Reviewerly/exHarmony huggingface dataset:
Multi-Class Classification of Actors in Movie Trailers

A trailer is a short version of the movie which gives the viewer information about the movie such as its genre, the cast and what the audience needs to expect from the movie. You should never judge a book by its cover but you can always judge a movie by its trailer. So in this paper, we aim to classify actors in movie trailers by extracting key frames from trailers and obtaining features from it through convolutional neural network and then classifying it using the output function. A trailer emphasises the type of movie it is marketing. Actors are one of the most important aspects in a movie and play a decisive role when analysing the overall popularity of the movie. A trailer always gives a sneak-peak of the actors that are going to play a crucial role in the movie. Recognising actors in a movie trailer thus becomes an important task as much of the viewer’s pre-judge a movie based on the actors that glorify its cast.
Assessing the Similarity of Smart Contracts by Clustering their Interfaces

Like most programs, smart contracts offer their functionality via entry points that constitute the interface. Interface standards, e.g. for tokens contracts, foster interoperability. Ethereum is the most prominent platform for smart contracts. The number of contract deployments approaches 30 million, corresponding to roughly 300 000 distinct contract codes. In view of these numbers, it is necessary to develop automated methods for classifying contracts regarding their purpose, if one aims at a qualitative and quantitative understanding of what blockchain applications are used for at large. We approach the task by considering contracts as similar if their interfaces are. We encode interfaces and their interrelationships as graphs and explore several algorithms regarding their ability to find clusters of functionally similar contracts. Our evaluation of the quality of clustering relies on a ground truth of token and wallet contracts identified in earlier work. Our analysis is based on the bytecodes deployed on the main chain of Ethereum up to block 10.5 million, mined on July 21, 2020.
E-Marketing and Its Impact on The Competitive Advantage

Competitive advantage is considered as a subject that haunts business organizations all over the world due to its importance in the organizations quest to increase its profitability and productivity. Hence, this forces the organizations to extend all their efforts, and to follow all the relevant methods and strategies to reach the competitive advantage.Therefore, this study is aimed at identifying the impact of E-Marketing on the competitive advantage by following three strategies for competitive advantage which are (Cost leadership, Focus, & Differentiation) in Al-Razi Pharmacies in the city of Irbid. The study population consisted of (120) pharmacy workers, for which the researchers distributed (92) questionnaires, where the study used the descriptive and analytical approach to describe the phenomenon in question. Finally, the study found out that E-Marketing has an effect on all the dimensions of the competitive advantage.
SAP on Premise to SAP S/4HANA Public Cloud Migration

SAP suite on premise to SAPS/4HANA on cloud conversion is a critical part of the organization’s Digital transformation narrative journey to gain greater agility, leverage cloud based intelligent digital technologies & optimize operations. Cloud adoption has been constantly increasing and it is becoming harder for companies to justify on premise implementation. SAP Public cloud has also improved a lot on security frontier leading no justification to stay with on premise option. Some customers are still wary of moving to cloud and this article provides a list of benefits of migration from on premise to cloud. This article builds a case for SAP public cloud migration and explains what are the respective key benefits which the customers can reap when they migrate to public cloud.
Design of Voice Privacy System using Linear Prediction

Speaker’s identity is the most crucial information exploited (implicitly) by an Automatic Speaker Verification (ASV) system. Numerous attacks can be obliterated simultaneously if privacy preservation is exercised for a speaker’s identity. The baseline of the Voice Privacy Challenge 2020 by INTERSPEECH uses the Linear Prediction (LP) model of speech, and McAdam’s coefficient for achieving speaker de-identification. The baseline approach focuses on altering only the pole angles using McAdam’s coefficient. However, from speech acoustics and digital resonator design, the radius of the poles is associated with various energy losses. The energy losses implicitly carry speaker-specific information during speech production. To that effect, the authors have brought fine-tuned changes in both pole angle and pole radius, resulting in 18.98% higher value of EER for Vctk-test-com dataset, and 5% lower WER for Libri-test dataset compared to the baseline. This means privacy-preservation is indeed improved by our approach. Furthermore, we have exploited the relatively poor spectral resolution of female speakers to our advantage for achieving effective anonymization. To that effect, gender-based analysis of the obtained results reveals that our approach leads to better speaker anonymization for females as compared to the male speakers.
Graph Pooling by Edge Cut

