Reviewerly/exHarmony huggingface dataset:
A Systems Perspective on Technical Debt

This paper explores applications of the Technical Debt metaphor, which originated in the agile software community, at a systems level. We present a summary of current understanding of the technical debt concept and propose a broader application of the approach at a system level. The technical debt metaphor provides a method to identify cases when design or implementation decisions are being made for expediency and assess their long term impact on productivity and risk. In addition, technical debt management offers a logical approach to tracking expedient decisions and determining when it makes sense to carry the debt and when it's better to pay it off by updating designed and retrofitting units. In this paper, we describe various kinds of technical debt that may be present in a system baseline and consider how the presence of debt impacts further evolution of the system. Types of debt include architectural debt, configuration debt, testing debt and deprecation debt among others. We also present use case studies that illustrate how technical debt assessment and management can have a positive impact on system realization. Cases include moving a baseline from R&D to operations, reusing a baseline in a new context, and planning a technical refresh. We discuss methods of managing technical debt in a baseline including identification, quantification, tracking and payoff.
Selective directed forgetting of motor sequences

We examined selective directed forgetting in motor memory using a new variant of a three-list approach, to distinguish between accounts of directed forgetting. Participants consecutively studied three lists (L1, L2, and L3) of four sequential four-finger movements each. After studying L2, participants in the forget group were instructed to selectively forget the just studied four items of L2 but to retain the previously studied four items of L1, whereas the remember group did not receive any forget instruction for L2 but was encouraged to retain the items of both lists. In addition, we switched (switch groups) or repeated the items-enacting hand (no-switch groups) between L2 and L3 for a manipulation of post-forget-cue material competition for L2. A final memory test assessed recall performance for all three lists. Selective directed forgetting (lower L2 recall in the forget group as compared to the remember group) only occurred if the same hand was used for L2 and L3 (high interference between L2 and L3 encoding) whereas no selective directed forgetting occurred if the hand switched between L2 and L3 (low interference between L2 and L3 encoding). These results suggest that an inhibitory mechanism caused (selective) directed-forgetting costs that was triggered when items studied after the forget instruction had the potential to interfere with already stored items (i.e. were to be enacted by the same hand). When subsequently studied items pertained to the other hand no directed-forgetting costs occurred.
SnakeGX: A Sneaky Attack Against SGX Enclaves

Intel Software Guard eXtension (SGX) is a technology to create enclaves (i.e., trusted memory regions) hardware isolated from a compromised operating system. Recently, researchers showed that unprivileged adversaries can mount code-reuse attacks to steal secrets from enclaves. However, modern operating systems can use memory-forensic techniques to detect their traces. To this end, we propose SnakeGX, an approach that allows stealthier attacks with a minimal footprint; SnakeGX is a framework to implant a persistent backdoor in legitimate enclaves. Our solution encompasses a new architecture specifically designed to overcome the challenges SGX environments pose, while preserving their integrity and functionality. We thoroughly evaluate SnakeGX against StealthDB, which demonstrates the feasibility of our approach.
CLoTH: A Lightning Network Simulator

Payment-channel networks are one of the most promising solution to the well-known issue of blockchain scalability. In this work we present CLoTH, a simulator of the Lightning Network — the mainstream payment-channel network, used in Bitcoin. CLoTH simulates the execution of payments in a payment-channel network and produces performance measures such as the probability of payment success and the average payment time. To the best of our knowledge, CLoTH is the only simulator that faithfully reproduces the Lightning Network code functions, and this ensures the reliability of simulation results. In this work we provide a detailed description of the new, refactored, publicly-usable version of CLoTH, and we show simulations on the multi-path-payment feature, a recent Lightning Network feature that aims to minimize payment failures.
Situation-Specific Business Model Development Methods for Mobile App Developers

The development of effective business models is an essential task in highly competitive markets like mobile ecosystems. Existing development methods for these business models do not specifically bring into focus that the development process profoundly depends on the situation (e.g., market size, regulations) of the mobile app developer. Here, a mismatch between method and situation can lead to poor resource management and longer development cycles. In software engineering, software projects use situational method engineering to configure a development method out of a method repository based on the project situation. Analogously, we support creating situation-specific business model development methods with a method base and new user roles. Here, the method engineer obtains the domain expert’s knowledge and stores it in the method base as elements, building blocks, and patterns. We derive the expert knowledge from a grey literature review on mobile development processes. After this, the method engineer constructs the development method based on the described situation of the business developer. We provide an open-source tool and evaluate it by constructing a local event platform’s business model development method.
Development of Non-Fiction Text Digital Learning Media in Narrative Writing Skills for Fourth Grade Elementary School Students

