Reviewerly/exHarmony huggingface dataset:
A secure and authenticated host-to-memory communication interface

Emerging non-volatile memories (NVMs) have the potential to change the memory-storage hierarchy in computing devices, and even to replace DRAM as main memories. In fact NVMs, beside offering byte-addressability and data persistence, promise better scalability and higher capacity than DRAM. However, from a security point of view, the persistent nature of emerging memories provides a larger time window to exfiltrate data from a device with respect to current DRAM-based main memories, and NVMs have in general lower write endurance than DRAM, thus requiring wear-out conscious encryption schemes. In this work we propose an architectural solution to secure non-volatile emerging memories, providing confidentiality, integrity and authenticity to the entire set of data, addresses and commands. Our solution relies on securing and authenticating the entire information transport between the host controller and the memory, enabling the storage of cleartext data inside the NVM. Such an approach allows to retain the advantage of differential write strategies without forsaking security. We validate our proposed architecture through the simulation of a set of software benchmarks on an embedded architecture, employing the gem5 trace-based architectural simulator.
Single-image reflection removal using conditional GANs

Removing undesired reflections from a single-image is inherently ill-posed problem since defining its exact physical model is almost impossible. Most previous works tackle this problem through the use of multiple images or hand-crafted features. These methods are still quite limited in terms of the generality and photo-realistic result. In this paper, we propose a conditional Generative Adversarial Networks based deep neural network structure to render realistic image and formulate our problem in a simple objective function. Specifically, we use gradient information to elaborate this formulation to preserve both low and high frequency details. Our proposed network does not rely on any physical prior information and performs effectively with a single-image. Experimental results demonstrate that proposed algorithm conducts favorably against existing algorithms from human perceptual aspect.
Characterising Relativised Strong Equivalence with Projection for Non-ground Answer-Set Programs

Starting with the seminal work on strong equivalence by Lifschitz, Pearce, and Valverde, many different advanced notions of program equivalence have been studied in the area of answer-set programming (ASP). In particular, relativised strong equivalence with projection has been introduced as a generalisation of strong equivalence by parameterising, on the one hand, the alphabet of the context programs used for checking program equivalence as well as, on the other hand, allowing the filtering of auxiliary atoms. Like many other advanced equivalence notions, it was introduced originally for propositional programs, along with model-theoretic concepts providing characterisations when equivalence between two programs hold. In this paper, we extend these concepts and characterisations to the general case of non-ground programs.
Decentralized Velocity-Aware Motion Planning for Multi-agent Coordination

Multi-agent motion planning is a crucial topic with applications of search and rescue missions, transportation missions, etc. The concerns of motion planning focus on path planning and inter-agent collision avoidance. Lyapunov-based models present great solutions for the problems. However it is usually empirical to set reasonable parameters for the models. Moreover, agents controlled by the models usually converge to destinations slowly. Heuristic planning algorithms are also mainstream approaches to guide agents without consideration of kinetics. This paper proposes decentralized velocity-aware planning algorithm and collision avoidance algorithm to serve motion planning of multiple agents. The velocity-aware algorithm generates paths with acceleration vectors that converge to the predefined destinations. The collision avoidance algorithm will be triggered to protect agents from collisions. The simulation results illustrate the efficiency of our algorithms. Furthermore, compared with the prioritized A* algorithm paired with Lyapunov-based low-level control laws, our approach could help agents take much shorter paths and much less time to move to destinations safely.
Standardization of the Shape of Ground Control Point (GCP) and the Methodology for Its Detection in Images for UAV-Based Mapping Applications

The challenge of georeferencing aerial images for an accurate object to image correspondence has gained significance over the past couple of years. There is an ever-increasing need to establish accurate georeferencing techniques for Unmanned Aerial Vehicles (UAVs) for tasks like aerial surveyance of mines/construction sites, change detection along national highways, inspection of major pipelines, intelligent farming, among others. With this paper, we aim to establish a standard method of georeferencing by proposing the design of a simple, white colored, L-shaped marker along with the pipeline for its detection. In a first, the less common DRGB color space is used along with the RGB color space to segment the characteristic white color of the marker. To carry out recognition, a scale and rotation invariant modification of the edge oriented histogram is used. To allow for accurate histograms, improvements are made on canny edge detection using adaptive approaches and exploiting contour properties. The histogram obtained displayed a characteristic distribution of peaks for GCP-markers. Thus, a new peak-detection and verification methodology is also proposed based on the normalized cross-correlation. Finally, a CNN model is trained on the Regions of Interest around the GCP-markers that are received after the filtering. The results from EOH and CNN were then used for classification. Regions with a diverse range of locality, terrain, soil quality were chosen to test the pipeline developed. The results of the design and the pipeline combined were quite impressive, with regards to the accuracy of detection as well as its reproducibility in diverse geographical locations.
A lightweight-client and privacy preserving content-based image retrieval in cloud environment

