Reviewerly/exHarmony huggingface dataset:
Generic and Practical Key Establishment from Lattice

In this work, we abstract some key ingredients in previous key establishment and public-key encryption schemes from LWE and its variants. Specifically, we explicitly formalize the building tool, referred to as key consensus (KC) and its asymmetric variant AKC. KC and AKC allow two communicating parties to reach consensus from close values, which plays the fundamental role in lattice-based cryptography. We then prove the upper bounds on parameters for any KC and AKC, which reveal the inherent constraints on the parameters among security, bandwidth, error probability, and consensus range. As a conceptual contribution, this simplifies the design and analysis of these cryptosystems in the future. Guided by the proved upper bounds, we design and analyze both generic and highly practical KC and AKC schemes, which are referred to as OKCN and AKCN respectively for presentation simplicity. We present a generic protocol structure for key establishment from learning with rounding (LWR), which can be instantiated with either KC or AKC. We then provide an analysis breaking the correlation between the rounded deterministic noise and the secret, and design an algorithm to calculate the error probability numerically. When applied to LWE-based key establishment, OKCN and AKCN can result in more practical or well-balanced schemes, compared to existing LWE-based protocols in the literature.
Non-linearity in gaze holding: Experimental results and possible mechanisms

Cerebellar impairment may cause deficits in horizontal gaze holding, leading to centrifugal gaze-evoked nystagmus during fixation of eccentric targets. Healthy individuals show a weak drift leading to physiological nystagmus only at large gaze angles. These drifts are due to imperfect memory of the neural circuitry generating the eye position signals by integration of velocity signals. The cerebellum plays a crucial role in reducing the "leakiness" of this neural integrator. This neural integrator has been traditionally modeled as a first order low-pass filter, implying a linear relation between drift velocity and eye eccentricity. Evidences of a non-linear behavior, however, can be found in the literature. In a recent series of papers we showed that the eye drift velocity (V) can be descriptively modeled as a tangent function of gaze eccentricity (P) with the following equation: V=k2/k1tan(k1P). Notably, the two parameters have distinct roles: k1 regulates the rate of compression of the tangent, exclusively determining the non-linearity; k2 is a pure scaling factor. This descriptive model robustly fitted the data of healthy individuals both at baseline (n=50) and following transient cerebellar impairment induced by controlled amounts of alcohol [blood alcohol content 0.06% (n=15) and 0.1% (n=15)] and of patients with chronic cerebellar impairment of various origin (n=20). Interestingly, alcohol selectively changed the scaling factor k2, evidencing that an overall, transient cerebellar impairment does not impact the non-linear behavior. Patients with cerebellar degeneration, on the other hand, showed a change in both k1 and k2, implying a role of the cerebellum in limiting the range of eye positions where the non-linearity becomes relevant. Non-linearity has been reported in literature for both the neural integrator and the eye plant. While previous models using a neural network attempted to reproduce the non-linear behavior of the brainstem, we propose a block diagram reproducing the observed PV tangent relation by introducing a position dependency in the parameters of the cerebellar feedback loop.
Quantum dialogue protocol based on Grover’s search algorithms

Among many classic heuristic search algorithms, the Grover quantum search algorithm (QSA) can play a role of secondary acceleration. Based on the properties of the two-qubit Grover QSA, a quantum dialogue (QD) protocol is proposed. In addition, our protocol also utilizes the unitary operations and single-particle measurements. The transmitted quantum state (except for the decoy state used for detection) can transmit two-bits of security information simultaneously. Theoretical analysis shows that the proposed protocol has high security.
Minimizing Data Center Uninterruptable Power Supply Overload by Server Power Capping

Recent analyses show an increase in data center (DC) service downtime due to the increase in power supply outages caused by overloaded uninterruptable power supplies (UPSs). As a solution to the UPS overload problem, this letter presents a developed power distributor manager (PDM) that caps the power consumption of the dc by enforcing a power restriction on the running servers based on a UPS power minimization cost function. The PDM proposed controls the power consumption per UPS in a dc by means of processor time-sharing and dynamic frequency scaling, which eliminates UPS overloading problems, and thus reduces server outages.
A single feature for human activity recognition using two-dimensional acoustic array

Human activity recognition is widely used in many fields, such as the monitoring of smart homes, fire detecting and rescuing, hospital patient management, etc. Acoustic waves are an effective method for human activity recognition. In traditional ways, one or a few ultrasonic sensors are used to receive signals, which require many feature quantities of extraction from the received data to improve recognition accuracy. In this study, we propose an approach for human activity recognition based on a two-dimensional acoustic array and convolutional neural networks. A single feature quantity is utilized to characterize the sound of human activities and identify those activities. The results show that the total accuracy of the activities is 97.5% for time-domain data and 100% for frequency-domain data. The influence of the array size on recognition accuracy is discussed, and the accuracy of the proposed approach is compared with traditional recognition approaches such as k-nearest neighbor and support vector machines where it outperformed them.
Towards Semantic Integration of Federated Research Data

