Reviewerly/exHarmony huggingface dataset:
Rethinking Semantic Image Compression: Scalable Representation With Cross-Modality Transfer

This article proposes the scalable cross-modality compression (SCMC) paradigm, in which the image compression problem is further cast into a representation task by hierarchically sketching the image with different modalities. Herein, we adopt the conceptual organization philosophy to model the overwhelmingly complicated visual patterns, based upon the semantic, structure, and signal level representation accounting for different tasks. The SCMC paradigm that incorporates the representation at different granularities supports diverse application scenarios, such as high-level semantic communication and low-level image reconstruction. The decoder, which enables the recovery of the visual information, benefits from the scalable coding based upon the semantic, structure, and signal layers. Qualitative and quantitative results demonstrate that the SCMC can convey accurate semantic and perceptual information of images, especially at low bitrates, and promising rate-distortion performance has been achieved compared to state-of-the-art methods. The code will be available online <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ppingzhang/SCMC</uri> .
Towards Hard Few-Shot Relation Classification

Few-shot relation classification (FSRC) focuses on recognizing novel relations by learning with merely a handful of annotated instances. Meta-learning has been widely adopted for such a task, which trains on randomly generated few-shot tasks to learn generic data representations. Despite impressive results achieved, existing models still perform suboptimally when handling hard FSRC tasks with similar categories that confuse the model to distinguish correctly. We argue this is largely due to two reasons, 1) ignoring pivotal and discriminate information that is crucial to distinguish confusing classes, and 2) training indiscriminately via randomly sampled tasks of varying difficulty. In this article, we introduce a novel prototypical network approach with contrastive learning that learns more informative and discriminative representations by exploiting relation label information. We further design two strategies that increase the difficulty of training tasks and allow the model to adaptively learn to focus on hard tasks. By doing so, our model can better represent subtle inter-relation variance and grow up through task difficulty. Extensive experiments on three standard benchmarks demonstrate the effectiveness of our method.
Multitarget Real-Time Path Planning Using Double Adaptive A Algorithm

MTRT is an extension of path planning that aims to traverse multiple targets in an unknown environment at the lowest cost. The MTRT is governed by the “multitarget” demand leading to a rather extensive search space and the “real-time” requirement that imposes fast planning. However, the traditional path planning algorithm A* is notoriously inefficient in large spaces. Therefore, spurred by the low efficiency of A* for MTRT, this article proposes the DAA, which implements an adaptive multitarget heuristic function and an adaptive node expansion method to accelerate planning. The suggested method is evaluated utilizing the following two approaches to establishing the search space: 1) decompose MTRT into traditional path planning problems and involve one target at a time; 2) consider all unsearched targets while planning. Simulations in discrete grid maps demonstrate that DAA provides at least equivalent searching paths to A* and other algorithms, but at much less running time.
Separating Separation Logic – Modular Verification of Red-Black Trees

Interactive theorem provers typically use abstract algebraic data structures to focus on algorithmic correctness. Verification of programs in real programming languages also has to deal with pointer structures, aliasing and, in the case of C, memory management. While progress has been made by using Separation Logic, direct verification of code still has to deal with both aspects at once. In this paper, we show a refinement-based approach that separates the two issues by using a suitable modular structure. We exemplify the approach with a correctness proof for red-black trees, demonstrating that our approach can generate efficient C code that uses parent pointers and avoids recursion. The proof is split into a large part almost identical to high-level algebraic proofs and a separate small part that uses Separation Logic to verify primitive operations on pointer structures.
Online multi-target intelligent tracking using a deep long-short term memory network

Multi-target tracking is facing the difficulties of modeling uncertain motion and observation noise. Traditional tracking algorithms are limited by specific models and priors that may mismatch a real-world scenario. In this paper, considering the model-free purpose, we present an online Multi-Target Intelligent Tracking (MTIT) algorithm based on a Deep Long-Short Term Memory (DLSTM) network for complex tracking requirements, named the MTIT-DLSTM algorithm. Firstly, to distinguish trajectories and concatenate the tracking task in a time sequence, we define a target tuple set that is the labeled Random Finite Set (RFS). Then, prediction and update blocks based on the DLSTM network are constructed to predict and estimate the state of targets, respectively. Further, the prediction block can learn the movement trend from the historical state sequence, while the update block can capture the noise characteristic from the historical measurement sequence. Finally, a data association scheme based on Hungarian algorithm and the heuristic track management strategy are employed to assign measurements to targets and adapt births and deaths. Experimental results manifest that, compared with the existing tracking algorithms, our proposed MTIT-DLSTM algorithm can improve effectively the accuracy and robustness in estimating the state of targets appearing at random positions, and be applied to linear and nonlinear multi-target tracking scenarios.
Assessing polygonal approximations: A new measurement and a comparative study

