Reviewerly/exHarmony huggingface dataset:
Employment of Secure Enclaves in Cheat Detection Hardening

Over the last years, it has become clear that online games are one of the most used applications on the Internet. This increasing popularity has attracted an influx of players, with some of them trying to gain an unfair advantage for economic reasons, e.g., eSports tournaments, through the use of cheats and exploits. From a different perspective, it is of utmost importance to start analyzing attacks from a defensive perspective to create novel mechanisms that can stop such behaviors. In this work, we introduce a novel solution that extends current anti-cheat solutions through Intel SGX. Our solution moves the core cheat detection engine to a secure enclave provided by SGX while making use of a kernel module for the necessary primitives for system-wide protection. With this, we can prevent client-side tampering in both game code and configuration data by creating a trusted execution environment isolated from the hosting operating system. We are making it capable of preventing the attacker from modifying the cheat detection engine and associated game files. This solution blocks known attacks in games such as CS:GO while maintaining the performance, ensuring gameplay integrity and fairness.
SPHINX

We present SPHINX, a system for metapath-based entity exploration in Heterogeneous Information Networks (HINs). SPHINX allows users to define different views over a HIN based on both automatically selected and user-defined meta-paths. Then, entity ranking and similarity search can be performed over these views to find and explore entities of interest, taking also into account any spatial or temporal properties of entities. A Web-based user interface is provided to facilitate users in performing the various functionalities supported by the system, including metapath-based view definition, index construction, search parameters specification, and visual comparison of the results.
2AIRTC: The Amharic Adhoc Information Retrieval Test Collection

Evaluation is highly important for designing, developing, and maintaining information retrieval (IR) systems. The IR community has developed shared tasks where evaluation framework, evaluation measures and test collections have been developed for different languages. Although Amharic is the official language of Ethiopia currently having an estimated population of over 110 million, it is one of the under-resourced languages and there is no Amharic adhoc IR test collection to date. In this paper, we promote the monolingual Amharic IR test collection that we build for the IR community. Following the framework of Cranfield project and TREC, the collection that we named 2AIRTC consists of 12,583 documents, 240 topics and the corresponding relevance judgments.
Towards the Assessment of Easy-to-Read Guidelines Using Artificial Intelligence Techniques

The Easy-to-Read (E2R) Methodology was created to improve the daily life of people with cognitive disabilities, who have difficulties in reading comprehension. The main goal of the E2R Methodology is to present clear and easily understood documents. This methodology includes a set of guidelines and recommendations that affect the writing of texts, the supporting images, the design and layout of documents, and the final editing format. Such guidelines are used in the manual processes of (a) adapting existing documents and (b) producing new materials. The process of adapting existing documents is cyclic and implies three activities: analysis, transformation, and validation. All these activities are human resource consuming, due to the need of involving people with cognitive disabilities as well as E2R experts. In order to alleviate such processes, we are currently investigating the development of methods, based on Artificial Intelligence (AI) techniques, to perform the analysis and transformation of documents in a (semi)-automatic fashion. In this paper we present our AI-based method for assessing a particular document with respect to the E2R guidelines as well as an initial implementation of such a method; our research on the transformation of documents is out of the scope of this paper. We carried out a comparative evaluation of the results obtained by our initial implementation against the results of the document analysis performed by people with cognitive disabilities.
Predicting 3D Motions from Single-Camera Optical Test Data

In a typical optical test, a stereo camera pair is required to measure the three-dimensional motion of a test article; one camera typically only measures motions in the image plane of the camera, and measurements in the out-of-plane direction are missing. Finite element expansion techniques provide a path to estimate responses from a test at unmeasured degrees of freedom. Treating the case of a single camera as a measurement with unmeasured degrees of freedom, a finite element model is used to expand to the missing third dimension of the image data, allowing a full-field, three-dimensional measurement to be obtained from a set of images from a single camera. The key to this technique relies on the mapping of finite element deformations to image deformations, creating a set of mode shape images that are used to filter the response in the image into modal responses. These modal responses are then applied to the finite element model to estimate physical responses at all finite element model degrees of freedom. The mapping from finite element model to image is achieved using synthetic images produced by a rendering software. The technique is applied first to a synthetic deformation image, and then is validated using an experimental set of images.
A decentralized prediction market platform based on blockchain and masternode technologies

