Miracle-dz/UniFAR-FTUs huggingface dataset:
Sustainability in Software Architecture: A Systematic Mapping Study

Sustainability is an increasingly-studied topic in software engineering in general, and in software architecture in particular. There are already a number of secondary studies addressing sustainability in software engineering, but no such study focusing explicitly on software architecture. This work aims to fill this gap by conducting a systematic mapping study on the intersection between sustainability and software architecture research with the intention of (i) reflecting on the current state of the art, and (ii) identifying the needs for further research. Our results show that, overall, existing works have focused disproportionately on specific aspects of sustainability, and in particular on the most technical and “inward facing” ones. This comes at the expense of the holistic perspective required to address a multi-faceted concern such as sustainability. Furthermore, more reflection-oriented research works, and better coverage of the activities in the architecting life cycle are required to further the maturity of the area. Based on our findings we then propose a research agenda for sustainability-aware software architecture.
Embedding Knowledge for Document Summarization: A Survey

Knowledge-aware methods have boosted a range of Natural Language Processing applications over the last decades. With the gathered momentum, knowledge recently has been pumped into enormous attention in document summarization research. Previous works proved that knowledge-embedded document summarizers excel at generating superior di-gests, especially in terms of informativeness, coherence, and fact consistency. This paper pursues to present the ﬁrst systematic survey for the state-of-the-art methodologies that embed knowledge into document summarizers. Particularly, we propose novel taxonomies to recapitulate knowledge and knowledge embeddings under the document summarization view. We further explore how embed-dings are generated in learning architectures of document summarization models, especially in deep learning models. At last, we discuss the challenges of this topic and future directions.
Interactivity in Constructive Cryptography : Modeling and Applications to Updatable Encryption and Private Information Retrieval

In this work, we extend the Constructive Cryptography (CC) framework introduced by Maurer in 2011 so as to handle interactive protocols. We design and construct a so-called Interactive Server Memory Resource (ISMR), that is an augmented version of the basic instantiation of a client-server protocol in CC, namely the Server Memory Resource. We then apply our ISMR construction to two types of interactive cryptographic protocols for remote storage : Updatable Encryption (UE) and Private Information Retrieval (PIR). Concerning UE, our results are a composable version of those protocols, clarifying the security guarantees achieved by any UE scheme. Namely, we give the relevant security notion to consider according to a given leakage context. Letting USMR denote our ISMR adapted to the UE application, we prove that IND - UE - CPA security is suﬃcient for a secure construction of a conﬁdential USMR that hides the age of ciphertexts; and IND - ( ENC + UPD ) - CPA security is suﬃcient for a secure construction of a conﬁdential USMR in case of unrestricted leakage. As a consequence, contrary to what was claimed before, the IND - UE security notion is not always stronger than the IND - ( ENC + UPD ) one. Another contribution to CC is a method of proof handling asymmetric challenges, namely game pairs of the form ( m,c ) , i.e. (plaintext, cipher-text), where the oracle either answers an encryption of m or an update of c . Those occur for instance when proving IND - UE - CPA security. Concerning PIR, we also give a composable version of PIR protocols, yielding a unique model that uniﬁes diﬀerent notions of PIR : IT-PIR, C-PIR, one- or multi- server PIR. Using the ﬂexibility of CC, we are also able to model PIR variants, such as SPIR.
Information and Decision-making Processes Leading to Corporate Failure: Enron and Red Flags

Enron is examined as a critical case study to understand the role of information in the Enron’s Board’s decision-making processes. Three major events in the Enron failure were analyzed in detail using thematic analysis. Three themes were identified regarding the communication and use of information in Enron: interdependency of authority relationships, information control, and decision protocol and policies that shaped the information that management sent to the Board during the approval process. The Board was dependent on advisors to provide approvals first, prior to Board approval. The relationships between advisors and management influenced the information sent to the Board and affected controls used to monitor deals. The Board maintained that they were unaware of red flags, such as warnings related to incomplete disclosures and conflict of interest issues. The Board received information for arrangements which required Board approval but it had limited access to information regarding Enron’s operations.
Privacy-Preserving Cloud Computing: Ecosystem, Life Cycle, Layered Architecture and Future Roadmap