Two proposals related to the evaluation of polygonal approximations are presented in this document. First, a new measurement, called normalized compression ratio and adjustment error (NCA), to provide a fair evaluation of the performance of the polygonal approximations of 2D closed curves is proposed. Second, a new methodology for evaluation of measurements for assessing polygonal approximations is also proposed. This methodology is based on the optimal quality curve concept, which can characterize the performance of the measurements. A simple visual analysis of the optimal quality curve allows possible drawbacks or weaknesses of the measurement to be detected. The new evaluation methodology is used to compare the performance of the proposed NCA and the most popular measurements, such as Rosin's Merit, FOM or versions of FOM. Experiments show that NCA obtains the best results and, therefore, may be used to fairly evaluate the performance of polygonal approximations.
Major elements concentration in soils. A case study from Campania Region (Italy)

In this study, 500 bottom soils and 7300 topsoils samples were collected to determine the major elements concentrations and to evaluate the correspondence of soil chemical contents and the local geology and to better identify background values. The <2 mm fraction of each sample was analysed for a total of 52 elements combining ICP-MS and ICP-ES techniques. This study focuses on the concentration and distribution of major elements: Al, Ca, Fe, K, Mg, Mn, Na, P and Ti. A combined methodological approach, which involved compositional data analysis, multifractal data interpolation, as well as enrichment factor analysis, was applied to the geochemical dataset. Results indicate that the compositional approach in the multivariate data analysis allows to better interpret the behaviour of elements in natural environment and in geochemical processes as compared to raw data and to improve the quality of related maps. The study has shown that the soils of the Campania region generally contain higher concentrations of investigated major elements than those assessed for European and Italian agricultural soils. In Campania, both bottom soils and topsoils contain higher concentrations of Al, K, Na, P and Ti in volcanic areas. Limestone and dolostone are responsible for higher Ca and Mg contents, while soils formed on siliciclastic deposits generally contain higher levels of Fe and Mn. P and Na concentrations are higher in volcanic soils, but the contribution of fertilizers, which is evident in some areas, should not be underestimated. Weathering processes play an essential role in the distribution of elements in soils, and this appears very evident mostly in volcanic areas. The observations are consistent with the results of the enrichment factors analysis.
Novel Co-SIFT Detector for Scanned Images Differentiation

We describe experiments with the hi-tech contactless scanner CRUSE CS 220ST1100 in the digitization of originals of natural and cultural heritage. The 2D scans guarantee high accuracy both in geometry and radiometry (48 bits for RGB colors). However, an inexperienced customer needs support in selecting the appropriate scan mode. To distinguish similar CRUSE scans, we proposed an image descriptor based on Harris corners and a topological structure embedding a planar subgraph. For some use-cases, the Harris approach did not perform well. We report on a novel SIFT type detector using concurrent color channels, hence the proposed name. We put our solution into the context of previous research and compare, on selected use-cases, the solution quality and/or disadvantages.
PointPillars Backbone Type Selection for Fast and Accurate LiDAR Object Detection

3D object detection from LiDAR sensor data is an important topic in the context of autonomous cars and drones. In this paper, we present the results of experiments on the impact of backbone selection of a deep convolutional neural network on detection accuracy and computation speed. We chose the PointPillars network, which is characterised by a simple architecture, high speed, and modularity that allows for easy expansion. During the experiments, we paid particular attention to the change in detection efficiency (measured by the mAP metric) and the total number of multiply-addition operations needed to process one point cloud. We tested 10 different convolutional neural network architectures that are widely used in image-based detection problems. For a backbone like MobilenetV1, we obtained an almost 4x speedup at the cost of a 1.13% decrease in mAP. On the other hand, for CSPDarknet we got an acceleration of more than 1.5x at an increase in mAP of 0.33%. We have thus demonstrated that it is possible to significantly speed up a 3D object detector in LiDAR point clouds with a small decrease in detection efficiency. This result can be used when PointPillars or similar algorithms are implemented in embedded systems, including SoC FPGAs. The code is available at https://github.com/vision-agh/pointpillars-backbone .
Future worldbuilding with synthetic biology: A case study in interdisciplinary scenario visualization

