Reviewerly/exHarmony huggingface dataset:
Impact of Resource-Constrained Networks on the Performance of NIST Round-3 PQC Candidates

The search for Quantum Resistant algorithms has been a priority in Cryptography for years. Increased network usage and the resulting resource constraints are at direct odds with the increasing need for secure and efficient communication. In light of this, this paper explores the relationship between constrained networks and their impact on and performance of NIST’s Round-3 Post Quantum Cryptography candidates.
Attention-based position-aware framework for aspect-based opinion mining using bidirectional long short-term memory

Aspect-based Opinion Mining is a form of fine-grained Sentiment Analysis and it models the semasiological relationship between aspect terms and context words in a sentence. The presence of a variety of context words has a significant impact on a sentence's sentiment polarity. As a result, while designing a model, it is necessary to consider the interaction of aspects and context words. Although existing approaches have taken into account an aspect’s position in a sentence, much of the research works have not explored the use of Sentiment Lexicons with the Deep Learning algorithms. In this paper, we propose a framework for an Attention-based position-aware Bidirectional Long Short-Term Memory network for Aspect-based Opinion Mining that incorporates a Sentiment Intensity Lexicon. The aspect word’s pre-trained vector is adjusted to be closer to semantically and sentimentally similar nearest neighbors and further away from sentimentally dissimilar neighbors. The proposed framework calculates aspect weights by concatenating the external knowledge in the form of lexicon sentiment intensity scores with word embeddings and position information. The framework experiments on the SemEval 2014 dataset. The results of the experiments illustrate that injecting external knowledge into the Bidirectional Long Short-Term Memory network can improve classification accuracy significantly.
Regularizing CTC in Expectation-Maximization Framework with Application to Handwritten Text Recognition

Connectionist Temporal Classification (CTC) is an objective function for sequence learning and has shown promising results in speech and text recognition tasks. However, its inherent mechanism has not been investigated thoroughly. In this paper, we propose a theoretical explanation of CTC from the perspective of the Expectation-Maximization (EM) algorithm. Based on the EM analysis, we propose a pseudo-label-based L1 regularization and voting decoding algorithm to improve the performance of text recognition. The L1 regularization can reduce the pseudo-label estimation error, while the voting decoding algorithm modifies the built-in decoding logic of CTC and introduces a voting mechanism to the inference process. Experiments of handwritten text recognition show that the proposed method consistently improves over the CTC baseline and yields state-of-the-art results on three benchmark datasets.
Improving cold-start recommendations using item-based stereotypes

Abstract Recommender systems (RSs) have become key components driving the success of e-commerce and other platforms where revenue and customer satisfaction is dependent on the user’s ability to discover desirable items in large catalogues. As the number of users and items on a platform grows, the computational complexity and the sparsity problem constitute important challenges for any recommendation algorithm. In addition, the most widely studied filtering-based RSs, while effective in providing suggestions for established users and items, are known for their poor performance for the new user and new item (cold-start) problems. Stereotypical modelling of users and items is a promising approach to solving these problems. A stereotype represents an aggregation of the characteristics of the items or users which can be used to create general user or item classes. We propose a set of methodologies for the automatic generation of stereotypes to address the cold-start problem. The novelty of the proposed approach rests on the findings that stereotypes built independently of the user-to-item ratings improve both recommendation metrics and computational performance during cold-start phases. The resulting RS can be used with any machine learning algorithm as a solver, and the improved performance gains due to rate-agnostic stereotypes are orthogonal to the gains obtained using more sophisticated solvers. The paper describes how such item-based stereotypes can be evaluated via a series of statistical tests prior to being used for recommendation. The proposed approach improves recommendation quality under a variety of metrics and significantly reduces the dimension of the recommendation model.
Design and Development of a Robotic Arm for Sorting Colored Object

In the present research, a robotic arm with four degrees of freedom is designed and developed which sorts object on the basis of color recognition technique. The arm is designed with the assistance of SolidWorks and constructed by using a light weight composite material known as plastic wood. Whenever an object is sensed by the color recognition module, it gets detected without any error. With the help of Inverse Kinematics algorithms, the robotic arm’s control input is enumerated and then received by Arduino Mega microcontroller board. After that, the Arduino runs the motors set on the arm to sort and place the objects based on their color. The successful sorting of different objects can be done by this robotic arm. Therefore, this kind of arm can be developed at low cost and used in industrial applications.
CSS-LM: A Contrastive Framework for Semi-Supervised Fine-Tuning of Pre-Trained Language Models

