Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis of Participant Comments in a User Centered Design Study for Degraded Visual Environment Sensor Visualization

Several methodologies in user centered research lead to the collection of large amounts of comments about a product or system. The growth of social media research has led to the development of sentiment analysis algorithms that computationally analyze the meaning of text. This paper utilized the Valence Aware Dictionary for sEntiment Reasoning (VADER) sentiment analysis technique to assess comments from a user centered design study for a rotorcraft degraded visual environment mitigation system. The sentiment analysis findings mirror results from the other measures of the user centered design study. This paper supports the use of sentiment analysis for large volumes of comment data from user centered design studies.
A Modality Converting Approach for Image Annotation to Overcome the Inconsistent Labels in Training Data

The automatic image annotation (AIA) task, in which a system specifies descriptive keywords for an input image, has been a shared task studied for long time, and still important because the annotation keywords enables users efficient access of ever-growing image data. However, the current performance of the AIA systems remains at low levels. One of the difficulties of the AIA comes from inconsistency of annotation keywords in the training data, which is naturally occurred in manual annotations, for many supervised methods. For example, annotation keywords for images of people may be “tourist” or “woman” depending on scenes of the images. This inconsistency makes it difficult to annotate images, which possibly have such similar keywords. For that difficulty, we propose a modality converting method that transforms an input image into an encyclopedic text of keywords assigned to the image. With the modality converting, similar keywords can share their features derived from texts with each other. In the proposed method, we pair images with Wikipedia articles, which have annotation keywords as their titles. We train a modality convertor from images to Wikipedia texts using a neural network with the paired data. Then, the method classifies the converted text into annotation keywords similar to the text classification. Experimental results show relatively high performance of our method based on the converted text compared with existing methods.
A party-adaptive variant of LowGear

Over the last decade, secure multi-party computation (MPC) has made a great stride from a major theoretical area to the multi-functional tool for building privacy protection applications. At EUROCRYPT’18, Keller et al. presented MPC protocol LowGear based on semi-homomorphic (addition-only) encryption. Due to its superior performance and high availability, LowGear has drew a lot of attention from industry. But in practical application environment, there are still requirements that LowGear does not satisfy. A major drawback is the inability to handle changes in the set of parties during online computing phase. In this paper, we present a party-adaptive variant to LowGear, with the goal to continue secure collaborative computing tasks when some new parties join in, some old parties drop out, or new parties replace old ones. This variant is based on tweaks to preprocessing phase to adapt existing preprocessed data to the computation over a new set of parties without regenerating new data by rerunning the entire preprocessing phase. It can effectively avoid the waste of data and time resources. Meanwhile the variant does not reduce security and efficiency of LowGear.
WITHDRAWN: Performance evaluation of the sequential neural network decoder with improved bit error rate

Due to the dimensionality of the neural network channel code decoder's training complexity increases exponentially with the length of the code term. While the computational capacity has advanced considerably, it is still difficult to manage the word code for long blocks. In this work, we proposed a decoder based on a neural network called the Sequential Neural Decoder (SNND). The SNND consists of several Sub-Models, and transfers the final state of the present Sub-Model as initial state to the following model. The output of the SNND's bit error rate (BER) continues unchanged as submodels increase, and the performance of Viterbi soft decisions in the white additive Gaussian noise (AWGN) channel is near. The output of the SNND, however has decreased along with the increased order of modulation.
Ellipse-FCN: Oil Tanks Detection from Remote Sensing Images with Fully Convolution Network

Oil is an essential asset for every country, and plays a key role in world trade system. The detection of oil tanks is a very important task for both military and commerce. Recently, researchers have shown an increasing interest in oil tanks detection in remote sensing imagery. However, the previous works almost used the methods of circle detection, but the real oil tanks in remote sensing imagery are more close to ellipses. In this paper, we propose an oil tanks detector base on an U-shape Fully Convolutional Network(FCN) in optical remote sensing images. The structure of our network consists of three parts: feature extraction part, feature merge part and the output layer. The output layer consists of two output branches, one branch is a score map branch, which generates confidence score to indicate the region of oil tanks at pixel wise, and the other ends up with several channels which regress the ellipse geometric parameters (center, horizontal axis and vertical axis). In addition, we also design a novel loss function adapted to our network. The experimental results conducted on our dataset collected from Google Earth show that this method achieves promising performance on oil tanks detection in terms of both efficiency and accuracy in high-resolution optical remote sensing images.
Performance Measures in Discrete Supervised Classification

