Reviewerly/exHarmony huggingface dataset:
Online Transaction Fraud Detection System

The growth in internet and e-commerce appears to involve the use of online credit/debit card transactions. The increase in the use of credit / debit cards is causing an increase in fraud. The frauds can be detected through various approaches, yet they lag in their accuracy and its own specific drawbacks. In this work, the behavior-based approach to classification using Support Vector Machines is used to improve its accuracy. If there are any changes in the conduct of the transaction, the frauds are predicted and taken for further process. Due to large amount of data credit / debit card fraud detection problem is rectified by the proposed method.
Efficient GPU-parallelization of batch plants design using metaheuristics with parameter tuning

We address a practice-relevant optimization problem: optimizing multi-product batch plants, with a real-world use case study – optimal design of chemical-engineering systems. Our contribution is a novel approach to parallelizing this optimization problem on GPU (Graphics Processing Units) by combining two metaheuristics – Simulated Annealing (SA) and Ant Colony Optimization (ACO). We improve the implementation performance by tuning particular parameters of the ACO metaheuristic. Our tuning approach improves on the previous methods in two respects: (1) we do not have to rely on additional mechanisms like fuzzy logic or algorithms for online tuning; and (2) we use the high computation performance of GPU to speedup the tuning process. By parallelizing the tuning process on modern GPUs, we allow the user to experiment with large volumes of input data and find the optimal values of the ACO parameters in feasible time. Our experiments on NVIDIA GPU show the efficiency of our approach to parameter tuning for the ACO metaheuristic.
Research on Community Consumer Behavior Based on Association Rules Analysis

In order to analyze the inner needs and purchase behavior of consumers and increase the sales of goods, a community consumer behavior analysis method based on association rules is proposed. First, to solve the problems of the traditional Apriori algorithm, this article optimizes the data set and improves the efficiency of pruning, then uses the optimized Apriori algorithm to mine the consumer purchase records of the community supermarket to find out the correlation between multiple products, which calculating the consumer's preference for goods and getting the corresponding association rules and marketing strategies. Finally, this research uses the shopping data of community supermarket retail to conduct experimental tests to consumer preferences. The results show that the optimized Apriori algorithm is more efficient and the correlation analysis result is more accurate.
A unified method based on pseudo-spectrum for track-before-detect of targets with motion model uncertainty

Track-before-detect (TBD) of weak targets with motion model uncertainty is of significance in practical applications, since targets may experience various motions. In this paper, a unified TBD framework is presented for multiframe detection of weak targets with constant velocity (CV) and coordinated turn (CT) motions. This enables detection and parameter estimation of weak CV and CT targets in a same state space. Firstly, according to common CT model in Cartesian coordinates, the state equation of the common model suitable for matched filtering based TBD is derived. In this equation, the speed, turn rate and initial heading are used to determine target motion. Secondly, a heading-speed-turn rate matched filter based on pseudo-spectrum in mixed coordinates is proposed for weak CT or CV target detection in sensor coordinates. The data search before multiframe integration is conducted according to the derived model in x-y plane by matching the constant target speed, turn rate and initial heading. Then, the echo energy is integrated based on the pseudo-spectrum in polar coordinates. This eliminates performance loss caused by model mismatch in sensor coordinates, and facilitates well-maintained target envelope. Thirdly, to deal with the problem that the target motion model may jump at any time during the processing batch, a joint motion parameter and jumping time matching based TBD in mixed coordinates is presented. The jumping time is considered as a matching parameter for multiframe integration in addition to speed, turn rate and heading. This method can supply complete information of the maneuvering target motion in the processing batch. Finally, simulations are conducted to demonstrate the effectiveness of the proposed methods.
The Multi-objective Cloud Tasks Scheduling Based on Hybrid Particle Swarm Optimization

This paper proposes a hybrid particle swarm optimization algorithm, namely HPSO, to reduce application's total execution time (ie., makespan) and balance the computation load, by solving the multi-objective task scheduling problem in cloud environments. The proposed algorithm starts with the strategy of pre-generating search particles that can improve particles' position quality during the search procedure by refining the solution space. In addition, we incorporate the strategy of artificial fish swarm algorithm into the position updating process of particles swarm optimization (PSO), providing various updating schemes to solve the multi-objective scheduling problem. Moreover, we define the weighted sum of the standardized total execution time and load balancing as the optimization goal of the scheduling problem. Experimental results demonstrate that compared with PSO and immune algorithm, our HPSO algorithm can achieve better scheduling result in terms of shorter makespan and smaller standard deviation of computation times among all computing resources.
LSTMs for Keyword Spotting with ReRAM-Based Compute-In-Memory Architectures