This development research was carried out improve the learning outcomes of Indonesian students learning non-fiction text material in narrative writing skills in fourth grade students. This type of research is a Research and Development with Prestest-Posttest One Group Design, using the 4D development model which includes four stages, namely Define, Design, Development and Dissemination. Research respondents of this research are fourth grade. The instrument used a validation questionnaire, observation, practicality questionnaires and learning outcomes tests. Results of this research indicate that the validation of the material obtained 89.4% and the validation of the media obtained 100%. The practicality of the teacher's questionnaire got a percentage of 95%, while the student questionnaire got a percentage of 98.6%, the results of teacher and student observations obtained a percentage of 92.85% and 95%. The effectiveness based on giving pretest and posttest to students totaled 25 students with an average pretest result of 79.52 while the average posttest was 91.96. The result of the t-count is -11.085 and the probability of 0.000 which shows that there is a significant difference in the value of the narrative writing skills of fourth grade elementary school students using the development of non-fiction text digital-based learning media.
Entanglement in Tavis-Cummings model with Kerr nonlinearity induced by a thermal noise

In this paper, we investigated the entanglement dynamics between two superconducting qubits interacting with a thermal one-mode field of lossless cavity with the Kerr media. We obtained the exact solution for time- dependent density matrix and calculated on its basis the qubi-qubit entanglement parameter-negativity. The results show that Kerr nonlinearity greatly affected the entanglement behaviour. More interestingly, that initial qubits coherence greatly enhances the degree of entanglement in the presence of the Kerr medium.
Necessity of Blockchain for Building Trust in Today’s Applications: An Useful Explanation from User’s Perspective

Building trust in machines and in people always became a critical issue. Due to recent development in technology, human has moved from wired devices to wireless devices (like internet connected devices), but there are several issues (for example, security, privacy, trust, etc.) also raised with this development. Also, a lot of work was presented (proposed) in the past decades to overcome (solve) such issues. But among these existing issues, trust issue is critical one to measure and put in scale. In 2008-09, a new (novel) concept was initiated called Blockchain, i.e., using blocks in from of chains (where each node is containing records about previous and next blocks). Blockchain provides sufficient level of trust and less number of malicious breaches over a database or transaction/ communication. Hence, this work discusses the facts behind using Blockchain and popularity of this proposed technology to build trust as a new wave. Every person, firm, and organization can easily trust on this technology and can utilize this respective technology in building trust with others.
Blind Quality Assessment of Night-Time Images Via Weak Illumination Analysis

Night-time images can be generated by various camera sensors in many practical applications, and hence how to effectively evaluate the quality of night-time images is an essential research topic. In this article, we propose a novel blind quality assessment method for night-time images. By analyzing the characteristic of night-time images, we first investigate the statistical properties of local luminance information based on the brightness level division, and then measure the masking effect on color and structure information caused by weak illumination. Finally, all extracted quality-aware features and the associated subjective ratings are trained via support vector regression to build the quality assessment model. Extensive experiments on a real-world night-image database validate the superiority of the proposed method over several state-of-the-art blind image quality metrics <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
SimpliFI: Hardware Simulation of Embedded Software Fault Attacks

Fault injection simulation on embedded software is typically captured using a high-level fault model that expresses fault behavior in terms of programmer-observable quantities. These fault models hide the true sensitivity of the underlying processor hardware to fault injection, and they are unable to correctly capture fault effects in the programmer-invisible part of the processor microarchitecture. We present SimpliFI, a simulation methodology to test fault attacks on embedded software using a hardware simulation of the processor running the software. We explain the purpose and advantage of SimpliFI, describe automation of the simulation framework, and apply SimpliFI on a BRISC-V embedded processor running an AES application.
Probabilistic message passing control for complex stochastic switching systems

In this paper, we propose a general decentralised probabilistic control framework for a class of complex stochastic systems with switching modes. Probabilistic state space models are exploited to characterise the subsystems’ dynamical behaviours constituting a complex dynamical system, thus providing a complete description of the subsystems components. To address the variations in the operational modes of the subsystems, the Mixture Density Network (MDN) is applied here to identify the subsystems modes and provides estimates for the system dynamic distributions. Besides, to harmonise the actions between the subsystems, the probabilistic message passing methodology is utilised to provide communication between neighbouring subsystems. Based on the MDN model and the neighbours subsystems information via message passing, the general solution of the fully probabilistic decentralised randomised controller which minimises the Kullback-Leibler divergence (KLD) between the actual and its ideal distributions is then obtained. Moreover, a numerical example is presented to illustrate the effectiveness and the usefulness of our novel proposed framework.
Optimizing the resource usage of actor-based systems

