Reviewerly/exHarmony huggingface dataset:
Pipe Profiling Using Digital Image Correlation

Pipes are among the important components of the U.S. infrastructure. Deflection monitoring of gravity pipes, such as culverts or sewer pipelines, provides an invaluable information of the pipe’s structural condition to the decision makers. Conventionally, laser profilers are used for dry pipes to accurately measure their profile. However, laser profilers are very expensive, cumbersome to transport, and they require an expert to operate. Digital image correlation (DIC) is an inexpensive alternative to the conventionally used laser profilers to measure the pipe’s profile and consequently its deflection accurately over time. In this paper, feasibility of DIC for pipe profiling is investigated and verified through several linear variable differential transformers (LVDTs) and cable displacement sensors (CDSs). Two 5.5-ft long, 60-in. circular corrugated metal pipes (CMPs) and one equivalent size arch CMP were embedded, instrumented and subjected to static load using an MTS hydraulic actuator under the laboratory condition. The inside of two circular CMPs were lined by a polymeric spray applied pipe lining (SAPL) to investigate the effect of liner brittle failure (i.e., cracking) on the accuracy of DIC. The pipes were loaded until the ultimate bearing capacity of the composite system was reached. The profiles of pipe samples were measured before, after and during the loading to calculate their respective deflections using DIC. In this study, MATLAB and a commercially available software, GOM Correlate, were used to obtain the pipe samples’ profile. The result shows an excellent agreement between DIC, LVDTs, and CDSs. It is recommended that potential use of DIC in the actual field conditions be investigated for laser profiler substitution.
A nonproprietary language for the command and control of cyber defenses – OpenC2

The fact that cyber attacks are getting increasingly sophisticated and performed at machine speed motivated the development of OpenC2. This paper presents Open Command and Control (OpenC2), a suite of specifications that enable command and control of cyber defense systems and components at machine speed and in a manner that is agnostic of the underlying technologies utilized or of any other aspects of particular implementations. OpenC2 provides the means to introduce standardized interfaces to cyber defense systems, enabling interoperability and allowing seamless integration, communication, and operation between decoupled blocks that perform cyber defense functions. The suit of specifications includes a semantic language that enables machine-to-machine communication for purposes of command and control of cyber defense components, actuator profiles that specify the subset of the OpenC2 language and may extend it in the context of specific cyber defense functions, and transfer specifications that utilize existing protocols and standards to implement OpenC2 in particular environments. Fundamentally, OpenC2 addresses the acting part of the Integrated Adaptive Cyber Defense (IACD) framework and is designed to be technology agnostic, concise, abstract, and extensible. Ultimately, OpenC2 is a building block for enabling coordinated defense in cyber-relevant time, shifting traditional monolithic cyber response approaches to more granular, flexible, and adaptive.
Huffman Coding with Gap Arrays for GPU Acceleration

Huffman coding is a fundamental lossless data compression scheme used in many data compression file formats such as gzip, zip, png, and jpeg. Huffman encoding is easily parallelized, because all 8-bit symbols can be converted into codewords independently. On the other hand, since an encoded codeword sequence has no separator to identify each codeword, parallelizing Huffman decoding is a much harder task. This work presents a new data structure called gap array to be attached to an encoded codeword sequence of Huffman coding for accelerating parallel Huffman decoding. In addition, it also shows that GPU Huffman encoding and decoding can be accelerated by several techniques including (1) the Single Kernel Soft Synchronization (SKSS), (2) wordwise global memory access and (3) compact codebooks. The experimental results for 10 files on NVIDIA Tesla V100 GPU show that our GPU Huffman encoding and decoding run 2.87x-7.70x times and 1.26x-2.63x times faster than previously presented GPU Huffman encoding and decoding, respectively. Also, Huffman decoding can be further accelerated by a factor of 1.67x-6450x if a gap array is attached to an encoded codeword sequence. Since the size and computing overhead of gap arrays in Huffman encoding are small, we can conclude that gap arrays should be introduced for GPU Huffman encoding and decoding.
When Less is More: Core-Restricted Container Provisioning for Serverless Computing

Cloud applications are exposed to workloads whose intensity can change unpredictably over time. Hence, the ability to quickly scale the amount of computing resources provisioned to applications is essential to minimize costs while providing reliable services. In this context, containers are deemed to be a promising technology to enable fast elasticity in resource allocation schemes. In this paper, we propose and experimentally test an efficient container-based cloud computing provisioning system. First, we address the container deployment problem and discuss how to manage container provisioning and scaling. Second, we devise a resource management mechanism leveraging on both admission control and auto-scaling techniques. We propose to drive auto-scaling decisions through a Q-Learning algorithm, which is agnostic to the specific computing environment, and proceeds based only on the load of the physical processors assigned to a container. We evaluate our solution in two experimental setups, and show that it yields significant advantages when compared to popular container managers such as Kubernetes.
Learning domain invariant unseen features for generalized zero-shot classification

