Reviewerly/exHarmony huggingface dataset:
Incremental multi-target domain adaptation for object detection with efficient domain transfer

Recent advances in unsupervised domain adaptation have significantly improved the recognition accuracy of CNNs by alleviating the domain shift between (labeled) source and (unlabeled) target data distributions. While the problem of single-target domain adaptation (STDA) for object detection has recently received much attention, multi-target domain adaptation (MTDA) remains largely unexplored, despite its practical relevance in several real-world applications, such as multi-camera video surveillance. Compared to the STDA problem that may involve large domain shifts between complex source and target distributions, MTDA faces additional challenges, most notably the computational requirements and catastrophic forgetting of previously-learned targets, which can depend on the order of target adaptations. STDA for detection can be applied to MTDA by adapting one model per target, or one common model with a mixture of data from target domains. However, these approaches are either costly or inaccurate. The only state-of-art MTDA method specialized for detection learns targets incrementally, one target at a time, and mitigates the loss of knowledge by using a duplicated detection model for knowledge distillation, which is computationally expensive and does not scale well to many domains. In this paper, we introduce an efficient approach for incremental learning that generalizes well to multiple target domains. Our MTDA approach is more suitable for real-world applications since it allows updating the detection model incrementally, without storing data from previous-learned target domains, nor retraining when a new target domain becomes available. Our approach leverages domain discriminators to train a novel Domain Transfer Module (DTM), which only incurs a modest overhead. The DTM transforms source images according to diverse target domains, allowing the model to access a joint representation of previously-learned target domains, and to effectively limit catastrophic forgetting. Our proposed method – called MTDA with DTM (MTDA-DTM) – is compared against state-of-the-art approaches on several MTDA detection benchmarks and Wildtrack, a benchmark for multi-camera pedestrian detection. Results indicate that MTDA-DTM achieves the highest level of detection accuracy across multiple target domains, yet requires significantly fewer computational resources. Our code is available.1
Monotone Split and Conquer for Anomaly Detection in IoT Sensory Data

Anomaly detection is essential to guarantee the correctness of sensory data collected from Internet of Things. The latest detection approaches only operate under one of the two general forms of short-term or long-term anomaly. In this article, we propose monotone split and conquer (MSC) scheme to tackle both anomaly forms. The proposed scheme exploits the spatial–temporal correlation between neighboring sensors to detect abnormal sensory data. MSC splits the collected data into monotonic subtrends in the training phase to establish the trend-based normal profiles for the online detection phase. To eradicate the overfitting phenomenon, we further develop a general formulation to estimate the square prediction error (SPE) control limit. Both monotone split and general formulation contribute to the advancement of MSC in terms of accuracy (ACC) and false positive rate (FPR) in the online detection phase. To evaluate the performance of MSC, we design a general anomaly model to generate artificial short-term and long-term anomalies. Numerical experiments with the Intel Berkeley Research Lab (IBRL) data set demonstrate that MSC obtains about 8% higher ACC and 5% lower FPR on average when compared to existing schemes. Remarkably, MSC requires only a few observations for anomaly detection as it is applicable to real-time systems.
Analysis and practice on the training of key ability of students majoring in electronic information in higher vocational education

In recent years, higher vocational colleges have developed rapidly and have nurtured a large number of electronic and information technology talents, however the professional counterparts rate of higher vocational colleges is low. There are "unemployed" and "Jobs-in-Wrong-Position" among higher vocational and technical colleges’ students, and enterprises cannot recruit suitable position talents in the present job market. In addition to higher vocational college students’ professional quality cannot meet the needs of enterprises, one of the main reasons for this kind of conflict is that key capabilities have not been explored. Therefore, vocational colleges must realize that the develospment of students’ key abilities is important for student growth and school development. Vocational colleges need to take the development of students’ key abilities as one of the important teaching tasks to improve the comprehensive quality of students.
Poltergeist: Acoustic Adversarial Machine Learning against Cameras and Computer Vision