Image storage and retrieval incur high storage and computation complexities and cloud computing offers an attractive choice for the image storage outsourcing and the on-demand access. Since the outsourced images often include sensitive information, privacy-preserving image outsourcing scheme becomes the biggest research hot spot. In this paper, we proposed a lightweight-client and privacy-preserving content-based image retrieval (CBIR) scheme, which has the following advantages: (1) to improve search accuracy, color and texture feature are integrated to construct index; (2) to reduce the client (owner and user) burden, computationally intensive tasks, texture feature extraction and index construction, are securely outsourced to cloud server; (3) to enhance search efficiency, a two-layer index structure is also adopted. The security analysis and the experiments show the security and efficiency of the proposed scheme.
Utilization of Fuzzy Logic in CPU Scheduling in Various Computing Environments

CPU Scheduling is a core functionality among all operating systems. The goal of the scheduler is to decide in what order a set of processes should be executed so that a system can run effectively. There is a demonstrated applicability of fuzzy inference in solving problems that are difficult to stochastically model or analyze. In this research, we use the burst time and static priority available to the CPU scheduler to feed a fuzzy inference system that classifies the priority of processes dynamically. Our results show that the fuzzy-priority scheduler had on average a lower waiting time than the priority and round-robin schedulers.
Communicative Aggression as a Communication Strategy in Digital Society

One of previous studies showed that among students of Saint-Petersburg social network sites and the Internet resources has replaced TV as a primary mass source of obtaining political information. The survey conducted between St. Petersburg State University students of journalism and pollical science (N=400) revealed next results. The Internet replaced television on a primary informative position. Surprisingly, the printed press ranks second as a source of political information. The highest aggression level was marked in the Internet and on television relatively. It turns out a remarkable links with sources of political information and aggression perceptiveness in received data frames. Respondents identify the lowest level of aggression in printed media. In general students of both educational programs’ indexes’ differences are not significant. The article provides detailed information about method of survey, its main results and directions for further research.
Image caption based on Visual Attention Mechanism

The generic neural encoder-decoder framework for image captioning typically uses a convolution neural network to extract the image features and then uses a recurrent neural network to generate a sentence describing this image. The residual attention network is a model that achieves good results in image classification task, which is proved that this network is better than the classical convolution neural network in the ability of feature extraction. In this paper, we propose a combination of the residual attention network and the classical convolution to extract image spatial features, and then input this image spatial features to our visual attention module. At last, we use the decoder which consists of two long short-term memory(Two-LSTM) to generate a sentence describing the image. Our design scheme validates the results of BLEU-N in the MSCOCO dataset.
On-Device Algorithms for Public-Private Data with Absolute Privacy

Motivated by the increasing need to preserve privacy in digital devices, we introduce the on-device public-private model of computation. Our motivation comes from social-network based recommender systems in which the users want to receive recommendations based on the information available on their devices, as well as the suggestions of their social contacts, without sharing such information or contacts with the central recommendation system. Our model allows us to solve many algorithmic problems while providing absolute (deterministic) guarantees of the privacy of on-device data and the user's contacts. In fact, we ensure that the private data and private contacts are never revealed to the central system. Our restrictive model of computation presents several interesting algorithmic challenges because any computation based on private information and contacts must be performed on local devices of limited capabilities. Despite these challenges, under realistic assumptions of inter-device communication, we show several efficient algorithms for fundamental data mining and machine learning problems, ranging from k-means clustering to heavy hitters. We complement this analysis with strong impossibility results for efficient private algorithms without allowing inter-device communication. In our experimental evaluation, we show that our private algorithms provide results almost as accurate as those of the non-private ones while speeding up the on-device computations by orders of magnitude.
Application of Internet segmentation research based on Natural Language Processing technology in enterprise public opinion risk monitoring