Fine-tuning pre-trained language models (PLMs) has demonstrated its effectiveness on various downstream NLP tasks recently. However, in many low-resource scenarios, the conventional fine-tuning strategies cannot sufficiently capture the important semantic features for downstream tasks. To address this issue, we introduce a novel framework (named "CSS-LM") to improve the fine-tuning phase of PLMs via contrastive semi-supervised learning. Specifically, given a specific task, we retrieve positive and negative instances from large-scale unlabeled corpora according to their domain-level and class-level semantic relatedness to the task. We then perform contrastive semi-supervised learning on both the retrieved unlabeled and original labeled instances to help PLMs capture crucial task-related semantic features. The experimental results show that CSS-LM achieves better results than the conventional fine-tuning strategy on a series of downstream tasks with few-shot settings, and outperforms the latest supervised contrastive fine-tuning strategies. Our datasets and source code will be available to provide more details.
CyCoSeg: A Cyclic Collaborative Framework for Automated Medical Image Segmentation

Deep neural networks have been tremendously successful at segmenting objects in images. However, it has been shown they still have limitations on challenging problems such as the segmentation of medical images. The main reason behind this lower success resides in the reduced size of the object in the image. In this paper we overcome this limitation through a cyclic collaborative framework, CyCoSeg. The proposed framework is based on a deep active shape model (D-ASM), which provides prior information about the shape of the object, and a semantic segmentation network (SSN). These two models collaborate to reach the desired segmentation by influencing each other: SSN helps D-ASM identify relevant keypoints in the image through an Expectation Maximization formulation, while D-ASM provides a segmentation proposal that guides the SSN. This cycle is repeated until both models converge. Extensive experimental evaluation shows CyCoSeg boosts the performance of the baseline models, including several popular SSNs, while avoiding major architectural modifications. The effectiveness of our method is demonstrated on the left ventricle segmentation on two benchmark datasets, where our approach achieves one of the most competitive results in segmentation accuracy. Furthermore, its generalization is demonstrated for lungs and kidneys segmentation in CT scans.
Attention in Reasoning: Dataset, Analysis, and Modeling

While attention has been an increasingly popular component in deep neural networks to both interpret and boost the performance of models, little work has examined how attention progresses to accomplish a task and whether it is reasonable. In this work, we propose an Attention with Reasoning capability (AiR) framework that uses attention to understand and improve the process leading to task outcomes. We first define an evaluation metric based on a sequence of atomic reasoning operations, enabling a quantitative measurement of attention that considers the reasoning process. We then collect human eye-tracking and answer correctness data, and analyze various machine and human attention mechanisms on their reasoning capability and how they impact task performance. To improve the attention and reasoning ability of visual question answering models, we propose to supervise the learning of attention progressively along the reasoning process and to differentiate the correct and incorrect attention patterns. We demonstrate the effectiveness of the proposed framework in analyzing and modeling attention with better reasoning capability and task performance. The code and data are available at https://github.com/szzexpoi/AiR.
Multi-Density Sketch-to-Image Translation Network

Sketch-to-image (S2I) translation plays an important role in image synthesis and manipulation tasks, such as photo editing and colorization. Some specific S2I translations, including sketch-to-photo and sketch-to-painting, can be used as powerful tools in the art design industry. However, previous methods only support S2I translation with a single level of density, which gives less flexibility to users for controlling the input sketches. In this work, we propose the first multi-level density sketch-to-image translation framework, which allows the input sketch to cover a wide range from rough object outlines to microstructures. Moreover, to tackle the problem of noncontinuous representation of multi-level density input sketches, we project the density level into a continuous latent space, which can then be linearly controlled by a parameter. This allows users to conveniently control the densities of input sketches and the generation of images. Moreover, our method has been successfully verified on various datasets for different applications, including face editing, multi-modal sketch-to-photo translation, and anime colorization, providing coarse-to-fine levels of controls to these applications.
Analyzing Multiagent Interactions in Traffic Scenes via Topological Braids

We focus on the problem of analyzing multiagent interactions in traffic domains. Understanding the space of behavior of real-world traffic may offer significant advantages for algorithmic design, data-driven methodologies, and bench-marking. However, the high dimensionality of the space and the stochasticity of human behavior may hinder the identification of important interaction patterns. Our key insight is that traffic environments feature significant geometric and temporal structure, leading to highly organized collective behaviors, often drawn from a small set of dominant modes. In this work, we propose a representation based on the formalism of topological braids that can summarize arbitrarily complex multiagent behavior into a compact object of dual geometric and symbolic nature, capturing critical events of interaction. This representation allows us to formally enumerate the space of outcomes in a traffic scene and characterize their complexity. We illustrate the value of the proposed representation in summarizing critical aspects of real-world traffic behavior through a case study on recent driving datasets. We show that despite the density of real-world traffic, observed behavior tends to follow highly organized patterns of low interaction. Our framework may be a valuable tool for evaluating the richness of driving datasets, but also for synthetically designing balanced training datasets or benchmarks.
Pay to Win: Cheap, Cross-Chain Bribing Attacks on PoW Cryptocurrencies

