Reviewerly/exHarmony huggingface dataset:
Anticipating Corporate Financial Performance from CEO Letters Utilizing Sentiment Analysis

With the emergence and tremendous growth of text mining, a computer-assisted approach for capturing sentiment viewpoints from textual data is gradually becoming a promising field, particularly when researchers are increasingly facing the problem of filtering bunches of useless information without capturing the essence in the big data era. This study aims at observing and classifying the sentiment orientation in CEO letters, digging the main corporate social responsibility (CSR) themes, and examining the effectiveness of CEO letters’ sentiment on forecasting financial performance. A specific sentiment dictionary has been proposed to identify and classify the sentiment orientation in CEO letters by utilizing the appraisal theory. Additionally, the qualitative data analysis software NVivo is applied to explore the CSR topics. Furthermore, a modified Altman’s Z-score model and machine-learning approach are employed to predict financial performance. The results of preliminary evaluations validate that approximately 62.14% of the texts represent positive polarity even when companies are not in a promising economic situation. The CSR themes mainly focus on business ethical responsibility, particularly ethical activities. Among various machine-learning approaches, the logistic regression approach is appropriate for predicting financial performance with the state-of-the-art accuracy of 70.46 %. The encouraging results indicate that the sentiment information inCEO letters is a vital factor for anticipating financial performance. This work not only offers a new analytic framework for associating linguistic theory with computer science and economic models but will also improve stakeholders’ decision-making.
3DUI and the Phantom Limb: Multisensory Experience for Embodiment of Amputation

In the context of the 3DUI Contest promoted by the IEEE VR 2020, we propose an application to allow users to experience having an amputated arm. By providing both visual and haptic feedback, our application offers a multisensory experience to enhance the sense of embodiment. The user of our application should still feel their real limb attached to their bodies, and yet see their virtual avatar and interact with the virtual environment as an amputee. A simple task of handling and positioning objects in a table is proposed for the users to experience the difficulties of having a missing arm. As most amputees have the sensation that an amputated or missing limb is still attached, our application should allow users to experience the same feeling.
SiSiMo: Towards Simulator Sickness Modeling for 360<sup>°</sup> Videos Viewed with an HMD

Users may experience symptoms of simulator sickness while watching 360 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> /VR videos with Head-Mounted Displays (HMDs). At present, practically no solution exists that can efficiently eradicate the symptoms of simulator sickness from virtual environments. Therefore, in the absence of a solution, it is required to at least quantify the amount of sickness. In this paper, we present initial work on our Simulator Sickness Model SiSiMo including a first component to predict simulator sickness scores over time. Using linear regression of short term scores already shows promising performance for predicting the scores collected from a number of user tests.
Violation Detection of Live Video Based on Deep Learning

With the rapid development of Internet technology, live broadcast industry has also flourished. However, in the public network live broadcast platform, live broadcast security issues have become increasingly prominent. The detection of suspected pornographic videos in live broadcast platforms is still in the manual detection stage, that is, through the supervision of administrators and user reports. At present, there are many online live broadcast platforms in China. In mainstream live streaming platforms, the number of live broadcasters at the same time can reach more than 100,000 people/times. Only through manual detection, there are a series of problems such as low efficiency, poor pertinence, and slow progress. This approach is obviously not up to the task requirements of real-time network supervision. For the identification of whether live broadcasts on the Internet contain pornographic content, a deep neural network model based on residual networks (ResNet-50) is proposed to detect pictures and videos in live broadcast platforms. The core idea of detection is to classify each image in the video into two categories: (1) pass and (2) violation. The experiments verify that the network proposed can heighten the efficiency of pornographic detection in webcasts. The detection method proposed in this article can improve the accuracy of detection on the one hand and can standardize the detection indicators in the detection process on the other. These detection indicators have a certain promotion effect on the classification of pornographic videos.
Lattice-based public-key encryption with equality test supporting flexible authorization in standard model

As cloud computing has developed rapidly, outsourcing data to cloud servers for remote storage has become an attractive trend. However, when cloud clients store their data in the cloud, the security and privacy of cloud data would be threatened due to accidental corruptions or purposive attacks caused by a semi-trusted cloud server. The widely used method of addressing the security and privacy of cloud data is to store encrypted data instead of plain data. As the resulting system is unusable, since the cloud can no longer search throughout the data, new cryptographic primitive such as public-key encryption with equality test (PKEET) has been introduced. PKEET has many interesting applications such as keyword search on encrypted data, encrypted data partitioning for efficient encrypted data management, personal health record systems, spam filtering in encrypted email systems and so forth. PKEET allows checking whether two ciphertexts encrypted under different public keys contain the same message or not. However, unrestricted access to equality tests can reveal information about the underlying data. This is not acceptable in respect of users' privacy. In 2015, Ma et al. introduce the notion of PKEET with flexible authorization (PKEET-FA) which strengthens privacy protection. Since 2015, there are several follow-up works on PKEET-FA. However, all are secure in the random oracle model and vulnerable to quantum attacks. In this paper, we provide two constructions of quantum-safe PKEET-FA secure in the standard model based on the hardness assumptions of integer lattices and ideal lattices. Finally, we implement the PKEET-FA scheme over ideal lattices.
Detecting Multi-Scale Faces Using Attention-Based Feature Fusion and Smoothed Context Enhancement