Generalized zero-shot classification is a challenging task to recognize test data which may come from seen or unseen classes. Existing methods suffer from the bias problem that the unseen images are easy to be misclassified to seen classes. Generating some fake unseen samples by Generative Adversarial Network has been a popular method. However, these models are not easy to train. In this paper, we proposed a method by learning domain invariant unseen features for generalized zero-shot classification. Specifically, we learn the support seen class set for each unseen class for transferring knowledge from source to target domain. The unseen samples of each class are generated based on the combinations of the samples from its support seen class set. In addition, for dealing with the domain shift problem between source and target domains, we learn domain invariant unseen features by minimizing the Maximum Mean Discrepancy distance of seen data, generated unseen data and then project target data to the common space. For dealing with the bias problem, we select some confident target unseen samples to augment training samples for training the classifier. In experiments, we demonstrate that the proposed method significantly outperforms other state-of-the-art methods.
AMRT: Anti-ECN Marking to Improve Utilization of Receiver-driven Transmission in Data Center

Cloud applications generate a variety of workloads ranging from delay-sensitive flows to bandwidth-hungry ones in data centers. Existing reactive or proactive congestion control protocols are hard to simultaneously achieve ultra-low latency and high link utilization across all workloads in data center networks. We present a new receiver-driven transport scheme using anti-ECN (Explicit Congestion Notification) marking to achieve both near-zero queueing delay and full link utilization by reasonably increasing sending rate in the case of under-utilization. Specifically, switches mark the ECN bit of data packets once detecting spare bandwidth. When receiving the anti-ECN marked packet, the receiver generates the corresponding marked grant to trigger more data packets. The experimental results of small-scale testbed implementation and large-scale NS2 simulation show that AMRT effectively reduces the average flow completion time (AFCT) by up to 40.8% and improves the link utilization by up to 36.8% under high workload over the state-of-the-art receiver-driven transmission schemes.
Novel metrics for computing semantic similarity with sense embeddings

In the last years many efforts have been spent to build word embeddings, a representational device in which word meanings are described through dense unit vectors of real numbers over a continuous, high-dimensional Euclidean space, where similarity can be interpreted as a metric. Afterwards, sense-level embeddings have been proposed to describe the meaning of senses, rather than terms. More recently, additional intermediate representations have been designed, providing a vector description for pairs 〈term,sense〉, and mapping both term and sense descriptions onto a shared semantic space. However, surprisingly enough, this wealth of approaches and resources has not been supported by a parallel refinement in the metrics used to compute semantic similarity: to date, the semantic similarity featuring two input entities is mostly computed as the maximization of some angular distance intervening between vector pairs, typically cosine similarity. In this work we introduce two novel similarity metrics to compare sense-level representations, and show that by exploiting the features of sense-embeddings it is possible to substantially improve on existing strategies, by obtaining enhanced correlation with human similarity ratings. Additionally, we argue that semantic similarity needs to be complemented by another task, involving the identification of the senses at the base of the similarity rating. We experimentally verified that the proposed metrics are beneficial when dealing with both semantic similarity task and sense identification task. The experimentation also provides a detailed how-to illustrating how six important sets of sense embeddings can be used to implement the proposed similarity metrics.
Balancing Graph Processing Workloads Using Work Stealing on Heterogeneous CPU-FPGA Systems

We propose, implement and evaluate a work stealing based scheduler, called HWS, for graph processing on heterogeneous CPU-FPGA systems that tightly couple the CPU and the FPGA to share system memory. HWS addresses unique concerns that arise with work stealing in the context of our target system. Our evaluation is conducted on the Intel Heterogeneous Architecture Research Platform (HARPv2), using three key processing kernels and seven real-world graphs. We show that HWS effectively balances workloads. Further, the use of HWS results in better graph processing performance compared to static scheduling and a representative of existing adaptive partitioning techniques, called HAP. Improvements vary by graph processing application, input graph and number of threads, and can be up to 100% over static scheduling, and up to 17% over HAP. We also compare to an oracle chunk self-scheduler, in which the best chunk size is known a priori for each number of threads and each input graph. HWS performs no worse than 1-3% in most cases. Finally, our graph processing throughput scales well with increasing threads. These results collectively demonstrate the effectiveness of work stealing for graph processing on our heterogeneous target platform.
Rendering Server Allocation for MMORPG Players in Cloud Gaming

