Reviewerly/exHarmony huggingface dataset:
ICS-Assist: Intelligent Customer Inquiry Resolution Recommendation in Online Customer Service for Large E-Commerce Businesses

Efficient and appropriate online customer service is essential to large e-commerce businesses. Existing solution recommendation methods for online customer service are unable to determine the best solutions at runtime, leading to poor satisfaction of end customers. This paper proposes a novel intelligent framework, called ICS-Assist, to recommend suitable customer service solutions for service staff at runtime. Specifically, we develop a generalizable two-stage machine learning model to identify customer service scenarios and determine customer service solutions based on a scenario-solution mapping table. A novel knowledge distillation network called “Panel-Student” is proposed to derive a small yet efficient distilled learning model. We implement ICS-Assist and evaluate it using an over 6-month field study with Alibaba Group. In our experiment, over 12,000 customer service staff use ICS-Assist to serve for over 230,000 cases per day on average. The experimental results show that ICS-Assist significantly outperforms the traditional manual method, and improves the solution acceptance rate, the solution coverage rate, the average service time, the customer satisfaction rate, and the business domain catering rate by up to 16%, 25%, 6%, 14% and 17% respectively, compared to the state-of-the-art methods.
Identifying Implicit Vulnerabilities Through Personas as Goal Models

When used in requirements processes and tools, personas have the potential to identify vulnerabilities resulting from misalignment between user expectations and system goals. Typically, however, this potential is unfulfilled as personas and system goals are captured with different mindsets, by different teams, and for different purposes. If personas are visualised as goal models, it may be easier for stakeholders to see implications of their goals being satisfied or denied, and designers to incorporate the creation and analysis of such models into the broader RE tool-chain. This paper outlines a tool-supported approach for finding implicit vulnerabilities from user and system goals by reframing personas as social goal models. We illustrate this approach with a case study where previously hidden vulnerabilities based on human behaviour were identified.
Controlled Feature Adjustment for Image Processing and Synthesis

Feature adjustment, understood as the process aimed at modifying at will global features of given signals, has cardinal importance for several signal processing applications, such as enhancement, restoration, style transfer, and synthesis. Despite of this, it has not yet been approached from a general, theory-grounded, perspective. This work proposes a new conceptual and practical methodology that we term Controlled Feature Adjustment (CFA). CFA provides methods for, given a set of parametric global features (scalar functions of discrete signals), (1) constructing a related set of deterministically decoupled features, and (2) adjusting these new features in a controlled way, i.e., each one independently of the others. We illustrate the application of CFA by devising a spectrally-based hierarchically decoupled feature set and applying it to obtain different types of image synthesis that are not achievable using traditional (coupled) feature sets.
Phrase-Based Named Entity Transliteration on Myanmar-English Terminology Dictionary

Named entity (NE) transliteration is mainly a phonetically based transcription of names across languages using different writing systems. For the Myanmar language, robust transliteration of named entities is still a challenging task, because of the complex writing system and the lack of data. The Myanmar NE transliteration dictionary has so far developed over 135,255 NE instance pairs of western person, organization and place names. We apply statistical experiments on Phrase-based statistical machine translation (PBSMT) model using 2-Grams, 3-Grams, 4-Grams, 5-Grams and 6-Grams language models in decoding. Different units in the Myanmar script, i.e., characters and syllables are compared. We perform experiments on 1,000 test data set and 1,000 development data set of our proposed dictionary and measure the performance of our system applying bilingual evaluation understudy (BLEU) score. We discuss detailed observations of our experiments in this paper. According to the evaluations, we got the significant results on syllable unit for Myanmar (Myan) to English (Eng) transliteration direction with 89.3% BLEU score and on character unit for English (Eng) to Myanmar (Myan) transliteration direction with 82.0% BLEU score.
An Efficient Finger Gesture Recognition System Using Image

Robots will usually interact with the people directly, and hence it is very important to find an easier way for user interface. Only few robotic systems are user interfaces that possess the ability of controlling the robot by natural means, while issues such as manipulation and navigation in the environment have been focused primarily by earlier works.To promote a beneficial solution to this requirement, a system has been implemented through which the user can give commands to a wireless robot using gestures. With the help of this method, the robot can be navigated by the user by gestures using fingers, and thereby providing a way for interaction with the robotic system. By using image processing, command signals are generated from those gestures. Those command signals are then passed to the robot to navigate it in the specified direction.
Concept Driven Search and Visualization System for Exploring Scientific Repositories