Prediction market can help to trade the outcome of events, which is very useful for risk hedging, especially when people are making business ventures. However, the traditional prediction market is centralized, which means the platform is completely controlled by just one person or team. This centralization has bad influence on the platform to obtaining trust from massive users. So it is important for the prediction market industry to decentralize the traditional platform into a decentralized one, and get its control from few person and give it back to a lot of people in the prediction market community. Blockchain is a very promising technology to make it true as it is a popular decentralizing technology. However, existing blockchain technologies like Bitcoin or Ethernum bring about new problems like high energy consumption, expensive fee, and very low system capacity, which is not suitable for the current prediction market. To solve these problems, we propose to combine masternode technology together with blockchain to serve as a decentralized node, and each masternode is deployed and running on an edge server in mobile edge network. The network of master-nodes serves together as the decentralized prediction market platform. As we know, it is not easy to run a masternode. So the number of masternodes is much less than the number of complete nodes of the blockchain. As a result, it is easier to reach the consensus among these masternodes. Theoretical analysis and experimental results show that our proposed method is useful.
Harden Deep Convolutional Classifiers via K-Means Reconstruction

Adversarial examples are carefully perturbed input examples that aim to mislead the deep neural network models into producing unexpected outputs. In this paper, we employ a K-means clustering algorithm as a pre-processing method to defend against adversarial examples. Specifically, we reconstruct adversarial examples according to their cluster assignments in pixel level to reduce the impact of the injected perturbation. Our approach does not rely on any neural network architectures and can also work with existing pre-processing defenses to provide better protection for modern classifiers. Comprehensive comparison and evaluation have been conducted to investigate our proposal, where the models protected by the proposed defense show substantial robustness to strong adversarial attacks. As a by-product of our exploration of ensemble defense, we identify that the order of defense methods has a crucial impact on the final performance. Additionally, the limitation of K-means reconstruction and the impact of the number of clusters have also been studied to provide an in-deep understanding of pre-processing defenses.
Multi-Source Domain Adaptation with Distribution Fusion and Relationship Extraction

Transfer learning is gaining increasing attention due to its ability to leverage previously acquired knowledge to assist in completing a prediction task in a similar domain. While many existing transfer learning methods deal with single source and single target problem without considering the fact that a target domain maybe similar to multiple source domains, this work proposes a multi-source domain adaptation method based on a deep neural network. Our method contains common feature extraction, specific predictor learning and target predictor estimation. Common feature extraction explores the relationship between source domains and target domain by distribution fusion and guarantees the strength of similar source domains during training, something which has not been well considered in existing works. Specific predictor learning trains source tasks with cross-domain distribution constraint and cross-domain predictor constraint to enhance the performance of single source. Target predictor estimation employs relationship extraction and selective strategy to improve the performance of the target task and to avoid negative transfer. Experiments on real-world visual datasets show the performance of the proposed method is superior to other deep learning baselines.
Can a Hand-Held Navigation Device Reduce Cognitive Load? A User-Centered Approach Evaluated by 18 Surgeons

During spinal fusion surgery, the orientation of the pedicle screw in the right angle plays a crucial role for the outcome of the operation. Local separation of navigation information and the surgical situs, in combination with intricate visualizations, can limit the benefits of surgical navigation systems. The present study addresses these problems by proposing a hand-held navigation device (HND) for pedicle screw placement. The in-situ visualization of graphically reduced interfaces, and the simple integration of the device into the surgical work flow, allow the surgeon to position the tool while keeping sight of the anatomical target. 18 surgeons participated in a study comparing the HND to the state-of-the-art visualization on an external screen. Our approach revealed significant improvements in mental demand and overall cognitive load, measured using NASA-TLX (\(p < 0.05\)). Moreover, surgical time (One-Way ANOVA \(p < 0.001\)) and system usability (Kruskal-Wallis test \(p < 0.05\)) were significantly improved.
Large-scale Data Exploration Using Explanatory Regression Functions

Analysts wishing to explore multivariate data spaces, typically issue queries involving selection operators, i.e., range or equality predicates, which define data subspaces of potential interest. Then, they use aggregation functions, the results of which determine a subspace’s interestingness for further exploration and deeper analysis. However, Aggregate Query (AQ) results are scalars and convey limited information and explainability about the queried subspaces for enhanced exploratory analysis. Analysts have no way of identifying how these results are derived or how they change w.r.t query (input) parameter values. We address this shortcoming by aiding analysts to explore and understand data subspaces by contributing a novel explanation mechanism based on machine learning. We explain AQ results using functions obtained by a three-fold joint optimization problem which assume the form of explainable piecewise-linear regression functions. A key feature of the proposed solution is that the explanation functions are estimated using past executed queries. These queries provide a coarse grained overview of the underlying aggregate function (generating the AQ results) to be learned. Explanations for future, previously unseen AQs can be computed without accessing the underlying data and can be used to further explore the queried data subspaces, without issuing more queries to the backend analytics engine. We evaluate the explanation accuracy and efficiency through theoretically grounded metrics over real-world and synthetic datasets and query workloads.
A Quantitative Understanding of Pattern Matching.

