Reviewerly/exHarmony huggingface dataset:
ADRL: An attention-based deep reinforcement learning framework for knowledge graph reasoning

Knowledge graph reasoning is one of the key technologies for knowledge graph construction, which plays an important part in application scenarios such as vertical search and intelligent question answering. It is intended to infer the desired entity from the entities and relations that already exist in the knowledge graph. Most current methods for reasoning, such as embedding-based methods, globally embed all entities and relations, and then use the similarity of vectors to infer relations between entities or whether given triples are true. However, in real application scenarios, we require a clear and interpretable target entity as the output answer. In this paper, we propose a novel attention-based deep reinforcement learning framework (ADRL) for learning multi-hop relational paths, which improves the efficiency, generalization capacity, and interpretability of conventional approaches through the structured perception of deep learning and relational reasoning of reinforcement learning. We define the entire process of reasoning as a Markov decision process. First, we employ CNN to map the knowledge graph to a low-dimensional space, and a message-passing mechanism to sense neighbor entities at each level, and then employ LSTM to memorize and generate a sequence of historical trajectories to form a policy and value functions. We design a relational module that includes a self-attention mechanism that can infer and share the weights of neighborhood entity vectors and relation vectors. Finally, we employ the actor–critic algorithm to optimize the entire framework. Experiments confirm the effectiveness and efficiency of our method on several benchmark data sets.
The Measurement of Eye Contact in Human Interactions: A Scoping Review

Abstract Eye contact is a fundamental aspect of nonverbal communication and therefore important for understanding human interaction. Eye contact has been the subject of research in many disciplines, including communication sciences, social psychology, and psychiatry, and a variety of techniques have been used to measure it. The choice of measurement method has consequences for research outcomes and their interpretation. To ensure that research findings align with study aims and populations, it is essential that methodological choices are well substantiated. Therefore, to enhance the effective examination of eye contact, we performed a literature review of the methods used to study eye contact. We searched Medline, PsycINFO and Web of Science for empirical peer-reviewed articles published in English that described quantitative studies on human eye contact and included a methodological description. The identified studies ( N = 109) used two approaches to assess eye contact: direct, i.e., assessing eye contact while it is occurring, and indirect, i.e., assessing eye contact retrospectively (e.g., from video recordings). Within these categories, eight specific techniques were distinguished. Variation was found regarding the reciprocity of eye contact between two individuals, the involvement of an assessor and the behavior of participants while being studied. Measures not involving the interactors in assessment of eye contact and have a higher spatial and temporal resolution, such as eye tracking, have gained popularity. Our results show wide methodological diversity regarding the measurement of eye contact. Although studies often define eye contact as gaze towards an exact location, this may not do justice to the subjective character of eye contact. The various methodologies have hardly ever been compared, limiting the ability to compare findings between studies. Future studies should take notice of the controversy surrounding eye contact measures.
Phishing Website Detection Based on Machine Learning: A Survey

Phishing attacks are cybercrime in today's world which are done by social engineering and malware based. It is one of the most dangerous threats that every individuals and organization faced. URLs are known as web links are by which users locate information on the internet. The review creates awareness of phishing attacks, detection of phishing attacks and encourages the practice of phishing prevention among the readers. In phishing, phishers use email or message, as a weapon to target individual or organization by send URL link to target people and to deceive them. With the huge number of phishing emails or messages received every day, companies or individuals are not able to detect all of them. Here, different reviews give for detection of phishing attack, by using machine learning. Here it is used for detecting the web links, i.e., either phishing or legitimate.
Deep Architecture With Cross Guidance Between Single Image and Sparse LiDAR Data for Depth Completion

It is challenging to apply depth maps generated from sparse laser scan data to computer vision tasks, such as robot vision and autonomous driving, because of the sparsity and noise in the data. To overcome this problem, depth completion tasks have been proposed to produce a dense depth map from sparse LiDAR data and a single RGB image. In this study, we developed a deep convolutional architecture with cross guidance for multi-modal feature fusion to compensate for the lack of representation power of their modality. Two encoders, which are part of the proposed architecture, receive different modalities as inputs. They interact with each other by exchanging information in each stage through the attention mechanism during encoding. We also propose a residual atrous spatial pyramid block, comprising multiple dilated convolutions with different dilation rates, which are used to derive highly significant features. The experimental results of the KITTI depth completion benchmark dataset demonstrate that the proposed architecture shows higher performance than that of the other models trained in a two-dimensional space without pre-training or fine-tuning other datasets.
NLP based Machine Learning Approaches for Text Summarization