Autonomous vehicles increasingly exploit computer-vision-based object detection systems to perceive environments and make critical driving decisions. To increase the quality of images, image stabilizers with inertial sensors are added to alleviate image blurring caused by camera jitters. However, such a trend opens a new attack surface. This paper identifies a system-level vulnerability resulting from the combination of the emerging image stabilizer hardware susceptible to acoustic manipulation and the object detection algorithms subject to adversarial examples. By emitting deliberately designed acoustic signals, an adversary can control the output of an inertial sensor, which triggers unnecessary motion compensation and results in a blurred image, even if the camera is stable. The blurred images can then induce object misclassification affecting safety-critical decision making. We model the feasibility of such acoustic manipulation and design an attack framework that can accomplish three types of attacks, i.e., hiding, creating, and altering objects. Evaluation results demonstrate the effectiveness of our attacks against four academic object detectors (YOLO V3/V4/V5 and Fast R-CNN), and one commercial detector (Apollo). We further introduce the concept of AMpLe attacks, a new class of system-level security vulnerabilities resulting from a combination of adversarial machine learning and physics-based injection of information-carrying signals into hardware.
Regulating blockchain for sustainability? The critical relationship between digital innovation, regulation, and electricity governance

Blockchain technology has found several innovative applications in the electricity industry. However, its potential has still to be discovered. This is partly due to the role that regulation plays in electricity markets. To be introduced, experimented with, and eventually adopted on a commercial scale, blockchain-supported innovations need to fit the existing regulatory framework or the rules to be reshaped or updated. We focus on energy regulators' possible responses to the blockchain-enhanced market operations (both from the incumbents and potential newcomers), suggesting a monitoring mechanism that can support innovation.
Make It Easy

Entity alignment (EA) is a prerequisite for enlarging the coverage of a unified knowledge graph. Previous EA approaches either restrain the performance due to inadequate information utilization or need labor-intensive pre-processing to get external or reliable information to perform the EA task. This paper proposes EASY, an effective end-to-end EA framework, which is able to (i) remove the labor-intensive pre-processing by fully discovering the name information provided by the entities themselves; and (ii) jointly fuse the features captured by the names of entities and the structural information of the graph to improve the EA results. Specifically, EASY first introduces NEAP, a highly effective name-based entity alignment procedure, to obtain an initial alignment that has reasonable accuracy and meanwhile does not require much memory consumption or any complex training process. Then, EASY invokes SRS, a novel structure-based refinement strategy, to iteratively correct the misaligned entities generated by NEAP to further enhance the entity alignment. Extensive experiments demonstrate the superiority of our proposed EASY with significant improvement against 13 existing state-of-the-art competitors.
SML: Semantic meta-learning for few-shot semantic segmentation☆

The significant amount of training data required for training Convolutional Neural Networks has become a bottleneck for applications like semantic segmentation. Few-shot semantic segmentation algorithms address this problem, with an aim to achieve good performance in the low-data regime, with few annotated training images. Recent approaches based on class-prototypes computed from available training data have achieved immense success for this task. In this work, we propose a novel meta-learning framework, Semantic Meta-Learning (SML), which incorporates class level semantic descriptions in the generated prototypes for this problem. In addition, we propose to use the well-established technique, ridge regression, to not only bring in the class-level semantic information, but also to effectively utilise the information available from multiple images present in the training data for prototype computation. This has a simple closed-form solution, and thus can be implemented easily and efficiently. Extensive experiments on the benchmark PASCAL-5i dataset under different experimental settings demonstrate the effectiveness of the proposed framework.
Robust Object Detection via Instance-Level Temporal Cycle Confusion

Building reliable object detectors that are robust to domain shifts, such as various changes in context, viewpoint, and object appearances, is critical for real-world applications. In this work, we study the effectiveness of auxiliary self-supervised tasks to improve the out-of-distribution generalization of object detectors. Inspired by the principle of maximum entropy, we introduce a novel self-supervised task, instance-level temporal cycle confusion (CycConf), which operates on the region features of the object detectors. For each object, the task is to find the most different object proposals in the adjacent frame in a video and then cycle back to itself for self-supervision. CycConf encourages the object detector to explore invariant structures across instances under various motions, which leads to improved model robustness in unseen domains at test time. We observe consistent out-of-domain performance improvements when training object detectors in tandem with self-supervised tasks on various do-main adaptation benchmarks with static images (Cityscapes, Foggy Cityscapes, Sim10K) and large-scale video datasets (BDD100K and Waymo open data) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Cookie Swap Party: Abusing First-Party Cookies for Web Tracking

As a step towards protecting user privacy, most web browsers perform some form of third-party HTTP cookie blocking or periodic deletion by default, while users typically have the option to select even stricter blocking policies. As a result, web trackers have shifted their efforts to work around these restrictions and retain or even improve the extent of their tracking capability.
On Lagrangian L2-norm pinball twin bounded support vector machine via unconstrained convex minimization

