Reviewerly/exHarmony huggingface dataset:
Mining high-utility itemsets in dynamic profit databases

High-Utility Itemset (HUI) mining is an important data-mining task which has gained popularity in recent years due to its applications in numerous fields. HUI mining aims at discovering itemsets that have high utility (e.g., yield a high profit) in transactional databases. Although several algorithms have been designed to enumerate all HUIs, an important issue is that they assume that the utilities (e.g., unit profits) of items are static. But this simplifying assumption does not hold in real-life situations. For example, the unit profits of items often vary over time in a retail store due to fluctuating supply costs and promotions. Ignoring this important characteristic of real-life transactional databases makes current HUI-mining algorithms inapplicable in many real-world applications. To address this critical limitation of current HUI-mining techniques, this paper studies the novel problem of mining HUIs in databases having dynamic unit profits. To accurately assess the utility of any itemset in this context, a redefined utility measure is introduced. Furthermore, a novel algorithm named MEFIM (Modified EFficient high-utility Itemset Mining), which relies on a novel compact database format to discover the desired itemsets efficiently, is designed. An improved version of the MEFIM algorithm, named iMEFIM, is also introduced. This algorithm employs a novel structure called P-set to reduce the number of transaction scans and to speed up the mining process. Experimental results show that the proposed algorithms considerably outperform the state-of-the-art HUI-mining algorithms on dynamic profit databases in terms of runtime, memory usage, and scalability.
Deep reinforcement learning for quantum gate control

How to implement multi-qubit gates efficiently with high precision is essential for realizing universal fault tolerant computing.For a physical system with some external controllable parameters, it is a great challenge to control the time dependence of these parameters to achieve a target multi-qubit gate efficiently and precisely.Here we construct a dueling double deep Q-learning neural network (DDDQN) to find out the optimized time dependence of controllable parameters to implement two typical quantum gates: a single-qubit Hadamard gate and a two-qubit CNOT gate.Compared with traditional optimal control methods, this deep reinforcement learning method can realize efficient and precise gate control without requiring any gradient information during the learning process.This work attempts to pave the way to investigate more quantum control problems with deep reinforcement learning techniques.
Balanced Self-Paced Learning for Generative Adversarial Clustering Network

Clustering is an important problem in various machine learning applications, but still a challenging task when dealing with complex real data. The existing clustering algorithms utilize either shallow models with insufficient capacity for capturing the non-linear nature of data, or deep models with large number of parameters prone to overfitting. In this paper, we propose a deep Generative Adversarial Clustering Network (ClusterGAN), which tackles the problems of training of deep clustering models in unsupervised manner. \emph{ClusterGAN} consists of three networks, a discriminator, a generator and a clusterer (i.e. a clustering network). We employ an adversarial game between these three players to synthesize realistic samples given discriminative latent variables via the generator, and learn the inverse mapping of the real samples to the discriminative embedding space via the clusterer. Moreover, we utilize a conditional entropy minimization loss to increase/decrease the similarity of intra/inter cluster samples. Since the ground-truth similarities are unknown in clustering task, we propose a novel balanced self-paced learning algorithm to gradually include samples into training from easy to difficult, while considering the diversity of selected samples from all clusters. Therefore, our method makes it possible to efficiently train clusterers with large depth by leveraging the proposed adversarial game and balanced self-paced learning algorithm. According our experiments, ClusterGAN achieves competitive results compared to the state-of-the-art clustering and hashing models on several datasets.
A novel background subtraction algorithm based on parallel vision and Bayesian GANs

To address the challenges of change detection in the wild, we present a novel background subtraction algorithm based on parallel vision and Bayesian generative adversarial networks (GANs). First, we use the median filtering algorithm for background image extraction. Then, we build the background subtraction model by using Bayesian GANs to classify all pixels into foreground and background, and use parallel vision theory to improve the background subtraction results in complex scenes. The proposed algorithm has been evaluated on the well-known, publicly available changedetection.net dataset. Experiment results show that the proposed algorithm results in better performance than many state-of-the-art ones. In addition, our model trained on CDnet dataset can generalize very well to unseen datasets, outperforming multiple state-of-art methods. The major contribution of this work is to apply parallel vision and Bayesian GANs to solve the difficulties in background subtraction, achieving high detection accuracy.
Confidence-aware motion prediction for real-time collision avoidance<sup>1</sup>

