Reviewerly/exHarmony huggingface dataset:
Club Ideas and Exertions: Aggregating Local Predictions for Action Recognition

Recognizing the actions performed in a video is challenging for an intelligent system since there are wide variations and enormous information in the video. Attention mechanism pays attention to key target areas, ignores irrelevant information and extracts more discriminant features. In recent years, attention mechanism has been introduced into video recognition. Although a rich literature has been spawned, most of the research on attention aims to aggregate local features by attention. Instead of feature aggregation, we propose to aggregate decisions based on local spatio-temporal attention regions for action recognition, which is inspired by ensemble learning. The proposed decision fusion module is easy to interpret and architecture-independent. In this article, the regions around the body joints are regarded as the key regions. We use the corresponding regions of the body joints in the 3-D feature maps as the basic local features for local classification. Finally, all the local classification results are combined to make a global decision. Furthermore, when training the network, we can selectively add supervision to the local and global decisions. We experimentally show that the proposed mechanism can improve the recognition performance on multiple datasets which demonstrates its effectiveness.
How to identify relevant product properties in the context of user-product interaction?

A smooth and pleasurable user-product interaction (UPI) forms the basis of a successful user-centred design. An essential part of achieving this is the consideration of the ‘right’ product properties whose identification currently requires huge monetary, temporal and personnel effort. This contribution presents a more sufficient approach for identifying relevant product properties in the context of UPI as well as its first evaluation. To identify relevant properties, the UPI is first described by standard interactions which are secondly used as input for searching a knowledge base. For the initial evaluation an application example (percussing drill) is conducted. It shows that the outcome of the approach is indeed plausible but usability should be improved. Future research focuses on a deeper evaluation and improvements regarding accessibility and traceability.
Information Scrambling by Lorenz System Based Sequences in OFDM

This paper presents the modeling results OFDM system with use chaotic scrambling of signal constellation. In this paper statistical characteristics, the bit error rate (BER) and Peak to Average Power Ratio the signals in the system are estimated. The correlation coefficient between adjacent pairs of chaotic permutation vectors as a assessing the ability to withstand a statistical attack is estimated. The scrambling degree (SD) is evaluated. The results can be used in the design of modern communication systems in order to increase the security of transmitted information.
Glyph: Efficient ML-Based Detection of Heap Spraying Attacks

Heap spraying is probably the most simple and effective memory corruption attack, which fills the memory with malicious payloads and then jumps at a random location in hopes of starting the attacker's routines. To counter this threat, GRAFFITI has been recently proposed as the first OS-agnostic framework for monitoring memory allocations of arbitrary applications at runtime; however, the main contributions of GRAFFITI are on the monitoring system, and its detection engine only considers simple heuristics which are tailored to certain attack vectors and are easily evaded. In this article, we aim to overcome this limitation and propose GLYPH as the first ML-based heap spraying detection system, which is designed to be effective, efficient, and resilient to evasive attackers. GLYPH relies on the information monitored by GRAFFITI, and we investigate the effectiveness of different feature spaces based on information entropy and memory n-grams, and discuss the several engineering challenges we have faced to make GLYPH efficient with an overhead compatible with that of GRAFFITI. To evaluate GLYPH, we build a representative dataset with several variants of heap spraying attacks, and assess GLYPH's resilience against evasive attackers through selective hold-out experiments. Results show that GLYPH achieves high accuracy in detecting spraying and is able to generalize well, outperforming the state-of-the-art approach for heap spraying detection, NOZZLE. Finally, we thoroughly discuss the trade-offs between detection performance and runtime overhead of GLYPH's different configurations.
Tibetan-Chinese Neural Machine Translation Combining Attention Mechanism

Abstract Neural Machine Translation (NMT) has developed rapidly in recent years, and translation effects in most languages have surpassed statistical machine translation methods. The Seq2Seq framework has brought great advantages to machine translation, but the model still has great limitations in the ability to capture long-distance information. Recurrent neural networks (RNN) and LSTM networks are proposed to improve this problem, but the effect is not obvious. However, the proposal and application of the attention mechanism effectively compensated for this defect. This paper uses the attention mechanism as the basis to construct the encoder-decoder framework. This method analyzes the mechanism and principle of the attention model to implement the Tibetan-Chinese neural machine translation system. Compared with the previous neural network translation models, the results show that the translation model that incorporates the attention mechanism can obtain better translation results.
Content Recommendation Based on Topic Modeling

