Reviewerly/exHarmony huggingface dataset:
TRACTOR: Traffic‐aware and power‐efficient virtual machine placement in edge‐cloud data centers using artificial bee colony optimization

Summary Technology providers heavily exploit the usage of edge‐cloud data centers (ECDCs) to meet user demand while the ECDCs are large energy consumers. Concerning the decrease of the energy expenditure of ECDCs, task placement is one of the most prominent solutions for effective allocation and consolidation of such tasks onto physical machine (PM). Such allocation must also consider additional optimizations beyond power and must include other objectives, including network‐traffic effectiveness. In this study, we present a multi‐objective virtual machine (VM) placement scheme (considering VMs as fog tasks) for ECDCs called TRACTOR , which utilizes an artificial bee colony optimization algorithm for power and network‐aware assignment of VMs onto PMs. The proposed scheme aims to minimize the network traffic of the interacting VMs and the power dissipation of the data center's switches and PMs. To evaluate the proposed VM placement solution, the Virtual Layer 2 (VL2) and three‐tier network topologies are modeled and integrated into the CloudSim toolkit to justify the effectiveness of the proposed solution in mitigating the network traffic and power consumption of the ECDC. Results indicate that our proposed method is able to reduce power energy consumption by 3.5% while decreasing network traffic and power by 15% and 30%, respectively, without affecting other QoS parameters.
HCI Tactics for Politics from Below: Meeting the Challenges of Smart Cities

As crucial public functions are transferred to computer systems, emerging technologies have public implications that are often shaped beyond public influence and oversight. "Smart city" and "modernization" projects are just some examples of such transformations. This paper focuses on struggles over the acquisition, control, and maintenance of these public, digital infrastructures. We focus on the forms of HCI knowledge and practice that proved useful to a coalition of community organizations claiming rights of input into and political oversight over surveillance technology. Their claims were a response to their exclusion from decision-making about smart city implementation in San Diego. We offer tactics "from below" as a way to attune HCI to the needs and practices of those excluded from power over widespread technology infrastructures. Ultimately, we argue that HCI cultivates a variety of capacities beyond design and redesign that can strengthen struggles to shape real-world technologies from below.
GAN-Based SAR-to-Optical Image Translation with Region Information

In this paper, we propose a SAR-to-optical image translation method based on conditional generative adversarial networks (cGANs). Though cGANs have achieved great success in image translation, some problems remain in SAR-to-optical image translation. One of the problems is the colorization error owing to the lack of color information in SAR data. Since the colors of optical images are varied, while SAR images have no color information, the generator network is confused and fail to generate correctly colorized optical images. To prevent it, we introduce a region information to the image translation network. Specifically, the feature vector from the pre-trained classification network is fed to the generator and discriminator network. Experimental results with SEN1-2 dataset show the advantage of our proposed method over the baseline method that does not use any additional information.
Dynamically Constrained Motion Planning Networks for Non-Holonomic Robots

Reliable real-time planning for robots is essential in today's rapidly expanding automated ecosystem. In such environments, traditional methods that plan by relaxing constraints become unreliable or slow-down for kinematically constrained robots. This paper describes the algorithm Dynamic Motion Planning Networks (Dynamic MPNet), an extension to Motion Planning Networks, for non-holonomic robots that address the challenge of real-time motion planning using a neural planning approach. We propose modifications to the training and planning networks that make it possible for real-time planning while improving the data efficiency of training and trained models' generalizability. We evaluate our model in simulation for planning tasks for a non-holonomic robot. We also demonstrate experimental results for an indoor navigation task using a Dubins car.
A Cervical Histopathology Dataset for Computer Aided Diagnosis of Precancerous Lesions

Cervical cancer, as one of the most frequently diagnosed cancers worldwide, is curable when detected early. Histopathology images play an important role in precision medicine of the cervical lesions. However, few computer aided algorithms have been explored on cervical histopathology images due to the lack of public datasets. In this article, we release a new cervical histopathology image dataset for automated precancerous diagnosis. Specifically, 100 slides from 71 patients are annotated by three independent pathologists. To show the difficulty of the task, benchmarks are obtained through both fully and weakly supervised learning. Extensive experiments based on typical classification and semantic segmentation networks are carried out to provide strong baselines. In particular, a strategy of assembling classification, segmentation, and pseudo-labeling is proposed to further improve the performance. The Dice coefficient reaches 0.7833, indicating the feasibility of computer aided diagnosis and the effectiveness of our weakly supervised ensemble algorithm. The dataset and evaluation codes are publicly available. To the best of our knowledge, it is the first public cervical histopathology dataset for automated precancerous segmentation. We believe that this work will attract researchers to explore novel algorithms on cervical automated diagnosis, thereby assisting doctors and patients clinically.
Linguistic Generative Steganography With Enhanced Cognitive-Imperceptibility

