Reviewerly/exHarmony huggingface dataset:
Shrinking Temporal Attention in Transformers for Video Action Recognition

Spatiotemporal modeling in an unified architecture is key for video action recognition. This paper proposes a Shrinking Temporal Attention Transformer (STAT), which efficiently builts spatiotemporal attention maps considering the attenuation of spatial attention in short and long temporal sequences. Specifically, for short-term temporal tokens, query token interacts with them in a fine-grained manner in dealing with short-range motion. It then shrinks to a coarse attention in neighborhood for long-term tokens, to provide larger receptive field for long-range spatial aggregation. Both of them are composed in a short-long temporal integrated block to build visual appearances and temporal structure concurrently with lower costly in computation. We conduct thorough ablation studies, and achieve state-of-the-art results on multiple action recognition benchmarks including Kinetics400 and Something-Something v2, outperforming prior methods with 50% less FLOPs and without any pretrained model.
Super Detector: An Ensemble Approach for Anomaly Detection in Industrial Control Systems

Industrial Control Systems encompass supervisory systems (SCADA) and cyber-physical components (sensors/actuators), which are typically deployed in critical infrastructure to control physical processes. Their interconnectedness and controllability leaves them vulnerable to cyber-physical attacks which could have detrimental impacts on the safety, security, and service of the infrastructure. Detection and defense against these attacks is, therefore, of utmost importance. Existing detection methods often fail to detect cyber-physical attacks of certain types, or produce false alarms by design. This paper proposes a novel anomaly detection method called the Super Detector for the robust prediction of cyber-physical attacks in these complex systems by design. It fuses the inputs from the real-time operational data from the system as well as individual anomaly detectors. The methods tested for the first prototype of Super Detector entail a stacked ensemble of Random Forest, Bagging with k-Nearest Neighbors and Gaussian Naïve Bayes algorithms to classify normal and anomalous data. This results in high accuracy of detection and a lower number of false alarms. The training and validation of our method has been carried out on the SWaT dataset collected during a real live-fire international cyber exercise. The operational and attack data is obtained from a real small-scale replica of an industrial water treatment plant. We present that Super Detector achieves much higher accuracy and a lower false alarm rate as compared to individual anomaly detection approaches.
Massive-scale Decoding for Text Generation using Lattices

Conditional neural text generation models generate high-quality outputs, but often concentrate around a mode when what we really want is a diverse set of options.We present a search algorithm to construct lattices encoding a massive number of generation options.First, we restructure decoding as a best-first search, which explores the space differently than beam search and improves efficiency by avoiding pruning paths.Second, we revisit the idea of hypothesis recombination: we can identify pairs of similar generation candidates during search and merge them as an approximation.On both summarization and machine translation, we show that our algorithm encodes thousands of diverse options that remain grammatical and high-quality into one lattice.This algorithm provides a foundation for building downstream generation applications on top of massive-scale diverse outputs. 1
Smart Count System Based on Object Detection Using Deep Learning

Object counting is an indispensable task in manufacturing and management. Recently, the development of image-processing techniques and deep learning object detection has achieved excellent performance in object-counting tasks. Accordingly, we propose a novel small-size smart counting system composed of a low-cost hardware device and a cloud-based object-counting software server to implement an accurate counting function and overcome the trade-off presented by the computing power of local hardware. The cloud-based object-counting software consists of a model adapted to the object-counting task through a novel DBC-NMS (our own technique) and hyperparameter tuning of deep-learning-based object-detection methods. With the power of DBC-NMS and hyperparameter tuning, the performance of the cloud-based object-counting software is competitive over commonly used public datasets (CARPK and SKU110K) and our custom dataset of small pills. Our cloud-based object-counting software achieves an mean absolute error (MAE) of 1.03 and a root mean squared error (RMSE) of 1.20 on the Pill dataset. These results demonstrate that the proposed smart counting system accurately detects and counts densely distributed object scenes. In addition, the proposed system shows a reasonable and efficient cost–performance ratio by converging low-cost hardware and cloud-based software.
An Analysis of Crosswalks from Research Data Schemas to Schema.org

ABSTRACT The increased number of data repositories has greatly increased the availability of open data. To enable broad discovery and access to research dataset, some data repositories have begun leveraging the web architecture by embedding structured metadata markup in dataset web landing pages using vocabularies from Schema.org and extensions. This paper aims to examine metadata interoperability for supporting global data discovery. Specifically, the paper reports a survey on which metadata schema has been adopted by participating data repositories, and presents an analysis of crosswalks from fourteen research data schemas to Schema.org. The analysis indicates most descriptive metadata are interoperable among the schemas, the most inconsistent mapping is the rights metadata, and a large gap exists in the structural metadata and controlled vocabularies to specify various property values. The analysis and collated crosswalks can serve as a reference for data repositories when they develop crosswalks from their own schemas to Schema.org, and provide the research data community a benchmark of structured metadata implementation.
Multi-task feature and structure learning for user-preference based knowledge-aware recommendation

