Reviewerly/exHarmony huggingface dataset:
A Modulo Function-Based Robust Asymmetric Variable Data Hiding Using DCT

This work presents a new asymmetric data hiding technique that hides a variable number of secret message bits in the discrete cosine transform (DCT) coefficients of a cover image using a modular distance technique. Prior to data hiding, the proposed framework transforms a cover image from a spatial domain to various frequency coefficients using DCT. The DCT coefficients are arranged in two groups: one with low-frequency coefficient, and the other with the medium and high-frequency coefficients. The medium and higher frequency coefficients are processed for variable data hiding asymmetrically. The proposed technique hides variable sets of secret information bits in different coefficients. The variation in hidden secret information is maintained using a key developed based on the modulo of distance of a coefficient from the reference point. The same key is also used to retrieve the confidential information at the receiver ends. The results reveal that the presented framework does not create any visually significant distortion, and thus the hidden information does not attract the human visual system (HVS). The technique also results in high data hiding efficiency.
Online multimedia retrieval on CPU–GPU platforms with adaptive work partition

Nearest neighbors search is a core operation found in several online multimedia services. These services have to handle very large databases, while, at the same time, they must minimize the query response times observed by users. This is specially complex because those services deal with fluctuating query workloads (rates). Consequently, they must adapt at run-time to minimize the response times as the load varies. In this paper, we address the aforementioned challenges with a distributed memory parallelization of the product quantization nearest neighbor search, also known as IVFADC, for hybrid CPU–GPU machines. Our parallel IVFADC implements an out-of-GPU memory execution scheme to use the GPU for databases in which the index does not fit in its memory, which is crucial for searching in very large databases. The careful use of CPU and GPU with work stealing led to an average response time reduction of 2.4× as compared to using the GPU only. Also, our approach to adapt the system to fluctuating loads, called Dynamic Query Processing Policy (DQPP), attained a response time reduction of up to 5× vs. the best static (BS) policy for moderate loads. The system has attained high query processing rates and near-linear scalability in all experiments. We have evaluated our system on a machine with up to 256 NVIDIA V100 GPUs processing a database of 256 billion SIFT features vectors.
Video Game Level Repair via Mixed Integer Linear Programming

Recent advancements in procedural content generation via machine learning enable the generation of video-game levels that are aesthetically similar to human-authored examples. However, the generated levels are often unplayable without additional editing. We propose a “generate-then-repair” framework for automatic generation of playable levels adhering to specific styles. The framework constructs levels using a generative adversarial network (GAN) trained with human-authored examples and repairs them using a mixed-integer linear program (MIP) with playability constraints. A key component of the framework is computing minimum cost edits between the GAN generated level and the solution of the MIP solver, which we cast as a minimum cost network flow problem. Results show that the proposed framework generates a diverse range of playable levels, that capture the spatial relationships between objects exhibited in the human-authored levels.
Fast Distributed Training of Deep Neural Networks: Dynamic Communication Thresholding for Model and Data Parallelism.

Data Parallelism (DP) and Model Parallelism (MP) are two common paradigms to enable large-scale distributed training of neural networks. Recent trends, such as the improved model performance of deeper and wider neural networks when trained with billions of data points, have prompted the use of hybrid parallelism---a paradigm that employs both DP and MP to scale further parallelization for machine learning. Hybrid training allows compute power to increase, but it runs up against the key bottleneck of communication overhead that hinders scalability. In this paper, we propose a compression framework called Dynamic Communication Thresholding (DCT) for communication-efficient hybrid training. DCT filters the entities to be communicated across the network through a simple hard-thresholding function, allowing only the most relevant information to pass through. For communication efficient DP, DCT compresses the parameter gradients sent to the parameter server during model synchronization, while compensating for the introduced errors with known techniques. For communication efficient MP, DCT incorporates a novel technique to compress the activations and gradients sent across the network during the forward and backward propagation, respectively. This is done by identifying and updating only the most relevant neurons of the neural network for each training sample in the data. Under modest assumptions, we show that the convergence of training is maintained with DCT. We evaluate DCT on natural language processing and recommender system models. DCT reduces overall communication by 20x, improving end-to-end training time on industry scale models by 37%. Moreover, we observe an improvement in the trained model performance, as the induced sparsity is possibly acting as an implicit sparsity based regularization.
New Framework for FCMs Using Dual Hesitant Fuzzy Sets with an Analysis of Risk Factors in Emergency Event