Graph neural networks (GNNs) are very efficient at solving several tasks in graphs such as node classification or graph classification. They come from an adaptation of convolutional neural networks on images to graph structured data. These models are very effective at finding patterns in images that can discriminate images from each others. Another aspect leading to their success is their ability to uncover hierarchical structures. This comes from the pooling operation that produces different versions of the input image at different scales. The same way, we want to identify patterns at different scales in graphs in order to improve the classification accuracy. Compared to the case of images, it is not trivial to develop a pooling layer on graphs. This is mainly due to the fact that in graphs nodes are not ordered and have irregular neighborhoods. To aleviate this issue, we propose a pooling layer based on edge cuts in graphs. This pooling layer works by computing edge scores that correspond to the importance of edges in the process of information propagation of the GNN. Moreover, we define a regularization function that aims at producing edge scores that minimize the minCUT problem. Finally, through extensive experiments we show that this architecture can compete with state-of- the-art methods.
Cataloging Principles and Objectives: History and Development

Cataloging principles and objectives guide the formation of cataloging rules governing the organization of information within the library catalog, as well as the function of the catalog itself. Changes in technologies wrought by the internet and the web have been the driving forces behind shifting cataloging practice and reconfigurations of cataloging rules. Modern cataloging principles and objectives started in 1841 with the creation of Panizzi’s 91 Rules for the British Museum and gained momentum with Charles Cutter’s Rules for Descriptive Cataloging (1904). The first Statement of International Cataloguing Principles (ICP) was adopted in 1961, holding their place through such codifications as AACR and AACR2 in the 1970s and 1980s. Revisions accelerated starting in 2003 with the three original FR models. The Library Reference Model (LRM) in 2017 acted as a catalyst for the evolution of principles and objectives culminating in the creation of Resource Description and Access (RDA) in 2013.
10 Things You Need to Know About Data in 2021

Abstract Vanessa Barnett, Technology and Data Partner at Keystone Law, scans the data regulation horizon to warn of what's to come in the year ahead.
Sar Metadata Standards: Single-Look Complex Data

Many formats and standards currently exist for metadata for Synthetic Aperture Radar data. One of the more promising standards for single-look complex data is being promoted by the United States National Geospatial-Intelligence Agency (NGA), the “Sensor Independent Complex Data” standard, or SICD for short. This standard provides an XML-encoding for the metadata as well as as a specification for the image data. While it does not cover all possible kinds of data, is it designed to be applicable to most current single-look complex datasets. However, it was developed independently of other geospatial standards and organizations, and so does not inter-operate with them. The current effort by the IEEE SAR Metadata Standard Working Group is aimed at harmionizing this standard with existing ISO standards so that interoperability is enhanced, and international adoption is encouraged. For now, the standard that is being developed only concerns the metadata, and not the image data. It also does not specify an encoding, rather allowing the marketplace to choose an appropriate encodeing based on current technology. This enables data providers to optimize how they provide their data to their customers without forcing them to use outdated technology. This standard is designed to enable providers of processing software to provide tools that can be applied to a much wider variety of SAR data than is currently possible.
Towards Edge-Fog-Cloud Continuum

Over time, two counteracting trends have been observed in the “world of computing”. One of them was a push from centralized towards decentralized solutions. The first wave of this process can be associated with the introduction of the Internet and personal computers and took place in early 1980th. The second was the move in the opposite direction. Here, the first wave of centralization can be associated with the ascent of cloud computing. These two seem to be similar to the thesis and antithesis, in Hegel’s philosophy. Interestingly, similarly to Hegel’s synthesis, we are approaching a unified model of edge-fog-cloud continuum. My talk will reflect on the journey and outline the proposed way forward.
Meta-Learned Attribute Self-Gating for Continual Generalized Zero-Shot Learning

Zero-shot learning (ZSL) has been shown to be a promising approach to generalizing a model to categories unseen during training by leveraging class attributes, but challenges still remain. Recently, methods using generative models to combat bias towards classes seen during training have pushed the state of the art of ZSL, but these generative models can be slow or computationally expensive to train. Additionally, while many previous ZSL methods assume a one-time adaptation to unseen classes, in reality, the world is always changing, necessitating a constant adjustment for deployed models. Models unprepared to handle a sequential stream of data are likely to experience catastrophic forgetting. We propose a meta-continual zero-shot learning (MCZSL) approach to address both these issues. In particular, by pairing self-gating of attributes and scaled class normalization with meta-learning based training, we are able to outperform state-of-the-art results while being able to train our models substantially faster ($>100\times$) than expensive generative-based approaches. We demonstrate this by performing experiments on five standard ZSL datasets (CUB, aPY, AWA1, AWA2 and SUN) in both generalized zero-shot learning and generalized continual zero-shot learning settings.
Private Post-GAN Boosting

