Reviewerly/exHarmony huggingface dataset:
Extractive Summarization of Chinese Judgment Documents via Sentence Embedding and Memory Network

A rapidly rising number of open judgment documents has increased the requirement for automatic summarization. Since Chinese judgment documents are characterized by a lengthy and logical structure, extractive summarization is an effective method for them. However, existing extractive models generally cannot capture information between sentences. In order to enable the model to obtain long-term information in the judgment documents, this paper proposes an extractive model using sentence embeddings and a two-layers memory network. A pre-trained language model is used to encode sentences in judgment documents. Then the whitening operation is applied to get isotropic sentence embeddings, which makes the subsequent classification more accurate. These embeddings are fed into a unidirectional memory network to fuse previous sentence embeddings. A bidirectional memory network is followed to introduce position information of sentences. The experimental results show that our proposed model outperforms the baseline methods on the SFZY dataset from CAIL2020.
Fixing Classification: A Viewpoint-Based Approach

The concept of classification as realized in most traditional object-oriented computer languages has certain limitations that may inhibit its application to modeling more complex phenomena. This is likely to prove problematic as modern software becomes increasingly more integrated with the highly dynamic physical world. In this paper, we first provide a detailed description of these limitations, followed by an outline of a novel approach to classification designed to overcome them. The proposed approach replaces the static multiple-inheritance hierarchy approach found in many object-oriented languages with multiple dynamic class hierarchies each based on different classification criteria. Furthermore, to better deal with ambiguous classification schemes, it supports potentially overlapping class membership within any given scheme. Also included is a brief overview of how this approach could be realized in the design of advanced computer languages.
New Approach for Use of the Potential Fields Method in Mobile Robotics

In the famous papers dedicated to the method of the potential fields in mobile robotics is attached to particular significance to the mechanism of the force action for the movement of robot which defines the law of the rate of change of its movement. In the present paper the new methodology of use of the virtual fields is suggested: the force aspect is excluded and the strength lines of the produced field give only desired trajectories of the robot's movement where the movement along the given trajectory is realized by two-contour control system: by the direction and the rate of robot’ movement. The question of the construction of the attracting and repelling potentials is considered. The results of the imitative simulation of the trajectory control by the movement of two-wheeled robot are reduced.
WIRE: Resource-efficient Scaling with Online Prediction for DAG-based Workflows

This paper introduces WIRE that manages resources for the DAG-based workflows on IaaS clouds. WIRE predicts and plans resources over the MAPE (Monitor-Analyze-Plan-Execute) loops to: 1) Estimate task performance with online data, 2) Conduct simulations to predict the upcoming loads based on online estimates and workflow DAGs, 3) Apply a resource-steering policy to size cloud instance pools for the maximal parallelism that is consistent with low cost. We implement WIRE on Pegasus WMS/HTCondor and evaluate its performance on the ExoGENI network cloud. The results show that WIRE attains low resource cost with the performance that is typically within a factor of two of optimal.
Smart Innovations, Systems, and Technologies in University Libraries

The article deals with the innovative technologies used in modern university libraries in the context of the UN Sustainable Development Goals agenda, the IFLA Strategy 2019–2024, the essential function of libraries. Examples of library technologies and systems from Germany, the USA, Canada, Russia, Brazil, Serbia, India, China, and Australia are given. The re-engineering of library processes has led to the application of RFID (Radio Frequency Identification), DRM (Digital Rights Management), BYOD (bring your own device) and other technologies in libraries. Multimedia digital libraries and their mobile applications with geolocation functions and augmented reality technologies have been created and are in operation. Huge data sets of libraries are deployed on data centres, which are accessed using VPN and VLAN technologies. The Elasticsearch, Logstash, and Kibana (ELK) technology stack is used to analyse digital libraries. Library processes are logically and seamlessly integrated into the teleinformation systems of universities: the Unified Teleinformation System at Perm State University (PSU), OpenAthens at the University of North Carolina manage identification and access to library resources through the university’s authentication service. The article considers the processes of library space organisation in different countries, including “maker” spaces with 3D printers and spaces contributing to the development of entrepreneurship. The results of scientific activities are promoted by libraries through author profiles in web-catalogues, databases and citation systems, retro-cataloguing, digitisation of collections, and the Open Science communication channel. Policies in shaping and providing access to world library resources facilitate international scholarly collaborations. In this article, analogies between the deployment of information technology in the library and information services sector and other industries, including tourism industry, are presented; the need to implement artificial intelligence (AI) technology in library processes is emphasized. In general, effectively used technologies, the latest equipment and qualified personnel make it possible to cope with the challenges and find new points of growth and development. University libraries have a great variety of opportunities to meet Sustainable Development Goals.
Establishment of Aero-Engine Improved On-Board Adaptive Model with Contracted Kalman Filter Estimation

