Reviewerly/exHarmony huggingface dataset:
On the Price of Source Anonymity in Heterogeneous Parametric Point Estimation

Parametric point estimation from anonymous and heterogeneous data is studied. For heterogeneity, we assume n samples are independently drawn, each following one of K possible distributions. For anonymity, we assume the estimator knows the number of samples drawn from each distribution, but which one each sample follows is hidden. In words, samples as a sequence are passed through an unknown permutation prior to being observed. The goal is to find an estimator that minimizes the worst-case statistical risk over all possible permutations. We prove that an optimal estimator depends only on the empirical distribution (type) of samples, and when the risk function is the mean squared error (MSE), it follows a non-trivial Cramer-Rao lower bound. We further characterize its asymptote as n → ∞, assuming the number of samples from each distribution is proportional to n. The lower bound is of the order of 1/n, and the reciprocal of its prefactor is the Fisher information of the mixture of the K distributions.
Need for Hybrid Lexicon Based Context Aware Sentiment Analysis for Handling Uncertainty—An Experimental Study

In this modern era, the rapid improvement of internet technologies makes the user comfortable in generating the data in an easier way. To analyse the user-generated data for classifying sentiment polarity into one of the three categories namely positive, negative and neutral, sentiment analysis is required. Sentiment analysis is the computational study of user opinions, moods, sentiments and other subjective elements of the text. Sentiment analysis can be implemented using lexicon based approaches and machine learning approaches (Bnadhane et al. in Procedia Comput Sci 45: 808-814 (2015), [1]). Sentiment lexicon can be used to maintain terms and their respective sentiment values. But, the existing sentiment lexicons cannot handle improved internet slang data and missing data. Hybrid lexicon can be generated by combining sentiment lexicon with domain-specific sentiment-bearing terms. In this work, we analyse and find the need for improving sentiment classification with derived knowledge from domain-specific contextual analysis and domain adopted lexicons. Finally, the analysis shows the need for the proposed sentiment classification to handle missing data.
A Highly Efficient Remote Access Trojan Detection Method

Nowadays, machine learning is popular in remote access Trojan (RAT) detection which can create patterns for decision-making. However, most research focus on improving the detection rate and reducing the false negative rate, therefore they ignore the result of abnormal samples. In addition, most classifiers select several proprietary applications and RATs as their training set, which makes them difficult to adapt to the real environment. In this article, the authors address the issue of imbalance dataset between normal and RAT samples, and propose a highly efficient method of detecting RATs in real traffic. In the authors method, they generate eight features by combining the size, the inter-arrival and the flag from one packet sequence. Then, they preprocess the imbalance dataset and implement a classifier by XGBoost algorithm. The classifier achieves a false negative rate of less than 0.18%. Moreover, the authors demonstrate that their classifier is capable of detecting unknown RAT.
Comparative Numerical and Experimental Modal Analysis of Aluminum and Composite Plates

The paper presents the comparative analysis of the dynamic behavior of two rectangular plates of different material, aluminum and composite. While their global geometric dimensions (length, width and thickness) are similar, their inner structures are quite different. Whereas aluminum plate can be considered isotropic, the composite plate is a unidirectional carbon-epoxy laminate. Modal characteristics of the two plates were determined both numerically and experimentally and a comparative analysis of the obtained results was performed. Responses of the plates were documented by an optical, contactless 3D digital image correlation (DIC) system that contains a set of high-speed cameras capable of recording the movement of the white-and-black stochastic pattern applied to the upper surfaces of the plates. Numerical simulations were performed by the finite element method (FEM) in the commercial software package ANSYS. The plates were excited by a modal hammer and allowed to freely oscillate. In order to determine the natural frequencies of the plates the recorded time-domain responses were post-processed, i.e. converted to the frequency domain by fast Fourier transform (FFT). The first three natural modes were successfully experimentally established and compared to the corresponding numerical values. Since the differences between the two sets of results are less than 5%, the applied experimental technique can be considered valid and suitable for a wide range of engineering problems involving vibrations.
Deep Learning Method Based Binary Descriptor for Object Detection

The wide applications of the object detection techniques in the domains like video surveillance, security, military, automated industry tasks, biometrics has attracted the interest of the researchers. Deep learning is one of the most effective and efficient techniques for the object detection nowadays and has brought quite a revolution in this field. This paper proposes CNN architecture for the extraction of compact binary descriptors using stacked convolutional auto encoders without labeled data. PASCAL and CALTECH standard object datasets are used to validate the experimental results. The results are presented in terms of recall and precision performance matrices. The results show that the proposed architecture using CNN outperforms the rest of the state-of the art descriptor of its class. The recall and precision for the CALTECH dataset for the proposed CNN architecture is 0.98 and 0.93 respectively.
Data Security Algorithm Using Circular Queue and Graycode

