Reviewerly/exHarmony huggingface dataset:
Towards Measuring Quality of Service in Untrusted Multi-Vendor Service Function Chains: Balancing Security and Resource Consumption

The IT infrastructure of large organizations consists of devices and software services purchased from multiple vendors. The problem of measuring the quality of service (QoS) of each of these vendor devices (and services) is challenging since the vendors may tamper with the measurements for monetary benefits or saving debugging efforts. Existing solutions for QoS measurement in trusted environments cannot be extended for this problem since the vendors can easily circumvent them. Solutions borrowed from other areas such as client-server QoS measurement do not help either since they incur unreasonable storage and network overheads, or require extensive modifications to the packet headers. In this paper, we propose the Measuring Tape scheme, comprised of (1) a novel data structure called evidence Bloom filter (e-BF) that can be deployed at the vendor devices (and services), and (2) unique querying techniques, which can be used by the administrator to query the e-BF to measure QoS. While e-BF uses storage and computational resources judiciously, the querying techniques ensure resilience to adversarial behavior. We evaluate our solution based on a few real-world and synthetic traces and with different adversaries. Our results highlight the trade-off between resources (i.e., storage and computation) and the accuracy of QoS predictions, as well as its implications on security. We also present an analytical model of e-BF that establishes the relationship between storage, prediction accuracy, and security. Further, we present security arguments to illustrate how our solution thwarts adversarial attempts to tamper QoS.
Renewable Powered Portable Weather Update Station

Weather estimate, in reality continuous weather gauge is essential for our day by day life particularly in agribusiness. National weather data does not generally contain the precise information of each area rather it contains the information of closest climate station for a timeframe. The fundamental aim of this project is to develop a RES powered Weather Station which will help to monitor the weather parameters. Such a project contains sensors for detecting temperature, humidity, raindrop, carbon mono-oxide, smoke, LPG in the environment, barometric pressure, altitude etc. The information from the sensors are gathered by the Arduino. Arduino sends the sensors information in LCD display. Additionally, the device sends an SMS which contains weather information to the user with the assistance of a GSM module. At the end of the project the results have been compared between the national weather data and the actual reading. According to the results, the percentage of deviation for Temperature is 1%, Humidity is 5% and Barometric Pressure is 8%.
Universal Quantum Control through Deep Reinforcement Learning

Emerging reinforcement learning techniques using deep neural networks have shown great promise in control optimization. They harness non-local regularities of noisy control trajectories and facilitate transfer learning between tasks. To leverage these powerful capabilities for quantum control optimization, we propose a new control framework to simultaneously optimize the speed and fidelity of quantum computation against both leakage and stochastic control errors. For a broad family of two-qubit unitary gates that are important for quantum simulation of many-electron systems, we improve the control robustness by adding control noise into training environments for reinforcement learning agents trained with trusted-region-policy-optimization. The agent control solutions demonstrate a two-order-of-magnitude reduction in average-gate-error over baseline stochastic-gradient-descent solutions and up to a one-order-of-magnitude reduction in gate time from optimal gate synthesis counterparts. These significant improvements in both fidelity and runtime are achieved by combining new physical understandings and state-of-the-art machine learning techniques. Our results open a venue for wider applications in quantum simulation, quantum chemistry and quantum supremacy tests using near-term quantum devices.
Generalized teleportation by means of discrete-time quantum walks on N-lines and N-cycles

Recently, Wang et al. [Wang et al., Quantum Inf. Process. 16 (2017) 221] developed generalized teleportation schemes based on different quantum walks structures. In their paper, an interesting open question is whether there are other graphs suitable for teleportation. Here, we extend the results of quantum teleportation of an unknown qubit state by means of discrete-time quantum walks and propose two kinds of schemes for quantum teleportation by means of discrete-time quantum walks on N-lines and N-cycles, respectively. Likewise, prior quantum entanglement is unnecessary for teleportation and quantum entanglement is generated by means of quantum walks. This further opens wider applications of quantum walks in quantum communication protocols.
Optimal Skipping Rates: Training Agents with Fine-Grained Control Using Deep Reinforcement Learning

These days game AI is one of the focused and active research areas in artificial intelligence because computer games are the best test-beds for testing theoretical ideas in AI before practically applying them in real life world. Similarly, ViZDoom is a game artificial intelligence research platform based on Doom used for visual deep reinforcement learning in 3D game environments such as first-person shooters (FPS). While training, the speed of the learning agent greatly depends on the number of frames the agent is permitted to skip. In this paper, how the frame skipping rate influences the agent’s learning and final performance is proposed, particularly using deep Q-learning, experience replay memory, and the ViZDoom Game AI research platform. The agent is trained and tested on Doom’s basic scenario(s) where the results are compared and found to be 10% better compared to the existing state-of-the-art research work on Doom-based agents. The experiments show that the profitable and optimal frame skipping rate falls in the range of 3 to 11 that provides the best balance between the learning speed and the final performance of the agent which exhibits human-like behavior and outperforms an average human player and inbuilt game agents.
A Hybrid Path-Planning Strategy for Mobile Robots with Limited Sensor Capabilities

