Reviewerly/exHarmony huggingface dataset:
Multimodal Memetic Framework for low-resolution protein structure prediction

In this paper, we propose a systematic design of evolutionary optimization, namely Multimodal Memetic Framework (MMF), to effectively search the vast complex energy landscape. Our proposed memetic framework is implemented in hierarchical stages with the optimization of each stage performed in parallel in three different states: Exploratory, Exploitative and Central. Each state, with its own set of sub-populations, either explores or exploits by beneficial mixing of potential solutions to direct the search towards a global solution. Instead of implementing identical genetic operators, the proposed approach employs different selection and survival criteria in each state according to their designated task. The Exploratory state employs a knowledge-based initial population generation technique with appropriately tuned genetic operators to guide the search to the “nearest peak”. The Exploitative state fine-tunes the individuals representing different regions by applying a building block based local search. Finally, by utilizing the imbibed knowledge from different peaks, the Central state carries out information-exchange among the highly fit solutions for exploring the undiscovered regions. The information exchange employs a novel non-random parental selection technique to distribute the reproduction opportunity intelligently among the individuals for making cross-over more effective. The method has been tested on a set of various benchmark protein sequences for 2D and 3D lattice models. The experimental results demonstrate the superiority of the proposed method over other state-of-the-art algorithms.
AI for Audience Prediction and Profiling to Power Innovative TV Content Recommendation Services

In contemporary TV audience prediction, outliers are considered mere anomalies in the otherwise cyclical trend and seasonality components that can be used to make predictions. In the ReTV project, we want to provide more accurate audience predictions in order to enable innovative services for TV content recommendation. This paper presents a concept for identifying the source of outliers and factoring TV content categories and the occurrence of events as additional features for training TV audience prediction. We show how this can improve the accuracy of the audience prediction. Finally, we outline how this work could also be combined with AI-enabled audience profiling to power new content recommendation services.
The impact of mobile phone use on farmers’ livelihoods in post-insurgency Northeast Nigeria

The most ubiquitous information and communications technology (ICT) in the hands of the common man today is the mobile phone. Most existing literature on the impact of mobile phones in farming has examined the various components of the farming cycle in isolation, and failed to holistically account for the complex interactions and relationships between these components. In this study, we combine the strengths of the Sustainable Livelihoods Framework (SLF) and Systems Theory (ST) as a theoretical lens to understand the impact of mobile phone use in farming and its developmental contributions on the livelihoods of smallholder farmers in post-insurgency northeast Nigeria. On the basis of empirical data and literature, we develop a qualitative system dynamics model depicting mobile phone use in farming. The main feedback loops show that the greatest bane to farming in the area is insecurity and climate variability. However, better access to information and communications afforded by mobile phones empowers farmers and enables them to overcome these vulnerabilities. The use of feedback loops in analysis provides rigour and depth to the findings due to their ability to demonstrate the interdependencies between system components. We contribute to knowledge and inform practice by extending the understanding of the impact of mobile phone use in farming through system dynmics modelling.
The effect of Electronic Jammers on GPS Signals

Global Positioning System (GPS) has found wide application in many areas of our daily life. However, it has a very week signal making it vulnerable to external attacks like blocking, jamming or spoofing. Jamming is a technique that blocks the GPS from receiving location signals making it nonfunctional. In this paper, several Electronic Jamming methods are introduced and a comparison is done between them to observe which of them has the most jamming effect on the GPS receiver. The methods that will be analyzed are Pulse Jamming (PJ), Spot Jamming (SJ), Barrage Noise Jamming (BNJ) and Sweep Jamming (SWJ). The Jamming signals are modelled using MATLAB and their effect on the GPS receiver is quantified by observing their Bit Error Rate (BER) for different Jamming to Signal ratio.
The Integer Factorization Algorithm With Pisano Period

Large integer factorization is one of the basic issues in number theory and is the subject of this paper. Our research shows that the Pisano period of the product of two prime numbers (or an integer multiple of it) can be derived from the two prime numbers themselves and their product, and we can therefore decompose the two prime numbers by means of the Pisano period of their product. We reduce the computational complexity of modulo operation through the “fast Fibonacci modulo algorithm” and design a stochastic algorithm for finding the Pisano periods of large integers. The Pisano period factorization method, which is proved to be slightly better than the quadratic sieve method and the elliptic curve method, consumes as much time as Fermat's method, the continued fractional factorization method and the Pollard p-1 method on small integer factorization cases. When factoring super-large integers, the Pisano period factorization method has shown as strong performance as subexponential complexity methods; thus, this method demonstrates a certain practicability. We suggest that this paper may provide a completely new idea in the area of integer factorization problems.
Risk Prioritization by Leveraging Latent Vulnerability Features in a Contested Environment