Data sparsity and long-tail items recommendation, two typical problems of recommender systems, can be effectively alleviated by leveraging user-side information and item-side information. Knowledge graph (KG) is a source of side information. Due to the specificity of data distribution, the current KG-enhanced recommendation algorithms have the problem of weak generalization and robustness (the performance varies greatly in multiple data sets). Moreover, the previous knowledge-aware recommendation models are insufficient to distill the collaborative signal and personal features from the collective behaviors of users while simultaneously distilling the connectivity and exclusive entity features from the knowledge graph on the item-side. In this paper, we propose a multi-task framework Multi-Rec with high flexibility and adaptability based on alternating learning, which consists of a recommender system (RS) task and a knowledge graph embedding (KGE) task. The RS task consists of user feature learning and user structure learning modules. Correspondingly, the KGE task consists of knowledge graph feature learning and knowledge graph structure learning modules. The correlation between each sub-learning task is carried out by designed cross units and exchange units. Through the end-to-end framework, our model generates high-efficiency node embeddings for downstream recommender tasks and outperforms several state-of-the-art baselines on four real-world datasets through extensive experiments.
Semantic analysis of blockchain intelligence with proposed agenda for future issues

Integrating blockchain technology with artificial intelligence (AI) i.e., blockchain Intelligence makes an extremely powerful tool that solves many multidimensional problems in several domains. Blockchain technology has the potential to provide links to shared data, transactions, and records in a decentralized, safe, and reliable manner, including the information and decision-making capability of AI which makes machines similar as capable as humans. This study is intended to present an updated systematic review of the integration of Blockchain and AI in various application areas. We have studied and summarized more than 100 research papers to present an updated version of the review. We also discuss the future of Blockchain technologies with AI. By integrating these two technologies results increases the security, efficiency, and productivity of the applications. Past works feature a few possible advantages of integration of Blockchain and AI, yet just give a restricted hypothetical system to depict forthcoming certifiable combination instances of the two advances. We survey and orchestrate surviving exploration on the integration of AI and Blockchain are other ways around to thoroughly build up a future research plan on the fusion of the two innovations. We also proposed an agenda to develop a secure system of cyber threat intelligence information exchange by using features of blockchain and artificial intelligence. This paper mainly focusses on explaining how collaboration of blockchain and AI gives immense boost in latest domains like Cybersecurity, Healthcare, Supply Chain Management, Finance and Banking and Social Media Analytics.
High‐Precision Wavelength Tuning of GeSn Nanobeam Lasers via Dynamically Controlled Strain Engineering

The technology to develop a large number of identical coherent light sources on an integrated photonics platform holds the key to the realization of scalable optical and quantum photonic circuits. Herein, a scalable technique is presented to produce identical on-chip lasers by dynamically controlled strain engineering. By using localized laser annealing that can control the strain in the laser gain medium, the emission wavelengths of several GeSn one-dimensional photonic crystal nanobeam lasers are precisely matched whose initial emission wavelengths are significantly varied. The method changes the GeSn crystal structure in a region far away from the gain medium by inducing Sn segregation in a dynamically controllable manner, enabling the emission wavelength tuning of more than 10 nm without degrading the laser emission properties such as intensity and linewidth. The authors believe that the work presents a new possibility to scale up the number of identical light sources for the realization of large-scale photonic-integrated circuits.
Explanatory Interactive Machine Learning

Abstract The most promising standard machine learning methods can deliver highly accurate classification results, often outperforming standard white-box methods. However, it is hardly possible for humans to fully understand the rationale behind the black-box results, and thus, these powerful methods hamper the creation of new knowledge on the part of humans and the broader acceptance of this technology. Explainable Artificial Intelligence attempts to overcome this problem by making the results more interpretable, while Interactive Machine Learning integrates humans into the process of insight discovery. The paper builds on recent successes in combining these two cutting-edge technologies and proposes how Explanatory Interactive Machine Learning (XIL) is embedded in a generalizable Action Design Research (ADR) process – called XIL-ADR. This approach can be used to analyze data, inspect models, and iteratively improve them. The paper shows the application of this process using the diagnosis of viral pneumonia, e.g., Covid-19, as an illustrative example. By these means, the paper also illustrates how XIL-ADR can help identify shortcomings of standard machine learning projects, gain new insights on the part of the human user, and thereby can help to unlock the full potential of AI-based systems for organizations and research.
Semi-supervised federated learning on evolving data streams

