Reviewerly/exHarmony huggingface dataset:
Hybrid Data Driven and Rule Based Sentiment Analysis on Greek Text

Sentiment analysis is a developing field dealing with the detection of sentiments or opinions expressed in a written text. In an age where online services are subject to immediate feedback by users and clients, sentiment analysis on bodies of online reviews is of particular interest to service-oriented businesses such as hospitality establishments. Methods based on Machine Learning are very widely used for this purpose. While some standardized methodologies exist, the sheer volume of data on which the analysis is to be performed in order to train a Deep Learning network, makes it important to come up with designs that are sufficiently complex so as to accurately detect sentiment but also simple enough so as to be scalable and efficient in terms of performance. In this paper, we examine trade-offs of accuracy versus efficiency by performing sentiment analysis on an annotated body of review texts collected from online hotel reserving resources. The reviews we use to train our Deep Learning models are pre-processed by a tool which uses linguistic rules to add sentiment tags to words and expressions in the text. The tags express sentiment polarity, i.e. whether the expression is positive or negative. We propose four different DL network designs, which receive as training input either the review texts, the review texts plus some information on the tag annotation, or the annotation of the text alone, and we present the trade-offs that each setup offers.
Omni-Directional Image Generation from Single Snapshot Image

An omni-directional image (ODI) is the image that has a field of view covering the entire sphere around the camera. The ODIs have begun to be used in a wide range of fields such as virtual reality (VR), robotics, and social network services. Although the contents using ODI have increased, the available images and videos are still limited, compared with widespread snapshot images. A large number of ODIs are desired not only for the VR contents, but also for training deep learning models for ODI. For these purposes, a novel computer vision task to generate ODI from a single snapshot image is proposed in this paper. To tackle this problem, the conditional generative adversarial network was applied in combination with class-conditioned convolution layers. With this novel task, VR images and videos will be easily created even with a smartphone camera.
Statically Aggregate Verifiable Random Functions and Application to E-Lottery

Cohen, Goldwasser, and Vaikuntanathan (TCC’15) introduced the concept of aggregate pseudo-random functions (PRFs), which allow efficiently computing the aggregate of PRF values over exponential-sized sets. In this paper, we explore the aggregation augmentation on verifiable random function (VRFs), introduced by Micali, Rabin and Vadhan (FOCS’99), as well as its application to e-lottery schemes. We introduce the notion of static aggregate verifiable random functions (Agg-VRFs), which perform aggregation for VRFs in a static setting. Our contributions can be summarized as follows: (1) we define static aggregate VRFs, which allow the efficient aggregation of VRF values and the corresponding proofs over super-polynomially large sets; (2) we present a static Agg-VRF construction over bit-fixing sets with respect to product aggregation based on the q-decisional Diffie–Hellman exponent assumption; (3) we test the performance of our static Agg-VRFs instantiation in comparison to a standard (non-aggregate) VRF in terms of costing time for the aggregation and verification processes, which shows that Agg-VRFs lower considerably the timing of verification of big sets; and (4) by employing Agg-VRFs, we propose an improved e-lottery scheme based on the framework of Chow et al.’s VRF-based e-lottery proposal (ICCSA’05). We evaluate the performance of Chow et al.’s e-lottery scheme and our improved scheme, and the latter shows a significant improvement in the efficiency of generating the winning number and the player verification.
Deep Learning Adoption Blockchain Secure Framework for Cyber Physical System

The growth of cyber-physical systems (CPS) has greatly improved industrial efficiency and consumer convenience. Nevertheless, the widespread deployment of 5G-CPS systems also poses new problems that are of major concern to security. In order to help create stable and robust 5G-CPS systems, systematic analyzes are carried out on CPS and IoT from individual smart devices that act as terminal nodes to the entire network. The results of the review will help to find weaknesses in CPS systems and/or provide guidance on how to create protection in these systems using deep learning and blockchain techniques. Risk reduction approaches that can help improve protection will also be addressed.
Peruvian Professors respond for teaching from virtual context - Key Success Factors in Engineering

This article describes the main challenges faced by Peruvian Professors in terms of the methodological change originated as a result of the confinement situation for Covid-19, which has forced the face-to-face academic processes to shift to digital face-to-face academic processes, in which changes have originated significant in the pedagogical component, digital skills, work rationalization, among others.A structured questionnaire is applied to Peruvian Professors with the aim of knowing the opportunities for improvement to strengthen the faculty.This study contrasts with Peruvian Professors in the field of engineering and the level of advancement or delay with respect to the rest of the Professors studied.
Sound’s Matter: ‘Deleuzian Sound Studies’ and the Problems of Sonic Materialism