Privacy-Preserving Cloud Computing is an emerging technology with many applications in various fields. Cloud computing is important because it allows for scalability, adaptability, and improved security. Likewise, privacy in cloud computing is important because it ensures that the integrity of data stored on the cloud maintains intact. This survey paper on privacy-preserving cloud computing can help pave the way for future research in related areas. This paper helps to identify existing trends by establishing a layered architecture along with a life cycle and an ecosystem for privacy-preserving cloud systems in addition to identifying the existing trends in research on this area.
A diploid population model for copy number variation of genetic elements

We study the following model for a diploid population of constant size $N$: Every individual carries a random number of (genetic) elements. Upon a reproduction event each of the two parents passes each element independently with probability $\tfrac 12$ on to the offspring. We study the process $X^N = (X^N(1), X^N(2),. )$, where $X_t^N(k)$ is the frequency of individuals at time $t$ that carry $k$ elements, and prove convergence (in some weak sense) of $X^N$ jointly with its empirical first moment $Z^N$ to the ``slow-fast'' system $(Z,X)$, where $X_t = \text{Poi}(Z_t)$ and $Z$ evolves according to a critical Feller branching process. We discuss heuristics explaining this finding and some extensions and limitations.
Deep Reinforcement Learning for Online Routing of Unmanned Aerial Vehicles with Wireless Power Transfer

The unmanned aerial vehicle (UAV) plays an vital role in various applications such as delivery, military mission, disaster rescue, communication, etc., due to its flexibility and versatility. This paper proposes a deep reinforcement learning method to solve the UAV online routing problem with wireless power transfer, which can charge the UAV remotely without wires, thus extending the capability of the battery-limited UAV. Our study considers the power consumption of the UAV and the wireless charging process. Unlike the previous works, we solve the problem by a designed deep neural network. The model is trained using a deep reinforcement learning method offline, and is used to optimize the UAV routing problem online. On small and large scale instances, the proposed model runs from four times to 500 times faster than Google OR-tools, the state-of-the-art combinatorial optimization solver, with identical solution quality. It also outperforms different types of heuristic and local search methods in terms of both run-time and optimality. In addition, once the model is trained, it can scale to new generated problem instances with arbitrary topology that are not seen during training. The proposed method is practically applicable when the problem scale is large and the response time is crucial.
Piecewise-Linear Activations or Analytic Activation Functions: Which Produce More Expressive Neural Networks?

Many currently available universal approximation theorems afﬁrm that deep feedforward networks deﬁned using any suitable activation functions can approximate any integrable function locally in L 1 -norm. Though different approximation rates are available for deep neural networks deﬁned using other classes of activation functions, there is little explanation for the empirically conﬁrmed advantage that ReLU networks exhibit over their classical (e.g. sigmoidal) counterparts. Our main result demonstrates that deep networks with piecewise linear activation (e.g. ReLU or PReLU) are fundamentally more expressive than deep feedforward networks with analytic (e.g. sigmoid, Swish, GeLU, or Softplus). More speciﬁcally, we construct a strict reﬁnement of the topology on the space L 1loc ( R d , R D ) of locally Lebesgue-integrable functions, in which the set of deep ReLU networks with (bilinear) pooling NN ReLU + Pool is dense (i.e. universal) but the set of deep feedforward networks deﬁned using any combination of analytic activation functions with (or without) pooling layers NN ω + Pool is not dense (i.e. not universal). The “separation phenomenon” persists when comparing deep ReLU networks with pooling to classical polynomial functions; which we show are also not dense in this space. Our main result is further explained by quantitatively demonstrating that this “separation phenomenon” between the networks in NN ReLU + Pool and those in NN ω + Pool by showing that the networks in NN ReLU are capable of approximate any compactly supported Lipschitz function while simultaneously approximating its essential support;
Optimal day-ahead offering strategy for large producers based on market price response learning

