Reviewerly/exHarmony huggingface dataset:
Adversarial Training-Based Mean Bayesian Personalized Ranking for Recommender System

Users' feedback information as the ground-truth has attracted a lot of attention in recommender systems. However, the feedback that could be contaminated by users' misoperations or malicious operations is probably not true in real scenarios. This work aims to develop a technique based on an improved Bayesian personalized ranking (BPR), called adversarial training-based mean Bayesian personalized ranking (AT-MBPR). In this method, we divide the feedback information into three categories based on the mean Bayesian personalized ranking (MBPR), then gain the implicit feedback from the mean and non-observed items of each user, following which, adversarial perturbations are added on the embedding vectors of the users and items by playing a minimax game to reduce the noise. The experiments demonstrate in five datasets that our approach outperforms the traditional BPR methods and state-of-the-art methods used for the recommendation. Our implementation is available at: https://github.com/HanXia001/Adversarial-Training-based-Mean-BPR-for-Recommender.
Differentiation of early mild cognitive impairment in brainstem MR images using multifractal detrended moving average singularity spectral features

Brainstem texture analysis can provide valuable information in the diagnosis of Early Mild Cognitive Impairment (EMCI) condition. In this work, 3D brainstem structure is segmented and analysed for texture alterations using multifractal features to differentiate EMCI from other Alzheimer's disease stages. The images obtained from public domain database are preprocessed for spatial registration, skull stripping and contrast enhancement. White matter volume is segmented from the preprocessed images using fuzzy 'C' means clustering algorithm. Midsagittal white matter tissue is used as the initial seed to segment the brainstem volume using sparse field level set method. Multifractal detrended moving average algorithm is used to compute the fluctuation function, generalized Hurst exponent and mass exponent to study the multifractal characteristics of brainstem structure. Features extracted from the multifractal spectrum are analysed to differentiate the images pertaining to EMCI subject group. Results indicate that the proposed technique is able to segment the brainstem structure from all the considered images. The fluctuation function is observed to have linear relationship with scale. The generalized Hurst exponent decreases with order and mass exponent follows a non-linear trend demonstrating the multifractal nature of brainstem. Singularity spectral features namely strength of multifractality, Holder exponent at f(2.8), tangent slope and maximum Holder exponent are found to be most significant in differentiating EMCI from subject groups. As this complex EMCI distinction is clinically important, the proposed approach is useful for early diagnosis of Alzheimer's condition.
Autonomous flight for Multi-copters flying in UTM -TCL4+ sharing common airspace

NASA's UAS Traffic management (UTM) research initiative has demonstrated the flight of multiple UAVs flying in urban environment. Most of the deconfliction has been demonstrated with separate operational volumes through different USSs. For high density operations in urban environments autonomous vehicles should be able to fly in the same shared airspace take on-board decisions to handle different scenarios and land safely beyond visual line of sight. In this paper we present a complete autonomous architecture for flying an UAV in dense urban environments. The planning architecture consists of a global planner and local planner. The global planner plans path to the goal and alternate landing sites along the way. The local planner is involved in avoiding obstacles along the approved path. An UTM client talks with the connected USS to get flight approvals from UTM and make contingency plans.The entire system is tested in the Reflection Simulation Architecture in the NASA Ames Campus simulation demonstration.
Hand Gesture Recognition for Deaf-Mute using Fuzzy-Neural Network

Communication is important for every individual to convey whatever information they want to people and viceversa. Hand gesture is one of the important methods of nonverbal communication for human beings. There are plenty of methods that are used to recognize hand gestures with different accuracies and precision, some has advantages and disadvantages. The general objective of this paper is to develop a hand gesture translator gloves with the use of fuzzy-neural network to eliminate the barrier of communication for deaf-mute and non-deaf person. This paper studied the effectiveness of combining fuzzy logic and neural network for hand gesture recognition. The study is successful with the objective of combining Fuzzy Logic algorithm with Neural Networks algorithm to improve the hand gesture recognition rate compared to as an individual. With the earning capability of the Neural Network combined with the simple interpretation and implementation by means of Fuzzy Logic, it unite their advantages and exclude disadvantages like the ability of Fuzzy Logic to interpret input to output that Neural Network is unable to do. The total percent of recognition rate was met with an average of 92.58%.
A Survey on Methods of Ontology Learning from Text