This paper introduces a strategy for the path planning problem for platforms with limited sensor and processing capabilities. The proposed algorithm does not require any prior information but assumes that a mapping algorithm is used. If enough information is available, a global path planner finds sub-optimal collision-free paths within the known map. For the real time obstacle avoidance task, a simple and cost-efficient local planner is used, making the algorithm a hybrid global and local planning solution. The strategy was tested in a real, cluttered environment experiment using the Pioneer P3-DX and the Xbox 360 kinect sensor, to validate and evaluate the algorithm efficiency.
Review of Ontology Based Approaches for Web Service Discovery

The Growth in the number of web services leads to rise of problems; therefore, users have a difficulty in finding a web service that has been developed and published. An important issue in web service is the discovery of web services. Semantic technologies facilitate specialization and generalization of service needs as well as service composition. Thus, a higher degree of automation and more precise results can be achieved. In this paper, we are going to provide a review of current ontology approaches for Web Service Discovery.
In-situ mapping of ferric iron variations in lunar glasses using X-ray absorption spectroscopy

This paper presents a new X-ray absorption spectroscopy (XAS) method for making two-dimensional maps of Fe3+ in-situ in polished glass samples, which opens the door to study redox changes associated with magmatic processes such as crystallization, assimilation, ascent, and eruption. Multivariate analysis (MVA) allows selection of specific channels in a spectrum to inform predictions of spectral characteristics. Here, the sparse model of the least absolute shrinkage and selection operator (Lasso) is used to select key channels in XAS channels that can be used to predict accurate in-situ Fe3+ analyses of silicate glasses. By tuning the model to use only six channels, analytical time is decreased enough to allow mapping of Fe3+ variations in samples by making gridded point analyses at the scale of the XAS beam (1–2 μm). Maps of Fe3+ concentration can then be constructed using freely available, open source software (http://cars.uchicago.edu/xraylarch/). This result shows the enormous potential of using MVA to select indicative spectral regions for predicting variables of interest across a wide variety of spectroscopic applications. Redox gradients in lunar picritic glass beads first observed with point analyses are confirmed through this XAS mapping and suggest degassing processes during ascent and eruption are responsible for the range of Fe3+ values measured in these samples.
Logical argumentation by dynamic proof systems

In this paper we provide a proof theoretical investigation of logical argumentation, where arguments are represented by sequents, conflicts between arguments are represented by sequent elimination rules, and deductions are made by dynamic proof systems extending standard sequent calculi. The idea is to imitate argumentative movements in which certain claims are introduced or withdrawn in the presence of counter-claims. This is done by a dynamic evaluation of sequences of sequents, in which the latter are considered 'derived' or 'not derived' according to the content of the sequence. We show that decisive conclusions of such a process correspond to well-accepted consequences of the underlying argumentation framework. The outcome is therefore a general and modular proof-theoretical approach for paraconsistent and non-monotonic reasoning with argumentation systems.
Forensic signature for tracking storage devices: Analysis of UEFI firmware image, disk signature and windows artifacts

Tracking storage devices is one of the important fields in digital forensics. The existing methods and tools about registry, event log or IconCache analysis help solving cases on confidential leakage, illegal copying, and security incident cases. However, previous approach has drawback in tracking storage devices such as HDD, SSD, and etc since it was based on the good performance of USB device tracking. Another drawback in previous approach is that it is vulnerable to anti-forensics because the artifacts are dependent on the operating system. This paper introduces a new definition of forensic signature for tracking various storage devices and reviews the known artifacts. Furthermore, this study introduces unidentified artifact stored in UEFI firmware image and independent of operating system. Moreover, this paper develops a methodology for tracking storage devices using forensic signature according to the storage type.
Document Summarization Using Sentence-Level Semantic Based on Word Embeddings