A B S T R A C TAs a kind of soft computing tool with strong knowledge representation and causal reasoning ability, fuzzy cognitive maps (FCMs) is a product of fuzzy logic and neural network.A limitation of the current FCMs method is its inability to model the uncertainty that is introduced into a complex system due to the hesitancy of people.Dual hesitant fuzzy sets (DHFSs), which considers the membership and nonmembership degrees by a set of possible values respectively, is an effective tool to model the hesitancy and epistemic uncertainty.Thus, a novel extension of FCMs model called dual hesitant fuzzy cognitive maps (DHFCMs) is proposed in this paper.Firstly, motivated by the idea of Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) method, a new similarity measure based on dual hesitant fuzzy distance measure is put forward, and its properties are also discussed.Then, detailed procedure and algorithm for DHFCMs are specified.Moreover, the application steps of the proposed method are provided.Finally, a case study on the huge explosion at Tianjin Port in China in 2015 is given to illustrate the rationality and effectiveness of the proposed method.
Smart Self-Checkout Carts Based on Deep Learning for Shopping Activity Recognition

Fast and reliable communication plays a major role in the success of smart shopping applications. In a “Just Walk Out” shopping scenario, a video camera is installed on the cart to monitor shopping activities and transmit images to the cloud for processing so that items in the cart can be tracked and checked out. This paper proposes a prototype of a smart shopping cart based on image-based action recognition. Firstly, deep learning networks such as Faster R-CNN, YOLOv2, and YOLOv2-Tiny are utilized to analyze the content of each video frame. Frames are classified into three classes: No Hand, Empty Hand, and Holding Items. The classification accuracy based on Faster R-CNN, YOLOv2, or YOLOv2-Tiny is between 93.0% and 90.3%, and the processing speed of the three networks can be up to 5 fps, 39 fps, and 50 fps, respectively. Secondly, based on the sequence of frame classes, the timeline is divided into No Hand intervals, Empty Hand intervals, and Holding Items intervals. The accuracy of action recognition is 96%, and the time error is 0.119s on average. Finally, we categorize the events into four cases: No Change, placing, Removing, and Swapping. Even including the correctness of the item recognition, the accuracy of shopping event detection is 97.9%, which is higher than the minimal requirement to deploy such a system in a smart shopping environment. A demo of the system and a link to download the data set used in the paper are in Smart Shopping Cart Prototype or found at this URL: https://hackmd.io/abEiC83rQoqxz7zpL4Kh2w.
Upaya Peningkatan Motivasi dan Hasil Belajar Mahasiswa dengan Penerapan Model Pembelajaran E–Learning Berbasis Google Classroom pada Masa Pandemi Covid-19

The purpose of this research is to improve students’ motivation and learning outcomes by applying the Google classroom-based E-Learning model in the Satya Wacana Christian University Economic Education Study Program. The research method used was a classroom action research with the application of 2 cycles. The research subjects were 17 students who took Basic Accounting courses and research objects that took place during the learning process. The data analysis technique used was quantitative descriptive with percentages used to measure the level of student motivation, and for learning outcomes an evaluation was conducted at the end of each lesson. Classroom action research results obtained by implementing Google Classroom could improve students’ motivation and learning outcomes. This improvement could be proved from the implementation of actions starting from pre-action, cycle I, and cycle II. Student learning motivation was said to succeed with an average of 75-100%. The results of the study obtained in the first cycle of action amounted to 74.11% and 80.11% in the second cycle. While the percentage of student learning outcomes had also increased. The increase occurred in pre-action from 50.75 to 73.53 in which the first cycle increased by 22.77, and from 73.53 to 87.35 in the second cycle meaning that an increase amounted to13.82.
An Intelligent Forensics Approach for Detecting Patch-Based Image Inpainting

Image inpainting algorithms have a wide range of applications, which can be used for object removal in digital images. With the development of semantic level image inpainting technology, this brings great challenges to blind image forensics. In this case, many conventional methods have been proposed which have disadvantages such as high time complexity and low robustness to postprocessing operations. Therefore, this paper proposes a mask regional convolutional neural network (Mask R-CNN) approach for patch-based inpainting detection. According to the current research, many deep learning methods have shown the capacity for segmentation tasks when labeled datasets are available, so we apply a deep neural network to the domain of inpainting forensics. This deep learning model can distinguish and obtain different features between the inpainted and noninpainted regions. To reduce the missed detection areas and improve detection accuracy, we also adjust the sizes of the anchor scales due to the inpainting images and replace the original nonmaximum suppression single threshold with an improved nonmaximum suppression (NMS). The experimental results demonstrate this intelligent method has better detection performance over recent approaches of image inpainting forensics.
DAIS: Automatic Channel Pruning via Differentiable Annealing Indicator Search

