Miracle-dz/UniFAR-FTUs huggingface dataset:
Scalable Game-Focused Learning of Adversary Models: Data-to-Decisions in Network Security Games

Previous approaches to adversary modeling in network security games (NSGs) have been caught in the paradigm of first building a full adversary model, either from expert input or historical attack data, and then solving the game. Motivated by the need to disrupt the multibillion dollar illegal smuggling networks, such as wildlife and drug trafficking, this paper introduces a fundamental shift in learning adversary behavior in NSGs by focusing on the accuracy of the model using the downstream game that will be solved. Further, the paper addresses technical challenges in building such a game-focused learning model by i) applying graph convolutional networks to NSGs to achieve tractability and differentiability and ii) using randomized block updates of the coefficients of the defender’s optimization in order to scale the approach to large networks. We show that our game-focused approach yields scalability and higher defender expected utility than models trained for accuracy only.
Guided Reinforcement Learning Under Partial Observability

Due to recent breakthroughs in artificial intelligence, reinforcement learning (RL) gained considerable attention in the last years. Especially combining RL with deep neural networks, which could already achieve great success in other research fields, has contributed to these breakthroughs. In many real-world scenarios it is not always possible to perceive the true and complete state of the environment. These scenarios are known as learning under uncertainty or under partial observability. Formulating such problems as partially observable Markov decision processes (POMDPs) allows us to solve decision making processes under uncertainty. Guided RL approaches address this problem by supporting the RL algorithms with additional state information during the learning process to increase their performance for solving POMDPs. However, these guided approaches are relatively rare in literature and most existing approaches are modelbased, meaning that they require to learn an appropriate model of the environment first. For this reason, we will propose a novel model-free guided RL approach, called guided reinforcement learning (GRL). The guidance is mainly based on mixing samples containing full or partial state information while we gradually decrease the amount of full state information during training which ends up with a policy compatible with partial observations. The general formulation of our simple GRL approach allows to combine this approach with a variety of existing model-free RL algorithms as well as a variety of settings where these algorithms can be applied to. We demonstrate that our GRL approach can outperform other baseline algorithms that are trained directly on partial observations on nine different tasks. These tasks include two instances of the well-known discrete action space problem RockSample and the continuous action space problem LunarLander-POMDP which is a partially observable modification of the LunarLanderContinuous-v2 environment. Further, the benchmark tasks also include six partially observable tasks that we have constructed based on continuous control problems, simulated in the MuJoCo physics simulator.
The cue-based retrieval theory of sentence comprehension: New findings and new challenges

An active question in psycholinguistics concerns how speakers mentally encode and retrieve linguistic representations in memory during sentence comprehension. In this paper, we briefly review contemporary perspectives on memory retrieval in sentence comprehension, focusing on accounts of dependency resolution, and present new puzzles and challenges for existing models. We propose that the current data support the conclusion that linguistic dependencies are resolved using a direct-access, cue-based retrieval mechanism that gives preferential weighting to syntactic information when navigating linguistic representations in memory. We then discuss current empirical and theoretical gaps, highlighting areas in need of further research.
TAF1L promotes development of oral squamous cell carcinoma via decreasing autophagy-dependent apoptosis

This study focused on investigating the relationships of TAF1L expression and clinical features or pathological stages of oral squamous cell carcinoma (OSCC), and its potential roles of TAF1L on OSCC development. Western blot and immunohistochemical staining were used to detect TAF1L expression in OSCC tissues and cells. Effects of TAF1L on OSCC cells in vitro were examined by cell proliferation assay, wound healing assay, transwell chamber assay, flow cytometry analysis and siRNA technique. Cellular key proteins related to cell autophagy and apoptosis were evaluated by Western blot and immunofluorescent staining. Moreover, functions of TAF1L on OSCC process were observed in nude mouse model. Testing results showed that expression of TAF1L protein was higher in OSCC tissues than that in normal oral epithelial or paracancerous tissues. Additionally, the level of TAF1L protein expression was upregulated in OSCC cell lines, compared to that in normal oral epithelial cells. Furthermore, cell proliferation, migration, autophagy and apoptosis were modulated post siRNA-TAF1L treatment in vitro. Especially, TAF1L knockdown-induced apoptotic activation on OSCC cells could be rescued by autophagic activator (Rapamycin). Moreover, that overexpression of TAF1L protein could promote the growth of OSCC cell xenografts was confirmed in nude mouse model. Taken together, it suggests that TAF1L may facilitate OSCC cells to escape cell apoptosis via autophagic activation for enhancing OSCC development.
IMAGE ANALISIS OF UNSTEADY STRUCTURE OF HIGH-SPEED CAVITATING JET

