Reviewerly/exHarmony huggingface dataset:
Learning to Undersampling for Class Imbalanced Credit Risk Forecasting

Credit risk forecasting generally aims to evaluate the default probability of users in financial service. It is usually regarded as a binary classification problem, which suffers from the severe class-imbalance problem due to the extremely limited throngs and the concept drift problem brought by the delayed verification. In this paper, we investigate these problems in credit risk forecasting and propose a semi-supervised meta-learning based approach called TRUST (TRainable Undersampling with Self Training) to resolve. First, it decides whether to sample the data through meta-learning based reinforcement learning. Secondly, it learns the distribution of the data that have not yet shown financial performance via self-training and updates the model trained in the first step. Finally, the updated model is evaluated on the validation dataset, the result of which will be fed back through the evaluator. These three steps will be iterated until the model converges. With the real-world industrial dataset containing 1.75 million users, we investigate the effectiveness of our method. Experimental results exhibit that the proposed method is able to improve AP over 5.94% on credit risk forecasting task compared with the recent methods.
Faster Approximate Pattern Matching: A Unified Approach

In the approximate pattern matching problem, given a text T, a pattern P, and a threshold k, the task is to find (the starting positions of) all substrings of T that are at distance at most k from P. We consider the two most fundamental string metrics: Under the Hamming distance, we search for substrings of T that have at most k mismatches with P, while under the edit distance, we search for substrings of T that can be transformed to P with at most k edits. Exact occurrences of P in T have a very simple structure: If we assume for simplicity that |P| <; |T| ≤ 3/2 |P| and that P occurs both as a prefix and as a suffix of T, then both P and T are periodic with a common period. However, an analogous characterization for occurrences with up to k mismatches was proved only recently by Bringmann et al. [SODA'19]: Either there are O(k <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) k-mismatch occurrences of P in T, or both P and T are at Hamming distance O(k) from strings with a common string period of length O(m/k). We tighten this characterization by showing that there are O(k) k-mismatch occurrences in the non-periodic case, and we lift it to the edit distance setting, where we tightly bound the number of k-edit occurrences by O(k <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) in the non-periodic case. Our proofs are constructive and let us obtain a unified framework for approximate pattern matching for both considered distances. In particular, we provide meta-algorithms that only rely on a small set of primitive operations. We showcase the generality of our meta-algorithms with results for the fully compressed setting, the dynamic setting, and the standard setting.
A Time-/Frequency-Domain Side-Channel Attack Resistant AES-128 and RSA-4K Crypto-Processor in 14-nm CMOS

A side-channel attack (SCA) hardened AES-128 and RSA crypto-processor in 14-nm CMOS with measured resistance to correlation power/electromagnetic analysis (CPA/CEMA) in both time and frequency domains is demonstrated. While previously reported linear low-dropout regulators (LDOs) offer improvements in minimum-time-to-disclose (MTD) of extracted key bytes in the time domain, their transformations are less effective against frequency-domain attacks. This article describes a non-linear digital LDO (NL-DLDO) with control loop randomizations that bolster SCA resistance in the frequency domain. The NL-DLDO cascaded with an AES engine augmented with arithmetic countermeasures enables > 250K× improvement in MTD, with no CPA/CEMA/DNN attacks detected after 1-B encryptions, with 8% power and 10% area overheads incurred by arithmetic techniques. The RSA-4K crypto-processor implements exponent magnitude and timing randomizations along with dynamic memory addressing to mitigate time- and frequency-domain attacks. The countermeasures enable 711× suppression in means separation in current/EM magnitudes from 3.1 mV to 4.35 μV, reducing attacker's accuracy to an ineffective random guess classification, while limiting area and performance overheads to <; 0.05% and 3.25%, respectively.
SiEVE: Semantically Encoded Video Analytics on Edge and Cloud

Recent advances in computer vision and neural networks have made it possible for more surveillance videos to be automatically searched and analyzed by algorithms rather than humans. This happened in parallel with advances in edge computing where videos are analyzed over hierarchical clusters that contain edge devices, close to the video source. However, the current video analysis pipeline has several disadvantages when dealing with such advances. For example, video encoders have been designed for a long time to please human viewers and be agnostic of the downstream analysis task (e.g., object detection). Moreover, most of the video analytics systems leverage 2-tier architecture where the encoded video is sent to either a remote cloud or a private edge server but does not efficiently leverage both of them. In response to these advances, we present SIEVE, a 3-tier video analytics system to reduce the latency and increase the throughput of analytics over video streams. In SIEVE, we present a novel technique to detect objects in compressed video streams. We refer to this technique as semantic video encoding because it allows video encoders to be aware of the semantics of the downstream task (e.g., object detection). Our results show that by leveraging semantic video encoding, we achieve close to 100% object detection accuracy with decompressing only 3.5% of the video frames which results in more than 100x speedup compared to classical approaches that decompress every video frame.
Deprecation of Packages and Releases in Software Ecosystems: A Case Study on NPM

