Reviewerly/exHarmony huggingface dataset:
Performance Analysis of Video Magnification Methods

Video is a huge source of information and the only way to extract that information is video processing. Depending upon the information to be retrieved or analyzed, the video processing techniques are applied. Some of the information consists of imperceptibly small motion, color changes, or sound changes. To estimate the small motions, which are not visible to naked eyes, various video magnification techniques are used. In this paper, various video magnification techniques- Eulerian video magnification, Phase-based video magnification with both Laplacian and Gaussian pyramid are implemented and their comparative performance analysis is presented. The various performance metrics used are PSNR, amplification factor, execution time, intensity of color and motion magnification. The effect of changing the level of pyramid and amplification factor on the performance of particular method is also analyzed. With increase in the level of pyramid quality of magnification reduced, while for higher amplification factor (>200) output exploited drastically. Results reveal that the Eulerian video magnification method is fast and simple but supports small amplification factors while the phase-based method is very slow and complex but supports the large amplification factors. To get better performance of the video magnification method, it is required to develop a new method using hybrid approach and its scope can be further extended in biomedical field to measure the biological parameters.
Dependable information processing method for reliable human-robot interactions in smart city applications

Human-robot interaction (HRI) is a multidisciplinary area that consists of several technologies that are used to create various smart city applications. The knowledge gain and analysis of the smart city environment improves response time. This paper introduces the dependable information processing (DIP) method for handling multi-attribute environmental information in a smart city application. Information sensed from the environment is categorized in the initial stage regarding how it meets application requirements. It helps to identify the need and response of the application through different interacting spans and previous trials. For attribute categorization and span validation, learning via K-means clustering is exploited to identify similar and dissimilar information attributes. This identification speeds up the process of successive responses with improved interaction sessions. The accuracy of user responses and latency-free assessment improves the reliability of DIP in smart city applications. The efficiency of the system is then evaluated using detection accuracy (97.34%), the response time (4.3 s), and interaction time (12.3 s), which is compared with other methods.
High-speed implementation of privacy amplification for continuous-variable quantum key distribution

Privacy amplification (PA) is an essential process for high-speed and real-time implementation of a continuous-variable quantum key distribution (CV-QKD) system. This work focuses on the improvement of the performance of PA, and we realize PA with Toeplitz matrix and accelerate it using fast Fourier transform (FFT) on graphic processing unit (GPU). Based on the architectural feature of FFT, we adjust its form of input length and obtained an average speed of PA about 2Gbps with input length ranges from 1Mbits to 128Mbits, which is length-adaptable to satisfy various requirements of CV-QKD systems at different transmission distances. Furthermore, we test this work with different compress ratios of PA, which can also achieve a high implementation speed around 2Gbps. With the method used in this paper, the requirements of PA for the high-speed and real-time CV-QKD system can be entirely satisfied.
Task-to-Task Transfer Learning with Parameter-Efficient Adapter

Many existing pre-trained language models have yielded strong performance on many NLP tasks. They depend on enough labeled data of downstream tasks, which are difficult to be trained on tasks with limited data. Transfer learning from large labeled task to narrow task based on the pre-trained language models can solve this problem. However, it always suffers from catastrophic forgetting. In this paper, we propose an effective task-to-task transfer learning method with parameter-efficient adapter based on pre-trained language model, which can be trained on new tasks without hindering the performance of those already learned. Our experiments include transfer learning from MNLI or SQUAD (as the source task) to some related small data tasks based on Bert. Experimental results show large gains in effectiveness over previous approaches on transfer learning and domain adaptation without forgetting. By adding less than 2.1% of the parameters, our method matches or outperforms vanilla fine-tuning and can overcome catastrophic forgetting.
Hierarchical Multi-view Attention for Neural Review-Based Recommendation

Many E-commerce platforms allow users to write their opinions towards products, and these reviews contain rich semantic information for users and items. Hence review analysis has been widely used in recommendation systems. However, most existing review-based recommendation methods focus on a single view of reviews and ignore the diversity of users and items since users always have multiple preferences and items always have various characteristics. In this paper, we propose a neural recommendation method with hierarchical multi-view attention which can effectively learn diverse user preferences and multiple item features from reviews. We design a review encoder with multi-view attention to learn representations of reviews from words, which can extract multiple points of a review. In addition, to learn representations of users and items from their reviews, we design a user/item encoder based on another multi-view attention. In this way, the diversity of user preference and item features can be fully exploited. Compared with the existing single attention approaches, the hierarchical multi-view attention in our method has the potential for better user and product modeling from reviews. We conduct extensive experiments on four recommendation datasets, and the results validate the advantage of our method for review based recommendation.
Salient points driven pedestrian group retrieval with fine-grained representation