Due to the limited number of sensors in the aeroengine, the estimation of performance degradation of the engine is inaccurate. In this paper, an improved on-board adaptive model with contracted Kalman filter is proposed. This is accomplished by constructing a transformation matrix to reduce the dimension of the health parameter vector, and taking the estimated deviation of the simplified Kalman filter as the goal of minimization, and using genetic algorithm and deep learning models to build more accurate health parameters to reflect the engine performance. Use the inverse transform to obtain the original health parameters. The simulation results show that the improved on-board adaptive model established in this paper can still estimate the health parameters with high accuracy when the measured parameter dimension is less than the health parameter dimension, and the accuracy of improved on-board adaptive model is very high.
An Efficient Deep Learning Accelerator Architecture for Compressed Video Analysis

Previous neural network accelerators tailored to video analysis only accept data of RGB/YUV domain, requiring decompressing the video that are often compressed before transmitted from the edge sensors. A compressed video processing accelerator can alleviate the decoding overhead, and gain performance speedup by operating on more compact input data. This work proposes a novel deep learning accelerator architecture, Alchemist, which is able to predict results directly from the compressed video bitstream instead of reconstructing the full RGB images. By utilizing the metadata of motion vector and critical blocks extracted from bitstreams, Alchemist contributes to a remarkable performance speedup of 5x with negligible accuracy loss. Nevertheless, we still find that the original compressed video coded by standard algorithms such as H.264 is not suitable to be directly manipulated, due to diverse compressed structures. Although obviating the requirement to recover all RGB frames, the accelerator must parse the entire compressed video bitstream to locate reference frames and extract useful metadata. If we combine the video codec with the proposed compressed video analysis, additional optimizations can be obtained. Therefore, to cope with the mismatch between current video coding algorithms, such as H.264 and neural network-based video analysis, we propose a specialized coding strategy to generate compressed video bitstreams more suitable for transmission and analysis, which further simplifies the decoding stage of video analysis and is capable of achieving significant storage reduction.
Style Agnostic 3D Reconstruction via Adversarial Style Transfer

Reconstructing the 3D geometry of an object from an image is a major challenge in computer vision. Recently introduced differentiable renderers can be leveraged to learn the 3D geometry of objects from 2D images, but those approaches require additional supervision to enable the renderer to produce an output that can be compared to the input image. This can be scene information or constraints such as object silhouettes, uniform backgrounds, material, texture, and lighting. In this paper, we propose an approach that enables a differentiable rendering-based learning of 3D objects from images with backgrounds without the need for silhouette supervision. Instead of trying to render an image close to the input, we propose an adversarial style-transfer and domain adaptation pipeline that allows to translate the input image domain to the rendered image domain. This allows us to directly compare between a translated image and the differentiable rendering of a 3D object reconstruction in order to train the 3D object reconstruction network. We show that the approach learns 3D geometry from images with backgrounds and provides a better performance than constrained methods for single-view 3D object reconstruction on this task.
Smart Technologies and New Business Models: Insights from Artificial Intelligence and Blockchain

Smart technologies are profoundly changing the way organizations conduct their business. Among such technologies, Artificial Intelligence (AI), and the Blockchain are considered the most disruptive ones. Our study investigates the potential impacts of AI and the Blockchain on business models by employing a structured literature review (SLR). We analyzed 182 documents between academic sources and professional reports, identifying the potential influences on the single building blocks of the business model. Results highlight 97 potential impacts for the AI, and 40 for the Blockchain, widespread among all the building blocks, including the society. Our findings open up to practical as well as research implications, offering food for thought to both organizations as well as academics.
Runtime Metric Analysis in NoSQL Database Performance Benchmarking

NoSQL database management systems are the best choice for managing big data. But with so many NoSQL products available, it may not be obvious which product is the best fit for certain application workloads. The actual performance of a NoSQL database management system in a given scenario is revealed by database performance benchmarking experiments. This paper presents a performance benchmarking study that analyzes the performance of MongoDB and Apache Cassandra, two NoSQL database management systems that are widely used. The analyzed performance metric is total runtime. The measured results show that Apache Cassandra outperforms MongoDB when the number of operations and degree of parallelism are high.
Gradual type theory