This article evaluates the theoretical and practical grounds of recent debates around Christoph Cox's realist project of a 'sonic materialism' by returning to Gilles Deleuze, a key theoretical resource for Cox. It argues that a close engagement with Deleuze's work, in fact, challenges many of the precepts of Cox's sonic materialism, and suggests a rethinking of materialism in the context of music. Turning to some aspects of Deleuze's work neglected by Cox, the 'realist' ontological inquiry Cox affirms is challenged through the 'onto-ethology' that Deleuze and Félix Guattari develop in their A Thousand Plateaus, with this diversely constructive theory of relations explicated through musical examples from John Cage and Pauline Oliveros. To conclude, this article suggests that Deleuze can indeed be understood as subscribing to a materialism, but a materialism that is practical rather than doctrinal.
Implementation of Artificial Neural Network to Predict S&amp;P 500 Stock Closing Price

Artificial Neural Network (ANN) is a learning method that can be used for prediction and classification. In this study, ANN back-propagation is implemented to predict the closing price of the S&P 500 stock exchange using historical data. Historical data consisting of five variables, namely open, high, low, close, and volume. The historical data are taken from finance.yahoo.com which stores historical data of daily stock prices up to 65 years earlier. The data is designed based on daily prediction scenarios of the closing stock price of the S&P 500 stock exchange. Using the scenario an application prototype using R and Java software has been successfully built. The prototype is dynamic during select the data set (10, 50, 100, 500 past data) to get the best stock closing prediction for the next day. The selection of the best stock closing prediction uses the MAPE (Mean Average Percentage Error) criterion which is in contrast to the prediction accuracy level. The smaller the MAPE value the better the predicted result. Based on the implementation results, the average MAPE for daily forecast for 1 month in April 2017 was 0.2307 indicating that the average daily prediction accuracy rate for 1 month was 99.77%. Our prototype application can make prediction automatically every day, every time when connected to the internet.
An Efficient Bengali Text Steganography Method Using Bengali Letters and Whitespace Characters

The present era is the era of information and communication. Everyday, billions of information pass from one digital device to another. Among this information and data, there are many sensitive and confidential data like banking credentials, national and international secrets, defense strategy, etc. These data must not go public and security must be ensured. It has become complicated to provide protection to these data. One solution to keep data privacy is steganography. It is a way of secure communication where a secret message is hidden within a cover object. In this paper, we propose a unique text steganography method using Bengali Unicode characters and white-space characters. In our method, we have encrypted our secret message within an ordinary plain text. It is impossible to differentiate between the normal plain text and stego-text in bear eyes. If anybody suspects the stego-text, he/she must have to break another secret pattern to decrypt the hidden message. We evaluate the efficiency of our proposed work in terms of security and embedding capacity. The comparison results suggest that our new scheme offers higher embedding capacity and more security than the existing methods.
Self-paced learning enhanced neural matrix factorization for noise-aware recommendation

Matrix factorization-based collaborative filtering, learning user and item latent features, has been one of the powerful recommendation techniques. Due to its simply modeling of user–item interactions by inner product of two vectors as a linear model, its efficiency needs an improvement. Neural Network-based matrix factorization has been proposed to deal with this issues. Usually, these methods are proposed on clean data, but in real applications, there are possibly unexpected noises and outliers, due to many subjective or objective reasons. The noisy instances would disturb the learning of normal instances and thus cause adverse affect as the model would also be easily over-fitted. Thus, we propose an enhanced neural matrix factorization model by introducing a self-paced learning (SPL) schema, which can automatically distinguish noisy instances and learn the model mostly based on clean instances. The main contribution of our model is that we design a bounded SPL learning schema with a parameter to control how many instances will be finally induced in the model learning. Thus, different from traditional SPL that gradually selects instances until all are selected, the bounded SPL mechanism tries to learn the model mainly on clean data and exclude noisy instances. The effectiveness of proposed method on collaborative filtering is demonstrated by extensive experiments on three widely used datasets.
Dynamic Bayesian Collective Awareness Models for a Network of Ego-Things

analyzed for the collective abnormality estimation of the agents.Rician wireless channel and the usage of two protocols (i.e., IEEE 802.11p and IEEE 802.15.4) along with different channel conditions are considered as well.
Bare-hand Depth Inpainting for 3D Tracking of Hand Interacting with Object

We propose a 3D hand tracking system using bare-hand depth inpainting from an RGB-depth image for a hand interacting with an object. The effectiveness of most existing hand-object tracking methods is impeded by the insufficiency of data, which do not include hand data occluded by the object, and their reliance on the information inferred from assuming the specific object type. We generate a sufficiently accurate bare-hand depth image from a hand interacting with an object using a conditional generative adversarial network, which is trained using the synthesized 2D silhouettes of the object to learn the morphology of the hand. We evaluate the proposed approach using a hierarchical particle filter-based hand tracker and prove that our approach utilizing the bare-hand tracker in the hand-object interaction dataset achieve state-of-the-art performance. The generalization of our work will enable visual-tactile interaction that is more natural in various wearable augmented reality applications.
Hypernegative Interpretation of Negatively Perceived Email at Work