People often take part in various social activities in the form of groups in public area. As the primary constituent units of crowd, groups retrieval has become one of the urgent issues for the security departments. In this paper, collection of stable individuals with some social relationship, called group, is selected as the research object, and a novel task of pedestrian group retrieval is introduced. Different from the individual person matching, groups often show high aggregation due to their inherent characteristics, occlusions in group individuals therefore are more serious. As a result, the performance of individual person based detection and matching will be affected. Meanwhile, group matching also needs to address the problems like variations in the shape or configuration. Therefore, we suggest that the group entirety may be disassembled into fine-grained representation and then design a salient points driven framework for pedestrian group retrieval. The work focuses on the problems of overall appearance characteristics extraction of a deformable pedestrian collection and matching of groups at varying scales. Experiments on Pedestrian-Groups2 dataset and Road Group dataset demonstrate the effectiveness of our proposed framework for Pedestrian Group retrieval.
Towards Greening MapReduce Clusters Considering Both Computation Energy and Cooling Energy

Increased processing power of MapReduce clusters generally enhances performance and availability at the cost of substantial energy consumption that often incurs higher operational costs (e.g., electricity bills) and negative environmental impacts (e.g., carbon dioxide emissions). There exist a few greening methods for computing clusters in the literature that focus mainly on computational energy consumption leaving cooling energy, which occupies a significant portion of the total energy consumed by the clusters. To this extent, in this article, we propose a machine learning-based approach that reduces the total energy consumption of a MapReduce cluster considering both computational energy and cooling energy. Our approach predicts the number of machines that results in minimum total energy consumption. We perform the prediction through applying different machine learning techniques over year-long data collected from a real setup. We evaluate performance of our approach through both real test-bed experimentation and simulation. Our evaluation reveals that our approach achieves substantial reduction in total energy consumption compared to other state-of-the-art alternatives while experiencing marginal performance degradation in a few cases.
Research on Integrated Detection of SQL Injection Behavior Based on Text Features and Traffic Features

With the rapid development of Internet technology, various network attack methods come out one after the other. SQL injection has become one of the most severe threats to Web applications and seriously threatens various Web application services and users’ data security. There are both traditional detection methods and emerging methods based on deep learning technology with higher detection accuracy for the detection of SQL injection. However, they are all for detecting a single statement and cannot determine the stage of the attack. To further improve the effect of SQL injection detection, this paper proposes an integrated detection framework for SQL injection behavior based on both text features and traffic features. We propose a SQL-LSTM model based on deep learning technology as the detection model at the text features level. Meanwhile, the features of the data traffic are merged. By this integrated method, the detection effect of SQL injection is further improved.
Collaboration Around an Interactive Tabletop Map: Comparing Voice Interactions and a Tangible Shape-changing Controller

Tabletop interactions can support various social activities such as collaborative review of dynamic sales information in the business setting or route planning with an interactive map among others. Sharing control between users of interactive surfaces can be difficult to ensure participation and efficiency for co-located contexts. We explore the differences between two modalities for interacting with a tabletop map including interactions with a voice assistant and tangible interactions with a shape-changing controller. To simulate a shared goal-oriented situation, we use the context of shared autonomous mobility and compare the tangible controller with voice input through simulated tasks of route planning and detours. The findings suggest that the most suitable interaction modality depends on user preferences, scenarios, and contexts. Tangible controls work well for communicating the state of the system and facilitating discussion, while voice can be more pragmatic and task oriented.
Camera-Based Selection with Cardboard Head-Mounted Displays

We present two experiments comparing selection techniques for low-cost mobile VR devices, such as Google Cardboard. Our objective was to assess the feasibility of computer vision tracking on mobile devices as an alternative to common head-ray selection methods. In the first experiment, we compared three selection techniques: air touch, head ray, and finger ray. Overall, hand-based selection (air touch) performed much worse than ray-based selection. In the second experiment, we compared different combinations of selection techniques and selection indication methods. The built-in Cardboard button worked well with the head ray technique. Using a hand gesture (air tap) with ray-based techniques resulted in slower selection times, but comparable accuracy. Our results suggest that camera-based mobile tracking is best used with ray-based techniques, but selection indication mechanisms remain problematic.
Comparative Methodologies for Evaluation of Ontology Design

