Reviewerly/exHarmony huggingface dataset:
Energy-efficient Workload Allocation and Computation Resource Configuration in Distributed Cloud/Edge Computing Systems With Stochastic Workloads

Energy efficiency is one of the most important concerns in cloud/edge computing systems. A major benefit of the Dynamic Voltage and Frequency Scaling (DVFS) technique is that a Virtual Machine (VM) can dynamically scale its computation frequency on an on-demand basis, which is helpful in reducing the energy cost of computation when dealing with stochastic workloads. In this paper, we study the joint workload allocation and computation resource configuration problem in distributed cloud/edge computing. We propose a new energy consumption model that considers the stochastic workloads for computation capacity reconfiguration-enabled VMs. We define Service Risk Probability (SRP) as the probability a VM fails to process the incoming workloads in the current time slot, and we study the energy-SRP tradeoff problem in single VM. Without specifying any distribution of the workloads, we prove that, theoretically there exists an optimal SRP that achieves minimal energy cost, and we derive the closed form of the condition to achieve this minimal energy point. We also derive the closed form for computing the optimal SRP when the workloads follow a Gaussian distribution. We then study the joint workload allocation and computation frequency configuration problem for multiple distributed VMs scenario, and we propose solutions to solve the problem for both Gaussian and unspecified distributions. Our performance evaluation results on both synthetic and real-world workload trace data demonstrate the effectiveness of the proposed model. The closeness between the simulation results and the analytical results prove that our proposed method can achieve lower energy consumption compared with fixed computation capacity configuration methods.
IntegrityChain: Provable Data Possession for Decentralized Storage

Outsourced storage enables data owners to host their data on remote storage resources without keeping a local copy so as to target their core business. However, a serious problem is data integrity in the sense that data owners lose their physical control over the remote-stored data. Existing provable data possession protocols are overwhelmingly designed for centralized storage such as cloud, in which the server is assumed dishonest but the client is reliable. Moreover, the centralized storage suffers single-point-of-failure threat. In this paper, to deal with these issues, we propose the notion of IntegrityChain, a decentralized storage framework supporting provable data possession (PDP) based on blockchain. We formalize the system model, in which a data owner can store files to the peers in a blockchain network and check the integrity of the outsourced data periodically by paying some cryptocurrencies while the hosts can earn money if honestly provide storage service and will be punished by losing the pre-made deposit if data loss happens. In the security model, we consider the fairness in trading between a host and a data user and the soundness of the underlying decentralized PDP in this system. We come up with a concrete construction by borrowing the idea of multi-replica PDP and proof-of-retrievability and present the security analysis of the proposal. The evaluation for the construction contain two segments: the offchain part, in which we implement the algorithms locally to test the time consumption, and onchain part, in which we program a smart contract and launch it in a test network to test the gas cost for the functions.
Mammographic Classification Based on XGBoost and DCNN With Multi Features

The classification of benign and malignant masses in mammograms by Computer-Aided Diagnosis (CAD) is one of the most difficult and important tasks in the development of CAD systems.This classification has commonly been automated by extracting a set of handcrafted features from mammograms and relating the responses to breast cancer.Recently, the application of Deep Learning (DL) technology in medical imaging informatics has been attracting extensive research interest.However, limited medical image datasets and feature expression often reduce the performance of DL-based schemes.Therefore, this study aims to develop a new combined feature CAD method based on DL for classifying mammographic masses into three classes: normal, benign and cancer (malignant) masses.Three kinds of breast masses were scored by using Deep Convolution Neural Network (DCNN) as a feature extractor.Then the scoring features are combined with the image texture features as input to the classifier.This features including the scoring features, Gray-Level Co-occurrence Matrix (GLCM) and Histogram of Oriented Gradient (HOT) were employed to extract the breast mass information in mammograms and the classifier of Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost) were trained for the classification task.Accuracy (ACC), Precision (Pre), Recall (Rec), F 1 -score (F 1 ), and Overall Accuracy (Overall ACC) are used to evaluate the performance of the proposed system and the results show that the proposed multi-features combination model performs the best results.The performance of the XGBoost classifier has proved to be better in comparison to the SVM classification algorithms.As a result, when XGBoost was used as a classifier, the correct identification rate of the Overall ACC was 92.80% and that of malignant tumors was 84%, with reasonable and best results.These results indicate that the proposed method may help in more accurately diagnosing cases that are difficult to classify on images.
ASR Error Correction and Domain Adaptation Using Machine Translation