In this paper we extend the attack landscape of bribing attacks on cryptocurrencies by presenting a new method, which we call Pay-To-Win (P2W). To the best of our knowledge, it is the first approach capable of facilitating double-spend collusion across different blockchains. Moreover, our technique can also be used to specifically incentivize transaction exclusion or (re)ordering. For our construction we rely on smart contracts to render the payment and receipt of bribes trustless for the briber as well as the bribee. Attacks using our approach are operated and financed out-of-band i.e., on a funding cryptocurrency, while the consequences are induced in a different target cryptocurrency. Hereby, the main requirement is that smart contracts on the funding cryptocurrency are able to verify consensus rules of the target. For a concrete instantiation of our P2W method, we choose Bitcoin as a target and Ethereum as a funding cryptocurrency. Our P2W method is designed in a way that reimburses collaborators even in the case of an unsuccessful attack. Interestingly, this actually renders our approach approximately one order of magnitude cheaper than comparable bribing techniques (e.g., the whale attack). We demonstrate the technical feasibility of P2W attacks through publishing all relevant artifacts of this paper, ranging from calculations of success probabilities to a fully functional proof-of-concept implementation, consisting of an Ethereum smart contract and a Python client.
Low-Shot Validation: Active Importance Sampling for Estimating Classifier Performance on Rare Categories

For machine learning models trained with limited labeled training data, validation stands to become the main bottleneck to reducing overall annotation costs. We propose a statistical validation algorithm that accurately estimates the F-score of binary classifiers for rare categories, where finding relevant examples to evaluate on is particularly challenging. Our key insight is that simultaneous calibration and importance sampling enables accurate estimates even in the low-sample regime (< 300 samples). Critically, we also derive an accurate single-trial estimator of the variance of our method and demonstrate that this estimator is empirically accurate at low sample counts, enabling a practitioner to know how well they can trust a given low-sample estimate. When validating state-of-the-art semi-supervised models on ImageNet and iNatural-ist2017, our method achieves the same estimates of model performance with up to 10× fewer labels than competing approaches. In particular, we can estimate model F1 scores with a variance of 0.005 using as few as 100 labels.
Reduce the Difficulty of Incremental Learning With Self-Supervised Learning

Incremental learning requires the model to learn new tasks without forgetting the learned tasks continuously. However, when the deep learning model learns new tasks, it will catastrophically forget the tasks it has learned before. Researchers have proposed methods to alleviate catastrophic forgetting; these methods only consider extracting features related to tasks learned before from learning samples, suppressing the model extracting features for unlearned tasks. As a result, when the model learns new tasks incrementally, the model needs to learn to extract the relevant features of the newly learned task quickly; this requires a significant change in the model’s behavior of extracting features, which increases the learning difficulty. Therefore, the model is caught in the dilemma of reducing the learning rate to retain existing knowledge or increasing the learning rate to learn new knowledge quickly; we believe that introducing self-supervised learning can alleviate this problem. Self-supervised learning methods learn to obtain supervised signals by customizing various pseudo-labels. Since the features extracted from the network model are based on self-supervised learning, independent of the specific subtasks of incremental learning, incorporating the supervised signal of self-supervised learning into the incremental learning process can help the model learn in incremental learning. The self-supervised learning process provides universal constraints, so that the model not only extracts features that are effective for the current learned task, but also allows the model to extract features that are suitable for other tasks that have not been learned, thereby allowing the network model parameters to iterate more smoothly and quickly to a point in the parameter space that satisfies the new and old tasks at the same time when learning a new task incrementally. The method of fusing selfsupervised and supervised learning signals significantly improve the adaptability of incremental learning without the need for additional labeled data. We verified that the introduction of self-supervised learning could significantly alleviate catastrophic forgetting in incremental learning algorithms based on sample replay by combining different databases and incremental learning model methods.
Friends Recommendation Based on KBERT-CNN Text Classification Model