Cloud gaming services enable users with heterogeneous device capabilities to get access to game titles with hardware demanding specifications. While visual quality requirements are to be satisfied by the cloud rendering servers, latency is one of the major problems which arises as most of the tasks are offloaded remotely. Cloud gaming players must experience latency below a certain acceptable limit for the game to be responsive with playable visual quality under limited bandwidth capacity. Playing multiplayer games in the cloud needs to cater for player interactions and their commonality especially if they are playing in the same virtual space. The main characteristic of cloud gaming is that the whole rendering pipeline occurs in the cloud; therefore, rendering optimization by reusing common information across players is possible by taking advantage of multi-view rendering. In this paper, we propose a cloud gaming architecture for MMORPGs which involves hundreds of players as well as online optimization heuristic algorithms on rendering server allocations in order to minimize rendering server rental cost from game provider’s point of view. In addition, we also compare the performance of those online heuristics with an offline lower bound in order to observe the scalability of the online scenario.
Personalized tourism route recommendation based on user’s active interests

Tourism is both an important industry and popular leisure activity undertaken by millions around the world. How to effectively mine the user’s travel mode and visit preferences based on the user’s historical travel data is a challenge. The tourism resources of different visiting areas, such as the popularity of POI (Point of Interest), influence the user’s interests dynamically. Therefore, a user’s interest preferences during traveling may differ between geographical region. In this paper, we introduce a personalized travel route recommendation framework, named PTDR, based on region dependent personal interest. PTDR consists of two parts, which are POI recommendation and itinerary generation. We analyzed the user’s history interest from check-in behavior in detail and constructed a convolutional neural network to extract the potential features of the target visiting area. Then the user’s active interest is learned from the user’s history interest and the potential features of the target area. Finally, we optimize the itinerary for a user based on the orienteering problem, which takes into account the user’s travel restrictions, such as time limits, starting attraction restrictions, and destination attraction restrictions. We evaluated the proposed algorithm on four cities of Flickr datasets and compared them to existing travel recommendations, including accuracy, recall, and F1. Experiments verify the effectiveness of the proposed method.
Developing Chatbot For Academic Record Monitoring in Higher Education Institution

Abstract Monitoring academic records at a higher education institution is highly needed by both students and parents of students. Although the system is usually already available in the form of a web site, but it is still considered too complicated because it must involve a troublesome authentication process, especially for parents. Nowadays, chat applications have been very widely used by the community both young people and even the elderly. There are many chat applications that are widely used including WhatsApp, LINE, Telegram, and Facebook Messenger. The chat application provides an Application Programming Interface (API) service for sending or receiving messages. Therefore, the API can be used to create applications (chatbot) that will serve users in the form of chat. In this study, chatbot was built using the services of Telegram. This is because sending messages via Telegram is free. The information to be served is information about students’ attendance at lectures, grades, and financial records. With this application, a college can provide facilities for students or parents of students to view academic records easily, cheaply, and can be accessed anytime. With this application, a college can provide facilities for students or parents of students to view academic records easily, cheaply, and can be accessed anytime.
Enhanced Cyber-physical Security of Steering Stability Control System for Four-Wheel Independent Drive Electric Vehicles

In this paper, we present a residual-based anomaly detection method to enhance the cyber-physical security of the steering stability control system (SSCS) in a four-wheel independent drive electric vehicle. With the approach of the linear quadratic regulator, the SSCS is developed through the yaw moment generated by the torque deviation between the motors, the goal of which is to improve the lateral stability of the vehicle body. To prevent the vehicle against cyber-physical attacks, e.g., integrity attacks, we propose a residual-based anomaly detection method. Compared to traditional residual-based anomaly detection, the presented method can deal with threats on both control inputs and sensor measurements by combining physics-based and learning-based approaches. Simulation results have shown the effectiveness of the proposed detection method.
Amplitude Suppression and Direction Activation in Networks for 1-bit Faster R-CNN

Recent advances in object detection have been driven by the success of deep convolutional neural networks (DCNNs). Deploying a DCNN detector on resource-limited hardware such as embedded devices and smart phones, however, remains challenging due to the massive number of parameters a typical model contains. In this paper, we propose an amplitude suppression and direction activation for Faster R-CNN (ASDA-FRCNN) framework to significantly compress DCNNs for highly efficient performance. The shared amplitude between the full-precision and the binary kernels can be significantly suppressed through a simple but effective loss, which is then incorporated into the existing Faster R-CNN detector. Furthermore, the ASDA module is generic and flexible to be incorporated into existing DCNNs for different tasks. Experiments demonstrate the superiority of 1-bit ASDA-FRCNN which achieves superior performance on various datasets. Specifically, ASDA-FRCNN shows the best speed-accuracy trade off with 63.4% at estimated 711 FPS and 19.4% mAP at and estimated 362 FPS with ResNet-18 on the PASCAL VOC 2007 and MS COCO validation datasets respectively, which demonstrate the superior performance and strong generalization of our method.
Hash-enhanced elliptic curve bit-string generator for medical image encryption

