hz. As the sampling frequency dropped from 200 to 10Hz, the accuracy of the TSAM ranged from 93.3% to 91.8%. The sensitivity and specificity ranges from 93.3% to 91.8%, and 98.3% to 97.9%, respectively for the same frequency range. Conclusion: Our algorithm can be utilized with energy-efficient wearable devices at low sampling frequencies to classify different types of falls. Significance: Our system can expedite medical assistance in emergency situations caused by falls by providing the necessary information to medical doctors or clinicians.
ACCEL: Accelerating the Bitcoin Blockchain for High-throughput, Low-latency Applications

The Bitcoin blockchain is a secure, distributed ledger that enables trusted transactions across untrusted entities. However, many applications need much faster transaction confirmation than that of the current Bitcoin blockchain. In this paper, we present a high-throughput, low-latency, deterministic confirmation mechanism called ACCEL for accelerating Bitcoin's block confirmation mechanism. Our key idea for achieving faster confirmation is the quick identification of singular blocks that provably belong to the blockchain. While it is impossible to determine with certainty if a block belongs to a blockchain when network delays are unbounded, singular block detection exploits the fact that the end-to-end latency between Bitcoin miners is substantially lower than the inter-block spacing and can be assumed to be upper bounded. ACCEL is especially suitable for low-latency, permissioned blockchains, where the block spacing can be optimized to the blockchain's small latencies to greatly improve throughput. We evaluate ACCEL's performance with extensive simulations and with a real implementation built with minimal changes to and fully compatible with the Bitcoin blockchain. We show that with appropriate bounds on the end-to-end latency, it is possible to reduce transaction confirmation latencies to milliseconds with ACCEL, and so meet the performance needs of a wide range of applications.
Special Issue: Embracing Plurality in Designing Social Innovation Practices

This Special Issue (SI) for Design and Culture is an outcome of many conversations over several years, catalyzed by a series of international symposia on Design and Social Innovation in Asia-Pacifi...
An efficient page ranking approach based on vector norms using sNorm(p) algorithm

In the whole world, the internet is exercised by millions of people every day for information retrieval. Even for a small to smaller task like fixing a fan, to cook food or even to iron clothes persons opt to search the web. To fulfill the information needs of people, there are billions of web pages, each having a different degree of relevance to the topic of interest (TOI), scattered throughout the web but this huge size makes manual information retrieval impossible. The page ranking algorithm is an integral part of search engines as it arranges web pages associated with a queried TOI in order of their relevance level. It, therefore, plays an important role in regulating the search quality and user experience for information retrieval. PageRank, HITS, and SALSA are well-known page ranking algorithm based on link structure analysis of a seed set, but ranking given by them has not yet been efficient. In this paper, we propose a variant of SALSA to give sNorm(p) for the efficient ranking of web pages. Our approach relies on a p-Norm from Vector Norm family in a novel way for the ranking of web pages as Vector Norms can reduce the impact of low authority weight in hub weight calculation in an efficient way. Our study, then compares the rankings given by PageRank, HITS, SALSA, and sNorm(p) to the same pages in the same query. The effectiveness of the proposed approach over state of the art methods has been shown using performance measurement technique, Mean Reciprocal Rank (MRR), Precision, Mean Average Precision (MAP), Discounted Cumulative Gain (DCG) and Normalized DCG (NDCG). The experimentation is performed on a dataset acquired after pre-processing of the results collected from initial few pages retrieved for a query by the Google search engine. Based on the type and amount of in-hand domain expertise 30 queries are designed. The extensive evaluation and result analysis are performed using MRR, [email protected], MAP, DCG, and NDCG as the performance measuring statistical metrics. Furthermore, results are statistically verified using a significance test. Findings show that our approach outperforms state of the art methods by attaining 0.8666 as MRR value, 0.7957 as MAP value. Thus contributing to the improvement in the ranking of web pages more efficiently as compared to its counterparts.
Hierarchical division clustering framework for categorical data

Although many divisive hierarchical clustering methods for processing categorical data have been presented in the literature, none have been systematically or comprehensively investigated. This paper presents a systematic analysis of existing methods, with respective advantages and disadvantages summarized to develop a unified divisive hierarchical clustering framework that follows three general steps: (1) select attributes for splitting a selected cluster; (2) based on these attributes, generate bipartitions of the cluster; and (3) determine which of the resulting clusters should be further split. Using the proposed framework, representative existing algorithms are compared, and better-performing algorithms are produced through improvements relevant to each step of the unified framework. Experimental results on fifteen UCI benchmark datasets reveal that application of the proposed framework significantly improves the clustering performance of a number of algorithms relative to baseline.
Genetic Types and Metallogenic Model for the Polymetallic Copper–Gold Deposits in the Tongling Ore District, Anhui Province, Eastern China