With the advent of the mobile Internet era, the network has become a distribution center of various information such as media, entertainment, sports, economy, politics and so on. A large amount of information is generated and disappeared on the network every day. How to effectively extract and identify the relevant data, and judge and analyze them is an important part of the corporate public opinion control. This paper uses natural language processing technology to study the word segmentation of text information on the network, and applies it to the risk detection of corporate public opinion.
ASiam: adaptive Siamese regression tracking with adversarial template generation and motion‐based failure recovery

Object tracking is challenged by the varying appearances of targets and the real-time requirement. Siamese regression trackers, being one of the most popular tracking paradigms, excel in efficiency but suffer at adaptability to cope with appearance variations. To improve their adaptability, the authors propose a new adaptive Siamese (ASiam) tracker, which integrates a novel adversarial template generation module and a motion-based failure recovery module. The template generation module exploits the temporal coherence and evolution of target appearance variations encoded in preceding tracklets and then generates an adaptive target template online which approximates the varying target in the coming frame. This generation module is optimised via adversarial learning to achieve accurate appearance prediction and sharp template quality. The generated template, together with a search region, are fed into a Siamese tracking backbone to compute an appearance response map via dense similarity computation in a sliding-window way. At frames where the Siamese tracking fails, the failure recovery module is invoked to perform deep frame differencing motion detection to provide a motion response map. By fusing different response maps, the drifted tracker can be re-calibrated. Extensive experiments on the OTB2013, OTB2015, and VOT2016 datasets prove the accuracy and efficiency of the proposed tracker.
Improving Performance of Convolutional Neural Networks via Feature Embedding

Recently convolutional neural networks (CNN) have shown exceptional performance with data where a feature structure is explicitly defined, for example image data. Real world data is often represented as d dimensional vectors and they lack such feature structure. If features could be embedded into a low dimensional space to introduce feature locality, CNNs could take advantage of the newly introduced feature structure and show better performance. In this paper, we present a technique of feature embedding to introduce feature locality so that non-image data exhibit image like feature structure. We achieve this by embedding features into a 1d or 2d space using t-SNE. We show that CNN performs better under the proposed approach.
CRLB for multi-sensor rotational bias estimation for passive sensors without target state estimation

Bias estimation is a significant problem in target tracking applications and passive sensors present additional challenges in this field. Biases in passive sensors are commonly represented as unknown rotations of the sensor coordinate frame and it is necessary to correct for such errors. Many methods have used simultaneous target state and bias estimation to register the sensors, however it may be advantageous to decouple state and bias estimation to simplify the estimation problem. This way bias estimation can be done for any arbitrary target motion. If measurements are converted into Cartesian coordinates and differenced then it is possible to isolate the effects of the biases. This bias pseudo-measurement approach has been used in bias estimation for many types of biases and sensors and this paper applies this method to 3D passive sensors with rotational biases. The Cram´er-Rao Lower Bound for the bias estimates is evaluated and it is shown to be attained, i.e., the bias estimates are statistically efficient.
The Visual SuperTree: similarity-based multi-scale visualization

Similarity-based exploration of multi-dimensional data sets is a difficult task, in which most techniques do not perform well with large data sets, particularly in handling clutter that invariably happens as data sets grow larger. In this paper, we introduce the Visual SuperTree (VST), a method to build a multi-scale similarity tree that can deal with large data sets at interactive rates, maintaining most of the accuracy and the data organization capabilities of other available methods. The VST is built on top of a clustered multi-level configuration of the data that allows the user to quickly explore data sets by similarity. The method is shown to be useful for both unlabeled and labeled data, and it is capable of revealing external and internal cluster structures. We demonstrate its application on artificial and real data sets, showing additional advantages of the approach when exploring data that can be summarized meaningfully.
Collusion attacks and fair time-locked deposits for fast-payment transactions in Bitcoin1

