Reviewerly/exHarmony huggingface dataset:
Multi-Level Knowledge Injecting for Visual Commonsense Reasoning

When glancing at an image, human can infer what is hidden in the image beyond what is visually obvious, such as objects' functions, people's intents and mental states. However, such a visual reasoning paradigm is tremendously difficult for computer, requiring knowledge about how the world works. To address this issue, we propose Commonsense Knowledge based Reasoning Model (CKRM) to acquire external knowledge to support Visual Commonsense Reasoning (VCR) task, where the computer is expected to answer challenging visual questions. Our key ideas are: (1) To bridge the gap between recognition-level and cognition-level image understanding, we inject external commonsense knowledge via multi-level knowledge transfer network, achieving cell-level, layer-level and attention-level joint information transfer. It can effectively capture knowledge from different perspectives, and perceive common sense of human in advance. (2) To further promote image understanding at cognitive level, we propose a knowledge based reasoning approach, which can relate the transferred knowledge to visual content and compose the reasoning cues to derive the final answer. Experiments are conducted on the challenging visual commonsense reasoning dataset VCR to verify the effectiveness of our proposed CKRM approach, which can significantly improve reasoning performance and achieve the state-of-the-art accuracy.
Enhanced-Blind Approach for Privacy Protection of IoT

The Internet of Thing (IoT) refers to a vast number of things (e.g., sensors) that are connected to the internet to share data such as in smart cities, intelligent transportation, and healthcare. However, these data may be subject to privacy breaching attacks that pose considerable challenges in the development and deployment of IoT. Therefore, many solutions have been introduced with the assumption of trusted parties' inclusion (e.g., Service Providers) to increase the level of privacy. For example, the blind approach solution depends on asymmetric keys in which the third parties anonymize the user's identity before forwarding the encrypted query to the service provider without being able to perusal the transferred data. But, what will happen if the anonymizer sends the request to the service provider itself without the user's knowledge, in this case, it will be able to read the answers and hacking the privacy of the user. In our research, we propose an enhanced technique for the blind approach to enable users to detect any unauthorized access to their data and then take action. A case-study will be presented in the paper to prove the effectiveness of the enhanced technique in addressing the drawback of the main Blind approach and thereby improving the level of user's privacy in IoT.
An Effective Approach of Named Entity Recognition for Cyber Threat Intelligence

Traditional methods of domain named entity recognition (NER) rely on manually-defined feature templates and domain experience. Aiming at domain NER task of unstructured cyber threat intelligence (CTI), this paper proposed an approach based on BiLSTM-CRF model and domain dictionary matching correction. This approach utilizes bi-directional Long Short-Term Memory (BiLSTM) to automatically capture features of context, Conditional Random Fields (CRF) to learn label constraint rule, and an ontology-based domain dictionary for matching correction. Due to the lack of available domain dataset, this paper adopts the pre-processed unstructured CTI text as dataset for domain NER experiment. The experimental results show that the proposed approach reaches 85% in F1 score, and can significantly reduce reliance on manually-defined features.
Sentiment Analysis on Google Play Store Data Using Deep Learning

In the past few years, there has been an astounding rise in the number of mobile app analytical firms where the developers and innovation teams have been gauging their entire focus onto bringing in more advancements in the field of data analytics, and thereby collaborate their own solutions to conduct competitive analysis or market research to understand customers. This can also help the firms in understanding various notable features like ranking, reviews, current price and how these have key roles in building the monetization of apps under question. The main idea is to track mobile apps and user interaction within the apps. The baseline of this research is to get valuable insights regarding the entire data extraction process being used for analysing the app-centric metrics and predict user ratings on the basis of it. We have implemented deep learning models on our refined dataset to study the various intricate details of the underlying data. We implemented few machine learning techniques like Naive Bayes, XGBoost as well as a deep learning classifier and MLP, and further implemented functional layers of Keras for combining all the features of mobile apps like text reviews and numerical features like installations, the total number of reviews and categorical columns, like broad app categories or user ratings. We evaluated the performance of these algorithms using the metrics of accuracy and area under the receiver operator characteristic (ROC).
Approach of Fuzzy Theory and Hill Climbing Based Recommender for Schedule of Life

For maintaining good health, it is important to take meals and exercises regularly. However, it has been reported that the number of jobs with irregular work times is increasing. In addition, determining meal and exercise times to maintain health during irregular work times is an NP-hard problem because it needs to resolve the multi-objective optimization. In this paper, we propose a recommender system based on fuzzy theory and hill climbing approach that presents a schedule of meals and exercises in a daily life including irregular work time. We show the simulation results and case study using the proposed system.
Urdu Sentiment Corpus (v1.0): Linguistic Exploration and Visualization of Labeled Dataset for Urdu Sentiment Analysis

