Reviewerly/exHarmony huggingface dataset:
New term weighting methods for classifying textual sentiment data

ABSTRACTIn current society, people can easily use social media to express their own opinions toward products and services. These online comments can influence other customers’ purchase behaviors. Especially those negative reviews and comments can hurt the images of companies. Consequently, to identify the sentiment of social media users from a large amount comments is one of crucial issues. In recent years, machine learning approaches have been considered as one of possible solutions for recognizing sentiment of text reviews. But, when using these methods to sentiment classification, traditional term weighting methods including Term Presence (TP), Term Frequency (TF), and Term Frequency-Inverse Document Frequency (TF-IDF) often have been utilized for describing the collected textual reviews. However, those conventional term weighting methods cannot have positive effect on improving the classification performance of text sentiment data. Therefore, this study aims to propose two new term weighting methods called Categorical Difference Weights (CDW) and TF-CDW by integrating class information into term weights of textual data to construct Term-Document Matrix (TDM). Then, Support Vector Machines (SVM) will be employed to build classifiers. Finally, we will use several actual cases to demonstrate the effectiveness of our presented methods. Compared to traditional term weighting methods, results showed that our methods indeed outperform TF, TP and TF-IDF.
Psychological Dimensions in Trust-Based Models in Cloud Computing

Cloud computing has been rapidly evolving and various organizations are updating their infrastructure and services to the cloud computing system. However, being a relatively new technology, users of cloud computing are facing various issues that need to be addressed. Identifying the risks associated and establishing trust in that is one such major issue that needs to be addressed. This article has focused on the various concepts related to trust in the cloud environment, technicalities of trust being used and evaluated, the trust models with an objective of identifying the inclusion of psychological dimensions and attributes in the assessment, evaluation, and management process. The results from the review have indicated that as such there are no models found which contribute to incorporating the psychological attributes. As an extension of the assessment, an integration of two important aspects are recommended in the existing trust model. The inclusion of psychological attributes that consist of motivation, perception, learning, and personality attributes; and an initial stage of assessment model in a top-down approach scheme for identifying the effectiveness of cloud service providers based on the collaborative trust information-sharing mechanism and by using techniques such as rankings and ratings.
The Impact of Applying Good Governance Principles on Job Satisfaction among Public Sector Employees in Jordan

This study aims to investigate the impact of the applying of good governance principles on job satisfaction among employees working in the public sector in Jordan.The study population consisted of all public sector employees who are subject to the Civil Service Code (219,000 employees), where the analysis and sampling unit included all job titles.The study sample size was 384 employees, with 315 returned questionnaires, and 308 questionnaires analyzed after excluding the questionnaires that are not suitable for analysis.The study found a statistically significant impact of applying good governance principles collectively (transparency, justice and equality, accountability, participation, and integrity) in each dimension of job satisfaction separately (salaries and compensations, direct supervision, training programs, promotions, and performance evaluation) to varying degrees.The results showed a statistically significant effect of applying good governance principles on job satisfaction among employees in the public sector in Jordan.
Towards a Smart Opponent for Board Games: Learning beyond Simulations

Reinforcement learning algorithms have been successfully trained for games like GO, Atari, and Chess in simulated environments. However, in cue sport-based games like Carrom, real world is unpredictable unlike in Chess and GO due to the stochastic nature of the gameplay as well as the effect of external factors such as friction combined with multiple collisions. Hence, solely training in a simulated platform for games like Billiard and Carrom, which need precise execution of a shot, would not be ideal in actual gameplay. This paper presents a real-time vision based efficient robotic system to play Carrom against a proficient human opponent. We demonstrate the challenges of adopting a reinforcement learning algorithm beyond simulations in implementing a strategic gameplay for the robotic system. We currently achieve an overall shot accuracy of 70.6% by combining heuristic and reinforcement learning algorithms. Analysis of the overall results suggests the possibility of adopting a real-world training for board games which need precise mechanical actuation beyond simulations.
A Linear-Color Contrast Based Algorithm for Fault Detection of Primary Spring in Train Bogie System

