Reviewerly/exHarmony huggingface dataset:
Learning to Predict Salient Faces: A Novel Visual-Audio Saliency Model

Recently, video streams have occupied a large proportion of Internet traffic, most of which contain human faces. Hence, it is necessary to predict saliency on multiple-face videos, which can provide attention cues for many content based applications. However, most of multiple-face saliency prediction works only consider visual information and ignore audio, which is not consistent with the naturalistic scenarios. Several behavioral studies have established that sound influences human attention, especially during the speech turn-taking in multiple-face videos. In this paper, we thoroughly investigate such influences by establishing a large-scale eye-tracking database of Multiple-face Video in Visual-Audio condition (MVVA). Inspired by the findings of our investigation, we propose a novel multi-modal video saliency model consisting of three branches: visual, audio and face. The visual branch takes the RGB frames as the input and encodes them into visual feature maps. The audio and face branches encode the audio signal and multiple cropped faces, respectively. A fusion module is introduced to integrate the information from three modalities, and to generate the final saliency map. Experimental results show that the proposed method outperforms 11 state-of-the-art saliency prediction works. It performs closer to human multi-modal attention.
Generating Artificial Outliers in the Absence of Genuine Ones — A Survey

By definition, outliers are rarely observed in reality, making them difficult to detect or analyze. Artificial outliers approximate such genuine outliers and can, for instance, help with the detection of genuine outliers or with benchmarking outlier-detection algorithms. The literature features different approaches to generate artificial outliers. However, systematic comparison of these approaches remains absent. This surveys and compares these approaches. We start by clarifying the terminology in the field, which varies from publication to publication, and we propose a general problem formulation. Our description of the connection of generating outliers to other research fields like experimental design or generative models frames the field of artificial outliers. Along with offering a concise description, we group the approaches by their general concepts and how they make use of genuine instances. An extensive experimental study reveals the differences between the generation approaches when ultimately being used for outlier detection. This survey shows that the existing approaches already cover a wide range of concepts underlying the generation, but also that the field still has potential for further development. Our experimental study does confirm the expectation that the quality of the generation approaches varies widely, for example, in terms of the dataset they are used on. Ultimately, to guide the choice of the generation approach in a specific context, we propose an appropriate general-decision process. In summary, this survey comprises, describes, and connects all relevant work regarding the generation of artificial outliers and may serve as a basis to guide further research in the field.
Color Image Self-Recovery and Tampering Detection Scheme Based on Fragile Watermarking with High Recovery Capability

In this paper, a fragile watermarking scheme for color image authentication and self-recovery with high tampering rates is proposed. The original image is sub-sampled and divided into non-overlapping blocks, where a watermark used for recovery purposes is generated for each one of them. Additionally, for each recovery watermark, the bitwise exclusive OR (XOR) operation is applied to obtain a single bit for the block authentication procedure. The embedding and extraction process can be implemented in three variants (1-LSB, 2-LSB or 3-LSB) to solve the tampering coincidence problem (TCP). Three, six or nine copies of the generated watermarks can be embedded according to the variant process. Additionally, the embedding stage is implemented in a bit adjustment phase, increasing the watermarked image quality. A particular procedure is applied during a post-processing step to detect the regions affected by the TCP in each recovery watermark, where a single faithful image used for recovery is generated. In addition, we involve an inpainting algorithm to fill the blocks that have been tampered with, significantly increasing the recovery image quality. Simulation results show that the proposed framework demonstrates higher quality for the watermarked images and an efficient ability to reconstruct tampered image regions with extremely high rates (up to 90%). The novel self-recovery scheme has confirmed superior performance in reconstructing altered image regions in terms of objective criteria values and subjective visual perception via the human visual system against other state-of-the-art approaches.
Deterministic spatial search using alternating quantum walks

This paper examines the performance of spatial search where the Grover diffusion operator is replaced by continuous-time quantum walks on a class of interdependent networks. We prove that for a set of optimal quantum walk times and marked vertex phase shifts, a deterministic algorithm for structured spatial search is established that finds the marked vertex with 100% probability. This improves on the original spatial search algorithm on the same class of graphs, which we show can only amplify to 50% probability. Our method uses $\left\lceil\frac{\pi}{2\sqrt{2}}\sqrt{N}\right\rceil$ marked vertex phase shifts for an $N$-vertex graph, making it comparable with Grover's algorithm for unstructured search. It is expected that this new framework can be readily extended to deterministic spatial search on other families of graph structures.
Multitask Recalibrated Aggregation Network for Medical Code Prediction

