Reviewerly/exHarmony huggingface dataset:
The merits of using Ethereum MainNet as a Coordination Blockchain for Ethereum Private Sidechains

Abstract A Coordination Blockchain is a blockchain that coordinates activities of multiple private blockchains. This paper discusses the pros and cons of using Ethereum MainNet, the public Ethereum blockchain, as a Coordination Blockchain. The requirements Ethereum MainNet needs to fulfil to perform this role are analyzed within the context of Ethereum Private Sidechains, a private blockchain technology which allows many blockchains to be operated in parallel, and allows atomic crosschain transactions to execute across blockchains. We found that Ethereum MainNet is best suited to storing long-term static data that need to be widely available, such as the Ethereum Registration Authority information. However, due to Ethereum MainNet’s probabilistic finality, it is not well suited to information that needs to be available and acted upon immediately, such as the Sidechain Public Keys and Atomic Crosschain Transaction state information that need to be accessible prior to the first atomic crosschain transaction being issued on a sidechain. Although this paper examined the use of Ethereum MainNet as a Coordination Blockchain within reference to Ethereum Private Sidechains, the discussions and observations of the typical tasks a Coordination Blockchain may be expected to perform are applicable more widely to any multi-blockchain system.
Jointly Optimizing the IT and Cooling Systems for Data Center Energy Efficiency based on Multi-Agent Deep Reinforcement Learning

With the development and application of cloud computing, the increasing amount of data centers has resulted in huge energy consumption and severe environmental problems. Improving the energy efficiency of data centers has become a necessity. In this paper, in order to improve the energy efficiency of both IT and cooling systems for data centers, a model-free deep reinforcement learning (DRL) based joint optimization approach MACEEC is proposed. To improve the cooperation between IT and cooling system while handling the high-dimensional state space and the large hybrid discrete-continuous action space, a hybrid AC-DDPG multi-agent structure is developed. A scheduling baseline comparison method is proposed to enhance the stability of the architecture. And an asynchronous control optimization algorithm is developed to solve the different responding time issue between IT and cooling system. Experiments based on real-world traces data validate that MACEEC can effectively improve the overall energy efficiency for data centers while ensuring the temperature constraint and service quality compared with existing joint optimization approaches.
Rationing bytes: managing demand for digital forensic examinations

With the growing sophistication and prevalence of digital devices such as mobile phones, computers, tablets, sat-navs, and domestic appliances, the extraction, analysis and interpretation of digital data has become increasingly central to intelligence gathering and criminal proceedings.However, the very extent of data available today challenges the ability of police agencies to turn seized devices into useful evidence.To date, most social science scholarship about forensics has concentrated on DNA profiling and its societal and ethical issues.In contrast, other forensic fields, including digital forensics, have had little analytical scrutiny.Based on unprecedented access to a forensic collaboration in England, this study addresses the question: In conditions of constrained resources, how do police agencies manage the insatiable demand for digital examinations?In doing so, we bring rationing classification schemes from healthcare studies into the field of criminology in order to characterise the techniques for reconciling demand with capacity.As detailed, formal attempts to ration demand are confounded by informal practices and procedures that can impact on the capacity of the workforce and the speed with which cases are processed.In addition, the rationing of digital devices has significant consequences for the definition and distribution of skills and expertise across criminal justice agencies.
A Novel TIGFET-based DFF Design for Improved Resilience to Power Side-Channel Attacks

Side-channel attacks (SCAs) represent a significant security threat, and aim to reveal otherwise secret data by analyzing a relevant circuit's behavior, e.g., its power consumption. While all circuit components are potential power side channels, D-flip-flops (DFFs) are often the primary source of information leakage to an SCA. This paper proposes a DFF design based on the three-independent-gate field-effect transistor (TTGFET) that reduces side-channel vulnerabilities of sequential circuits. Notably, we find that the I-V characteristics of the TIGFET itself leads to inherent side-channel resilience, which in turn enables simpler and more efficient cryptographic hardware. Our proposed design is based on a prior TIGFET-based true single-phase clock (TSPC) DFF design, which offers high performance and reduced area. More specifically, our modified TSPC (mTSPC) design exploits the symmetric I-V characteristics of TIGFETs, which results in pull-up and pull-down currents that are nearly identical. When combined with additional circuit modifications (made possible by the unique characteristics of the TIGFET), the mTSPC circuit draws almost the same amount of supply currents under all possible input transitions (less than 1% variation for different transitions), which can in turn mask information leakage. Using a 10nm TIGFET technology model, simulation results show that the proposed TIGFET-based DFF circuit leads to decreased power consumption (up to 96.9% when compared to the prior secured designs), has a low delay (15.2 ps), and employs only 12 TIGFET devices. Furthermore, an 8-bit S-box whose output is sampled by a group of eight mTSPC DFFs was simulated. A correlation power analysis attack on the simulated S-box with 256 power traces shows that the key is not revealed, which confirms the SCA resiliency of the proposed DFF design.
On constructions and properties of (n, m)-functions with maximal number of bent components