Off-the-shelf pre-trained Automatic Speech Recognition (ASR) systems are an increasingly viable service for companies of any size building speech-based products. While these ASR systems are trained on large amounts of data, domain mismatch is still an issue for many such parties that want to use this service as-is leading to not so optimal results for their task. We propose a simple technique to perform domain adaptation for ASR error correction via machine translation. The machine translation model is a strong candidate to learn a mapping from out-of-domain ASR errors to in-domain terms in the corresponding reference files. We use two off-the-shelf ASR systems in this work: Google ASR (commercial) and the ASPIRE model (open-source). We observe 7% absolute improvement in word error rate and 4 point absolute improvement in BLEU score in Google ASR output via our proposed method. We also evaluate ASR error correction via a downstream task of Speaker Diarization that captures speaker style, syntax, structure and semantic improvements we obtain via ASR correction.
Efficient Ladder-Style DenseNets for Semantic Segmentation of Large Images

Recent progress of deep image classification models provides great potential for improving related computer vision tasks. However, the transition to semantic segmentation is hampered by strict memory limitations of contemporary GPUs. The extent of feature map caching required by convolutional backprop poses significant challenges even for moderately sized Pascal images, while requiring careful architectural considerations when input resolution is in the megapixel range. To address these concerns, we propose a novel ladder-style DenseNet-based architecture which features high modelling power, efficient upsampling, and inherent spatial efficiency which we unlock with checkpointing. The resulting models deliver high performance and allow training at megapixel resolution on commodity hardware. The presented experimental results outperform the state-of-the-art in terms of prediction accuracy and execution speed on Cityscapes, VOC 2012, CamVid and ROB 2018 datasets. Source code at https://github.com/ivankreso/LDN.
Leaking Information Through Cache LRU States

The Least-Recently Used cache replacement policy and its variants are widely deployed in modern processors. This paper shows for the first time in detail that the LRU states of caches can be used to leak information: any access to a cache by a sender will modify the LRU state, and the receiver is able to observe this through a timing measurement. This paper presents LRU timing-based channels both when the sender and the receiver have shared memory, e.g., shared library data pages, and when they are separate processes without shared memory. In addition, the new LRU timing-based channels are demonstrated on both Intel and AMD processors in scenarios where the sender and the receiver are sharing the cache in both hyper-threaded setting and time-sliced setting. The transmission rate of the LRU channels can be up to 600Kbps per cache set in the hyper-threaded setting. Different from the majority of existing cache channels which require the sender to trigger cache misses, the new LRU channels work with the sender only having cache hits, making the channel faster and more stealthy. This paper also demonstrates that the new LRU channels can be used in transient execution attacks, e.g., Spectre. Further, this paper shows that the LRU channels pose threats to existing secure cache designs, and this work demonstrates the LRU channels affect the secure PL cache. The paper finishes by discussing and evaluating possible defenses.
Deepfake: A Survey on Facial Forgery Technique Using Generative Adversarial Network

"Deepfake" it is an incipiently emerging face video forgery technique predicated on AI technology which is used for creating the fake video. It takes images and video as source and it coalesces these to make a new video using the generative adversarial network and the output is very convincing. This technique is utilized for generating the unauthentic spurious video and it is capable of making it possible to generate an unauthentic spurious video of authentic people verbally expressing and doing things that they never did by swapping the face of the person in the video. Deepfake can create disputes in countries by influencing their election process by defaming the character of the politician. This technique is now being used for character defamation of celebrities and high-profile politician just by swapping the face with someone else. If it is utilized in unethical ways, this could lead to a serious problem. Someone can use this technique for taking revenge from the person by swapping face in video and then posting it to a social media platform. In this paper, working of Deepfake technique along with how it can swap faces with maximum precision in the video has been presented. Further explained are the different ways through which we can identify if the video is generated by Deepfake and its advantages and drawback have been listed.
iTLS: Lightweight Transport-Layer Security Protocol for IoT With Minimal Latency and Perfect Forward Secrecy

