Reviewerly/exHarmony huggingface dataset:
Augmenting Co-Training With Recommendations to Classify Human Rights Violations

In the recent past, many human rights organizations have started using social media to identify, collect and document human rights violations. To manually extract relevant data from the large corpus of this social network data is difficult and time-consuming and expensive. Furthermore, with the advent of technology, the context and significance of the human rights abuses has and will change over time and advice from experts is needed to perform any kind quantitative analysis on this data. There are applications and systems that help structure this data into relevant categories, but detecting underlying latent patterns, finding similar annotated patterns and continuously upgrading the system to perform exploratory analysis requires high maintenance and cost. This paper proposes a solution to address this problem by integrating semi-supervised learning (with Matrix Factorization) and similarity measures algorithms to classify the large unstructured corpus into stories that have been labelled with one or more types of human rights abuses. In the last few decades, recommender systems have come across as powerful machine learning tools to infer from data and provide value-added content. Along the same context, semi-supervised algorithms mitigate situations where there is a relatively small labelled training data, but a large unlabeled data-set. This paper tries to combine both these algorithms to discover patterns in unlabeled victim survivor stories and recommends labels from other similar stories, thus updating the initial labelled set. The efficiency of the algorithm is evaluated using state of art evaluation metrics. Experimental results show a correlation between new and labelled stories. Real-world results show that the algorithm outplays some of in house recommendation algorithms.
VCPU Prioritization Interface for Improving the Performance of Latency-Critical Tasks

Virtualization supports multi-tenancy in the form of virtual machine (VM) and tends to oversubscribe hardware resources to improve resource utilization. However, this server consolidation comes at performance degradation, especially for latency-critical tasks. In such environments, a physical CPU (PCPU) is often time-shared by multiple virtual CPUs (VCPUs), and this time-sharing of PCPU limits guest OS-level supports for latency-critical (LC) tasks. To address this problem, this paper presents VCPU prioritization interface (VPI) that directly prioritizes the VCPU executing an LC task over other VCPUs hosting background ones. Our evaluation on real-world applications shows that VPI significantly reduces the average and 99th percentile latency of LC tasks by up to 66.6% and 60.5%, respectively.
Explainable Visualization for Interactive Exploration of CNN on Wikipedia Vandal Detection

Machine learning (ML) algorithms have greatly improved the performances of many computationally expensive operations, including detection of frauds and attacks for security applications. However, these algorithms are often hard for users to interpret or modify due to their hidden processes. This paper presents an explainable visualization approach to provide a set of developer tools that enable users to apply ML algorithms efficiently without requiring users to understand the algorithms completely. We use an algorithm of convolutional neural network (CNN) for Wikipedia vandal detection as an example. Our highly coordinated visualizations serve as a visual analytics interface for developers to analyze the behaviors of algorithms and large-scale data. It enables users to study hidden relationships among the spaces of data, algorithm parameters, and results that are essential to understand the CNN detection mechanism. We provide several case studies to demonstrate how our approach can be used to explore the clusters of parameter space, identify outlier of parameters or data, study the parameter sensitivity ranges, and select suitable ranges of parameter for combined criteria (e.g., covering selected important cases and maintaining relative high success ratio) that are hard to describe with single objective functions. Due to the complexity of the problem, we also discuss the limitations of our approach and future work.
Fusing Visual and Textual Information to Determine Content Safety

In advertising, identifying the content safety of web pages is a significant concern since advertisers do not want brands to be associated with threatening content. At the same time, publishers would like to maximize the number of web pages on which they can place ads. Thus, a fine balance must be achieved while classifying content safety in order to satisfy both advertisers and publishers. In this paper, we propose a multimodal machine learning framework that fuses visual and textual information from web pages to improve current predictions of content safety. The primary focus is on late fusion, which involves combining final model outputs of separate modalities, such as images and text, to arrive at a single decision. This paper presents a fully automated machine learning framework that performs binary and multilabel classification using late fusion techniques. We also introduce additional work in early fusion, which involves extracting and fusing intermediate features from the two separate models. Our algorithms are applied to data extracted from relevant web pages in the advertising industry. Both of our late and early fusion methods obtain significant improvements over algorithms currently in use.
Helicity rotation induced by Lorentz boosts