The increasingly central role of speech based human computer interaction necessitates on-device, low-latency, low- power, high-accuracy key word spotting (KWS). State-of-the- art accuracies on speech-related tasks have been achieved by long short-term memory (LSTM) neural network (NN) models. Such models are typically computationally intensive because of their heavy use of Matrix vector multiplication (MVM) operations. Compute-in-Memory (CIM) architectures, while well suited to MVM operations, have not seen widespread adoption for LSTMs. In this paper we adapt resistive random access memory based CIM architectures for KWS using LSTMs. We find that a hybrid system composed of CIM cores and digital cores achieves 90% test accuracy on the google speech data set at the cost of 25 uJ/decision. Our optimized architecture uses 5-bit inputs, and analog weights to produce 6-bit outputs. All digital computation are performed with 8-bit precision leading to a 3.7× improvement in computational efficiency compared to equivalent digital systems at that accuracy.
Stochastic Gradient Descent with Noise of Machine Learning Type Part I: Discrete Time Analysis

Stochastic gradient descent (SGD) is one of the most popular algorithms in modern machine learning. The noise encountered in these applications is different from that in many theoretical analyses of stochastic gradient algorithms. In this article, we discuss some of the common properties of energy landscapes and stochastic noise encountered in machine learning problems, and how they affect SGD-based optimization. In particular, we show that the learning rate in SGD with machine learning noise can be chosen to be small, but uniformly positive for all times if the energy landscape resembles that of overparametrized deep learning problems. If the objective function satisfies a Łojasiewicz inequality, SGD converges to the global minimum exponentially fast, and even for functions which may have local minima, we establish almost sure convergence to the global minimum at an exponential rate from any finite energy initialization. The assumptions that we make in this result concern the behavior where the objective function is either small or large and the nature of the gradient noise, but the energy landscape is fairly unconstrained on the domain where the objective function takes values in an intermediate regime.
Reinforcement Learning in Sparse-Reward Environments With Hindsight Policy Gradients

A reinforcement learning agent that needs to pursue different goals across episodes requires a goal-conditional policy. In addition to their potential to generalize desirable behavior to unseen goals, such policies may also enable higher-level planning based on subgoals. In sparse-reward environments, the capacity to exploit information about the degree to which an arbitrary goal has been achieved while another goal was intended appears crucial to enabling sample efficient learning. However, reinforcement learning agents have only recently been endowed with such capacity for hindsight. In this letter, we demonstrate how hindsight can be introduced to policy gradient methods, generalizing this idea to a broad class of successful algorithms. Our experiments on a diverse selection of sparse-reward environments show that hindsight leads to a remarkable increase in sample efficiency.
Scale-aware attention-based multi-resolution representation for multi-person pose estimation

The performance of multi-person pose estimation has significantly improved with the development of deep convolutional neural networks. However, two challenging issues are still ignored but are key factors causing deterioration in the keypoint localization. These two issues are scale variation of human body parts and huge information loss caused by consecutive striding in multiple upsampling. In this paper, we present a novel network named ‘Scale-aware attention-based multi-resolution representation network’ (SaMr-Net) which targets to make the proposed method against scale variation and prevent the detail information loss in upsampling, leading more precisely keypoint estimation. The proposed architecture adopts the high-resolution network (HRNet) as the backbone, we first introduce dilated convolution into the backbone to expand the receptive field. Then, attention-based multi-scale feature fusion module is devised to modify the exchange units in the HRNet, allowing the network to learn the weights of each fusion component. Finally, we design a scale-aware keypoint regressor model that gradually integrates features from low to high resolution, enhancing the invariance in different scales of pose parts keypoint estimation. We demonstrate the superiority of the proposed algorithm over two benchmark datasets: (1) the MS COCO keypoint benchmark, and (2) the MPII human pose dataset. The comparison shows that our approach achieves superior results.
Deep Learning-Based Anomaly Detection in LAN from Raw Network Traffic Measurement

