Reviewerly/exHarmony huggingface dataset:
Research on Investment Decision of Substation Project Based on Life Cycle Cost

In this paper, the life cycle cost theory is applied to the study of investment decision-making of substation engineering, and the scientific, reasonable and objective comprehensive evaluation of substation engineering is realized. Based on the analysis of the calculation method of life cycle cost, a theory-based investment decision model for substation engineering is established. Finally, taking a substation project as an example, it proves that the model can effectively analyse the investment cost of different schemes, objectively reflects the economic differences between schemes, and provides a favorable judgment basis for investment decision-making of Power Grid Substation projects, which has a certain practical value.
Multiple Videos Captioning Model for Video Storytelling

In this paper, We propose a novel video captioning model that utilizes context information of correlated clips. Unlike the ordinary “one clip - one caption” algorithms, we concatenate multiple neighboring clips as a chunk and train the network in “one chunk - multiple caption” manner. We train and evaluate our algorithm using M-VAD dataset and report the performance of caption and keyword generation. Our model is a foundation model for generating a video story using several captions. Therefore, in this paper, we focus on caption generation for several videos and trend analysis of the generated captions. In the experiments, we show the performance of intermediate results of our model in both qualitative and quantitative aspects.
Meta-evaluation of Dynamic Search: How Do Metrics Capture Topical Relevance, Diversity and User Effort?

Complex dynamic search tasks typically involve multi-aspect information needs and repeated interactions with an information retrieval system. Various metrics have been proposed to evaluate dynamic search systems, including the Cube Test, Expected Utility, and Session Discounted Cumulative Gain. While these complex metrics attempt to measure overall system "goodness" based on a combination of dimensions – such as topical relevance, novelty, or user effort – it remains an open question how well each of the competing evaluation dimensions is reflected in the final score. To investigate this, we adapt two meta-analysis frameworks: the Intuitiveness Test and Metric Unanimity. This study is the first to apply these frameworks to the analysis of dynamic search metrics and also to study how well these two approaches agree with each other. Our analysis shows that the complex metrics differ markedly in the extent to which they reflect these dimensions, and also demonstrates that the behaviors of the metrics change as a session progresses. Finally, our investigation of the two meta-analysis frameworks demonstrates a high level of agreement between the two approaches. Our findings can help to inform the choice and design of appropriate metrics for the evaluation of dynamic search systems.
Simple Techniques for Cross-Collection Relevance Feedback

We tackle the problem of transferring relevance judgments across document collections for specific information needs by reproducing and generalizing the work of Grossman and Cormack from the TREC 2017 Common Core Track. Their approach involves training relevance classifiers using human judgments on one or more existing (source) document collections and then applying those classifiers to a new (target) document collection. Evaluation results show that their approach, based on logistic regression using word-level tf-idf features, is both simple and effective, with average precision scores close to human-in-the-loop runs. The original approach required inference on every document in the target collection, which we reformulated into a more efficient reranking architecture using widely-available open-source tools. Our efforts to reproduce the TREC results were successful, and additional experiments demonstrate that relevance judgments can be effectively transferred across collections in different combinations. We affirm that this approach to cross-collection relevance feedback is simple, robust, and effective.
Deskbuddy

We present Deskbuddy, a vibration-based system that can track a user's activities through their desk. Tracking sitting and other office related activities let us remind the user to have healthier working habits, as well as giving information about how office spaces are used. Many solutions have been proposed for office activity tracking, but they either require the user to wear a device, or they use cameras or microphones, which can make subjects uncomfortable. Our demo includes a small vibration sensor that sits on a table that can detect four office related activities. We capture the signal from the vibration sensor, extract features, and perform classification on the resulting features. The full functionality of the system will be shown in a video. In order for our demo to be more effective in a crowded environment, we have re-trained it to detect only typing versus not typing.
Learning How to Design Apps through the Cognitive Apprenticeship Approach and Collaborative Learning in a Taiwanese Classroom

It is never a matter for human beings possessing creativity, but a matter of having much or less of it.Creativity is like intelligence, some people are rich in creativity, yet some lack a bit.Scholars have mentioned that Taiwan's traditional teaching method emphasizes on memorizing facts and knowledge, but neglects the enlightening of problem understanding and creative thinking.In addition, in recent years, learning computer programming has become a global trend.Educational institutions in various countries have promoted computer programming in schools.Hence, this study wants to use the cognitive apprenticeship approach and the collaborative learning in an Apps design class in the university.The study focuses on investigating students' attitudes toward the course design.The findings suggest that students' interests and motivation have been increased through the proposed approach.These findings have implications for teachers who are teaching computer programming.
The effect of morphological structure on semantic transparency ratings