In this paper, we calculate the helicity rotation angle induced by Lorentz boosts. This is relevant for the study of Lorentz boost effects on quantum entanglement encoded in pairs of massive fermions, which are described in terms of positive energy solutions of the Dirac equation with definite helicity. A Lorentz boost describing the change to an inertial frame moving at uniform speed will in general rotate the particle’s helicity. We obtain the coefficients of the helicity superposition in the boosted frame and specialize our results for a perpendicular boost geometry. We verify that the helicity rotation angle can be obtained in terms of the Wigner rotation angle for spin [Formula: see text] states, bridging the framework considered in our previous works to the one of the Wigner rotations. Finally, we calculate the boost-induced spin-parity entanglement for a single particle.
Effect of feedback and target size on eye gaze accuracy in an off-screen task

Eye gaze interfaces have been used by people with severe physical impairment to interact with various assistive technologies. If used to control robots, it would be beneficial if individuals could gaze directly at targets in the physical environment rather than have to switch their gaze between a screen with representations of robot commands and the physical environment to see the response of their selection. By using a homogeneous transformation technique, eye gaze coordinates can be mapped between the reference coordinate frame of eye tracker and the coordinate frame of objects in the physical environment. Feedback about where the eye tracker has determined the eye gaze is fixated is needed so users can select targets more accurately. Screen-based assistive technologies can use visual feedback, but in a physical environment, other forms of feedback need to be examined.In this study, an eye gaze system with different feedback conditions (i.e., visual, auditory, vibrotactile, and no-feedback) was tested when participants received visual feedback on a display (on-screen) and when looking directly at the physical environment (off-screen). Target selection tasks in both screen conditions were performed by ten non-disabled adults, three non-disabled children, and two adults and one child with cerebral palsy.Tasks performed with gaze fixation feedback modalities were accomplished faster and with higher success than tasks performed without feedback, and similar results were observed in both screen conditions. No significant difference was observed in performance across the feedback modalities, but participants had personal preferences.The homogeneous transformation technique enabled the use of a stationary eye tracker to select target objects in the physical environment, and auditory and vibrotactile feedback enabled participants to be more accurate selecting targets than without it.Implications for RehabilitationBeing able to select target objects in the physical environment by eye gaze could make it easier for children with disabilities to control assistive robots, because in this way they do not have to change their focus between a computer screen with commands and the robot.Providing auditory or vibrotactile feedback when using an eye gaze system made it faster and easier to know if a target was being gazed upon.Being able to select targets in the environment using eye gaze could be beneficial for other assistive technology, too, such as destination selection for power wheelchairs.
The Analysis Knowledge Management System Of Electronic Government South Tangerang Based On Usability Evaluation Using SUMI (Software Usability Measurement Inventory)

E-Government performance in the application of quality management information communication technology is very necessary. A website-based system is also used to improve the quality of services and community participation in development. The context that becomes the material that needs to be examined is public service, the quality of websites managed by the government and user satisfaction so that there is a two-way interaction between the government and the community. The purpose of this study consisted of three stages which were first to determine the role of E-government by utilizing technology to support the development of a reliable information system in South Tangerang Regency. The second is to find out the extent to which the system is used in South Tangerang E-government through Usability Evaluation using SUMI (Software Usability Measurement Inventory). The method used in this study is a method adopted from the Knowledge Management System Life Cycle. The stages of the method are Evaluate Existing Infrastructure, Form The KM Team, Knowledge Capture, Implement the KM system. The results of this study are in the form of usability values on the E-Government website of South Tangerang and usability testing which becomes a benchmark for the success of a system with score scores for 85 effectiveness categories, 81.5
Wavelet optimized upwind conservative method for traffic flow problems

Numerical schemes, namely, upwind nonconservative, upwind conservative, Lax–Friedrichs, Lax–Wendroff, MacCormack and Godunov are applied and compared on traffic flow problems. The best scheme, namely, upwind conservative is used for wavelet-optimized method using Daubechies wavelet for numerically solving the same traffic flow problems. Numerical results corresponding to the traffic flow problem with the help of wavelet-optimized, adaptive grid, upwind conservative method have been given. Moreover, the run time carried out by the developed technique have been compared to that of run time carried out by finite difference technique. It is observed that, in terms of run time, the proposed method performs better.
Self-managed Computer Systems: Foundations and Examples