This paper describes the statistical characteristics of unsteady structure of a cavitating water jet issuing into a stagnant fluid of water. The structure of the cavitating jet is visualized by the shadowgraph method and the images taken by the high-speed camera are analyzed by the proper orthogonal decomposition (POD). The experimental result indicates that the cavitating jet is fluctuating in streamwise direction and the maximum oscillation appears around the cloud center, while the disappearance of cloud occurs in the downstream with the opposite phase of oscillation. The reconstructed cavitation images show that a series of growing, reentrant and shedding motion of the cloud is represented by the lower POD modes.
The UTokyo System for Blizzard Challenge 2016

In this paper, we mainly introduce the UTokyo speech synthesis system for Blizzard Challenge 2016. Our system is a typical statistical parametric speech synthesis system. Its duration model is built by using HTS toolkit, and its acoustic model is made using Bidirectional Long Short-Term Memory with Recurrent Neural Network (BLSTM-RNN). In the synthesizing phase, sentence-level waveforms are ﬁrstly generated. Then these waveforms are concatenated into a paragraph. Because the evaluations of our system are not satisfactory, the defects and problems in the system are also discussed in this paper
Security deﬁnitions, attacker models and veriﬁcation techniques for protocols based on weak secrets

This deliverable concerns the Task 3 of the Sequoia project: Low-entropy secrets. We have proposed new deﬁnitions and techniques for verifying protocols that rely on low-entropy (or weak) secrets. Our results allowed us to study several protocols for authenticated data transmission. These protocols are part of the ISO/IEC 9798-6:2010 standard. Our results have been published in [3].
Pipelining Bottom-up Data Flow Analysis

Bottom-up program analysis has been traditionally easy to parallelize because functions without caller-callee relations can be analyzed independently. However, such function-level parallelism is significantly limited by the calling dependence - functions with caller-callee relations have to be analyzed sequentially because the analysis of a function depends on the analysis results, a.k. a. , function summaries, of its callees. We observe that the calling dependence can be relaxed in many cases and, as a result, the parallelism can be improved. In this paper, we present Coyote, a framework of bottom-up data flow analysis, in which the analysis task of each function is elaborately partitioned into multiple sub-tasks to generate pipelineable function summaries. These sub-tasks are pipelined and run in parallel, even though the calling dependence exists. We formalize our idea under the IFDS/IDE framework and have implemented an application to checking null-dereference bugs and taint issues in C/C++ programs. We evaluate Coyote on a series of standard benchmark programs and open-source software systems, which demonstrates significant speedup over a conventional parallel design.
Distributed frequent sequence mining with declarative subsequence constraints

Frequent sequence mining extracts frequently occurring patterns from sequential data. Some algorithms allow users to specify constraints to control which sequences are of interest. Each algorithm usually supports a particular subset of available constraints. Recently, based on regular expressions, a declarative approach to specifying many of these constraints in a unified way was proposed. Scalable algorithms are essential to mine large datasets efficiently. Such algorithms exist for particular subsets of constraints. However, no scalable algorithm with support for many constraints has been put forward yet. We propose the distributed two-stage algorithm DDIN based on item-based partitioning. It processes input sequences in parallel and constructs partitions that can mine for frequent sequences in parallel. We use nondeterministic finite automata as an efficient representation for the intermediary sequences we send to the partitions. Our experimental evaluation on four real-world datasets suggests that DDIN outperforms naive approaches for declarative constraints, is competitive to a state-of-the-art algorithm for traditional constraints, offers linear scalability, and makes it possible to mine datasets that cannot be mined efficiently using sequential algorithms.
Small-holder Farming, Food Security and Climate Change in South Africa: Male-Female and Urban-Rural Differences

With ongoing climate change, food insecurity is likely to become more widespread in most small-holder and subsistence farm households in sub-Saharan Africa. However, the existence and extent of gendered food (in)security remains unclear. This study extends existing knowledge by assessing gender inequality in food (in)security amongst small-holder farm households in urban and rural areas of South Africa. To do so, we use the gender of the head of household in a treatment effects framework. Our results show that male-headed farm households are more food secure compared to female-headed households, with the latter depending more on agriculture. We further observe that chronic food insecurity is greater and the gender gap in food security is wider amongst rural than urban households. Our results suggest that the current policy interest in promoting both rural and urban agriculture is likely to increase food security in both maleand female-headed households, and to reduce the gender gap.
Ubiquitous and Low Power Vehicles Speed Monitoring for Intelligent Transport Systems