Though tremendous strides have been made in face detection, face detection remains a challenging problem due to scale variance. In this paper, we propose a smoothed attention network for performing scale-invariant face detection by taking advantage of feature fusion and context enhancement, which is dubbed SANet. To reduce the noise in the fused features at different levels, an Attention-guided Feature Fusion Module (AFFM) is designed. In addition, an exhaustive analysis of the role of attention mechanism on performance is conducted, which considers channel-wise, spatial-wise attentions and their combinations. To enrich the contextual information by using dilated convolution and avoid the gridding artifacts problem produced by dilated convolution, we propose a Smoothed Context Enhancement Module (SCEM). Our method achieves state-of-the-art results on the UFDD dataset and comparable performance on the WIDER FACE, FDDB, PASCAL Faces, and AFW datasets.
Assessment of Direct Normal Irradiance Forecasts Based on IFS/ECMWF Data and Observations in the South of Portugal

Direct Normal Irradiance (DNI) predictions obtained from the Integrated Forecasting System of the European Centre for Medium-Range Weather Forecast (IFS/ECMWF) were compared against ground-based observational data for one location at the south of Portugal (Évora). Hourly and daily DNI values were analyzed for different temporal forecast horizons (1 to 3 days ahead) and results show that the IFS/ECMWF slightly overestimates DNI for the period of analysis (1 August 2018 until 31 July 2019) with a fairly good agreement between model and observations. Hourly basis evaluation shows relatively high errors, independently of the forecast day. Root mean square error increases as the forecast time increases with a relative error of ~45% between the first and the last forecast. Similar patterns are observed in the daily analysis with comparable magnitude errors. The correlation coefficients between forecast and observed data are above 0.7 for both hourly and daily data. A methodology based on a new DNI attenuation Index (DAI) was developed to estimate cloud fraction from hourly values integrated over a day and, with that, to correlate the accuracy of the forecast with sky conditions. This correlation with DAI reveals that in IFS/ECMWF model, the atmosphere as being more transparent than reality since cloud cover is underestimated in the majority of the months of the year, taking the ground-based measurements as a reference. The use of the DAI estimator confirms that the errors in IFS/ECMWF are larger under cloudy skies than under clear sky. The development and application of a post-processing methodology improves the DNI predictions from the IFS/ECMWF outputs, with a decrease of error of the order of ~30%, when compared with raw data.
EDGE20: A Cross Spectral Evaluation Dataset for Multiple Surveillance Problems

Surveillance-related datasets that have been released in recent years focus only on one specific problem at a time (e.g., pedestrian detection, face detection, or face recognition), while most of them were collected using visible spectrum (VIS) cameras. Even though some cross-spectral datasets were presented in the past, they were acquired in a constrained setup, which limited the performance of methods for the aforementioned problems under a cross-spectral setting. This work introduces a new dataset, named EDGE20, that can be used in addressing the problems of pedestrian detection, face detection, and face recognition in images captured using trail cameras under the VIS and NIR spectra. Data acquisition was performed in an outdoor environment, during both day and night, under unconstrained acquisition conditions. The collection of images is accompanied by a rich set of annotations, consisting of person and facial bounding boxes, unique subject identifiers, and labels that characterize facial images as frontal, profile, or back faces. Moreover, the performance of several state-of-the-art methods was evaluated for each of the scenarios covered by our dataset. The baseline results we obtained highlight the difficulty of current methods in the tasks of cross-spectral pedestrian detection, face detection, and face recognition due to unconstrained conditions, including low resolution, pose variation, illumination variation, occlusions, and motion blur.
Performance Improvement in Handwritten Devanagari Character Classification