The traditional approach to managing complex computer systems is to use a cadre of skilled IT professionals who use monitoring tools in order to detect when problems arise. They are then able to use their skills and experience to determine what actions should be taken to solve the problems. This approach is no longer viable for highly complex, networked computer information systems that have numerous configuration knobs, and operate in environments that vary with time at a very high rate. In this case, one cannot expect that design-time configurations will make the system operate optimally at run-time. For that reason, complex systems need to manage themselves using controllers that make the systems self-configuring, self-optimizing, self-healing, and self-protecting. This paper provides a formalism to describe self-managed systems and discusses concrete examples that illustrate how these properties are enforced by controllers in a variety of domains including cloud computing, fog/cloud computing, Internet datacenters, distributed software systems, and secure database systems.
Low Power BCH Decoder Using Verification Algorithm and Two-Step Parallel Chien Search Architecture

Bose-Chaudhuri-Hocquenghem (BCH) code is normally utilized in communication systems in order to enhance its reliability. The main computational complexity and high power consumption stages in BCH come from its two main stages, the key equation solving (KES) and Chien search (CS). This paper presents two different algorithms to reduce the computational process and hence reduce the total power consumption. These algorithms are the verification algorithm and the two-step parallel CS architecture which are utilized in KES and CS stages, respectively. The whole proposed system is implemented using LABVIEW tools from NI. The results show that the proposed BCH algorithm provides an enhanced performance when compared to the conventional one.
An IOT based secure transaction system using Micro field communication

Abstract The humans have always tried to improve quality of living and technology plays a major role in this cause. Modern technology has been evolving with the emergence of smart technologies. Cash Based transaction involves lot of human effort of withdrawing and then storing the cash. It is also an insecure way of payment involving fears of theft etc… With large amounts of data transfer and e-payment needs, NFC is one of the prime technologies contributing towards fast and secure transmission of electronic signals, data and money. This paper focuses on NFC technology utilisation for a simplified and secure unified transaction system using IOT. Internet of things is a technology of present and future which has its applications in various fields like healthcare, transportation, manufacturing etc… There has already been a lot of research and developments on utilisation of NFC technology for transactions, but we propose a secure wireless transaction system by providing features such as database connectivity and user authentication in the system
Deep Reinforcement Learning Based Intelligent Decision Making for Two-player Sequential Game with Uncertain Irrational Player

In this paper, two player sequential game with an unknown non-stationary irrational player is investigated for cooperative autonomous robots decision making applications. In practice, the irrationality of agent can seriously degrade the effectiveness of decision making especially for distributed cooperative tasks with applications to multi-robot systems. Specifically, The irrationality can be caused by the cooperation agent's mechanical failure or sensor flaw. To handle this issue, a novel dynamic evaluation system, which includes two important parameters, i.e. cooperation index and competitive flag, is designed to efficiently quantify the player's level of cooperation or competition firstly. Then, the continuous deep Q network space is proposed to predict the action value with respect to a continuous cooperation index. Inspired from the framework of "Friend or Foe" algorithm, a novel hybrid online multi-agent deep reinforcement learning algorithm is proposed. The designed algorithm can evaluate the cooperator's cooperative level as well as maximize the total payoff by learning in a continuous deep Q network space. Eventually, numerical simulation and experimental tests are provided to demonstrate the effectiveness of the designed algorithm.
ClusterClean: a Weak Semi-Supervised Approach for Cleaning Data Labels

Every day, tens of thousands of Amazon customers message Amazon's selling partners inquiring about orders, products and services. Unfortunately, we have observed over time that buyers may send unsolicited and often times mal intended messages to sellers through the buyer-seller messaging (BSM) service. Although the BSM service gives the ability to sellers to report such messages, most of them do not make use of this feature. Hence, collecting training and testing data with clean labels to build machine learning models in order to proactively block unsolicited messages and help prevent and mitigate losses for Amazon is extremely challenging. To address this problem we propose ClusterClean, an algorithm that automatically cleans data labels with little to no human effort. ClusterClean has the ability to a) accurately infer the labels of unlabeled data points based on an initial small labeled set and b) detect new data patterns such as incoming unobserved spam attacks by soliciting feedback from users to decide on their label. Experiments on approximately 150,000 real messages from Amazon customers showed that ClusterClean can accurately clean the labels of these messages in a few minutes, drastically reducing the human effort and time spent on this task.
Learning Compressible 360° Video Isomers

