Miracle-dz/UniFAR-FTUs huggingface dataset:
Formalization of Concurrent Revisions

Concurrent revisions is a concurrency control model developed by Microsoft Research [1]. It has many interesting properties that distinguish it from other well-known models such as transactional memory. One of these properties is determinacy : programs written within the model always produce the same outcome, independent of scheduling activity. The concurrent revisions model has an operational semantics, with an informal proof of determinacy [2]. This document contains an Isabelle/HOL formalization of this semantics and the proof of determinacy. It is part of my master’s thesis [3], which describes it in more detail.
Large-Screen Interactive Imaging System with Switching Federated Filter Method Based on 3D Sensor

Large-screen human-computer interaction technology is reflected in all aspects of daily life. The dynamic gesture tracking algorithm commonly used in recent large-screen interactive technologies demonstrates compelling results but suffers from accuracy and real-time problems. This paper systematically addresses these issues by a switching federated filter method that combines particle filtering and Mean Shifting algorithms based on a 3D sensor. Compared with several algorithms, the results show that the one-hand and two-hand large-screen gesture tracking based on the switched federated filtering algorithm work well, and there is no tracking failure and loss of target. Therefore, the switching federated tracking positioning algorithm can be well applied to the design of human-computer interaction system and provide new ideas for future human-computer interaction.
Autism, social connectedness, and minimal social acts

Autism spectrum condition (henceforth ASC) is a complex psychopathological condition characterized by repetitive and restricted patterns of behaviors, as well as by impairments in social interaction and communication. This article focuses on the idea that ASC involves impairments in the capacity to connect with the feelings and actions of others. The metaphor of social connectedness might be considered somewhat uninformative, hardly specific of ASC, and ultimately compatible with a variety of competing approaches to social impairments in ASC. Nevertheless, here I develop an account of social connectedness which plays a distinctive and informative role in further understanding ASC. My strategy is to explore the role of social reciprocity in relation to the difficulties that persons with ASC have with social connectedness. Drawing on the work of Peter Hobson, I propose that such difficulties primarily involve experiences and actions that require the uptake or response from another subject for their fulfillment. I clarify and develop this idea by introducing the concept of minimal social act, inspired by the work of the phenomenologists Adolf Reinach and Dietrich von Hildebrand, and by discussing some 4E (i.e., embodied, enactive, embedded, and extended) approaches to ASC. On the current proposal, minimal social acts are pervasive and developmentally critical experiences that have built into their conditions of success a receptiveness or responsiveness from the subject to whom they are directed.
Modelling and Analysis of a Host-Parasitoid Impulsive Ecosystem under Resource Limitation

With a long history of theoretical development, biological model has focused on the interaction of a parasitoid and its host. In this paper, two Nicholson-Bailey models with a nonlinear pulse control strategy are proposed and analyzed to examine how limited resource affects the pest control. For a fixed-time discrete impulsive model, the existence and stability of the host-free periodic solution are derived. Threshold analysis suggests that it is critical to release parasitoid in an optimal number in case of the happening of the intra-specific competition, which will seriously affect the pest control. Bifurcation analysis reveals that the model exists complex dynamics including period doubling, chaotic solutions, coexistence of multiple attractors, and so on. For a state-dependent discrete impulsive model, the numerical simulations for bifurcation analysis are studied, the results show that how the key parameters and the initial densities of both populations affect the pest outbreaks, and consequently the relative biological implications with respect to pest control are discussed.
Objective and Subjective Evaluation of Virtual Relighting from Reflectance Transformation Imaging Data

Reﬂectance Transformation Imaging (RTI) is widely used to produce relightable models from multi-light image collections. These models are used for a variety of tasks in the Cultural Heritage ﬁeld. In this work, we carry out an objective and subjective evaluation of RTI data visualization. We start from the acquisition of a series of objects with different geometry and appearance characteristics using a common dome-based conﬁguration. We then transform the acquired data into relightable representations using different approaches: PTM, HSH, and RBF. We then perform an objective error estimation by comparing ground truth images with relighted ones in a leave-one-out framework using PSNR and SSIM error metrics. Moreover, we carry out a subjective investigation through perceptual experiments involving end users with a variety of backgrounds. Objective and subjective tests are shown to behave consistently, and signiﬁcant differences are found between the various methods. While the proposed analysis has been performed on three common and state-of-the-art RTI visualization methods, our approach is general enough to be extended and applied in the future to new developed multi-light processing pipelines and rendering solutions, to assess their numerical precision and accuracy, and their perceptual visual quality.
Recurrent Patterns of User Behavior in Different Electoral Campaigns: A Twitter Analysis of the Spanish General Elections of 2015 and 2016

