Reviewerly/exHarmony huggingface dataset:
Robust Object Detection Against Adversarial Perturbations with Gabor Filter

Adversarial attacks are one of the most critical threats in the machine learning field, which raises doubts about the application of deep neural networks (DNNs). Despite the recent advances in DNNs, the adversarial robustness in DNNs has yet to reach an acceptable level, especially against different kinds of perturbations. In this paper, we aim to enhance the robustness of object detection against adversarial perturbations. To this end, we adversarially train YOLOv3 model with different backbones by means of parameterized Gabor convolutional layers. To assess the robustness of our trained models, we have adopted TOG vanishing, TOG fabrication, and TOG mislabeling adversarial attacks. These perturbations are crafted on PASCAL VOC and MSCOCO datasets to simulate three types of targeted specificity, including object-vanishing, object-fabrication, and object-mislabeling, respectively. Extensive evaluations demonstrate that our model equipped with the Gabor filters gain consideration adversarial robustness in addition to the high generalization performance on clean data.
Quality-Aware Distributed Computation for Cost-Effective Non-Convex and Asynchronous Wireless Federated Learning

Wireless federated learning (WFL) trains machine learning (ML) models on wireless edge devices in a distributed manner without the need of collecting data from users. In WFL, the quality of a local model update depends on the variance of the local stochastic gradient, determined by the mini-batch data size used to compute the update. In this paper, we study quality-aware distributed computation for WFL with non-convex problems and asynchronous algorithms, using mini-batch size as a "knob" to control the quality of users' local updates. We first characterize performance bounds on the training loss as a function of local updates' quality over the training process, for both non-convex and asynchronous settings. Our findings reveal that the impact of a local update's quality on the training loss 1) increases with the stepsize used for that local update for non- convex learning, and 2) increases when there are more other users' local updates which are coupled with that local update (depending on the update delays) for asynchronous learning. Based on these useful insights, we design channel-aware adaptive algorithms that determine users' mini-batch sizes over the training process, based on the impacts of local updates' quality on the training loss as well as users' wireless channel conditions (which determine the update delays) and computation costs. We evaluate the proposed quality- aware adaptive algorithms using simulations, which demonstrate improved learning accuracy and learning cost.
Partial Off-policy Learning: Balance Accuracy and Diversity for Human-Oriented Image Captioning

Human-oriented image captioning with both high diversity and accuracy is a challenging task in vision+language modeling. The reinforcement learning (RL) based frameworks promote the accuracy of image captioning, yet seriously hurt the diversity. In contrast, other methods based on variational auto-encoder (VAE) or generative adversarial network (GAN) can produce diverse yet less accurate captions. In this work, we devote our attention to promote the diversity of RL-based image captioning. To be specific, we devise a partial off-policy learning scheme to balance accuracy and diversity. First, we keep the model exposed to varied candidate captions by sampling from the initial state before RL launched. Second, a novel criterion named max-CIDEr is proposed to serve as the reward for promoting diversity. We combine the above-mentioned offpolicy strategy with the on-policy one to moderate the exploration effect, further balancing the diversity and accuracy for human-like image captioning. Experiments show that our method locates the closest to human performance in the diversity-accuracy space, and achieves the highest Pearson correlation as 0.337 with human performance.
Toward Practical Adversarial Attacks on Face Verification Systems

DNN-based face verification systems are vulnerable to adversarial examples. The previous paper’s evaluation protocol (scenario), which we called the probe-dependent attack scenario, was unrealistic. We define a more practical attack scenario, the probe-agnostic attack. We empirically show that these attacks are more challenging than probe-dependent ones. We propose a simple and effective method, PAMTAM, to improve the attack success rate for probe-agnostic attacks. We show that PAMTAM successfully improves the attack success rate in a wide variety of experimental settings.
Cutting the Request Completion Time in Key-value Stores with Distributed Adaptive Scheduler