Cyber network defenders face an overwhelming volume of software vulnerabilities. Resource limitations preclude them mitigating all but a small number of vulnerabilities on an enterprise network, so proper prioritization of defensive actions are of paramount importance. Current methods of risk prioritization are predominantly expert-based, and many include leveraging Common Vulnerability Scoring System (CVSS) risk scores. These scores are assigned by subject matter experts according to conventional methods of qualifying risk. Vulnerability mitigation strategies are then often applied in CVSS score order. Our vulnerability assessment system, in contrast, takes a predominantly data-driven approach. In general, we associate a risk metric of vulnerabilities with existence of corresponding exploits. Our assumption is that if an entity has invested time and money to exploit a particular vulnerability, this is a critical gauge of that vulnerability's importance, and hence risk.
A Multi-domain Named Entity Recognition Method Based on Part-of-Speech Attention Mechanism

Named entity recognition is an important and basic work in text mining. To overcome the shortcomings of existing multi-domain named entity recognition methods, a multi-domain named entity recognition method based on the part-of-speech attention mechanism, called BiLSTM-ATTENTION-CRF, was proposed in this paper. The domain dictionary was constructed to represent multi-domain semantic information and the BiLSTM network was used to capture the grammatical and syntactic features, as well as multi-domain semantic features in context information. A part-of-speech attention mechanism was designed to obtain the contribution weight of part-of-speech for entity recognition. Finally, a group of experiments were performed on the multi-domain dataset to compare various fusion strategies of multi-level entity information. The experimental results show that BiLSTM-ATTENTION-CRF has a high precision and recall rate, and can effectively recognizes the multi-domain named entities.
Discovering causal graphs with cycles and latent confounders: An exact branch-and-bound approach

Understanding causal relationships is a central challenge in many research endeavours. Recent research has shown the importance of accounting for feedback (cycles) and latent confounding variables, as they are prominently present in many data analysis settings. However, allowing for cycles and latent confounders makes the structure learning task especially challenging. The constraint-based approach is able to learn causal graphs even over such general search spaces, but to obtain high accuracy, the conflicting (in)dependence information in sample data need to be resolved optimally. In this work, we develop a new practical algorithmic approach to solve this computationally challenging combinatorial optimization problem. While recent advances in exact algorithmic approaches for constraint-based causal discovery build upon off-the-shelf declarative optimization solvers, we propose a first specialized branch-and-bound style exact search algorithm. Our problem-oriented approach enables directly incorporating domain knowledge for developing a wider range of specialized search techniques for the problem, including problem-specific propagators and reasoning rules, and branching heuristics together with linear programming based bounding techniques, as well as directly incorporating different constraints on the search space, such as sparsity and acyclicity constraints. We empirically evaluate our implementation of the approach, showing that it outperforms current state of art in exact constraint-based causal discovery on real-world instances.
Studio Thinking Framework in Higher Education: Exploring Options for Shaping Immersive Experiences Across Virtual Reality/Augmented Reality Curricula

With an increase in the number of colleges and universities using virtual reality and augmented reality integrated programs, specific insight for exploring immersive learning approaches utilizing virtual and augmented reality tools and applications in a variety of disciplines is needed. In some instances, pedagogical approaches for creating immersive learning experiences require a sound conceptual framework for course or content design with emphasis on developing opportunities for higher order thinking in virtual reality/augmented reality educational experiences. Public access data were used in this qualitative-directed content analysis study to examine course goals, objectives, and learning outcomes of 14 U.S. Artificial Intelligence universities as to the potential for developing creative and cognitive skills, as described within the pedagogical framework of Studio Thinking Framework. Findings indicated multiple opportunities for creative and cognitive thinking as Studio Thinking Framework was integrated into these immersive spaces.
Inhomogeneously Stacked RNN for Recognizing Hand Gestures from Magnetometer Data

Hand gesture recognition systems relying on biosignal data exclusively are mandatory for a variety of applications. In general, these systems have to meet requirements such as affordability, reliability, and mobility. In general, surface electrodes are used to obtain signals caused by the contraction of underlying muscles of the forearm. These data are then used to decode hand gestures. In this work, we evaluate the possibility of replacing the electrodes by magnetometers that are cheap and can be easily implemented in mobile devices. We propose an inhomogeneously stacked recurrent neural network for classifying hand gestures given magnetometer data. The experiments reveal that the comparably small network significantly outperforms state-of-the-art hand gesture recognition systems relying on multi-modal data. Furthermore, the proposed network requires significantly shorter windows and enables a quickly responding classification system. Also, the experiments show that the performance of the proposed system does not vary much between subjects and works outstandingly for amputees.
Enhancing Communication and Awareness in Asymmetric Games