The primary spring buffers the impact and vibration of track on train. Hence, once the primary spring breaks or shifts, it will cause serious safety accidents. To detect whether the primary spring has shifted or not, the linear-color contrast (LC) algorithm is adopted to locate a marked white line on the primary spring. More specifically, the salient value of each pixel in the image is calculated to binarize the image. Then, by calculating whether the abscissa difference of the white line's contour between the template image and the image to be detected is less than a given threshold, the primary spring in the image to be detected is determined to be shifted or not. By comparing with other threshold processing techniques for image segmentation tasks, including basic global threshold processing, OSTU (is also called maximum between-cluster variance (MBV)) and Sauvola, the LC algorithm achieves the most impressive performance in primary spring fault detection task.
Reversible Data Hiding in Encrypted Image using Checkerboard Pattern based Pixel Inversion

Reversible data hiding (RDH) is a widely studied domain due to its applications in cloud computing and in the transmission of the medical images. A new block-wise RDH scheme in encrypted images that uses a checkerboard pattern-based pixel selection and pixel inversion technique is introduced in this paper. The use of a checkerboard pattern to classify the pixels into two classes (black pixels and white pixels) and the inversion process on the pixels during data hiding ensures the exact recovery of the image in most of the cases. The extraction of the secret data and the original image recovery is possible by analyzing the correlation between pixels within the decrypted image blocks. A new smoothness measure is introduced in this paper which considers all the 3 $\times$ 3 pixels in the overlapping windows of an image block. The new measure introduced in this paper along with the checkerboard pattern-based pixel inversion ensures a low bit error rate. The results claim that the new scheme performs well as compared to the existing techniques.
Anticipatory Governance in the Technology Sector: Processes, Critiques and Principles for Addressing Grand Challenges in Computing

With growing understanding of negative social and environmental impacts of computing technologies and increasingly urgent calls to mitigate these impacts, the sector now faces thorny questions around whether and how to govern computing technologies. This workshop aims to bring together researchers and practitioners across a wide range of disciplines to explore critical perspectives on and solutions to anticipatory governance in the computing sector. We will draw on participants’ diverse expertise to develop a practical and ethical governance roadmap that attends to the computing sector’s responsibility to mitigate its own contribution to the climate emergency. Having developed strategies within this specific context, we will then produce a set of governance principles that could be useful to mitigate other harms resulting from computing, nominally those pertaining to efforts around responsible AI, data protection, and mis/disinformation.
The Research on the Construction of the “Online + Offline” Hybrid Teaching Mode of College English Under “Internet +” Background

Driven by the technology of network and information, especially the concept of “Internet +”, it has had a great impact on English teaching of college. The traditional classroom-based teaching model cannot meet the social development basic needs at the current stage. Under the “Internet +” background, the “online + offline” college English hybrid teaching model has ushered in new development opportunities. Based on actual teaching experience, the author analyzes the chance and challenges faced in college English teaching at the current stage, and proposes the construction of a “online + offline” hybrid teaching model for college English under the “Internet +” background, in that to improve the effect of English teaching in college positive effects.
L<sub>1</sub>-PLS Based on Incremental Extreme Learning Machine

In order to improve the accuracy of the regression model, an L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> norm partial least square method (IELM-L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -PLS) based on an incremental limit learning machine is proposed. The data processing process of the incremental limit learning machine is nested into the framework based on the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> norm partial least square method, and the original data is upgraded by extracting the hidden node output matrix in the incremental limit learning machine, and then Regression analysis was performed on the upgraded data using L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -PLS. This method is used for experimental verification of actual data. The results show that the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -PLS method based on the incremental limit learning machine can perform better regression analysis on the data.
Geographical variation in mobile phone ownership and SMS literacy among women (age 15–49) in India: A cross-sectional analysis based on National Family Health Survey-4

In India, studies on mobile phone ownership and digital literacy have focused on the gender dimension, perspectives of women and men towards mobile phone use, and socio-economic determinants of ownership. There are no studies with a large sample size that present the variation in mobile phone ownership and ability to read short message services (SMS) message among women across different geographic levels. We use multilevel models to estimate the contribution of geographic levels in explaining the variation in mobile phone ownership and SMS literacy. The data from the fourth round of National Family Health Survey (NFHS) which collected information on health and nutrition indicators was used for our analysis. 122,351 women were interviewed about mobile phone ownership and SMS literacy. Information on mobile phone ownership and ability to read text messages were used to create the dependent variables. The independent variables included women's education, age, religion, social class, wealth quintile and place of residence. Two and four level variants of a multilevel model, with individuals (level-1) nested within primary sampling unit (level 2), districts (level 3) and States (level 4), were used to estimate the probability of mobile phone ownership and ability to read text message. The results from multilevel model and variance partition analysis indicate that contribution of State and primary sampling unit towards explaining variation in mobile phone ownership and ability to read text message is greater than the districts. The variance estimates are sensitive to the number of levels included in the multilevel model, so relying on estimates obtained for a particular level could lead to a bias. Our findings suggest that understanding the magnitude of inequalities at different geographic levels should be warranted more attention before tackling the socio-economic factors. State estimates should be supplemented with information available for lower geographic levels. It is important to identify the geographical clusters with high and low coverage of mobile ownership and ability to read text messages before implementing mhealth interventions.
Multi-Stage Network Embedding for Exploring Heterogeneous Edges

