Reviewerly/exHarmony huggingface dataset:
Predictive Student Modeling in Educational Games with Multi-Task Learning

Modeling student knowledge is critical in adaptive learning environments. Predictive student modeling enables formative assessment of student knowledge and skills, and it drives personalized support to create learning experiences that are both effective and engaging. Traditional approaches to predictive student modeling utilize features extracted from students’ interaction trace data to predict student test performance, aggregating student test performance as a single output label. We reformulate predictive student modeling as a multi-task learning problem, modeling questions from student test data as distinct “tasks.” We demonstrate the effectiveness of this approach by utilizing student data from a series of laboratory-based and classroom-based studies conducted with a game-based learning environment for microbiology education, Crystal Island. Using sequential representations of student gameplay, results show that multi-task stacked LSTMs with residual connections significantly outperform baseline models that do not use the multi-task formulation. Additionally, the accuracy of predictive student models is improved as the number of tasks increases. These findings have significant implications for the design and development of predictive student models in adaptive learning environments.
Efficient Incremental Learning Using Dynamic Correction Vector

One major challenge for modern artificial neural networks (ANNs) is that they typically does not handle incremental learning well. In other words, while learning the new features, the performances of existing features usually deteriorate. This phenomenon is called catastrophic forgetting, which causes great problems for continuous, incremental, and intelligent learning. In this work, we propose a dynamic correction vector based algorithm to address both the bias problem from knowledge distillation and the overfitting problem. Specifically, we have made the following contributions: 1) we have designed a novel dynamic correction vector based algorithm; 2) we have proposed new loss functions accordingly. Experimental results on MNIST and CIFAR-100 datasets demonstrate that our technique can outperform state-of-the-art incremental learning methods by 4% on large datasets.
Deep Embedded Non-Redundant Clustering

Complex data types like images can be clustered in multiple valid ways. Non-redundant clustering aims at extracting those meaningful groupings by discouraging redundancy between clusterings. Unfortunately, clustering images in pixel space directly has been shown to work unsatisfactory. This has increased interest in combining the high representational power of deep learning with clustering, termed deep clustering. Algorithms of this type combine the non-linear embedding of an autoencoder with a clustering objective and optimize both simultaneously. None of these algorithms try to find multiple non-redundant clusterings. In this paper, we propose the novel Embedded Non-Redundant Clustering algorithm (ENRC). It is the first algorithm that combines neural-network-based representation learning with non-redundant clustering. ENRC can find multiple highly non-redundant clusterings of different dimensionalities within a data set. This is achieved by (softly) assigning each dimension of the embedded space to the different clusterings. For instance, in image data sets it can group the objects by color, material and shape, without the need for explicit feature engineering. We show the viability of ENRC in extensive experiments and empirically demonstrate the advantage of combining non-linear representation learning with non-redundant clustering.
Attack-Resistant Federated Learning with Residual-based Reweighting

Federated learning has a variety of applications in multiple domains by utilizing private training data stored on different devices. However, the aggregation process in federated learning is highly vulnerable to adversarial attacks so that the global model may behave abnormally under attacks. To tackle this challenge, we present a novel aggregation algorithm with residual-based reweighting to defend federated learning. Our aggregation algorithm combines repeated median regression with the reweighting scheme in iteratively reweighted least squares. Our experiments show that our aggregation algorithm outperforms other alternative algorithms in the presence of label-flipping and backdoor attacks. We also provide theoretical analysis for our aggregation algorithm.
Are These Birds Similar: Learning Branched Networks for Fine-grained Representations

Fine-grained image classification is a challenging task due to the presence of hierarchical coarse-to-fine-grained distribution in the dataset. Generally, parts are used to discriminate various objects in fine-grained datasets, however, not all parts are beneficial and indispensable. In recent years, natural language descriptions are used to obtain information on discriminative parts of the object. This paper leverages on natural language description and proposes a strategy for learning the joint representation of natural language description and images using a two-branch network with multiple layers to improve the fine-grained classification task. Extensive experiments show that our approach gains significant improvements in accuracy for the fine-grained image classification task. Furthermore, our method achieves new state-of-the-art results on the CUB-200-2011 dataset.
Protecting the 4G and 5G Cellular Paging Protocols against Security and Privacy Attacks