Bit-string generator (BSG) is based on the hardness of known number theoretical problems, such as the discrete logarithm problem with the elliptic curve (ECDLP). Such type of generators will have good randomness and unpredictability properties as it is challenged to find a solution regarding this mathematical dilemma. Hash functions in turn play a remarkable role in many cryptographic tasks to accomplish different security levels. Hash-enhanced elliptic curve bit-string generator (HEECBSG) mechanism is proposed in this study based on the ECDLP and secure hash function. The cryptographic hash function is used to achieve integrity and security of the obtained bit-strings for highly sensitive plain data. The main contribution of the proposed HEECBSG is transforming the x-coordinate of the elliptic curve points using a hash function H to generate bit-strings of any desirable length. The obtained pseudo-random bits are tested by the NIST test suite to analyze and verify its statistical and randomness properties. The resulted bit-string is utilized here for encrypting various medical images of the vital organs, i.e. the brain, bone, fetuses, and lungs. Then, extensive evaluation metrics have been applied to analyze the successful performance of the cipherimage, including key-space analysis, histogram analysis, correlation analysis, entropy analysis and sensitivity analysis. The results demonstrated that our proposed HEECBSG mechanism is feasible for achieving security and privacy purposes of the medical image transmission over unsecure communication networks.
A 4.45 ms Low-Latency 3D Point-Cloud-Based Neural Network Processor for Hand Pose Estimation in Immersive Wearable Devices

A 3D point-cloud-based neural network (PNN) processor is proposed for the low-latency hand pose estimation (HPE) system. The processor adopts the heterogeneous core architecture to accelerate both convolution layers (CLs) and sampling-grouping layers (SGLs). The proposed window-based sampling-grouping (WSG) directly samples and groups the 3D points from the streaming depth image to boost up the throughput by ×2.34. Furthermore, the max pooling prediction (MPP) predicts the 64- and 128-to-1 max pooling outputs with ×1.31 throughput enhancement. In addition, the tiled data based MPP (TMPP) performs the MPP with the tiled input data to hide the latency of the MPP. As a result, the processor achieves 4.45 ms latency on the HPE system.
An Efficient Text-Based Image Retrieval Using Natural Language Processing (NLP) Techniques

The image retrieval system is a computer system for browsing, searching and retrieving images from a large database of digital images or text. Most traditional and common methods of image retrieval utilize some method of adding metadata such as captioning, keywords or descriptions to the images so that retrieval can be performed over the annotation words. Manual image annotation is time-consuming, laborious and expensive. To address this, there has been a large amount of research done on automatic image annotation. With the rapid development of information technology, the number of electronic documents and digital content within documents exceeds the capacity of manual control and management. The usage of images is increased in real time. So, the proposed system concentrates on retrieving image by using the text-based image retrieval system. Text documents are given as input to the preprocessing stage, and features are extracted using TF-IDF. Finally, document clustering method can be used to automatically group the retrieved documents into list of meaningful categories. Document clustering clusters the document of different domains and latent Dirichlet allocation (LDA), each document may be viewed as a mixture of various topics where each document is considered to have a set of topics, and after that relevant documents are retrieved and then images of those relevant documents are retrieved.
Efficient Layout Hotspot Detection via Binarized Residual Neural Network Ensemble

Layout hotspot detection is of great importance in the physical verification flow. Deep neural network models have been applied to hotspot detection and achieved great successes. The layouts can be viewed as binary images. The binarized neural network (BNN) can thus be suitable for the hotspot detection problem. In this article, we propose a new deep learning architecture based on BNNs to speed up the neural networks in hotspot detection. A new binarized residual neural network is carefully designed for hotspot detection. Experimental results on ICCAD 2012 and 2019 benchmarks show that our architecture outperforms previous hotspot detectors in detecting accuracy and has an 8× speedup over the best deep learning-based solution. Since the BNN-based model is quite computationally efficient, a good tradeoff can be achieved between the efficiency and performance of the hotspot detector by applying ensemble learning approaches. Experimental results show that the ensemble models achieve better hotspot detection performance than the original with acceptable speed loss.
The anthropophagic studio: towards a critical pedagogy for interaction design

The design studio is the standard approach for interaction design education in the Global North. Nevertheless, in the Global South, this approach is not directly applicable due to authoritarian educational systems founded on colonialist ideologies. This research reports on an attempt to appropriate the design studio and fundamental interaction design concepts in Brazil. Following the anthropophagy tradition of hybridization, the foreign concepts were not rejected but devoured and digested together with Global South concepts, such as radical alterity, mediation, and oppression to form what we call the anthropophagic studio. The process gradually revealed to students and researchers the role of interaction design in reproducing other historical oppressions beyond colonialism. This finding points to the need for a critical pedagogy that can aptly tackle technology-mediated oppression.