Digitization of the research (data) lifecycle has created a galaxy of data nodes that are often characterized by sparse interoperability. With the start of the European Open Science Cloud in November 2018 and facing the upcoming call for the creation of the National Research Data Infrastructure (NFDI), researchers and infrastructure providers will need to harmonize their data efforts. In this article, we propose a recently initiated proof-of-concept towards a network of semantically harmonized Research Data Management (RDM) systems. This includes a network of research data management and publication systems with semantic integration at three levels, namely, data, metadata, and schema. As such, an ecosystem for agile, evolutionary ontology development, and the community-driven definition of quality criteria and classification schemes for scientific domains will be created. In contrast to the classical data repository approach, this process will allow for cross-repository as well as cross-domain data discovery, integration, and collaboration and will lead to open and interoperable data portals throughout the scientific domains. At the joint lab of L3S research center and TIB Leibniz Information Center for Science and Technology in Hanover, we are developing a solution based on a customized distribution of CKAN called the Leibniz Data Manager (LDM). LDM utilizes the CKAN’s harvesting functionality to exchange metadata using the DCAT vocabulary. By adding the concept of semantic schema to LDM, it will contribute to realizing the FAIR paradigm. Variables, their attributes and relationships of a dataset will improve findability and accessibility and can be processed by humans or machines across scientific domains. We argue that it is crucial for the RDM development in Germany that domain-specific data silos should be the exception, and that a semantically-linked network of generic and domain-specific research data systems and services at national, regional, and organization levels should be promoted within the NFDI initiative.
Screen corner detection using polarization camera for cross-ratio based gaze estimation

Eye tracking, which measures line of sight, is expected to advance as an intuitive and rapid input method for user interfaces, and a cross-ratio based method that calculates the point-of-gaze using homography matrices has attracted attention because it does not require hardware calibration to determine the geometric relationship between an eye camera and a screen. However, this method requires near-infrared (NIR) light-emitting diodes (LEDs) attached to the display in order to detect screen corners. Consequently, LEDs must be installed around the display to estimate the point-of-gaze. Without these requirements, cross-ratio based gaze estimation can be distributed smoothly. Therefore, we propose the use of a polarization camera for detecting the screen area reflected on a corneal surface. The reflection area of display light is easily detected by the polarized image because the light radiated from the display is polarized linearly by the internal polarization filter. With the proposed method, the screen corners can be determined without using NIR LEDs, and the point-of-gaze can be estimated using the detected corners on the corneal surface. We investigated the accuracy of the estimated point-of-gaze based on a cross-ratio method under various illumination and display conditions. Cross-ratio based gaze estimation is expected to be utilized widely in commercial products because the proposed method does not require infrared light sources at display corners.
A Flexible Hybrid BCH Decoder for Modern NAND Flash Memories Using General Purpose Graphical Processing Units (GPGPUs)

Bose-Chaudhuri-Hocquenghem (BCH) codes are broadly used to correct errors in flash memory systems and digital communications. These codes are cyclic block codes and have their arithmetic fixed over the splitting field of their generator polynomial. There are many solutions proposed using CPUs, hardware, and Graphical Processing Units (GPUs) for the BCH decoders. The performance of these BCH decoders is of ultimate importance for systems involving flash memory. However, it is essential to have a flexible solution to correct multiple bit errors over the different finite fields (GF(2 m )). In this paper, we propose a pragmatic approach to decode BCH codes over the different finite fields using hardware circuits and GPUs in tandem. We propose to employ hardware design for a modified syndrome generator and GPUs for a key-equation solver and an error corrector. Using the above partition, we have shown the ability to support multiple bit errors across different BCH block codes without compromising on the performance. Furthermore, the proposed method to generate modified syndrome has zero latency for scenarios where there are no errors. When there is an error detected, the GPUs are deployed to correct the errors using the iBM and Chien search algorithm. The results have shown that using the modified syndrome approach, we can support different multiple finite fields with high throughput.
Exploring simple neural network architectures for eye movement classification

