Reviewerly/exHarmony huggingface dataset:
Pruning Long-term Recurrent Convolutional Networks for Video Classification and captioning

Images, videos and speech are three frequent data fields found everywhere. Nearly 6 billion YouTube videos are watched daily. Video classification is an emerging application with few works proposed and indulging a lot of challenges. Video is a collection of frames (images). Simple algorithms utterly fail. Long-term recurrent convolutional networks (LRCN) are apt for video classification tasks as they are capable of capturing spatial and temporal behaviour. Though this network achieved significant accuracy, it passes every frame of a video through a CNN and LSTM which makes it computationally challenging. The time taken for training or testing a video is too high as CNN has to process several frames. So, in our work, this long-term recurrent convolutional network have been pruned. Several pruning techniques have been analysed. Here by providing metric evidence claimed that our pruned LRCN became 2x times faster with just a drop of 4% accuracy after pruning 50% of filters. The time is taken to classify test videos gradually decreased by 40% when Resnet101 is considered. In the video captioning task, Usage of simple CNN only yielded 19.3 BLE-U4 scores whereas pruned RSNET-101 produced 26.3 BLEU-4 Score. So, our work suggests to use pruned deep CNNS as an encoder instead of Custom-built simple CNNS as processing time of both networks is made equal. The parallel computing of frames is not considered by the user has been assumed.
Chinese Copyright Law and Computer-Generated Works in the Era of Artificial Intelligence

Artificial intelligence (AI) technology has been widely applied by innovative industries with a large number of outputs indistinguishable from human works. These outputs raise debates on the legal status of computer-generated works (CGWs) under copyright law, questions which are directly related to certain key issues of copyright law, including copyrightability and authorship. This article, based on the analysis of China’s copyright system, raises the question about whether the judgment criterion of the originality of computer-generated works should take into account the author’s personality and creativity. It argues that some existing specific rules of Chinese copyright law, such as “the work created in the course of employment” and “the work of legal person or entity”, allow for the separation of the authorship and ownership. Under the basic principle of “the author is the owner of copyright”, these rules value the importance of investors, which provides a possible approach to the protection of CGWs by the current copyright law in the future.
502 Bad Gateway: Rebooting Smart Contracts

Smart contracts, which were once theorised, are now somewhat realised, thanks to recent developments in distributed ledger technology. Yet, these self-executing agreements written in code are not a panacea to businesses’ and individuals’ contracting woes. It is argued that smart contracts worsen existing power asymmetries between contracting parties, thus, fallbacks must be provided for.
Recurrent Neural Network Channel Estimation Using Measured Massive MIMO Data

In this work, we develop a novel channel estimation method using recurrent neural networks (RNNs) for massive multiple-input multiple-output (MIMO) systems. The proposed framework alleviates the need for channel-state-information (CSI) feedback and pilot assignment through exploiting the inherent time and frequency correlations in practical propagation environments. We carry out the analysis using empirical MIMO channel measurements between a 64T64R active antenna system and a state-of-the-art multi-antenna scanner for both mobile and stationary use-cases. We also capture and analyze similar MIMO channel data from a legacy 2T2R base station (BS) for comparison purposes. Our findings confirm the applicability of utilising the proposed RNN-based massive MIMO channel acquisition scheme particularly for channels with long time coherence and hardening effects. In our practical setup, the proposed method reduced the number of pilots used by 25%.
Human-Robot Assembly: Methodical Design and Assessment of an Immersive Virtual Environment for Real-World Significance

Virtual reality is a powerful tool for industrial applications. The article at hand addresses designers of industrial virtual environments. It summarizes key aspects to design immersive and coherent virtual environments. Furthermore, relevant influencing factors for a high quality virtual environment and tools to quantify this quality are presented. So far, a methodology to design, evaluate, and transfer knowledge from virtual environments into reality has been missing and is of high value for industrial applications. The proposed methodical approach includes the steps application analysis, technology selection and integration, design of virtual environment, evaluation of simulator quality, as well as discussion of the real-world validity. The method is shown on the example of a virtual human-robot working cell used to analyze the human perception of robot behavior during mutual assembly processes. The quality of the virtual environment is evaluated to be adequate for those purposes and the transfer of knowledge gained in virtuality on a corresponding real-world application is discussed. To the best of our knowing a system like the presented one, including full-body tracking, finger tracking, a virtual avatar and a head-mounted display has not been used for industrial use cases and human-robot cooperation before.
EG^2N: enhanced gradient guiding network for single MR image super-resolution

