Reviewerly/exHarmony huggingface dataset:
Visual Transfer Between Atari Games Using Competitive Reinforcement Learning

Modern deep Reinforcement Learning (RL) methods are highly effective at selecting optimal policies to maximize rewards. The combination of these methods with Deep Learning approaches shows promise for challenging tasks by leveraging rich visual information for policy selection. In this paper, we explore the use of visual representations to transfer the knowledge of an RL agent from one domain to another. More specifically, we propose a method that can generalize for a target game using an RL agent trained for a source game in Atari 2600 environment. Instead of fine-tuning a pre-trained model for the target game, we propose a learning approach to update the model using multiple RL agents trained in parallel with different representations of the target game. The visual representations of the target game are generated by learning a visual mapping between the source game and the target game in an unsupervised manner. The visual mapping between sequences of transfer pairs has been shown to derive new representations of the target game; training on which improves the RL agent updates in terms of performance, data efficiency and stability. In order to demonstrate the effectiveness of this approach, the transfer learning procedure is evaluated on two pairs of Atari games taken in contrasting settings.
Anonymous State Pinning for Private Blockchains

Public blockchains such as Ethereum and Bitcoin provide transparency and accountability, and have strong non-repudiation properties, but fall far short of enterprise privacy requirements for business processes. Consequently consortiums are exploring private blockchains to keep their membership and transactions private. However, private blockchains do not provide adequate protection against potential collusion by consortium members to revert the state of the blockchain. To countenance this, the private blockchain state may be "pinned" to a tamper resistant public blockchain. Existing solutions offering pinning to the public blockchain would reveal the transaction rate of the private blockchain, and do not provide a mechanism to contest the validity of a pin. Moreover, they require that all transactions and members of the private blockchain be revealed. These challenges are hampering the wider adoption of private blockchain technology. We describe the primary author's "Anonymous State Pinning approach", which overcomes these limitations and present a security proof to demonstrate pins can be challenged without compromising these properties. We perform a gas cost analysis of the implementation to estimate the operating cost of this technology, which shows that pinning a private blockchain at the rate of one pin per hour would cost US$508 per year. A hierarchical pinning approach is proposed which would allow many private blockchains to pin to a management blockchain which would then pin to Ethereum MainNet. This approach saves money, but at the cost of increased finality times.
A new image edge detection algorithm based on improved Canny

Due to the filter and threshold setting method, the traditional Canny algorithm has the disadvantages of limited denoising ability and poor adaptive ability. Focusing on the filtering part and the threshold setting part, a new image edge detection al
Random 2.5D U-net for Fully 3D Segmentation

Convolutional neural networks are state-of-the-art for various segmentation tasks. While for 2D images these networks are also computationally efficient, 3D convolutions have huge storage requirements and therefore, end-to-end training is limited by GPU memory and data size. To overcome this issue, we introduce a network structure for volumetric data without 3D convolution layers. The main idea is to include projections from different directions to transform the volumetric data to a sequence of images, where each image contains information of the full data. We then apply 2D convolutions to these projection images and lift them again to volumetric data using a trainable reconstruction algorithm. The proposed architecture can be applied end-to-end to very large data volumes without cropping or sliding-window techniques. For a tested sparse binary segmentation task, it outperforms already known standard approaches and is more resistant to generation of artefacts.
Automatic Difficulty Management and Testing in Games using a Framework Based on Behavior Trees and Genetic Algorithms

The diversity of agent behaviors is an important topic for the quality of video games and virtual environments in general. Offering the most compelling experience for users with different skills is a difficult task, and usually needs important manual human effort for tuning existing code. This can get even harder when dealing with adaptive difficulty systems. Our paper's main purpose is to create a framework that can automatically create behaviors for game agents of different difficulty classes and enough diversity. In parallel with this, a second purpose is to create more automated tests for showing defects in the source code or possible logic exploits with less human effort.
Application of magnetic field sensors for hand gesture recognition with neural networks

Hand motion tracking represents one of the most widely used human-computer interface. Those technologies are essential for virtual reality (VR) systems recognition of human movements or the interpretation of gestures. For such tasks numerous systems are utilized, which often combine magnetic field and inertial measurement systems. This is done to enable a six degree of freedom (DOF)position. Often time extensive modeling effort is necessary. In the following a sensing methodology is presented, which distinguishes itself via the exclusive application of magnetic field sensing. The recorded positions of this measurements are the input of a neural network that is trained to recognize the gesture of the hand depending on the position of the fingers.
The Encoder-Decoder Framework and Its Applications