Enabling end-to-end secure communication is essential for many Internet-of-Things (IoT) application scenarios. Transport-layer security (TLS) and datagram TLS (DTLS) are the de-facto protocols for communication security in the IP-based IoT. However, the current authentication approaches of TLS are confronted with the heavy overhead and security issues in the resource-constrained IoT scenario. On the other hand, the identity-based cryptography (IBC) becomes an attractive cryptographic solution for the IoT. Unfortunately, the current IBC-based proposals exist the problem of either high communication latency or low level of security. In this article, we propose the first lightweight secure transport protocol called iTLS, which delivers protected data in the first flight with perfect forward secrecy, and provides implicit mutual authentication without certificates. iTLS dynamically generates identity-based early keys before receiving a server response, allowing clients to send the encrypted data without additional round trips. Furthermore, it employs the ephemeral secret ticket to obtain an ephemeral server key in the previous connection. Therefore, the early key established afterward can provide full forward secrecy. Design and implementation in the form of extension make iTLS fully compatible with TLS 1.3 and easy to be converted to a DTLS version. Our evaluation shows that iTLS reduces the network traffic overhead by at least 61.2%, and handshake latency on an ideal network by at least 60% compared to the certificate-based TLS. The results demonstrate iTLS achieves strong adaptability on the low-power and lossy IoT networks.
Software Cost Estimation Based on Dolphin Algorithm

Accurate Software Effort Estimation is of high importance with regard to Software Project Management.It can be specified as the process for predicting Effort regarding costs, needed for developing software products.A lot of techniques related to software effort estimation were carried out for developing models that are generating optimal estimation accuracy.Swarm intelligence is one such technique.The process-related in selecting the optimum estimation algorithm is expert dependent and complex.The presented study optimizes the estimation using the COCOMO II models by two models: the first model applied the dolphin algorithm, the second model applied suggested hybrid dolphin and bat algorithm (DolBat).By applying the two models on two data set and evaluate with the use of Magnitude of Relative Error(MRE) and Mean Magnitude of Relative Error(MMRE).The results indicate that the dolphin algorithm has better than previous algorithms but the (DolBat) is the best to get the coefficient value of the COCOMO II model.
High-Throughput Docking Using Quantum Mechanical Scoring

Today high-throughput docking is one of the most commonly used computational tools in drug lead discovery. While there has been an impressive methodological improvement in docking accuracy, docking scoring still remains an open challenge. Most docking programs are rooted in classical molecular mechanics. However, to better characterize protein-ligand interactions, the use of a more accurate quantum mechanical (QM) description would be necessary. In this work, we introduce a QM-based docking scoring function for high-throughput docking and evaluate it on 10 protein systems belonging to diverse protein families, and with different binding site characteristics. Outstanding results were obtained, with our QM scoring function displaying much higher enrichment (screening power) than a traditional docking method. It is acknowledged that developments in quantum mechanics theory, algorithms and computer hardware throughout the upcoming years will allow semi-empirical (or low-cost) quantum mechanical methods to slowly replace force-field calculations. It is thus urgently needed to develop and validate novel quantum mechanical-based scoring functions for high-throughput docking toward more accurate methods for the identification and optimization of modulators of pharmaceutically relevant targets.
Deep Sub-Region Network for Salient Object Detection

Saliency detection is a fundamental and challenging task in computer vision, which aims at distinguishing the most conspicuous objects or regions in an image. Existing deep-learning methods mainly rely on the entire image to learn the global context information for saliency detection, which loses the spatial relation and results in ambiguity in predicting saliency maps. In this paper, we propose a novel deep sub-region network (DSR-Net) equipped with a sequence of sub-region dilated blocks (SRDB) by aggregating multi-scale salient context information of multiple sub-regions, such that the global context information from the whole image and local contexts from sub-regions are fused together, making the saliency prediction more accurate. Our SRDB separates the input feature map at different layers of a convolutional neural network (CNN) into different sub-regions and then designs a parallel ASPP module to refine feature maps at each sub-region. Experiments on the five widely-used saliency benchmark datasets demonstrate that our network outperforms recent state-of-the-art saliency detectors quantitatively and qualitatively on all the benchmarks.
Sensitive loss: Improving accuracy and fairness of face representations with discrimination-aware deep learning

We propose a discrimination-aware learning method to improve both the accuracy and fairness of biased face recognition algorithms. The most popular face recognition benchmarks assume a distribution of subjects without paying much attention to their demographic attributes. In this work, we perform a comprehensive discrimination-aware experimentation of deep learning-based face recognition. We also propose a notational framework for algorithmic discrimination with application to face biometrics. The experiments include three popular face recognition models and three public databases composed of 64,000 identities from different demographic groups characterized by sex and ethnicity. We experimentally show that learning processes based on the most used face databases have led to popular pre-trained deep face models that present evidence of strong algorithmic discrimination. Finally, we propose a discrimination-aware learning method, Sensitive Loss, based on the popular triplet loss function and a sensitive triplet generator. Our approach works as an add-on to pre-trained networks and is used to improve their performance in terms of average accuracy and fairness. The method shows results comparable to state-of-the-art de-biasing networks and represents a step forward to prevent discriminatory automatic systems.
Visibility-Aware Point-Based Multi-View Stereo Network