The significance of the labeled dataset is not obscure from artificial intelligence practitioners. We have seen much phenomenal work, in natural language processing, for many languages (like English, Chinese, and Arabic, etc.), due to the reason for the availability of substantial data. For the Urdu language, despite the third largest spoken language in the world, very little research work is shown; hence, it is adjudged as a `morphologically rich' but `resource-poor' language. Further, the researchers working on Urdu natural language processing are in a quandary due to the lack of availability of labeled/annotated datasets. This paper shares the data, “Urdu Sentiment Corpus” (USC), and insights therein, of Urdu tweets for the sentiment analysis and polarity detection. The dataset is consisting of tweets, such that it casts a political shadow and presents a competitive environment between two separate political parties versus the government of Pakistan. Overall, the dataset is comprising over 17, 185 tokens with 52% records as positive, and 48 % records as negative. This paper shares the visual insights (from document-level to word-level) into the textual similarities, manifold-learning, etc. In addition to it, this paper also presents a Part-of-Speech wise analysis and an unpretentious technique for the extraction of sentiment lexicons from the corpus.
Dataset for Eye Tracking on a Virtual Reality Platform

We present a large scale data set of eye-images captured using a virtual-reality (VR) head mounted display mounted with two synchronized eye-facing cameras at a frame rate of 200 Hz under controlled illumination. This dataset is compiled from video capture of the eye-region collected from 152 individual participants and is divided into four subsets: (i) 12,759 images with pixel-level annotations for key eye-regions: iris, pupil and sclera (ii) 252,690 unlabeled eye-images, (iii) 91,200 frames from randomly selected video sequences of 1.5 seconds in duration, and (iv) 143 pairs of left and right point cloud data compiled from corneal topography of eye regions collected from a subset, 143 out of 152, participants in the study. A baseline experiment has been evaluated on the dataset for the task of semantic segmentation of pupil, iris, sclera and background, with the mean intersection-over-union (mIoU) of 98.3 %. We anticipate that this dataset will create opportunities to researchers in the eye tracking community and the broader machine learning and computer vision community to advance the state of eye-tracking for VR applications, which in its turn will have greater implications in Human-Computer Interaction.
Smart Digital Signatures (SDS): A post-quantum digital signature scheme for distributed ledgers

The upcoming quantum era is believed to be an end for the elliptic curve digital signature algorithm (ECDSA) and other number-theoretic digital signature schemes. Hence, the technologies which incorporate ECDSA would be at risk once quantum computers are available at large scale. Distributed ledger technology is one of the potential victims of powerful quantum computers. Fortunately, post-quantum digital signature schemes are already available. Hash-based digital signatures (HBS) schemes due to their simplicity and efficiency have gained tremendous attention from the research community. However, large size of key and signature are the major drawbacks of HBS schemes. This paper proposes a compact and efficient HBS scheme “Smart Digital Signatures” (SDS), which is closer to an existing popular HBS scheme, XMSS. SDS incorporates a novel one-time signature (OTS) scheme in XMSS, namely SDS-OTS. Furthermore, SDS uses a slightly modified version of the key compression tree as compared to XMSS. We have compared SDS with XMSS-WOTS and XMSS-WOTS+. The results reveal a significant reduction in hash tree construction time compared to XMSS, and key and signature sizes compared to WOTS and WOTS+. Finally, we have also proposed a model for incorporating SDS into a distributed ledger, with the help of High-Level Petri-nets.
Adaptive Image-Based Visual Servoing Using Reinforcement Learning With Fuzzy State Coding

Image-based visual servoing (IBVS) allows precise control of positioning and motion for relatively stationary targets using visual feedback. For IBVS, a mixture parameter β allows better approximation of the image Jacobian matrix, which has a significant effect on the performance of IBVS. However, the setting for the mixture parameter depends on the camera's realtime posture; there is no clear way to define the change rules for most IBVS applications. Using simple model-free reinforcement learning, Q-learning, this article proposes a method to adaptively adjust the image Jacobian matrix for IBVS. If the state-space is discretized, traditional Q-learning encounters problems with the resolution that can cause sudden changes in the action, so the visual servoing system performs poorly. Besides, a robot in a real-world environment also cannot learn on as large a scale as virtual agents, so the efficiency with which agents learn must be increased. This article proposes a method that uses fuzzy state coding to accelerate learning during the training phase and to produce a smooth output in the application phase of the learning experience. A method that compensates for delay also allows more accurate extraction of features in a real environment. The results for simulation and experiment demonstrate that the proposed method performs better than other methods, in terms of learning speed, movement trajectory, and convergence time.
Optimization‐based workload distribution in geographically distributed data centers: A survey