In this paper we present a scientific document retrieval system targeted at answering specific information needs for a diverse set of professionals. This became a crying need in the wake of the pandemic when researchers, clinicians, virologists, epidemiologists and health policy makers are regularly exploring a large collection of scientific literature related SARS-COV2 and COVID-19. The system facilitates exploration and easy comprehension of the results at multiple granularity through visual and textual summaries. The system uses novel NLP techniques to extract, aggregate and analyse textual information. The system also creates unique visual components to view key information of interest at individual and aggregate levels. The system is implemented as a web application.
Cognitive Method Discussion on Communication Jamming Intelligent Decision

The application of reconfigurable, programmable and cognitive radio technologies in communication systems has put forward new requirements for communication jamming decision. In this paper, the overall scheme of cognition-based jamming communication jamming decision methods is presented first. Then the method of intelligent communication jamming decision based on reinforcement learning is discussed under the condition of a large number of data samples of the communication signal, and how to build the element library of jamming strategy and how to describe the behavior of communication signal are described in detail. Moreover, the real-time dynamic jamming effect evaluation method based on target communication signal behavior learning is also discussed. As a result, an online, self-learning and self-optimizing cognitive jamming closed-loop is realized.
Deep Transform Learning for Multi-Sensor Fusion

This paper presents a Deep Transform Learning based framework for multi-sensor fusion. Deep representations are learnt for each of the sensors by stacking one transform after another. Subsequently, a common transform is utilized to fuse the deep representations of all sensors to estimate the output. Restricting to a regression use case, a joint optimization formulation is presented for learning the sensor-specific deep transforms, their coefficients, the common transform, its coefficient and the regression weights together. The requisite solution steps and the derivation of closed form updates for the transforms and associated coefficients are given. The performance of the proposed method is evaluated using two real-life datasets and comparisons with the state-of-the-art dictionary and transform learning techniques for regression are presented. Results show that the deep network has superior performance compared to other methods as it is able to learn the data representation more effectively than the other shallow variants. In addition to the multi-sensor case, estimation results with single sensors alone are also provided to demonstrate the importance of multi-sensor fusion.
Scene Novelty Prediction from Unsupervised Discriminative Feature Learning

Deep learning approaches are widely explored in safety-critical autonomous driving systems on various tasks. Network models, trained on big data, map input to probable prediction results. However, it is unclear how to get a measure of confidence on this prediction at the test time.Our approach to gain this additional information is to estimate how similar test data is to the training data that the model was trained on. We map training instances onto a feature space that is the most discriminative among them. We then model the entire training set as a Gaussian distribution in that feature space. The novelty of the test data is characterized by its low probability of being in that distribution, or equivalently a large Mahalanobis distance in the feature space.Our distance metric in the discriminative feature space achieves a better novelty prediction performance than the state-of-the-art methods on most classes in CIFAR-10 and ImageNet. Using semantic segmentation as a proxy task often needed for autonomous driving, we show that our unsupervised novelty prediction correlates with the performance of a segmentation network trained on full pixel-wise annotations. These experimental results demonstrate potential applications of our method upon identifying scene familiarity and quantifying the confidence in autonomous driving actions.
Securing PKES: Integrating Artificial Intelligence Into PKES Forcefield For Anomaly Detection

Passive Keyless Entry and Start System (PKES) is a convenient feature found in newer cars that allows the user to enter their car without having to press a button on a key fob. Users can simply walk up to their vehicle and once they are in close enough proximity, it unlocks and the user is granted access. However, the convenience of PKES comes at a price; vulnerability to relay attacks and amplified relay attacks. Our proposed method, PKES Forcefield, would protect the user from these attacks without sacrificing any convenience. PKES Forcefield utilizes coordinate tracing and multifactor authentication to secure the user. To add more security to PKES Forcefield, we propose AI integration to construct a profile for each user of the vehicle. This profile will store data on the habits of the user (location, walking speed, time, etc.). Once each user has a profile built from their input data, our system will be able to detect any behaviors that differ from the user's profile (anomalies) and then proper security measures will be taken depending on if the anomaly is considered to be a Low Risk Anomaly or a High Risk Anomaly.
Batched Predecessor and Sorting with Size-Priced Information in External Memory