One of the most difficult challenges in robot motion planning is to account for the behavior of other moving agents, such as humans. Commonly, practitioners employ predictive models to reason about where other agents are going to move. Though there has been much recent work in building predictive models, no model is ever perfect: an agent can always move unexpectedly, in a way that is not predicted or not assigned sufficient probability. In such cases, the robot may plan trajectories that appear safe but, in fact, lead to collision. Rather than trust a model’s predictions blindly, we propose that the robot should use the model’s current predictive accuracy to inform the degree of confidence in its future predictions. This model confidence inference allows us to generate probabilistic motion predictions that exploit modeled structure when the structure successfully explains human motion, and degrade gracefully whenever the human moves unexpectedly. We accomplish this by maintaining a Bayesian belief over a single parameter that governs the variance of our human motion model. We couple this prediction algorithm with a recently proposed robust motion planner and controller to guide the construction of robot trajectories that are, to a good approximation, collision-free with a high, user-specified probability. We provide extensive analysis of the combined approach and its overall safety properties by establishing a connection to reachability analysis, and conclude with a hardware demonstration in which a small quadcopter operates safely in the same space as a human pedestrian.
MRI Breast Tumor Segmentation Using Different Encoder and Decoder CNN Architectures

Breast tumor segmentation in medical images is a decisive step for diagnosis and treatment follow-up. Automating this challenging task helps radiologists to reduce the high manual workload of breast cancer analysis. In this paper, we propose two deep learning approaches to automate the breast tumor segmentation in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) by building two fully convolutional neural networks (CNN) based on SegNet and U-Net. The obtained models can handle both detection and segmentation on each single DCE-MRI slice. In this study, we used a dataset of 86 DCE-MRIs, acquired before and after two cycles of chemotherapy, of 43 patients with local advanced breast cancer, a total of 5452 slices were used to train and validate the proposed models. The data were annotated manually by an experienced radiologist. To reduce the training time, a high-performance architecture composed of graphic processing units was used. The model was trained and validated, respectively, on 85% and 15% of the data. A mean intersection over union (IoU) of 68.88 was achieved using SegNet and 76.14% using U-Net architecture.
Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

In the last few years, there has been a growing expectation created about the analysis of large amounts of data often available in organizations, which has been both scrutinized by the academic world and successfully exploited by industry. Nowadays, two of the most common terms heard in scientific circles are Big Data and Deep Learning. In this double review, we aim to shed some light on the current state of these different, yet somehow related branches of Data Science, in order to understand the current state and future evolution within the healthcare area. We start by giving a simple description of the technical elements of Big Data technologies, as well as an overview of the elements of Deep Learning techniques, according to their usual description in scientific literature. Then, we pay attention to the application fields that can be said to have delivered relevant real-world success stories, with emphasis on examples from large technology companies and financial institutions, among others. The academic effort that has been put into bringing these technologies to the healthcare sector are then summarized and analyzed from a twofold view as follows: first, the landscape of application examples is globally scrutinized according to the varying nature of medical data, including the data forms in electronic health recordings, medical time signals, and medical images; second, a specific application field is given special attention, in particular the electrocardiographic signal analysis, where a number of works have been published in the last two years. A set of toy application examples are provided with the publicly-available MIMIC dataset, aiming to help the beginners start with some principled, basic, and structured material and available code. Critical discussion is provided for current and forthcoming challenges on the use of both sets of techniques in our future healthcare.
Lessons Learned from Using a Deep Tree-Based Model for Software Defect Prediction in Practice

Defects are common in software systems and cause many problems for software users. Different methods have been developed to make early prediction about the most likely defective modules in large codebases. Most focus on designing features (e.g. complexity metrics) that correlate with potentially defective code. Those approaches however do not sufficiently capture the syntax and multiple levels of semantics of source code, a potentially important capability for building accurate prediction models. In this paper, we report on our experience of deploying a new deep learning tree-based defect prediction model in practice. This model is built upon the tree-structured Long Short Term Memory network which directly matches with the Abstract Syntax Tree representation of source code. We discuss a number of lessons learned from developing the model and evaluating it on two datasets, one from open source projects contributed by our industry partner Samsung and the other from the public PROMISE repository.
Head-Mounted Display Virtual Reality in Post-secondary Education and Skill Training