The relationships between objects in a network are typically diverse and complex, leading to the heterogeneous edges with different semantic information. In this article, we focus on exploring the heterogeneous edges for network representation learning. By considering each relationship as a view that depicts a specific type of proximity between nodes, we propose a multi-stage non-negative matrix factorization (MNMF) model, committed to utilizing abundant information in multiple views to learn robust network representations. In fact, most existing network embedding methods are closely related to implicitly factorizing the complex proximity matrix. However, the approximation error is usually quite large, since a single low-rank matrix is insufficient to capture the original information. Through a multi-stage matrix factorization process motivated by gradient boosting, our MNMF model achieves lower approximation error. Meanwhile, the multi-stage structure of MNMF gives the feasibility of designing two kinds of non-negative matrix factorization (NMF) manners to preserve network information better. The united NMF aims to preserve the consensus information between different views, and the independent NMF aims to preserve unique information of each view. Concrete experimental results on realistic datasets indicate that our model outperforms three types of baselines in practical applications.
Depth Estimation for Small Obstacles Based on Monocular Vision

In order to solve the problem of small obstacles avoidance in ultra-low altitude flying of unmanned aerial vehicles (UAVs), a depth estimation algorithm based on monocular visual for small tower obstacles is proposed by combining deep learning and traditional methods. With the advantage of convolutional neural networks to extract the salient features of the images, a lightweight and multi-level residual convolution neural network for obstacle segmentation is proposed. The cross-entropy loss function is optimized by adding the weight of the background and obstacles area. A method based on pairs of coplanar points is introduced to complete the depth estimation of a single frame image. Moreover, the recursive formula of sequence images depth results is derived, and a multi-frame voting optimization algorithm is proposed. Finally, the simulation results show that the proposed algorithm can effectively realize the fast segmentation and accurate depth estimation of the small tower obstacles in the sequence images.
Distributing Parallel Virtual Image Application using Continuous Integrity/Continuous Delivery Based on Cloud Infrastructure

Integration and distribution applications to several production servers take a long time by the classical method. In this case, the application requires several libraries to run. Automated continuous integration/continuous delivery is a method that can make software development activity is more effective. The stages of development such as coding, executing, and distributing applications can be done automatically by system tools. This paper used the method to distribute generating named entity recognition model applications. In this case, the experiment named entity recognition model with computation parallel on more than one server. The results of the named entity recognition model experiment are faster and can reach more results than using one server. The infrastructure of architecture used to run automated continuous integration/continuous delivery uses cloud service. The implementation results of this automated continuous integration/continuous delivery run successfully and run multiple jobs on continuous integration/continuous delivery. The tools to implement this scenario such as GitLab, Jenkins, Docker, Private Image Repository, and the server operations system is Ubuntu server 18.04 Long Term Support 64 bit. The benefits of this paper, operational development activities by virtual image applications can integrate and distributed to several servers by automatically so that requires less time.
Semantic Composition of Word-Embeddings with Genetic Programming

Word-embeddings are vectorized numerical representations of words increasingly applied in natural language processing. Spaces that comprise the embedding representations can capture semantic and other relationships between the words. In this paper we show that it is possible to learn methods for word composition in semantic spaces using genetic programming (GP). We propose to address the creation of word embeddings that have a target semantic content as an automatic program generation problem. We solve this problem using GP. Using a word analogy task as benchmark, we also show that GP-generated programs are able to obtain accuracy values above those produced by the commonly used human-designed rule for algebraic manipulation of word vectors. Finally, we show the robustness of our approach by executing the evolved programs on the word2vec GoogleNews vectors, learned over 3 billion running words, and assessing their accuracy in the same word analogy task.
D-Sketch: A Differentiated Sketch Strategy for Double-Stack Networks