The fundamental problems of sorting and searching, traditionally studied in the unit-cost comparison model, have been generalized to include priced information, where different pairs of items have different comparison costs. These costs can be arbitrary (Charikar et al. STOC 2000), structured (Gupta et al. FOCS 2001), or stochastic (Angelov et al. LATIN 2008). Motivated by the database setting where the comparison cost depends on the sizes of the records, we consider the problems of sorting and batched predecessor where two non-uniform sets of items A and B are given as input. In the RAM model, pairwise comparisons (A-A, A-B and B-B) have respective comparison costs a, b and c. We give upper and lower bounds for the case $$a \le b \le c$$ , which serves as a warmup for the generalization to the external-memory model. In the Disk-Access Model (DAM), where transferring elements between disk and RAM is the main bottleneck, we consider the scenario where elements in B are larger than elements in A. All items are required in their entirety for comparisons in RAM. A key observation is that the complexity of sorting depends on the interleaving of the small and large items in the final sorted order, and with a high degree of interleaving, the lower bound is dominated by an associated batched predecessor problem. We give output-sensitive bounds on the batched predecessor and sorting; our bounds are tight in most cases. Our lower bounds require novel generalizations of lower bound techniques in external memory to accommodate non-uniform keys.
A Lightweight Deep Learning Model for Vehicle Viewpoint Estimation from Dashcam Images

Vehicle viewpoint estimation from vehicle cameras is a crucial component of road scene understanding. In this paper, we propose a deep lightweight method to predict vehicle viewpoint from a single RGB dashcam image. To this aim, we customize and adapt state-of-the-art deep learning techniques for general object viewpoint estimation to the vehicle viewpoint estimation task. Furthermore, we define a novel objective function that takes into account errors at different granularity to improve neural network training. To keep the model lightweight and fast, we rely upon MobileNetV2 as backbone. Tested both on benchmark viewpoint estimation data (Pascal3D+) and on actual vehicle camera data (nuScenes), our method is shown to outperform the state of the art in vehicle viewpoint estimation, in terms of both accuracy and memory footprint.
Joint Embedding based Text-to-Image Synthesis

Learning joint embedding between image and text is significant for text-to-image synthesis as it bridges the semantic gap between image and text. Most existing text-to-image generation methods depend on the quality of the text embedding. If the text features are not be extracted well, it is difficult for subsequent processes to generate satisfactory images. However, these methods are disturbed by the text expression form in the process of extracting text features, resulting in the ideal text features cannot be generated well. In this paper, we propose a new text encoder that learns joint embedding to capture semantic information shared by the real images and the input text, and eliminates the interference of textual expression forms. The main difference with existing works is that for different texts describing the same image, although their expressions are different, because they contain the same semantic information, the proposed text encoder extracts the similar semantic features from these different texts. Meanwhile, a special auxiliary classifier for discriminator is adopted to retain low-level features to generate fine-detailed images. We evaluate this work on the Caltech-UCSD Birds 200 (CUB) and the Oxford-102 flower dataset, experiments show that our work has better performance than the state-of-the-art works.
Different Long Short-Term Memory Approaches to Enhance Prediction-Based Satellite Telemetry Compression

Motivated by the success of deep learning in recent years, prediction-based methods are used to compress satellite telemetry data. In this paper, two-stage lossless compression methods for telemetry data are demonstrated. In the first stage, different approaches of long short-term memory (LSTM) based on one-to-one, many-to-one, and many-to-many network architectures are presented. The framework of implementing each approach, as a predictor, is discussed. In the second stage, a set of competing entropy coding methods are tested and evaluated. The presented approaches are capable of exploring correlation dependencies between consecutive samples in the individual and/or successive telemetry frames. The proposed approaches are introduced in two different versions: stacked- and nonstacked-based LSTM architectures trying to achieve higher prediction efficiency. The proposed approaches are tested on real telemetry data, in frames of different data word widths, in distinct FUNcube satellite sessions. The performance of each presented approach, as a predictor, is evaluated based on prediction gain, and reduction in entropy. However, the performance of the whole two-stage lossless compression method is assessed by compression ratio. Comparative analysis is preformed among the proposed approaches, and the improvements are verified against the state-of-the-art prediction-based approaches.
Towards Quantized DCT Coefficients Restoration for Compressed Images

Images and videos suffer from undesirable visual artifacts at high compression ratios, which is due to the use of the discrete cosine transform and scalar quantization in the compression. To restore the quantized coefficients via producing the quantization error, we propose a coefficients restoration convolutional neural network in the frequency domain (FD-CRNet). Taking advantage of residual learning, the proposed FD-CRNet efficiently exploits the related distribution of different frequency components. The squeeze-and-excitation block (SE block) is adopted to reduce the computational complexity and better restoration performance. Experimental results show the quantized coefficients are recovered near the lossless coefficients effectively, which outperforms the existed coefficients restoration methods. In addition, the performance of methods in the spatial domain is significantly improved by FD-CRNet with more authentic details and sharper edges when removing the compression artifacts.
Automated Normalization and Analysis of Historical Texts