Abstract Medical coding translates professionally written medical reports into standardized codes, which is an essential part of medical information systems and health insurance reimbursement. Manual coding by trained human coders is time-consuming and error-prone. Thus, automated coding algorithms have been developed, building especially on the recent advances in machine learning and deep neural networks. To solve the challenges of encoding lengthy and noisy clinical documents and capturing code associations, we propose a multitask recalibrated aggregation network. In particular, multitask learning shares information across different coding schemes and captures the dependencies between different medical codes. Feature recalibration and aggregation in shared modules enhance representation learning for lengthy notes. Experiments with a real-world MIMIC-III dataset show significantly improved predictive performance.
Improving Encryption Digital Watermark by Using Blue Monkey Algorithm

Watermarking enables the users to share the digital contents in public domain without any issue. In the present day, the tremendous development in the digital technologies and networks caused an increase in the threats of unauthorized copying, tampering of digital media and image theft. To face these threats, digital watermark technology can be applied. However, the current paper uses new technique with two main algorithms that are the following ones: improved honey algorithm that is used to encrypt the digital watermark and blue monkey meta-heuristic algorithm which is used to find the best location in the host image to hide the digital watermark. Furthermore, in order to check the security and the robustness of the proposed method against various common image processing attacks such as Gaussian noise, Rotation, Salt and pepper noise, Sharpen, Median filter, Average filter, compression and Cropping is computed, certain performance metrics such as Peak to Signal Noise Ratio (PSNR)and Mean Square Error (MSE) also are computed. Likewise, Normalized Correlation (NC) is used to check similarity between the original and extracted digital watermark. The results demonstrate that the proposed method is efficient, provides high security and robustness against most attacks compared to the pervious methods.
Investigating reading comprehension strategies used by teachers during English First Additional Language reading instruction

Since the purpose of reading is comprehension, the major goal of reading comprehension instruction is to help learners develop knowledge, skills, and strategies so that they become strategic readers who read for comprehension. Language teachers use reading comprehension strategies as an instruction tool to assist learners to develop into strategic readers. However, the range of strategies used by teachers is crucial. This paper presents classroom observations of reading comprehension strategy instruction used by four purposively selected English First Additional Language (FAL) teachers. The study was qualitative in nature and a case study design was chosen. The findings of the study reveal that teachers did not provide opportunity to support learners’ independent comprehension strategy use. Furthermore, the study has disclosed that the teachers’ inability to engage learners in reading comprehension strategies might signify the teachers’ lack of knowledge of how to incorporate reading comprehension strategies as an instructional tool during reading comprehension lessons. The authors recommend that the Department of Basic Education institute interventions to empower teachers on how to teach reading comprehension strategies.
Virtual Machine Placement Using Energy Efficient Particle Swarm Optimization in Cloud Datacenter

Abstract Efficient resource allocation through Virtual machine placement in a cloud datacenter is an ever-growing demand. Different Virtual Machine optimization techniques are constructed for different optimization problems. Particle Swam Optimization (PSO) Algorithm is one of the optimization techniques to solve the multidimensional virtual machine placement problem. In the algorithm being proposed we use the combination of Modified First Fit Decreasing Algorithm (MFFD) with Particle Swarm Optimization Algorithm, used to solve the best Virtual Machine packing in active Physical Machines to reduce energy consumption; we first screen all Physical Machines for possible accommodation in each Physical Machine and then the Modified Particle Swam Optimization (MPSO) Algorithm is used to get the best fit solution.. In our paper, we discuss how to improve the efficiency of Particle Swarm Intelligence by adapting the efficient mechanism being proposed. The obtained result shows that the proposed algorithm provides an optimized solution compared to the existing algorithms.
Topological classification of time-asymmetry in unitary quantum processes

Understanding which physical processes are symmetric with respect to time inversion is a ubiquitous problem in physics. In quantum physics, effective gauge fields allow emulation of matter under strong magnetic fields, realizing the Harper-Hofstadter, the Haldane models, demonstrating one-way waveguides and topologically protected edge states. Central to these discoveries is the chirality induced by time-symmetry breaking. In quantum walk algorithms, recent work has discovered implications time-reversal symmetry breaking has on the transport of quantum states which has enabled a host of new experimental implementations. We provide a full topological classification of the Hamiltonians of operators breaking time-reversal symmetry in their induced transition probabilities between elements in a preferred site-basis. We prove that a quantum process is necessarily time-symmetric for any choice of time-independent Hamiltonian precisely when the underlying support graph is bipartite or no Aharonov-Bohm phases are present in the gauge field. We further prove that certain bipartite graphs exhibit transition probability suppression, but not broken time-reversal symmetry. Furthermore, our development of a general framework characterizes gauge potentials on combinatorial graphs. These results and techniques fill an important missing gap in understanding the role this omnipresent effect has in quantum information and computation.
Complex-Valued Reservoir Computing for Interferometric SAR Applications With Low Computational Cost and High Resolution