Abstract This paper focuses on protecting the cellular paging protocol — which balances between the quality-of-service and battery consumption of a device — against security and privacy attacks. Attacks against this protocol can have severe repercussions, for instance, allowing attacker to infer a victim’s location, leak a victim’s IMSI, and inject fabricated emergency alerts. To secure the protocol, we first identify the underlying design weaknesses enabling such attacks and then propose efficient and backward-compatible approaches to address these weaknesses. We also demonstrate the deployment feasibility of our enhanced paging protocol by implementing it on an open-source cellular protocol library and commodity hardware. Our evaluation demonstrates that the enhanced protocol can thwart attacks without incurring substantial overhead.
Bi-Modal Learning With Channel-Wise Attention for Multi-Label Image Classification

Multi-label image classification is more in line with the real-world applications. This problem is difficult due to the the fact that complex label space makes it hard to get label-level attention regions and deal with semantic relationships among labels. Common deep network-based methods utilize CNN to extract features and consider the labels as a sequence or a graph, thus handling the label correlations with RNN or graph-theoretical algorithms. In this paper, we propose a novel CNN-RNN-based model, bi-modal multi-label learning(BMML) framework. Firstly, an improved channel-wise attention mechanism is presented to propose regional attention maps and connect them to relative labels. After that, based on the assumption that objects in a semantic scene always have high-level relevance among visual and textual corpus, we further embed the labels through different pre-trained language models and determine the label sequence in a “semantic space” constructed on large-scale textual data, thereby handling the labels in their semantic context. In addition, a cross-modal feature aligning module is introduced in BMML framework. Experimental results show that BMML is able to achieve better accuracies then those mainstream multi-label classification methods on several benchmark data sets.
How Fast Do Quantum Walks Mix?

The fundamental problem of sampling from the limiting distribution of quantum walks on networks, known as \emph{mixing}, finds widespread applications in several areas of quantum information and computation. Of particular interest in most of these applications, is the minimum time beyond which the instantaneous probability distribution of the quantum walk remains close to this limiting distribution, known as the \emph{quantum mixing time}. However this quantity is only known for a handful of specific networks. In this letter, we prove an upper bound on the quantum mixing time for \emph{almost all networks}, i.e.\ the fraction of networks for which our bound holds, goes to one in the asymptotic limit. To this end, using several results in random matrix theory, we find the quantum mixing time of Erd\"os-Renyi random networks: networks of $n$ nodes where each edge exists with probability $p$ independently. For example for dense random networks, where $p$ is a constant, we show that the quantum mixing time is $\mathcal{O}\left(n^{3/2 + o(1)}\right)$. Besides opening avenues for the analytical study of quantum dynamics on random networks, our work could find applications beyond quantum information processing. Owing to the universality of Wigner random matrices, our results on the spectral properties of random graphs hold for general classes of random matrices that are ubiquitous in several areas of physics. In particular, our results could lead to novel insights into the equilibration times of isolated quantum systems defined by random Hamiltonians, a foundational problem in quantum statistical mechanics.
Communication Efficient Federated Learning over Multiple Access Channels

In this work, we study the problem of federated learning (FL), where distributed users aim to jointly train a machine learning model with the help of a parameter server (PS). In each iteration of FL, users compute local gradients, followed by transmission of the quantized gradients for subsequent aggregation and model updates at PS. One of the challenges of FL is that of communication overhead due to FL's iterative nature and large model sizes. One recent direction to alleviate communication bottleneck in FL is to let users communicate simultaneously over a multiple access channel (MAC), possibly making better use of the communication resources. In this paper, we consider the problem of FL learning over a MAC. In particular, we focus on the design of digital gradient transmission schemes over a MAC, where gradients at each user are first quantized, and then transmitted over a MAC to be decoded individually at the PS. When designing digital FL schemes over MACs, there are new opportunities to assign different amount of resources (such as rate or bandwidth) to different users based on a) the informativeness of the gradients at each user, and b) the underlying channel conditions. We propose a stochastic gradient quantization scheme, where the quantization parameters are optimized based on the capacity region of the MAC. We show that such channel aware quantization for FL outperforms uniform quantization, particularly when users experience different channel conditions, and when have gradients with varying levels of informativeness.
Image encryption using a combination of Grain‐128a algorithm and Zaslavsky chaotic map

Encryption is a very important way to secure data in storage and communication, and it is a process of encoding messages or information in such a manner that only authorised persons can access it. Different techniques are used to protect confidential image data against illicit access. In image encryption using chaotic systems, most authors use or design algorithms to generate the initial parameters’ values from the secret key. However, as the key size depends on the number of these parameters, the used algorithms show little sensitivity to small changes in the key. To enhance both security and sensitivity in the choice of the initial parameters, this work combines the use of the Grain-128a stream cipher algorithm with two-dimensional Zaslavsky chaotic map. Firstly, the Grain-128a algorithm is applied to generate the required parameters of Zaslavsky's chaotic map from a fixed length 256-bit secret key. Secondly, the sequences generated by the chaotic map are used to encrypt the image using a bit confusion and diffusion process. The simulation results on greyscale, colour, binary, indexed, and medical images together with the scores obtained in the evaluation of the algorithm show that the proposed method is very sure and effective in encrypting images of any size and any type.
Spin Me Right Round Rotational Symmetry for FPGA-Specific AES: Extended Version