Due to the plethora of data available today, text summarization has become very essential to gain just the right amount of information from huge texts. We see long articles in news websites, blogs, customers' review websites, and so on. This review paper presents various approaches to generate summary of huge texts. Various papers have been studied for different methods that have been used so far for text summarization. Mostly, the methods described in this paper produce Abstractive (ABS) or Extractive (EXT) summaries of text documents. Query-based summarization techniques are also discussed. The paper mostly discusses about the structured based and semantic based approaches for summarization of the text documents. Various datasets were used to test the summaries produced by these models, such as the CNN corpus, DUC2000, single and multiple text documents etc. We have studied these methods and also the tendencies, achievements, past work and future scope of them in text summarization as well as other fields.
Uncertainty-based Object Detector for Autonomous Driving Embedded Platforms

For self-driving cars that operate based on battery-generated power, detection and control are commonly performed in embedded systems to reduce power consumption. To drive safely without driver intervention, it is essential to operate object detection algorithms with high accuracy and fast detection speed within autonomous driving embedded systems. This paper proposes new methods to predict the localization uncertainty by applying Gaussian modeling to the DNN-based tiny YOLOv3 algorithm and consequently, to drastically improve accuracy at the expense of a slight penalty of detection speed by using it in post-processing. Compared to the baseline algorithm (i.e., tiny YOLOv3), the proposed algorithm, tiny Gaussian YOLOv3, improves the mean average precision (mAP) by 2.62 and 4.6 on the Berkeley deep drive (BDD) and KITTI datasets, respectively. Nevertheless, the proposed algorithm is capable of performing real-time detection at 55.56 frames per second (fps) on the BDD dataset and 69.74 fps on the KITTI dataset, respectively, under the mode 0 of the autonomous driving embedded platform, Jetson AGX Xavier.
Voice Recognition and Voice Comparison using Machine Learning Techniques: A Survey

Voice comparison is a variant of speaker recognition or voice recognition. Voice comparison plays a significant role in the forensic science field and security systems. Precise voice comparison is a challenging problem. Traditionally, different classification and comparison models were used by the researchers to solve the speaker recognition and the voice comparison, respectively but deep learning is gaining popularity because of its strength in accuracy when trained with large amounts of data. This paper focuses on an elaborated literature survey on both traditional and deep learning-based methods of speaker recognition and voice comparison. This paper also discusses publicly available datasets that are used for speaker recognition and voice comparison by researchers. This concise paper would provide substantial input to beginners and researchers for understanding the domain of voice recognition and voice comparison.
Hierarchical Soft Quantization for Skeleton-Based Human Action Recognition

In daily life, human beings rely on hands and body parts to complete particular actions cooperatively. These selected body parts and their cooperative relationships are essential cues to distinguish these actions. However, most existing action recognition methods, which try to model the body appearance or spatial relations in skeleton sequences, often ignore the essential cooperation relationship among joints. Differently, in this paper, we propose a spatio-temporal hierarchical soft quantization method to extract the congenerous motion features, which reflect the cooperation relations among joints and body parts. Specifically, we design a hierarchical network with multiple soft quantization layers to extract congenerous features. The hierarchical network not only models the spatial hierarchy of skeleton structure for joint, part, and body, but also extracts the temporal hierarchy with sliding windows for frame, fragment, and sequence. Moreover, the features in each layer are visually explainable, which reflect the cooperation among body parts. The trainable parameters in the network are also significantly reduced, which reduces computational cost. Extensive experiments conducted on four benchmarks demonstrate that our method can provide competitive results compared with state-of-the-arts. The visualized congenerous features also validate that our approach can effectively perceive the essential cooperation relations.
Collaborating personalized recommender system and content-based recommender system using TextCorpus

Recommender systems aim to get the relevant data, based on the user's interests. One of the key problems of the recommender systems is to maintain the dataset and to retrieve the data, which is relevant to the user. A common solution is to track the user's preferences and showing the relevant results, however, it is a complex task in terms of time and space. The user data need to be analyzed and learnt using efficient algorithms. To address this problem, we have proposed a method to format the data in the dataset using POS-taggers using NLTK framework. In this paper, we have proposed a user-profile model which uses this tagging mechanism to provide better recommendations compared to the existing state-of-the-art recommender techniques.
Artificial Intelligence Aided Joint Bit Rate Selection and Radio Resource Allocation for Adaptive Video Streaming over F-RANs