Abstract The Tongling ore district is one of the most economically important ore areas in the Middle–Lower Yangtze River Metallogenic Belt, eastern China. It contains hundreds of polymetallic copper–gold deposits and occurrences. Those deposits are mainly clustered (from west to east) within the Tongguanshan, Shizishan, Xinqiao, Fenghuangshan, and Shatanjiao orefields. Until recently, the majority of these deposits were thought to be skarn‐ or porphyry–skarn‐type deposits; however there have been recent discoveries of numerous vein‐type Au, Ag, and Pb‐Zn deposits that do not fall into either of these categories. This indicates that there is some uncertainty over this classification. Here, we present the results of several systematic geological studies of representative deposits in the Tongling ore district. From investigation of the ore‐controlling structures, lithology of the host rock, mineral assemblages, and the characteristics of the mineralization and alteration within these deposits, three genetic types of deposits (skarn‐, porphyry‐, and vein‐type deposits) have been identified. The spatial and temporal relationships between the orebodies and Yanshanian intrusions combined with the sources of the ore‐forming fluids and metals, as well as the geodynamic setting of this ore district, indicate that all three deposit types are genetically related each other and constitute a magmatic–hydrothermal system. This study outlines a model that relates the polymetallic copper–gold porphyry‐, skarn‐, and vein‐type deposits within the Tongling ore district. This model provides a theoretical basis to guide exploration for deep‐seated and concealed porphyry‐type Cu (–Mo, –Au) deposits as well as shallow vein‐type Au, Ag, and Pb–Zn deposits in this area and elsewhere.
Knowledge-Context in Search Systems

In this perspectives paper we define knowledge-context as meta information that searchers use when making sense of information displayed in and accessible from a search engine results page (SERP). We argue that enriching the knowledge-context in SERPs has great potential for facilitating human learning, critical thinking, and creativity by expanding searchers' information-literate actions such as comparing, evaluating, and differentiating between information sources. Thus it supports the development of learning-centric search systems. Using theories and empirical findings from psychology and the learning sciences, we first discuss general effects of Web search on memory and learning. After reviewing selected research addressing metacognition and self-regulated learning, we discuss design goals for search systems that support metacognitive skills required for long-term learning, creativity, and critical thinking. We then propose that SERPs make both bibliographic and inferential knowledge-context readily accessible to motivate and facilitate information-literate actions for learning and creative endeavors. A brief discussion of related ideas, designs, and prototypes found in prior work follows. We conclude the paper by presenting future research directions and questions on knowledge-context, information-literate actions, and learning-centric search systems.
A novel algorithm for privacy preserving utility mining based on integer linear programming

As an important research topic, high-utility itemset mining (HUIM) has, of late years, attracted increasing attention, where both the significance and quantity factors of items are taken into account to mine high-utility itemsets (HUIs). Privacy breaches have always been a major issue existing in the field of data mining, which usually inevitably arise, especially when private data collections are publicly published or shared by organizations. To tackle this problem, plentiful methodologies regarding privacy-preserving data mining (PPDM) have been proposed. Due to the high practicality of HUIM, in recent years, privacy-preserving utility mining (PPUM) has become a popular research orientation in PPDM. The main goal of PPUM is to hide sensitive HUIs (SHUIs) so as to leave no confidential information uncovered in the resulting sanitized database. However, all the previously proposed approaches have suffered from the defect of introducing numerous side effects by performing database perturbation. To alleviate this issue, in this paper, a novel algorithm based on integer linear programming (ILP) is proposed to obtain a lower ratio of side effects produced in the hiding process while does not reveal any sensitive information in the sanitized database. We formulate the hiding process as a constraint satisfaction problem (CSP), which pursuing the protection of SHUIs as well as the minimization of side effects. A solution to the hiding problem is expected to be obtained by exploiting ILP technique to solve the mapped problem, which properly indicates the processing manner of perturbation operation. In addition, a relaxation procedure is also adopted in the designed algorithm to provide an approximate solution of the CSP when the optimal one does not exist. Extensive experimental evaluations between our proposed method and other state-of-the-art algorithms are conducted on several real-world datasets. The comparative results demonstrate the superiorities of the proposed algorithm with respect to running time and the ability to minimize side effects.
Synergy of physics-based reasoning and machine learning in biomedical applications: towards unlimited deep learning with limited data