In recent years, linguistic generative steganography has been greatly developed. The previous works are mainly to optimize the perceptual-imperceptibility and statistical-imperceptibility of the generated steganographic text, and the latest developments show that they have been able to generate steganographic texts that look authentic enough. However, we noticed that these works generally cannot control the semantic expression of the generated steganographic text, and we believe this will bring potential security risks. We named this kind of security challenges as cognitive-imperceptibility. We think this is a new challenge that the generative steganography models must strive to overcome in the future. In this letter, we conduct some preliminary attempts to solve this challenge. Experimental results show that the proposed methods can further constrain the semantic expression of the generated steganographic text on the basis of ensuring certain perceptual-imperceptibility and statistical-imperceptibility, so as to enhance its cognitive-imperceptibility.
Tunnel vision optimization method for VR flood scenes based on Gaussian blur

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Kraken: Memory-Efficient Continual Learning for Large-Scale Real-Time Recommendations

Modern recommendation systems in industry often use deep learning (DL) models that achieve better model accuracy with more data and model parameters. However, current opensource DL frameworks, such as TensorFlow and PyTorch, show relatively low scalability on training recommendation models with terabytes of parameters. To efficiently learn large-scale recommendation models from data streams that generate hundreds of terabytes training data daily, we introduce a continual learning system called Kraken. Kraken contains a special parameter server implementation that dynamically adapts to the rapidly changing set of sparse features for the continual training and serving of recommendation models. Kraken provides a sparsity-aware training system that uses different learning optimizers for dense and sparse parameters to reduce memory overhead. Extensive experiments using real-world datasets confirm the effectiveness and scalability of Kraken. Kraken can benefit the accuracy of recommendation tasks with the same memory resources, or trisect the memory usage while keeping model performance.
Visual Navigation Among Humans With Optimal Control as a Supervisor

Real world visual navigation requires robots to operate in unfamiliar, human-occupied dynamic environments. Navigation around humans is especially difficult because it requires anticipating their future motion, which can be quite challenging. We propose an approach that combines learning-based perception with model-based optimal control to navigate among humans based only on monocular, first-person RGB images. Our approach is enabled by our novel data-generation tool, HumANav, that allows for photorealistic renderings of indoor environment scenes with humans in them, which are then used to train the perception module entirely in simulation. Through simulations and experiments on a mobile robot, we demonstrate that the learned navigation policies can anticipate and react to humans without explicitly predicting future human motion, generalize to previously unseen environments and human behaviors, and transfer directly from simulation to reality. Videos describing our approach and experiments, as well as a demo of HumANav are available on the project website. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Benchmarking and survey of explanation methods for black box models

Abstract The rise of sophisticated black-box machine learning models in Artificial Intelligence systems has prompted the need for explanation methods that reveal how these models work in an understandable way to users and decision makers. Unsurprisingly, the state-of-the-art exhibits currently a plethora of explainers providing many different types of explanations. With the aim of providing a compass for researchers and practitioners, this paper proposes a categorization of explanation methods from the perspective of the type of explanation they return, also considering the different input data formats. The paper accounts for the most representative explainers to date, also discussing similarities and discrepancies of returned explanations through their visual appearance. A companion website to the paper is provided as a continuous update to new explainers as they appear. Moreover, a subset of the most robust and widely adopted explainers, are benchmarked with respect to a repertoire of quantitative metrics.
Hybrid ensemble approaches to online harassment detection in highly imbalanced data

Online harassment is a major threat to users of social media platforms, especially young adults and women. It can cause mental illnesses and impacts deeply and negatively economic institutions experiencing cyberbully attacks by losing their credibility and business. This makes automatic detection of online harassment extremely important. Most of current studies within this context apply machine-learning algorithms that assume balanced class distribution. However, this assumption does not hold for most real datasets. This research provides a comprehensive investigation of various approaches that combine diverse techniques under three dimensions: feature representation, imbalanced data handling, and supervised learning. For the first dimension, three word-embedding models have been considered, namely: word2vec, Glove, and SSWE. For the other two dimensions, nine techniques for balancing skewed class distributions have been employed to feed several learning models. In particular, resampling methods, cost-sensitive learning, and Weight-Selection strategy-based methods have been used with deep neural networks. The ultimate goal of this study is to evaluate the potential of using such hybrid approaches to handle the online harassment detection task efficiently using highly-imbalanced Twitter data and to select the best combination concerning the intended purpose. An extensive comparative study has been conducted, and the results have been discussed in terms of three evaluation metrics widely used for imbalanced classification. As main findings, Glove has been found as the best feature representation and some combinations as the best performing most notably LSTM and BLSTM with cost-sensitive learning and VL strategy.
Parallel Blockwise Knowledge Distillation for Deep Neural Network Compression