With the introduction of the regularization term in the formulation of the well-known twin support vector machine (TWSVM) for classification , twin bounded support vector machine (TBSVM) method was proposed recently as an improved version by implementing the structural risk minimization principle. However, TBSVM employs hinge loss function and it is sensitive to noise and unstable to re-sampling. Since the pinball loss function related to quantile distance enjoys noise insensitivity property , a novel TBSVM method with squared pinball loss function for classification is proposed. The noise insensitivity and scatter minimization properties are discussed. Our formulation is further simplified as a pair of unconstrained strongly convex minimization problems in the dual space free of matrix inversion terms and having only m variables where m is the number of training examples. As opposed to TWSVM and TBSVM wherein approximate kernel generated surfaces are constructed, kernel trick is applied directly in our formulation and thereby elegant formulation as in the classical support vector machine (SVM) is achieved. Numerical experiments performed on a synthetic and thirteen benchmark datasets with noise where better or comparable generalization performance with faster learning speed by the proposed method confirms its suitability and applicability to problems of interest.
A Path-Velocity Decomposition Approach to Collision Avoidance for Autonomous Passenger Ferries in Confined Waters

A deliberate collision avoidance approach for autonomous surface vehicles operating in confined waters with high traffic is presented. The approach focuses on dynamic obstacles, by assuming a predefined set of paths that are collision-free with respect to static obstacles. Hence, the collision avoidance problem is reduced to a velocity planning problem, which is solved by first transforming all dynamic obstacles to a path-time space and subsequently constructing a conditioned visibility graph and traversing it with Dijkstra's algorithm. The performance of this approach is demonstrated through both simulations and full-scale experiments in Trondheim harbor, using the NTNU milliAmpere ferry platform and virtual dynamic obstacles.
Population management in metaheuristic algorithms: Could less be more?

In this paper, a new parameter for population management is implemented in the Differential Evolution algorithm. The new scheme integrates a set of operators that analyze the exploration and exploitation effects during its operation. With these operators, the new method obtains important knowledge about its population diversity during its evolution. Under such conditions, the proposed method can reduce its population when the diversity is too low in order to reduce its computational cost and improve its search capacities simultaneously. To test the new additions, the proposed algorithm has been tested in a set of 29 complex functions and two interplanetary trajectory design problems. The outcome of the tests demonstrates a greatly improved performance when compared to the original Differential Evolution algorithm, a few of its most successful variants, and other algorithms.
Linguistically-Enriched and Context-AwareZero-shot Slot Filling

Slot filling is identifying contiguous spans of words in an utterance that correspond to certain parameters (i.e., slots) of a user request/query. Slot filling is one of the most important challenges in modern task-oriented dialog systems. Supervised approaches have proven effective at tackling this challenge, but they need a significant amount of labeled training data in a given domain. However, new domains (i.e., unseen in training) may emerge after deployment. Thus, it is imperative that these models seamlessly adapt and fill slots from both seen and unseen domains – unseen domains contain unseen slot types with no training data, and even seen slots in unseen domains are typically presented in different contexts. This setting is commonly referred to as zero-shot slot filling. Little work has focused on this setting, with limited experimental evaluation. Existing models that mainly rely on context-independent embedding-based similarity measures fail to detect slot values in unseen domains or do so only partially. We propose a new zero-shot slot filling neural model, , which works in three steps. Step one acquires domain-oblivious, context-aware representations of utterance words by exploiting (a) linguistic features such as part-of-speech tags; (b) named entity recognition cues; and (c) contextual embeddings from pre-trained language models. Step two fine-tunes these rich representations and produces slot-independent tags for each word. Step three exploits generalizable context-aware utterance-slot similarity features at the word level, uses slot-independent tags, and contextualizes them to produce slot-specific predictions for each word. Our thorough evaluation on four diverse public datasets demonstrates that our approach consistently outperforms state-of-the-art models by 17.52%, 22.15%, 17.42%, and 17.95% on average for unseen domains on SNIPS, ATIS, MultiWOZ, and SGD datasets, respectively.
Towards a Decomposable Metric for Explainable Evaluation of Text Generation from AMR