In general, it is advisable to evaluate ontology designed quality before developing an Information System based on Ontologies. Principles of ontology design, focus on ontologies design that can be reusable, easy-to-use, maintain and update over time. In this work a model for quality verification of the ontology design is proposed, it is based on ontology design principles of [, , ]. Methodology starts with an analysis of design principles, then principles are grouped into verification or evaluation collections and following verification techniques was established: 1) minimalist; 2) consistency; 3) flexibility; 4) standardization; 5) redundancy; and 6) efficiency. The main contribution of this work is a qualitative and quantitative model for the verification of an ontology applying design principles. As an application case, quality evaluation of ontological model for OntoPAA is performed, results show that ontology evaluated complies with design techniques that guarantee an adequate level of quality.
Mobile Based Learning Development for Improving Quality of Nursing Education in Indonesia

Internet users in Indonesia tend to use mobile devices in accessing the Internet at around 47.6%. This means that there is already a preference for Internet users in terms of using devices from PC and laptop computers to mobile devices. With the development of technology and the transition to mobile, the education sector is also experiencing a change from face-to-face in the classroom (conventional) to online learning. The researcher proposes a prototype of a mobile-based online learning management system (m-LMS) that will be used and adopted by students and lecturers at the nursing academy. The study aims to determine the preferences of prospective nurses in using smartphone devices as m-learning media and specific needs in m- learning situations, as well as their opinions on the appropriateness of learning through smartphones. Retrieval of data using interview techniques to students and lecturers at the nursing academy. The sampling technique of this study used simple random sampling. The results obtained were enthusiastic nursing students with online learning via smartphones and the feature most wanted by students was the availability of learning material that could be downloaded and read whenever they needed. The research resulted in the design of a mobile-based learning development model for nursing school students and lecturers.
Implementing Resource Adequate Service Robot Behavior by Systematic Management of Non-Functional Properties: An Intralogistics Use Case

The primary focus in the development of service robotic systems is still mostly on purely functional concerns (what to do?). However, an important further aspect so far often neglected are the non-functional properties (NFPs) of task execution (how to do it?). In this paper, we briefly introduce our general approach for composing NFPs and for deciding about adequate execution alternatives. Then, we present a model for the time composition of an order picking task. Finally, we show in a practical experiment how a service robot executing an order picking task can balance the fulfilment of external nonfunctional requirements (meet deadline for order picking) and the consideration of internal needs (save energy).
Deep neural annealing model for the semantic representation of documents

As a result of the growing production of unstructured textual data, techniques for representing words and documents in the vector space have emerged recently. The Brazilian Public Ministry has received several textual requests that are send by citizens with different needs, such as those involved in cases of domestic violence against women, others requesting intensive care unit admissions, and more. The time spent in classifying, detecting similar requests and distributing them is essential to optimize and save public resources. Therefore, we adopted the neural model with the Simulated Annealing (SA), a classic global optimization algorithm with low computational complexity, because of the need to reduce the daily training time, providing a more friendly graphic visualization of documents in high dimensions, supporting the judicial decision process. The physical analogy of the SA meta-heuristic associated with the continuous representation of documents in the vector space contribute greatly to the friendly visualization of a high-dimensional dataset, maintaining a comparable score with other deep models and optimization algorithms, such as Covariance Matrix Adaptation Evolution Strategy (CMA-ES) and Bayesian Optimization (BO).
Fill Students' Knowledge Gap by Recommending Remedial Learning Materials

Identifying and filling the knowledge gap is one solution to improve students' learning performance. Several challenges are involved, such as how to identify the gap and how to fill the gap. In this paper, we propose to identify students' knowledge gap at the early stage by tracking their in-class emotions, and fill the gap by recommending remedial learning materials on time. The framework and workflow have been described in details in this work.
A Bayesian goodness-of-fit test for regression

Regression models are widely used statistical procedures, and the validation of their assumptions plays a crucial role in the data analysis process. Unfortunately, validating assumptions usually depends on the availability of tests tailored to the specific model of interest. A novel Bayesian goodness-of-fit hypothesis testing approach is presented for a broad class of regression models the response variable of which is univariate and continuous. The proposed approach relies on a suitable transformation of the response variable and a Bayesian prior induced by a predictor-dependent mixture model. Hypothesis testing is performed via Bayes factor, the asymptotic properties of which are discussed. The method is implemented by means of a Markov chain Monte Carlo algorithm, and its performance is illustrated using simulated and real data sets.
The Right to Process Data for Machine Learning Purposes in the EU