In day-ahead electricity markets based on uniform marginal pricing, small variations in the offering and bidding curves may substantially modify the resulting market outcomes. In this work, we deal with the problem of finding the optimal offering curve for a risk-averse profit-maximizing generating company (GENCO) in a data-driven context. In particular, a large GENCO's market share may imply that her offering strategy can alter the marginal price formation, which can be used to increase profit. We tackle this problem from a novel perspective. First, we propose a optimization-based methodology to summarize each GENCO's step-wise supply curves into a subset of representative price-energy blocks. Then, the relationship between the market price and the resulting energy block offering prices is modeled through a Bayesian linear regression approach, which also allows us to generate stochastic scenarios for the sensibility of the market towards the GENCO strategy, represented by the regression coefficient probabilistic distributions. Finally, this predictive model is embedded in the stochastic optimization model by employing a constraint learning approach. Results show how allowing the GENCO to deviate from her true marginal costs renders significant changes in her profits and the market marginal price. Furthermore, these results have also been tested in an out-of-sample validation setting, showing how this optimal offering strategy is also effective in a real-world market contest.
Hybrid ISTA: Unfolding ISTA With Convergence Guarantees Using Free-Form Deep Neural Networks

It is promising to solve linear inverse problems by unfolding iterative algorithms (e.g., iterative shrinkage thresholding algorithm (ISTA)) as deep neural networks (DNNs) with learnable parameters. However, existing ISTA-based unfolded algorithms restrict the network architectures for iterative updates with the partial weight coupling structure to guarantee convergence. In this paper, we propose hybrid ISTA to unfold ISTA with both pre-computed and learned parameters by incorporating free-form DNNs (i.e., DNNs with arbitrary feasible and reasonable network architectures), while ensuring theoretical convergence. We first develop HCISTA to improve the efficiency and flexibility of classical ISTA (with pre-computed parameters) without compromising the convergence rate in theory. Furthermore, the DNN-based hybrid algorithm is generalized to popular variants of learned ISTA, dubbed HLISTA, to enable a free architecture of learned parameters with a guarantee of linear convergence. To our best knowledge, this paper is the first to provide a convergence-provable framework that enables free-form DNNs in ISTA-based unfolded algorithms. This framework is general to endow arbitrary DNNs for solving linear inverse problems with convergence guarantees. Extensive experiments demonstrate that hybrid ISTA can reduce the reconstruction error with an improved convergence rate in the tasks of sparse recovery and compressive sensing.
Long-Range ICN for the IoT: Exploring a LoRa System Design

This paper presents LoRa-ICN, a comprehensive IoT networking system based on a common long-range communication layer (LoRa) combined with Information-Centric Networking (ICN) principles. We have replaced the LoRaWAN MAC layer with an IEEE 802.15. 4 Deterministic and Synchronous Multi-Channel Extension (DSME). This multifaceted MAC layer allows for different mappings of ICN message semantics, which we explore to enable new LoRa scenarios. We designed LoRa-ICN from the ground-up to improve reliability and to reduce dependency on centralized components in LoRa IoT scenarios. We have implemented a feature-complete prototype in a common network simulator to validate our approach. Our results show design trade-offs of different mapping alternatives in terms of robustness and efficiency.
Few-cycle vortex beam generated from self-compression of midinfrared femtosecond vortices in thin plates