For any positive integers $$n=2k$$ and m such that $$m\ge k,$$ in this paper we show that the maximal number of bent components of any (n, m)-function is equal to $$2^{m}-2^{m-k},$$ and for those attaining the equality, their algebraic degree is at most k. It is easily seen that all (n, m)-functions of the form $$G(x)=(F(x),0),$$ with F(x) being any vectorial bent (n, k)-function, have the maximal number of bent components. Those simple functions G are called trivial in this paper. We show that for a power (n, n)-function, it has the maximal number of bent components if and only if it is trivial. We also consider the (n, n)-function of the form $$F(x)=xh(\mathrm{Tr}^{n}_{e}(x)),$$ where $$h: \mathbb {F}_{2^{e}} \rightarrow \mathbb {F}_{2^{e}},$$ and show that F has the maximal number of bent components if and only if $$e=k,$$ and h is a permutation over $$\mathbb {F}_{2^{e}}.$$ It essentially shows that all previously known nontrivial functions with maximal number of bent components are subclasses of the class described by F. Based on the Maiorana–McFarland class, we present constructions of large numbers of (n, m)-functions with maximal number of bent components for any integer m in bivariate representation. We also determine the differential spectra and Walsh spectra of the constructed functions. It turns out that our constructions can also provide new plateaued vectorial functions.
Efficient Implementations of Echo State Network Cross-Validation

Cross-Validation (CV) is still uncommon in time series modeling. Echo State Networks (ESNs), as a prime example of Reservoir Computing (RC) models, are known for their fast and precise one-shot learning, that often benefit from good hyper-parameter tuning. This makes them ideal to change the status quo. We discuss CV of time series for predicting a concrete time interval of interest, suggest several schemes for cross-validating ESNs and introduce an efficient algorithm for implementing them. This algorithm is presented as two levels of optimizations of doing k-fold CV. Training an RC model typically consists of two stages: (i) running the reservoir with the data and (ii) computing the optimal readouts. The first level of our optimization addresses the most computationally expensive part (i) and makes it remain constant irrespective of k. It dramatically reduces reservoir computations in any type of RC system and is enough if k is small. The second level of optimization also makes the (ii) part remain constant irrespective of large k, as long as the dimension of the output is low. We discuss when the proposed validation schemes for ESNs could be beneficial, three options for producing the final model and empirically investigate them on six different real-world datasets, as well as do empirical computation time experiments. We provide the code in an online repository. Proposed CV schemes give better and more stable test performance in all the six different real-world datasets, three task types. Empirical run times confirm our complexity analysis. In most situations, k-fold CV of ESNs and many other RC models can be done for virtually the same time and space complexity as a simple single-split validation. This enables CV to become a standard practice in RC.
Attention-aware scoring learning for person re-identification

Person re-identification (re-ID) refers to matching people across multiple camera views at different times and locations. The challenge is mainly about the huge variance of visual appearance of a specific pedestrian owing to pose variations, illumination changes and various camera-styles. In this paper, an Attention-Aware Scoring Learning (AASL) framework is proposed to address these issues. The proposed AASL framework consists of two attention modules and a score learning head. Specifically, the two modules, Spatial Attention Grid and Channel Attention Grid, embedded respectively in the shallow and deep layer in the convolutional neural network, are put forward to help the network learn the most discriminative visual features. Furthermore, an adaptive module termed score learning head is proposed to optimize the parameters of the attention modules. The present paper carries out extensive experiments on three large-scale datasets, including Market-1501, DukeMTMC-reID and CUHK03, after which it is found that our Attention-Aware Scoring Learning framework significantly outperforms the baseline model and achieves a competitive performance compared with the state-of-the-art person re-ID methods.
Modeling frequency and severity of claims with the zero-inflated generalized cluster-weighted models

To facilitate applications in general insurance, some extensions are proposed to cluster-weighted models (CWMs). First, we extend CWMs to have generalized cluster-weighted models (GCWMs) by allowing modeling of non-Gaussian distribution of the continuous covariates, as they frequently occur in insurance practice. Secondly, we introduce a zero-inflated extension of GCWM (ZI-GCWM) for modeling insurance claims data with excess zeros coming from heterogeneous sources. Additionally, we give two expectation–optimization (EM) algorithms for parameter estimation given in the proposed models. An appropriate simulation study shows that, for various settings and in contrast to the existing mixture-based approaches, both extended models perform well. Finally, a real data set based on French auto-mobile policies is used to illustrate the application of the proposed extensions.
Simulation System of Electric-Powered Wheelchairs for Training Purposes