Ontologies continue to emerge in performing a greater function in various business processes and knowledge sharing in the field of modern information systems. They help to exchange and extend, from syntax to semantic, data and knowledge. They are viewed as a remedy for interoperable semantics which power semantic web. Since the turn of the millennium, ontology learning (OL) from text has received a considerable attention with a sudden surge of textual information for promising research. Remarkably, with intermingling of various disciplines like text mining (TM), statistical analysis techniques (SAT), machine learning (ML), knowledge representation (KR), natural language processing (NLP), etc., more research is on in the area of Ontological Engineering and OL from text. This survey brings out several outcomes of foregone research, discusses challenges and limitations of research in the past decade and points to new challenges in future.
Information hiding methods for E-Healthcare

As the volume of medicinal information stored electronically grows, the necessity to boost the security increases too. Health care systems are now very common in the world. A healthcare system must maintain privacy while preventing patient information from being transmitted. Therefore, securing such systems is crucial for gaining public confidence in the use of network-based healthcare systems. Information hiding is particularly used for security applications to protect the secret message from an unauthorized person. Employing information hiding in healthcare systems, deals with storing, sharing, and managing medical data in a reliable and secure manner. This paper reviews the algorithms used to provide security in healthcare systems. Furthermore, these algorithms are categorized in this paper based on the application and the way of providing security. Finally, the challenges in implementing the security of healthcare systems are described.
Neural Stance Detection With Hierarchical Linguistic Representations

Stance detection aims to assign a stance label (i.e., favor or against) to a post towards a specific target. Recently, there is a growing interest in adopting neural models to detect stance of a document. However, most of these works focus on modeling the sequence of words to learn document representation, though other linguistic information, such as sentiment and arguments, are correlated with the stance of document, and may inspire us to explore the stance. In this article, we propose a hierarchical attention neural model to well study various linguistic information to better represent a document via hierarchical linguistic representations. In addition, we propose a hierarchical network with attention mechanism to weight the importance of various kinds of linguistic information, and learn the mutual attention between document and linguistic information. Detail evaluation on two benchmark datasets demonstrates the effectiveness of proposed hierarchical network with attention mechanism.
Purely Image-Based Pose Stabilization of Nonholonomic Mobile Robots With a Truly Uncalibrated Overhead Camera

Although many vision-based control methods have been proposed for nonholonomic mobile robots, in their implementation, it is usually necessary to calibrate the camera intrinsic and/or extrinsic parameters using offline/online parameter estimation algorithms or online adaptation laws. To avoid the tediousness of camera calibration and to make the system performance highly robust to camera parameter uncertainties, in this article, we propose novel image-based pose stabilization control approaches for nonholonomic mobile robots with a truly uncalibrated overhead fixed camera. In the proposed approaches, only image position information of three feature points from an overhead camera is used for controller design, while information from other sensors (such as wheel encoders) is not required. Furthermore, either offline or online camera calibration is not necessary, and no knowledge about the camera intrinsic and extrinsic parameters is needed, which also can greatly simplify the controller implementation. Simulation and experimental results are given to demonstrate the feasibility and effectiveness of the proposed purely image-based pose stabilization approaches.
Women and the Spatial Politics of Community Networks

Community Networks (CNs) offer many positive impacts for rural inhabitants of low-income regions beyond cheap communications, including different benefits to local economies, social welfare and personal well-being. However, spatial politics compromise diverse inclusion in these benefits by rendering women invisible when the wireless technologies used by CNs are designed, deployed and referred to in discussions about regulation. I support this claim by drawing together insights from multiple case research of rural CNs in India, Indonesia, Argentina Mexico, South Africa and Uganda, and my engagement with activists advocating for CNs. The spatial politics shaping debates about spectrum regulation, and the wireless technologies to which they apply, are at different scales than women in rural villages experience. Gendered spatial politics perform orthogonally across all scales and dimensions to constrain women's proximal access to WiFi, capacity to learn about and undertake technical tasks and involvement in decisions about CNs; however, advocacy for CNs works across vast geographies.
Task-Optimized Word Embeddings for Text Classification Representations

Word embeddings have introduced a compact and efficient way of representing text for further downstream natural language processing (NLP) tasks. Most word embedding algorithms are optimized at the word level. However, many NLP applications require text representations of groups of words, like sentences or paragraphs. In this paper, we propose a supervised algorithm that produces a task-optimized weighted average of word embeddings for a given task. Our proposed text embedding algorithm combines the compactness and expressiveness of the word-embedding representations with the word-level insights of a BoW-type model, where weights correspond to actual words. Numerical experiments across different domains show the competence of our algorithm.
Daily life patients sentiment analysis model based on well-encoded embedding vocabulary for related-medication text