We demonstrate theoretically that few-cycle vortex beam with subterawatt peak power can be generated by self-compression of mid-infrared femtosecond vortex beam using the thin-plate scheme. The 3 {\mu}m femtosecond vortex beam with input duration of 90 fs is compressed to 15.1 fs with the vortex characteristics preserved. The conversion efficiency is as high as 91.5% and the peak power reaches 0.18 TW. The generation of the high-peak-power few-cycle vortex beam is owing to the proper spatiotemporal match by this novel scheme, where the spectrum is broadened enough, the negative group velocity dispersion can compensate the positive chirp induced by nonlinear effects, and multiple filamentation is inhibited for the keeping of the vortex characteristics. Our work will help to generate isolated attosecond vortices, opening a new perspective in ultrafast science.
Kernel of Arithmetic Jet Spaces

we construct m -shifted π -typical Witt vectors W mn ( B ) for any O algebra B of length m + n + 1 . They are a generalization of the usual π -typical Witt vectors. Along with it we construct a lift of Frobenius, called the lateral Frobenius ˜ F : W mn ( B ) → W m ( n − 1) ( B ) and show that it satisﬁes a natural identity with the usual Frobenius map. Now a group scheme G deﬁnedover Spec R , where R is an O -algebra with a ﬁxed π -derivation δ on it, one naturally considers the n -th arithmetic jet space J n G whose points are the Witt ring valued points of G . This leads to a natural projection map of group schemes u : J m + n G → J m G N mn G the kernel of u . in the case when G an group scheme, we show that the lateral Frobenius ˜ F turns the system of schemes { N mn G } ∞ n =1 into a prolongation sequence. One of our main results then prove that for n ≥ 1 , N mn G is naturally isomorphic to J n − 1 ( N m 1 G ) as group schemes. Hence this implies that for any π -formal group scheme ˆ G over Spf R , N mn ˆ G is isomorphic to J n − 1 ( N m 1 G ) . As an application, if ˆ G is a smooth commutative π -formal group scheme of dimension d and R is of characteristic 0 whose ramiﬁcation is bounded above by p − 2 , then our result implies that J n G is a canonical extension of ˆ G by ( W n − 1 ) d where W n − 1 is the π -formal group scheme ˆ A n endowed with the group law of addition of Witt vectors. Our results also give a geometric characterization of G ( π n +1 R ) which is the subgroup of points of G ( R ) that reduces to identity under the modulo π n +1 map.
Federated Geometric Monte Carlo Clustering to Counter Non-IID Datasets

—Federated learning allows clients to collaboratively train models on datasets that are acquired in different locations and that cannot be exchanged because of their size or regulations. Such collected data is increasingly non-independent and non-identically distributed (non-IID), negatively affecting training accuracy. Previous works tried to mitigate the effects of non-IID datasets on training accuracy, focusing mainly on non-IID labels, however practical datasets often also contain non-IID features. To address both non-IID labels and features, we propose FedGMCC 1 , a novel framework where a central server aggregates client models that it can cluster together. FedGMCC clustering relies on a Monte Carlo procedure that samples the output space of client models, infers their position in the weight space on a loss manifold and computes their geometric connection via an afﬁne curve parametrization. FedGMCC aggregates connected models along their path connectivity to produce a richer global model, incorporating knowledge of all connected client models. FedGMCC outperforms FedAvg and FedProx in terms of convergence rates on the EMNIST62 and a genomic sequence classiﬁcation datasets (by up to +63%). FedGMCC yields an improved accuracy (+4 % ) on the genomic dataset with respect to CFL, in high non-IID feature space settings and label incongruency.
Enabling High-Performance and Energy-Efficient Hybrid Transactional/Analytical Databases with Hardware/Software Cooperation