In the era of information overload, text summarization has become a focus of attention in a number of diverse fields such as, question answering systems, intelligence analysis, news recommendation systems, search results in web search engines, and so on. A good document representation is the key point in any successful summarizer. Learning this representation becomes a very active research in natural language processing field (NLP). Traditional approaches mostly fail to deliver a good representation. Word embedding has proved an excellent performance in learning the representation. In this paper, a modified BM25 with Word Embeddings are used to build the sentence vectors from word vectors. The entire document is represented as a set of sentence vectors. Then, the similarity between every pair of sentence vectors is computed. After that, TextRank, a graph-based model, is used to rank the sentences. The summary is generated by picking the top-ranked sentences according to the compression rate. Two well-known datasets, DUC2002 and DUC2004, are used to evaluate the models. The experimental results show that the proposed models perform comprehensively better compared to the state-of-the-art methods.
Multi-Scale Interpretation Model for Convolutional Neural Networks: Building Trust Based on Hierarchical Interpretation

With the rapid development of deep learning models, their performances in various tasks have improved; meanwhile, their increasingly intricate architectures make them difficult to interpret. To tackle this challenge, model interpretability is essential and has been investigated in a wide range of applications. For end users, model interpretability can be used to build trust in the deployed machine learning models. For practitioners, interpretability plays a critical role in model explanation, model validation, and model improvement to develop a faithful model. In this paper, we propose a novel Multi-scale Interpretation (MINT) model for convolutional neural networks using both the perturbation-based and the gradient-based interpretation approaches. It learns the class-discriminative interpretable knowledge from the multi-scale perturbation of feature information in different layers of deep networks. The proposed MINT model provides the coarse-scale and the fine-scale interpretations for the attention in the deep layer and specific features in the shallow layer, respectively. Experimental results show that the MINT model presents the class-discriminative interpretation of the network decision and explains the significance of the hierarchical network structure.
Detecting Anomaly in Large-scale Network using Mobile Crowdsourcing

In this paper, we propose a tree modeling-based data mining method to detect anomalies from crowdsourced network data. We design an algorithm to extract potential network anomalies from decision trees. Moreover, we propose a criteria to evaluate the severity of anomaly in terms of three factors: standard deviation, weight sum and impurity decrease. To enhance generalization performance, we randomly generate sample subspace of the original dataset as the input for each subtree and compact detected anomalies from all subtrees. We carry out experiments based on the crowdsourced network measurement dataset containing five million samples, which contains round trip time (RTT) from more than 5,000 users. Experiments show that the proposed method can effectively detect high-latency network anomalies. Moreover, the random forest-based approach can achieve an improvement of approximately 25% of generalization performance compared to the single decision tree approach.
LISA: Visible light based initialization and SMS based authentication of constrained IoT devices

Future healthcare systems, smart homes, power managementsystems will all involve a large number of smart inter-connected wireless devices (such as wireless sensor and IoT nodes). One of the major challenges to securing these systems presents loading initial cryptographic credentials into a relatively large number of wireless devices. This challenge is further aggravated by the fact that many of these technologies and systems involve low-cost and highly interface constrained devices — lacking usual wired interfaces, displays, keypads, and alike. In this paper we propose two novel multichannel key deployment schemes for wireless (sensor) networks, LISA and LISAT, that only require a presence of a light source device, such as a multi-touch screen, tablet or a smartphone device. Both key deployment schemes are secret key-based multichannel protocols and are suitable for interface/resource constrained wireless devices. We use one-way visible light channel (VLC) of multitouch screens (the flashing displays) to initialize sensor devices in a secure, usable and scalable way. More interestingly, LISAT protocol is suited for initialization of geographically remote devices via a trusted third party device. We provide formal analysis of the proposed protocols using the automated verification tool. From the user’s perspective of protocol execution, device initialization is quite simple as user simply places the devices on the top of the multitouch screen after which the remaining process is fully automatized. Indeed, through the experiments with 34 users we showed that our solution is user-friendly and has a good performance concerning the time consumption.
A Self-Adaptive Step Glowworm Swarm Optimization Approach

When the basic glowworm swarm optimization (GSO) algorithm optimizes the multi-peak function, the solution accuracy is not high, the later convergence is slow. To solve these problems, the fluorescent factor is introduced to adaptively adjust the step length of the firefly, an adaptive step length firefly optimization algorithm is proposed, this algorithm is an improved self-adaptive step glowworm swarm optimization (ASGSO). In this algorithm, the behavior of glowworms are developed, the step size is dynamically adjusted by the fluorescent factor, the algorithm avoids falling into a local optimum and improves the optimization speed and accuracy. The simulation results show that the improved ASGSO can search for global optimization more quickly and precisely.
Boosting Texture-Based Classification by Describing Statistical Information of Gray-Levels Differences