Analysis of eye-gaze is a critical tool for studying human-computer interaction and visualization. Yet eye tracking systems only report eye-gaze on the scene by producing large volumes of coordinate time series data. To be able to use this data, we must first extract salient events such as eye fixations, saccades, and post-saccadic oscillations (PSO). Manually extracting these events is time-consuming, labor-intensive and subject to variability. In this paper, we present and evaluate simple and fast automatic solutions for eye-gaze analysis based on supervised learning. Similar to some recent studies, we developed different simple neural networks demonstrating that feature learning produces superior results in identifying events from sequences of gaze coordinates. We do not apply any ad-hoc post-processing, thus creating a fully automated end-to-end algorithms that perform as good as current state-of-the-art architectures. Once trained they are fast enough to be run in a near real time setting.
Cryptanalysis of ID based Proxy-Blind signature scheme over lattice

In this article we review the ID Based Proxy-Blind signature scheme proposed by Zhang et al. and we give an attack on the scheme revealing the master secret key used in the construction of their scheme.
Retrieval-Augmented Convolutional Neural Networks Against Adversarial Examples

We propose a retrieval-augmented convolutional network (RaCNN) and propose to train it with local mixup, a novel variant of the recently proposed mixup algorithm. The proposed hybrid architecture combining a convolutional network and an off-the-shelf retrieval engine was designed to mitigate the adverse effect of off-manifold adversarial examples, while the proposed local mixup addresses on-manifold ones by explicitly encouraging the classifier to locally behave linearly on the data manifold. Our evaluation of the proposed approach against seven readilyavailable adversarial attacks on three datasets-CIFAR-10, SVHN and ImageNet-demonstrate the improved robustness compared to a vanilla convolutional network, and comparable performance with the state-of-the-art reactive defense approaches.
ARCH-COMP19 Category Report: Artificial Intelligence and Neural Network Control Systems (AINNCS) for Continuous and Hybrid Systems Plants

This report presents the results of a friendly competition for formal verification of continuous and hybrid systems with artificial intelligence (AI) components. Specifically, machine learning (ML) components in cyber-physical systems (CPS), such as feedforward neural networks used as feedback controllers in closed-loop systems are considered, which is a class of systems classically known as intelligent control systems, or in more modern and specific terms, neural network control systems (NNCS). For future iterations, we more broadly refer to this category as AI and NNCS (AINNCS). The friendly competition took place as part of the workshop Applied Verification for Continuous and Hybrid Systems (ARCH) in 2019. In the first edition of this AINNCS category at ARCH-COMP, three tools have been applied to solve five different benchmark problems, (in alphabetical order): NNV, Sherlock, and Verisig. This report is a snapshot of the current landscape of tools and the types of benchmarks for which these tools are suited. Due to the diversity of problems and that this is the first iteration of this category, we are not ranking tools in terms of performance, yet the presented results probably provide the most complete assessment of tools for the safety verification of NNCS.
The Missing Framework for Adaptation of Agile Software Development Projects

This paper focuses on the study of the Software Development Life Cycle (SDLC) implementation of software development project in an insurance company name “Preeminence” in Jakarta, Indonesia. Initial findings in this corporation reveal that several key people who had using Agile SDLC, mentioned they need some accommodation when implementing Agile during a project. Previous studies had introduced the additional process to help a project using Agile. However, studies that explored the importance of experience-based learning process during Agile implementation are noticeably lacking. The needs of the experience-based learning process to explore the learning process as a factor for adaptation of Agile implementation, which hopes to increase the success of a project when using the Agile framework. Methods carried out in this research are based on archival research strategy by examining extant research on Agile adaptation for software projects implementation which published from 1999. Many studies focus on Agile implementation related to content and context to put additional process as an accommodation to easier a project using Agile. However, only a few discussed the process, particularly the learning process which is mean that research gap exists in the area of Agile implementation relates to the learning process.
A Deeper Look into ‘Deep Learning of Aftershock Patterns Following Large Earthquakes’: Illustrating First Principles in Neural Network Physical Interpretability

In the last years, deep learning has solved seemingly intractable problems, boosting the hope to find (approximate) solutions to problems that now are considered unsolvable. Earthquake prediction - a recognized moonshot challenge - is obviously worthwhile exploring with deep learning. Although encouraging results have been obtained recently, deep neural networks (DNN) may sometimes create the illusion that patterns hidden in data are complex when this is not necessarily the case. We investigate the results of De Vries et al. [Nature, vol. 560, 2018] who defined a DNN of 6 hidden layers with 50 nodes each, and with an input layer of 12 stress features, to predict aftershock patterns in space. The performance of their DNN was assessed using ROC with AUC = 0.85 obtained. We first show that a simple artificial neural network (ANN) of 1 hidden layer yields a similar performance, suggesting that aftershock patterns are not necessarily highly abstract objects. Following first principle guidance, we then bypass the elastic stress change tensor computation, making profit of the tensorial nature of neural networks. AUC = 0.85 is again reached with an ANN, now with only two geometric and kinematic features. Not only seems deep learning to be “excessive” in the present case, the simpler ANN streamlines the process of aftershock forecasting, limits model bias, and provides better insights into aftershock physics and possible model improvement. Complexification is a controversial trend in all of Science and first principles should be applied wherever possible to gain physical interpretations of neural networks.
Participatory design with teens