For some people with severe physical disabilities, the main assistive device to improve their independence and to enhance overall well-being is an electric-powered wheelchair (EPW). However, there is a necessity to offer users EPW training. In this work, the Simcadrom is introduced, which is a virtual reality simulator for EPW driving learning purposes, testing of driving skills and performance, and testing of input interfaces. This simulator uses a joystick as the main input interface, and a virtual reality head-mounted display. However, it can also be used with an eye-tracker device as an alternative input interface and a projector to display the virtual environment (VE). Sense of presence, and user experience questionnaires were implemented to evaluate this version of the Simcadrom in addition to some statistical tests for performance parameters like: total elapsed time, path following error, and total number of commands. A test protocol was proposed and, considering the overall results, the system proved to simulate, very realistically, the usability, kinematics, and dynamics of a real EPW in a VE. Most subjects were able to improve their EPW driving performance in the training session. Furthermore, all skills learned are feasible to be transferred to a real EPW.
p for political: Participation Without Agency Is Not Enough

Participatory Design's vision of democratic participation assumes participants' feelings of agency in envisioning a collective future. But this assumption may be leaky when dealing with vulnerable populations. We reflect on the results of a series of activities aimed at supporting agentic-future-envisionment with a group of sex-trafficking survivors in Nepal. We observed a growing sense among the survivors that they could play a role in bringing about change in their families. They also became aware of how they could interact with available institutional resources. Reflecting on the observations, we argue that building participant agency on the small and personal interactions is necessary before demanding larger Political participation. In particular, a value of PD, especially for vulnerable populations, can lie in the process itself if it helps participants position themselves as actors in the larger world.
Outlier Detection using Clustering Techniques – K-means and K-median

Outlier detection in data mining helps to identify and dispose of irregular data objects from the given dataset. In this work, various clustering and outlier techniques are initially reviewed. Secondly, the problem is identified using the K-means clustering algorithm for outlier detection Finally, an optimal solution is proposed from K-median clustering and compared it with the weight-based K-mean grouping. To test the algorithm of outlier detection, the real dataset is considered and the above process is simulated in MATLAB. The experimental result is compared with the proposed K-median weight-based approach and has the maximum weight value in the frame.
Mind the map? Mapping the academic, citizen and professional stakeholder views on buildings and heating behaviour in Spain

The residential building sector is a major driver of current and future energy consumption and associated CO2 emissions. The main use of energy by households is for heating. Consumers’ heating behaviour results from the interactions of internal and external drivers, which makes it a complex system. We used Fuzzy Cognitive Mapping method to represent key drivers and interactions in that system. Maps were drawn up at three focus groups representing different social groups from Spain – academics, citizens and energy experts – in order to capture heterogeneity of behaviours. Maps seek to identify and set out the factors that influence heating costs as well as private and public adaptation measures to minimise them. The core common concepts of the maps deal with consumer behaviour regarding investment in energy efficiency technologies such insulation or thermostat, attitudes regarding the environment or the thermal comfort temperature, economic factors such as price and income and regulatory interventions. The most significant differences between the groups were that the academics and energy experts considered that taxes could improve energy savings. The results shown in this paper may be helpful in designing effective policies on heating consumption.
Modeling of Pruning Techniques for Simplifying Deep Neural Networks

Convolutional Neural Networks (CNNs) suffer from different issues such as computational complexity and the number of parameters. In recent years pruning techniques are employed to reduce the number of operations and model size in CNNs. Different pruning methods are proposed, which are based on pruning the connections, channels, and filters. Various techniques and tricks accompany pruning methods, and there is not a unifying framework to model all the pruning methods. In this paper pruning methods are investigated, and a general model which is contained the majority of pruning techniques is proposed. The advantages and disadvantages of the pruning methods can be identified, and all of them can be summarized under this model. The final goal of this model can be providing a specific method for all the pruning methods with different structures and applications.
Wireless in the Weather-world and Community Networks Made to Last