Representing traffic with sketch data structures to provide flow statistics is a fundamental strategy in network measurement. In the current double-stack circumstance, we observe that IPv4 and IPv6 flows are inequivalent in terms of both flow cardinality and flow size in Internet. The existing sketches, however, represent IPv4 and IPv6 flows with no differentiation. As a consequence, the IPv4 flows, which can be massive and large, while the IPv6 flows, which are usually handful and small. Such an unbalance or asymmetry feature may lead to unnecessary hash collision errors, especially for IPv6 flows. To this end, in this letter, we present D-Sketch, a new sketch optimization strategy to represent IP flows. At its core, D-Sketch differentiates IPv4 flows from IPv6 ones, thereafter represents them with isolated sketches whose capacities are proportional to the corresponding flow cardinality. Given the same space overhead, trace-driven evaluations further commit that D-Sketch decreases the ARE of per-flow measurement up to 52.5%, with an average of 24.0%, compared with the existing one-sketch and large-small strategies.
Blockchain-Based Platform for Trusted Collaborations on Data and AI Models

Data analytics and artificial intelligence are extensively used by enterprises today and they increasingly span organization boundaries. Such collaboration between organizations today happens in an ad hoc manner, with very little visibility and systemic control on who is accessing the data, how, and for what purpose. When sharing data and AI models with other organizations, the owners desire the ability to control access, have visibility into the entire data pipeline and lineage, and ensure integrity. In this work, we present a decentralized trusted data and model platform for collaborative AI, that leverages blockchain as an immutable metadata store of data and model resources and operations performed on them, to support and enforce ownership, authenticity, integrity, lineage and auditability properties. Smart contracts enforce policies specified on data, including hierarchical and composite policies that are uniquely enabled by the use of blockchain. We demonstrate that our system is light-weight and can support over 1000 transactions per second with sub-second latency, significantly lower than the time taken to execute data pipelines.
A Computational Treatment of Anaphora and Its Algorithmic Implementation

Abstract In this paper, we propose a framework capable of dealing with anaphora and ellipsis which is both general and algorithmic. This generality is ensured by the compination of two general ideas. First, we use a dynamic semantics which reperent effects using a monad structure. Second we treat scopes flexibly, extending them as needed. We additionally implement this framework as an algorithm which translates abstract syntax to logical formulas. We argue that this framework can provide a unified account of a large number of anaphoric phenomena. Specifically, we show its effectiveness in dealing with pronominal and VP-anaphora, strict and lazy pronouns, lazy identity, bound variable anaphora, e-type pronouns, and cataphora. This means that in particular we can handle complex cases like Bach–Peters sentences, which require an account dealing simultaneously with several phenomena. We use Haskell as a meta-language to present the theory, which also consitutes an implementation of all the phenomena discussed in the paper. To demonstrate coverage, we propose a test suite that can be used to evaluate computational approaches to anaphora.
Foreground-aware Siamese tracker with dynamic template in wireless sensor networks

Accuracy and reliability are the main problems to be considered when using wireless sensor networks for target tracking. Deep learning has a good prospect in visual object tracking, in which Siamese based trackers achieve a balance between performance and computational efficiency.Template adaptability and feature expression of the target are crucial for Siamese trackers which aim to locate the target by learning a decision-making based similarity evaluation. However, the fixed template can hardly handle the severe target variation and the background information in the complex scene will also interfere the similarity evaluation learning process.In these work, we propose a foreground-aware Siamese tracker with dynamic template to tackle these issues. Firstly, we construct a local effective template feature attention network to guide the template updating according to context tracking information. Then the similarity attention update mask is generated to enhance the discriminative ability of tracker to adapt to complex tracking task. Further, we propose a foreground-aware channel selection module which let the tracker pay more attention to the foreground semantic feature channel in the target search area, thus our tracker can suppress the tracking drift of Siamese trackers in the background clutter scene. Extensive experiments demonstrate that our tracker can significantly improve the performance of our baseline and performs favorably against many state-of-the-art trackers.
Attendance System with Face Recognition

For every organization, today attendance is the most important thing to record the presence of someone. The presence of someone in an organization is a sign that the person is carrying out their obligations to come to the agency or organization. Usually, attendance is done manually. It can be signed or called one by one. In this digital age, there must be a change from this absence to be able to accelerate and provide time efficiency. We can use face recognition to record attendance from everyone present in an organization. In this face recognition, many algorithms are performed to dissect and capture images of someone's face, such as Machine Learning and Deep Learning. With this algorithm, the system can recognize a person's face and record attendance from that person so that attendance activities are more efficient and faster.