The digitalization occurring in various industries is bringing more information transmitted through networks. More resilient and efficient network traffic monitoring systems are in high demand to safeguard network flows. In this article, we presented a combined approach of anomaly detection in LAN based on raw network traffic observation and measurement, the collected data being converted to regulated chunks of 480 bits. A network traffic dataset including multi-type anomalies from a honeypot device in LAN was employed, with a total of two weeks' data. By further integrating the representation with supervised learning and knowledge-based labeling methods, we aim to classify raw network traffic thus detecting anomaly from raw data measurement without using manually crafted features. We conducted the model training against accuracy and evaluated the scheme based on a separated validation set against a metric of precision. Finally, we achieved a validation precision score of 0.980 for detecting ARP flooding, a score of 0.801 for detecting malicious SMB, and a score of 0.815 for detecting TCP SYN flooding respectively.
Lightweight design of SM4 algorithm and realization of threshold scheme

Abstract Aiming at the lightweight design of SM4 cryptographic algorithm, a lightweight cryptographic algorithm scheme is proposed and implemented in hardware. Based on the work of Li et al., a 4-bit S-box is selected. The original 8-bit look-up table S-box in the SM4 algorithm is replaced with a 4-bit cyclic shift S-box, and the same circuit structure is used to save resources in hardware implementation. A threshold scheme based on the principle of secret sharing is proposed to improve the overall security of the scheme against side-channel attacks. The input information is divided into three groups, and all operations are converted from GF (2 4 ) to GF (2 2 ) through a multiplier and a squarer, which further reduces the circuit consumption and improves the security of the algorithm. In the realization of the threshold scheme, virtual values are intro-duced to ensure that the scheme meets the uniformity, and the decomposition method is used in the inverter to re-duce the number of operations and the proportion of space. After theoretical deduction, the threshold scheme as a whole has the ability to resist first-order and second-order DPA attacks. The overall hardware implementation of the program uses a total of 2803 logic elements, which proves that it achieves the goal of lightweight while ensuring safety.
A GDPR-compliant Ecosystem for Speech Recognition with Transfer, Federated, and Evolutionary Learning

Automatic Speech Recognition (ASR) is playing a vital role in a wide range of real-world applications. However, Commercial ASR solutions are typically “one-size-fits-all” products and clients are inevitably faced with the risk of severe performance degradation in field test. Meanwhile, with new data regulations such as the European Union’s General Data Protection Regulation (GDPR) coming into force, ASR vendors, which traditionally utilize the speech training data in a centralized approach, are becoming increasingly helpless to solve this problem, since accessing clients’ speech data is prohibited. Here, we show that by seamlessly integrating three machine learning paradigms (i.e., T ransfer learning, F ederated learning, and E volutionary learning (TFE)), we can successfully build a win-win ecosystem for ASR clients and vendors and solve all the aforementioned problems plaguing them. Through large-scale quantitative experiments, we show that with TFE, the clients can enjoy far better ASR solutions than the “one-size-fits-all” counterpart, and the vendors can exploit the abundance of clients’ data to effectively refine their own ASR products.
PG-CAS: Patterned-Ground Co-Planar Capacitive Asymmetry Sensing for mm-Range EM Side-Channel Attack Probe Detection

Electromagnetic (EM) side-channel analysis (SCA) attack, which breaks cryptographic implementations, has become a major concern in the design of circuits and systems. This paper presents the design and analysis of the EM side-channel attack detection system utilizing patterned-ground co-planar capacitive asymmetry sensing (PG-CAS) for approaching probe, targeting to improve sensitivity, detection range, and power consumption compared to LC oscillator utilizing inductive sensing. The PG-CAS consists of a grid of four metal plates of the same size at the top metal layer and a patterned ground plane at a lower metal. As an EM probe approaches, electric field lines between the plates and plate-ground get distorted, thereby breaking the symmetry of the inter-plate and the plate-ground capacitance system and this change in capacitance is sensed. The PG-CAS circuit consists of two LC oscillators, mixer, low pass filter (LPF), resistive feedback amplifier (RFA) and a digital logic. By down-converting sensing signal to low-frequency using mixer, LPF, RFA and digital logic, the detection range is significantly improved. At a distance of 1 mm between the sensing metal plates and the approaching EM probe, system-level simulation results using TSMC 65nm technology and Ansys Maxwell show a > 10% change in the output frequency from the baseline frequency, leading to a > 10× improvement in the detection range and a ~ 3× improvement in power consumption over existing inductive sensing methods.
GCCNet: Grouped channel composition network for scene text detection