Synthetic aperture radar (SAR) is widely used for ground surface classification since it utilizes information on vegetation and soil unavailable in optical observation. Image classification often employs convolutional neural networks. However, they have serious problems such as long learning time and resolution degradation in their convolution and pooling processes. In this article, we propose complex-valued reservoir computing (CVRC) to deal with complex-valued images in interferometric SAR (InSAR). We classify InSAR image data by using CVRC successfully with a higher resolution and a lower computational cost, i.e., one-hundredth learning time and one-fifth classification time, than convolutional neural networks. We also conduct experiments on slope angle estimation. CVRC is found applicable to quantitative tasks dealing with continuous values as well as discrete classification tasks with a higher accuracy.
Breast Mass Detection With Faster R-CNN: On the Feasibility of Learning From Noisy Annotations

In this work we study the impact of noise on the training of object detection networks for the medical domain, and how it can be mitigated by improving the training procedure. Annotating large medical datasets for training data-hungry deep learning models is expensive and time consuming. Leveraging information that is already collected in clinical practice, in the form of text reports, bookmarks or lesion measurements would substantially reduce this cost. Obtaining precise lesion bounding boxes through automatic mining procedures, however, is difficult. We provide here a quantitative evaluation of the effect of bounding box coordinate noise on the performance of Faster R-CNN object detection networks for breast mass detection. Varying degrees of noise are simulated by randomly modifying the bounding boxes: in our experiments, bounding boxes could be enlarged up to six times the original size. The noise is injected in the CBIS-DDSM collection, a well curated public mammography dataset for which accurate lesion location is available. We show how, due to an imperfect matching between the ground truth and the network bounding box proposals, the noise is propagated during training and reduces the ability of the network to correctly classify lesions from background. When using the standard Intersection over Union criterion, the area under the FROC curve decreases by up to 9%. A novel matching criterion is proposed to improve tolerance to noise.
On the capability of a class of quantum sensors

Quantum sensors may provide extremely high sensitivity and precision to extract key information in a quantum or classical physical system.A fundamental question is whether a quantum sensor is capable of uniquely inferring unknown parameters in a system for a given structure of the quantum sensor and admissible measurement on the sensor.In this paper, we investigate the capability of a class of quantum sensors which consist of either a single qubit or two qubits.A quantum sensor is coupled to a spin chain system to extract information of unknown parameters in the system.With given initialisation and measurement schemes, we employ the similarity transformation approach and the Gröbner basis method to prove that a single-qubit quantum sensor cannot effectively estimate the unknown parameters in the spin chain system while the two-qubit quantum sensor can.The work demonstrates that it is a feasible method to enhance the capability of quantum sensors by increasing the number of qubits in the quantum sensors for some practical applications.
LIGA: a cryptosystem based on the hardness of rank-metric list and interleaved decoding

Abstract We propose the new rank-metric code-based cryptosystem which is based on the hardness of l ist decoding and i nterleaved decoding of Ga bidulin codes. is an improved variant of the Faure–Loidreau (FL) system, which was broken in a structural attack by Gaborit, Otmani, and Talé Kalachi (GOT, 2018). We keep the FL encryption and decryption algorithms, but modify the insecure key generation algorithm. Our crucial observation is that the GOT attack is equivalent to decoding an interleaved Gabidulin code. The new key generation algorithm constructs public keys for which all polynomial-time interleaved decoders fail—hence resists the GOT attack. We also prove that the public-key encryption version of is IND-CPA secure in the standard model and the key encapsulation mechanisms version is IND-CCA2 secure in the random oracle model, both under hardness assumptions of formally defined problems related to list decoding and interleaved decoding of Gabidulin codes. We propose and analyze various exponential-time attacks on these problems, calculate their work factors, and compare the resulting parameters to NIST proposals. The strengths of are short ciphertext sizes and (relatively) small key sizes. Further, guarantees correct decryption and has no decryption failure rate. It is not based on hiding the structure of a code. Since there are efficient and constant-time algorithms for encoding and decoding Gabidulin codes, timing attacks on the encryption and decryption algorithms can be easily prevented.
Recurrent residual U-Net with EfficientNet encoder for medical image segmentation