This paper shows that the recent approach to quantitative typing systems for programming languages can be extended to pattern matching features. Indeed, we define two resource aware type systems, named U and E, for a lambda-calculus equipped with pairs for both patterns and terms. Our typing systems borrow some basic ideas from [BKRDR15], which characterises (head) normalisation in a qualitative way, in the sense that typability and normalisation coincide. But in contrast to [BKRDR15], our (static) systems also provides quantitative information about the dynamics of the calculus. Indeed, system U provides upper bounds for the length of normalisation sequences plus the size of their corresponding normal forms, while system E, which can be seen as a refinement of system U, produces exact bounds for each of them. This is achieved by means of a non-idempotent intersection type system equipped with different technical tools. First of all, we use product types to type pairs, instead of the disjoint unions in [BKRDR15], thus avoiding an overlap between being a pair and being duplicable, resulting in an essential tool to reason about quantitativity. Secondly, typing sequents in system E are decorated with tuples of integers, which provide quantitative information about normalisation sequences, notably time (c.f. length) and space (c.f. size). Another key tool of system E is that the type system distinguishes between consuming (contributing to time) and persistent (contributing to space) constructors. Moreover, the time resource information is remarkably refined, because it discriminates between different kinds of reduction steps performed during evaluation, so that beta reduction, substitution and matching steps are counted separately.
Design and Development of Weak Coherent Pulse Source for Quantum Key Distribution System

Single photon source is indispensable for realization of photon based quantum communication technologies such as quantum key distribution (QKD). Weak coherent pulse source is a practical, efficient and robust solution to generate stream of single photons, probabilistically. In this paper, a methodology for design and development of weak coherent pulse (WCP) source with high count rate of upto 10 Mcps is proposed and implemented. The generation of optical pulses with narrow pulse width and repetition rate of up to 100 MHz is achieved by developing a miniaturized pulse laser module. The weak coherent pulses are generated by heavily attenuating the laser pulses so as to reach the mean photon no. per pulse, μ, close to 0.1. WCP source performance in terms of mean photon number, second order coherence and photon count rate is also investigated. The measured g <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(2)</sup> [0] of ~0.98 shows that the developed WCP source has a very good quality.
An Imu-Based Wearable Ring For On-Surface Handwriting Recognition

We propose a finger-worn, on-surface fingerwriting recognition system based on an inertial sensor. The acceleration and the angular velocity data from the finger are sent by Bluetooth (BLE) to a host computer for conversion into words. The motion data are segmented by a long short-term memory (LSTM) model before recognition by a Convolutional Neural Network (CNN) or an LSTM model. Experiment results show the proposed system achieves 1.05% CER and 7.28% WER, making it a viable system as a text input interface.
ClipUp: A Simple and Powerful Optimizer for Distribution-Based Policy Evolution

Distribution-based search algorithms are a powerful approach for evolutionary reinforcement learning of neural network controllers. In these algorithms, gradients of the reward function with respect to the policy parameters are estimated using a population of solutions drawn from a search distribution, and then used for policy optimization with stochastic gradient ascent. A common choice is to use the Adam optimization algorithm for obtaining an adaptive behavior during gradient ascent, due to its success in a variety of supervised learning settings. As an alternative to Adam, we propose to enhance classical momentum-based gradient ascent with two simple-yet-effective techniques: gradient normalization and update clipping. We argue that the resulting optimizer called ClipUp (short for clipped updates) is a better choice for distribution-based policy evolution because its working principles are simple and easy to understand and its hyperparameters can be tuned more intuitively in practice. Moreover, it avoids the need to re-tune hyperparameters if the reward scale changes. Experiments show that ClipUp is competitive with Adam despite its simplicity and is effective at some of the most challenging continuous control benchmarks, including the Humanoid control task based on the Bullet physics simulator.
Evaluating Traffic Signs Detection using Faster R-CNN for Autonomous driving

