Reviewerly/exHarmony huggingface dataset:
Probabilistic Relational Connectivity Analysis of Bigram Models

This study demonstrates an application of probabilistic relational graph connectivity analysis in bigram models (2-gram) of texts. The probabilistic relation matrices are calculated by estimating bigram co-occurrence probabilities in word sequence of a given short text. Then, deeper probabilistic connectivity relations among word sequences can be considered by calculating powers of probabilistic relation matrix of bigram models. Cosine similarity measure is used to evaluate similarity of probabilistic connectivity patterns of lexemes in a given message. Illustrative analysis examples are presented to discuss results of sentence-wise and short text analyses.
Flexible POI Recommendation Based on User Situation

Location-based social networks (LBSNs) become an essential part of our lives as these services can assist users in finding interesting point-of-interest (POI). Many studies have been conducted to perform POI recommendations with various factors, such as user's check-in records, geographic information, and social relationship. However, existing studies use only fixed values without considering the user's current situations that frequently change. In this paper, we propose a flexible POI recommendation based on the user's situation. For this, we first construct a graph model of three factors, such as trajectory, distance, and preference. We then use a weight-threshold algorithm (w-TA) to adjust the weight of each factor based on the user's current situation. The experiment results show the flexibility of the proposed method in various conditions.
Object Motion Detection Methods for Real-Time Video Surveillance: A Survey with Empirical Evaluation

Automated moving object detection and analysis assumes a great significance in video surveillance. This article presents a comprehensive survey on the techniques of object-in-motion detection for video surveillance. In this paper, eight methods of object detection in video streams are implemented and evaluated empirically on five quality parameters for identifying the efficiency and effectiveness of these methods. For objective assessments of these methods, a standard dataset “CDnet2012” is used which consists of six different rigorous scenarios. In conclusion, an attempt has been made to identify the best method for different scenarios, employable in real-time video surveillance.
Designing Temporary Resident Information Systems: Case Study of a Village in Bali

Temporary Resident data processing is the main activity carried out by the village administration on an ongoing basis. The current system still often finds difficulties when conducting data collection, searching population data, and making Temporary Resident reports. Manual registration process also causes damage and loss of data due to storage media only in the form of physical documents. This conventional system caused the activities of Temporary Resident data collection in the village to be less effective and efficient. This research aims to produce a faster, more effective and efficient, Temporary Resident data's information system in the village. The method used in developing this system is a System Development Life Cycle with the Waterfall process model, while observation and interviews were used for the data collection. The Temporary Resident Information System is expected to help facilitate Residents in registering so that village administrators will produce a faster, more effective, and efficient work. The results of this study are to facilitate the village in the process of managing Temporary Resident data, search, and report periodically on population numbers and print Temporary Resident identity cards.
Towards Efficient Medical Video Super-Resolution Based on Deep Back-Projection Networks

Medical video super-resolution reconstruction helps doctors clearly observe the patient's lesions, and increases the likelihood of the disease being diagnosed and cured. In this paper, we propose an efficient medical video super-resolution method based on deep back-projection networks. First, three medical video super-resolution models based on deep back-projection networks are trained for real-time, high-quality and high-magnification medical video super-resolution reconstruction. Second, an easy-to-use interface is designed. Third, our proposed model is used to reconstruct different kinds of medical videos, and calculate their peak signal-to-noise ratio and structural similarity. The experimental results show that the proposed medical video super-resolution method achieves superior performance over the other compared methods in this work.
Introspection unit in memory network: Learning to generalize inference in OOV scenarios

Inference in natural language processing (NLP) is a tough task. Although plenty of models have been proposed in recent years, they are usually restricted to infer within a limited vocabulary or handcrafted training templates. In this paper, we propose the introspection unit (IU), a new neural module which can be incorporated with memory networks to deal with inference tasks in out of vocabulary (OOV) and rare named entities (RNEs) scenarios. Specifically, when encountering a new word, IU compares its part-of-speech context with the training dataset to extract a similar sample, and then embeds the new word into a target position to construct a simulated sample. The target position is located by the result of part-of-speech tagging. Finally, using the simulated sample, IU helps memory networks to learn the context and characteristic of the new word. In experiments, we evaluate the effectiveness of IU with the memory network on four inference datasets: a name OOV dataset, a place OOV dataset, a more challenging synthetical mixture OOV dataset and a realistic dialogue dataset. The experimental results demonstrate that IU effectively generalizes the inference ability of memory networks to OOV scenarios and improves the inference accuracies significantly. Furthermore, we visualize both the introspection process and the effect of IU in word embeddings and memories.
CamaLeon