The evaluation of results in Cluster Analysis frequently appears in the literature, and a variety of evaluation measures have been proposed. On the contrary, in supervised classification, particularly in the discrete case, the subject of results’ evaluation is relatively scarce in this field of the literature. This is the motto underlying this study. The evaluation of the performance of any model of supervised classification is, generally, based on the number of cases correctly or incorrectly predicted by the model. However, these measures can lead to a misleading evaluation when the data is not balanced. More recently, other types of measures have been studied as association or agreement coefficients, the Huberty index, Mutual information, and even ROC curves. Exploratory studies were conducted in this study to understand the relationship between each measure and data characteristics, namely, sample size, balance, and separability of classes. To this end, simulated data and a Beta regression model in the performance of the models were used.
Uncertainty maximization in partially observable domains: A cognitive perspective

Faced with an ever-increasing complexity of their domains of application, artificial learning agents are now able to scale up in their ability to process an overwhelming amount of data. However, this comes at the cost of encoding and processing an increasing amount of redundant information. This work exploits the possibility of learning systems, applied in partially observable domains, to selectively focus on the specific type of information that is more likely related to the causal interaction among transitioning states. A temporal difference displacement criterion is defined to implement adaptive masking of the observations. It can enable a significant improvement of convergence of temporal difference algorithms applied to partially observable Markov processes, as shown by experiments performed under a variety of machine learning problems, ranging from highly complex visuals as Atari games to simple textbook control problems such as CartPole. The proposed framework can be added to most RL algorithms since it only affects the observation process, selecting the parts more promising to explain the dynamics of the environment and reducing the dimension of the observation space.
Evidence-Based Technological Solutions for Migrant Workers: Is a Technological Information Platform for Thai Migrant Workers in Israel a Solution?

CIMI is a professional NGO that has partnered with the government of Israel to oversee the arrival of Thai migrants and implement best practices as part of that process. Approximately 25,000 Thai migrants work in remote agricultural areas around Israel, and have little knowledge about life and working conditions in the country. Studies show that increased internet access, specifically mobile information and communications technology (ICT), can lead to a significant improvement in migrants’ effective access to information, informed decision-making and work standards. Building on literature that examines how internet usage gaps still exist across social categories despite equal access, the study presented in this paper examines how Thai migrant workers in Israel use mobile ICT, in order to find ways to enhance and utilize it to improve their wellbeing. As a next step and based on a survey conducted among this population for this research, CIMI would aim to apply the findings and operationalize solutions for internet usage which can benefit these migrants by targeting their specific needs and assets. A technological platform to deliver reliable information and utilities can have a significant impact on over 25,000 Thai migrants in Israel. The project also has the potential of being expanded to include other migrant workers’ communities in Israel, ranging from over 40,000 Filipino caretakers to over 10,000 Moldovan, Ukrainian and Chinese construction workers.
Scalable Distributed Pivot Analysis over Massive Big Data: Models, Paradigms, New Advancements

Pivot analysis is a powerful analytics tool that has been traditionally used in Business-Intelligence/OLAP systems. Surprisingly, pivot analysis turns to be very handful in modern big data analytics systems, where the main issue is that of extracting useful, actionable knowledge from massive amounts of (distributed) data. Energy management application scenarios are classical settings where pivot analysis shows its benefits, and the integration with big data features is a very exciting research direction. Following these considerations, this paper proposes a new vision on scalable distributed pivot analysis over massive big data, by analyzing actual trends and studying further advancements.
Modified forms of Tscherning ellipses

Abstract Third‐order equations are well known for determining sagittal and tangential powers of a thin lens, corresponding to an eye rotating behind a lens to view objects away from the optical axis of the lens. These equations are referenced to the back surface of the lens and do not take into account the peripheral thickness of the lens. They do not give the same results as finite raytracing at small angles in which powers are referenced to the vertex sphere, which is the same distance from the centre‐of‐rotation for all object angles. Modified forms of the third‐order sagittal and tangential image vergence error equations are developed to overcome the discrepancies. These are used to determine Tscherning ellipses for zero oblique astigmatism and zero mean oblique power error. While solutions to oblique astigmatism are not affected by the modifications, there are considerable changes to mean oblique error solutions.
A study of student evaluation of teaching in applied colleges