During magnetic resonance imaging (MRI), the strong response to the signal is usually displayed as structural edges and textures, which is important for distinguishing different tissues and lesions. In the current superresolution (SR) methods with the usage of deep learning, some low-level structural information tends to gradually disappear as the network deepens, resulting in excessive smoothness in high-frequency regions. This phenomenon is particularly noticeable in MRI with poor brightness contrast and small gray dynamic range. Although the generative adversarial network (GAN) can repair structured textures well in natural images, it is likely to learn patterns that do not exist in the images, which poses risks to the reconstruction of medical images. Therefore, we propose an enhanced gradient guiding network (EG<sup>2</sup>N) to alleviate these problems. On the one hand, for improving the contrast and suppress the noise effectively, we use a multi-scale wavelet enhancement for preprocessing, where the enhanced gradient map is considered as the structural prior. On the other hand, blindly using dense connections in the feed-forward network will bring about redundancy, so structural features from an additional branch are added to specific layers as a supplement to high-level features and constrain optimization. We add a feedback mechanism to promote cross-layer flow between low-level and high-level features. In addition, the perceptual loss is added to avoid distortion caused by excessive smoothing. The experimental results show that our method achieves the best visual results and excellent performance compared with state-of-the-art methods on most popular MR images SR benchmarks.
An Analysis of the Applicability of VoiceXML as Basis for a Dialog Control Flow in Industrial Interaction Management

Industry 4.0 (I4.0) looks to enable intelligent production by connecting and evaluating data. The asset administration shell, the Industry 4.0 specification of a digital twin describes various concepts to realize this data exchange. One part of the asset administration shell is the I4.0-language, which intends to standardize complex interactions between machines by using interaction protocols. VoiceXML is a W3C-standard from the field of interactive voice response, which has been established for several years. In this paper we analyze to which extend a future I4.0 interaction manager could derive VoiceXML-concepts to the asset administration shell. For this purpose, parallels between VoiceXML and the I4.0-language are shown and compared by implementing a selected interaction protocol from the VDI 2193 using VoiceXML.
POSTER: SecretSVM -- Secret Sharing-Based SVM for Preventing Collusion in IoT Data Analysis

Support vector machine (SVM) is widely used because of its efficiency in data processing. Single source data may not train a nice SVM, since a single entity isn't having enough data with adequate attributes. Thus, multiple source data need to share data to combine a dataset with different attributes, and then jointly train a classifier. However, outsourcing data to a cloud for training induces two security concerns, data privacy and collusion with the cloud and providers. In this paper, we consider a distributed scenario without any centralized party in which IoT data providers will jointly serve as the leader to obtain some parameters. We propose a privacy-preserving and collusion-free SVM (so-called SecretSVM)built from secret sharing and distributed consensus. Participants train intermediate values to provide the necessary interaction andprevent against collusion attacks.
The underwater active imaging detection with linear polarization

The underwater optical polarization imaging with active light source illumination is studied. The mechanism and modeling of active polarization detection process with the linear polarization are demonstrated through theoretical and experimental analysis. The corresponding experiments were carried out for different objects with different distances and materials in water with different turbidity. In particular, the imaging effects with different wavelengths of light sources were compared at low turbidity, the imaging effects with 532 nm wavelength light are better than that of 633 nm wavelength. These results may benefit the underwater optical polarization imaging for achieving more diversified and flexible functions.
A Parallel Computing Framework for Cloud Services

In recent years, the field of big data processing has increasingly demanded computer performance for data processing. In order to enable users to use the big data processing platform conveniently and quickly, and put more attention on the writing of parallel computing algorithms, this paper focuses on cloud services, parallel computing, IronPython and virtualization as the main research content, and uses Python for the client, Service node and computing node module design, using the reflexive and self-observing capabilities of Python, calling library functions to achieve functions that cannot be achieved in other languages. The parallel computing framework based on Python designed in this paper can be easily implemented on the virtual machine of the cloud computing platform, and can efficiently and intelligently distribute computing requests.
Lifelong Learning for Dynamic Churn Prediction