This article presents and examines the implications of a multidisciplinary case study using worldbuilding and narrative scenario visualization. The case study focuses on the research area of synthetic biology, more specifically, the production of industrially relevant plant metabolites in microalgae. The approach taken addresses what we describe as the ‘Promises Paradox’ in scientific research, whereby scientists are at once required to implicitly imagine world-shaping implications for their research, while remaining significantly constrained with regard to explicitly envisioning the diverse tones and textures such worlds might take once scientific innovations become part of everyday life. The article contributes to existing research in the fields of design futuring, design fiction, speculative design and scenario-based planning and design. Focus is given to analyzing the development of character, narrative and visualization in the creative process and how these aspects of worldbuilding both afford opportunities and possess limitations with regard to framing plausible futures that might engage both expert and non-expert audiences.
Augmenting the training database with the method of gradual similarity ratios in the face recognition systems

In face recognition systems, light direction, reflection, and emotional and physical changes on the face are some of the main factors that make recognition difficult. Deep metric learning algorithms called representative learning are frequently preferred in this field. However, in addition to the model's success in feature extraction, factors such as the distribution of samples in this database and appropriate classifier preferences also affect the overall performance of the face recognition system. This study it is aimed to create integrity in the database of a pre-trained deep neural network model by obtaining augmented data for classes with a limited number of samples. Thanks to this method called Graded Similarity Rates (GSR), augmented data that could disrupt class integrity has been removed from the database. This way, classes with limited examples are kept integrity, and classifier behavior is used more effectively. The model proposed in the experimental study reached 99.38% accuracy values compared to traditional data augmentation models. Experimental results have shown that the database has an acceptable level of success even at smaller vector sizes and is more organized.
ACTS: A Near-Memory FPGA Graph Processing Framework

Despite the high off-chip bandwidth and on-chip parallelism offered by today's near-memory accelerators, software-based (CPU and GPU) graph processing frameworks still suffer performance degradation from under-utilization of available memory bandwidth because graph traversal often exhibits poor locality. Emerging FPGAbased graph accelerators tackle this challenge by designing specialized graph processing pipelines and application-specific memory subsystems to maximize bandwidth utilization and efficiently utilize high-speed on-chip memory. To use the limited on-chip (BRAM) memory effectively while handling larger graph sizes, several FPGAbased solutions resort to some form of graph slicing or partitioning during preprocessing to stage vertex property data into the BRAM. While this has demonstrated performance superiority for small graphs, this approach breaks down with larger graph sizes. For example, GraphLily [19], a recent high-performance FPGA-based graph accelerator, experiences up to 11X performance degradation between graphs having 3M vertices and 28M vertices. This makes prior FPGA approaches impractical for large graphs.
A Framework for Monte-Carlo Tree Search on CPU-FPGA Heterogeneous Platform via on-chip Dynamic Tree Management

Monte Carlo Tree Search (MCTS) is a widely used search technique in Artificial Intelligence (AI) applications. MCTS manages a dynamically evolving decision tree (i.e., one whose depth and height evolve at run-time) to guide an AI agent toward an optimal policy. In-tree operations are memory-bound leading to a critical performance bottleneck for large-scale parallel MCTS on general-purpose processors. CPU-FPGA accelerators can alleviate the memory bottleneck of in-tree operations. However, a major challenge for existing FPGA accelerators is the lack of dynamic memory management due to which they cannot efficiently support dynamically evolving MCTS trees. In this work, we address this challenge by proposing an MCTS acceleration framework that (1) incorporates an algorithm-hardware co-optimized accelerator design that supports in-tree operations on dynamically evolving trees without expensive hardware reconfiguration; (2) adopts a hybrid parallel execution model to fully exploit the compute power in a CPU-FPGA heterogeneous system; (3) supports Python-based programming API for easy integration of the proposed accelerator with RL domain-specific bench-marking libraries at run-time. We show that by using our framework, we achieve up to 6.8× speedup and superior scalability of parallel workers than state-of-the-art parallel MCTS on multi-core systems.
A novel concept-cognitive learning method: A perspective from competences