As one of the commonly employed methods in teaching quality evaluation, student evaluation of teaching (SET) plays an important role in teaching quality evaluation in colleges and universities. Traditional SET-based research usually takes a small portion of data to measure the influencing factors in SET. However, there are quite a few influencing factors of SET, and the reliability of previous conclusions need verification in a larger volume of data. Based on a bigger value of SET-related data from an applied college, we made an analysis of the influencing factors including students’ curriculum performance, teacher information, and curriculum settings. The analysis affirms that students’ curriculum performance correlates little with SET, and that teacher information does indeed have some impact on SET, but most importantly, curriculum settings have a heavy impact on SET results.
A Novel Framework for Enhancement of Diagnostic Information in MR Imaging using Super-Resolution

Magnetic resonance imaging (MRI) is the most preferred modality for imaging soft tissues. In spite of several advantages, it has a limitation of slow data acquisition, which restricts its application in high resolution (HR) imaging. One viable solution is the post-processing approach using super-resolution (SR) to generate HR images within a clinically feasible time. The state-of-the-art SR methods are commonly producing good quality HR images, but most of them are not designed for clinical application and also involve high computational time. In this paper, we propose a region-of-interest based framework to enhance the diagnostic information in MR images for pathological use. Several experiments are carried out with pathological MRI datasets to evaluate the performance of the proposed technique. From the experimental results, we can conclude that the proposed method is a clinically feasible solution for the enhancement of diagnostic information in MR images.
IFEPE: On the Impact of Facial Expression in Head Pose Estimation

The Head Pose Estimation (HPE) is the study of the angular rotations of the head along the Pitch, Yaw, and Roll axes. Widely used in facial involving methods, as face frontalization, driver attention and best surveillance frame selection, is strongly related to facial features. In this study we examine the impact of facial expressions (FE) on HPE and, in particular, we put in relation the axis more affected by the error when a specific facial expression is observable. The HPE method chosen for this purpose is based on the Partitioned Iterated Function System (PIFS). For its construction this method is dependent on the facial appearance and self-similarity. Basing on this, and using a FER network, we observed that there is an evident relation between facial expressions and pose errors. This relation go thought the facial keypoints distances and can be discriminated along the three axes, by providing an estimate of the percentages of variation in errors related to a percentage of variation in distances.
Fast Duplicate Bug Reports Detector Training using Sampling for Dimension Reduction: Using Instance-based Learning for Continous Query in Real-World

Duplicate bug report detection (DBRD) is a famous problem in software triage systems like Bugzilla. It is vital to update the internal machine learning (ML) models of DBRD for real-world usage and continuous query of new bug reports. The training phase of ML algorithms is time-consumable and dependent on the training dataset volume. Instance-based learning (IbL) is an ML technique that reduces the number of samples in the training dataset to achieve fast learning for the incremental database. This research introduces a hybrid approach using clustering and straight forward sampling to improve the runtime and validation performance of DBRD. Two bug report datasets of Android and Mozilla Firefox are used to evaluate the proposed approach. The experimental evaluation shows acceptable results and improvement in both runtime and validation performance of DBRD versus the traditional approach without IbL.
Optimizing Vertex Pressure Dynamic Graph Partitioning in Many-Core Systems

With the rise of graph-based algorithms in many applications, dynamic graphs have become critical for applications that work with real-time or time-series relationship data. Due to their large storage footprint, these applications require high parallelism and locality. Many-core architectures offer high parallelism for both static and dynamic graphs. However, systems operating on dynamic graphs must continuously repartition graph data across storage units to achieve good locality, load balancing, and performance. In this work, we examine the effectiveness and efficiency of different vertex-pressure repartitioning schemes, which move vertices so to co-locate them near their most relevant neighbors. We describe key repartitioning design choices and provide a thorough evaluation of the impact of a range of design features with different datasets. Our evaluation indicates that optimized dynamic repartitioning techniques can often provide over 2x performance speedup over state-of-the-art static solutions.
The phenomenon of photo fakes and a problem of manipulating a context of a photograph in an information and semantic warfare

The article deals with the analysis of the photo fakes phenomenon in contemporary informational space as well as the principles of their fabrication, spreading, and influence on audience. The examples illustrating a practice of changing and counterfeiting a context of photographic image are discussed in historical dimension as well as on the basis of Russia’s information and semantic warfare against Ukraine in 2014 – 2020. The causes of stable existence of such manipulations and their rebuttal resistance are shown.
MCCNet: Multi-Color Cascade Network with Weight Transfer for Single Image Depth Prediction on Outdoor Relief Images