We have retrieved and analyzed several millions of Twitter messages corresponding to the Spanish general elections held on the 20th of December 2015 and repeated on the 26th of June 2016. The availability of data from two electoral campaigns that are very close in time allows us to compare collective behaviors of two analogous social systems with a similar context. By computing and analyzing the time series of daily activity, we have found a significant linear correlation between both elections. Additionally, we have revealed that the daily number of tweets, retweets, and mentions follow a power law with respect to the number of unique users that take part in the conversation. Furthermore, we have verified that the topologies of the networks of mentions and retweets do not change from one election to the other, indicating that their underlying dynamics are robust in the face of a change in social context. Hence, in the light of our results, there are several recurrent collective behavioral patterns that exhibit similar and consistent properties in different electoral campaigns.
Towards the Reconstruction of Wide Historical Sites: A Local Graph-based Representation to Resample Gigantic Acquisitions

Nowadays, LiDAR scanners are able to digitize very wide historical sites, leading to point clouds composed of billions of points. These point clouds are able to describe very small objects or elements disseminated in these sites, but also exhibit numerous defects in terms of sampling quality. Moreover, they sometimes contain too many samples to be processed as they are. In this paper, we propose a local graph-based structure to deal with the set of LiDAR acquisitions of a digitization campaign. Each acquisition is considered as a graph representing the local behavior of the captured surface. Those local graphs are then connected together to obtain a single and global representation of the original scene. This structure is particularly suitable for resampling gigantic points clouds. We show how we can reduce the number of points drastically while preserving the visual quality of large and complex sites, whatever the number of acquisitions.
A Maturity Model for Assessing IS Risk Management Activity Considering the Dependencies Between Its Elements

The information systems (IS) are a key asset for organizations. Therefore, managing IS risks becomes more and more important especially within a world in perpetual change. Since IS risk management creates added-value, it must follow a process of continuous improvement orchestrated by a maturity model that figures out available pathways for a better improvement. The studied literature shows the lack of an IS risk management maturity model that considers all IS components and specificities of risk management activity. The present article shows first this lack in the section related to the comparative analysis of the existing models. Then, it proposes a maturity model to address this issue. The proposed model aims to assess the information system risk management activity while considering the dependencies between its elements.
Multiobjective Optimization of Tool Geometric Parameters Using Genetic Algorithm

Tool geometric parameters have a huge impact on tool wear. Up to now, there are only a few researches on tool geometric parameters and optimization, and the single objective function of parameter optimization used by researchers during high-speed machining (HSM) mainly is the minimum cutting force. However, the elevated cutting temperature also greatly affects tool wear due to the numerous cutting heat generation. Thus, to reduce tool wear, it is the most fundamental approach to taking into account the comprehensive control of the cutting force and cutting temperature because they are the two most important physical quantities in metal cutting processes. This work proposes a new optimization idea of the cutting-tool’s multi geometric parameters (three main parameters: rake angle, clearance angle, and cutting edge radius) with two objective functions (the cutting force and the temperature). Based on the response surface method (RSM), we have established the modified functional relation models of the influence of tool geometric parameters on the cutting force and temperature according to the finite element simulation results in high-speed cutting of Ti6Al4V. Then the models are solved by using a genetic algorithm, and the optimal tool geometric parameters values that can concurrently control the two objectives in their minimum values are obtained. The advantages lie in the strategy of the separate models of the cutting force and cutting temperature owing to their different dimensions and the solution of the models through giving the cutting force and cutting temperature different weight coefficients. The optimal results are verified by experiments, which shows that the optimal tool geometric parameters are very effective and vital for ensuring both the cutting force and the cutting temperature not too high. This work is of great significance to the cutting tool design theory and its manufacturing for reducing tool wear.
Looking Under the Hood of Z-Wave