Asymmetric games rely on players taking on different gameplay roles, often with associated different views of the game world and different input modalities. This asymmetry can lead to difficulties in establishing common referents as players collaborate. To explore communication and group awareness in asymmetric games, we present a novel asymmetric game, combining a tablet presenting a 2D top-down view and a virtual reality headset providing an immersive 3D view of the gameworld. We demonstrate how communication can be afforded between the two types of views via interaction techniques supporting deixis, shared reference, and awareness. These techniques are bi-directional, enabling an equitable collaboration. A pilot study has shown that players adapt well to the system's two roles, and find the collaborative interaction techniques to be effective.
Performance and design optimization of single-axis multi-position sun-tracking PV panels

In this article, the photovoltaic (PV) and sun-tracking performance of single-axis multiposition sun-tracking PV panels (MP-PV) is investigated based on solar geometry and dependence of PV conversion efficiency on the incident angle of solar rays on PV panels. For such PV systems, the azimuth angle of the PV panels is adjusted daily M times from an eastward direction in the morning to a westward direction in the afternoon by rotating the PV panels about the inclined north–south axis (INSA) to ensure that the projected incident angle (PIA) of solar rays on the plane perpendicular to the INSA is always less than the angle θa, the specified maximum PIA. Results show that the annual average sun-tracking efficiency (ηst) and the annual electricity generation (AEG) of the MP-PV system increase as M increases, but this increase is less than 1% when M &amp;gt; 5. The optimal θa of the MP-PV system for maximizing AEG and the ratio (Cp,MP-0) of AEG of the optimized MP-PV system to that of a similar fixed PV system are highly dependent on the annual average atmospheric clearness index (Kt) but only slightly dependent on the site latitude (λ). For an MP-PV with a tilt angle (β) of the INSA that is fixed yearly, the AEG of 3P-, 5P-, and 7P-PV systems is about 92%, 94%, and 95%, respectively, of that obtained from a similar dual-axis tracking PV panel (2A-PV). However, for an MP-PV whose β is adjusted four times a year at three tilts, the AEG of 3P-, 5P-, and 7P-PV systems is about 96%, 98%, and 99%, respectively, of that of a similar 2A-PV system. Empirical correlations for quick estimation of the optimal θa and Cp,MP-0 of the MP-PV optimized for maximizing AEG are suggested and examined based on monthly global radiation on the horizon at 30 sites around China.
Governance Challenges for European Cybersecurity Policies: Stakeholder Views

We outline possible approaches to cybersecurity governance and compare them against the EU proposal of a Network of Competence Centers, which should manage all European cybersecurity funding.We test such policy proposal against the opinions of key stakeholders (senior administrators from European Agencies, Data Protection Authorities, CISOs, managers, and academics).
Blockchain-based accountability for multi-party oblivious RAM

Recently, oblivious random access machine (ORAM) has been widely used to prevent privacy leakage from user’s access pattern. However, in multi-user scenarios, the obliviousness property of ORAM facilitates the malicious data modification by unauthorized users, which brings a new security challenge of user accountability to ORAM applications. Moreover, based on our observations, existing user accountability schemes for multi-user ORAM induce the extremely unacceptable overhead in both time and storage. What is worse, it is still inherent the traditional cloud accountability problem that the untrusted cloud server may have misbehavior on storing the outsourced data. In this paper, we focus on the issue that how to do accountability for both malicious users and untrusted cloud server without the independent trusted third party server. To address the above problem, we design and implement a Traceable Oblivious RAM, or T-ORAM for short, a cryptographic system that protects the privacy of users and the integrity of outsourced data based on group signatures. It can detect malicious users quickly by utilizing the traceability property of group signatures, and cost less storage overhead comparing with the existing solutions. Then, we further propose a more secure solution of Blockchain-based Traceable Oblivious RAM (BT-ORAM). Specifically, by introducing the blockchain technology, BT-ORAM can detect the malicious behavior from both malicious users and untrusted cloud server. BT-ORAM is the first accountability work for multi-user ORAM that deal with both malicious users and the untrusted cloud server. Finally, security analysis and experimental results show that our method outperforms the state-of-the-art accountability work for oblivious RAM, S-GORAM, in both security and performance.
New Features of Authentication Scheme for the IoT

With the development of cloud computing and artificial intelligence, the Internet of Things (IoT) products are gradually entering every corner of our lives and changing the way we live. Yet traditional computer authentication protocol cannot be used directly in the Internet of things devices, because of its limitations (e.g., simple CPU structure, low storage capacity, weak computing power, etc.). Therefore, the authentication protocol for the Internet of Things is an essential ingredient for securing the internet of things.
A Novel Robust Feature Descriptor for Multi-Source Remote Sensing Image Registration