I describe grassroots innovation and recursive engagement in Argentina by members of rural Community Networks (CNs), or decentralized telecommunications that people build and operate themselves. Hackers began the CNs to resist the dominant internet regime through peer collaboration but, these days, their members' technical competences, life experiences and perspectives on communality and solidarity are diverse. I apply Tim Ingold's concepts in analysing links between technology, social relations and bodies in the CNs, and how co-design of an app unfolded along paths of growth and repair. The WiFi network materialises the liveliness of processes that go into forming it, and the app materialises CN members' efforts to enact awareness of their own nodes to maintain the network. The Argentine CNs and Ingold's perspective on commoning illustrate that being aware of local details and adapting to the ways things are going in the living world is vital to making shared resources last.
Neural Correlates of Fixated Low- and High-level Scene Properties during Active Scene Viewing

During real-world scene perception, viewers actively direct their attention through a scene in a controlled sequence of eye fixations. During each fixation, local scene properties are attended, analyzed, and interpreted. What is the relationship between fixated scene properties and neural activity in the visual cortex? Participants inspected photographs of real-world scenes in an MRI scanner while their eye movements were recorded. Fixation-related fMRI was used to measure activation as a function of lower- and higher-level scene properties at fixation, operationalized as edge density and meaning maps, respectively. We found that edge density at fixation was most associated with activation in early visual areas, whereas semantic content at fixation was most associated with activation along the ventral visual stream including core object and scene-selective areas (lateral occipital complex, parahippocampal place area, occipital place area, and retrosplenial cortex). The observed activation from semantic content was not accounted for by differences in edge density. The results are consistent with active vision models in which fixation gates detailed visual analysis for fixated scene regions, and this gating influences both lower and higher levels of scene analysis.
LanguageLogger: A Mobile Keyboard Application for Studying Language Use in Everyday Text Communication in the Wild

We present a concept and tool for studying language use in everyday mobile text communication (e.g. chats). Our approach for the first time enables researchers to collect comprehensive data on language use during unconstrained natural typing (i.e. no study tasks) without logging readable messages to preserve privacy. We achieve this with a combination of three customisable text abstraction methods that run directly on participants' phones. We report on our implementation as an Android keyboard app and two evaluations: First, we simulate text reconstruction attempts on a large text corpus to inform conditions for minimising privacy risks. Second, we assess people's experiences in a two-week field deployment (N=20). We release our app as an open source project to the community to facilitate research on open questions in HCI, Linguistics and Psychology. We conclude with concrete ideas for future studies in these areas.
Searchable Encrypted Image Retrieval Based on Multi-Feature Adaptive Late-Fusion

Recently, searchable encrypted image retrieval in a cloud environment has been widely studied. However, the inappropriate encryption mechanism and single feature description make it hard to achieve the expected effects. Therefore, a major challenge of encrypted image retrieval is how to extract and fuse multiple efficient features to improve performance. Towards this end, this paper proposes a searchable encrypted image retrieval based on multi-feature adaptive late-fusion in a cloud environment. Firstly, the image encryption is completed by designing the encryption function in an RGB color channel, bit plane and pixel position of the image. Secondly, the encrypted images are uploaded to the cloud server and the convolutional neural network (CNN) is fine-tuned to build a semantic feature extractor. Then, low-level features and semantic features are extracted. Finally, the similarity score curves of each feature are calculated, and adaptive late-fusion is performed by the area under the curve. A large number of experiments on public dateset are used to validate the effectiveness of our method.
Formal techniques for consistency checking of orchestrations of semantic Web services

Web service orchestrations aim at offering value-added services that meet the increasing complex requirements of business processes. Since it becomes harder to manually check the correctness of such systems, we propose a formal method for their consistency checking by first translating OWL-S composite services and WS-BPEL orchestrator into communicating automata using an iterative process driven by control structures of these languages. Thereafter, we build the synchronization product of the resulting automata and proceed to its compatibility checking in order to unveil forbidden states, which depict incompatibilities in that orchestration interaction protocol. Last, this automaton consistency is checked against the behavioral model of WS-CDL choreography seen as its specification to which it has to conform.
A Multi-Level Supervision Model: A novel approach for Thermal Image Super Resolution

This paper proposes a novel architecture for thermal image super-resolution. A very large dataset is provided by PBVS 2020 in their super-resolution challenge. This dataset contains the images with three different resolution scales(low, medium, high) [1]. This dataset is used to train the proposed architecture to generate the super-resolution images in x2, x3, x4 scales. The proposed architecture is based on the residual blocks as the base units of the network. Along with this, the coordinate convolution layer and the convolutional block attention Module (CBAM) are also used in the architecture. Further, the multi-level supervision is implemented to supervise the output image resolution similarity with the real image at each block during training. To test the robustness of the proposed model, we evaluated our model on the Thermal-6 dataset [13]. The results show that our model is efficient to achieve the state of art results on the PBVS'2020 dataset. Further the results on the Thermal-6 dataset show that the model has a decent generalization capacity.