Abstract Gradually typed languages are designed to support both dynamically typed and statically typed programming styles while preserving the benefits of each. Sound gradually typed languages dynamically check types at runtime at the boundary between statically typed and dynamically typed modules. However, there is much disagreement in the gradual typing literature over how to enforce complex types such as tuples, lists, functions and objects. In this paper, we propose a new perspective on the design of runtime gradual type enforcement: runtime type casts exist precisely to ensure the correctness of certain type-based refactorings and optimizations. For instance, for simple types, a language designer might desire that beta-eta equality is valid. We show that this perspective is useful by demonstrating that a cast semantics can be derived from beta-eta equality. We do this by providing an axiomatic account program equivalence in a gradual cast calculus in a logic we call gradual type theory (GTT). Based on Levy’s call-by-push-value, GTT allows us to axiomatize both call-by-value and call-by-name gradual languages. We then show that we can derive the behavior of casts for simple types from the corresponding eta equality principle and the assumption that the language satisfies a property called graduality , also known as the dynamic gradual guarantee. Since we can derive the semantics from the assumption of eta equality, we also receive a useful contrapositive: any observably different cast semantics that satisfies graduality must violate the eta equality. We show the consistency and applicability of our axiomatic theory by proving that a contract-based implementation using the lazy cast semantics gives a logical relations model of our type theory, where equivalence in GTT implies contextual equivalence of the programs. Since GTT also axiomatizes the dynamic gradual guarantee, our model also establishes this central theorem of gradual typing. The model is parameterized by the implementation of the dynamic types, and so gives a family of implementations that validate type-based optimization and the gradual guarantee.
Written News Search Engines and Retrieval Systems of the Databases of Spanish Digital Newspapers

ABSTRACT In light of the dearth of empirical studies of the quality of the databases of Spanish digital newspapers, this paper delves into the issue. Accordingly, the databases of 55 Spanish digital general newspapers were examined for the purpose of determining the quality of their retrospective written news search engines and retrieval systems. The results indicate the following: (1) the vast majority of the databases analysed employ search models based exclusively on a calendar or text box, thus hindering the selective retrieval of information; (b) a large proportion of these databases do not allow users to employ several fields in order to filter and narrow down their searches; and (3) all the online news databases lack search guides and controlled vocabularies or thesauruses. From these finding it is possible to infer that the majority of the databases of the Spanish digital press are, as a rule, rudimentary and inefficient.
A Simple Post-Quantum Non-interactive Zero-Knowledge Proof from Garbled Circuits

We construct a simple public-coin zero-knowledge proof system solely based on symmetric primitives, from which we can apply the Fiat-Shamir heuristic to make it non-interactive. Our construction can be regarded as a simplified cut-and-choose-based malicious secure two-party computation for the zero-knowledge functionality. Our protocol is suitable for pedagogical purpose for its simplicity (code is only 728 lines).
Optimal Multidimensional Cyclic Convolution Algorithms For Deep Learning And Computer Vision Applications

1D, 2D and multidimensional convolutions are basic tools in deep learning, notably in convolutional neural networks (CNNs) and in computer vision (template matching, correlation trackers). Therefore, fast 1D/2D/3D convolution algorithms are essential for advanced machine learning and computer vision. This paper presents: 1) novel optimal n-D cyclic convolution algorithms having minimal multiplicative complexity that are much faster than any competing convolution algorithm internationally and 2) methods for speeding up such optimal convolution algorithms on GPUs and multicore CPUs. Such a speedup is very important both for CNN training and CNN testing, particularly in embedded environments (e.g., on drones) and real-time applications (e.g., fast CNN inference for object detection and correlation trackers for embedded real-time object tracking).
Trajectory is not Enough

In outdoor crimes such as robbery and kidnapping, suspects generally secretly follow their victims in public places and then look for opportunities to commit crimes. Video anomaly detection (VAD) has achieved fruitful results through deep neural networks (DNN). However, as an abnormal behavior without obvious abnormal physical features, hidden following is highly similar to ordinary walking and accompanying behaviors, so it is difficult to effectively detect hidden dangerous followers using video anomaly detection methods or traditional trajectory analysis methods. We propose "hidden follower'' detection (HFD) task and a HFD model based on gaze pattern extraction. It extracts gaze pattern features of pedestrians from gaze-interval-series and introduces a time series classification model to classify pedestrians with or without hidden following purposes. Based on this model, we propose a hidden follower detection framework (HFDF) to detect hidden followers from normal pedestrians, which utilizes the trajectories and gaze patterns extracted from videos. To cope with the lack of test data, we construct a dataset of 1200 pedestrians from the crowd simulation model to simulate scenes including hidden followers, and we also collected a surveillance video dataset including the hidden following behaviors. The experiments conducted on these two datasets show that HFDF can consistently outperform the state-of-the-art method by a notable margin in the HFD task on the commonly-used F1 benchmark.
HAIN: Hierarchical Aggregation and Inference Network for Document-Level Relation Extraction