Deprecation is used by developers to discourage the usage of certain features of a software system. Prior studies have focused on the deprecation of source code features, such as API methods. With the advent of software ecosystems, package managers started to allow developers to deprecate higher-level features, such as package releases. This study examines how the deprecation mechanism offered by the <inline-formula><tex-math notation="LaTeX">${\sf npm}$</tex-math></inline-formula> package manager is used to deprecate releases that are published in the ecosystem. We propose two research questions. In our first RQ, we examine how often package releases are deprecated in <inline-formula><tex-math notation="LaTeX">${\sf npm}$</tex-math></inline-formula> , ultimately revealing the importance of a deprecation mechanism to the package manager. We found that the proportion of packages that have at least one deprecated release is 3.7 percent and that 66 percent of such packages have deprecated all their releases, preventing client packages to migrate from a deprecated to a replacement release. Also, 31 percent of the partially deprecated packages do not have any replacement release. In addition, we investigate the content of the deprecation messages and identify five rationales behind the deprecation of releases, namely: withdrawal, supersession, defect, test, and incompatibility. In our second RQ, we examine how client packages adopt deprecated releases. We found that, at the time of our data collection, 27 percent of all client packages directly adopt at least one deprecated release and that 54 percent of all client packages transitively adopt at least one deprecated release. The direct adoption of deprecated releases is highly skewed, with the top 40 popular deprecated releases accounting for more than half of all deprecated releases adoption. As a discussion that derives from our findings, we highlight the rudimentary aspect of the deprecation mechanism employed by <inline-formula><tex-math notation="LaTeX">${\sf npm}$</tex-math></inline-formula> and recommend a set of improvements to this mechanism. These recommendations aim at supporting client packages in detecting deprecated releases, understanding their impact, and coping with them.
Socially-driven multi-interaction attentive group representation learning for group recommendation

Group recommendation has attracted much attention since group activities information has become increasing available in many online applications. A fundamental challenge in group recommendation is how to aggregate individuals’ preferences to infer the decision of a group. However, most existing group representation methods do not take into account the static and dynamic preferences of groups synchronously, leading to the suboptimal group recommendation performance. In this work, we propose a socially-driven multi-interaction group representation approach to learn static and dynamic group preference coherently. Specifically, we inject the social homophily and social influence into capturing static and dynamic preference of a group. Furthermore, we explore latent user-item and group-item multiple interactions with bipartite graphs for group representation. Extensive experimental results on two real-world datasets verify the effectiveness of our proposed approach.
Rough fuzzy model based feature discretization in intelligent data preprocess

Abstract Feature discretization is an important preprocessing technology for massive data in industrial control. It improves the efficiency of edge-cloud computing by transforming continuous features into discrete ones, so as to meet the requirements of high-quality cloud services. Compared with other discretization methods, the discretization based on rough set has achieved good results in many applications because it can make full use of the known knowledge base without any prior information. However, the equivalence class of rough set is an ordinary set, which is difficult to describe the fuzzy components in the data, and the accuracy is low in some complex data types in big data environment. Therefore, we propose a rough fuzzy model based discretization algorithm (RFMD). Firstly, we use fuzzy c -means clustering to get the membership of each sample to each category. Then, we fuzzify the equivalence class of rough set by the obtained membership, and establish the fitness function of genetic algorithm based on rough fuzzy model to select the optimal discrete breakpoints on the continuous features. Finally, we compare the proposed method with the discretization algorithm based on rough set, the discretization algorithm based on information entropy, and the discretization algorithm based on chi-square test on remote sensing datasets. The experimental results verify the effectiveness of our method.
A Multi-Task Comparator Framework for Kinship Verification