Deep neural networks (DNNs) have been extremely successful in solving many challenging AI tasks in natural language processing, speech recognition, and computer vision nowadays. However, DNNs are typically computation intensive, memory demanding, and power hungry, which significantly limits their usage on platforms with constrained resources. Therefore, a variety of compression techniques (e.g., quantization, pruning, and knowledge distillation) have been proposed to reduce the size and power consumption of DNNs. Blockwise knowledge distillation is one of the compression techniques that can effectively reduce the size of a highly complex DNN. However, it is not widely adopted due to its long training time. In this article, we propose a novel parallel blockwise distillation algorithm to accelerate the distillation process of sophisticated DNNs. Our algorithm leverages local information to conduct independent blockwise distillation, utilizes depthwise separable layers as the efficient replacement block architecture, and properly addresses limiting factors (e.g., dependency, synchronization, and load balancing) that affect parallelism. The experimental results running on an AMD server with four Geforce RTX 2080Ti GPUs show that our algorithm can achieve 3x speedup plus 19 percent energy savings on VGG distillation, and 3.5x speedup plus 29 percent energy savings on ResNet distillation, both with negligible accuracy loss. The speedup of ResNet distillation can be further improved to 3.87 when using four RTX6000 GPUs in a distributed cluster.
Federated Bandit: A Gossiping Approach

We study Federated Bandit, a decentralized Multi-Armed Bandit (MAB) problem with a set of N agents, who can only communicate their local data with neighbors described by a connected graph G. Each agent makes a sequence of decisions on selecting an arm from M candidates, yet they only have access to local and potentially biased feedback/evaluation of the true reward for each action taken. Learning only locally will lead agents to sub-optimal actions while converging to a no-regret strategy requires a collection of distributed data. Motivated by the proposal of federated learning, we aim for a solution with which agents will never share their local observations with a central entity, and will be allowed to only share a private copy of his/her own information with their neighbors. We first propose a decentralized bandit algorithm GossipUCB, which is a coupling of variants of both the classical gossiping algorithm and the celebrated Upper Confidence Bound (UCB) bandit algorithm. We show that GossipUCB successfully adapts local bandit learning into a global gossiping process for sharing information among connected agents, and achieves guaranteed regret at the order of O(max(poly(N,M) log T, poly(N,M) logλ2-1 N)) for all N agents, where λ2∈(0,1) is the second largest eigenvalue of the expected gossip matrix, which is a function of G. We then propose FedUCB, a differentially private version of GossipUCB, in which the agents preserve ε-differential privacy of their local data while achieving O(max poly(N,M)/ε log2.5 T, poly(N,M) (logλ2-1 N + log T)) regret.
Continuous, Real-Time Object Detection on Mobile Devices without Offloading

This paper presents AdaVP, a continuous and real-time video processing system for mobile devices without offloading. AdaVP uses Deep Neural Network (DNN) based tools like YOLOv3 for object detection. Since DNN computation is time-consuming, multiple frames may be captured by the camera during the processing of one frame. To support real-time video processing, we develop a mobile parallel detection and tracking (MPDT) pipeline that executes object detection and tracking in parallel. When the object detector is processing a new frame, a light-weight object tracker is used to track the objects in the accumulated frames. As the tracking accuracy decreases gradually, due to the accumulation of tracking error and the appearance of new objects, new object detection results are used to calibrate the tracking accuracy periodically. In addition, a large DNN model produces high accuracy, but requires long processing latency, resulting in a great degradation for tracking accuracy. Based on our experiments, we find that the tracking accuracy degradation is also related to the variation of video content, e.g., for a dynamically changing video, the tracking accuracy degrades fast. A model adaptation algorithm is thus developed to adapt the DNN models according to the change rate of video content. We implement AdaVP on Jetson TX2 and conduct a variety of experiments on a large video dataset. The experiment results reveal that AdaVP improves the accuracy of the state-of-the-art solution by up to 43.9%.
Distributed Ledger Technology-based land transaction system with trusted nodes consensus mechanism