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A Comparative Study of the Influence of Interactive AR-Based Experiential Teaching on Cognitive Ability in a Chemical Electrolytic Cell Course

The integration of Augmented Reality (AR) and experiential teaching plays a vital role in teaching practice. This research designs and implements an interactive AR-based experiential course about electrolytic cells in chemistry. The influences are also explored on high school students' cognitive ability, including remembering, understanding and applying dimensions, through classroom comparative experiments. 60 high school students of similar level, who participated in the study, were randomly divided into an experimental group and a control group. The experimental group used an interactive AR program, and the control group used traditional flash animation. The data was collected through pretest, posttest, interviews and questionnaire surveys. The results show that the experimental group performs better on the applying dimension, while the control group performs better on the remembering dimension, and the two groups performs similarly on the understanding dimension. It is concluded that both traditional teaching with flash and AR-based experiential teaching can improve students' cognitive ability. But experiential teaching based on interactive AR can improve students' cognitive ability better on the applying dimension through hands-on practice.
Remote Sensing Image Retrieval Based on DenseNet Model and CBAM

As a retrieval method, Hash is widely used in the field of image retrieval because it can meet the retrieval time requirements for large-scale image retrieval. In order to solve the problem that most of the current image retrieval models are weak in representing remote sensing images, especially for remote sensing images with complex background, it is easy to confuse the background with the image subject. This paper proposes a remote sensing image retrieval model based on DenseNet. On the ground of original DenseNet model, the method in this paper adds CBAM to each Dense Block of the model, extracts the image features with regional attention and channel attention, and adopts the triple loss function to achieve the retrieve of remote sensing images. The experiment results show the retrieval accuracy of this presented method is significantly improved based on global features in the common datasets of CIFAR-10 and NUS-WIDE as well as the remote sensing image dataset of UC-Merced compared with the existing retrieval methods.
Implementing a Blended Learning Approach to Foreign Language Teaching at SPbPU

Abstract One of the most significant trends in higher education today is combining conventional classroom instruction with online learning, or blended learning. Implementing blended learning for foreign language teaching has proven effective due to a variety of factors. Universities in Russia also switch to this new teaching model and design online courses to accompany and support traditional full-time teaching. In accordance with the SPbPU Provision on online-learning, a blended course of General English was launched in the Institute of Industrial Management, Economics and Trade (IIMET). The paper describes the aspects of course development and its structure. With the aim of evaluating the pilot blended project, we have conducted a survey to define the complexity level and elicit the most difficult aspects of the course, give account of students’ motivation level and ability to work self-consistently and responsibly, and measure the satisfaction level with the course. Test results show that much bigger number of students in pilot groups got excellent and good grades which increased by 40% compared to the results of control groups. Although some aspects of the experimental course were rather challenging and caused some complications, the majority of students opted for blended learning model, as it gives more flexibility, convenience and allows students to achieve good learning outcomes.
Bridging Visual Perception with Contextual Semantics for Understanding Robot Manipulation Tasks

Understanding manipulation scenarios allows intelligent robots to plan for appropriate actions to complete a manipulation task successfully. It is essential for intelligent robots to semantically interpret manipulation knowledge by describing entities, relations and attributes in a structural manner. In this paper, we propose an implementing framework to generate high-level conceptual dynamic knowledge graphs from video clips. A combination of a Vision-Language model and an ontology system, in correspondence with visual perception and contextual semantics, is used to represent robot manipulation knowledge with Entity-Relation-Entity (E-R-E) and Entity-Attribute-Value (E-A-V) tuples. The proposed method is flexible and well-versed. Using the framework, we present a case study where robot performs manipulation actions in a kitchen environment, bridging visual perception with contextual semantics using the generated dynamic knowledge graphs.
Modeling Interventions for Insider Threat

The threat of malicious actions by trusted insiders is a difficult issue to predict and combat. This paper presents an agent-based model that simulates a way in which social interventions can curb or eliminate the occurrence of malicious insider attacks, or “insider threats”, within an organization. There are several precursors to an individual taking malicious action against an organization. One such factor, social isolation from one’s peers, may lend itself to proactive remediation techniques. Here we attempt to model the occurrence of malicious insider attacks for which social isolation is a precursor, then demonstrate the effects of social interventions on the emergence of such behavior.