Nowadays, the distributed key-value stores have become the basic building block for large scale cloud applications. In large-scale distributed key-value stores, many key-value access operations, which will be processed in parallel on different servers, are usually generated for the data required by a single end-user request. Hence, the completion time of the end request is determined by the last completed key-value access operation. Accordingly, scheduling the order of key-value access operations of different end requests can effectively reduce their completion time, improving the user experience. However, existing algorithms are either hard to employ in distributed key-value stores due to the relatively large cooperation overhead for centralized information or unable to adapt to the time-varying load and server performance under different traffic patterns. In this paper, we first formalize the scheduling problem for small mean request completion time. As a step further, because of the NP-hardness of this problem, we heuristically design the distributed adaptive scheduler (DAS) for distributed key-value stores. DAS reduces the average request completion time by a distributed combination of the largest remaining processing time last and shortest remaining process time first algorithms. Moreover, DAS is adaptive to the time-varying server load and performance. Extensive simulations show that DAS reduces the mean request completion time by more than 15 ~ 50% compared to the default first come first served algorithm and outperforms the existing Rein-SBF algorithm under various scenarios.
The Role of Legal Algorithmic Language in the Identification, Disclosure, and Investigation of Extremist Crimes Committed Through Use of the Internet

For the proper detection, disclosure, and investigation of extremist crimes committed through use of the Internet, it is necessary to create methods for the study of electronic documents and other information contained in systems of various types. At the same time, an important role can be played by information technologies using elements of artificial intelligence, which provide increased capabilities for investigators intellectual activities as a result of interaction with them using a legal algorithmic language. Toward this end, it is necessary to create several types of local thesauri and formalize the relationships between the concepts included therein. The features of control over the formation of detailed criminal law characteristics of crimes of the type under consideration, as well as the processing of information in electronic digital form to obtain the necessary evidence, along with their verification and evaluation, are discussed.
Statistical analysis of concentric objects estimation problem under the heteroscedastic Berman model

The problem of fitting two concentric objects (circles and ellipses) to noisy data plays a vital role in many fields. In this paper, statistical methodologies are deployed by additionally assuming that the angular differences between successively measured data points are known, which is known as Berman's model. The heteroscedasticity between covariates is also assumed here, and hence, several estimators for the problem of fitting concentric circles and ellipses are developed and their statistical properties are established. Unlike concentric circles, the problem of fitting concentric ellipses turns out to be nonlinear even with Berman's assumption, and as such, iterative algorithms are implemented. Extensive numerical experiments were conducted to validate our results.
Boundary Information Aggregation and Adaptive Keypoint Combination Enhanced Object Detection

Keypoint-based methods achieve increasing attention and competitive performance in the field of object detection. In this paper, we propose a new keypoint-based object detection method in order to better locate center keypoints of objects and adaptively combine keypoints to obtain more accurate bounding boxes. Specifically, to better locate center keypoints of objects, we aggregate boundary information by adding the center pooling operation to the original center keypoints prediction branch. The boundary information is the location of object boundary which is more easier to predict than object center. Furthermore, to obtain more accurate bounding boxes, we propose an adaptive keypoint combination algorithm to map all keypoints back to the original image so that the keypoints are combined with less localization errors. Experiments have demonstrated the effectiveness of the our proposed methods.
Automatic Calibration Algorithm for English Text Translation Based on Semantic Features

At present, the existing methods of English article flip calibration neglect to extract English semantic features, which leads to errors in English flip results and has a great impact on the accuracy and time consumption of translation sentence calibration. Therefore, a semantic feature-based automatic text flipping calibration algorithm is proposed. According to the features of semantic information in machine translation, a semantic grammar tree is constructed to complete the machine turning of English articles. The CART decision tree attribute is obtained, and the random forest method is introduced to extract the input matrix and output matrix of the corpus feature as samples to determine the spatial attribute feature of the mistranslated sentences. Choose 10000 English sentences about human body parts as the experimental object and design the simulation experiment. The experimental results show that the minimum and maximum accuracy rates are 95.4% and 100.0%, respectively. The proposed algorithm is time-consuming, and the KSMR value is lower than that of the traditional method. It is proved that the error rate of English article flipping is significantly reduced.
An Agent Architecture for Knowledge Discovery and Evolution

The abductive theory of method (ATOM) was recently proposed to describe the process that scientists use for knowledge discovery. In this paper we propose an agent architecture for knowledge discovery and evolution (KDE) based on ATOM. The agent incorporates a combination of ontologies, rules and Bayesian networks for representing different aspects of its internal knowledge. The agent uses an external AI service to detect unexpected situations from incoming observations. It then uses rules to analyse the current situation and a Bayesian network for finding plausible explanations for unexpected situations. The architecture is evaluated and analysed on a use case application for monitoring daily household electricity consumption patterns.
Coarse-to-Fine Secure Image Deduplication with Merkle-Hash and Image Features for Cloud Storage