Learning continually, accumulating knowledge, and using it to learn new tasks was the characteristic of lifelong learning. Alternatively, also known as Continual learning, the concept takes benefit from the one that called the previous tasks to solve the new task-this schema can learn on a series of tasks across diverse sources (or domains or even modality) through the selection of the right mechanism. Elastic weight consolidation (EWC) method proposed by Google Deepmind, provides a way of calculating the importance of weight preserving the previously acquired information then selectively adjusts the plasticity. The term plasticity is the main reason for catastrophic forgetting since the weight that previously learned in the first task can be easily modified or even abruptly lost. The probability perspective underlies the formation of the EWC; the method assumes that the second task's solution is closely related to the solution space for the first task. In this paper, the EWC exploited to tackle the sequence tasks of predicting churn activity. The tasks involve two distinct datasets as two sequential tasks from the domain of Telecom. Learning sequential tasks without applying the lifelong learning concept shows the worst result with no sign of recovery. However, the experiment that applies the EWC method shows enhancement (maximizing accuracy while suppressing the loss value) on both task performance. Lifelong learning offers a more flexible way of learning to further research in dynamic learning.
Implementation of A Fuzzy Logic Progression For Alcohol Addicts Using Fuzzy Control System(FCS)

The use of the Reduced Alcohol Intake (RAI) logic model is to help monitor alcoholic addicts and help them reduce alcohol intake for a better life and improved academic performance. The purpose of this study is to use the Mamdani Fuzzy Inference System (MFIS) component, which includes the application of a T-S (Takagi-Sugeno) model-based Fuzzy Control System (FCS) and a Fuzzy Logic System (FLS) as a rule-based system (Fuzzy Control System - FCS) to assemble all inputs in the RAI model to achieve classified fuzzy outputs. As initiated in the theoretical logic model (the RAI logic model), there is a direct identification of breakpoints for a transition between phases in the model. Thus, it can be used as an AI system that is efficient to monitor and examine the progression of alcohol addicts (to know what percentage of improvement they have reached overtime). Using the T-S method, the core parameters (motivation and self-determined state) of the RAI model were analyzed to find a linear interaction between the existing variables. In this paper, the variables of motivation and self-determined state are scaled between 0 to 10 to predict the level of improvement in percentage (%).
SCAAT: Secure Cache Alternative Address Table for mitigating cache logical side-channel attacks

Interest in memory systems' security has increased during the last decade due to their vulnerabilities to be exploited by logical side channels attacks. A promising approach for attack detection at run-time is to monitor the cache memory's behavior. However, designing an environment capable of detecting and mitigating these attacks is very challenging. In current monitoring systems, attack mitigation has been largely neglected. To overcome these shortcomings, in this work, we present a secure cache called SCAAT. SCAAT is equipped with an attack mitigation system to handle attacks by remapping where data is stored in the cache to random locations. In addition, SCAAT uses an attack monitor that identifies suspicious behavior that indicates cache logical side-channel attacks. The effectiveness of SCAAT is analyzed and evaluated for several cache configurations in terms of area overhead and performance.
Information Retrieval-Based Question Answering System on Foods and Recipes

Question Answering (QA) is an emerging domain of research that retrieves a textual segment from the set of documents in response to user’s queries. To recommend the answer in response to cooking recipe related questions is just an early stage of research and requires the significant refinement. In this paper, we have developed a question answering system on cooking recipes by using Natural Language Processing (NLP) and Information Retrieval (IR) technique. In recent years, with the rapid growth of information, the IR system has more importance in question answering domain. Users can also face difficulties to find expected answers from a huge amount of information. QA solves the information-overloading problem and IR returns the precise answers to the users. Answers from search engines are not only the results for a user’s query but these collective words should justify the questions. We have a standard dataset on recipes and foods from famous cities in India which is collected from various Indian recipe websites. We have used Apache Lucene for information retrieval and we have prepared the gold standard dataset for the question answering system on cooking recipes.
Efficient Zero-Knowledge Proofs of Graph Signature for Connectivity and Isolation Using Bilinear-Map Accumulator

To prove the graph relations such as the connectivity and the isolation for a certified graph, the system of graph signature and proofs have been proposed. In this system, an issuer generates a signature certifying the topology of an undirected graph, and issues a prover the signature. The prover can prove the knowledge of the signature and the graph in the zero-knowledge, i.e., the signature and the signed graph are hidden. In addition, the prover can prove relations on the certified graph such as the connectivity and isolation between two vertexes. In the previous system, using integer commitments on RSA modulus, the graph relations are proved. However, the RSA modulus needs a longer size of each element. Furthermore, the proof size and the verification cost depend on the total numbers of vertexes and edges. In this paper, we propose a graph signature and proof system, where these are computed on bilinear groups without the RSA modulus. Moreover, using a bilinear map accumulator, the prover can prove the connectivity and isolation on a graph, where the proof size and verification cost become independent from the total numbers of vertexes and edges.
Negative Feedback Aware Hybrid Sequential Neural Recommendation Model