Differentially private GANs have proven to be a promising approach for generating realistic synthetic data without compromising the privacy of individuals. Due to the privacy-protective noise introduced in the training, the convergence of GANs becomes even more elusive, which often leads to poor utility in the output generator at the end of training. We propose Private post-GAN boosting (Private PGB), a differentially private method that combines samples produced by the sequence of generators obtained during GAN training to create a high-quality synthetic dataset. To that end, our method leverages the Private Multiplicative Weights method (Hardt and Rothblum, 2010) to reweight generated samples. We evaluate Private PGB on two dimensional toy data, MNIST images, US Census data and a standard machine learning prediction task. Our experiments show that Private PGB improves upon a standard private GAN approach across a collection of quality measures. We also provide a non-private variant of PGB that improves the data quality of standard GAN training.
Retracted: “Sensorless Control of Tubular Permanent Magnet Synchronous Linear Motor Considering the End Effect” [ASME 2020 International Mechanical Engineering Congress and Exposition, Volume 2B: Advanced Manufacturing, Virtual, Online, November 16–19, 2020, Conference Sponsors: ASME, ISBN: 978-0-7918-8449-2, Copyright © 2020 by ASME. Paper No. IMECE2020-23676, V02BT02A042; 6 pages; doi: 10.1115/IMECE2020-23676]

Abstract This paper was removed from publication at the author’s request, March 8, 2021. Copyright © 2021 by ASME
Dissecting the Computational Roles of the Social Brain

Social interactions routinely lead to neural activity in a so-called “social brain network” comprising, among other regions, the temporoparietal junction (TPJ) and the dorsomedial prefrontal cortex (dmPFC). But what is the behavioral function of these brain areas – are they specialized for interacting with others in social contexts or do they implement general-purpose computations required for dealing with any reactive process, even non-living entities? Here, we approach this question using functional magnetic resonance imaging (fMRI) and a novel game paradigm separating the need for these two (usually correlated) aspects of cognition. We find that most areas in the social-brain network respond to both social and non-social reactivity, rather than specifically to human opponents. However, the TPJ shows a functional dissociation from the dmPFC: Its activation and connectivity with reward-coding brain areas additionally differentiate between social and non-social contexts. Our results characterize an overarching computational property underlying the engagement of the social-brain network but also suggest specialized roles for different subregions of this network.
A Dummy-Based Privacy Protection Scheme for Location-Based Services under Spatiotemporal Correlation

The dummy-based method has been commonly used to protect the users location privacy in location-based services, since it can provide precise results and generally do not rely on a third party or key sharing. However, the close spatiotemporal correlation between the consecutively reported locations enables the adversary to identify some dummies, which lead to the existing dummy-based schemes fail to protect the users location privacy completely. To address this limit, this paper proposes a new algorithm to produce dummy location by generating dummy trajectory, which naturally takes into account of the spatiotemporal correlation all round. Firstly, the historical trajectories similar to the user's travel route are chosen as the dummy trajectories which depend on the distance between two trajectories with the help of home gateway. Then, the dummy is generated from the dummy trajectory by taking into account of time reachability, historical query similarity and the computation of in-degree/out-degree. Security analysis shows that the proposed scheme successfully perturbs the spatiotemporal correlation between neighboring location sets, therefore, it is infeasible for the adversary to distinguish the users real location from the dummies. Furthermore, extensive experiments indicate that the proposal is able to protect the users location privacy effectively and efficiently.
Designing for Tussle in (Encrypted) DNS

Recent concerns over the privacy implications of the Domain Name System (DNS) have led to encrypting DNS queries and responses through protocols like DNS-over-HTTPS (DoH) and DNS-over-TLS (DoT). While the trend towards encryption is a positive development, the resulting centralization of the DNS has fomented tussles involving ISPs, browser and device vendors, content delivery networks, and users. Current deployment trends, should they continue, result in dynamics that will increase barriers to entry to competition and threaten consumer protection. This development makes it necessary for us to re-think name resolution to allow tussles to play out within the context of the design of the Internet architecture. This paper articulates several current DNS tussles and offers principles to guide system design and implementation such that all stakeholders in the space could participate. We then explore how a refactored client DNS mechanism can open up new possibilities for de-centralized name resolution, preserving the benefits of encrypted DNS while satisfying other architectural desiderata, including performance, resilience, and privacy.
Ethereum 2.0 Blockchain in Healthcare and Healthcare Based Internet-of-Things Devices

Healthcare encompasses multiple domains of engineering and technology for its core functioning. Electronic health records of patients are used to store the medical history of patients and make them readily available and consumable for the user. This data is also prone to cyber-attacks and malicious activities. A Proof-Of-Stake implementation of Blockchain is proposed to design a decentralized healthcare management system that provides interoperability between hospitals besides being safe and secure using a 256-Bit encryption. This provides sufficient decentralization with better speeds of handling data in a secure environment, readily access of information to all users concerned in a private Blockchain network for a healthcare system.
The problem of determining the subject of copyright for works created by artificial intelligence