The paper presents an original method for processing historical texts. A historical text is converted into its modernized equivalent by a tool called diachronic normalizer, embedded into a linguistic toolkit. The solution has a few merits. Firstly, the toolkit architecture allows for imposing the morphological constraints on diachronization rules. Secondly, the diachronic normalizer may be launched in the pipeline together with other NLP tools, such as parsers or translators. Lastly, the toolkit makes it possible to efficiently apply, in the diachronic normalization, a long list of diachronic pairs, found out with the aid of word distribution vectors in historical corpora.
Security-Aware QoS Forecasting in Mobile Edge Computing based on Federated Learning

This paper proposes a novel security-aware QoS (Quality of Service) forecasting approach - Edge QoS Per-PM (Edge QoS forecasting with Personalized training based on Public Models in mobile edge computing) by migrating the principle of integrating cooperative learning and independent learning from federated learning. Edge QoS Per-PM can make fast and accurate forecasting on the premise of ensuring enhanced security. We train private model based on public model for personalized forecasting. The private models are invisible to other users to ensure the absolute security. At regular intervals, a Long Short-Term Memory (LSTM) model is trained based on the latest private data to meet the realtime requirements of the dynamic edge environment and ensure the accuracy of prediction results. A series of experiments is conducted based on public network data sets. The results demonstrate that Edge QoS Per-PM can train appropriate models and achieve faster convergence and higher accuracy.
Differentially Private Profiling of Anonymized Customer Purchase Records

In assessing accurately the risk of being compromised, anonymized data must consider the balance between utility and security. In this paper, we propose a new model for profiling customer purchase records. Our model uses a fixed-size vector that indicates the set of all goods that a customer has purchased. Aggregating all records for n customers gives a profile matrix for a customer base. Our interest is in assessing the risk of re-identification by an adversary who has access to the profile matrix as an adversarial knowledge. To evaluate the privacy budget of the differential privacy, we estimate the probability that a dataset has a profile under some reasonable assumptions. This profile probability allows us to estimate not only the privacy of the profile, but also its utility in the form of its mean absolute error. We have examined the privacy gain expected by performing representative anonymizations, including top/bottom coding, sampling/suppression, and generalization (the fundamental techniques in k-anonymity). These anonymization are modeled by means of simple factors, which allow us to estimate the privacy loss and the mean absolute error under the assumption that the profile’s bit errors occur as a sum of independent and identically distributed random variables characterized by the number of records.
An Ultrasound Body Area Network for Arm Gesture Recognition

This contribution describes the concept and implementation of a wearable arm gesture recognition system. The system relies on an ultrasound body area network for communication and to enable distance measurements between torso and arms. The network nodes use new capacitive micromachined ultrasonic transducer technology. The operation and performance is shown with the help of a real time demonstrator system.
Audio Watermarking System Robust to Offset Distortion in Acoustic Channel Transmission

Audio watermarking is a technology for enriching multimedia services by hiding and delivering useful information in an audio signal. To extend its potential, audio watermarking has recently been applied to short-range communication between a speaker and microphone for easy accessibility to enriched data in a second portable device. Although many challengeable difficulties are expected to occur under such a scenario, the most important aspect is achieving robust synchronization by overcoming offset distortion in the time and frequency domains. In this paper, a polarized sequence-based audio watermarking system in the DFT domain is proposed. The main strength of this work is providing reliable synchronization that is robust to any distortion for time and frequency offset in an acoustic channel. Moreover, based on the results of an evaluation, performance aspects such as the synchronization under acoustic channel distortion, and preservation of the sound quality, were confirmed.
EmnDash: M-sequence Dashed Markers on Vector-based Laser Projection for Robust High-speed Spatial Tracking

Camera pose estimation is commonly used for augmented reality, and it is currently expected to be integrated into sports assistant technologies. However, conventional methods face difficulties in simultaneously achieving fast estimation in milliseconds or less for sports, bright lighting environments of the outdoors, and capturing of large activity areas. In this paper, we propose EmnDash, M-sequence dashed markers on vector-based laser projection for an asynchronous high-speed dynamic camera, which provides both a graphical information display for humans and markers for the wearable high-speed camera with a high S/N ratio from a distance. One of the main notions is drawing a vector projection image with a single stroke using two dashed lines as markers. The other involves embedding the binary M-sequence as the length of each dashed line and its recognition method using locality. The recognition of the M-sequence dashed line requires only a one-shot image, which increases the robustness of tracking both in terms of camera orientation and occlusion. We experimentally confirm an increase in recognizable posture, sufficient tracking accuracy, and low-computational cost in the evaluation of a static camera. We also show good tracking ability and demonstrate immediate recovery from occlusion in the evaluation of a dynamic camera.