Millions of health-related messages and fresh communications can reveal important public health issues. New Drugs, Diseases, Adverse Drug Reactions (ADRs) keep appearing on social media in new Unicode versions. In particular, generative Model for both Sentiment analysis (SA) and Naturel Language Understanding (NLU) requires medical human labeled data or making use of resources for weak supervision that operates with the ignorance and the inability to define related-medication targets, and results in inaccurate sentiment prediction performance. The frequent use of informal medical language, nonstandard format and abbreviation forms, as well as typos in social media messages has to be taken into account. We probe the transition-based approach between patients language used in social media messages and formal medical language used in the descriptions of medical concepts in a standard ontology[21] to be formal input of our neural network model. At this end, we propose daily life patients Sentiment Analysis model based on hybrid embedding vocabulary for related-medication text under distributed dependency, and concepts translation methodology by incorporating medical knowledge from social media and real life medical science systems. The proposed neural network layers is shared between medical concept Normalization model and sentiment prediction model in order to understand and leverage related-sentiment information behind conceptualized features in Multiple context. The experiments were performed on various real world scenarios where limited resources in this case.
Proximity-aware heterogeneous information network embedding

Network embedding, which aims to learn a high-quality low-dimensional representation for each node in a network, has attracted increasing attention recently. Heterogeneous information networks, with distinguishing types of nodes and relations, are one of the most significant networks. In the past years, heterogeneous information network embedding has been intensively studied. Most popular methods generate a set of node sequences, and feed them into an unsupervised feature learning model to obtain a low-dimensional vector for each node. However, the limitations of these approaches are that their generative node sequences neglect the different importances of diverse relations and they ignore the great value of proximity information which reveals whether two nodes are close or not in the network. To tackle these limitations, this paper presents a novel framework named Proximity-Aware Heterogeneous Information Network Embedding (PAHINE). The native information of a network is extracted from node sequences, which are generated by walking on a probability-sensitive metagraph. Afterwards, the extracted information is fed into deep neural networks to derive the desired embedding vectors. The experimental results on four different heterogeneous networks indicate that the proposed method is efficient and it outperforms the state-of-the-art heterogeneous networks embedding algorithms.
Decreasing the execution time of reducers by revising clustering based on the futuristic greedy approach

Abstract MapReduce is used within the Hadoop framework, which handles two important tasks: mapping and reducing. Data clustering in mappers and reducers can decrease the execution time, as similar data can be assigned to the same reducer with one key. Our proposed method decreases the overall execution time by clustering and lowering the number of reducers. Our proposed algorithm is composed of five phases. In the first phase, data are stored in the Hadoop structure. In the second phase, we cluster data using the MR-DBSCAN-KD method in order to determine all of the outliers and clusters. Then, the outliers are assigned to the existing clusters using the futuristic greedy method. At the end of the second phase, similar clusters are merged together. In the third phase, clusters are assigned to the reducers. Note that fewer reducers are required for this task by applying approximated load balancing between the reducers. In the fourth phase, the reducers execute their jobs in each cluster. Eventually, in the final phase, reducers return the output. Decreasing the number of reducers and revising the clustering helped reducers to perform their jobs almost simultaneously. Our research results indicate that the proposed algorithm improves the execution time by about 3.9% less than the fastest algorithm in our experiments.
An Accurate and Efficient Voting Scheme for a Maximally All-Inlier 3D Correspondence Set

We present a highly accurate and efficient, yet simple, two-stage voting scheme for distinguishing inlier 3D correspondences by densely assessing and ranking their local and global geometric consistencies. The strength of the proposed method stems from both the novel idea of post-validated voting set, as well as single-point superimposition transforms, which are computationally cheap and avoid rotational ambiguities. Using a well-known dataset consisting of various 3D models and numerous scenes that include different occlusion rates, the proposed scheme is evaluated against state-of-the-art 3D voting schemes, in terms of both the correspondence PR (precision-recall) AUC (area under curve), and the execution time. A total of 374 experiments were conducted for each method, which involved a combination of four models, 50 scenes, and two down-samplings. The proposed scheme outperforms the state-of-the-art 3D voting schemes in terms of both accuracy and speed. Quantitatively, the proposed scheme scores 97.0% ±12.9%97.0%±12.9%<; inline-graphic xlink:href="sahloul-ieq1-2963980.gif"/> on the PR AUC metric, averaged over all of the experiments, while the two state-of-the-art schemes score 74.2% ±22.2%74.2%±22.2%<; inline-graphic xlink:href="sahloul-ieq2-2963980.gif"/> and 78.3% ±26.4%78.3%±26.4%<; inline-graphic xlink:href="sahloul-ieq3-2963980.gif"/>. Furthermore, the proposed scheme requires only 24.1% ±6.0%24.1%±6.0%<; inline-graphic xlink:href="sahloul-ieq4-2963980.gif"/> of the time consumed by the fastest state-of-the-art scheme. The proposed voting scheme also demonstrates high robustness against occlusions and scarce inliers.
An improved gray-level coding method for absolute phase measurement based on half-period correction