Approaches for kinship verification often rely on cosine distances between face identification features. However, due to gender bias inherent in these features, it is hard to reliably predict whether two opposite-gender pairs are related. Instead of fine tuning the feature extractor network on kinship verification, we propose a comparator network to cope with this bias. After concatenating both features, cascaded local expert networks extract the information most relevant for their corresponding kinship relation. We demonstrate that our framework is robust against this gender bias and achieves comparable results on two tracks of the RFIW Challenge 2020. Moreover, we show how our framework can be further extended to handle partially known or unknown kinship relations.
FPGA-based Dual Core TRNG Design Using Ring and Runge-Kutta-Butcher based on Chaotic Oscillator

Despite the fact that chaotic systems do not have very complex circuit structures, interest in chaotic systems has increased considerably in recent years due to their interesting dynamic properties. Thanks to the noise-like properties of chaotic oscillators and the ability to mask information signals, great efforts have been made in recent years to develop chaos-based TRNG structures. In this study, a new chaos-based dual entropy core TRNG with high operating frequency and high bit generation rate was realized using 3D Pehlivan-Wei Chaotic Oscillator (PWCO) structure designed utilizing RK-Butcher numerical algorithm on FPGA and ring oscillator structure. In the FPGA-based TRNG model of the system, 32-bit IQ-Math fixed-point number standard is used. The developed model is coded using VHDL. The designed TRNG unit was synthesized for Virtex-7 XC7VX485T-2FFG1761 chip produced by Xilinx. Then, the statistics of the parameters of FPGA chip resource usage and unit clock speed were examined. The data processing time of the TRNG unit was achieved by using the Xilinx ISE Design Tools 14.2 simulation program, with a high bit production rate of 437.043 Mbit/s. In addition, number sequences obtained from FPGA-based TRNG were subjected to the internationally valid statistical NIST 800-22 Test Suite and all the randomness tests of NIST 800-22 Test Suite were successful.
Design and Implementation of Curriculum System Based on Knowledge Graph

With the fact that the knowledge in each field in university is keeping increasing, the number of university courses is becoming larger, and the content and curriculum system is becoming much more complicated than it used to be, which bring many inconveniences to the course arrangement and analysis. In this paper, we aim to construct a method to visualize all courses based on Google Knowledge Graph. By analysing the properties of the courses and their preceding requirements, we want to extract the relationship between the precursors and the successors, so as to build the knowledge graph of the curriculum system. Using the graph database Neo4j [7] as the core aspect for data storage and display for our new curriculum system will be our approach to implement our knowledge graph. Based on this graph, the venation relationship between courses can be clearly analysed, and some difficult information can be obtained, which can help to combine the outline of courses and the need to quickly query the venation information of courses.
Deep NRSfM++: Towards Unsupervised 2D-3D Lifting in the Wild

The recovery of 3D shape and pose from 2D landmarks stemming from a large ensemble of images can be viewed as a non-rigid structure from motion (NRSfM) problem. Classical NRSfM approaches, however, are problematic as they rely on heuristic priors on the 3D structure (e.g. low rank) that do not scale well to large datasets. Learning-based methods are showing the potential to reconstruct a much broader set of 3D structures than classical methods - dramatically expanding the importance of NRSfM to a temporal unsupervised 2D to 3D lifting. Hitherto, these learning approaches have not been able to effectively model perspective cameras or handle missing/occluded points - limiting their applicability to in-the-wild datasets. In this paper, we present a generalized strategy for improving learning-based NRSfM methods [32] to tackle the above issues. Our approach, Deep NRSfM++, achieves state-of-the-art performance across numerous large-scale benchmarks, outperforming both classical and learning-based 2D-3D lifting methods.
Novel Results on the Number of Runs of the Burrows-Wheeler-Transform

The Burrows-Wheeler-Transform (BWT), a reversible string transformation, is one of the fundamental components of many current data structures in string processing. It is central in data compression, as well as in efficient query algorithms for sequence data, such as webpages, genomic and other biological sequences, or indeed any textual data. The BWT lends itself well to compression because its number of equal-letter-runs (usually referred to as r) is often considerably lower than that of the original string; in particular, it is well suited for strings with many repeated factors. In fact, much attention has been paid to the r parameter as measure of repetitiveness, especially to evaluate the performance in terms of both space and time of compressed indexing data structures.
High Throughput Pipelined Implementation of the SHA-3 Cryptoprocessor