Document-level relation extraction (RE) aims to extract relations between entities within a document. Unlike sentence-level RE, it requires integrating evidences across multiple sentences. However, current models still lack the ability to effectively obtain relevant evidences for relation inference from multi-granularity information in the document. In this paper, we propose Hierarchical Aggregation and Inference Network (HAIN), performing the model to effectively predict relations by using global and local information from the document. Specifically, HAIN first constructs a meta dependency graph (mDG) to capture rich long distance global dependency information across the document. It also constructs a mention interaction graph (MG) to model complex local interactions among different mentions. Finally, it creates an entity inference graph (EG), based on which we design a novel hybrid attention mechanism to integrate relevant global and local information for entities. Experimental results demonstrate that our model achieves superior performance on a large-scale document-level dataset (DocRED). Extensive analyses also show that the model is particularly effective in extracting relations between entities across multiple sentences and mentions.
CASQ: Accelerate Distributed Deep Learning with Sketch-Based Gradient Quantization

Gradient quantization has been widely used in distributed training of deep neural network (DNN) models to reduce communication costs. However, existing quantization methods overlook that gradients have a nonuniform distribution changing over time, which can lead to significant gradient variance that requires a higher number of quantization bits (and consequently higher communication cost) to keep the validation accuracy as high as stochastic gradient descent (SGD). In this paper, we propose Cluster-Aware Sketch Quantization (CASQ), a novel sketch-based gradient quantization method for SGD. CASQ models the nonuniform distribution of gradients via clustering, and adaptively allocates appropriate numbers of hash buckets based on the statistics of different clusters to compress gradients. The extensive evaluation shows that compared to existing quantization methods CASQ-based SGD (i) achieves the same validation accuracy when decreasing quantization level from 3 bits to 2 bits, and (ii) reduces the training time to convergence by up to 43% for the same training loss.
Motion information based avoidance control for 3-D multi-agent systems

This paper presents a closed-form cooperative avoidance control design for 3-dimensional rigid-body agents. New avoidance functions are designed to integrate the collision risk with the addition of motion and posture information. Time-varying sensing regions enable the conflict resolution actions only when there is a potential risk of collision, thus further reducing some unnecessary interference with other objectives. These characteristics lead to the less-conservative avoidance response, smoother maneuvers, and milder motion trajectories. The stability of the proposed method is analyzed and the corresponding proofs and guaranties for collision-free maneuvering, are provided. Performance comparisons are presented for two simulation scenarios, which illustrate the effectiveness and reliability of the new method.
Neighbourhood Semantics for Graded Modal Logic

We introduce a class of neighbourhood frames for graded modal logic embedding Kripke frames into neighbourhood frames. This class of neighbourhood frames is shown to be first-order definable but not modally definable. We also obtain a new definition of graded bisimulation with respect to Kripke frames by modifying the definition of monotonic bisimulation.
Multi-View Multi-Modal Head-Gaze Estimation for Advanced Indoor User Interaction

Gaze estimation is one of the most promising technologies for supporting indoor monitoring and interaction systems. However, previous gaze estimation techniques generally work only in a controlled laboratory environment because they require a number of high-resolution eye images. This makes them unsuitable for welfare and healthcare facilities with the following challenging characteristics: 1) users’ continuous movements, 2) various lighting conditions, and 3) a limited amount of available data. To address these issues, we introduce a multi-view multi-modal head-gaze estimation system that translates the user’s head orientation into the gaze direction. The proposed system captures the user using multiple cameras with depth and infrared modalities to train more robust gaze estimators under the aforementioned conditions. To this end, we implemented a deep learning pipeline that can handle different types and combinations of data. The proposed system was evaluated using the data collected from 10 volunteer participants to analyze how the use of single/multiple cameras and modalities affect the performance of head-gaze estimators. Through various experiments, we found that 1) an infrared-modality provides more useful features than a depth-modality, 2) multi-view multi-modal approaches provide better accuracy than single-view single-modal approaches, and 3) the proposed estimators achieve a high inference efficiency that can be used in real-time applications.
The EnMAP Satellite - Mission Status and Science Preparatory Activities

The Environmental Mapping and Analysis Program (EnMAP) is a spaceborne German hyperspectral satellite mission that aims at monitoring and characterizing the Earth's environment on a global scale. EnMAP core themes are environmental changes, ecosystem responses to human activities, and management of natural resources. After several years delay, the instrument is finished and in the final stage of environmental characterization and assembly for a launch early 2022. This paper presents an update of the mission status and activities in the frame of the science preparation and mission support project led by the German Research Center for Geosciences (GFZ) Potsdam. Further, this paper presents a specific focus on the planning for the independent EnMAP data product validation.