Concept-cognitive learning has been widely used in simulating the human brain to learn concepts. However, the existing concept-cognitive learning models mainly focused on how to acquire knowledge and its properties, but not how to solve the problems. The underlying skills for solving problems are ignored in the cognitive learning process. Indeed, a concept-cognitive learning process is always accompanied with problem solving and skill learning, and the skills are necessary for solving problems. Knowledge space theory is an effective mathematical analysis approach for knowledge assessment. Nevertheless, the existing learning paths for skill were evaluated by constructing the concept lattice, which is a NP hard problem. To overcome these limitations and problems, a novel concept-cognitive learning model from a perspective of competences is proposed. Firstly, knowledge and skills can be represented by item sets and skill sets. And a good semantic explanation between knowledge and skills is provided by a competence-based concept, which presents that one can acquire the most knowledge with the least amount of skills. Secondly, a competence-based concept-cognitive learning model and its properties are put forward to describe the concept-cognitive learning through skills. Moreover, by analyzing the sufficient and necessary relationship between skills and knowledge, a competence-based information granule structure is constructed. Finally, a transformation method of information granules is proposed to convert a general information granule into sufficient and necessary competence-based information granules (i.e., competence-based concepts). And the experimental results of UCI data sets show that the competence-based concept-cognitive learning model is feasible and effective.
Minimizing Resource Allocation for Cloud-Native Microservices

Abstract With the continuous progress of cloud computing, many microservices and complex multi-component applications arise for which resource planning is a great challenge. For example, when it comes to data-intensive cloud-native applications, the tenant might be eager to provision cloud resources in an economical manner while ensuring that the application performance meets the requirements in terms of data throughput. However, due to the complexity of the interplay between the building blocks, adequately setting resource limits of the components separately for various data rates is nearly impossible. In this paper, we propose a comprehensive approach that consists of measuring the resource footprint and data throughput performance of such a microservices-based application, analyzing the measurement results by data mining techniques, and finally formulating an optimization problem that aims to minimize the allocated resources given the performance constraints. We illustrate the benefits of the proposed approach on Cortex, an extension to Prometheus for storing monitored metrics data. The data-intensive nature of this illustrative example stems from real-time monitoring of metrics exposed by a multitude of applications running in a data center and the continuous analysis performed on the collected data that can be fetched from Cortex. We present Cortex’s performance vs resource footprint trade-off, and then we build regression models to predict the microservices’ resource consumption and draw a mathematical programming formulation to optimize the most important configuration parameters. Our most important finding is the linear relationship between resource consumption and application performance, which allows for applying linear regression and linear programming models. After the optimization, we compare our results to Cortex’s recommendation, leading to a CPU reservation reduced by 50–80%.
Fleet: Improving Quality of Experience for Low-Latency Live Video Streaming

Low-latency live video streaming proposes more challenges on designing Adaptive Bit Rate (ABR) algorithms than video-on-demand streaming. In this paper, we present Fleet, a solution that improves user quality of experience (QoE) in terms of video quality, video switches, video freezes, and latency. Fleet is designed to meet two operational challenges at the same time: 1) optimizing QoE under dynamic mobile network conditions and 2) ensuring a low latency experience with minimal visual quality degradation. The core of Fleet is a stochastic model predictive controller that incorporates network conditions and client states for bitrate adaptation. The idle period problem presents great challenges for bandwidth measurement and client state prediction. Fleet consists of an HTTP chunk level bandwidth measurement algorithm, and a practical live video streaming evolution model for client state prediction. Besides, a throughput probability predictor is trained to capture the mobile network’s uncertainty. And, a triple threshold playback speed controller is designed for latency management. Fleet is practically implemented in <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dash.js</monospace> and evaluated over both synthetic and real-world mobile network conditions. Our study shows that compared with the state-of-the-art solution, Fleet achieves an average QoE improvement of 39.7% and 37.9%, respectively. Moreover, Fleet has a good generalization, outperforming existing algorithms even on new video traces and network conditions.
Target detecting deep neural network countermeasure technology for space-based remote sensing and its application to mobile radar

Space-based remote sensing is an important way of detecting many types of land targets. For the purpose of taking cover, land targets have a strong demand for avoiding space-based remote sensing reconnaissance. In practice, space-based reconnaissance will produce huge data, which are unbearable for human-beings. Therefore, the data processing must rely on artificial intelligence technology such as deep neural network. Many previous works show that the existing intelligent target detection algorithm based on deep neural network will be affected by perturbations. Firstly, this paper establishes a target detection method based on the Faster RCNN framework, and then three types of disturbances methods are studied to help the mobile radar to counter the typical space-based artificial intelligence detection algorithm. The simulation results show that the three types of disturbances methods can fool the typical target detection technology based on deep neural network.