Extensive commentary cautions about the consequences of poor email etiquette, including emotional miscommunication and conflict escalation at work. This research considers the role of the receiver in negative email exchanges. Participants identified examples of negatively perceived emails received from coworkers, provided the text of these emails, and reported their perceptions and accounts of the messages. Results document a negative intensification bias in receiver perceptions of emails. Receivers perceived the emails more negatively than did observers, and receiver ratings had only weak relationships to characteristics of messages, including negative language and flaming. Negative intensification bias occurred more in poor communication climates and among individuals in subordinate positions. Qualitative results highlight the potential for ambiguous emails to be perceived in multiple ways leading to unanticipated consequences.
Exploration for concealed fracture controlled uranium mineralization: A case from Shivaramapuram-Nutankalva tract in basement granitoids, south of Cuddapah basin, Andhra Pradesh, India

The application of hydro geochemical exploration for sub-surface uranium mineralization is becoming significant due to the geochemical properties of uranium. The oxygen in natural water enhances the mobility of uranium by the complexing action of carbonates and bicarbonates with oxidized uranyl ions. The uranium and its decay products (i.e., radium group) can be used as good tracers for pinpointing uranium mineralization. The basement complex in the south of Cuddapah basin comprises granite, gneiss-migmatites and patches of greenstone belts. There are different generation of fractures and mafic dykes trending E–W, NS, NW-SE and NE–SW directions. The NNE–SSW to NE–SW trending fractures are youngest and often show higher uranium values in water along the same trend indicating possible mineralization. The fractures are often intruded by younger basic intrusive rocks and quartz reefs which often show high uranium content. Several NE–SW to NNE–SSW trending fracture zones with uranium mineralization are observed in the areas around Shivaramapuram-Nutankalva, Kamaguttapalle and T. Sundupalle villages. These sympathetic fracture zones are important for further sub-surface exploration. The present focus is given on a NE-SW fracture zone along Sivaramapuram–Nutanakalva tract extends over a strike length of ~15 km with a plan width of ~1 to 1.5 km. The tract is mostly soil covered and rock outcrops are scanty. Radioactive anomalies are located at small outcrops in fractured basic dyke, fractured and altered granite, deformed quartz vein, sludge samples collected from private village drilling sites, Pincha river channel sands and soils (U3O8 values up to 0.19% (β/γ); n = 44;). Hydrogeochemical exploration along this fracture zone has indicated anomalous concentration of uranium in water samples ranging from 100 to 19,434 ppb U (n = 110). Selective borewells have been monitored for 8 months to check the seasonal variation. The data remain persistent and indicated encouraging outcome. This NE–SW fracture zone has affected the Nagari quartzite of Cuddapah Supergroup, thus indicate Post Nallamalai Mesoproterozoic Grenville orogeny, which is metallogenically favourable epoch for fracture controlled uranium mineralization. The surface anomalies vis-a-vis the hydro uranium anomalies and suitable geochemical condition along the NE-SW trending fracture make this zone a potential target for further sub-surface exploration.
Solving Imperfect Information Poker Games Using Monte Carlo Search and POMDP Models

Recent advances achieved in the field of reinforcement learning have led AI algorithms capable of beating world champions in some perfect information games like Chess and Go. However, the AI approach to imperfect information games (such as Poker) is much more difficult because the complexities in estimating hidden information and behaviors of opponents may become extremely challenging. Since Markov Decision Process (MDP) is the underlying mathematical model of reinforcement learning with perfect information games, Partially Observable Markov Decision Process (POMDP) deserves research attention for studying the games with imperfect information. In this paper, we study a 16-cards Rhode Island Hold'em poker game and present a POMDP model to formulate this imperfect information extensive game. Based on the POMDP model, we use Bayesian approach to estimate the opponent's hand and transform the original problem to several perfect information games. Furthermore, to handle the challenge of explosively huge storage space and computation burdens, we develop a Monte Carlo optimization algorithm to estimate the action values of the POMDP model. Finally, we conduct numerical experiments in the Rhode Island Hold'em poker game to demonstrate the effectiveness of our approach.
GraphPB: Graphical Representations of Prosody Boundary in Speech Synthesis