Federated learning allows multiple clients to jointly train a model on their private data without revealing their local data to a centralized server. Thereby, federated learning has attracted increasing attention in recent years, and many algorithms have been proposed. However, existing federated learning algorithms often focus on static data, which tend to fail in the scenarios of data streams. Due to the varying distributions/concepts within and among the clients over time, the joint learning model must learn these different emerging concepts dynamically and simultaneously. The task becomes more challenging when the continuous arriving data are partially labeled for the participating clients. In this paper, we propose SFLEDS (Semi-supervised Federated Learning on Evolving Data Streams), a new federated learning prototype-based method to tackle the problems of label scarcity, concept drift, and privacy preservation in the federated semi-supervised evolving data stream environment. Extensive experiments show that SFLEDS outperforms both state-of-the-art semi-supervised and supervised algorithms. The source code for the proposed method is publicly available on github (https://github.com/mvisionai/FedLimited).
Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminating

Unsupervised graph representation learning (UGRL) has drawn increasing research attention and achieved promising results in several graph analytic tasks. Relying on the homophily assumption, existing UGRL methods tend to smooth the learned node representations along all edges, ignoring the existence of heterophilic edges that connect nodes with distinct attributes. As a result, current methods are hard to generalize to heterophilic graphs where dissimilar nodes are widely connected, and also vulnerable to adversarial attacks. To address this issue, we propose a novel unsupervised Graph Representation learning method with Edge hEterophily discriminaTing (GREET) which learns representations by discriminating and leveraging homophilic edges and heterophilic edges. To distinguish two types of edges, we build an edge discriminator that infers edge homophily/heterophily from feature and structure information. We train the edge discriminator in an unsupervised way through minimizing the crafted pivot-anchored ranking loss, with randomly sampled node pairs acting as pivots. Node representations are learned through contrasting the dual-channel encodings obtained from the discriminated homophilic and heterophilic edges. With an effective interplaying scheme, edge discriminating and representation learning can mutually boost each other during the training phase. We conducted extensive experiments on 14 benchmark datasets and multiple learning scenarios to demonstrate the superiority of GREET.
HCI Design Rationales as a Learning Resource

We report on our experiences applying human-computer interaction (HCI) design rationales as a learning resource for inexperienced designers. The lessons revealed by a series of prototypes are described. A pilot study within an industrial setting developed a framework for access to HCI design rationales, reflecting potential usage scenarios. A prototype system built as an extension to a commercial user-interface toolkit showed that generic design rationale components could guide inexperienced designers, and also revealed aspects of current toolkits that hinder effective access to HCI design rationales. Our current prototype explores the problem of encouraging &quot;use-oriented&quot; design, by addressing representation and rationales of high-level design issues. We discuss how the application of HCI design rationales as a learning resource provides leverage for ongoing design capture and reuse.
Straight-Ahead Jazz with GenJam: A Quick Demonstration

The author’s GenJam project has spanned nearly 20 years of development and performance. This demonstration provides a brief overview of GenJam’s current configuration illustrated with a visually annotated performance of a tune. After a brief explanation of GenJam’s real-time interactive capabilities, we’ll play more tunes, as time permits.
Hermitian codes and complete intersections

In this paper we present a geometrical characterization for the minimum-weight codewords of the Hermitian codes over the fields $\mathbb{F}_{q^2}$ in the third and fourth phase, namely with distance $d \geq q^2-q$. We consider the unique writing $\mu q + \lambda (q+1)$ of the distance $d$ with $\mu, \lambda$ non negative integers, and $\mu \leq q$, and prove that the minimum-weight codewords correspond to complete intersection divisors cut on the Hermitian curve $\mathcal{H}$ by curves $\mathcal X$ of degree $\mu+\lambda$ having $x^\mu y^\lambda$ as leading term w.r.t. the $\texttt{DegRevLex}$ term ordering (with $y>x$). Moreover, we show that any such curve $\mathcal X$ corresponds to minimum-weight codewords provided that the complete intersection divisor $\mathcal{H}\cap \mathcal X$ is made of simple $\mathbb{F}_{q^2}$-points. Finally, using this geometric characterization, we propose an algorithm to compute the number of minimum weight codewords and we present comparison tables between our algorithm and MAGMA command $\mathtt{MinimumWords}$.
Leveraging Browsing Patterns for Topic Discovery and Photostream Recommendation