This paper presents a new texture descriptor booster, Complete Local Oriented Statistical Information Booster (CLOSIB), based on statistical information of the image. Our proposal uses the statistical information of the texture provided by the image gray-levels differences to increase the discriminative capability of Local Binary Patterns (LBP)-based and other texture descriptors. We demonstrated that Half-CLOSIB and M-CLOSIB versions are more efficient and precise than the general one. H-CLOSIB may eliminate redundant statistical information and the multi-scale version, M-CLOSIB, is more robust. We evaluated our method using four datasets: KTH TIPS (2-a) for material recognition, UIUC and USPTex for general texture recognition and JAFFE for face recognition. The results show that when we combine CLOSIB with well-known LBP-based descriptors, the hit rate increases in all the cases, introducing in this way the idea that CLOSIB can be used to enhance the description of texture in a significant number of situations. Additionally, a comparison with recent algorithms demonstrates that a combination of LBP methods with CLOSIB variants obtains comparable results to those of the state-of-the-art.
Minicrypt Primitives with Algebraic Structure and Applications

Algebraic structure lies at the heart of Cryptomania as we know it. An interesting question is the following: instead of building (Cryptomania) primitives from concrete assumptions, can we build them from simple Minicrypt primitives endowed with some additional algebraic structure? In this work, we affirmatively answer this question by adding algebraic structure to the following Minicrypt primitives: The algebraic structure that we consider is group homomorphism over the input/output spaces of these primitives. We also consider a "bounded" notion of homomorphism where the primitive only supports an a priori bounded number of homomorphic operations in order to capture lattice-based and other "noisy" assumptions. We show that these structured primitives can be used to construct many cryptographic protocols. In particular, we prove that: In addition, we show how to realize any CDH/DDH-based protocol with certain properties in a generic manner using IHwUFs/IHwPRFs, and how to instantiate such a protocol from many concrete assumptions. We also consider primitives with substantially richer structure, namely Ring IHwPRFs and L-composable IHwPRFs. In particular, we show the following: Our framework allows us to categorize many cryptographic protocols based on which structured Minicrypt primitive implies them. In addition, it potentially makes showing the existence of many cryptosystems from novel assumptions substantially easier in the future.
SoftME: A Software-Based Memory Protection Approach for TEE System to Resist Physical Attacks

The development of the Internet of Things has made embedded devices widely used. Embedded devices are often used to process sensitive data, making them the target of attackers. ARM TrustZone technology is used to protect embedded device data from compromised operating systems and applications. But as the value of the data stored in embedded devices increases, more and more effective physical attacks have emerged. However, TrustZone cannot resist physical attacks. We propose SoftME, an approach that utilizes the on-chip memory space to provide a trusted execution environment for sensitive applications. We protect the confidentiality and integrity of the data stored on the off-chip memory. In addition, we design task scheduling in the encryption process. We implement a prototype system of our approach on the development board supporting TrustZone and evaluate the overhead of our approach. The experimental results show that our approach improves the security of the system, and there is no significant increase in system overhead.
Quantification of the diagenesis-designating metals in sediments by ICP-MS: Comparison of different sample preparation methods

Concentrations of specific metals in sediments are of immense importance in geochemical studies related to diagenetic processes, and quantification can be most reliably performed by ICP-MS after bringing the sample into solution. It is the latter analytical operation that generates a discord as no common sample preparation approach has been yet accepted. To resolve this challenge, we compared different modes of acidic sample digestion (in open and closed systems, applying in the latter case conventional or microwave heating) using a lake sediment reference sample. It was shown that the sample treatment in an autoclave with resistance heating provides the best fit (relative deviation < 7%) to certified values for metals generally used as diagenetic proxies (U, Sm, Nd, Th, La, Sc, Sr, etc.). Furthermore, the thermally convective pressure digestion appears to be more cost-efficient than microwave-assisted digestion (because of lower reagent consumption and cheaper removable PFE liners), an important asset for systematic geochemical mapping. In order to prove the applicability of the developed digestion procedure for different sediments, a marine sample from the East Siberian Sea was analyzed independently in two laboratories and across all representative metals the results acquired were found in good agreement.
Foraging-based optimization of pervasive displays

The article addresses a key challenge in the design of content for pervasive displays: how to engage passers-by who have limited time and attention? To achieve this, we apply a novel approach for computational design of interesting display content using tiled layouts. We present a model of display foraging based on information foraging theory to describe the behavior of a rational but time-limited user looking at a display. Accordingly, our work aims to maximize the information gain for tiled displays. This complex problem is divided into two phases: (1) generating designs of tiled layouts and (2) assigning content options to individual tiles based on what predicted by display foraging. Accordingly, a proof-of-concept system was realized then evaluated computationally and empirically with a control study and field study. The results show that the proposed system can engage significantly more people than typical digital signage.