The convolutional neural network (CNN) has achieved great success in fulfilling computer vision tasks despite large computation overhead against efficient deployment. Channel pruning is usually applied to reduce the model redundancy while preserving the network structure, such that the pruned network can be easily deployed in practice. However, existing channel pruning methods require hand-crafted rules, which can result in a degraded model performance with respect to the tremendous potential pruning space given large neural networks. In this article, we introduce differentiable annealing indicator search (DAIS) that leverages the strength of neural architecture search in the channel pruning and automatically searches for the effective pruned model with given constraints on computation overhead. Specifically, DAIS relaxes the binarized channel indicators to be continuous and then jointly learns both indicators and model parameters via bi-level optimization. To bridge the non-negligible discrepancy between the continuous model and the target binarized model, DAIS proposes an annealing-based procedure to steer the indicator convergence toward binarized states. Moreover, DAIS designs various regularizations based on a priori structural knowledge to control the pruning sparsity and to improve model performance. Experimental results show that DAIS outperforms state-of-the-art pruning methods on CIFAR-10, CIFAR-100, and ImageNet.
Who, Where, and What to Wear?

Fashion knowledge helps people to dress properly and addresses not only physiological needs of users, but also the demands of social activities and conventions. It usually involves three mutually related aspects of: occasion, person and clothing. However, there are few works focusing on extracting such knowledge, which will greatly benefit many downstream applications, such as fashion recommendation. In this paper, we propose a novel method to automatically harvest fashion knowledge from social media. We unify three tasks of occasion, person and clothing discovery from multiple modalities of images, texts and metadata. For person detection and analysis, we use the off-the-shelf tools due to their flexibility and satisfactory performance. For clothing recognition and occasion prediction, we unify the two tasks by using a contextualized fashion concept learning module, which captures the dependencies and correlations among different fashion concepts. To alleviate the heavy burden of human annotations, we introduce a weak label modeling module which can effectively exploit machine-labeled data, a complementary of clean data. In experiments, we contribute a benchmark dataset and conduct extensive experiments from both quantitative and qualitative perspectives. The results demonstrate the effectiveness of our model in fashion concept prediction, and the usefulness of extracted knowledge with comprehensive analysis.
Simple Hierarchical Multi-Task Neural End-To-End Entity Linking for Biomedical Text

Recognising and linking entities is a crucial first step to many tasks in biomedical text analysis, such as relation extraction and target identification.Traditionally, biomedical entity linking methods rely heavily on heuristic rules and predefined, often domain-specific features.The features try to capture the properties of entities and complex multi-step architectures to detect, and subsequently link entity mentions.We propose a significant simplification to the biomedical entity linking setup that does not rely on any heuristic methods.The system performs all the steps of the entity linking task jointly in either single or two stages.We explore the use of hierarchical multi-task learning, using mention recognition and entity typing tasks as auxiliary tasks.We show that hierarchical multi-task models consistently outperform single-task models when trained tasks are homogeneous.We evaluate the performance of our models on the biomedical entity linking benchmarks using MedMentions and BC5CDR datasets.We achieve state-of-theart results on the challenging MedMentions dataset, and comparable results on BC5CDR.
KoBE: Knowledge-Based Machine Translation Evaluation

We propose a simple and effective method for machine translation evaluation which does not require reference translations.Our approach is based on (1) grounding the entity mentions found in each source sentence and candidate translation against a large-scale multilingual knowledge base, and (2) measuring the recall of the grounded entities found in the candidate vs. those found in the source.Our approach achieves the highest correlation with human judgements on 9 out of the 18 language pairs from the WMT19 benchmark for evaluation without references, which is the largest number of wins for a single evaluation method on this task.On 4 language pairs, we also achieve higher correlation with human judgements than BLEU.To foster further research, we release a dataset containing 1.8 million grounded entity mentions across 18 language pairs from the WMT19 metrics track data.
One-Class-Based Intelligent Classifier for Detecting Anomalous Situations During the Anesthetic Process

Abstract Closed-loop administration of propofol for the control of hypnosis in anesthesia has evidenced an outperformance when comparing it with manual administration in terms of drug consumption and post-operative recovery of patients. Unlike other systems, the success of this strategy lies on the availability of a feedback variable capable of quantifying the current hypnotic state of the patient. However, the appearance of anomalies during the anesthetic process may result in inaccurate actions of the automatic controller. These anomalies may come from the monitors, the syringe pumps, the actions of the surgeon or even from alterations in patients. This could produce adverse side effects that can affect the patient postoperative and reduce the safety of the patient in the operating room. Then, the use of anomaly detection techniques plays a significant role to avoid this undesirable situations. This work assesses different one-class intelligent techniques to detect anomalies in patients undergoing general anesthesia. Due to the difficulty of obtaining real data from anomaly situations, artificial outliers are generated to check the performance of each classifier. The final model presents successful performance.
Probing exciton/exciton interactions with entangled photons: Theory