Z-Wave is a proprietary Internet of Things substrate providing distributed home and office automation services. The proprietary nature of Z-Wave devices makes it difficult to determine their security aptitude. While there are a variety of open source tools for analyzing Z-Wave frames, inspecting non-volatile memory, and disassembling firmware, there are no dynamic analysis tools allowing one to inspect the internal state of a Z-Wave transceiver while it is running. In this work, a memory introspection capability is developed for three Z-Wave devices containing a ZW0301, a Z-Wave transceiver system-on-chip. In all three devices, the firmware image is modified to include the memory introspection capability by hooking an existing data exfiltration mechanism used by the device. The memory introspection capability is applied to determine how nonces are generated by Z-Wave devices to prevent replay attacks. Through a combination of static and dynamic analysis, the nonce generating algorithm is found to be based on a nonce round key that updates every secure frame transaction.
Compiling Efficiently with Arithmetic Emulation for the Custom-Width Connex Vector Processor

Compiling from sequential C programs using LLVM for the wide Connex vector accelerator, a competitive customizable architecture for embedded applications with 32 to 4096 16-bit integer lanes, is challenging. Our compiler targets Opincaa, a JIT assembler and coordination C++ library for Connex, which is able to run portable programs w.r. t. the vector width. For this to work, our back end needs to handle symbolic C/C++ expressions represented as adjacent inline assembly strings, which are used as scalar immediate operands in the vector code. Also, our back end for Connex needs to lower code to emulate efficiently arithmetic operations for non-native types such as 32-bit integer and 16-bit floating point. To simplify the work of the compiler writer we conceive a method to code generate how we lower these operations inside LLVM's instruction selection pass. We report speedup factors of up to 12.24 when running on a Connex processor with 128 lanes w.r. t. the dual-core ARM Cortex A9 clocked at a frequency 6.67 times higher, and an energy efficiency improvement average of 1.07 times. However, note that a Connex IC can achieve an order of magnitude more energy efficiency than our FPGA implementation.
MalAlert: Detecting Malware in Large-Scale Network Traffic Using Statistical Features

In recent years, we witness the spreading of a significant variety of malware, which operate and propagate relying on network communications. Due to the staggering growth of traffic in the last years, detecting malicious software has become infeasible on a packet-by-packet basis. In this paper, we address this challenge by investigating malware behaviors and designing a method to detect them relying only on network flow-level data. In our analysis we identify malware types with regards to their impact on a network and the way they achieve their malicious purposes. Leveraging this knowledge, we propose a machine learning-based and privacy-preserving method to detect malware. We evaluate our results on two malware datasets (MalRec and CTU-13) containing traffic of over 65,000 malware samples, as well as one month of network traffic from the University of Oxford containing over 23 billion flows. We show that despite the coarse-grained information provided by network flows and the imbalance between legitimate and malicious traffic, MalAlert can distinguish between different types of malware with the F1 score of 90%.
Days of our lives: family experiences of digital technology use

This paper describes findings from a workshop, with 11 parents of children under 12 years of age, that explored family experiences of digital technology use. We found that technology experiences within everyday family life are complicated and interlinked. We highlight four experiences that featured most prominently with our participants: apprehension, ambivalence, compromise and conflict. In addition, we discuss how family values govern these experiences and how families use digital technology. This work contributes to current understandings of how family values guide technology practices. These early findings suggest that deeper understandings of family values; how they are shared, negotiated and put into action, will help inform the design of future technologies that not only support families' practices and activities, but also their experiences and aspirations.
A Survey of Cloudlet-Based Mobile Augmentation Approaches for Resource Optimization

Mobile devices (MDs) face resource scarcity challenges owing to limited energy and computational resources. Mobile cloud computing (MCC) offers a resource-rich environment to MDs for offloading compute-intensive tasks encountering resource scarcity challenges. However, users are unable to exploit its full potential owing to challenges of distance, limited bandwidth, and seamless connectivity between the remote cloud (RC) and MDs in the conventional MCC model. The cloudlet-based solution is widely used to address these challenges. The response of the cloudlet-based solution is faster than the conventional mobile cloud-computing model, rendering it suitable for the Internet of Things (IoT) and Smart Cities (SC). However, with the increase in devices and workloads, the cloudlet-based solution has to deal with resource-scarcity challenges, thus, forwarding the requests to remote clouds. This study has been carried out to provide an insight into existing cloudlet-based mobile augmentation (CtMA) approaches and highlights the underlying limitations for resource optimization. Furthermore, numerous performance parameters have been identified and their detailed comparative analysis has been used to quantify the efficiency of CtMA approaches.
Distributed Learning in Ad-Hoc Networks with Unknown Number of Players