Despite work on smart spaces, nowadays a lot of knowledge work happens in the wild: at home, in coffee places, trains, buses, planes, and of course in crowded open office cubicles. Conducting web conferences in these settings creates privacy issues, and can also distract participants, leading to a perceived lack of professionalism from the remote peer(s). To solve this common problem, we implemented CamaLeon, a browser-based tool that uses real-time machine vision powered by deep learning to change the webcam stream sent by the remote peer. Specifically, CamaLeon dynamically changes the "wild" background into one that resembles that of the office workers. In order to detect the background in disparate settings, we designed and trained a fast UNet model on head and shoulder images. CamaLeon also uses a face detector to determine whether it should stream the person's face, depending on its location (or lack of presence). It uses face recognition to make sure it streams only a face that belongs to the user who connected to the meeting. We tested the system during a few real video conferencing calls at our company in which two workers are remote. Both parties felt a sense of enhanced co-presence, and the remote participants felt more professional with their background replaced.
CHAMELEON: Hides privacy in cloud IoT system by LSB and CSE

Summary Recently, in a variety of Internet of Things (IoT) application scenarios, fingerprint image as a common biological information has been widely collected. For example, in safe city projects, civil fingerprints representing unique identification are vastly assembled, stored, and transmitted wirelessly. Under such situations, data privacy protection becomes increasingly valued. In our study, this fundamental problem was centered. In our work, the method to remain secure in such an universal cloud based IoT system and the approach to hide important information in biologically collected data such are fully discussed. A new lightweight algorithm in protecting the privacy and integrity of data in a cloud based IoT system is badly needed. We believe that it is a way to hide the core information in the surrounding environment just like “Chameleon” to protect the data security. Thus, in this paper, we propose “CHAMELEON” by using least significant bits (LSB) to hide important information in fingerprint images and chaotic sequence encryption (CSE) to securely transmit useful data. The experimental results of this algorithm show that Chameleon can resist static attacks and protect the privacy of users regarding to the information security and integrity.
Grasping the Intimate Immensity: Acousmatic compositional techniques in sound art as ‘something to hold on to’

This article explores the accessibility of acousmatic compositional approaches to sound and installation art. Principally of concern is the consideration of intimacy to create a means of ‘connecting’ with an audience. Installations might be said to explore ideas of intimacy in two ways which increase accessibility for the installation visitor: through cultivating installation–visitor relationships, and through encouraging visitor–visitor relationships. A variety of ways in which various acousmatic compositional techniques relating to intimacy might be brought to bear on and operate as a way of drawing a listener into a work are explored, in particular as they relate to the consideration of space and spatial relationships. These include recording techniques, types of sound materials chosen, and the creation of particular spatial environments and listening conditions. Along with a number of instances of sound art provided by way of examples, my ongoing GRIDs series of sound sculptures will provide a case study of works related to an acousmatic aesthetic where these concerns find an outlet.
Choosing Protection: User Investments in Security Measures for Cyber Risk Management

Firewalls, Intrusion Detection Systems (IDS), and cyber-insurance are widely used to protect against cyber-attacks and their consequences. The optimal investment in each of these security measures depends on the likelihood of threats and the severity of the damage they cause, on the user’s ability to distinguish between malicious and non-malicious content, and on the properties of the different security measures and their costs. We present a model of the optimal investment in the security measures, given that the effectiveness of each measure depends partly on the performance of the others. We also conducted an online experiment in which participants classified events as malicious or non-malicious, based on the value of an observed variable. They could protect themselves by investing in a firewall, an IDS or insurance. Four experimental conditions differed in the optimal investment in the different measures. Participants tended to invest preferably in the IDS, irrespective of the benefits from this investment. They were able to identify the firewall and insurance conditions in which investments were beneficial, but they did not invest optimally in these measures. The results imply that users’ intuitive decisions to invest resources in risk management measures are likely to be non-optimal. It is important to develop methods to help users in their decisions.
Lightweight implementation of SILC, CLOC, AES-JAMBU and COLM authenticated ciphers