Recently, fog-computing-based radio access networks (F-RANs) have been conceptualized to provide high quality of experience (QoE) for adaptive bit rate (ABR) streaming, where additional computing capacity is supplemented on fog nodes to facilitate complicated cross-layer optimization (i.e., joint bit rate selection and radio resource allocation). However, finding an optimal global solution with acceptable complexity is still infeasible by the conventional optimization methods. In this work, we propose an artificial intelligence (AI) aided joint bit rate selection and radio resource allocation scheme referred to as iABR, which provides a new vision for handling the over-complicated optimization in F-RANs. Based on multi-agent hierarchy deep reinforcement learning, the proposed iABR can dynamically allocate radio resource and select bit rate in a multiuser scenario, by perceiving the network environment and clients' player information. Moreover, long short-term memory (LSTM) is employed by the iABR algorithm, which enables accurate prediction of the change of channel quality by learning the history of the wireless channel. Hence, iABR is able to adjust the action policy in advance to accommodate the future channel quality for avoiding bit rate fluctuation. According to the experimental results, the iABR exhibits higher QoE in terms of high average bit rate, low rebuffering ratio, and average bit rate variance. Last but not least, the QoE performance of all the clients are fairly guaranteed by the iABR algorithm, enhancing the practicality of AI-driven F-RANs for multimedia service delivery.
Monocular 3D vehicle detection with multi-instance depth and geometry reasoning for autonomous driving

We propose MCK-NET, a novel monocular framework for 3D target detection, location and pose estimation in autonomous driving scenarios, in which the key challenge is how to effectively mine depth information from a monocular image. To tackle this ill-posed problem, we combine the relative instance-depth of multiple corners in a monocular image to explicitly construct the corresponding depth relations between interest regions, from which MCK-NET learns to detect and locate objects based on geometric reasoning. In addition, there are two significant features existing in MCK-NET: One is to use the relative relationship between the 2D center and the 3D center projection to help locate the 3D center. The other is that the geometric constraints are established, including semantic keypoints, 2D box center and the projection of 3D center, which can enhance the 3D corner detection and improve the estimation accuracy of 3D centers. Experiments on the KITTI datasets show that MCK-NET achieves the most advanced results in all three tasks and outperforms the current monocular methods.
Robot Time-Optimal Trajectory Planning Based on Improved Cuckoo Search Algorithm

The trajectory planning time is uncertain when there is no velocity constraint in the joint space of an operating robot.This paper proposes a 3-5-3 polynomial interpolation trajectory planning algorithm based on improved cuckoo search algorithm (ICS) which functions under a velocity constraint.Kinematics analysis of the robot is conducted and the 3-5-3 polynomial interpolation function is established as the foundation for trajectory planning.The size of the step size control factor is set as a variant which varies with the number of iterations, which accelerates the algorithm's convergence and prevents premature falling into the local optimal solution, both problems in the traditional cuckoo search algorithm where the step size control factor is fixed.The proposed algorithm was used to optimize the trajectory of a test robot with interpolation time as the search space for optimization.Any interpolation time that did not meet the speed constraint was eliminated to secure the shortest possible robot running time.The UR robot was taken as a research object to simulate the joint motion trajectory planning in MATLAB.The proposed algorithm performed well and better realized the time-optimal trajectory under the constraint of speed compared to the traditional cuckoo search algorithm, particle swarm optimization algorithm, and genetic algorithm.
Performance Evaluation of Machine Learning Algorithms for Email Spam Detection

Sending huge number of unwanted mails causes security threat to users. In spite of various security approaches, spammers cause much vulnerability in the internet. This paper discusses the efficient methods of using some of the popular algorithms for building a machine learning model which can classify whether a mail is a spam or ham. UCI Machine Learning Repository Spambase Data Set is used for the experiment. The performance of five important machine learning classification algorithms viz. Logistic Regression, Decision Tree, Naive Bayes, KNN and SVM are evaluated in order to train and build an effective machine learning model for email spam detection. Weka tool is used for training and testing the data set.
Learning manual assembly through real-time motion capture for operator training with augmented reality