Design requirements can be gathered through a variety of ways; however, engaging teen audiences in design process can be challenging. We present a novel method for engaging teens in design through a social robot design challenge. Groups of teens participated in the challenge to prototype a social robot that would live in their high school and help address stress, a persistent and pervasive problem for this age group. In this paper, we present our methods and share preliminary findings.
A novel information hiding scheme based on social networking site viewers’ public comments

In earlier reported chat-based steganography techniques, only direct communication between the sender and receiver was considered which can raise suspicion for further investigations by an attacker. If the adversary traces the communication between the two, he may investigate to find out the data shared during communication, can alter the content, or destroy it. In such a situation, a steganography system is required which can bypass adverse attention through indirect communication instead of direct communication. In this paper, we propose a new framework to camouflage hidden communication between the transceivers. The framework is based on online social networking and video-sharing website's communication. The secret message is communicated using comment features of these social networking and video-sharing websites in a totally new way compared with earlier approaches. Stego-comment is generated by performing synonym-substitution based on Huffman code on the auto-summarized previous comments of a chosen post. The stego-comment does not raise any suspicion to the intermediary by being technically similar to other comments which may not have any hidden message, which makes this method successful. Similarity indices of the stego-comment with respect to other comments are calculated using a String Similarity Tool which uses fuzzy comparison functions between strings for one sample dataset. The proposed method gives average bit-rate (as a measure of embedding efficiency) of 9.04 bits per sentence for a set of five different case studies which is high compared with average bit rates found in the literature. This communication cannot be traced between the communicators through traffic analysis in any easy way due to the absence of any direct communication.
The application research of application decision model based on internet of things in enterprise supply chain management

There are many problems in the supply chain management of Chinese enterprises, which restricts the development of enterprises and the improvement of benefits. In order to improve the decision-making ability of Internet of Things (IoT) applications in enterprise supply chain management, the collabor ative filtering algorithm is optimized. The algorithm determines the collaboration type according to the nature of the optimization target, and calculates the user similarity based on the user browsing behavior, so as to seek the recommendation function of the user to best evaluate the individual. According to the two objectives of the shortest time and the highest overall satisfaction in emergency dispatching in enterprise supply chain management, a two-level planning model of commodity scheduling is constructed, the time function of the supply chain delivery process calculated. The simulation results of the model show that the demand forecasting algorithm based on neighborhood rough set and GA-SVM is used to predict the demand of chain retail supply chain, which achieves high prediction accuracy. The maximum error is 5.71%, the minimum error is 0.60. The average error is %, which is 2.84%. The research in this paper has implications for enterprise application of IoT in supply chain management. The use of decision-making model can greatly improve the operational efficiency of the supply chain.
Exploring Tangible Interaction and Diegetic Feedback in an AR Math Game for Children

Augmented Reality is becoming an emerging trend in the development of play-and-learn experience and increasingly accessible to children. However, there is a lack of understanding of how to design AR games to effectively improve children's learning experience, especially with respect to the novel interaction and representation paradigms that AR affords. In this study, we describe the design and the implementation process of different interaction types (screen-touch and tangible interaction) and feedback mechanisms (non-diegetic feedback and diegetic feedback) in an AR math game for children aged 7 and 8. We report the insights based on our current prototypes and discuss the design implications for our future work.
Improving human scoring of prosody using parametric speech synthesis

This paper proposes a method that utilizes parametric speech synthesis to improve human scoring of non-native speaker utterances. Instead of assessing each prosodic feature by directly listening to the utterance itself, in order to focus only on the target prosodic feature, the unassessed features are substituted with those of the native speakers. We used parametric speech synthesis to generate the features for substitution. In this study, HMM-based speech synthesis from an average model of native speakers was utilized. The experimental result shows that the proposed method can improve scoring reliability, which is confirmed by an increase in the inter-rater correlation. We also build an automatic pronunciation evaluation system trained from non-native speech databases with scores given by either the conventional and proposed methods, and compare the performance of the systems. The result shows that the predicted pronunciation scores matched the human-rated scores; the human-machine correlation produced a score of 0.87, while the conventional scoring method produced a score of 0.74.