In Bitcoin network, the distributed storage of multiple copies of the block chain opens up possibilities for double-spending, i.e., a payer issues two separate transactions to two different payees transferring the same coins. While Bitcoin has inherent security mechanism to prevent double-spending attacks, it requires a certain amount of time to detect the double-spending attacks after the transaction has been initiated. Therefore, it is impractical to protect the payees from suffering in double-spending attacks in fast payment scenarios where the time between the exchange of currency and goods or services is shorten to few seconds. Although we cannot prevent double-spending attacks immediately for fast payments, decentralized non-equivocation contracts have been proposed to penalize the malicious payer after the attacks have been detected. The basic idea of these contracts is that the payer locks some coins in a deposit when he initiates a transaction with the payee. If the payer double-spends, a cryptographic primitive called accountable assertions can be used to reveal his Bitcoin credentials for the deposit. Thus, the malicious payer could be penalized by the loss of deposit coins. However, such decentralized non-equivocation contracts are subjected to collusion attacks where the payer colludes with the beneficiary of the depoist and transfers the Bitcoin deposit back to himself when he double-spends, resulting in no penalties. On the other hand, even if the beneficiary behaves honestly, the victim payee cannot get any compensation directly from the deposit in the original design. To prevent such collusion attacks, we design fair time-locked deposits for Bitcoin transactions to defend against double-spending. The fair deposits ensure that the payer will be penalized by the loss of his deposit coins if he double-spends and the victim payee’s loss will be compensated within a locked time period. We start with the protocols of making a deposit for one transaction. In particular, for the transaction with single input and output and the transaction with multiple inputs and outputs, we provide different designs of the deposits. We analyze the performance of deposits made for one transaction and show how the fair deposits work efficiently in Bitcoin. We also provide protocols of making a deposit for multiple transactions, which can reduce the burdens of a honest payer. In the end, we extend the fair deposits to non-equivocation contracts for other distributed systems.
Classification of real estate images using transfer learning

With the growing mobility of the population and popularity of the Internet, real estate agents have larger database to manage. This paper presents a solution to classify images of a certain house, such as living room, kitchen, bathroom, layout sketch and external appearance collected by a real estate agent using transfer learning. The pictures are like those images posted on the real estate agent website to help people find out what’s the house looks like inside and outside. We employ a transfer learning approach for VGG-19 architecture. Using a network pre-trained on the general ImageNet dataset, we perform supervised fine-tuning on the last full connect layer and change the output size from 1000 to 5. Experimental results achieved with 5-fold cross-validation show that after training, this fine-tuning approach achieves high test accuracy of 99.4%.
Moving object tracking in video surveillance using YOLOv3 and MeanShift

Video surveillance is widely used and plays a huge role in society. Due to surveillance videos are often continuously produced, using these videos to track objects is a challenge for conventional moving object tracking methods. In this paper, in order to deal with the fast moving object and the problem of target occlusion, we propose an object tracking method based on YOLOv3 and MeanShift combined with Kalman filter aiming to improve the speed and accuracy of tracking. We use YOLOv3 to realize the detection and use the MeanShift combined with Kalman filter to track the target. The results of the experiment show that our method has achieved good results.
Data rescue in manuscripts: a hydrological modelling study example

Recently, data statements have been required in hydrological manuscripts. Authors are usually confused by different data management policies. Thus, a frequent response is to write down “data available upon request from the corresponding author”. Such a simplified response may lead to dormancy or permanent loss of data. We present an example of data management from a published hydrological modelling study. We started from figures with complete scientific stories in the publication and traced backwards to find relevant source data through workflows (linking). In this manner, the source data can be identified (archiving) and preserved (hosting). Such simple practices of data linking, archiving and hosting can rescue data and require only limited effort by the authors. We suggest that this data rescue process(es) should become routine in the scientific publication process. The data rescued with workflows can significantly improve the research transparency and reusability, which leads to a fairer and more open culture in hydrology.
Closed-World Semantics for Conjunctive Queries with Negation over $$\mathcal {ELH}_\bot $$ Ontologies

Ontology-mediated query answering is a popular paradigm for enriching answers to user queries with background knowledge. For querying the absence of information, however, there exist only few ontology-based approaches. Moreover, these proposals conflate the closed-domain and closed-world assumption, and therefore are not suited to deal with the anonymous objects that are common in ontological reasoning. We propose a new closed-world semantics for answering conjunctive queries with negation over ontologies formulated in the description logic , which is based on the minimal canonical model. We propose a rewriting strategy for dealing with negated query atoms, which shows that query answering is possible in polynomial time in data complexity.
Code Search on Bytecode for Mobile App Development

Code search is an essential task in software development. Existing code search engines often rely on open-source software code repositories to build their search database. However, in mobile app development, high quality code is rarely available as open-source. In this paper, we propose SUSHI, a novel code search system for mobile app development that exploits the vast amount of high-quality, production-ready code from millions of mobile apps publicly available on online app stores. SUSHI extracts names and usages of APIs directly from apps' bytecode and learns vector based representations of technical terms and statistical models of API usages. Then, it uses those vectors and models to calculate the relevance of queries and code snippets collected from mobile apps or other source code corpata if compatible.