We introduce VA-Point-MVSNet, a novel visibility-aware point-based deep framework for multi-view stereo (MVS). Distinct from existing cost volume approaches, our method directly processes the target scene as point clouds. More specifically, our method predicts the depth in a coarse-to-fine manner. We first generate a coarse depth map, convert it into a point cloud and refine the point cloud iteratively by estimating the residual between the depth of the current iteration and that of the ground truth. Our network leverages 3D geometry priors and 2D texture information jointly and effectively by fusing them into a feature-augmented point cloud, and processes the point cloud to estimate the 3D flow for each point. This point-based architecture allows higher accuracy, more computational efficiency and more flexibility than cost-volume-based counterparts. Furthermore, our visibility-aware multi-view feature aggregation allows the network to aggregate multi-view appearance cues while taking into account visibility. Experimental results show that our approach achieves a significant improvement in reconstruction quality compared with state-of-the-art methods on the DTU and the Tanks and Temples dataset. The code of VA-Point-MVSNet proposed in this work will be released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/callmeray/PointMVSNet</uri> .
Norm-Preservation: Why Residual Networks Can Become Extremely Deep?

Augmenting neural networks with skip connections, as introduced in the so-called ResNet architecture, surprised the community by enabling the training of networks of more than 1,000 layers with significant performance gains. This paper deciphers ResNet by analyzing the effect of skip connections, and puts forward new theoretical results on the advantages of identity skip connections in neural networks. We prove that the skip connections in the residual blocks facilitate preserving the norm of the gradient, and lead to stable back-propagation, which is desirable from optimization perspective. We also show that, perhaps surprisingly, as more residual blocks are stacked, the norm-preservation of the network is enhanced. Our theoretical arguments are supported by extensive empirical evidence. Can we push for extra norm-preservation? We answer this question by proposing an efficient method to regularize the singular values of the convolution operator and making the ResNet's transition layers extra norm-preserving. Our numerical investigations demonstrate that the learning dynamics and the classification performance of ResNet can be improved by making it even more norm preserving. Our results and the introduced modification for ResNet, referred to as Procrustes ResNets, can be used as a guide for training deeper networks and can also inspire new deeper architectures.
A new approach to data access and research transparency (DART)

Recent debates on transparency and replicability suggest that JIBS needs to update its approach on data access and research transparency (DART). We propose a series of initiatives, knowing well that there is a balance to be struck. There are clear benefits on the one hand, chief among these the potential for learning and knowledge accumulation, and equally manifest challenges on the other: the imperative to respect privacy, confidentiality, and intellectual property rights. Without addressing these challenges, will there be the high-quality data on which the benefits depend? We present access and transparency objectives, and set out how an actionable and effective approach towards DART will be implemented, but also address ethical, legal, and organizational challenges of concern to us as a scholarly community.
User Experience of Public Speaking Practice in Virtual Reality

This study investigated how the sense of presence and the plausibility illusion of high-immersion virtual reality (VR) impacted students' public speaking anxiety when presenting in a foreign language. In the study, the students gave eight presentations in a VR classroom while using a high-immersion VR headset. The students' virtual audience resembled classmates who were programmed to show nonverbal behavior, such as gestures, mimicry, and body motion. Analysis of subsequent individual semi-structured interviews with the students showed that they experienced a sense of presence and plausibility illusion about the virtual audience and the virtual space. The participants also saw VR as an effective tool for practicing public speaking and reducing any attendant anxiety.
Optical Measurement of Highly Reflective Surfaces From a Single Exposure

Three-dimensional structured light (SL) measurement of highly reflective surface is a challenge faced in industrial metrology. The high dynamic range (HDR) technique provides a solution by fusing images under multiple exposures; however, the process is highly time-consuming. This article reports a new SL-based method to measure parts with highly reflective surfaces from only a single exposure. A new quantitative metric is defined to optimally select camera exposure for capturing input single-exposure images. Different from existing image gradient or entropy-based metrics, the new metric incorporates both intensity modulation and overexposure. A skip pyramid context aggregation network (SP-CAN) is proposed to enhance the single exposure-captured images. Compared with existing image enhancement methods, SP-CAN effectively preserves detailed encoded phase information near edges and corners during enhancement. Experiments with various industrial parts demonstrated that the average time cost of the proposed method was 0.6 s, which was only one tenth of the HDR method (ten exposures), and the two methods achieved similar coverage rates (97.6% versus 98.0%) and measurement accuracy (0.040 mm versus 0.038 mm).