This paper presents a high scalability real-time intelligent traffic monitoring system, based on Radio Frequency Identification (RFID). The main features of this system are low cost, low power consumption, traffic monitoring, and connectivity. The system’s architecture includes an RFID reader, a passive tag, and a Raspberry Pi. Our solution collects vehicle information from the labels and stores the data into a database by employing only one antenna. The main challenge is that the RFID module is not robust enough to recognize the information in the vehicle’s RFID tag on each information query, while the label is in the reading zone. This instability does not allow us to know precisely when and where a vehicle enters or leaves the sensing zone. What is more, the high random error in the power signal and the complexity of its characteristic curve pattern add difficulty to the speed calculation, when we reduce the number of antennas to one. For this reason, an innovative approach has been designed, using customized modular neural network (MNN). This method fits the collected data (power signal vs time) affected by acute random noise, to the characteristic correspondence function among the signal power and the position of the terminal, which domains are dimensionally different. As a result, we can estimate the vehicle speeds and obtain the whole vehicle information. Under this novel method, we are able to reduce the hardware, in comparison with previous approaches, making it cheaper and decreasing power consumption.
Morphologization, lexicalization and historical classification*

This paper examines the irregular application of the sound change commonly known as ‘Bantu Spirantization (BS)’ — a particular type of assibilation — in front of certain common Bantu morphemes. This irregularity can to a large extent be explained as the result of the progressive morphologization (through ‘dephonologization’) and lexicalization to which the sound shift was exposed across Bantu. The interaction with another common Bantu sound change, i.e. the 7-to-5-vowel merger, created the conditions necessary for the morphologization of BS, while analogy played an important role in its blocking and retraction from certain morphological domains. Differing morpho-prosodic constraints are at the origin of the varying heteromorphemic conditioning of BS. These uneven morphologization patterns, especially before the agentive suffix -i, were entrenched in the lexicon thanks to the lexicalization of agent nouns. The typology of Agent Noun Spirantization (ans) developed in this paper not only contributes to a better understanding of the historical processes underlying the varying patterns of BS morphologization and lexicalization, but also to internal Bantu classification. The different ANS types are geographically distributed in such a way that they allow to distinguish major Bantu subgroups. From a methodological point of view, this article thus shows how differential morphologization and lexicalization patterns can be used as tools for historical classification.
Distribution mapping and conservation of Rhopaloblaste augusta (Kurz) H. E. Moore in Nicobar Islands, India

Rhopaloblaste augusta (Kurz) H. E. Moore is one of the threatened palm species endemic to Nicobar Islands. In order to know the distribution and conservation status of this palm, Maxent ecological niche modelling was used to predict the accurate distribution. The study revealed that high potential distribution of R. augusta was found in Great Nicobar and Car Nicobar islands; of the ten spatially unique points, six were from Great Nicobar, two from Camorta and one each from Katchall and Car Nicobar Islands. For islands like Tarassa, Chowra, Battimlav, Tinket and Nancowry, the model predicted significant influence of environmental variables in the distribution of species. High precipitation in warmest quarter, above 18 °C annual mean temperature and less temperature seasonality are the variables influencing distribution of this species. The islands of Nicobar group are rich in palm resources; however, habitats are extremely fragile owing to the prevalence of intense biotic and abiotic pressure. Urgent conservation initiatives are required to protect the existing endemic species richness of these islands.
Observing User Behavior in Group Recommender Systems

Recommender systems help users decide among a multitude of choices, and thus address the information overload present in our everyday lives. Research in the field received rekindled interest following the Netflix prize, where advanced Collaborative Filtering (CF) techniques demonstrated their superiority [7]. CF is based on the premise that the behavior of users in the past, in terms of their consumption and feedback, can help predict future behavior. In many cases, a recommendation to a group of people rather than a single person is required. For example, consider some friends planning their summer vacation destination [1], or a family deciding on a TV program to watch [9]. The additional challenge in making group recommendations is how to combine individual preferences. For this problem, ideas from Social Choice theory have been employed. In particular and similar to how other computer science fields were influenced e.g., [5], various aggregation strategies of individual preferences have been proposed with the understanding that no single one can be optimal in a formal sense [2]. To design better group recommender systems, it is first important to improve our understanding of which aggregation strategy works better and in what setting. Towards this direction, two distinct paradigms have been followed. The first is by case studies, where the goal is to observe how people actually combine preferences and make group decisions [8, 4]. The second is to define evaluation metrics of aggregation strategies and then apply them over semi-synthetic data [3] (datasets with actual feedback from groups are scarce, often non-open). However, each paradigm has its own drawbacks. The former suffers in generalizability and cannot scale to the order of millions of users and thousands of items, where recommender systems typically apply. The latter suffers from experimenter bias, as the choices made in designing the evaluation setting tend to favor group recommenders with specific aggregation strategies. Our first contribution directly addresses the aforementioned shortcomings. We design a technique to extrapolate the observations from small-scale studies to meaningful real-life scales. We try to match well-known aggregation strategies from social choice theory to the observed group decisions, and identify a mixture of strategies that appears to match well group behavior. We then apply these strategies over large publicly available datasets, typically used in recommender system research, to generate more realistic semi-synthetic group behavior. Our evaluation setting minimizes experimenter bias by considering multiple evaluation criteria. Our second contribution is a novel CF machine learning technique that attempts to discover how a group behaves.1 It does so by observing user behavior individually and within groups,
Racism without a Face: Predictive Statistics in the Criminal Justice System