Quantum entangled photons provide a sensitive probe of many-body interactions and offer a unique experimental portal for quantifying many-body correlations in a material system. In this paper, we present a theoretical demonstration of how photon-photon entanglement can be generated via interactions between coupled qubits. Here, we develop a model for the scattering of an entangled pair of photons from a molecular dimer. We develop a diagrammatic theory for the scattering matrix and show that one can correlate the von Neumann entropy of the outgoing bi-photon wave function with exciton exchange and repulsion interactions. We conclude by discussing possible experimental scenarios for realizing these ideas.
Fixpoints for the masses: programming with first-class Datalog constraints

Datalog is a declarative logic programming language that has been used in a variety of applications, including big-data analytics, language processing, networking and distributed systems, and program analysis. In this paper, we propose first-class Datalog constraints as a mechanism to construct, compose, and solve Datalog programs at run time. The benefits are twofold: We gain the full power of a functional programming language to operate on Datalog constraints-as-values, while simultaneously we can use Datalog where it really shines: to declaratively express and solve fixpoint problems. We present an extension of the lambda calculus with first-class Datalog constraints, including its semantics and a type system with row polymorphism based on Hindley-Milner. We prove soundness of the type system and implement it as an extension of the Flix programming language.
On the Computational Power of Transformers and Its Implications in Sequence Modeling

Transformers are being used extensively across several sequence modeling tasks.Significant research effort has been devoted to experimentally probe the inner workings of Transformers.However, our conceptual and theoretical understanding of their power and inherent limitations is still nascent.In particular, the roles of various components in Transformers such as positional encodings, attention heads, residual connections, and feedforward networks, are not clear.In this paper, we take a step towards answering these questions.We analyze the computational power as captured by Turing-completeness.We first provide an alternate and simpler proof to show that vanilla Transformers are Turing-complete and then we prove that Transformers with only positional masking and without any positional encoding are also Turing-complete.We further analyze the necessity of each component for the Turing-completeness of the network; interestingly, we find that a particular type of residual connection is necessary.We demonstrate the practical implications of our results via experiments on machine translation and synthetic tasks.
Controllable Meaning Representation to Text Generation: Linearization and Data Augmentation Strategies

We study the degree to which neural sequenceto-sequence models exhibit fine-grained controllability when performing natural language generation from a meaning representation.Using two task-oriented dialogue generation benchmarks, we systematically compare the effect of four input linearization strategies on controllability and faithfulness.Additionally, we evaluate how a phrase-based data augmentation method can improve performance.We find that properly aligning input sequences during training leads to highly controllable generation, both when training from scratch or when fine-tuning a larger pre-trained model.Data augmentation further improves control on difficult, randomly generated utterance plans.
CryptoNAS: Private inference on a ReLU budget

Machine learning as a service has given raise to privacy concerns surrounding clients' data and providers' models and has catalyzed research in private inference (PI): methods to process inferences without disclosing inputs. Recently, researchers have adapted cryptographic techniques to show PI is possible, however all solutions increase inference latency beyond practical limits. This paper makes the observation that existing models are ill-suited for PI and proposes a novel NAS method, named CryptoNAS, for finding and tailoring models to the needs of PI. The key insight is that in PI operator latency cost are non-linear operations (e.g., ReLU) dominate latency, while linear layers become effectively free. We develop the idea of a ReLU budget as a proxy for inference latency and use CryptoNAS to build models that maximize accuracy within a given budget. CryptoNAS improves accuracy by 3.4% and latency by 2.4x over the state-of-the-art.
“What Do You Mean by That?” A Parser-Independent Interactive Approach for Enhancing Text-to-SQL

In Natural Language Interfaces to Databases systems, the text-to-SQL technique allows users to query databases by using natural language questions. Though significant progress in this area has been made recently, most parsers may fall short when they are deployed in real systems. One main reason stems from the difficulty of fully understanding the users’ natural language questions. In this paper, we include human in the loop and present a novel parser-independent interactive approach (PIIA) that interacts with users using multi-choice questions and can easily work with arbitrary parsers. Experiments were conducted on two cross-domain datasets, the WikiSQL and the more complex Spider, with five state-of-the-art parsers. These demonstrated that PIIA is capable of enhancing the text-to-SQL performance with limited interaction turns by using both simulation and human evaluation.
Dual Quantum Zeno Superdense Coding

Abstract Quantum superdense coding enables a sender to encode a two-bit classical message in one qubit using the preshared entanglement. In this paper, we develop a superdense coding protocol using a dual quantum Zeno (DQZ) gate to take the full advantage of quantum superdense coding from the complete Bell-state analysis. We verify that the DQZ gate allows remote parties to achieve the distinguishability of orthonormal Bell states in a semi-counterfactual manner and the DQZ superdense coding achieves high throughput efficiency as a function of cycle numbers for the Bell-state analyzer.