A growth in data volume, combined with increasing demand for real-time analysis (using the most recent data), has resulted in the emergence of database systems that concurrently support transactions and data analytics. These hybrid transactional and analytical processing (HTAP) database systems can support real-time data analysis without the high costs of synchronizing across separate single-purpose databases. Unfortunately, for many applications that perform a high rate of data updates, state-of-the-art HTAP systems incur significant losses in transactional (up to 74.6%) and/or analytical (up to 49.8%) throughput compared to performing only transactional or only analytical queries in isolation, due to (1) data movement between the CPU and memory, (2) data update propagation from transactional to analytical workloads, and (3) the cost to maintain a consistent view of data across the system. We propose Polynesia, a hardware-software co-designed system for in-memory HTAP databases that avoids the large throughput losses of traditional HTAP systems. Polynesia (1) divides the HTAP system into transactional and analytical processing islands, (2) implements new custom hardware that unlocks software optimizations to reduce the costs of update propagation and consistency, and (3) exploits processing-in-memory for the analytical islands to alleviate data movement overheads. Our evaluation shows that Polynesia outperforms three state-of-the-art HTAP systems, with average transactional/analytical throughput improvements of 1.7x/3.7x, and reduces energy consumption by 48% over the prior lowest-energy HTAP system.
CellDefectNet: A Machine-designed Attention Condenser Network for Electroluminescence-based Photovoltaic Cell Defect Inspection

Photovoltaic cells are electronic devices that convert light energy to electricity, forming the backbone of solar energy harvesting systems. An essential step in the manufacturing process for photovoltaic cells is visual quality inspection using electroluminescence imaging to identify defects such as cracks, finger interruptions, and broken cells. A big challenge faced by industry in photovoltaic cell visual inspection is the fact that it is currently done manually by human inspectors, which is extremely time consuming, laborious, and prone to human error. While deep learning approaches holds great potential to automating this inspection, the hardware resource-constrained manufac-turing scenario makes it challenging for deploying complex deep neural network architectures. In this work, we introduce CellDefectNet, a highly efficient attention condenser network designed via machine-driven design exploration specifically for electroluminesence-based photovoltaic cell defect detection on the edge. We demonstrate the efficacy of CellDetectNet on a benchmark dataset comprising of a diversity of photovoltaic cells captured using electroluminescence imagery, achieving an accuracy of $\sim 86.3\%$ while possessing just 410K parameters $(\sim 13\times$ lower than EfficientNet-B0, respectively) and $\sim 115\mathrm{M}$ FLOPs $(\sim 12\times$ lower than EfficientNet-B0) and $\sim 13\times$ faster on an ARM Cortex A-72 embedded processor when compared to EfficientNet-B0.
Constructions of near MDS codes which are optimal locally recoverable codes

A linear code with parameters $[n,k,n-k]$ is said to be almost maximum distance separable (AMDS for short). An AMDS code whose dual is also AMDS is referred to as an near maximum distance separable (NMDS for short) code. NMDS codes have nice applications in finite geometry, combinatorics, cryptography and data storage. In this paper, we first present several constructions of NMDS codes and determine their weight enumerators. In particular, some constructions produce NMDS codes with the same parameters but different weight enumerators. Then we determine the locality of the NMDS codes and obtain many families of distance-optimal and dimension-optimal locally repairable codes.
Open-domain Dialogue Generation Grounded with Dynamic Multi-form Knowledge Fusion

Open-domain multi-turn conversations normally face the challenges of how to enrich and expand the content of the conversation. Recently, many approaches based on external knowledge are proposed to generate rich semantic and information conversation. Two types of knowledge have been studied for knowledge-aware open-domain dialogue generation: structured triples from knowledge graphs and unstructured texts from documents. To take both advantages of abundant unstructured latent knowledge in the documents and the information expansion capabilities of the structured knowledge graph, this paper presents a new dialogue generation model, Dynamic Multi-form Knowledge Fusion based Open-domain Chatt-ing Machine (DMKCM). In particular, DMKCM applies an indexed text (a virtual Knowledge Base) to locate relevant documents as 1st hop and then expands the content of the dialogue and its 1st hop using a commonsense knowledge graph to get apposite triples as 2nd hop. To merge these two forms of knowledge into the dialogue effectively, we design a dynamic virtual knowledge selector and a controller that help to enrich and expand knowledge space. Moreover, DMKCM adopts a novel dynamic knowledge memory module that effectively uses historical reasoning knowledge to generate better responses. Experimental results indicate the effectiveness of our method in terms of dialogue coherence and informativeness.