Authenticated encryption schemes provide both confidentiality and integrity services, simultaneously. CAESAR competition will identify a portfolio of authenticated ciphers, which is expected to be suitable for widespread adoption and offers advantages over AES-GCM. An important criterion for selecting the final candidates, besides security, is the hardware performance in resource-limited environments. In this paper, SILC, CLOC, AES-JAMBU, and COLM authenticated ciphers have been selected from the third round of the CAESAR competition for hardware evaluation. The main reasons to choose these schemes are their lightweight design, sufficient security level, and the use of the AES algorithm as their underlying block cipher. To the best our knowledge, it is the first time that an 8-bit lightweight architecture which is compatible with API v2 is presented for the selected schemes. To implement AES, the Atomic-AES v2 which is one of the smallest implementations has been adopted according to the requirements of the selected schemes. Furthermore, to reduce the area in the hardware implementation, several techniques are used, including implementing one AES core in the datapath, sharing registers to store intermediate values, implementing the tweak functions with the shuffling of wires, and implementing doubling on the GF(2128) with 8-bit architecture to construct the higher-order multipliers. The implementation results are presented on ASIC and FPGA platforms. The proposed architecture for each scheme on the two platforms is similar, but different optimization techniques are used for each platform, e.g. the AES S-box is implemented as ROM-based and logic-based on FPGA and ASIC, respectively. The comparing of the results with 128-bit implementations shows that the area on FPGA and ASIC is reduced up to 65% and 88%, respectively. The results of the current study demonstrate that AES-JAMBU has the lowest hardware area and the highest throughput and performance on both platforms. Besides, CLOC has the highest area reduction on both platforms, compared with those of the 128-bit implementations.
Towards Detecting and Mitigating Conflicts for Privacy and Security Requirements

Requirement engineering live in a world were contradiction is the norm. Hence, development of software engineering is usually an adjustable and upgrading cyclical process. We found in the literature that some requirements conflict with other requirements. We will focus in this study on identification and resolution of conflicts between security and privacy requirements. Although, most the recent studies focus on identifying conflicts without proposing a solution to resolve it. This paper presents an approach to identifying and resolving conflicting privacy and security requirements as patterns. By using patterns to describe the problem we can propose a solution for each conflict.
Efficient Panorama Database Indexing for Indoor Localization

We consider the task of indoor localization in large-scale environments using visual search on a database of geo-tagged panoramas. In this work we propose an efficient way to represent the database so as to maximize the search accuracy while minimizing the amount of computation required per query. The success of our method is due to a combination of (i) a hierarchical indexing method based on panorama image region information, and (ii) image descriptors aggregated from multiple views sampled finely over the panorama using generalized max pooling (GMP). Experiments on a large indoor dataset show that the complexity is reduced compared to common state-of-the-art retrieval methods such as FLANN (Fast Library for Approximate Nearest Neighbors): our scheme is more than twice as fast as an index based on FLANN while maintaining a similar retrieval performance.
Towards a reference context model for adaptive learning

Adaptive and context-aware learning provides learning content according to a learner's context. In order to achieve this goal, the dimensions that constitute the context in the current learner's state have to be determined. There are several existing works within this field and each of these are taking care of a subset of context parameters - like learning styles, learner location, etc. But, a standardized model that helps to capture a learner's context in its entirety is not available. The requirement to define context more precisely and in a uniform way has been identified by several researchers because a general and precise definition of context can facilitate the identification of what does and does not constitute context and can enable reuse and share of contextual data over applications. To this end, this work proposes a reference context model that helps to define a learner's context. The proposed model is developed by consolidating the various context parameters used in the existing adaptive systems and organizing them into an appropriate structure.
Improving the Hough Transform Through Three New Morphological Operators

Despite being one of the most famous line detectors, Hough Transform has reliability issues. Hough Transform works better on long lines. This article introduces three new binary morphological operators that stretches binary edge images. The first of these operators thins the output of the binary edge image so that the second one can stretch it. The third operator, smooth, is designed for smoothing the lines that are stretched. Results show that these two operators improve Hough Transform results considerably at a very low computational expense. The author publicly shared the executable of the algorithm and the dataset on the Github to prove the efficiency of the method.
A New Strategy for Arabic OCR

Current OCR has limited capability for Arabic because of script models lacking scientific basis. We propose a new OCR strategy for Arabic, based on 1. Islamic script grammar including extended shaping and 2. treating Arabic script as a multi-layered writing system.
ResidualDenseNetwork: A Simple Approach for Video Person Identification