The optical character recognition models have the capability to recognize the characters in real-time. Extensive research work in the field of optical character recognition has led to the development of robust recognition mechanisms for various languages. The concepts of Artificial Intelligence and Deep learning have played a significant role in technological advancements in this field. But there are still some languages that don't have efficient Optical Character Recognition (OCR) systems but have vast ancient literature in the form of scriptures and manuscripts which are still relevant in the present. In recent years, the conventional Convolutional Neural Network (CNN) has performed distinctly in image processing and pattern recognition applications. But the pooling operation in CNN ignores the important spatial information, which proves to be an essential attribute in many cases. The proposed Capsule Network extracts spatial information and improves the capabilities of traditional CNN. It uses capsules to describe features in multiple dimensions and dynamic routing to increase the performance of the network.
The Use of Static Telecytology Applications for Accreditational Purposes

This study aims to examine the feasibility of developing a proficiency testing scheme for cytology labs wishing to be accredited according to ISO 15189:2012, by using low cost telemedical applications. Two hundred forty-eight fine-needle aspiration specimens from patients preoperatively aspirated under ultrasonographic guidance and 208 pap smears with histological confirmation were retrospectively selected from the department's registry. Three diagnostic categories of cytological reports were used. Ten characteristic images from each case were transferred via file transfer protocol to password-protected accounts for remote review by three independent cytopathologists. Contributor's and reviewer's diagnoses were collected, recorded and statistically evaluated. No significant difference in diagnostic accuracy could be detected between the diagnoses proffered on the basis of digitized images and conventional slides.
Electromagnetically Aware Design of Capacitive Touch Sensor Panel Electrodes for Antenna Integration

Modern electronic devices increasingly rely on efficient antenna integration strategies for supporting high-speed wireless connectivity. Especially for bezel-less displays, these antennas are generally placed underneath the touch sensor panel (TSP) where they interact strongly with the sensing electrodes. To overcome this problem, we engineer the shapes of these electrodes to facilitate the transmission of electromagnetic waves having frequency identical to the operating frequency of the integrated antenna. We thereby propose a new design methodology for TSP electrodes and demonstrate its advantages with respect to antenna integration. As an example, we present the design of a TSP that has a pass-band at 2.51GHz and 5.66GHz for TE polarization and 3.01GHz for TM polarization. We integrate a printed dipole antenna with the proposed as well as a standard TSP geometry, placing it right underneath rather than beside the panel. Comparing the radiation patterns at 2.5GHz, we show that the pattern is significantly different than its free-space one, when a standard TSP geometry is used. Yet, the proposed one leads to a pattern that is almost identical (improved by 3.93dB as compared to standard TSP) to that in free-space.
Omni-Directional Obstacle Detection for Vehicles Based on Depth Camera

At present, the vehicle obstacle detection system usually uses different devices or sensors to perceive and obtain the obstacle information. However, omni-directional obstacle detection is difficult to realize because these devices or sensors are usually easy to be affected by environmental lighting and the material properties of the obstacle surface. Furthermore, most sensors have limited information regarding distance, which limits their application to omni-directional obstacle detection. To solve this problem, this paper proposes a method using depth camera for omni-directional obstacle detection. A method applying region growth for depth image and a fast inpainting method for depth image are proposed to extract and repair the obstacle regions in the depth images obtained by installing depth cameras around the car body. An improved method applying iterative normalized cut is also proposed to cluster and segment fragmentary and irregular obstacle regions to generate the complete obstacle regions. Finally, the obstacle regions are overviewed using a three-dimensional visualization method to realize omni-directional obstacle viewing. The results of experiments conducted in an environment with different obstacles during the day and night demonstrate that, compared with other methods, our proposed approach can effectively promote the ability to detect complex obstacles, and largely improve the detection speed; furthermore, obstacle detection using our method will be unaffected by environmental lighting. Each of these advantages provided by our method can significantly promote the driving safety of unmanned or other types of vehicles.
Offline handwritten character classification of the same scriptural family languages by using transfer learning techniques

Transfer Learning by using Convolutional Neural Network has shown its outstanding performance in large scale image classification. India is a multi-script and multilingual country. Out of all languages, Telugu and Kannada have shared almost similar structure characters. Offline character recognition of both handwriting characters is a challenging task. Different feature extraction models have been used in character recognition studies. Convolutional Neural Network is used as efficiently supervised feature vector extraction. Feature vectors from large scale pre-trained ImageNet or COCO were proved more efficient than other script datasets for better result. Fine-tuning model of transfer learning was used in the studies for comparison studies.
Improvement on a Masked White-Box Cryptographic Implementation