The neural encoder-decoder framework has advanced the state-of-the-art in machine translation significantly. Many researchers in recent years have employed the encoder-decoder based models to solve sophisticated tasks such as image/video captioning, textual/visual question answering, and text summarization. In this work we study the baseline encoder-decoder framework in machine translation and take a brief look at the encoder structures proposed to cope with the difficulties of feature extraction. Furthermore, an empirical study of solutions to enable decoders to generate richer fine-grained output sentences is provided. Finally, the attention mechanism which is a technique to cope with long-term dependencies and to improve the encoder-decoder performance on sophisticated tasks is studied.
Automatic Video Colorization Using 3D Conditional Generative Adversarial Networks

In this work, we present a method for automatic colorization of grayscale videos. The core of the method is a Generative Adversarial Network that is trained and tested on sequences of frames in a sliding window manner. Network convolutional and deconvolutional layers are three-dimensional, with frame height, width and time as the dimensions taken into account. Multiple chrominance estimates per frame are aggregated and combined with available luminance information to recreate a colored sequence. Colorization trials are run successfully on a dataset of old black-and-white films. The usefulness of our method is also validated with numerical results, computed with a newly proposed metric that measures colorization consistency over a frame sequence.
Poster

Machine learning has proved to be a promising technology to determine whether a piece of software is malicious or benign. However, the accuracy of this approach comes sometimes at the expense of its robustness and probing these systems against adversarial examples is not always a priority. In this work, we present a gradient-based approach that can carefully generate valid executable malicious files that are classified as benign by state-of-the-art detectors. Initial results demonstrate that our approach is able to automatically find optimal adversarial examples in a more efficient way, which can provide a good support for building more robust models in the future.
A Multilingual Topic Model for Learning Weighted Topic Links Across Corpora with Low Comparability

Multilingual topic models (MTMs) learn topics on documents in multiple languages.Past models align topics across languages by implicitly assuming the documents in different languages are highly comparable, often a false assumption.We introduce a new model that does not rely on this assumption, particularly useful in important low-resource language scenarios.Our MTM learns weighted topic links and connects cross-lingual topics only when the dominant words defining them are similar, outperforming LDA and previous MTMs in classification tasks using documents' topic posteriors as features.It also learns coherent topics on documents with low comparability.
Design and implementation of distributed traceability system for smart factories based on blockchain technology

Manufacturing enterprises are facing the challenges of productivity improvement, product and process quality management due to the lack of data interaction and manufacturing process traceability. In modern factory, number of machineries are pipelined into assembly/production lines which may merge and branch to meet the procedure requirements. Each machine on the assembly line generates huge amount of sensing data and manufacturing data. However, many manufacturing system are not ready to manage big data. This paper aims on developing the distributed traceability system with Blockchain technology on IoT devices in order to improve the stability of the factory production line, to reduce defect rate and to bring the operational performance to a new level. Without depending on centralized storage, it is a robust, truly distributed peer-to-peer system and capable of node failure tolerance. With better scalability and data interaction, this distributed ledger network enables the use of data collection with security and potential of public traceability system protocol which allows multiple factories to participate, build an ecosystem of traceability together.
Deep Learning for Analysis of Electronic Health Records (EHR)

In current scenario, every medical equipment, clinical instrument, lab setup in healthcare centres and hospitals, is linked with digital devices which has brought about digital data explosion. Due to this, the amount of digital information generated and stored in Electronic Health Records (EHRs) has increased exponentially. Therefore, EHRs have become an area of booming research, as EHRs can provide a host of untouched possibilities which, the data contained in them, can bring about. EHRs have several classification schema and controlled vocabularies are present to record relevant medical information and events. Thus, harmonizing and analysing data among institutions and across terminologies is an ongoing field of research. Several clinical code representation forms have been proposed by various deep learning EHR systems that share themselves easily to cross institutional analysis and applications. EHR records have primary use in storing patient information such as patient medical history, progress, demography, diagnosis and medications. But researchers across the globe have invented secondary use of EHRs for several clinical and health informatics applications. Secondary usage of electronic health records (EHRs) promises to boost clinical research and result into better informed clinical decision making. Challenge in summarizing and representing patient data prevents widespread practice to predict the future of patients using EHRs. Simultaneously, over the span of time, the machine learning field has witnessed widespread advancements in the area of deep learning. The current research in healthcare informatics focusses on applying deep learning based on EHRs to clinical tasks. In this context, the deep learning techniques described here can be applied to various types of clinical applications such as extraction of information, representation learning, outcome prediction, phenotyping and de-identification. Several limitations of current research have been identified like model interpretability and heterogeneity of data.
RETRACTED: Automatic Image Captioning Using Convolution Neural Networks and LSTM