Blockchain technology is an emerging solution to various financial applications that require a secure and tamper-proof transactions system. One such application is the land registry. Management of transactions for land registration is a tedious task. It is highly insecure and subject to forgery land records, Issues in verification, mediators, etc. This article presents a scalable and novel property/land registration framework based on blockchain technology. The transparent and immutable nature of blockchain technology makes it reliable for intrusive and fraudulent land registration activities. The framework uses the InterPlanetary File System (IPFS), a Peer-to-Peer (P2P) swarm network. It allows the sharing and storing of data in an efficient, decentralized, and transparent manner. This article also proposes a Trusted Node Consensus Algorithm (TNCA) based on the miners' trust value that minimizes the computational overhead of broadcasting the new block. The proposed approach - trusted node consensus algorithm requires lesser time to add the block into the blockchain, and message exchange per consensus is 58.94% lesser than the traditional Proof-of-Work (PoW) approach and 26.44% lesser time than the existing load-balanced method.
Acceptance and Usability of Immersive Virtual Reality in Older Adults with Objective and Subjective Cognitive Decline

Background: Virtual reality (VR) has recently emerged as a promising means for the administration of cognitive training of seniors at risk of dementia. Immersive VR could result in increased engagement and performances; however, its acceptance in older adults with cognitive deficits still has to be assessed. Objective: To assess acceptance and usability of an immersive VR environment requiring real walking and active participants’ interaction. Methods: 58 seniors with mild cognitive impairment (MCI, n = 24) or subjective cognitive decline (SCD, n = 31) performed a shopping task in a virtual supermarket displayed through a head-mounted display. Subjective and objective outcomes were evaluated. Results: Immersive VR was well-accepted by all but one participant (TAM3 positive subscales &gt; 5.33), irrespective of the extent of cognitive decline. Participants enjoyed the experience (spatial presence 3.51±0.50, engagement 3.85±0.68, naturalness 3.85±0.82) and reported negligible side-effects (SSQ: 3.74; q1-q3:0–16.83). The environment was considered extremely realistic, such as to induce potentially harmful behaviors: one participant fell while trying to lean on a virtual shelf. Older participants needed more time to conclude trials. Participants with MCI committed more errors in grocery items’ selection and experienced less “perceived control” over the environment. Conclusion: Immersive VR was acceptable and enjoyable for older adults in both groups. Cognitive deficits could induce risky behaviors, and cause issues in the interactions with virtual items. Further studies are needed to confirm acceptance of immersive VR in individuals at risk of dementia, and to extend the results to people with more severe symptoms.
An Ensemble Method for Radicalization and Hate Speech Detection Online Empowered by Sentic Computing

The dramatic growth of the Web has motivated researchers to extract knowledge from enormous repositories and to exploit the knowledge in myriad applications. In this study, we focus on natural language processing (NLP) and, more concretely, the emerging field of affective computing to explore the automation of understanding human emotions from texts. This paper continues previous efforts to utilize and adapt affective techniques into different areas to gain new insights. This paper proposes two novel feature extraction methods that use the previous sentic computing resources AffectiveSpace and SenticNet. These methods are efficient approaches for extracting affect-aware representations from text. In addition, this paper presents a machine learning framework using an ensemble of different features to improve the overall classification performance. Following the description of this approach, we also study the effects of known feature extraction methods such as TF-IDF and SIMilarity-based sentiment projectiON (SIMON). We perform a thorough evaluation of the proposed features across five different datasets that cover radicalization and hate speech detection tasks. To compare the different approaches fairly, we conducted a statistical test that ranks the studied methods. The obtained results indicate that combining affect-aware features with the studied textual representations effectively improves performance. We also propose a criterion considering both classification performance and computational complexity to select among the different methods.
RDIC: A blockchain-based remote data integrity checking scheme for IoT in 5G networks

Internet of things (IoT) is one of the main application scenarios of 5th generation mobile networks (5G). Along with the rapid development of 5G, IoT terminal devices will create big data. Generally, IoT terminal devices are lightweight user equipments, for example, wearable devices. In order to take use of these lightweight terminal devices, it is a feasible way to outsource these created big data to the public cloud. When these data are out of the client’s control, it is of crucial importance to ensure the remote data integrity. To solve the weaknesses of the existing remote data integrity checking schemes, we propose the concept of blockchain-based remote data integrity checking (RDIC) scheme for big data. The new concept makes use of blockchain technique which greatly improves the efficiency and security of RDIC. First, the system model and security definition are given for the proposed RDIC scheme by using blockchain. Then, by using efficient modular arithmetic, RSA digital signature, blockchain, etc, we construct a lightweight blockchain-based RDIC scheme. Finally, we analyze its security and performance. The detailed analysis shows that our scheme is provably secure and lightweight.