The article examines the status of the author of works created as a result of the activity of artificial intelligence. Since the duration of legal protection depends on the duration of the author's life, in conjunction with the norms of Article 1257 of the Civil Code of the Russian Federation, we can conclude that a work as an object of copyright can only be created by a person. Nevertheless, the presence of the figure of a secondary copyright holder allows us to talk about the possibility of removing the personalist approach to copyright in a work. In this regard, it is quite acceptable to assume the possibility of expanding or reinterpreting the concept of "author of a work" in the context of works created by artificial intelligence, which would allow us to consider a wide range of subjects as authors, and not limit it only to individuals - the creators of the work.
Multivariate and Statistical Approaches for the Source Apportionment and Evaluation of Trace Elements Pollution at Mining Areas (Case study: Mehdi Abad Pb/Zn mine)

Mining activities are among the main sources of trace elements in the environment, which constitute a real concern worldwide, especially in developing countries. This study aimed to investigate the multivariate approaches such as Correlation Matrix and Hierarchical Cluster Analysis (HCA) for the identification of probable sources of trace elements in the deposited dust near the Mehdi Abad Pb/Zn mine located in Mehriz, Yazd province, as well as the evaluation of dust contamination based on the Geo-accumulation Index (I geo), Nemerow Pollution Index (PI Nemerow), Improved Nemerow Index (IN), and Combined Pollution Index (CPI). In addition, an anthropogenic index was used to determine the sources of the elements.  For this purpose, deposited dust was collected in nine sites using a marble dust collector (MDCO). Next, the chemical analysis of dust was determined using Inductively Coupled Plasma-Mass Spectrometry (ICP-MS). Statistics analysis was carried out using SPSS 22.0. The results showed that concentrations of zinc, thallium, silver, aluminium, arsenic, bismuth, calcium, cadmium, cerium, caesium, erbium, europium, gadolinium, hafnium, potassium, lutetium, manganese, sodium, phosphorus, antimony, scandium, and samarium in the deposited dust was higher than the background value. HCA identified two origins for the elements. The anthropogenic index confirmed the geogenic origin of elements in the deposited dust. Furthermore, CPI and PI Nemerow indices values showed that all sampling sites were in the heavily contaminated class. The results of IN Index showed that 56% of sampling sites were in the heavily contaminated class. The analysis of I geo, PI Nemerow, and IN indices showed that arsenic caused extreme contamination of the deposited dust at sampling sites.
Nepali Speech Recognition using CNN and Sequence Models

Speech-To-Text(STT) is becoming an important way that humans interact with computers. STT systems are quite popular nowadays and are widely used for many applications and are available in many languages. However, for Nepali language, such systems are not readily available. In this paper, we implement several models for STT, based on Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN, GRU). We also compare these different models and their combinations. The models we present transcribe audio character-by-character. The predictions are made and the highest probability transcription is selected using a beam search decoder. For training, a dataset from OpenSLR was used. The best trained model (CNN-RNN) has an character error rate of 23.72% on the validation data.
Reactive Online Motion Re-Planning for Crash Mitigation in Autonomous Vehicles Using Bezier Curve Optimization

Abstract Crash avoidance and mitigation is still one of the major challenges faced by the autonomous vehicles industry. Path planning in autonomous vehicles is a crucial phase during crash avoidance with other vehicles. It is important for the path planning algorithm to be reactive in uncertain situations which usually arise in urban road scenarios. This study proposes a reactive online path planning strategy for obstacle avoidance and crash mitigation for an imminent collision with another vehicle. A cubic Bezier curve trajectory generation method is used for creating a maneuver around the obstacle vehicle. Relative hitting heading angle is considered to account for the crash severity between two vehicles. Two cases are considered where one is an imminent crash scenario and the other is where an obstacle can be avoided with a minimum safety distance. This obstacle avoidance problem is then converted to an optimization problem where potential crash severity, vehicle kinematic constraints and path smoothness are considered as constraints. The resulting cost function consists of a quadratic convex function and a piecewise defined function. This is further solved using a novel methodology where the piecewise function is included in the inequality constraints, so that the problem can be solved using quadratic programming method. This will also lead to a quick real time implementation. It is shown that the proposed method is able to avoid collisions and also minimizes the crash severity in case of an imminent collision.
Steganographic Distortion Function for Enhanced Images

Contrast enhancement is widely used to improve the visual quality of images. This paper proposes a distortion function for steganography in enhanced images. The pixel prediction error and the cost of the pixel value itself are joint to fit the unique properties of enhanced image. Given a cover image, each pixel is predicted by its neighbors. The prediction error, which reflects the texture complexity, are combined with the cost of the pixel value itself to form the final distortion function. With the proposed distortion function, secret data can be embedded into the enhanced cover image with minimal image distortion using syndrome trellis coding (STC). As a result, less detectable artifacts left in the stego images, so that high undetectability is achieved.