This paper introduces a graphical representation approach of prosody boundary (GraphPB) in the task of Chinese speech synthesis, intending to parse the semantic and syntactic relationship of input sequences in a graphical domain for improving the prosody performance. The nodes of the graph embedding are formed by prosodic words, and the edges are formed by the other prosodic boundaries, namely prosodic phrase boundary (PPH) and intonation phrase boundary (IPH). Different Graph Neural Networks (GNN) like Gated Graph Neural Network (GGNN) and Graph Long Short-term Memory (G-LSTM) are utilised as graph encoders to exploit the graphical prosody boundary information. Graph-to-sequence model is proposed and formed by a graph encoder and an attentional decoder. Two techniques are proposed to embed sequential information into the graph-to-sequence text-to-speech model. The experimental results show that this proposed approach can encode the phonetic and prosody rhythm of an utterance. The mean opinion score (MOS) of these GNN models shows comparative results with the state-of-the-art sequence-to-sequence models with better performance in the aspect of prosody. This provides an alternative approach for prosody modelling in end-to-end speech synthesis.
OraclesLink: An architecture for secure oracle usage

Smart contracts encode critical application logic for realizing digital agreements in a tamper-proof form. Blockchains guarantee that smart contracts cannot be altered after the first deployment and that the execution is strictly followed. Smart contracts can only operate on data available on-chain. Oracles are bridging the gap between on-chain and off-chain data. Oracles introduce a wide range of security risks, which were already exploited in publicly known hacks. In this paper OraclesLink is proposed, which is a secure and developer-friendly architecture for using oracles within smart contracts. The goal of the architecture is to eliminate single points of failure and single sources of truth through distribution. In order to demonstrate feasibility, a proof-of-concept implementation is provided.
Multi-scale Attentive Residual Dense Network for Single Image Rain Removal

Single image deraining is an urgent yet challenging task since rain streaks severely degrade the image quality and hamper the practical application. The investigation on rain removal has thus been attracting, while the performances of existing deraining have limitations owing to over smoothing effect, poor generalization capability and rain intensity varies both in spatial locations and color channels. To address these issues, we proposed a Multi-scale Attentive Residual Dense Network called MARD-Net in end-to-end manner, to exactly extract the negative rain streaks from rainy images while precisely preserving the image details. The architecture of modified dense network can be used to exploit the rain streaks details representation through feature reuse and propagation. Further, the Multi-scale Attentive Residual Block (MARB) is involved in the dense network to guide the rain streaks feature extraction and representation capability. Since contextual information is very critical for deraining, MARB first uses different convolution kernels along with fusion to extract multi-scale rain features and employs feature attention module to identify rain streaks regions and color channels, as well as has the skip connections to aggregate features at multiple levels and accelerate convergence. The proposed method is extensively evaluated on several frequent-use synthetic and real-world datasets. The quantitative and qualitative results show that the designed framework performs better than the recent state-of-the-art deraining approaches on promoting the rain removal performance and preserving image details under various rain streaks cases.
A Hierarchical Trajectory Planning Framework for Autonomous Driving

In this paper, we introduce a layered trajectory planning method for autonomous vehicles. The trajectory planning problem is decomposed into three parts: path planning, speed planning, and path-speed iteration. In the path planning section, we combine the advantages of search-based and optimization based methods. Firstly, we use different search-based methods for safe navigation and then smooth the searched path to get a high-quality reference path. Secondly, the path planning problem under the Frenet frame is formulated as a quadratic program (QP), which can be solved efficiently. Then a speed profile is generated based on the planned path. Furthermore, we propose an iterative strategy for path planning and speed planning to ensure the feasibility of the planned trajectory. Our algorithm is implemented in C++, and the path planning part is open-sourced on our GitHub page.
Detecting User Significant Intention via Sentiment-Preference Correlation Analysis for Continuous App Improvement

Detecting users’ significant intentions (e.g., new features wanted) timely and precisely is crucial for developers to update and maintain their apps in the competitive mobile app market. Sentiment and preference mining from crowd reviews provide an opportunity to proactively collect app users’ intentions, e.g., bug fixing and feature refinement. However, users’ sentiment and preferences often change over time due to either internal factors (e.g., new bugs) or external factors (e.g., new competitors). This makes it difficult for app developers to grasp users’ sentiment and preferences in time. In this paper, we propose a novel and automated framework named DSISP for detecting users’ significant intentions effectively via sentiment-preference correlation analysis. DSISP first employs sentiment analysis and NLP (Natural Language Processing) techniques to obtain sentence-level sentiment scores and fine-grained user preference features from app reviews in different time slices. Then, the temporal correlation between user sentiment and preferences is analyzed, which can be used to monitor users’ sentiment tendency and preferences in time. Finally, DSISP identifies users’ dramatically-changing sentiment (e.g., sentiment valley) to detect users’ significant intentions. We evaluate the feasibility and performance of DSISP by using real-world app reviews and app official changelogs. The experimental results show that DSISP can detect users’ significant intentions effectively and efficiently, with a precision of 0.962 on average. It can help app developers keep track of how their users’ intentions evolve over time so that they can improve their apps correspondingly and continuously.