In this paper, we propose a U-net architecture that integrates a residual skip connections and recurrent feedback with EfficientNet as a pretrained encoder. Residual connections help feature propagation in deep neural networks and significantly improve performance against networks with a similar number of parameters while recurrent connections ameliorate gradient learning. We also propose a second model that utilizes densely connected layers aiding deeper neural networks. EfficientNet is a family of powerful pretrained encoders that streamline neural network design. The proposed networks are evaluated against state-of-the-art deep learning based segmentation techniques to demonstrate their superior performance.
Evaluating Virtual Human Role-Players for the Practice and Development of Leadership Skills

This article reports on a study to evaluate the effectiveness of virtual human (VH) role-players as leadership training tools within two computer-generated environments, virtual reality (VR) and mixed reality (MR), compared to a traditional training method, real human (RH) role-players in a real-world (RW) environment. We developed an experimental training platform to assess the three conditions: RH role-players in RW (RH-RW), VH role-players in VR (VH-VR), and VH role-players in MR (VH-MR), during two practice-type opportunities, namely pre-session and post-session. We conducted a user study where 30 participants played the role of leaders in interacting with either RHs or VHs before and after receiving a leadership training session. We then investigated (1) if VH role-players were as effective as RH role-players during pre- and post-sessions, and (2) the impact that the human-type (RH, VH) in conjunction with the environment-type (RW, VR, MR) had on the outcomes. We also collected user reactions and learning data from the overall training experience. The results showed a regular increase in performance from pre- to post-sessions in all three conditions. However, we did not find a significant difference between VHs and RHs. Interestingly, the VH-MR condition had a more significant influence on performance and task engagement compared to the VH-VR and RH-RW conditions. Based on our findings, we conclude that VH role-players can be as effective as RH role-players to support the practice of leadership skills, where VH-MR could be the best method due to its effectiveness.
Discourse Understanding and Factual Consistency in Abstractive Summarization

We introduce a general framework for abstractive summarization with factual consistency and distinct modeling of the narrative flow in an output summary.Our work addresses current limitations of models for abstractive summarization that often hallucinate information or generate summaries with coherence issues.To generate abstractive summaries with factual consistency and narrative flow, we propose Cooperative Generator -Discriminator Networks (Co-opNet), a novel transformerbased framework where a generator works with a discriminator architecture to compose coherent long-form summaries.We explore four different discriminator objectives which each capture a different aspect of coherence, including whether salient spans of generated abstracts are hallucinated or appear in the input context, and the likelihood of sentence adjacency in generated abstracts.We measure the ability of Co-opNet to learn these objectives with arXiv scientific papers, using the abstracts as a proxy for gold longform scientific article summaries.Empirical results from automatic and human evaluations demonstrate that Co-opNet learns to summarize with considerably improved global coherence compared to competitive baselines. 1The labels are {BACKGROUND, METHOD, OBJEC-TIVE, RESULT, OTHER}.2 See (Cohan et al., 2019) for model and training details.
Cost-based Energy Efficient Scheduling Technique for Dynamic Voltage and Frequency Scaling System in cloud computing

Cloud computing is used as a backbone infrastructure to meet exponentially increasing computational and storage demands. This increase in service demands in smart cities will result in escalating energy consumption in cloud datacenters. Such a rise in energy consumption will result in upsurge of operational costs and emission of greenhouse gases. In this work, a green cloud computing algorithm named "Cost-based Energy Efficient Scheduling Technique for Dynamic Volage Frequency Scaling (DVFS) Systems (CEEST)" is proposed. The proposed algorithm reduces energy consumption without compromising the quality of service (QoS). The goal of this algorithm is optimization and management of servers in the datacenters by utilizing maximum resources of the servers and powering off the underutilized servers. CEEST utilizes the scaling of virtual machines to finish jobs in the deadlines to reduce violations of service level agreement (SLA). Simulation results prove that the proposed algorithm outperforms the existing algorithms in terms of execution time, energy consumption, resource utilization, and SLA violations. The proposed algorithm saves energy up to 30% in comparison to existing algorithms. The utilization of resources is also significantly increased by to 30%. In terms of SLA violations, the proposed algorithm reduced SLA violations up to 50%.