Video identification is an important task in the practical application and industry. Based on the iQIYI-VID-2019 dataset, ACM International Conference on Multimedia and iQIYI co-hosted the celebrity video identification challenge. We take part in the competition, propose a new feature fusion method and design a residual dense network which can improve video identification performance in the complex scenes. Only with face features, we achieve 0.9035 in mean Average Precision(mAP) which win the second place on the leadboard. At the same time, it is the best score only with official features. It is worth mention that the flops of our model is only 0.5G and the time required to predict the entire test dataset is only 2 sim 5 minutes. Our method takes accuracy and speed into account, which has a strong practical significance.
Real-Time Visualization of Neural Network Training to Supplement Machine Learning Education

In machine learning, neural networks have excelled at performing tasks at a high level with a simple and flexible implementation. Neural networks are particularly well-suited for novice programmers due to the availability of open-source libraries like TensorFlow and Caffe. However, novice programmers often neglect to learn beyond the black-box behaviors that these libraries provide. Introductory college students often lack the understanding of neural network internals, such as hidden layers and activation functions, and their interactions during training, which are crucial to efficiently solving more complex problems. Here, we present Omega <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> , a device that opens up the black-box of neural networks by visually representing how hidden layers behave during training in real-time. In addition, Omega <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> provides an engaging tactile and visual educational experience to students, and waives the requirement for a strong programming background in order to learn about neural networks. In this paper, we will discuss the fabrication and set-up of Omega <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> as well as evaluate and compare Omega <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> to traditional lecture-based learning.
A SVM-PSO Classifier for Robot Motion in Environment with Obstacles

Support vector machines (SVM) is a popular method of classification problems. In this study, the sensor data obtained from the movement of the robot SCITOS G5 along the road is classified. Sensor data includes numerical values of the movements of the robot against obstacles. C and Gamma values were optimized with Particle Swarm Optimization (PSO) from the most important parameters affecting the accuracy of classification in SVMs and the most appropriate values were used. In this paper, it has been investigated whether any robot can exhibit humanoid movements with SVM method against an obstacle.
Fusing Feature Distribution Entropy with R-MAC Features in Image Retrieval

Image retrieval based on a convolutional neural network (CNN) has attracted great attention among researchers because of the high performance. The pooling method has become a research hotpot in the task of image retrieval in recent years. In this paper, we propose the feature distribution entropy (FDE) to measure the difference of regional distribution information in the feature maps from CNNs. We propose a novel pooling method, which fuses our proposed FDE with region maximum activations of convolutions (R-MAC) features to improve the performance of image retrieval, as it takes the advantage of regional distribution information in the feature maps. Compared with the descriptors computed by R-MAC pooling, our proposed method considers not only the most significant feature values of each region in feature map, but also the distribution difference in different regions. We utilize the histogram of feature values to calculate regional distribution entropy and concatenate the regional distribution entropy into FDE, which is further normalized and fused with R-MAC feature vectors by weighted summation to generate the final feature descriptors. We have conducted experiments on public datasets and the results demonstrate that our proposed method could produce better retrieval performances than existing state-of-the-art algorithms. Further, higher performance could be achieved by performing these post-processing on the improved feature descriptors.
Research on the test of communication protocol of power quality monitoring platform

During the scale expansion of the power quality monitoring network, aiming at the problem of unification in communication protocol of power quality monitor, a consistency test method for communication protocol of power quality monitoring platform is proposed in this paper. The construction of smart grid and digital substation put forward new requirements for power quality monitor. Communication protocol test is particularly important in network monitoring. The communication protocol test is implemented on the power quality monitoring platform based on SISCO mms-ease lite and IEEE P1159.3. It effectively improves the reliability of the power quality monitor.
Algorithm Selection via Meta-Learning and Active Meta-Learning

To find most suitable classifier is possible either through cross-validation, which suffers from computational cost or through expert advice which is not always feasible to have. Meta-Learning can be a better approach to automate this process, by generating Meta-Examples which is a combination of performance results of classification algorithms on input datasets and Meta-Features. With the increasing number of datasets and underlying complexity of algorithms, makes even the Meta-Learning process expensive. So, Active Meta-Learning can help by optimizing the generation of Meta-Examples along with maintaining the performance of classification algorithms. Proposed work here provides a ranking of classifiers using SRR and ARR ranking method and compares Meta-Learning with Active Meta-Learning. In this work, evaluation methodology based on ideal ranking is presented, which shows that proposed method leads to significantly better ranking with reduced Meta-Examples. The executed experiments discovered a considerable improvement in Meta-Learning performance that supports nonexperts users in the selection of classification algorithms.