Runtime environments for IoT data processing systems based on the actor model often apply a thread pool to serve data streams. In this paper, we propose an approach based on Reinforcement Learning (RL) to find a trade-off between the resource (thread pool in server machines) usage and the quality of service for data streams. We compare our approach and the Thread Pool Executor of Akka, an open-source software toolkit. Simulation results show that our approach outperforms ThreadPoolExecutor with the timeout rule when the thread start times are not negligible. Furthermore, the tuning of our approach is not tedious as the application of the timeout rule requires.
A Re-Analysis and Synthesis of Data on Affect Dynamics in Learning

Affect dynamics, the study of how affect develops and manifests over time, has become a popular area of research in affective computing for learning. In this paper, we first provide a detailed analysis of prior affect dynamics studies, elaborating both their findings and the contextual and methodological differences between these studies. We then address methodological concerns that have not been previously addressed in the literature, discussing how various edge cases should be treated. Next, we present mathematical evidence that several past studies applied the transition metric (L) incorrectly - leading to invalid conclusions of statistical significance - and provide a corrected method. Using this corrected analysis method, we reanalyze ten past affect datasets collected in diverse contexts and synthesize the results, determining that the findings do not match the most popular theoretical model of affect dynamics. Instead, our results highlight the need to focus on cultural factors in future affect dynamics research.
Envisioning, designing, and rapid prototyping heritage installations with a tangible interaction toolkit

This paper discusses an empirical study on the use and appropriation of a toolkit forcultural heritage professionals and other non-technical experts (such as exhibitiondesigners) to envision, design and prototype tangible interactive installations for visitorengagement. The Toolkit is composed of an online editor (the software) and a set ofinterconnected physical components and devices (the hardware). The software enablescreating, adapting, deploying and testing installations together with the digital contentpopulating them, while the hardware underpins their hands-on interactivity. The Toolkitwas evaluated holistically in workshops with participants from the cultural sector. Wedetail aspects of the Toolkit design and how it was used in practice by collaboratingteams. We discuss results showing how the Toolkit supported three phases ofdevelopment of tangible heritage installations: envisioning, designing, and rapidprototyping. This paper contributes new research in HCI on digital interaction in theheritage domain, and on designing and assessing tools supporting user-drivendevelopment of novel technologies.
Contactless Transaction Using Wearable Ring with Biometric Fingerprint Security Feature

Card transaction is one of the most widely used transaction methods in the payment industry. There are more than 50.6 million credit cards and 840.6 million debit cards in India. These cards are so confidential that special care has to be taken while carrying and handling these cards. Carrying these cards everywhere and every time is fewer burdens on the user. Latest near field communication (NFC) enabled smart cards are not asking for personal identification number (PIN) while doing transaction so this is considered as another problem. If such NFC enabled card is stolen then there will be a huge loss. The intention behind this paper is to showcase the idea to solve the above problems in a more secured way. This research problem motivates to come with an appropriate solution with the idea of the wearable ring which has a smart card chip installed in it and has biometric fingerprint scanner support. Anyone can wear this ring so no burden to carry bank cards. It has biometric fingerprint sensor so without user’s authentication transaction will not be carried out, this is the great advantage over existing card payments and rings. This is the proposed idea with properly tested on paper considering parameters like reading range of NFC communication frequency, power source needs to run biometric fingerprint sensor and NFC tag, personalize the MasterCard applet in-ring etc. Future scope of this idea is the actual implementation of hardware that brings the new product in the market.
Adaptive differential privacy preserving based on multi‐objective optimization in deep neural networks

Summary Privacy data security has become an important bottleneck for the overall development of artificial intelligence and a key challenge that needs to be broken in the Internet era. The current research mainly considers differential privacy to effectively protect the private information in the data. However, as the noise increases, the precision of the training model will decrease. In order to solve above problem, an adaptive differential privacy (ADP) method is constructed and applied to deep neural networks. ADP adds noise adaptively in the training process according to the importance of features. We also build the differential privacy multi‐objective optimization model (DPMOM). DPMOM adopts multi‐objective optimization characteristics, takes accuracy and privacy protection as the optimization objectives. It optimizes the super parameters of deep neural networks and the noise of differential privacy. In addition, to better solve the ADP model, with the NSGA‐II algorithm as the basic framework, a multi‐objective optimization algorithm based on differential privacy protection (DPPMOA) is designed. Simulation experiments show that compared with other machine learning methods and differentially private stochastic gradient descent, the accuracy of ADP is higher under the same amount of noise. Through comparison with NSGA‐II, IBEA, PESA‐II, and AGE‐II, DPPMOA is proved that the solution set of this algorithm is better.
A neural network application to assess magma diversity in the Etendeka igneous province, Namibia