Content-based (CB) and collaborative filtering (CF) are two classical types of recommendation methods that widely applied in various online services. Recently, sequential based recommender systems achieved good performance. However, how to integrate the advantages of these recommendation systems has not been well studied yet. Besides, most previous algorithms conduct negative sampling for each user based on items the user has not interacted with for model training, while it is unreasonable when there is known users’ negative feedback over items. We believe that a user’s negative feedback is valuable and should be used to better model users’ preferences. In this study, we propose a novel negative feedback aware hybrid sequential recommendation model (NFHS) to take the advantages of these three types of recommendation systems and to directly utilize negative feedback. There are two modules in our algorithm: 1) a static module to model the interaction history and the content features of the user and the current item. 2) a sequence module to distill a user’s interaction sequence features, negative feedback has also been directly introduced into this module. The experimental results on two real-world datasets from distinct scenarios demonstrate our model significantly outperforms various state-of-the-art approaches.
Essential Requirements of IoT’s Cryptographic Algorithms: Case Study

Internet of Things (IoT) devices are increasing rapidly in today’s world, but the security of devices remains a major concern due to the unavailability of the memory and processing power in these devices, which is because of their smaller size. The trade-off lies between security and performance, i.e. if security is increased, which will come with high complexity and hence would deter the performance. On the other hand, if performance has to be increased, it would come with a cost in terms of security. Also, IoT devices can be used as bots as they are globally accessible without much of a security. The most secure cryptographic algorithms use a lot of resources, and in case of IoT, resources are not available on that scale, so there is a need to design a secure algorithm (lightweight cryptography) that would use less resources and hence won’t affect the performance either.
Pre-emptive Approach for Resource Scheduling in Cloud Computing

Cloud Computing has become an ideal service provider for Information Technology and based on the users' demands, the resources can be provided through internet. On the internet, workload are often changing but incessantly changing pattern is still available. Presently for automatic scaling of resource management, low cost and cultivating resource utility in the cloud, workload prophecy scheme has become a very bright packer. Various schemes are available for workload prophecy, which is based on the single model prophecy approach. However, because of the internet that provides a large scale heterogeneity data over the cloud, it is very hard to find out a reasonable result by means of a traditional model. This research work has proposed a pre-emptive approach for resource allocation by analyzing a large scale heterogeneity data in the cloud. This paper shows a better result on resource prophecy accuracy with low cost and less time than the existing approach.
MCP-LSTM Network for Sentence-Level Sentiment Classification

In this paper, an MCP-LSTM network (Multichannel word vector via Convolution and Pooling to concatenate LSTM) is proposed to generate more complete feature representations for sentence sentiment classification. The proposed model combines LSTM and CNN-multichannel network. It uses two set of word vectors, one is a static word vector and the other is a non-static word vector. The two channel word vectors are concatenated. Then the model performs convolution and max pooling on multichannel word vector, convolution layer produces multiple feature maps and max-pooling layer enables the network to focus on the most important feature. Combining the important feature with long sequence information extracted by LSTM, a more complete feature representation of sentences can be obtained. The experimental results demonstrated that the proposed model improves the accuracy of 1.36%, 0.89% and the AUC values of 1.2%, 1.6% on MPQA and MR datasets respectively.
An Approach for Morphological Analyzer Rules for Dravidian Telugu Language

Machine translation is a computational method for automating user queries or information posed over search engine or social media in local Dravidian language such as Telugu. Computer based translation has become global due to majority of the domains are likely to use local languages for universal resources accessing. Machine translation is an application of the major area for transforming one language to another target universal language. In this era analyzing Telugu language at syntax granularity level is essential to tackle through grammar. This article emphasizes on classification of approaches for machine translation syntactical grammar for Telugu Dravidian language. The authors also significantly presented investigations noticeable research issues in this article towards Telugu language for machine translations.
Video-Based Traffic Flow Monitoring Algorithm

Intelligent traffic is the main trend of urban development at present. In view of the increasing number of vehicles and traffic congestion, this paper presents a video-based traffic flow monitoring algorithm. The proposed algorithm extracts the vehicles using characteristics of the video, detects the moving vehicles and the non-motor vehicles by the pixel sizes and positions. According to the results of vehicle detection, the number of vehicles is analyzed, speed is calculated by moving distance, and combined with traffic flow to judge current road conditions are smooth, general, or congested. This algorithm adopts the three-frame difference method to detect moving vehicles to ensure the real-time performance of the algorithm and displays the number of vehicles, speed information, the number of vehicles per lane in a direct visual way, and vehicles entering the region of interest can be identified. The experiment results show the proposed algorithm can judge the current traffic situations in real-time based on video, and vehicle detection is more accurate.