Data is one of the most valuable things in the world. It can be text, image, audio or video. Millions of data are transmitted in every second. Hence, securing the data is one of the crucial topics in network environment. Lots of algorithms are available for data security. A low complexity, symmetric cryptographic algorithm with circular queue and gray code is developed here. The security algorithms, which are using circular queue, can make decryption of ciphered message more difficult. Gray code is an ordering of numeral binary system such that two successive differ in only one bit. This algorithm uses the dimension of circular queue and starting of the chosen keyword letter as the flexible factors. Using these adjustable features is the forte of this algorithm and makes retrieval of the plaintext by invaders more problematic. This algorithm can be used for securing text, image, audio, and video files. This algorithm will be less complex compared to the Multiple Access Circular Queue algorithm (MACQ). The circular queue and graycode in this algorithm makes the retrieval of plaintext by attackers more difficult.
Teaching Experience Design Using Poems as Cultural Probes

Training user experience (UX) design students to develop interactive products valuing the subjective experience as a catalyst is a challenge. Experience design implies a deep understanding of the values, culture, beliefs and practices of a target user group and also sensitivity to consciously integrate them in products with distinctive qualities. This paper presents a method experimented along three years of the Master’s Degree Program in Industrial Design at Eindhoven University of Technology. The method uses poems as cultural probes and as an access door to experience cultural elements and to embed them in the design of meaningful interactions. Beside the presentation of the method, the paper illustrates student projects that exemplify the approach. It concludes with recommendations to experience designers for transforming socio-cultural factors into product features which promote a valuable subjective experience.
Temporal information effect on personalized recommendation

As information explosively grows, it requires to find out the highly efficient data from the global database. In this paper, based on two real systems, i.e., the MovieLens and Netflix, we investigate the temporal information effect on personalized recommendation for three collaborative filtering algorithms, by considering both the volume and time of the training data. It is observed that, for an increasing training data of a certain time range, the recommendation accuracy using the time-dependent training data outperforms that using the random training data of the same size. Moreover, the increase of data volume would not always improve the recommendation accuracy for the time-dependent training data case, whereas the recent temporal training data are important to elevate the recommendation accuracy. Further study on a hybrid algorithm of heat conduction and mass diffusion shows that, using the recent temporal information as training data, the algorithm reaches the optimal recommendation accuracy, when it returns to the original mass diffusion algorithm, which suggests no necessity of introducing an extra parameter. And the simple mass diffusion algorithm is found to be more advantageous than the previous three algorithms.
A Real Time Fall Detection System Using Tri-Axial Accelerometer and Clinometer Based on Smart Phones

In this paper, we design a method to use smart phone to detect when fall accident happened, it can inform outside people or organization automatically to get help from them. The smart phone has implemented several sensors, such as the tri-axial accelerometer, electronic compass, global positioning system (GPS) etc. We will use those sensors to do fall detection. This detection system is used by placing in the waist pocket. Because people activity can be recorded real time by center of gravity in the body. We collect data for normal movements and fall events to setup a database, and then do the data analysis real time to identify if it is normal movement or fall event. To offload system operation loading and increase the efficiency on the fall detection, this paper will be divided into two parts. In the first part, in order to make the identification more lightweight, the collected data will be used to do the data blurring by weighted moving average. This way can make system easy to comparison and will not lose fall event feature. In the second part, we input the processed data to do fall detection. Three features weightlessness, impact and stillness are used to identify if it is fall event or not. If fall accident is true, system will send warning message and location automatically to the people or organization who we predefined in the system to get help. The results of our research can be used by everyone and everywhere if wireless network connection is valid. This system can be used in various environments and it is very convenient to hand carry and easy to use. So the acceptance from general people should be very high for them can use this system to get help in time to save their life or mitigate the damage.
Hadoop Scalability and Performance Testing in Homogeneous Clusters

Big data is a term used to refer to the datasets that are too large (Ex. GBs, TBs, PBs, ZBs, etc.) or complex for traditional data processing application software. Distributed and parallel processing becomes increasingly important for big data. There are two most popular parallel and distributed processing frameworks available, namely Hadoop and Spark. Hadoop and Spark are open-source software frameworks for reliable, scalable, and distributed computing. Hadoop is created by Apache Software Foundation. This framework allows the processing of extremely large datasets on clusters of computers using a simple programming model called MapReduce. It works on a distributed file system called HDFS (Hadoop Distributed File System) to run on commodity hardware. It is designed to scale up horizontally from a single machine to thousands of machines, each offering local computation and storage. Performance of Hadoop cluster depends on the application and several parameters. In this paper we aim to study the performance of Hadoop homogeneous cluster by tuning a few parameters like cluster size, dataset size, and HDFS block size, etc.
Hash Vectorizer Based Movie Genre Identification