Abstract The geochemical discrimination of different magma types in Large Igneous Provinces is conventionally based on a few, pre-selected variables that are regarded to have petrological meaning. An alternative approach explored in this study is to apply the neural network technique of self-organising maps (SOM) to identify inherent groupings in data without knowledge or assumptions (unsupervised learning). The dataset used in this study comprises whole-rock analyses from extrusive (lava) and intrusive (dykes, sills) mafic suites in the Etendeka province, Namibia, taken from published sources and augmented by 103 new chemical analyses of dykes. Six SOM-classified groups are identified, which are unevenly distributed among the extrusive and the intrusive rock suites. The lava samples are dominated by just three of the six SOM groups (95% of all samples) and one group is absent entirely, whereas all six groups are present in the intrusive suite and five of them each comprise more than 5% of the samples. The geographic distribution of SOM-grouped dykes is heterogeneous and groups that are under-represented in the lava suite occur preferentially in a region of the pre-Etendeka basement where few lavas are preserved. Thus, the difference in magma diversity between intrusive and extrusive suites may be partly an artefact of erosion, which implies that a proper assessment of magma diversity in this and other LIPs must include the intrusive components. The correspondence of our SOM groupings with magma types in the Etendeka province that were established from petrologically defined variables is reasonably good for most trace-element abundances and ratios. However, some of the SOM groups have a wide range of initial Sr–Nd isotope ratios and a poor correspondence with the established magma types. We conclude that the SOM approach is useful for sorting out large and complex geochemical datasets but the method gives all input variables equal weight, which may be problematic if they have different responses to processes in the system under study (e.g., partial melting, fractional crystallisation, degassing, alteration). It is no substitute for expert petrological knowledge in discriminating genetically distinct magma types in an application like the present one.
A Generalized Enhancement Framework for Hazy Images With Complex Illumination

Images captured under low-light conditions are generally characterized by poor illumination, low contrast, and nonignorable large amount of noise. In order to improve the visibility in weak illumination scenes, multiple artificial light sources are used, which leads to severe uneven illumination of the scene. The main challenges of dehazing images with complex illumination are to suppress the boosting of unsightly noise when enhancing contrast and avoid overenhancement in bright glow regions. To circumvent problems above, this letter proposes a generalized enhancement framework, which works well not only in uniform light conditions but also in strongly nonuniform illumination low-light scenes. To achieve this, we first decompose the input hazy image into a structure layer containing low-frequency illumination variance and a texture layer containing large amount of high-frequency details. Sequentially, benefit from two derived masks that are intrinsically similar to weight maps, the proposed framework can perform regional adaptive brightness adjustment on the structure layer according to the distribution of light in the input image. Meanwhile, regions of effective details in the texture layer are assigned higher weights, while regions that belong to noise are suppressed. Finally, adding the enhanced texture layer back to the brightened structure layer, visually appealing results are generated. Experimental results on various scenarios demonstrate the superiority of the proposed framework over state-of-the-art methods in terms of both qualitative and quantitative.
Architecture and Operation Adaptive Network for Online Recommendations

Learning feature interactions is crucial for model performance in online recommendations. Extensive studies are devoted to designing effective structures for learning interactive information in an explicit way and tangible progress has been made. However, the core interaction calculations of these models are artificially specified, such as inner product, outer product and self-attention, which results in high dependence on domain knowledge. Hence model effect is bounded by both restriction of human experience and the finiteness of candidate operations. In this paper, we propose a generalized interaction paradigm to lift the limitation, where operations adopted by existing models can be regarded as its special form. Based on this paradigm, we design a novel model to adaptively explore and optimize the operation itself according to data, named generalized interaction network(GIN). We proved that GIN is a generalized form of a wide range of state-of-the-art models, which means GIN can automatically search for the best operation among these models as well as a broader underlying architecture space. Finally, an architecture adaptation method is introduced to further boost the performance of GIN by discriminating important interactions. Thereby, architecture and operation adaptive network(AOANet) is presented. Experiment results on two large scale datasets show the superiority of our model. AOANet has been deployed to industrial production. In a 7-day A/B test, the click-through rate increased by 10.94%, which represents considerable business benefits.