Systems that generate natural language text from abstract meaning representations such as AMR are typically evaluated using automatic surface matching metrics that compare the generated texts to reference texts from which the input meaning representations were constructed.We show that besides wellknown issues from which such metrics suffer, an additional problem arises when applying these metrics for AMR-to-text evaluation, since an abstract meaning representation allows for numerous surface realizations.In this work we aim to alleviate these issues by proposing MF β , a decomposable metric that builds on two pillars.The first is the principle of meaning preservation M: it measures to what extent a given AMR can be reconstructed from the generated sentence using SOTA AMR parsers and applying (finegrained) AMR evaluation metrics to measure the distance between the original and the reconstructed AMR.The second pillar builds on a principle of (grammatical) form F that measures the linguistic quality of the generated text, which we implement using SOTA language models.In two extensive pilot studies we show that fulfillment of both principles offers benefits for AMR-to-text evaluation, including explainability of scores.Since MF β does not necessarily rely on gold AMRs, it may extend to other text generation tasks.
A Real-Time Prescriptive Solution for Explainable Cyber-Fraud Detection Within the iGaming Industry

This paper presents a real-time fully autonomous prescriptive solution for explainable cyber-fraud detection within the iGaming industry. We demonstrate how our solution facilitates the time-consuming task of player risk and fraud assessment through prescriptive analytics. Our tool leverages machine learning algorithms and advancements in the field of eXplainable AI to derive smarter predictions empowered by local interpretable explanations in real-time. Our best-performing pipeline was able to predict fraudulent behaviour with an average precision of 84.2% and an area under the receiver operating characteristics of 0.82 on our dataset. We also addressed the phenomenon of concept-drift and discussed our empirical and data-driven strategy for detecting and dealing with this problem. Finally, we cover how local interpretable explanations can help adopt a pro-active stance in fighting fraud.
Signal Detection and Classification in Shared Spectrum: A Deep Learning Approach

Accurate identification of the signal type in shared-spectrum networks is critical for efficient resource allocation and fair coexistence. It can be used for scheduling transmission opportunities to avoid collisions and improve system throughput, especially when the environment changes rapidly. In this paper, we develop deep neural networks (DNNs) to detect coexisting signal types based on In-phase/Quadrature (I/Q) samples without decoding them. By using segments of the samples of the received signal as input, a Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN) are combined and trained using categorical cross-entropy (CE) optimization. Classification results for coexisting Wi-Fi, LTE LAA, and 5G NR-U signals in the 5-6 GHz unlicensed band show high accuracy of the proposed design. We then exploit spectrum analysis of the I/Q sequences to further improve the classification accuracy. By applying Short-time Fourier Transform (STFT), additional information in the frequency domain can be presented as a spectrogram. Accordingly, we enlarge the input size of the DNN. To verify the effectiveness of the proposed detection framework, we conduct over-the-air (OTA) experiments using USRP radios. The proposed approach can achieve accurate classification in both simulations and hardware experiments.
Real-Time Bangla Sign Language Detection with Sentence and Speech Generation

Sign language, the non-verbal language used by the people with hearing and speaking disability, known as the deaf and mute, to connect the bridge of communication with others. Being a visual means of communication, it deprives the mutes to communicate with people having a visual impairment. A medium recognizing sign language and converting it into text and speech could fill the gap. Recently lots of research has been on Bangla Sign Language(BdSL) classification, not on Bangla sentence and speech generation. Most of them classify either digits or alphabets and also face time delay. This paper proposes a system for BdSL recognition that can interpret BdSL from a sequence of images or a video stream and generate both textual sentences and speech in real-time. We have used YOLOv4 as the object detection model. We have also proposed three new signs for the sentence generation task and built a dataset consisting of 12.5k BdSL images of 49 different classes where 39 are Bangla alphabets, 10 are Bangla digits, and the three new proposed signs.
An Encrypted Traffic Classification Method Combining Graph Convolutional Network and Autoencoder

The increase in the source and size of encrypted network traffic brings significant challenges for network traffic analysis. The challenging problem in the encrypted traffic classification field is obtaining high classification accuracy with small number of labeled samples. To solve this problem, we propose a novel encryption traffic classification method that learns the feature representation from the traffic structure and the traffic flow data in this paper. We construct a K-Nearest Neighbor (KNN) traffic graph to represent the structure of traffic data, which contains more similarity information about the traffic. We utilize a two-layer Graph Convolutional Network (GCN) architecture for flows feature extraction and encrypted traffic classification. We further use the autoencoder to learn the representation of the flow data itself and integrate it into the GCN-learned representation to form a more complete feature representation. The proposed method leverages the benefits of the GCN and the autoencoder, which can obtain higher classification performance with only very few labeled data. The experimental results on two public datasets demonstrate that our method achieves impressive results compared to the state-of-the-art competitors.