Today, in the modern world of digital communications, sensitive information is transmitted via public networks. It is essential to ensure the privacy of their transmission with confidentiality, security and integrity using techniques like hashing. An optimized and high throughput implementation of Secure Hash Algorithm-3 (SHA-3) in hardware is a vital issue for the efficient operation of many modern and demanding systems that utilize this operation in high bandwidth links. In this paper, we propose a pipelined architecture of the SHA-3 256 algorithm in order to increase the hash function calculation speed in Field Programmable Gate Array (FPGA). The proposed architecture is able to function in both single block and multi block messages. Our design is designed and verified in the Very High Speed Integrated Circuit Hardware Description language (VHDL) and synthesized in the FPGA Arria 10 GX. The results show that a significant improvement in throughput, frequency and efficiency is achieved compared to the published literature.
Dual Translation of International and Indian Regional Language using Recent Machine Translation

Neural Machine Translation (NMT) is a modern and powerful approach that has resulted in major improvements compared to traditional techniques of machine translation for translating one language into another. In the world, India is a very multicultural and multilingual country. People of India from various regions use their own regional communication languages, making India stand at the second position in the world to have maximum languages. In India, English is provided as the second extra official language. But the usage of English in India is very less forming a communication gap. To minimize this gap by translating one language into another language is almost impossible for humans. It can be achieved by a machine translation. This paper focuses on translating Indic languages using the translation technique of Neural technology. A Sequence to Sequence model with encoder-decoder attention mechanism of neural machine translation is proposed for Telugu language conversion into English and vice versa.
Classifying and Solving Arithmetic Math Word Problems—An Intelligent Math Solver

Solving mathematical (math) word problems (MWP) automatically is a challenging research problem in natural language processing, machine learning, and education (learning) technology domains, which has gained momentum in the recent years. Applications of solving varieties of MWPs can increase the efficacy of teaching-learning systems, such as e-learning systems, intelligent tutoring systems, etc., to help improve learning (or teaching) to solve word problems by providing interactive computer support for peer math tutoring. This article is specifically intended to benefit such teaching-learning systems on arithmetic word problems solving by adding an interactive and intelligent word problem solver to assess an individual's learning outcome. This article presents arithmetic mathematical word problems solver (AMWPS), an educational software application for solving arithmetic word problems involving single equation with single operation. This article is based on a combination of a machine learning based (classification) approach and a rule-based approach. We start with classification of arithmetic word problems into four categories (Change, Compare, Combine, and Division-Multiplication) along with their subcategories, followed by the classification of operations (+, -, *, and /) related to different subcategories. Our system processes an input arithmetic word problem, predicts the category and subcategory, predicts the operation, identifies and retrieves the relevant quantities within the problem with respect to answer generation, and formulates and evaluates the mathematical expression to generate the final answer. AMWPS outperformed similar systems on the standard AddSub and SingleOp datasets and produced new state-of-the-art result (94.22% accuracy).
Mobile Robot Path Planning based on Improved Genetic Algorithm With A-star Heuristic Method

This paper proposes an improved genetic algorithm to achieve efficient searching capabilities of path planning in complicated maps for mobile robot. The improved genetic algorithm uses the evaluation function of A-Star (A*) algorithm. Firstly, the grid environment model is constructed. The evaluation function of A* algorithm and the bending suppression operator are introduced to improve the heuristic information of the genetic algorithm, which accelerates the convergence speed during the search. Secondly, the insertion operators and deletion operators are introduced into the traditional genetic operators, meanwhile, the consistency of path planning is considered in fitness function, which calculating the fitness values of each path. Output the path with the highest fitness value as the optimal path. The simulation results show that the improved genetic algorithm has less iteration number and can get a better solution than the traditional genetic algorithm.
Sarcasm Detection in Newspaper Headlines

Sarcasm is an important part of communication, and detecting sarcasm is difficult for humans, let alone computers. Newspapers often seem to employ sarcasm in their headlines to grab the readers' attention. However, more often than not, the readers find it difficult to detect the irony in the headlines, thus getting a wrong idea about that particular news and further passing on their understanding to their friends, colleagues, etc. Thus, a system which can automatically and reliably detect sarcasm is more important now than ever. We build sarcasm detectors using neural networks and attempt to understand how a computer learns the patterns of sarcasm. The input to our project consists of sequences that are labeled sarcastic or non-sarcastic. These sequences come from two different datasets containing news headlines and social media commentary. Our classifiers are evaluated on their accuracies. Our model performs highly and is capable of reliably classifying sarcastic or non-sarcastic phrases.