Abstract PC vision has turned out to be universal in our general public, with applications in a few fields. Given a lot of pictures, with its inscription, make a prescient model which produces regular, inventive, and intriguing subtitles for the concealed picture. A speedy look at a picture is adequate for a human to call attention to and portray a monstrous measure of insights regarding the visual scene. To rearrange the current issue of producing inscriptions for pictures by making a model which would give exact subtitles to these pictures which can be additionally utilized in other helpful applications and use cases. Be that as it may, this momentous capacity has ended up being a tricky errand for our visual acknowledgment models. Most of the past research in scene acknowledgment has concentrated on naming pictures with a predetermined arrangement of visual classifications and extraordinary advancement has been accom-plished in these undertakings. For a question picture, the past strategies recover pertinent hopeful normal language states by outwardly contrasting the inquiry picture with database pictures. In any case, while shut vocabularies of visual ideas comprise a helpful demonstrating suspicion, they are boundlessly prohibitive when contrasted with the colossal measure of rich depictions that a human can form. These methodologies forced a breaking point on the assortment of inscriptions produced. The model ought to be exempt of suppositions regarding explicit pre decided formats, standards or classes and rather depend on figuring out how to create sentences from the preparation information. The model proposed utilizes Convolution Neural Networks which help to separate highlights of the picture whose subtitle is to be created and afterward by utilizing a probabilistic methodology and Natural Language Processing Techniques reasonable sentences are framed and inscriptions are produced.
A study: query expansion methods in information retrieval

Abstract This paper study about a review of the literature on Query Expansion (QE) methods. The main aspects of the review study are the methods, limitations of the QE methods, and datasets used in QE. As the results of this study, we can conclude several methods used on QE i.e., Ontology, Association Rules, Wordnet, Methathesaurus, Synonym mapping, Concept-based, Local Co-occurrence, and Latent Semantic Indexing (LSI). Each of the methods still has limitations. For datasets used in QE, many researchers use public datasets.
Video on demand recommender system for internet protocol television service based on explicit information fusion

Internet protocol television (IPTV) provides video on demand (VOD), internet service, and real-time broadcasting to users as a service that combines broadcasting and communication technology. Among various services, the sales of VOD are profitable because VODs offer relatively strong direct revenue models in IPTV services. However, the development of a VOD recommender system for IPTV service is highly challenging owing to the lack of explicit preference information of users in an IPTV environment. Previous studies for IPTV VOD recommender systems have attempted to solve the data sparsity problem through implicit preference information; however, it is better to utilize explicit preference information to improve the performance of system. Recently, IPTV service providers have provided their own over-the-top (OTT) services such that explicit preference information of users for items can be combined. Therefore, we proposed a novel information fusion method for an IPTV VOD recommender system that integrates the explicit information of both IPTV and OTT services. In addition, we utilized the probabilistic matrix factorization, that guarantees high performance in most recommender systems, as a recommender algorithm in this study. Finally, we conducted comparative evaluations based on various metrics and validated that the information fusion of IPTV and OTT services contribute to the IPTV VOD recommender system.
An Effective Network with ConvLSTM for Low-Light Image Enhancement

Low-light image enhancement is a fundamental problem in computer vision. The artifact, noise, insufficient contrast and color distortion are common challenging problems in low-light image enhancement. In this paper, we present a convolutional Long Short-Term Memory (ConvLSTM) network based method to directly restore a normal image from a low-light image, which can be learned in an end-to-end way. Specifically, our base network employs the encoder-decoder structure. Meanwhile, considering that a normal image may correspond to low-light images of different illuminance levels, we adopt a multi-branch structure combined with ConvLSTM to solve this problem. The extensive experiments on two low-light datasets show that our method outperforms the state-of-the-art traditional and deep learning based methods vertified by both quantitative and qualitative evaluation.
You Shall Not Join