Abstract Semantic transparency deals with the interface between lexical semantics and morphology. It is an important linguistic phenomenon in Chinese in the context of prediction of meanings of compounds from their constituents. Given prominence of compounding in Chinese morpho-lexical processes, to date there is no semantic transparency dataset available to support verifiable and replicable quantitative analysis of semantic transparency in Mandarin Chinese. In addition, the relation between semantic transparency and morphological structure has not been systematically examined. This paper reports a crowdsourcing-based experiment designed for the construction of a large semantic transparency dataset of Chinese compounds which includes semantic transparency ratings of both the compound and each constituent root of the compound. We also present an analysis of the effects of morphological structure on semantic transparency using the constructed dataset. Our study found that in a transparent modifier-head compound, the head tends to get greater semantic transparency rating than the modifier. Interestingly, no such effect is observed in coordinative compounds. This result suggests that compounds of different morphological structures are processed differently and that the concept of head plays an important role in the word-formation process of compounding. We advocate that crowdsourcing can be a highly instrumental method to collect linguistic judgments and to construct language resources in Chinese language studies. In addition, the proposed methodology of comparing constituent transparency and word transparency sheds light on the relation between morpho-lexical structure and cognitive processing of lexical meanings.
Design and assessment of the data analysis process for a wrist-worn smart object to detect atomic activities in the smart home

The ability to accurately identify the different activities of daily living (ADLs) is considered as one of the basis to foster new technological solutions inside the smart home. Current ADL recognition proposals, still however, struggle to accurately and robustly identify the range of different activities that can be performed at home, namely static, dynamic and transient activities, and the high variety of technologies and data analysis possibilities to classify the information gathered by the sensors. In this paper, we describe the methodological approach that we have followed for the processing, analysis and classification of data obtained by a simple and non-intrusive smart object with the objective to detect atomic (i.e. non-divisible) activities inside the smart home. The smart object consists of a wrist-worn 3D accelerometer, which presents as its advantages its customizability and usability. We have performed a set of systematic experiments involving ten people and have followed the steps from data gathering to the comparison of different classification techniques, to find out that it is possible to select a complete succession of data processing steps in order to detect, with high accuracy, a set of atomic activities of daily life with the selected smart object, which performs well with different independent datasets besides ours.
Creation of intellectual decision-making systems in industry based on hybrid computing methods

This paper describes the promising approach in creating applied intellectual decision-making systems in industry (different types) of the new generation, based on authors synergetic collective decision-making model and methods of hybrid computational intelligence, which allows us to significantly increase the quality of the results of computer-aided solutions to complex applied problems in information variety and heterogeneity as well as to enhance decision-making by reducing losses from erroneous and individual solutions which are irrelevant to the problem complexity. There is an example of successful application of proposed methods in modeling significant class of complex application systems in medical industry, i.e. multicomponent, poorly formalized systems functioning in various types of uncertainty. The paper presents perspective approach in the field of creation of applied intellectual systems of decision-making of new generation, on the basis of author's synergetic model of collective decision-making and methods of hybrid computing intelligence. This approach allows to significantly increase quality of results of the automated solution of complex applied challenges in the conditions of a variety and heterogeneity of information, and also to increase efficiency of decision-making due to reduction of losses from wrong and irrelevant difficulties of a task of individual decisions that results of laboratory experiments confirm.
The Role of a WhatsApp Group of a Professional Learning Community of Chemistry Teachers in the Development of Their Knowledge

The authors analyze chemistry teachers' discourse in a WhatsApp group. This online communication platform is used for continually studying the communication behavior of leading chemistry teachers who are members of a professional learning community (PLC). They describe the network of chemistry teachers' PLC in Israel, which provides the context for the study. WhatsApp enables sustained ongoing, intensive interaction, and sharing of knowledge that is practical, directly related to the members' needs, and is participant driven and constructivist in nature. A theoretical perspective of teachers' knowledge and professional development (PD) was developed in 2015 by Gess-Newsome, which was applied to examine the mechanism underlying teachers' knowledge development.
Ensemble of penalized logistic models for classification of high-dimensional data

Classification of High-dimensional data, such as gene expression data having far more variables (genes) than observations, is challenging. Classifiers aggregation known as ensemble method has proven improved classification accuracy in a wide range of applications. In this study, we propose an Ensemble of Penalized Logistic models (EPL) which utilizes Ridge regression, Lasso and Elastic net as base learners for ensemble generation. Classification is done on the basis of majority votes of the models. The EPL is assessed on both simulated data and bench mark microarray data sets. Its performance in terms of classification accuracy is compared with state-of-the-art classifiers, i.e., Support Vector Machines (SVM), K-Nearest Neighbors (KNN) and Random Forest (RF). The experimental comparisons show that the EPL has high classification accuracy as compared to the other classifiers considered here.
Grasping and Attached Mode in Human-Computer Interaction in the Study of Mouse Substitution