In photo-sharing websites and in social networks, photographs are most often browsed as a sequence: users who view a photo are likely to click on those that follow. The sequences of photos (which we call photostreams), as opposed to individual images, can therefore be considered to be very important content units in their own right. In spite of their importance, those sequences have received little attention even though they are at the core of how people consume image content. In this paper, we focus on photostreams. First, we perform an analysis of a large dataset of user logs containing over 100 million pageviews, examining navigation patterns between photostreams. Based on observations from the analysis, we build a stream transition graph to analyze common stream topic transitions (e.g., users often view “train” photostreams followed by “firetruck” photostreams). We then implement two stream recommendation algorithms, based on collaborative filtering and on photo tags, and report the results of a user study involving 40 participants. Our analysis yields interesting insights into how people navigate between photostreams, while the results of the user study provide useful feedback for evaluating the performance and characteristics of the recommendation algorithms.
Keeping the Player on an Emotional Trajectory in Interactive Storytelling

Artificial Intelligence (AI) techniques have been widely used in video games to control non-playable characters. More recently, AI has been applied to automated story generation with the objective of managing the player's experience in an interactive narrative. Such AI experience managers can generate and adapt narrative dynamically, often in response to the player's in-game actions. We implement and evaluate a recently proposed AI experience manager, PACE, which predicts the player's emotional response to a narrative event and uses such predictions to shape the narrative to keep the player on an author-supplied target emotional curve.
A generalization of the theorems of Chevalley-Warning and Ax-Katz via polynomial substitutions

We give conditions under which the number of solutions of a system of polynomial equations over a finite field F_q of characteristic p is divisible by p. Our setup involves the substitution t_i |-> f_i(t_i) for auxiliary polynomials f_1,...,f_n in F_q[t]. We recover as special cases results of Chevalley-Warning and Morlaye-Joly. Then we investigate higher p-adic divisibilities, proving a result that recovers the Ax-Katz Theorem. We also consider p-weight degrees, recovering work of Moreno-Moreno, Moreno-Castro and Castro-Castro-Velez.
Traffic System Anomaly Detection using Spatiotemporal Pattern Networks

Traffic dynamics in the urban interstate system are critical in terms of highway safety and mobility. This paper proposes a systematic data mining technique to detect traffic system-level anomalies in a batch-processing fashion. Built on the concepts of symbolic dynamics, a spatiotemporal pattern network (STPN) architecture is developed to capture the system characteristics. This novel spatiotemporal graphical modeling approach is shown to be able to extract salient time series features and discover spatial and temporal patterns for a traffic system. An information-theoretic metric is used to quantify the causal relationships between sub-systems. By comparing the structural similarity of the information-theoretic metrics of the STPNs learnt from each day, a day with anomalous system characteristics can be identified. A case study is conducted on an urban interstate in Iowa, USA, with 11 roadside radar sensors collecting 20-second resolution speed and volume data. After applying the proposed methods on one-month data (Feb. 2017), several system-level anomalies are detected. The potential causes that include inclement weather condition and non-recurring congestion are also verified to demonstrate the efficacies of the proposed technique. Compared to the traditional predefined performance measures for the traffic systems, the proposed framework has advantages in capturing spatiotemporal features in a fast and scalable manner.
Bayesian Parametric Bootstrap for Models with Intractable Likelihoods

In this paper it is demonstrated how the Bayesian parametric bootstrap can be adapted to models with intractable likelihoods.The approach is most appealing when the computationally efficient semi-automatic approximate Bayesian computation (ABC) summary statistics are selected.The parametric bootstrap approximation is used to form a proposal distribution in ABC algorithms to improve the computational efficiency.The new approach is demonstrated through the sequential Monte Carlo and the ABC importance and rejection sampling algorithms.We found efficiency gains in two simulation studies, the univariate g-and-k quantile distribution, a toggle switch model in dynamic bionetworks, and in a stochastic model describing expanding melanoma cell colonies.
Control and Readout Software for Superconducting Quantum Computing

Digital-to-analog converter (DAC) and analog-to-digital converter (ADC) as an important part of the superconducting quantum computer are used to control and readout the qubit states. The complexity of instrument manipulation increases rapidly as the number of qubits grows. Low-speed data transmission, imperfections of realistic instruments and coherent control of qubits are gradually highlighted which have become the bottlenecks in scaling up the number of qubits. To deal with the challenges, we present a solution in this study. Based on client-server (C/S) model, we develop two servers called Readout Server and Control Server for managing self-innovation digitizer, arbitrary waveform generator (AWG) and ultra-precision DC source which enable to implement physical experiments rapidly. Both Control Server and Readout Server consist three parts: resource manager, waveform engine and communication interface. The resource manager maps the resources of separate instruments to a unified virtual instrument and automatically aligns the timing of waveform channels. The waveform engine generates and processes the waveform for AWGs or captures and analyzes the data from digitizers. The communication interface is responsible for sending and receiving data in an efficient manner. We design a simple data link protocol for digitizers and a multi-threaded communication mechanism for AWGs. By using different network optimization strategies, both data transmission speed of digitizers and AWGs reach hundreds of Mbps through a single Gigabit-NIC.