Cryptocurrencies are digital assets which depend upon the use of distributed peer-to-peer networks. The method a new peer uses to initially join a peer-to-peer network is known as bootstrapping. The ability to bootstrap without the use of a centralized resource is an unresolved challenge. In this paper we survey the bootstrapping techniques used by 74 cryptocurrencies and find that censorship-prone methods such as DNS seeding and IP hard-coding are the most prevalent. In response to this finding, we test two other bootstrapping techniques less susceptible to censorship, Tor and ZMap, to determine if they are operationally feasible alternatives more resilient to censorship. We perform a global measurement study of DNS query responses for each the 92 DNS seeds discovered across 42 countries using the distributed RIPE Atlas network. This provides details of each cryptocurrencies' peer-to-peer network topology and also highlights instances of DNS outages and query manipulation impacting the bootstrapping process. Our study also reveals that the source code of the cryptocurrencies researched comes from only five main repositories; hence accounting for the inheritance of legacy bootstrapping methods. Finally, we discuss the implications of our findings and provide recommendations to mitigate the risks exposed.
PatMat: A Distributed Pattern Matching Engine with Cypher

Graph pattern matching is one of the most fundamental problems in graph database and is associated with a wide spectrum of applications. Due to its computational intensiveness, researchers have primarily devoted their efforts to improving the performance of the algorithm while constraining the graphs to have singular labels on vertices (edges) or no label. Whereas in practice graphs are typically associated with rich properties, thus the main focus in the industry is instead on powerful query languages that can express a sufficient number of pattern matching scenarios. We demo PatMat in this work to glue together the academic efforts on performance and the industrial efforts on expressiveness. To do so, we leverage the state-of-the-art join-based algorithms in the distributed contexts and Cypher query language - the most widely-adopted declarative language for graph pattern matching. The experiments demonstrate how we are capable of turning complex Cypher semantics into a distributed solution with high performance.
Location-Privacy Evaluation Within the Extreme Points Privacy (EPP) Scheme for VANET Users

The main purpose of designing vehicular ad-hoc networks (VANETs) is to achieve safety by periodically broadcasting the vehicle's coordinates with a high precision. This advantage brings a threat represented in the possible tracking and identification of the vehicles. A possible solution is to use and change pseudonyms. However, even by changing pseudonyms, the vehicle could still be tracked if the adversary has a prior knowledge about the potential start and end points of a particular driver who has social interactions (e.g., with neighbors) which introduces the concept of vehicular social networks (VSNs). This article extends the authors previous work, namely: “EPP Extreme Points Privacy for Trips and Home Identification in Vehicular Social Networks,” which exploits the nature of the end points that are common between VSN users in order to create shared zones to anonymize them. The extension is represented by (a) the evaluation of the enjoyed location privacy of VSN users after quitting the district in addition to (b) detailing the used environment during the evaluation.
Verified Verifiers for Verifying Elections

The security and trustworthiness of elections is critical to democracy; alas, securing elections is notoriously hard. Powerful cryptographic techniques for verifying the integrity of electronic voting have been developed and are in increasingly common use. The claimed security guarantees of most of these techniques have been formally proved. However, implementing the cryptographic verifiers which utilize these techniques is a technical and error prone process, and often leads to critical errors appearing in the gap between the implementation and the formally verified design. We significantly reduce the gap between theory and practice by using machine checked proofs coupled with code extraction to produce cryptographic verifiers that are themselves formally verified. We demonstrate the feasibility of our technique by producing a formally verified verifier which we use to check the 2018 International Association for Cryptologic Research (IACR) directors election.
KASLR-MT: Kernel Address Space Layout Randomization for Multi-Tenant cloud systems

Cloud computing has completely changed our lives. This technology dramatically impacted on how we play, work and live. It has been widely adopted in many sectors mainly because it reduces the cost of performing tasks in a flexible, scalable and reliable way. To provide a secure cloud computing architecture, the highest possible level of protection must be applied. Unfortunately, the cloud computing paradigm introduces new scenarios where security protection techniques are weakened or disabled to obtain a better performance and resources exploitation. Kernel ASLR (KASLR) is a widely adopted protection technique present in all modern operating systems. KASLR is a very effective technique that thwarts unknown attacks but unfortunately its randomness have a significant impact on memory deduplication savings. Both techniques are very desired by the industry, the first one because of the high level of security that it provides and the latter to obtain better performance and resources exploitation. In this paper, we propose KASLR-MT, a new Linux kernel randomization approach compatible with memory deduplication. We identify why the most widely and effective technique used to mitigate attacks at kernel level, KASLR, fails to provide protection and shareability at the same time. We analyze the current Linux kernel randomization and how it affects to the shared memory of each kernel region. Then, based on the analysis, we propose KASLR-MT, the first effective and practical Kernel ASLR memory protection that maximizes the memory deduplication savings rate while providing a strong security. Our tests reveal that KASLR-MT is not intrusive, very scalable and provides strong protection without sacrificing the shareability.