Single image depth prediction is considerably difficult since depth cannot be estimated from pixel correspondences. Thus, prior knowledge, such as registered pixel and depth information from the user is required. Another problem rises when targeting a specific domain requirement as the number of freely available training datasets is limited. Due to color problem in relief images, we present a new outdoor Registered Relief Depth (RRD) Prambanan dataset, consisting of outdoor images of Prambanan temple relief with registered depth information supervised by archaeologists and computer scientists. In order to solve the problem, we also propose a new depth predictor, called Multi-Color Cascade Network (MCCNet), with weight transfer. Applied on the new RRD Prambanan dataset, our method performs better in different materials than the baseline with 2.53 mm RMSE. In the NYU Depth V2 dataset, our method’s performance is better than the baselines and in line with other state-of-the-art works.
Text quantitative analysis on administrative punishment for the construction of a case analysis knowledge graph

The administrative punishment announcements of financial fraud issued by the China Securities Regulatory Commission (CSRC) are unstructured texts, and it is difficult for them to effectively play their role of reference and decision-assisting without pre-processing and resorting. This paper uses quantitative analysis method to analyze the administrative punishment announcements texts of financial fraud issued by the CSRC. It counts the number of the administrative punishment announcements issued from 2011 to 2019, analyzes the trends, and summarizes the categories of financial fraud and punishment methods. This paper analyzes the content of the administrative punishment announcements and designs the conceptual model for the administrative punishment case, which is conducive to the construction of a case analysis knowledge graph of administrative punishment for financial fraud and realizing the text analysis and case reasoning based on artificial intelligence.
Encryption Issues in Traffic Control Systems in Smart Cities and Traffic Signal Control Optimization

In this contribution, the most widely-used and popular cryptographic algorithms, both symmetric and asymmetric, are compared in terms of security, and in terms of performance and efficiency when implemented in MATLAB. Among symmetric algorithms, Data Encryption Standard (DES) and Advanced Encryption Standard (AES) are taken into consideration, and among asymmetric algorithms, the RSA cipher is analyzed. Following that, encryption issues in smart traffic control systems are discussed, and the possibility of integrating the studied cryptographic algorithms to those systems is introduced. Lastly, the paper suggests an advanced time-plan in order to optimize the traffic signal timing based on different demand patterns on different time-intervals of the day, while eliminating the encryption and decryption burdens by using different techniques like pipelining and queueing.
New Division Property Propagation Table: Applications to Block Ciphers with Large S-boxes

Abstract The division property method is a technique for automatic searching integral distinguishers on block ciphers. Previous methods only use word-based division property to search integral distinguishers for block ciphers with large S-boxes. Since using bit-based division property may find longer integral distinguishers than word-based division property, we propose a method to automatically search the integral distinguishers based on bit-based division property for block ciphers with large S-boxes. To achieve this goal, we propose a new division property propagation table for S-boxes. Theoretically, we prove that using both the new table and the traditional method to describe the bit-based division property propagation rule of S-box will lead to the same integral distinguishers. Technically, we design a mixed-integer linear programming-based tool to search the integral distinguisher based on the new table, which helps to search new integral distinguishers for block ciphers with large S-boxes efficiently. As a result, we apply our tool to derive new integral distinguishers and get the tight bound on the rounds that no integral distinguishers exist for ICEBERG, KHAZAD, Camellia, CS-Cipher, ITUbee and SMS4. Besides, to show the availability of our integral distinguishers, we form the present best five-round and the first six-round integral attack for ICEBERG as an example.
Revenue Maximization using Multitask Learning for Promotion Recommendation

This paper proposes and evaluates a multitask transfer learning approach to collectively optimize customer loyalty, retail revenue, and promotional revenue. Multitask neural network is employed to predict a customer's propensity to purchase within fine-grained categories. The network is then fine-tuned using transfer learning for a specific promotional campaign. Lastly, retail revenue and promotional revenue are jointly optimized conditioned on customer loyalty. Experiments are conducted using a large retail dataset that shows the efficacy of the proposed method compared to baselines used in the industry. A large retailer is currently adopting the proposed methodology in promotional campaigning owing to significant overall revenue and loyalty gains.
pcSVF: An Evaluation of Side-Channel Vulnerability of Port Contention

The threats from side-channel attacks to modern processors has become a serious problem, especially under the enhancement of the microarchitecture characteristics with multicore and resource sharing. Therefore, the research and measurement of the vulnerability of the side-channel attack of the system is of great significance for computer designers. Most of the current evaluation methods proposed by researchers are only for typical cache side-channel attacks. In this paper, we propose a method to measure systems' vulnerability to side-channel attacks caused by port contention called pcSVF. We collected the traces of the victim and attacker and computed the correlation coefficient between them, thus we can measure the vulnerability of the system against side-channel attack. Then we analyzed the effectiveness of the method through the results under different system defense schemes.