Technological advancements enable collecting vast data, i.e., Big Data, in science and industry including biomedical field. Increased computational power allows expedient analysis of collected data using statistical and machine-learning approaches. Historical data incompleteness problem and curse of dimensionality diminish practical value of pure data-driven approaches, especially in biomedicine. Advancements in deep learning (DL) frameworks based on deep neural networks (DNN) improved accuracy in image recognition, natural language processing, and other applications yet severe data limitations and/or absence of transfer-learning-relevant problems drastically reduce advantages of DNN-based DL. Our earlier works demonstrate that hierarchical data representation can be alternatively implemented without NN, using boosting-like algorithms for utilization of existing domain knowledge, tolerating significant data incompleteness, and boosting accuracy of low-complexity models within the classifier ensemble, as illustrated in physiological-data analysis. Beyond obvious use in initial-factor selection, existing simplified models are effectively employed for generation of realistic synthetic data for later DNN pre-training. We review existing machine learning approaches, focusing on limitations caused by training-data incompleteness. We outline our hybrid framework that leverages existing domain-expert models/knowledge, boosting-like model combination, DNN-based DL and other machine learning algorithms for drastic reduction of training-data requirements. Applying this framework is illustrated in context of analyzing physiological data.
Facial expression detection using Six Facial Expressions Hexagon (SFEH) model

Within the diverse field of image processing, facial expression detection is tremendously an interesting part of face recognition. In order to track and locate facial expressions unusual parts of face movements like eyes, nose, mouth, cheeks etc must be observed. In this paper, six basic facial expressions are detected using Six Facial Expressions Hexagon (SFEH) Model. The SFEH model provides the general representation of six facial expressions on six edges of surface hexagon (S-Hex). The S-hex is the outer boundary of face and can be classified into three parts as upper triangle, middle rectangle and lower triangle. The partitioning of facial features into three parts narrows down the processing area of face where expression originates and is very helpful to locate the varied region. Moreover, Facial Action Coding System (FACS) and Facial Animation Parameters System (FAPS) are used as intermediate frame to analyze the proposed SFEH model. The scope of the proposed SFEH model is to customize only the varied region of the face using morphological operations under reasonable computational cost.
Number “Not Used” Once - Practical Fault Attack on pqm4 Implementations of NIST Candidates

In this paper, we demonstrate practical fault attacks over a number of lattice-based schemes, in particular NewHope, Kyber, Frodo, Dilithium which are based on the hardness of the Learning with Errors (LWE) problem. One of the common traits of all the considered LWE schemes is the use of nonces as domain separators to sample the secret components of the LWE instance. We show that simple faults targeting the usage of nonce can result in a nonce-reuse scenario which allows key recovery and message recovery attacks. To the best of our knowledge, we propose the first practical fault attack on lattice-based Key encapsulation schemes secure in the CCA model. We perform experimental validation of our attack using Electromagnetic fault injection on reference implementations of the aforementioned schemes taken from the pqm4 library, a benchmarking and testing framework for post quantum cryptographic implementations for the ARM Cortex-M4. We use the instruction skip fault model, which is very practical and popular in microcontroller based implementations. Our attack requires to inject a very few number of faults (numbering less than 10 for recommended parameter sets) and can be repeated with a 100% accuracy with our Electromagnetic fault injection setup.
Weighted Voting on the Blockchain: Improving Consensus in Proof of Stake Protocols

Proof of Stake (PoS) protocols rely on voting mechanisms to reach consensus on the current state. If an enhanced majority of staking nodes, also called validators, agree on a proposed block, then this block is appended to the blockchain. Yet, these protocols remain vulnerable to faults caused by validators who abstain either accidentally or maliciously. To protect against such faults while retaining the PoS selection and reward allocation schemes, we study weighted voting in validator committees. We formalize the block creation process and introduce validators' voting profiles which we update by a multiplicative weights algorithm relative to validators' voting behavior and aggregate blockchain rewards. Using this framework, we leverage weighted majority voting rules that optimize collective decision making to show, both numerically and analytically, that the consensus mechanism is more robust if validators' votes are appropriately scaled. We raise potential issues and limitations of weighted voting in trustless, decentralized networks and relate our results to the design of current PoS protocols.