With the proliferation in Internet usage and communicating devices, plenty amount of information is available at user disposal but on other side, it leads to a challenge to provide the fruitful information to end users. To overcome this problem, recommendation system plays a decisive role in providing pragmatic information to end users at appropriate time. This paper proposes a topic modeling based recommendation system to provide contents related to end users interest. Recommendation systems are based on different filtering mechanisms which are classified as content based, collaborative based, knowledge based, utility based and hybrid filtering, etc. The objective of this research is thus to proffer a recommendation system based on topic modeling. Benefit of latent Dirichlet allocation (LDA) is to uncover latent semantic structure from the text documents. By analyzing the contents using topic modeling, system can recommend the right articles to end users based on user interest.
Mixed Reality based Collaboration for Design Processes

Abstract Due to constantly and rapidly growing digitization, requirements for international cooperation are changing. Tools for collaborative work such as video telephony are already an integral part of today’s communication across companies. However, these tools are not sufficient to represent the full physical presence of an employee or a product as well as its components in another location, since the representation of information in a two-dimensional way and the resulting limited communication loses concrete objectivity. Thus, we present a novel object-centered approach that compromises of Augmented and Virtual Reality technology as well as design suggestions for remote collaboration. Furthermore, we identify current key areas for future research and specify a design space for the use of Augmented and Virtual Reality remote collaboration in the manufacturing process in the automotive industry.
Analysis of Efficiency of Long-Term Multi-Scanning Radar Observation Methods

In this paper we consider a surveillance radar using a multi-scanning observation method based on the well-known “track-before-detect” procedure. We analyse different approaches to estimating efficiency of long-term multi-scanning observation methods. The expected benefit of the methods is usually associated with detection range of radar. Detection range is increased due to long-term accumulation of signal energy. But it is necessary to consider not only detection range gain, but also the benefit related to obtaining of track data. We analyse the accuracy of trajectory parameters estimation and increase in tracks initiation range.
Full polarization control of fiber-delivered light in a dilution refrigerator

Birefringence in optical fibers poses a challenge to controllably delivering polarized light. Strain-induced birefringence caused by bends in the fiber, vibrations, or a large temperature gradient can significantly alter the polarization, making it particularly difficult to deliver polarization states to low-temperature environments by fiber. In this paper, we investigate the transmission of polarized light through a fiber and discuss a method we have developed for delivering arbitrarily polarized light to the base stage of a dilution refrigerator using a standard optical fiber. We have created a compact, cryogenic optical system to identify the polarization of the delivered light, while room-temperature waveplates and a mathematical fiber model are used to fully characterize and compensate for the fiber's birefringent effects. We show here that we are able to deliver horizontal, vertical, diagonal, anti-diagonal, right circular, and left circular polarization states to milli-Kelvin temperatures, with state fidelities of greater than 0.96 being achieved in all cases. Additionally, we demonstrate that we can deliver randomly selected elliptical states through a standard fiber to the refrigerator. This opens up new opportunities for fiber-based optical experiments using polarized light, such as quantum information experiments using quantum states encoded in the polarization of single photons.
How to Interact and Change? Abstractive Dialogue Summarization with Dialogue Act Weight and Topic Change Info

Conventional sequence-to-sequence frameworks in neural abstractive summarization treat every document as a single topic text without interaction, so the results are often unsatisfactory when given dialogues. To tackle this problem, we propose a novel method to make the model generate a better summary with multiple topics and interactions among people. Compared with traditional abstractive summarization models, our approach makes two contributions: (1) To solve the interactive problem, we use dialogue act as an interactive pattern to represent important external information and propose Dialogue Act Weight to elaborately calculate the weight that dialogue acts can provide; (2) To make the model handle multiple topics better, we introduce Topic Change Info as a signal to indicate which utterance in dialogue is important and whether the topic has been changed. Compared to baselines, experiments show that our methods significantly improve the abstractive summarization performance on the modified AMI meeting corpus (The source code is available at https://github.com/d1jiasheng/DialogueSum ).
Understanding Nigerian e-mail scams: A computational content analysis approach

e-mail scams have been around for a long time, and they are still remarkably successful. Understanding these scams can help combat fraud by raising public awareness and improving anti-fraud technologies. We conducted an in-depth analysis of e-mail contents with a mixed-method approach using machine-learning-based content analysis and qualitative analysis. We identified five stories (i.e., topics) scammers used in their e-mails. Further, we explored similarities between messages from the same scammers. Our findings suggest some scammers resend a message with the same story as before but with highly textual differences. We argue this might be a deceptive maneuver to avoid spam blockers.
Logical Inference as Cost Minimization in Vector Spaces