Non-linear radiation difference (NRD) will lead to the corresponding features cannot be mapped one by one, so the traditional image feature matching methods based on intensity or gradient fail to be directly applied to the multisource remote sensing image registration. In this paper, a new robust feature descriptor is proposed, which has the invariance of radiation, scale and rotation. The nonlinear diffusion function which is insensitive to the radiation difference is used to construct the scale space so that the descriptors can be used in images with different resolutions. A pixel-by-pixel local phase congruency algorithm is used to extract the corresponding points, and then the features are described by means of rotation invariance description. Feature matching is completed based on feature vector constructed by the descriptor, thus to realize image registration. In the experimental part, three kinds of multisource remote sensing images with large radiation differences were used to test the descriptors. The results showed that the proposed method effectively extracted the corresponding features, and achieved the best effect in the quantitative evaluation of image registration.
Using stream-side groundwater discharge for geochemical exploration in mountainous terrain

Groundwater chemistry has been predominantly used in geochemical exploration studies to identify mineralized targets concealed under transported cover in areas with gentle topography. Another potentially valuable application that has received little attention is using groundwater chemistry to identify deposits concealed within mountain ridges. A number of geochemical exploration studies have employed surface water chemistry in hilly and mountainous terrain and have demonstrated mixed success. However, groundwater chemistry is potentially a more refined and powerful tool because groundwater sample locations can be selected to target a specific hillslope zone, whereas surface water integrates the geochemical signature of the entire watershed area above the sample site. In this study, we propose an approach that utilizes shallow groundwater samples collected from near-stream, hand-installed sampling points. We tested this approach in Handcart Gulch, an unmined catchment in the Montezuma Mining District, Colorado, USA, where previous studies suggested the possible presence of a concealed mineral occurrence. Seventeen near-stream mini-boreholes were drilled in a stream-parallel 3.3-km-long transect to depths of ~2 m in ferricrete (iron-oxide cemented colluvium and alluvium) using a handheld rock-coring drill. Groundwater in the mini-boreholes was sampled for major and trace element chemistry, as well as environmental tracers including stable water isotopes, dissolved noble gases, helium isotopes, and tritium, to identify possible variations in groundwater age and recharge conditions/sources. Chemistry data delineate a well-defined 900-m-wide zone of elevated SO4, Cu, and other metal concentrations on the east side of the stream. In this zone, Cu concentrations up to 2670 μg/L are 1–3 orders of magnitude greater than concentrations in upstream and downstream areas. Environmental tracer data show no corresponding anomalies in groundwater age or recharge conditions/sources within the zone, suggesting that the source of the elevated metals is a change in bedrock mineralogy as opposed to other hydrologic factors. The groundwater samples therefore successfully identify and refine the location of a potential concealed high-Cu mineral occurrence in the watershed, demonstrating that near-stream groundwater discharge may be a valuable medium for geochemical exploration in mountainous areas.
Comparison of Wavelet and RID-Rihaczek Based Methods for Phase-Amplitude Coupling

Oscillatory phenomena and linear/nonlinear interactions across different oscillatory frequencies are commonly seen between the elements of complex systems. One particular form of interaction that is widely encountered is the modulation of the amplitude of broadband high-frequency oscillations by the phase of low-frequency oscillations, defined as phase-amplitude coupling (PAC). Conventional methods for assessing PAC mostly rely on the Hilbert transform. Previous studies have shown that this method produces biased or spurious PAC estimates as it is highly dependent on the parameters of the bandpass filter used to extract narrowband signals from broadband signals. Recently, wavelet-based methods have been proposed to address some of these limitations. In this letter, we provide a thorough comparison between wavelet-based PAC measures and our recently introduced Reduced Interference Rihaczek (RID-Rihaczek) distribution based PAC both analytically and through simulations. The proposed method is shown to yield higher frequency resolution PAC estimates compared to wavelet-based methods.
Dynamic Block Sparse Reparameterization of Convolutional Neural Networks

Sparse neural networks are efficient in both memory and compute when compared to dense neural networks. But on parallel hardware such as GPU, sparse neural networks result in small or no runtime performance gains. On the other hand, structured sparsity patterns like filter, channel and block sparsity result in large performance gains due to regularity induced by structure. Among structured sparsities, block sparsity is a generic structured sparsity pattern with filter and channel sparsity being sub cases of block sparsity. In this work, we focus on block sparsity and generate efficient block sparse convolutional neural networks using our approach DBSR (Dynamic block sparse reparameterization). Our DBSR approach, when applied on image classification task over Imagenet dataset, decreases parameters and FLOPS of ResneXt50 by a factor of 2x with only increase of 0.48 in Top-1 error. And when extended to the task of semantic segmentation, our approach reduces parameters and FLOPS by 30% and 20% respectively with only 1% decrease in mIoU for ERFNet over Cityscapes dataset. To ease developments in this line of work, we open sourced our code on github (https://github.com/idharmateja/bsnn).