Some disabilities caused by sensory malfunction, accident, or congenital disorder could cause people difficulty in grasping the computer mouse. A well-known substitute uses an inertial sensor to monitor the body's orientation. This paper evaluates the efficiency and effectiveness of different methods of holding the sensor. There are two modes of holding the sensor, the grasping mode and the attached mode; we also evaluate a mouse as a baseline. The attached mode works by placing the sensor on the back of user's hand. The quantitative and qualitative evaluation procedure is based on ISO/TS 9241 part 411: Ergonomics of human-system interaction standard. The test consists of four levels of difficulty and indicates that the throughput and task completion times were not statistically different between grasping and attached mode. We also found that grasping mode and attached mode did not show significantly statistical differences in comfort and fatigue based on the questionnaires. The results also suggest that the orientation of the hand used in grasping and attached modes is suitable only for lower-impact computer use.
Data-flow bending: On the effectiveness of data-flow integrity

Most software attacks subvert the intended data-flow of a program via exploiting the memory corruption vulnerabilities. Data-Flow Integrity (DFI) is a generic defense against such attacks. Its security guarantee mainly depends on the accuracy of the static Data-Flow Graph (DFG) generated from Data-Flow Analysis (DFA), but the static DFG is conservatively over-approximated due to the imprecision of DFA. Hence a natural question is: what is the real protective power of DFI and how to measure it? In this work, we first evaluate the effectiveness of DFI based on the constructed memory corruption offense-defense model and the proposed attack Data-Flow Bending (DFB). We show how DFB corrupts memory data while adhering to DFI through a proof-of-concept exploit. Furthermore, we verify the possibility of the state-of-the-art data-oriented attacks using practical cases in the presence of DFI. Our work indicates that DFI may be ineffective against the exploitation of memory corruption vulnerabilities in certain circumstances, and that DFB can circumvent DFI to carry out memory corruption attacks.
Progressive Filtering for Feature Matching

In this paper, we propose a simple yet efficient method termed as Progressive Filtering for Feature Matching, which is able to establish accurate correspondences between two images of common or similar scenes. Our algorithm first grids the correspondence space and calculates a typical motion vector for each cell, and then removes false matches by checking the consistency between each putative match and the typical motion vector in the corresponding cell, which is achieved by a convolution operation. By refining the typical motion vector in an iterative manner, we further introduce a progressive matching strategy based on the coarse-to-fine theory to promote the matching accuracy gradually. The density estimation is utilized to address the island samples and accelerate the convergency of the mismatch removal procedure. In addition, our method is quite efficient where the gridding strategy enables it to achieve linear time complexity. Extensive experiments on several representative real images involving different types of geometric transformations demonstrate the superiority of our approach over the state-of-the-art.
Integer Wavelet Transform Based Medical Image Watermarking for Tamper Detection

Watermarking is used to hide sensitive information from being misused and to reserve integrity. The security of medical images is a prime concern now-a-days and tampers may be introduced by any third party. In this paper, we propose a new integer wavelet transform (IWT) based fragile medical image watermarking for tamper detection. The medical image is first segmented into two regions: Region of interest (ROI), region of non-interest (RONI). The watermark image is scrambled using logistic mapping. Hash value of ROI is generated using SHA-512 algorithm. Last bits of average value of each 5×5 mask of ROI is generated. Then, scrambled watermark; hash value of ROI and compressed last bits are embedded in specific regions of RONI. The specific regions of RONI are chosen based on the position and size of the RONI. Logistic mapping is used to increase security of the proposed technique so that one cannot just easily gain access of the information. Hash value and last bits are used for two level tamper detection. If the hash values of ROI changes, then last bits are compared to detect tampered regions. Results of experiments applied on medical images shows that the proposed method produces high quality watermarked medical images, identifies the presence of tampers inside ROI. Results are evaluated using Peak signal to noise ratio (PSNR), Mean squared error(MSE), Normalized correlation(NC).
Using PCA to Improve the Detection of Medical Insurance Fraud in SOFM Neural Networks