Standard video encoders developed for conventional narrow field-of-view video are widely applied to 360 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> video as well, with reasonable results. However, while this approach commits arbitrarily to a projection of the spherical frames, we observe that some orientations of a 360 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">° </sup> video, once projected, are more compressible than others. We introduce an approach to predict the sphere rotation that will yield the maximal compression rate. Given video clips in their original encoding, a convolutional neural network learns the association between a clip's visual content and its compressibility at different rotations of a cubemap projection. Given a novel video, our learning-based approach efficiently infers the most compressible direction in one shot, without repeated rendering and compression of the source video. We validate our idea on thousands of video clips and multiple popular video codecs. The results show that this untapped dimension of 360 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> compression has substantial potential-“good” rotations are typically 8-18 percent more compressible than bad ones, and our learning approach can predict them reliably 78 percent of the time.
Classification and Feature Extraction for User Identification for Smart Home Networks Based on Apps Access History

Advancements in smartphones has led to increasing dependency on them as an essential part of IoT smart home networks. Although this increase provides convenience to users, many challenges, including user identification and authentication, need to be considered. Many related works consider smartphone user authentication that assumes that the same user will be using the device throughout the access session. However, after the login stage, the device could be used by others, either in the home environment or outside, leading to undesired access to home appliances. In this paper, we present a continuous user identification approach that uses implicit features that can be integrated as a second layer of identification beyond the login step, based on user behavior on smartphones. The proposed method is mainly based on user interaction on the smartphone, which thus reflects the user's pattern that in turn provides the impetus to employ user identification as a complementary approach beyond the user login stage.
Scalar multiplication in elliptic curve libraries

With the advent of Elliptic curve cryptography (ECC); it grew unusually quickly because it had a smaller key size compared with that of the RSA cryptography system. Many researches are being done nowadays to make this type of cryptography more efficient. These investigations are about improving efficiency of both algorithm and method of implementations. This paper studies and analyzes some software libraries which support elliptic curve computations. Scalar multiplication is a major operation in ECC. Therefore, this study implements some methods of scalar multiplication by two software libraries LiDIA and MIRACL and compares the results at the end.
Exploring Adversaries to Defend Audio CAPTCHA

CAPTCHA is a web-based authentication method used by websites to distinguish between humans (valid users) and bots (attackers). Audio captcha is an accessible captcha meant for the visually disabled section of users such as color-blind, blind, near-sighted users. Firstly, this paper analyzes how secure current audio captchas are from attacks using machine learning (ML) and deep learning (DL) models. Each audio captcha is made up of five, seven or ten random digits[0-9] spoken one after the other along with varying background noise throughout the length of the audio. If the ML or DL model is able to correctly identify all spoken digits and in the correct order of occurance in a single audio captcha, we consider that captcha to be broken and the attack to be successful. Throughout the paper, accuracy refers to the attack model's success at breaking audio captchas. The higher the attack accuracy, the more unsecure the audio captchas are. In our baseline experiments, we found that attack models could break audio captchas that had no background noise or medium background noise with any number of spoken digits with nearly 99% to 100% accuracy. Whereas, audio captchas with high background noise were relatively more secure with attack accuracy of 85%. Secondly, we propose that the concepts of adversarial examples algorithms can be used to create a new kind of audio captcha that is more resilient towards attacks. We found that even after retraining the models on the new adversarial audio data, the attack accuracy remained as low as 25% to 36% only. Lastly, we explore the benefits of creating adversarial audio captcha through different algorithms such as Basic Iterative Method (BIM) and deepFool. We found that as long as the attacker has less than 45% sample from each kinds of adversarial audio datasets, the defense will be successful at preventing attacks.
RAP-Net: Recurrent Attention Pooling Networks for Dialogue Response Selection

The response selection has been an emerging research topic due to the growing interest in dialogue modeling, where the goal of the task is to select an appropriate response for continuing dialogues. To further push the end-to-end dialogue model toward real-world scenarios, the seventh Dialog System Technology Challenge (DSTC7) proposed a challenge track based on real chatlog datasets. The competition focuses on dialogue modeling with several advanced characteristics: (1) natural language diversity, (2) capability of precisely selecting a proper response from a large set of candidates or the scenario without any correct answer, and (3) knowledge grounding. This paper introduces recurrent attention pooling networks (RAP-Net), a novel framework for response selection, which can well estimate the relevance between the dialogue contexts and the candidates. The proposed RAP-Net is shown to be effective and can be generalize across different datasets and settings in the DSTC7 experiments.