Absolute phase retrieval is important for three-dimensional (3D) shape measurement in fringe projection technique. Numerous phase unwrapping methods have been proposed for addressing this issue, and gray-level is one such technique employed to determine fringe orders for wrapped phase. However, the codeword detection can fail due to random noise or cameras/projectors defocus. We propose an improved method to determine the codewords in this paper. Specifically, three sinusoidal fringe patterns are used to obtain the wrapped phase and three n-ary gray-level (nGL) patterns are employed to obtain the fringe order. Two opposite masks can be calculated through wrapped phase binarization. The masks and the nGL patterns are roughly used to obtain codewords. After half-period correction, the codeword is accurately reconstructed, and the fringe order is also determined. Simulations and experiments demonstrated the robustness and effectiveness of the proposed algorithm to measure isolated objects with complex surfaces.
2D Non-adjacent coupled map lattice with q and its applications in image encryption

In this paper, a novel high dimensional spatiotemporal chaotic system based on 2D non-adjacent coupled map lattice (2DNACML) model is proposed. Each local lattice is influenced by other random lattices in different dimensions and the local map is defined as a fractional-like style. From the theoretical analysis and numerical simulation, it is found that ranges of system parameters are expanded and unfixed in the novel 2DNACML model. As a result, the changed system is improved and been applied in the image encryption with a mixed scrambling scheme with chaotic method and knights tour method and an efficient diffusion scheme for different images to show the effectiveness of the proposed system.
HarTBleed: Using Hardware Trojans for Data Leakage Exploits

Data and information leakage is an important security concern in current systems. Several data leakage prevention (DLP) techniques have been proposed in the literature to prevent external as well as internal data leakage. Most of these solutions try to trace data flow and perform privilege checks to ensure the security of the data at the software and system level. Architecture level leakage vulnerabilities such as Spectre and Meltdown can be mitigated by performance-expensive software patches or by modifying the architecture itself. However, these solutions assume that the underlying hardware platform is secure and free from tampering. In this article, we present HarTBleed, a class of system attacks involving hardware compromised with a Trojan embedded in the CPU. We show that attacks crafted specifically to make use of the Trojan can be used to obtain sensitive information from the address space of a process. We propose the use of a capacitor-based Trojan trigger that exploits the virtual addressing of L1 cache to activate a Trojan payload that resets a target translation lookaside buffer (TLB) entry to maliciously map to sensitive data in memory. Extensive circuit simulation indicates that the proposed Trojan trigger is not activated during test or normal operation even under a wide range of process/temperature conditions. Therefore, it remains undetected. A successful HarTBleed-based exploit is demonstrated using an attack code by modeling the Trojan effects in the GEM5 simulator.
An Empirical Study of Bug Bounty Programs

The task of identifying vulnerabilities is commonly outsourced to hackers participating in bug bounty programs. As of July 2019, bug bounty platforms such as HackerOne have over 200 publicly listed programs, with programs listed on HackerOne being responsible for the discovery of tens of thousands of vulnerabilities since 2013. We report the results of an empirical analysis that was undertaken using the data available from two bug bounty platforms to understand the costs and benefits of bug bounty programs both to participants and to organisations. We consider the economics of bug bounty programs, investigating the costs and benefits to those running such programs and the hackers that participate in finding vulnerabilities. We find that the average cost of operating a bug bounty program for a year is now less than the cost of hiring two additional software engineers.
Self-adapted optimization-based video magnification for revealing subtle changes

Video magnification techniques can reveal subtle temporal variations that are difficult or even impossible to see with the naked eye and display them in an indicative manner. State-of-the-art approaches rely on hand-designed filters or precise prior knowledge of the target signal and produce magnif ied outputs that are always bounded by the spatial support of the related pyramids. This paper proposes a method for adaptively magnifying subtle video changes by directly solving three key optimization problems. To efficiently model the magnification transformation, alternating direction method of multipliers is employed to solve convex variation-detection optimization problems. Following the transformation step, the perturbation problem is innovatively solved using a forward additive iterative approach to iteratively minimize the dissimilarity between the original and magnified sequences based on the enhanced correlation coefficient. The proposed method can be applied to videos overlaid with different types of temporal change to obtain motion and color magnification. Quantitative and qualitative experimental comparison of the proposed method with state-of-the-art techniques reveals that it produces magnified videos with improved visual quality and substantially fewer artifacts or blurring.