Background: This review focused on how immersive head-mounted display virtual reality (VR) was used in post-secondary level education and skill training, with the aim to better understand its state of the art as found from the literature. While numerous studies describe the use of immersive VR within a specific educational setting, they are often standalone events not fully detailed regarding their curricular integration. This review aims to analyse these events, with a focus on immersive VR’s incorporation into post-secondary education. Objectives: O1) Review the existing literature on the use of immersive VR in post-secondary settings, determining where and how it has been used within each educational discipline. This criterion focused on literature featuring the use of immersive VR, due to its influence on a user’s perceived levels of presence and imagination. O2) Identify favourable outcomes from the use of immersive VR when compared to other learning methods. O3) Determine the conceptual rationale (purpose) for each implementation of immersive VR as found throughout the literature. O4) Identify learning theories and recommendations for the utilization of immersive VR in post-secondary education. Methods: A literature review was undertaken with searches of Education Research Complete, ERIC, MEDLINE, EMBASE, IEEE Xplore, Scopus and Web of Science: Core Collection to locate reports on the use of immersive VR in post-secondary curricula. Results: 119 articles were identified, featuring disciplines across Arts and Humanities, Health Sciences, Military and Aerospace, Science and Technology. 35 out of 38 experiments reported to have found a positive outcome for immersive VR, after being compared with a non-immersive platform. Each simulation’s purpose included one or more of the following designations: skill training, convenience, engagement, safety, highlighting, interactivity, team building and suggestion. Recommendations for immersive VR in post-secondary education emphasize experiential learning and social constructivist approaches, including student-created virtual environments that are mainly led by the students themselves under team collaboration. Conclusion: Immersive VR brings convenient, engaging and interactive alternatives to traditional classroom settings as well as offers additional capability over traditional methods. There is a diverse assortment of educational disciplines that have each attempted to harness the power of this technological medium.
He Gives C-Sieves on the CSIDH

Recently, Castryck, Lange, Martindale, Panny, and Renes proposed CSIDH (pronounced "sea-side") as a candidate post-quantum "commutative group action." It has attracted much attention and interest, in part because it enables noninteractive Diffie–Hellman-like key exchange with quite small communication. Subsequently, CSIDH has also been used as a foundation for digital signatures. In 2003–04, Kuperberg and then Regev gave asymptotically subexponential quantum algorithms for "hidden shift" problems, which can be used to recover the CSIDH secret key from a public key. In late 2011, Kuperberg gave a follow-up quantum algorithm called the collimation sieve ("c-sieve" for short), which improves the prior ones, in particular by using exponentially less quantum memory and offering more parameter tradeoffs. While recent works have analyzed the concrete cost of the original algorithms (and variants) against CSIDH, nothing of this nature was previously available for the c-sieve. This work fills that gap. Specifically, we generalize Kuperberg's collimation sieve to work for arbitrary finite cyclic groups, provide some practical efficiency improvements, give a classical (i.e., non-quantum) simulator, run experiments for a wide range of parameters up to the actual CSIDH-512 group order, and concretely quantify the complexity of the c-sieve against CSIDH. Our main conclusion is that the proposed CSIDH parameters provide relatively little quantum security beyond what is given by the cost of quantumly evaluating the CSIDH group action itself (on a uniform superposition). For example, the cost of CSIDH-512 key recovery is only about $$2^{16}$$ quantum evaluations using $$2^{40}$$ bits of quantumly accessible classical memory (plus relatively small other resources). This improves upon a prior estimate of $$2^{32.5}$$ evaluations and $$2^{31}$$ qubits of quantum memory, for a variant of Kuperberg's original sieve. Under the plausible assumption that quantum evaluation does not cost much more than what is given by a recent "best case" analysis, CSIDH-512 can therefore be broken using significantly less than $$2^{64}$$ quantum T-gates. This strongly invalidates its claimed NIST level 1 quantum security, especially when accounting for the MAXDEPTH restriction. Moreover, under analogous assumptions for CSIDH-1024 and -1792, which target higher NIST security levels, except near the high end of the MAXDEPTH range even these instantiations fall short of level 1.
Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark

Researchers have proposed hardware, software, and algorithmic optimizations to improve the computational performance of deep learning. While some of these optimizations perform the same operations faster (e.g., increasing GPU clock speed), many others modify the semantics of the training procedure (e.g., reduced precision), and can impact the final model's accuracy on unseen data. Due to a lack of standard evaluation criteria that considers these trade-offs, it is difficult to directly compare these optimizations. To address this problem, we recently introduced DAWNBENCH, a benchmark competition focused on end-to-end training time to achieve near-state-of-the-art accuracy on an unseen dataset-a combined metric called time-to-accuracy (TTA). In this work, we analyze the entries from DAWNBENCH, which received optimized submissions from multiple industrial groups, to investigate the behavior of TTA as a metric as well as trends in the best-performing entries. We show that TTA has a low coefficient of variation and that models optimized for TTA generalize nearly as well as those trained using standard methods. Additionally, even though DAWNBENCH entries were able to train ImageNet models in under 3 minutes, we find they still underutilize hardware capabilities such as Tensor Cores. Furthermore, we find that distributed entries can spend more than half of their time on communication. We show similar findings with entries to the MLPERF v0.5 benchmark.
Blockchain-Based Personal Health Records Sharing Scheme With Data Integrity Verifiable

The sharing of personal health records can help to improve the accuracy of the doctor's diagnosis and to promote the progress of medical research. Currently, to reduce the maintenance cost of data, personal health records are usually outsourced to a third party such as the cloud service provider. In this case, patients may lose direct control over their personal health records and the semi-trusted cloud service provider may tamper with or reveal personal health records. Therefore, ensuring the privacy and integrity of personal health records and realizing the fine-grained access control are crucial issues when personal health records are shared. As a distributed architecture with decentralized and tamper-proof features, blockchain provides a new way to protect the personal health records sharing system. In this paper, we propose a new personal health records sharing scheme with data integrity verifiable based on blockchain. Aiming at the problems of privacy disclosure, limited keyword search ability and loss of control rights in the process of personal health record sharing, the new scheme uses searchable symmetric encryption and attribute-based encryption techniques to achieve privacy protection, keyword search, and fine-grained access control. Compared with the existing similar schemes, the new scheme allows patients to distribute attribute private key for users, avoiding many security problems caused by the existing of attribute authority in the scheme. Furthermore, the new scheme uses blockchain to manage keys in the scheme, avoiding the single point failure problem of centralized key management. In particular, the new scheme stores the hash values of encrypted personal health records in blockchain, and the related index set is stored in smart contract, which can further improve the efficiency of data integrity verification. Finally, performance evaluation and security analysis indicate that our scheme is secure and feasible for practical use.
Explanatory learner models: Why machine learning (alone) is not the answer

Abstract Using data to understand learning and improve education has great promise. However, the promise will not be achieved simply by AI and Machine Learning researchers developing innovative models that more accurately predict labeled data. As AI advances, modeling techniques and the models they produce are getting increasingly complex, often involving tens of thousands of parameters or more. Though strides towards interpretation of complex models are being made in core machine learning communities, it remains true in these cases of “black box” modeling that research teams may have little possibility to peer inside to try understand how, why, or even whether such models will work when applied beyond the data on which they were built. Rather than relying on AI expertise alone, we suggest that learning engineering teams bring interdisciplinary expertise to bear to develop explanatory learner models that provide interpretable and actionable insights in addition to accurate prediction. We describe examples that illustrate use of different kinds of data (eg, click stream and discourse data) in different course content (eg, math and writing) and toward different goals (eg, improving student models and generating actionable feedback). We recommend learning engineering teams, shared infrastructure and funder incentives toward better explanatory learner model development that advances learning science, produces better pedagogical practices and demonstrably improves student learning. Practitioner Notes What is already known about this topic Researchers in learning analytics and educational data mining have been successful in creating innovative models of data that optimize prediction. Some of these models produce scientific or practical insights and fewer have been put into use and demonstrated to enhance student learning. What this paper adds We provide examples of development of explanatory models of learners that not only accurately predict data but also provide scientific insights and yield practical outcomes. In particular, researchers with expertise in cognitive science and math education content use AI‐based data analytics to discover previously unrecognized barriers to geometry student learning. They use model‐derived insights to redesign an online tutoring system and “close‐the‐loop” by experimentally demonstrating that the new system produces better student learning than the original. Implications for practice and/or policy We define explanatory learning models and provide an articulation of a process for generating them that involves interdisciplinary teams employing human–computer interaction and learning engineering methods. Based on our experiences, we recommend learning engineering teams, shared infrastructure and funder incentives toward better explanatory learner model development that advances learning science, produces better pedagogical practices and demonstrably improves student learning.