We propose a differentiable framework for logic program inference as a step toward realizing flexible and scalable logical inference. The basic idea is to replace symbolic search appearing in logical inference by the minimization of a cost function $$ {\mathbf{J}} $$ in a continuous space. $$ {\mathbf{J}} $$ is made up of matrix (tensor), Frobenius norm and non-linear functions just like neural networks and specifically designed for each task (relation abduction, answer set computation, etc) in such a way that $$ {\mathbf{J}} ( {\mathbf{X}} ) \ge 0$$ and $$ {\mathbf{J}} ( {\mathbf{X}} ) = 0$$ holds if-and-only-if $$ {\mathbf{X}} $$ is a 0–1 tensor representing a solution for the task. We compute the minimizer X of $$ {\mathbf{J}} $$ giving $$ {\mathbf{J}} ( {\mathbf{X}} ) = 0$$ by gradient descent or Newton’s method. Using artificial data and real data, we empirically show the potential of our approach by a variety of tasks including abduction, random SAT, rule refinement and probabilistic modeling based on answer set (supported model) sampling.
Social Behavioral Biometrics in Smart Societies

Smart societies of the future will increasingly rely on harvesting rich information generated by day-to-day activities and interactions of its inhabitants. Among the multitude of such interactions, web-based social networking activities became an integral part of everyday human communication. Flickr, Facebook, Twitter, and LinkedIn are currently used by millions of users worldwide as a source of information, which is growing exponentially over time. In addition to idiosyncratic personal characteristics, web-based social data include person-to-person communication, online activity patterns, and temporal information, among others. However, analysis of social interaction-based data has been studied from the perspective of person identification only recently. In this chapter, the authors elaborate on the concept of using interaction-based features from online social networking platforms as a part of social behavioral biometrics research domain. They place this research in the context of smart societies and discuss novel social biometric features and their potential use in various applications.
Comparing Virtual Reality SDK Potentials for Engineering Education

The paper at hand aims to help educators to grasp the potential and difficulties related to virtual reality hardware and software development kits when freshly starting off in the mixed reality realm. Therefore, it will present and compare major VR hardware devices and SDK’s potentials while considering individual features and advantages. As a common ground to judge the capabilities, a virtual test environment is created featuring common interactions needed in engineering education scenarios when interacting with virtual representation of technical equipment. The final verdict contains two main parts, one of which is the actual judgement of current VR SDK capabilities. The second part is a taxonomy with an overview over selection criteria and solution spaces when dealing with hardware choices.
Non-Parametric Clustering Using Deep Neural Networks

In this paper, a novel algorithm for non-parametric image clustering, is proposed. Non-parametric clustering methods operate by considering the number of clusters unknown as opposed to parametric clustering, where the number of clusters is known a priori. In the present work, a deep neural network is trained, in order to decide whether an arbitrary sized group of elements can be considered as a unique cluster or it consists of more than one clusters. Using this trained neural network as clustering criterion, an iterative algorithm is built, able to cluster any given dataset. Evaluation of the proposed method on several public datasets shows that the proposed method is either on par or outperforms state-of-the-art methods even when compared to parametric image clustering methods. The proposed method is additionally able to correctly cluster input samples from a completely different dataset than the one it has been trained on, as well as data coming from different modalities. Results on cross-dataset clustering show evidence of the generalization potential of the proposed method.
A GPU Unified Platform to Secure Big Data Transportation Using an Error-Prone Elliptic Curve Cryptography

The time and space optimization, protecting data loss and safeguarding privacy as well as integrity are the priorities in any large file transmission system. However, the current literature is lacking to provide such a system which can take care of each of these aspects very strongly in a unified way. Hence, this research proposes a Graphics Processing Unit (GPU)-based error-prone elliptic curve cryptography with an advanced memory management mechanism. It comprises the elliptic curve public-key cryptography (ECC) to offer higher defiance against the various privacy and integrity attacks. It further includes an advanced dual round of error regulator which can sense and spot any numbers of discrete or continuous faults. This error control technique can regenerate the data if there is any accidental loss during transportation. The usages of advanced parallel execution with a GPU and a unique memory management mechanism make it enable to handle required memory and time during the execution very efficiently. The achieved result shows that the projected unified approach offers higher Throughput on every incident which signifies that it is time-efficient. Additionally, it offers higher SNR, Avalanche Effect and a curtailed proportion of Data Loss. It specifies that the suggested mingled technique is proficient to protect privacy, integrity and mishap of data in big data hauling with the efficient management of available internal memory.
Things Are Looking Up: Using Cloud-Based Technology Tools in Collection Management Workflows