Summary Energy efficiency is a contemporary and challenging issue in geographically distributed data centers. These data centers consume significantly high energy and cast a negative impact on the energy resources and environment. To minimize the energy cost and the environmental impacts, Internet service providers use different approaches such as geographical load balancing ( G L B ). GLB refers to the placement of data centers in diverse geolocations to exploit variations in electricity prices with the objective to minimize the total energy cost. GLB helps to minimize the overall energy cost, achieve quality of service, and maximize resource utilization in geo‐distributed data centers by employing optimal workload distribution and resource utilization in the real time. In this paper, we summarize various optimization‐based workload distribution strategies and optimization techniques proposed in recent research works based on commonly used optimization factors such as workload type, load balancer, availability of renewable energy, energy storage, and data center server specification in geographically distributed data centers. The survey presents a systemized and a novel taxonomy of workload distribution in data centers. Moreover, we also debate various challenges and open research issues along with their possible solutions.
Fast, Compact, and Expressive Attribute-Based Encryption

Attribute-based encryption (ABE) is an advanced cryptographic tool and useful to build various types of access control systems. Toward the goal of making ABE more practical, we propose key-policy (KP) and ciphertext-policy (CP) ABE schemes, which first support unbounded sizes of attribute sets and policies with negation and multi-use of attributes, allow fast decryption, and are adaptively secure under a standard assumption, simultaneously. Our schemes are more expressive than previous schemes and efficient enough. To achieve the adaptive security along with the other properties, we refine the technique introduced by Kowalczyk and Wee (Eurocrypt'19) so that we can apply the technique more expressive ABE schemes. Furthermore, we also present a new proof technique that allows us to remove redundant elements used in their ABE schemes. We implement our schemes in 128-bit security level and present their benchmarks for an ordinary personal computer and smartphones. They show that all algorithms run in one second with the personal computer when they handle any policy or attribute set with one hundred attributes.
Microsoft Recommenders: Best Practices for Production-Ready Recommendation Systems

Recommendation algorithms have been widely applied in various contemporary business areas, however the process of implementing them in production systems is complex and has to address significant challenges. We present Microsoft Recommenders, an open-source Github repository for helping researchers, developers and non-experts in general to prototype, experiment with and bring to production both classic and state-of-the-art recommendation algorithms. A focus of this repository is on best practices in development of recommendation systems. We have also incorporated learnings from our experience with recommendation systems in production, in order to enhance ease of use; speed of implementation and deployment; scalability and performance.
A Defect Estimator for Source Code

An important issue faced during software development is to identify defects and the properties of those defects, if found, in a given source file. Determining defectiveness of source code assumes significance due to its implications on software development and maintenance cost. We present a novel system to estimate the presence of defects in source code and detect attributes of the possible defects, such as the severity of defects. The salient elements of our system are: (i) a dataset of newly introduced source code metrics, called PRO gramming CON struct (PROCON) metrics, and (ii) a novel M achine- L earning (ML)-based system, called D efect E stimator for S ource Co de (DESCo), that makes use of PROCON dataset for predicting defectiveness in a given scenario. The dataset was created by processing 30,400+ source files written in four popular programming languages, viz., C, C++, Java, and Python. The results of our experiments show that DESCo system outperforms one of the state-of-the-art methods with an improvement of 44.9%. To verify the correctness of our system, we compared the performance of 12 different ML algorithms with 50+ different combinations of their key parameters. Our system achieves the best results with SVM technique with a mean accuracy measure of 80.8%.
Multiplication fusion of sparse and collaborative-competitive representation for image classification

Representation based classification methods have become a hot research topic during the past few years, and the two most prominent approaches are sparse representation based classification (SRC) and collaborative representation based classification (CRC). CRC reveals that it is the collaborative representation rather than the sparsity that makes SRC successful. Nevertheless, the dense representation of CRC may not be discriminative which will degrade its performance for classification tasks. To alleviate this problem to some extent, we propose a new method called sparse and collaborative-competitive representation based classification (SCCRC) for image classification. Firstly, the coefficients of the test sample are obtained by SRC and CCRC, respectively. Then the fused coefficient is derived by multiplying the coefficients of SRC and CCRC. Finally, the test sample is designated to the class that has the minimum residual. Experimental results on several benchmark databases demonstrate the efficacy of our proposed SCCRC. The source code of SCCRC is accessible at https://github.com/li-zi-qi/SCCRC .
New objective QoE models for evaluating ABR algorithms in DASH