In social networking platforms, the text published by users is usually the most direct way to express users' interests. This paper studies the method of text classification to mine user's interests for friends recommendation. And by fine-tuning the BERT (Bidirectional Encoder Representation from Transformers) pre-training language model to complete text classification tasks. Aiming at the problem of local information missing in the output of BERT pre-training language model. This paper proposes the KBERT-CNN (K_layers Bidirectional Encoder Representation from Transformers and Text Convolutional Neural Networks) text classification model. The model uses the output of the last four layers Transformers of the BERT pre-training language model as text vector, and combines with TextCNN(Text Convolution Neural Network) to build a text classification model. Then this paper uses the probability distribution of user's texts categories to calculate the interest similarity between users to achieve Top-N friends recommendation. Experimental results show that the F1 of the KBERT-CNN text classification model reaches 92.26%, which is better than other text classification models. The Precision of friends recommendation based on text classification is ahead of other content-based friends recommendation methods.
The Degrees of Monotony-Dilemma in Abstract Argumentation

In this paper, we introduce the notion of the degree of monotony to abstract argumentation, a well-established method for drawing inferences in face of conflicts in non-monotonic reasoning. Roughly speaking, the degree of monotony allows us, given an abstract argumentation semantics and an abstract argumentation framework to be as monotonic as possible, when iteratively drawing inferences and expanding the argumentation framework. However, we also show that when expanding an argumentation framework several times using so-called normal expansions, an agent may, at any given step, select a conclusion that has the highest degree of monotony w.r.t. the previous conclusion (considering the constraints of the semantics), but end up with a conclusion that has a suboptimal degree of monotony w.r.t. one or several conclusions that precede the previous conclusion. We formalize this observation as the degrees of monotony-dilemma.
The Bipartisan Case for Labeling as a Content Moderation Method: Findings from a National Survey

Social media companies have increasingly been using labeling strategies to identify, highlight, and mark content that may be problematic in some way but not sufficiently violating to justify removing it. Such labeling strategies, which are now being used by most major social platforms, present a host of new challenges and questions. This report, based on a national survey conducted in the U.S. in summer 2021 (N = 1,464), provides new insights into public preferences around social media company policy and interventions in the media environment. It is often assumed that there are highly polarized views about content moderation. However, we find relatively strong, bipartisan support for the basic strategy and general goals of labeling.
Automotive UX design and data-driven development: Narrowing the gap to support practitioners

The development and evaluation of In-Vehicle Information Systems (IVISs) is strongly based on insights from qualitative studies conducted in artificial contexts (e.g., driving simulators or lab experiments).However, the growing complexity of the systems and the uncertainty about the context in which they are used, create a need to augment qualitative data with quantitative data, collected during real-world driving.In contrast to many digital companies that are already successfully using data-driven methods, Original Equipment Manufacturers (OEMs) are not yet succeeding in releasing the potentials such methods offer.We aim to understand what prevents automotive OEMs from applying data-driven methods, what needs practitioners formulate, and how collecting and analyzing usage data from vehicles can enhance UX activities.We adopted a Multiphase Mixed Methods approach comprising two interview studies with more than 15 UX practitioners and two action research studies conducted with two different OEMs.From the four studies, we synthesize the needs of UX designers, extract limitations within the domain that hinder the application of data-driven methods, elaborate on unleveraged potentials, and formulate recommendations to improve the usage of vehicle data.We conclude that, in addition to modernizing the legal, technical, and organizational infrastructure, UX and Data Science must be brought closer together by reducing silo mentality and increasing interdisciplinary collaboration.New tools and methods need to be developed and UX experts must be empowered to make data-based evidence an integral part of the UX design process.
Sentiment Analysis for MOOC Course Reviews

Currently, increasing users are using MOOC platforms to choose courses and leave text comments with emotional overtones. The traditional words vector representation method uses static method to extract text information, which ignores the text position information. The traditional convolutional neural network fails to make full use of the semantic features and association information of the text between channels, which will cause inaccurate text sentiment classification. In order to solve the problems, a text classification model based on Albert and Capsule Network and attention mechanism is proposed. The model was verified on the MOOC comment data set, and compared with the traditional user comment sentiment analysis model. The results show the accuracy of the model was improved to a certain extent.
Realtime single-stage instance segmentation network based on anchors

In this paper, we propose an instance segmentation method uses a single-stage detector. Compared to the two-stage method, the single-stage method is simpler and easier to train. Not rely on the traditional region proposal, it directly uses pixels, which reduces the complexity of the network and significantly increases the speed. Our segmentation method is based on anchor boxes, which performs multi-scale detection by setting anchors of different sizes on multi-scale feature maps. We add a new branch to the prediction head to generate prototype masks and mask coefficients, then linearly combine them to generate mask. In our experiments, the proposed model had better performance, we got 35.12 fps on a single NVIDIA GEFORCE GTX 2080 GPU, which proves that our method is simple, effective, and faster.