Anchor mechanism is widely applied in scene text detection methods and demonstrates promising performance. However, existing anchor mechanisms have two major limitations, namely handcrafted anchor design and hard-wired anchor assignment. We propose a novel Grouped Channels Composition(GCC) block to achieve the data-driven anchor design and adaptive anchor assignment. To be more specific, our GCC block uses optimizable anchor functions rather than handcrafted ones to achieve data-drive anchor design. In our GCC block, an adaptive anchor assignment is achieved with the attention mechanism instead of empirically assigning anchor according to the Intersection Over Union (IoU) between ground truth and targets. We then build a corresponding network named GCCNet with our GCC blocks. We also propose a Unified Loss Weighting module to alleviate the inconsistency between classification score and localization accuracy. Experiments conducted on publicly available datasets demonstrate the state-of-the-art performance of our methods.
Secure Blockchain for Admission Processing in Educational Institutions

Blockchain technology with its secure mechanism of maintaining data and transactions in shared, immutable, distributed ledgers has become very relevant today and is increasingly used for financial applications. This paper proposes the use of consortium blockchain and smart contracts for secure, transparent and automated processing of student applications received by educational institutions. The students applying for admissions in educational institutions need assurance of a safe, secure and transparent platform that does not compromise their privacy. On the other hand, educational institutions too need assurance about the authenticity of the documents and the applicant. The use of consortium blockchain and smart contracts incorporating business logic for validating, verifying and filtering of valid applications provides a safe and secure platform for processing student applications. This paper looks at blockchain application beyond finance and explains how the student registration and admission process can be made safe and secure for all stakeholders. It promotes a seamless mechanism with reduced turnaround time and increased security and transparency.
Machine Translation Verbosity Control for Automatic Dubbing

Automatic dubbing aims at seamlessly replacing the speech in a video document with synthetic speech in a different language. The task implies many challenges, one of which is generating translations that not only convey the original content, but also match the duration of the corresponding utterances. In this paper, we focus on the problem of controlling the verbosity of machine translation out-put, so that subsequent steps of our automatic dubbing pipeline can generate dubs of better quality. We propose new methods to control the verbosity of MT output and compare them against the state of the art with both intrinsic and extrinsic evaluations. For our experiments we use a public data set to dub English speeches into French, Italian, German and Spanish. Finally, we report extensive subjective tests that measure the impact of MT verbosity control on the final quality of dubbed video clips.
Modeling Reading Behaviors: An Automatic Approach to Eye Movement Analytics

Critical reading plays an important role in science learning, and previous studies have endeavored to objectively and precisely capture readers’ cognitive processing in reading scientific texts. Since many factors affect readers’ initiation and comprehension of scientific texts, studying the interactions of these factors was technically challenging for earlier studies. Recently, the use of artificial intelligent techniques for analyzing physiological signals has gained significant research attention, but exploitation of the educational data for proactive instructional use is still limited. This study proposed and evaluated an automatic approach incorporating the K-means++ clustering method for eye movement analytics. In this study, 64 undergraduate and graduate students read a multi-page popular science text while their eye movements were recorded. The results of the cluster analysis identified three patterns of reading behavior that were consistent and comparable to those of previous studies using self-reported measures and post-analysis analytics. Findings of the study support the potential and validity of a bottom-up, data-driven approach that can directly examine and analyze reading behaviors without interruption, and the contribution of the study to research and practice is outlined.
Shielding Federated Learning: A New Attack Approach and Its Defense

Federated learning (FL) is a newly emerging distributed learning framework that is communication-efficient with user privacy guarantee. Wireless end-user devices can collaboratively train a global model while keeping their local training data private. Nevertheless, recent studies show that FL is highly susceptible to attacks from malicious users since the server cannot directly access and audit the user's local training data. In this work, we identify a new kind of attack surface that is much easier to be carried out while remaining a high attack success rate. By exploiting the inherent flaw of the weight assignment strategy in the standard federated learning process, our attack can bypass the existing defense methods and damage the performance of the global model effectively. We then propose a new density-based detection strategy to defend against such attack by modeling the problem as anomaly detection to effectively detect anomalous updates. Experimental results on two typical datasets, MNIST and CIFAR-10, show that our attack can significantly affect the convergence of the aggregated model and reduce the accuracy of the global model. This holds true even the state-of-the-art defense strategies are deployed, while our newly proposed defense can effectively mitigate such attack.
Non-normal modal logics and conditional logics: Semantic analysis and proof theory

We introduce proper display calculi for basic monotonic modal logic, the conditional logic CK and a number of their axiomatic extensions. These calculi are sound, complete, conservative, and enjoy cut elimination and subformula property. Our proposal applies the multi-type methodology in the design of proper display calculi, starting from a semantic analysis which motivates syntactic translations from single-type non-normal modal logics to multi-type normal poly-modal logics.