Studies show that the total digital resource has already exceeded the global storage capacity due to the information explosion due to the speed of the development of digital data. It has become crucial to store this data securely and effectively. The deduplication concept helps to identify redundant data which can be removed leaving only one copy. To handle this deduplication authentically, we propose a three-phase coarse-to-fine model to identify duplicate images in a faster and efficient way for cloud image data storage. The phases are Global features based, Local features based, and Merkle hash tree-based image deduplication. It is observed that the proposed model is fast and efficient.
Fast and Consistent Remote Direct Access to Non-volatile Memory

For performance benefits, recent trends of modern data centers tend to use NVM (Non-volatile Memory) as storage and utilize one-sided primitives of RDMA (Remote Direct Memory Access) to directly access NVM for I/O requests. However, one-sided RDMA doesn’t have durability semantics currently, and data may partially exist in the NVM when crashes happen, thus causing unexpected data inconsistency.
Modeling and Analysis of Typical Generator Self-starting and Research on Black Start Capability Index Evaluation System in Urban Power Grid

Aiming at the problem of black start in urban power grid, based on the layout of typical generator units in urban power grid, this paper makes a qualitative analysis on the feasibility of various typical types of generators as capable units. Based on the start-up operation data of generator units, a typical unit self start model of urban power grid based on data-driven and model driven is established, and the self start process curve is depicted. According to the operation characteristics of urban power grid units, the management level of power plants and the output capacity of generating units, an index evaluation system for black start capacity of generating units is established, and a feasible index evaluation weight value is given as a reference. Finally, the black start capability of typical generating units in Beijing power grid is evaluated, and the evaluation results can provide a reference for urban power grid multi-source black start planning.
Contagion of Offensive Behavior for Transient Interactions in Online Commenting Platforms

Online aggression is an increasingly significant problem in Internet-based communication systems (e.g., commenting platforms) with potentially negative effects on online participation and quality of online discourse. Similar to offline aggression, online aggression has been shown to be contagious and to spread through online platforms as a result of exposure to aggressive content. Among other demographics, young adults report experiencing online aggression at alarming rates. In this paper, we examine the short-term effects of exposure to different levels of online aggressive behavior, and investigate whether it leads to an increase in online aggression among 118 young adults. Using an emulated commenting environment, we found evidence for the contagious nature of aggressive commenting. Even when individuals knew comments were synthetic (and not generated by actual peers), exposure to higher levels of aggressive comments resulted in a statistically significant increase in user aggression. Surprisingly, our results show that anonymity neither increased nor decreased the aggression response, which could indicate a change in user behavior since the advent of Internet 2.0 technologies or qualms related to experimenter-subject anonymity. Consequently, the results in this paper support the hypothesis that even in transient online interactions, young adult participants mimic their peers in an attempt to minimize social stress. Moreover, this mimicking occurs even when the participants know they are participating in an artificial social system.
Toward Robust Detection of Puppet Attacks via Characterizing Fingertip-Touch Behaviors

Fingerprint authentication has gained increasing popularity on mobile devices in recent years. However, it is vulnerable to presentation attacks, which include that an attacker spoofs with an artificial replica. Many liveness detection solutions have been proposed to defeat such presentation attacks; however, they all fail to defend against a particular type of presentation attack, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">puppet attack</i> , in which an attacker places an unwilling victim's finger on the fingerprint sensor. In this article, we propose <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FinAuth</small> , an effective and efficient software-only solution, to complement fingerprint authentication by defeating both synthetic spoofs and puppet attacks using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fingertip-touch</i> characteristics. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FinAuth</small> characterizes intrinsic fingertip-touch behaviors including the acceleration and the rotation angle of mobile devices when a legitimate user authenticates. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FinAuth</small> only utilizes common sensors equipped on mobile devices and does not introduce extra usability burdens on users. To evaluate the effectiveness of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FinAuth</small> , we carried out experiments on datasets collected from 90 subjects after the IRB approval. The results show that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FinAuth</small> can achieve the average balanced accuracy of 96.04% with 5 training data points and 99.28% with 100 training data points. Security experiments also demonstrate that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FinAuth</small> is resilient against possible attacks. In addition, we report the usability analysis results of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FinAuth</small> , including user authentication delay and overhead.
PupilMeter: Modeling User Preference with Time-Series Features of Pupillary Response