Between 2018 and 2019, two librarians at the College of Charleston Libraries incorporated cloud-based technologies into their collection management workflows. The following paper was derived from a presentation they prepared for the 2020 North Carolina Serials Conference, which was to be held on March 27, 2020 in Chapel Hill, North Carolina but was canceled due to the COVID-19 pandemic. The paper highlights which cloud technologies they use and provides an overview of the literature that has been published on project management and cloud-based technologies in libraries. It includes an explanation of how they use each tool, and provides examples for setting up each tool in different scenarios so that other collection management librarians may implement them into their own routines. The paper ends with an exploration of the advantages and disadvantages of integrating these tools into collection management workflows. Although the librarians had not prepared to discuss the role these technologies may play when working remotely, they have both found them invaluable during this unprecedented time when many librarians are working from home.
Accurate and Fast Deep Evolutionary Networks Structured Representation Through Activating and Freezing Dense Networks

Deep neural networks have been scaled up to thousands of layers with the intent to improve their accuracy. Unfortunately, after some point, doubling the number of layers leads to only minor improvements, while the training difficulties increase substantially. In this article, we present an approach for constructing high-accuracy deep evolutionary networks and train them by activating and freezing dense networks (AFNets). The activating and freezing strategy enables us to reduce the classification error of test and reduce the training time required for deeper dense networks. We activate the layers that are being trained and construct a freezing box to freeze the idle and pretrained network layers in order to minimize memory consumption. The training speed in the early stage is not fast enough because many layers are activated for training. As the epochs gradually increase, the training speed becomes faster and faster since fewer and fewer layers are activated. Our method improves the convergence to the optimal performance within a limited number of epochs. Comprehensive experiments on a variety of data sets show that the proposed model achieves better performance when compared to the other state-of-the-art network models.
Mechanisms for Hiding Sensitive Genotypes with Information-Theoretic Privacy

The growing availability of personal genomics services comes with increasing concerns for genomic privacy. Individuals may wish to withhold sensitive genotypes that contain critical health-related information when sharing their data with such services. A straightforward solution that masks only the sensitive genotypes does not ensure privacy due to the correlation structure within the genome. Here, we develop an information-theoretic mechanism for masking sensitive genotypes, which ensures no information about the sensitive genotypes is leaked. We also propose an efficient algorithmic implementation of our mechanism for genomic data governed by hidden Markov models. Our work is a step towards more rigorous control of privacy in genomic data sharing.
Identification and Sorting of Objects based on Shape and Colour using robotic arm

Robotic Automation is favored since human eyes are unable to constantly distinguish colors which will eventually reduce the work efficiency. In this work, a system that sorts objects based on their shape and color is proposed and implemented using a robotic arm. The system captures a real-time image from a web camera and is preprocessed by performing RGB to HSV conversion and noise removal by using a median filter. Colour detection of the objects is then carried out using the lower and upper HSV values. The system then performs an object’s shape detection using the contour detection technique. In this technique the contours are identified first using the modified boundary fill approach and the shapes are then detected from the contours using the Douglas-Peucker algorithm. A Robotic arm is constructed to sort objects based on their identified shape and color. The system is tested for three object shapes: square, triangle, and rectangle.
Max-affine regression with universal parameter estimation for small-ball designs

We study the max-affine regression model, where the unknown regression function is modeled as a maximum of a fixed number of affine functions. In recent work [1], we showed that end-to-end parameter estimates were obtainable using this model with an alternating minimization (AM) algorithm provided the covariates (or designs) were normally distributed, and chosen independently of the underlying parameters. In this paper, we show that AM is significantly more robust than the setting of [1]: It converges locally under small-ball design assumptions (which is a much broader class, including bounded log-concave distributions), and even when the underlying parameters are chosen with knowledge of the realized covariates. Once again, the final rate obtained by the procedure is near-parametric and minimax optimal (up to a polylogarithmic factor) as a function of the dimension, sample size, and noise variance. As a by-product of our analysis, we obtain convergence guarantees on a classical algorithm for the (real) phase retrieval problem in the presence of noise under considerably weaker assumptions on the design distribution than was previously known.