In view of the inefficiency and difficulty of detection in the traditional methods of abnormal detection of medical insurance fraud in patients, this paper combines data mining technology to realize the algorithm suitable for large-scale medical insurance data mining, and establishes effective anomaly feature extraction. model. The principal component analysis (PCA) method is used to reduce the dimension of the feature data, and the abnormal medical insurance information is analyzed, identified and processed. Then, the self-organizing feature map (SOFM) neural network is established to process the medical insurance fraud behavior detection. The test results show that the method can effectively extract the characteristics of medical insurance fraud behavior and can be effectively used for medical insurance abnormality detection.
A Brainmorphic Computing Hardware Paradigm Through Complex Nonlinear Dynamics

In a brainmorphic computing paradigm, a hardware system should process information imitating the anatomical and physiological mechanisms of the brain by naturally using physical and dynamical characteristics of the constituent devices, especially through nonlinear analog circuits and devices. The latest knowledge from brain science, especially, on high-order brain functions emerged from high-dimensional complex neuro-dynamics, are reflected in the design of brainmorphic hardware. In addition, the bodily and environmental constraints are considered and utilized as embodiment in this hardware paradigm. In this paper, we propose a brain/body whole organism computation paradigm where brain-intrinsic efficient and distinct information-processing styles and functions are expected to emerge through high-dimensional complex nonlinear dynamics and the embodiment. In particular, we employ a chaotic neuron in a reservoir neural network to emerge the reference-self in the brain/body whole organism computing framework. Chaotic behavior is usually avoided in the reservoir computing because it will violate the echo state property. However, we deliberately introduce high-dimensional chaotic dynamics through the chaotic neurons, but preserving the echo state property. The high-dimensional chaotic dynamics create a rich variety of neural patterns, and at the same time, integrate information in the neural patterns into a unique dynamical state as a high-dimensional attractor. We show preliminary results for chaotic time-series predictions through the chaotic reservoir neural network to demonstrate feasibility of the chaotic dynamics introduced in the reservoir.
Adversarial Multi-task Deep Features and Unsupervised Back-end Adaptation for Language Recognition

This paper presents an investigation into speaker-invariant feature learning and domain adaptation for language recognition (LR) with short utterances. While following the conventional design of i-vector front-end and probabilistic linear discriminant analysis (PLDA) back-end, we propose to apply speaker adversarial multi-task learning (AMTL) to aim explicitly at learning speaker-invariant multilingual bottleneck features and perform unsupervised PLDA adaptation to alleviate performance degradation caused by domain mismatch between training and test data. Through a demo experiment, we show the adverse effect of domain mismatch and motivate the necessity of domain adaptation. LR experiments are carried out with the AP17-OLR challenge dataset to evaluate the effectiveness of the proposed methods in comparison with the state of the art. The results show that both speaker AMTL and unsupervised PLDA adaptation contribute significantly to performance improvement on the short-duration LR task. The effectiveness of PLDA adaptation is found to be insensitive to the number of clusters assumed in unsupervised data labeling. Our best system outperforms the state-of-the-art system of AP17-OLR and shows relative improvements of 6.98% in terms of C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">avg</sub> and 4.80% in terms of EER on 1-second test set.
Research on vocal sounding based on spectrum image analysis

The improvement of vocal singing technology involves many factors, and it is difficult to achieve the desired effect by human analysis alone. Based on this, this study based on spectrum image analysis uses the base-2 time-selection FFT algorithm as the research algorithm, uses the wavelet transform algorithm as the denoising algorithm, and combines comparative analysis to discuss the mechanism of vocal music, the state of vocalization, and the vocal quality of vocalists in vocal music teaching. Simultaneously, this study compares the singer’s frequency, pitch, overtones, harmonics, singer formants, etc., and derives the characteristics of vocal vocalization under different conditions, and can be extended to all music vocal studies. Research shows that this research method has certain practicality and can provide theoretical reference for related research.
Turning a Vulnerability into an Asset: Accelerating Facial Identification with Morphing

In recent years, morphing of facial images has arisen as an important attack vector on biometric systems. Detection of morphed images has proven challenging for automated systems and human experts alike. Likewise, in recent years, the importance of efficient (fast) biometric identification has been emphasised by the rapid rise and growth of large-scale bio-metric systems around the world.In this paper, the aforementioned, hitherto unrelated, topics within the biometrics domain are combined: the properties of morphed images are exploited for the purpose of improving the transaction times of a biometric identification system. Specifically, morphs of two or more samples are used in the pre-selection step of a two-stage biometric identification system. In a proof-of-concept experimental evaluation using two state-of-the-art open-source facial recognition frameworks it is shown, that the proposed system achieves hit rates comparable to that of an exhaustive search-based baseline, while significantly reducing the penetration rate (and thus the computational workload) associated with the biometric identification transactions.