The task of movie genre classification is a small subset of the wider class of text classification applications. One of the major uses of movie genre classification is in the study of the interests of a regional demographic. This paper seeks to improve the process of classifying movies according to their genres by analyzing the textual plots of the movies and proposing a methodology to decrease the overall time complexity while maintaining a satisfactory level of performance.
A Multi-pattern Matching Algorithm for Chinese-Hmong Mixed Strings

To solve the problem of rapid retrieval of Chinese-Hmong mixed text, a multi-pattern matching algorithm in double-bytes unit combined with the idea of AC algorithm and the mismatch processing strategy of Horspool algorithm is proposed for the Chinese-Hmong mixed strings. In this algorithm, a deterministic finite automaton is constructed based on the pattern-set according to the idea of AC algorithm, and the moving distance of the pattern is calculated by the bad-character rule of the Horspool algorithm, and the text is only traversed once to complete the quick search task of all patterns by using the finite automata. The experimental results show that the proposed algorithm has a good performance in multi-pattern matching for Chinese-Hmong mixed texts in different scale, even for the mixed texts containing more than 100,000 characters, the matching efficiency is also significantly higher than the AC algorithm.
Probabilistic Word Embeddings in Neural IR

In this paper, we discuss how a promising word vector representation based on Probabilistic Word Embeddings (PWE) can be applied to Neural Information Retrieval (NeuIR). We illustrate PWE pros for text retrieval, and identify the core issues which prevent a full exploitation of their potential. In particular, we focus on the application of elliptical probabilistic embeddings, a type of PWE, to a NeuIR system (i.e., MatchPyramid). The main contributions of this paper are: (i) an analysis of the pros and cons of PWE in NeuIR; (ii) an in-depth comparison of PWE against pre-trained Word2Vec, FastText and WordNet word embeddings; (iii) an extension of the MatchPyramid model to take advantage of broader word relations information from WordNet; (iv) a topic-level evaluation of the MatchPyramid ranking models employing the considered word embeddings. Finally, we discuss some lessons learned and outline some open research problems to employ PWE in NeuIR systems more effectively.
A thickness measuring device of metal sheet based on dual triangulation through side irradiation with single camera

A classical thickness measuring device based on dual triangulation places the laser sources over and under the sample. According to reports from factories, the tail warping of the metal sheet usually strikes the laser sources and damages them. In order to solve the problem, the dot laser sources are replaced by laser light section generators and placed on the side to avoid the tail warping crash. Moreover, only one camera is employed to monitor both upper and lower line faculae simultaneously, which solves the asynchronism problem. The optical structure and measuring mechanism of the improved device are described in this paper. A nonlinear fitting is utilized to calibrate the device, and the experimental result reveals that the maximum relative error is lower than 1.6% when the measured thickness ranges from 0.2 to 1 mm. Furthermore, even if the object is inclined within 5°, the maximum relative error is still lower than 2.55%.
Security Culture at Production Facility. Conclusions from Employees’ Training

Abstract A high level of safety culture is a factor affecting the quality of production. The article contains conclusions and recommendations regarding creating a safety culture based on the awareness of threats and the ability to respond to them. The aim of the article is to analyze trainings on AT (antiterrorist) security procedures conducted in critical infrastructure (CI) enterprises and enterprises which are subject to mandatory protection. The theoretical system of reference is based on the issues regarding security culture and sociology of work. Observation of employee training, followed by analysis of employees' behavior and interviews with their supervisors, allowed to assess the functionality of solutions and develop recommendations for the training of with and blue collar workers.
Improved Soft Decoding of Reed-Solomon Codes on Gilbert-Elliott Channels

It was shown by Guruswami and Rudra that Reed-Solomon codes can be list decoded to recover from phased burst errors (i.e. errors occurring within fixed regular intervals) up to the information-theoretic limit and, in particular, beyond the Guruswami-Sudan bound. In this paper, we present evidence that the algorithm developed by Guruswami and Rudra can also give improvement for more "irregular" burst errors. We develop a low-complexity multiplicity assignment scheme for soft decoding of Reed-Solomon (RS) codes. Specifically, we present simulation results where such soft decoding of RS codes outperforms the existing soft decision decoding algorithms of Koetter and Vardy as well as the algorithm of Das and Vardy on Gilbert-Elliott channels (under QAM and BPSK modulations) for channels that are more bursty. We also present a theoretical result that shows that for certain Gilbert-Elliott channels, with high probability of errors, the output list size for list decoding RS codes is one.
Color Extraction from Lyrics