The current fourth industrial revolution significantly impacts on production processes. The personalized production paradigm which distinguishes Industry 4.0 enables customers to order unique products, defined by the specific features selected. The operators involved in the manual assembly of such workpieces have to process an enormous component variety adapting their tasks from product to product with limited learning opportunities. On the other hand, digital technologies significantly evolved in the last decade and their adoption in industrial shop floors in increasingly wider. In particular, camera-based marker-less motion capture achieved a large popularity since it represents a cheap, reliable and non-invasive solution to track, trace and digitalize human movements in different environments. Considering the presented framework, this research proposes an original hardware/software architecture to assist in real-time operators involved in manual assembly processes during the training phase to support their learning process, both in terms of rate and quality. A marker-less depth camera captures human motions in relation with the workstation environment whereas an augmented reality application based on visual feedback guides the operator through consecutive assembly tasks during the training phase. An experimental campaign is performed at the Learning factory of the Digital production university laboratory to validate the proposed architecture compared to traditional paper-based instructions provided for trainings. A real industrial case study is adopted to test and quantitatively evaluate the benefits of the developed technology compared to traditional approach in terms learning rate, which increases by 22% with a reduction in manual process duration up to -51% during the first assembly cycles.
Optimized co-scheduling of mixed-precision neural network accelerator for real-time multitasking applications

Neural networks are increasingly applied into real-time and embedded Artificial Intelligent (AI) systems like autonomous driving system. Such resource-constrained systems cannot support the execution of neural network based tasks due to their high execution overheads on general processors. Hence, we are approaching to design real-time AI applications on embedded systems with CPU and FPGA (Field Programmable Gate Array) coprocessors. We use dedicated FPGA to accelerate the neural network job and utilize CPU to process the rest jobs of real-time multitasking applications. We devise an Idle-Aware Earliest Deadline First policy to co-schedule the AI applications on hybrid CPU and FPGA coprocessors. Since the implementation of neural network job on FPGA accelerator with different precision configuration will result in different execution time and accuracy, we are also interested in the design optimization of real-time AI applications running on mixed-precision neural network accelerator, with the purpose of maximizing the accuracy related rewards of all applications subject to real-time related constraints. We address the problem as a multi-stage decision procedure, and propose an efficient dynamic programming approach with two pruning policies to reduce the intermediate searching states. Extensive experiments and real-life case evaluations demonstrate the efficiency of the proposed approaches.
Classification of breast density categories based on SE-Attention neural networks

Background and objective: Breast density (BD) is an independent predictor of breast cancer risk factor. The automatic classification of BD has yet to resolve. In this paper, we propose an improved convolutional neural network (CNN) framework that integrates innovative SE-Attention mechanism to learn discriminative features, aiming for automatic BD classification in mammography. Methods: A new benchmarking dataset was constructed from 18157 BD images, manually segmented into 4 levels based on Breast Imaging and Reporting Data System (BI-RADS): A (fatty), B (fibro-glandular), C (heterogeneously dense) and D (extremely dense). The proposed method consists of three main phases: (i) data enhancement and normalization of breast images (ii) SE-Attention training for feature re-calibration and fusion to better classify density and (iii) designing the auxiliary loss. We adopt an attention approach where SE-Attention mechanism is used to learn the density features, which is different from previous works. Results: Experimental results demonstrate that the proposed framework obtains higher classification accuracy than the original network, such as Inception-V4, ResNeXt, DenseNet, increasing the performance from 89.97% to 92.17%, 89.64% to 91.57%, 89.20% to 91.79% respectively. Among them, improved Inception-V4 possesses the highest accuracy meanwhile DenseNet improves in the largest extent, both the original and improved methods are more effective than other state-of-the-art image descriptors regarding classification. Conclusions: We insist that our method will help radiologists provide reliable BD diagnostic services at the expert level, allowing them to focus on patients who are really in need.
Digitalization boosted recycling: Gamification as an inspiration for young adults to do enhanced waste sorting

In August 2018, the City of Kuopio, student apartments (Kuopion Opiskelija-asunnot Oy), regional waste management and recycling company (Jätekukko Oy), and Savonia University of Applied Sciences set up an event called 3R Game Jam. The aim of the event was to different sources of motivation, which could improve young adults’ interests towards recycling. Additionally, the goal was to study gamified solutions, designed to boost recycling activities within the students age group. The game called “Fox the Recycler” won the competition and was selected for further development. In the fall of 2018, the next version of the game was piloted in student apartments where 90 % of all young adults (250) installed the game. The best rate of monthly users was 235, daily users over 50 to 30 persons. After the pilot, the recycling rate of biowaste increased from 76 % to 97 %. The recycling rate of plastic also increased significantly: 84 % of the inhabitants currently recycle plastic, while only 25 % of them did so before the pilot. The study found that the gamification-based solutions can be used efficiently to support a change in consumers’ behavior. such solutions have a wide range of applications in many different recycling and other contexts, and in different environments. The 2.0 version of the game will be piloted for primary school children to further study educational and cultural change in recycling during 2019.