As users become more demanding with regards to the consumption of multimedia content, the importance of measuring their level of satisfaction is growing. The difficulty in terms of time and resources for assessing the Quality of Experience (QoE) has popularized the use of objective QoE models, which try to emulate human behavior regarding the playback of multimedia streaming. Some objective QoE models existing in the literature are based on the bitrate. However, the PSNR (Peak Signal-to-Noise Ratio) or VMAF (Video Multimethod Assessment Fusion) have been proved to be metrics with a closer relationship with the QoE than the bitrate. This paper proposes three new models to measure the QoE analytically in DASH (Dynamic Adaptive Streaming over HTTP) video services. The first is based on the bitrate of the displayed video segments, whereas the second and the third are based on the PSNR and VMAF of each video segment, respectively. The proposed models are compared to the ITU-T standard P.1203 as well as the bitrate-based QoE model proposed by Yin et al. Moreover, the paper presents a subjective study, which confirms the validity of the proposed models. The models are validated by using different DASH adaptation algorithms. In this sense, this paper also presents a DASH ABR (Adaptive Bitrate Streaming) algorithm called Look Ahead, which takes into account the inherent bitrate variability of the video encoding process in order to calculate, in real time, the appropriate quality level that minimizes the number of stalls during the playback.
A Fuzzy based Data Perturbation Technique for Privacy Preserved Data Mining

The main objective of data mining algorithms are to mine previously unknown patterns from the enormous size of data. In current days there is a remarkable expansion in data collection due to the development in the field of information technology. The results, driven by data mining algorithm can be used in many domains such as Image Analysis, Marketing, weather forecasting etc. Some sensitive data is also exposed this is a side effect of the mining algorithm. Here we can preserve the privacy by using PPDM (privacy-preserving data mining) techniques. In this undertaking, fuzzy data transformation technique is proposed for privacy preservation in database surroundings. The triangular fuzzy membership function is a method of fuzzy data transformation to change the original dataset.
Fast, High-Quality Two-Arm Rearrangement in Synchronous, Monotone Tabletop Setups

Rearranging objects on a planar surface arises in a variety of robotic applications, such as product packaging. Using two arms can improve efficiency but introduces new computational challenges. This article studies the problem structure of object rearrangement using two arms in synchronous, monotone tabletop setups and develops an optimal mixed-integer model. It then describes an efficient and scalable algorithm, which first minimizes the cost of object transfers and then moves between objects. This is motivated by the fact that, asymptotically, object transfers dominate the cost of solutions. Moreover, a lazy strategy minimizes the number of motion planning calls and results in significant speedups. Theoretical arguments support the benefits of using two arms and indicate that synchronous execution, in which the two arms perform together either transfers or moves, introduces only a small overhead. Experiments support these claims and show that the scalable method can quickly compute solutions close to the optimal for the considered setup. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Monotone tabletop rearrangement challenges arise in a variety of automation scenarios, including product sorting or packing. Performing this task with two robotic manipulators introduces the overhead of coordinating them in the shared workspace, as well as an increase in the size of the underling search space. The objective of this work is to study the feasibility of such dual-arm solutions, providing both theoretical bounds, as well as a fast, and approximate solution. The approach leverages an effective algorithmic decomposition of the problem so as to take advantage of efficient motion planners and mixed-integer linear programming solvers. The proposed solution has been evaluated in settings that include delta robots as well as seven-degree-of-freedom (DOF) manipulators. Interesting extensions of this work correspond to studying the case of additional arms, nonmonotone, and general manipulation scenarios.
Towards a Hypothetical Framework to Secure DevOps Adoption

Security in DevOps is a challenging feature because traditional existing methods of security are not fulfilling the exact requirements of DevOps. To make DevOps activities successful for organizations this study will help to identify concerns about DevOps security in real world by interviewing the practitioners having DevOps working experience. The Classical grounded theory approach has been used to build our theory. We interviewed 13 practitioners working across five companies from different regions. We contributed to identify security concerns in DevOps and try to present a theoretical model to improve understanding and guidance of DevOps adoption. The security concerns were marked as functional and nonfunctional based upon the interviews concepts to help practitioners having understanding about particular concerns. Therefore, this theory will highlight security concerns that are hindering the adoption of DevOps successfully.