Abstract The effort in reducing the area of AES implementations has largely been focused on application-specific integrated circuits (ASICs) in which a tower field construction leads to a small design of the AES S-box. In contrast, a naive implementation of the AES S-box has been the status-quo on field-programmable gate arrays (FPGAs). A similar discrepancy holds for masking schemes—a well-known side-channel analysis countermeasure—which are commonly optimized to achieve minimal area in ASICs. In this paper, we demonstrate a representation of the AES S-box exploiting rotational symmetry which leads to a 50% reduction in the area footprint on FPGA devices. We present new AES implementations which improve on the state-of-the-art and explore various trade-offs between area and latency. For instance, at the cost of increasing 4.5 times the latency, one of our design variants requires 25% less look-up tables (LUTs) than the smallest known AES on Xilinx FPGAs by Sasdrich and Güneysu at ASAP 2016. We further explore the protection of such implementations against side-channel attacks. We introduce a generic methodology for masking any n -bit Boolean functions of degree t with protection order d . The methodology is exact for first-order and heuristic for higher orders. Its application to our new construction of the AES S-box allows us to improve previous results and introduce the smallest first-order masked AES implementation on Xilinx FPGAs, to date.
Effective Diversity in Population Based Reinforcement Learning

Exploration is a key problem in reinforcement learning, since agents can only learn from data they acquire in the environment. With that in mind, maintaining a population of agents is an attractive method, as it allows data be collected with a diverse set of behaviors. This behavioral diversity is often boosted via multi-objective loss functions. However, those approaches typically leverage mean field updates based on pairwise distances, which makes them susceptible to cycling behaviors and increased redundancy. In addition, explicitly boosting diversity often has a detrimental impact on optimizing already fruitful behaviors for rewards. As such, the reward-diversity trade off typically relies on heuristics. Finally, such methods require behavioral representations, often handcrafted and domain specific. In this paper, we introduce an approach to optimize all members of a population simultaneously. Rather than using pairwise distance, we measure the volume of the entire population in a behavioral manifold, defined by task-agnostic behavioral embeddings. In addition, our algorithm Diversity via Determinants (DvD), adapts the degree of diversity during training using online learning techniques. We introduce both evolutionary and gradient-based instantiations of DvD and show they effectively improve exploration without reducing performance when better exploration is not required.
Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods

As machine learning black boxes are increasingly being deployed in domains such as healthcare and criminal justice, there is growing emphasis on building tools and techniques for explaining these black boxes in an interpretable manner. Such explanations are being leveraged by domain experts to diagnose systematic errors and underlying biases of black boxes. In this paper, we demonstrate that post hoc explanations techniques that rely on input perturbations, such as LIME and SHAP, are not reliable. Specifically, we propose a novel scaffolding technique that effectively hides the biases of any given classifier by allowing an adversarial entity to craft an arbitrary desired explanation. Our approach can be used to scaffold any biased classifier in such a way that its predictions on the input data distribution still remain biased, but the post hoc explanations of the scaffolded classifier look innocuous. Using extensive evaluation with multiple real-world datasets (including COMPAS), we demonstrate how extremely biased (racist) classifiers crafted by our framework can easily fool popular explanation techniques such as LIME and SHAP into generating innocuous explanations which do not reflect the underlying biases.
Personalized Recommendation Model: An Online Comment Sentiment Based Analysis

Traditional recommendation algorithms measure users’ online ratings of goods and services but ignore the information contained in written reviews, resulting in lowered personalized recommendation accuracy. Users’ reviews express opinions and reflect implicit preferences and emotions towards the features of products or services. This paper proposes a model for the fine-grained analysis of emotions expressed in users’ online written reviews, using film reviews on the Chinese social networking site Douban.com as an example. The model extracts feature-sentiment word pairs in user reviews according to four syntactic dependencies, examines film features, and scores the sentiment values of film features according to user preferences. User group personalized recommendations are realized through user clustering and user similarity calculation. Experiments show that the extraction of user feature-sentiment word pairs based on four syntactic dependencies can better identify the implicit preferences of users, apply them to recommendations and thereby increase recommendation accuracy.
A real time expert system for anomaly detection of aerators based on computer vision and surveillance cameras