We study algorithms for distributed learning in ad-hoc cognitive networks where no central controller is available. In such networks, the players cannot communicate with each other and even may not know how many other players are present in the network. If multiple players select a common channel they collide, which results in loss of throughput for the colliding players. We consider both the static and dynamic scenarios where the number of players remains fixed throughout the game in the former case and can change in the later. We provide algorithms based on a novel 'trekking approach' that guarantees with high probability constant regret for the static case and sub-linear regret for the dynamic case. The trekking approach gives improved aggregate throughput and also results in fewer collisions compared to the state-of-the-art algorithms.
Making the MOST out of smartphone opportunities for mental health

Modern smartphones come equipped with an array of sensors, and the data from these sensors can be collected to obtain contextual and behavioural information about the phone user. Furthermore, the interesting idea that analysed patterns in this data can indicate psychological conditions or states has gained attention in recent years. In this paper we discuss some incipient work on bringing therapy components from the MOST (moderated online social therapy) web platform into a mobile system that delivers personalised real-time therapy recommendations based on such smartphone sensing information.
Braille Technology Beyond the Financial Barriers: A Braille Literacy Platform to Effectively Combat Braille Literacy Crisis

The braille literacy crisis is a reality among blind individuals. A major barrier is the high cost of technologies that support the learning process of braille. In this scenario, correlated works on the state of the art try to offer low-cost solutions which bring other adoption barriers. In this paper we aim to propose a modular computational architecture mediated by a minimal viable tangible user interface (TUI) prototype. The proposed computational architecture is software-based only and intend to offer flexibility for different implementations on user activities based on any kind of braille literacy context. We conducted experiments with 4 blind literate subjects making use of functional prototype instance of the specified architecture. The results showed that the use of a common haptic artifact for different kind of activities was accepted by the users and made them engaged do explore the tool. We concluded that our proposal solves the gap of low-cost braille technologies without additional barriers and has the potential to combat the braille literacy crisis with future development based on participatory design principles.
RESTful API Automated Test Case Generation with EvoMaster

RESTful APIs are widespread in industry, especially in enterprise applications developed with a microservice architecture. A RESTful web service will provide data via an API over the network using HTTP, possibly interacting with databases and other web services. Testing a RESTful API poses challenges, because inputs/outputs are sequences of HTTP requests/responses to a remote server. Many approaches in the literature do black-box testing, because often the tested API is a remote service whose code is not available. In this article, we consider testing from the point of view of the developers, who have full access to the code that they are writing. Therefore, we propose a fully automated white-box testing approach, where test cases are automatically generated using an evolutionary algorithm. Tests are rewarded based on code coverage and fault-finding metrics. However, REST is not a protocol but rather a set of guidelines on how to design resources accessed over HTTP endpoints. For example, there are guidelines on how related resources should be structured with hierarchical URIs and how the different HTTP verbs should be used to represent well-defined actions on those resources. Test-case generation for RESTful APIs that only rely on white-box information of the source code might not be able to identify how to create prerequisite resources needed before being able to test some of the REST endpoints. Smart sampling techniques that exploit the knowledge of best practices in RESTful API design are needed to generate tests with predefined structures to speed up the search. We implemented our technique in a tool called EvoMaster, which is open source. Experiments on five open-source, yet non-trivial, RESTful services show that our novel technique automatically found 80 real bugs in those applications. However, obtained code coverage is lower than the one achieved by the manually written test suites already existing in those services. Research directions on how to further improve such an approach are therefore discussed, such as the handling of SQL databases.
On the Triviality of the Assignment Statement

The assignment statement is a basic programming operation which is used to assign a value to a variable. The assignment statement is needed in all programs which manipulate data. Most programmers assume that this statement is trivial and therefore should not pose conceptual problems for novice programmers. This article investigates the validity of this assumption. We describe a number of misconceptions of the assignment statement in the form of inappropriate mental models for the assignment statement. These were established through a literature review. We used a questionnaire to identify the prior programming experience of the students in a cohort of 1058 students enrolled for an introductory programming course. We classify the respondents into three categories namely those who have never programmed before; those who have previously tried but failed the introductory programming course; and those who claim that they have had prior programming experience. We then asked the students to complete ten questions which ask the outcome of a short program executing assignment statements. The multiple choice questions were designed to identify the mental model of the respondent regarding the assignment statement for a number of cases. We compare the occurrence of correct and wrong metal models in the three groups and discuss our observations as well as their consequences for teaching introductory programming.