Modeling user preferences is a challenging problem in the wide application of recommendation services. Existing methods mainly exploit multiple activities irrelevant to user's inner feeling to build user preference model, which may raise model uncertainty and bring about prediction error. In this paper, we present PupilMeter - the first system that moves one step forward towards exploring the correlation between user preference and the instant pupillary response. Specifically, we conduct extensive experiments to dig into the generic physiological process of pupillary response while viewing specific content on smart devices, and further figure out six key time-series features relevant to users' preference degree by using Random Forest. However, the diversity of pupillary responses caused by inherent individual difference poses significant challenges to the generality of learned model. To solve this problem, we use Multilayer Perceptron to automatically train and adjust the importance of key features for each individual and then generate a personalized user preference model associated with user's pupillary response. We have prototyped PupilMeter and conducted both test experiments and in-the-wild studies to comprehensively evaluate the effectiveness of PupilMeter by recruiting 30 volunteers. Experimental results demonstrate that PupilMeter can accurately identify users' preference.
A Convolution-Involution Hybrid Framework for Monocular 3D Object Detection

Monocular 3D object detection is a prevalent research topic with several issues yet to be explored. In this paper, we present a novel framework for monocular 3D object detection with autonomous driving as the target application. As the first contribution, wed esign a novel Convolution-Involution Hybrid module for the 3D regression of geometric properties. The peculiar characteristics of involutions are delicately combined with the mature design of convolutions to yield enhanced network performance. The second contribution of us is the Dynamic Error- Tolerant loss. A tolerant threshold is integrated into the disentangled L1 loss to allow for minor errors from the network output. The threshold gradually changes in accordance with both the training iterations and the depth of objects to diminish the impact of outlying objects. To corroborate the effectiveness of the proposed method, extensive experiments and ablation study have been conducted on the prestigious KITTI dataset, and the experimental results demonstrate that our network outperforms the baseline by a large margin while retaining a real-time inference speed.
High-Performance Residue Arithmetic Based Elliptic Curve Cryptosystem Over GF(p) for Hardware Security

Hardware security is an inevitable one in embedded portable devices. Hardware uniqueness is the key advantage of hardware-based security and a viable alternative to software security. In this paper, residue arithmetic circuits are used to design the point operations in Elliptic Curve Cryptography (ECC) over Galois Field GF (p). Point Multiplication operations in ECC over GF(p) using signed residue arithmetic architectures are developed. A Verilog HDL code is used to model the entire ECC architecture and implemented in Zynq FPGA (XC7Z020CLG484-1) for Proof of Concept (PoC) using TSMC 180 nm, 90 nm and 45 nm standard cell libraries. The ASIC synthesis results, Proposed ECC Point multiplication architectures save the chip area by 7%, improve the operation speed by 17% and PDP savings of 15% compared with direct implementation of point multiplication. Key generation, Encryption, and Decryption operations are done using ECC over GF(p), which shows evidence that this system can secure embedded portable devices.
Virtual Assistant for Enhancing English Speaking Skills

Over the years with the advent of social media and messaging apps, people have been using jargon, abbreviated words, and casual language while chatting with other people. This leads to a lack of conversational skills during interviews, job meetings, or even daily conversations. Poorly spoken English has been a prime factor due to which students are unsuccessful in clearing the interviews for a job. There are many studies that indicate that an overwhelming percentage of engineers in the country cannot speak English fluently which is required for high-end consulting jobs. Present-day institutions provide solutions for improving English speaking but are expensive. Hence, there is a need for an instantly available conversing partner to hone communication skills. We propose a virtual assistant that can communicate with the user in an attempt to improve English speaking skills. The system consists of SynQG model for question generation, RoBERTa Grammar Error Correction model and praat-parselmouth for speech analysis. The user practices English speaking by answering the questions generated by the system. A thorough speech analysis report is provided to the user based on these answers highlighting mistakes as well as strengths in areas like grammar and pronunciation.