Aerators are essential and crucial auxiliary devices in intensive culture, especially in industrial culture in China. In this paper, we propose a real-time expert system for anomaly detection of aerators based on computer vision technology and existing surveillance cameras. The expert system includes two modules, i.e., object region detection and working state detection. First, we present a small object region detection method based on the region proposal idea. Moreover, we propose a novel algorithm called reference frame Kanade-Lucas-Tomasi (RF-KLT) algorithm for motion feature extraction in fixed regions. Then, we describe a dimension reduction method of time series for establishing a feature dataset with obvious boundaries between classes. Finally, we use machine learning algorithms to build the feature classifier. The proposed expert system can realize real-time, robust and cost-free anomaly detection of aerators in both the actual video dataset and the augmented video dataset. Demo is available at https://youtu.be/xThHRwu_cnI.
Complementary quantum correlations among multipartite systems

We study the monogamy and polygamy relations related to quantum correlations for multipartite quantum systems. General monogamy relations are presented for the $$\alpha $$th $$(0\le \alpha \le \gamma , \gamma \ge 2)$$ power of quantum correlation, and general polygamy relations are given for the $$\beta $$th $$(\beta \ge \delta , 0\le \delta \le 1)$$ power of quantum correlation. These monogamy and polygamy inequalities are complementary to the existing ones with different parameter regions of $$\alpha $$ and $$\beta $$. Applying these results to specific quantum correlations, the corresponding new classes of monogamy and polygamy relations are obtained, which include the existing ones as special cases. Detailed examples are given.
Learning to Solve Network Flow Problems via Neural Decoding

Many decision-making problems in engineering applications such as transportation, power system and operations research require repeatedly solving large-scale linear programming problems with a large number of different inputs. For example, in energy systems with high levels of uncertain renewable resources, tens of thousands of scenarios may need to be solved every few minutes. Standard iterative algorithms for linear network flow problems, even though highly efficient, becomes a bottleneck in these applications. In this work, we propose a novel learning approach to accelerate the solving process. By leveraging the rich theory and economic interpretations of LP duality, we interpret the output of the neural network as a noisy codeword, where the codebook is given by the optimization problem's KKT conditions. We propose a feedforward decoding strategy that finds the optimal set of active constraints. This design is error correcting and can offer orders of magnitude speedup compared to current state-of-the-art iterative solvers, while providing much better solutions in terms of feasibility and optimality compared to end-to-end learning approaches.
Tabu Search and Machine-Learning Classification of Benign and Malignant Proliferative Breast Lesions

Breast cancer is the most diagnosed cancer among women around the world. The development of computer-aided diagnosis tools is essential to help pathologists to accurately interpret and discriminate between malignant and benign tumors. This paper proposes the development of an automated proliferative breast lesion diagnosis based on machine-learning algorithms. We used Tabu search to select the most significant features. The evaluation of the feature is based on the dependency degree of each attribute in the rough set. The categorization of reduced features was built using five machine-learning algorithms. The proposed models were applied to the BIDMC-MGH and Wisconsin Diagnostic Breast Cancer datasets. The performance measures of the used models were evaluated owing to five criteria. The top performing models were AdaBoost and logistic regression. Comparisons with others works prove the efficiency of the proposed method for superior diagnosis of breast cancer against the reviewed classification techniques.
Detecting Binge Drinking and Alcohol-Related Risky Behaviours from Twitter’s Users: An Exploratory Content- and Topology-Based Analysis

Binge Drinking (BD) is a common risky behaviour that people hardly report to healthcare professionals, although it is not uncommon to find, instead, personal communications related to alcohol-related behaviors on social media. By following a data-driven approach focusing on User-Generated Content, we aimed to detect potential binge drinkers through the investigation of their language and shared topics. First, we gathered Twitter threads quoting BD and alcohol-related behaviours, by considering unequivocal keywords, identified by experts, from previous evidence on BD. Subsequently, a random sample of the gathered tweets was manually labelled, and two supervised learning classifiers were trained on both linguistic and metadata features, to classify tweets of genuine unique users with respect to media, bot, and commercial accounts. Based on this classification, we observed that approximately 55% of the 1 million alcohol-related collected tweets was automatically identified as belonging to non-genuine users. A third classifier was then trained on a subset of manually labelled tweets among those previously identified as belonging to genuine accounts, to automatically identify potential binge drinkers based only on linguistic features. On average, users classified as binge drinkers were quite similar to the standard genuine Twitter users in our sample. Nonetheless, the analysis of social media contents of genuine users reporting risky behaviours remains a promising source for informed preventive programs.