Traffic signs, which provide visual representation, play key role in autonomous navigation. Thus, detection and classification of traffic signs are one of the key requirements in autonomous vehicles (AVs). AVs heavily rely on object detection techniques to classify the traffic signs. In recent years, deep convolutional neural networks (CNNs) such as Faster R-CNN have achieved incredible success on object detection such as traffic signs. This paper focuses on the evaluation of state-of-the-art traffic signs detection techniques using deep learning algorithms and determination of the optimal one that can efficiently detect the traffic signs in real-time. Applying Faster R-CNN, the real-time traffic sign detection shall allow the autonomous vehicles to make decisions in real-time.
Development of Multilingual Thematic Picture Dictionary: A Support for Literacy

The study aimed at developing a multilingual thematic picture dictionary as a media to support literacy in English language for fifth grade elementary school students in Bali. This study used Design and Development (D&amp;D) model by Richey and Klein (2014). The steps were (1) analysis, (2) design, (3) development, and (4) evaluation. Fifth grade elementary school students and English teachers from rural and urban area were the research subjects. They were chosen through purposeful sampling technique. In analysis, the data were collected through observation, interview, questionnaire, and document analysis. The result of needs analysis found that there is an urgency of developing multilingual thematic picture dictionary. The students needed dictionary while learning English because it helped them in finding the word’s meaning independently and could help them prepare for additional point while participating in teaching learning process content validity judgement showed that the dictionary was highly valid to criteria of a good multilingual thematic picture dictionary. The result showed that the multilingual thematic picture dictionary was categorized as ‘excellent’ media. Thus, it can be used as a media to support fifth grade elementary school students in developing their literacy skills.
An architectural framework for information integration using machine learning approaches for smart city security profiling

In the past few decades, the whole world has been badly affected by terrorism and other law-and-order situations. The newspapers have been covering terrorism and other law-and-order issues with relevant details. However, to the best of our knowledge, there is no existing information system that is capable of accumulating and analyzing these events to help in devising strategies to avoid and minimize such incidents in future. This research aims to provide a generic architectural framework to semi-automatically accumulate law-and-order-related news through different news portals and classify them using machine learning approaches. The proposed architectural framework discusses all the important components that include data ingestion, preprocessor, reporting and visualization, and pattern recognition. The information extractor and news classifier have been implemented, whereby the classification sub-component employs widely used text classifiers for a news data set comprising almost 5000 news manually compiled for this purpose. The results reveal that both support vector machine and multinomial Naïve Bayes classifiers exhibit almost 90% accuracy. Finally, a generic method for calculating security profile of a city or a region has been developed, which is augmented by visualization and reporting components that maps this information onto maps using geographical information system.
Moroccan Data-Driven Spelling Normalization Using Character Neural Embedding

With the increase of Web use in Morocco today, Internet has become an important source of information. Specifically, across social media, the Moroccan people use several languages in their communication leaving behind unstructured user-generated text (UGT) that presents several opportunities for Natural Language Processing. Among the languages found in this data, Moroccan Arabic (MA) stands with an important content and several features. In this paper, we investigate online written text generated by Moroccan users in social media with an emphasis on Moroccan Arabic. For this purpose, we follow several steps, using some tools such as a language identification system, in order to conduct a deep study of this data. The most interesting findings that have emerged are the use of code-switching, multi-script and low amount of words in the Moroccan UGT. Moreover, we used the investigated data in order to build a new Moroccan language resource. The latter consists in building a Moroccan words orthographic variants lexicon following an unsupervised approach and using character neural embedding. This lexicon can be useful for several NLP tasks such as spelling normalization.
A Security and Privacy Focused KYC Data Sharing Platform

Banks in Europe must comply with new EU regulation and legislation. Recent legislation has focused on personal data, Know Your Customer (KYC), and anti-money laundering. As a result, the cost of KYC compliance is higher than ever, requiring time consuming work by both the banks and their customers in the form of document collection and verification. In this paper we detail a system designed to ease the burden of compliance for banks within the EU and save their customers time through the secure and permissioned sharing of digital KYC data. In order to share data, banks need a secure system capable of protecting the privacy of both them and their clients. We detail a system which uses blockchain technology and various privacy and security enhancing techniques to provide banks with a fast and secure way to share documents required for know your customer compliance. The system was built to be aligned with the GDPR, meaning each participating bank must have explicit permission for a customer to access one or more of their documents. These permissions are stored on a private blockchain shared by the banks. Moreover, we detail methods to anonymise on-chain data where necessary. The use of a private blockchain to achieve consensus on the veracity of customer-granted permissions to data enables participating banks to trust one another as each permission and request is observed, agreed upon, and stored on-chain. To the best of our knowledge we propose the first data sharing system under which there is no outsourcing of data storage. This allows the banks to retain full control of storage security and encryption.