White-box cryptography is a software technique to protect secret keys of cryptographic algorithms from attackers who have access to memory. By adapting techniques of differential power analysis to computation traces consisting of runtime information, Differential Computation Analysis (DCA) has recovered the secret keys from white-box cryptographic implementations. In order to thwart DCA, a masked white-box implementation was suggested. It was a customized masking technique that randomizes all the values in the lookup tables with different masks. However, the round output was only permuted by byte encodings, not protected by masking. This is the main reason behind the success of DCA variants on the masked white-box implementation. In this paper, we improve the masked white-box cryptography in such a way to protect against DCA variants by obfuscating the round output with random masks. Specifically, we introduce a white-box AES (WB-AES) implementation applying the masking technique to the key-dependent intermediate value and the several outer-round outputs computed by partial bits of the key. Our analysis and experimental results show that the proposed WB-AES can protect against DCA variants including DCA with a 2-byte key guess, collision, and bucketing attacks. This work requires approximately 3.7 times the table size and 0.7 times the number of lookups compared to the previous masked WB-AES.
A Survey on IOT Based on Renewable Energy for Efficient Energy Conservation Using Machine Learning Approaches

The Internet of Things has a vision in which the web extends into this present reality which is followed by daily posts. The IoT allows articles to be detected or remotely controlled over existing system frameworks, opening doors for the unadulterated incorporation of the physical world into PC-based frameworks and providing improved efficiency, accuracy, and monetary advantage despite reduced human mediation. This breakthrough has various applications, such as urban communities focused on solar power, smart cities, micro matrices, and lights on Solar Road, etc. AI is when calculations decode gigantic knowledge arrangements to the PCs so that they can function without specific programming. This, for the most part, focuses on the development of different PC programs which may change when exposed to new information. During this period renewable vitality developed at a rate faster than some other time in history. These days people groups confronting the issue of confinement of non-sustainable power sources, so to take care of this issue the best arrangement is to utilize sustainable power sources like solar oriented vitality. Solar dependence is the planet's fastest-growing sustainable power source, steadily increasing by a standard of 40 percent in the overall limit. AI can be utilized in Probabilistic Energy Forecasting. The fundamental reason behind this is to gauge the likelihood appropriation of solar oriented power age from more than one solar-based ranch all the while. In this paper, we examined -the study on how IOT assumes a significant job in solar-powered vitality and how AI approaches are utilized in solar oriented vitality.
Implementation of 5-Block Convolutional Neural Network (CNN) for Saliency Improvement on Flying Object Detection in Videos

In this paper an algorithm for detecting a flying object is proposed from the recorded videos using stationary as well as moving camera. Firstly the methods which required less computational power and have less complexity are used. The signature saliency which performs very well and there are very few misses of object detection is used. At the last stage only if required the CNN block is called when the false positive result from previous stages is more.
SketchTransfer: A Challenging New Task for Exploring Detail-Invariance and the Abstractions Learned by Deep Networks

Deep networks have achieved excellent results in perceptual tasks, yet their ability to generalize to variations not seen during training has come under increasing scrutiny. In this work we focus on their ability to have invariance towards the presence or absence of details. For example, humans are able to watch cartoons, which are missing many visual details, without being explicitly trained to do so. As another example, 3D rendering software is a relatively recent development, yet people are able to understand such rendered scenes even though they are missing details (consider a film like Toy Story). The failure of ma- chine learning algorithms to do this indicates a significant gap in generalization between human abilities and the abilities of deep networks. We propose a dataset that will make it easier to study the detail-invariance problem concretely. We produce a concrete task for this: SketchTransfer, and we show that state-of-the-art domain transfer algorithms still struggle with this task. The state-of-the-art technique which achieves over 95% on MNIST → SVHN transfer only achieves 59% accuracy on the SketchTransfer task, which is much better than random (11% accuracy) but falls short of the 87% accuracy of a classifier trained directly on labeled sketches. This indicates that this task is approachable with today’s best methods but has substantial room for improvement.
Eye Contact Correction using Deep Neural Networks

In a typical video conferencing setup, it is hard to maintain eye contact during a call since it requires looking into the camera rather than the display. We propose an eye contact correction model that restores the eye contact regardless of the relative position of the camera and display. Unlike previous solutions, our model redirects the gaze from an arbitrary direction to the center without requiring a redirection angle or camera/display/user geometry as inputs. We use a deep convolutional neural network that inputs a monocular image and produces a vector field and a brightness map to correct the gaze. We train this model in a bi-directional way on a large set of synthetically generated photorealistic images with perfect labels. The learned model is a robust eye contact corrector which also predicts the input gaze implicitly at no additional cost.Our system is primarily designed to improve the quality of video conferencing experience. Therefore, we use a set of control mechanisms to prevent creepy results and to ensure a smooth and natural video conferencing experience. The entire eye contact correction system runs end-to-end in real-time on a commodity CPU and does not require any dedicated hardware, making our solution feasible for a variety of devices.