We establish a procedure for color extraction from lyrics based on the distributed semantic representation obtained by word2vec. To extract colors from lyrics, we exploit the fact that the cosine similarities between words and color names tend to reflect the actual colors of the things the words indicate. We select important words that characterize the lyrics according to the tf-idf statistic and estimate the colors of the important words using the cosine similarities between the words and color names. For evaluation of the color extraction procedure, we implement a software system that automatically generate images based on the colors extracted from given English lyrics. We conduct a subjective evaluation of the color extraction procedure using the software system and the lyrics of three Beatles songs.
Ensuring That Test Takers Can Use New Chemistry Assessments Made Possible by Technology

Technology affords the ability to make new measurements that are not available with traditional paper-and-pencil assessments. Many of these measurements can be achieved because of new interactions made possible between test takers and the technology used in the assessment. Because the goal of any such assessment tool is to measure content, it is imperative that these new interactions do not introduce extraneous factors by which measurement errors can occur. Usability testing can be employed to prevent measurement errors from occurring by ensuring that users understand how to effectively use new technologies. In this chapter, a usability study completed for a computer-based chemistry assessment system that measures how students use multiple external representations is discussed to understand how a person’s perceived usability of the system can be measured while accounting for the possible influence of their assessment outcomes.
Anomaly Detection Using Adaptive Suppression

The paper presents an unsupervised method for anomaly detection named Adaptive Suppression (AS). The method relies on distance metrics between data points and receives hypothesis about percent of anomalies in dataset. Data points having initial accumulator value, “suppress” each other based on distance. Data points with high retained accumulator values considered as anomalous. We tested the method using anomaly video datasets (QMUL and UCSD). We extract scene dynamics using grid of short-time trackers (TrackGrid) and apply Adaptive Suppression to every TrackLoop (element of TrackGrid) separately, and then aggregate values using maximum. Using proposed method, we obtain competitive results: 0.746 AUC on QMUL, 0.7 on UCSD Ped1, 0.82 AUC on UCSD Ped2. Also, we apply Adaptive Suppression to CIFAR-10 dataset over space of reduced dimensionality (using UMAP) to provide an example of applicability of AS to image data. Possibly, AS has simple implementation and can be applied to any kind of data for which distance (or similarity metrics) exists, allowing to estimate anomaly/uniqueness of texts, images and other types of datasets using Deep Learning methods.
Engaging Communities in Public Safety via Social Media

The importance and effectiveness of community-based rescue efforts have become clear in recent emergency response operations of Hurricanes Harvey, Irma and Maria. Social media age has made it possible for the most physically disconnected residential areas to not only notify their loved ones of their safety by a single touch to update their status but also request help. In this paper, we propose a communication protocol and a Public Safety architecture over social media platforms like Twitter that allows a person in distress to send SOS messages to the Public Safety Network and for the response to be routed automatically to the closest dispatch unit or volunteer registered within the system. This allows distressed people to request help, like an “online 911” service, and lets the system take care of connecting both volunteers from the community as well as first responders to the victims. The proposed approach could enable communities to learn how to become part of the extended network of dispatch units by learning the skills necessary and registering with the Public Safety Network.
Secure Provisioning for Achieving End-to-End Secure Communications

The growth of the Internet of Things (IoT) is raising significant impact in several contexts, e.g., in cities, at home, and even attached to the human body. This digital transformation is happening at a high pace and causing a great impact in our daily lives, namely in our attempt to make cities smarter in an attempt to increase their efficiency while reducing costs and increasing safety. However, this effort is being supported by the massive deployment of sensors throughout cities worldwide, leading to increase concerns regarding security and privacy. While some of these issues have already been tackled, device authentication remains without a viable solution, specially when considering a resilient decentralized approach that is the most suitable for this scenario, as it avoids some issues related to centralization, e.g., censorship and data leakage or profit from corporations. The provisioning is usually an arduous task that encompasses device configuration, including identity and key provisioning. Given the potential large number of devices, this process must be scalable and semi-autonomous, at least. This work presents a novel approach for provisioning IoT devices that adopts an architecture where other device acts as a manager that represents a CA, allowing it to be switched on/off during the provisioning phase to reduce single point of failure (SPOF) problems. Our solution combines One Time Password (OTP) on a secure token and cryptographic algorithms on a hybrid authentication system.