The use of statistical risk assessment tools in the criminal justice system is an increasingly pressing issue. From determining needs and allocating resources post-release, to helping determine how much time a person will spend in cages, wide-spread adoption of these predictive tools continues. Their expanded use and the promise of a more equitable and effective criminal justice system has garnered hope and excitement for some, while their real-world implementation is a concern for others. Recent studies have demonstrated that a prominent risk assessment tool, called COMPAS, is having disparate impact across different demographic variables. My thesis is motivated by two central questions concerning COMPAS and risk assessment tools more generally. First, what drives disparate impact across race? More specifically, what affect does biased data have on this disparate impact? I conclude that biased data, where bias is defined as over-representation of a group, leads to biased algorithms and therefore biased results in the form of disparate impact. Furthermore, I claim that these results indicate a need for analyzing and understanding the prison industrial complex and the interlocking systems of domination that lead to over-representation of black defendants in the criminal justice system. Moreover, these results beg for a multidisciplinary, comprehensive approach to both the building and implementation of risk assessment tools.
Interaction Techniques for CSG Modeling in VR

Many ﬁelds use 3D virtual objects and environments which can be modeled using diﬀerent techniques: sketching, primitives, mesh manipulation, or using CSG, a powerful tool that allows the generation of more complex models using boolean operations on simpler ones. This content is created mainly with traditional 2D input and output. This diﬀerence in degrees of freedom to everyday physical interactions is counterproductive. Advances in VR hardware made user tracking and HMD more accurate, comfortable and aﬀordable. VR lets users operate with the same degrees of freedom as they would in the physical world, which has the power to accelerate 3D manipulation and modelling tasks. Moreover, in VR, users can’t see their physical body, making it necessary to ﬁnd strategies to give users the notion of their body, or at least of the parts of their bodies that matter for the tasks they are developing. We proposed two techniques to perform boolean operations between two objects in Virtual Reality based on 3D gestures and menus. To compare those approaches with a baseline approach, with physical controllers, we developed a prototype that implemented all three alternatives and had two types of body representation: a full body avatar or two oval objects for hands. We conducted a user evaluation and results showed that there was no signiﬁcant diﬀerence in eﬃciency between the three techniques. Users preferred the menu based approach due to its immediate feedback. We also found that having a full body avatar had negatively impacted the eﬃciency on this type of tasks.
It takes two to salsa: The interplay between HR systems and leadership attachment styles for employee creativity and innovation

Human resource (HR) management sees people as one of the main sources of firms’ competitive advantage, and it examines the role of different employee groups as well as the approaches in managing them (Purcell, 1999). In line with the devolution-to-the-line perspective in HR (Perry & Kulik, 2008), the role of immediate supervisors is becoming increasingly important for work performance, with respect to shaping the context of work and immediately influencing it. However, it remains to be asserted how various leadership approaches act within the higher context of HR. The existing literature fails to clearly answer how leadership interacts with HR, which should act as a top-down catalyst or a co-creator of how employees perceive their relationships with their supervisors (Gustafsson, Abbey, & Hope Hailey, 2016) and what the outcomes of such interactions are. The aim of this paper is to bring together literature on strategic HR management and leadership in order to develop and test hypotheses about the cross-level interactive role of attachment styles that employees perceive to have developed with leaders (Hinojosa, Davis McCauley, Randolph-Seng, & Gardner, 2014) and HR systems (cf. Lepak & Snell, 2002) in influencing desirable outcomes, specifically the employee innovation processes at the individual level. The importance of HR systems as a contextual top-down catalyst for innovation has only recently gained momentum in the extant literature (Zhou, Hong, & Liu, 2013). Similar is true for attachment styles (i.e., relational schemas that correspond to strategies of affect regulation that result from different patterns of interactions that followers possess with their supervisors), which are lately becoming increasingly investigated in the leadership literature (Davidovitz, Mikulincer, Shaver, Izsak, & Popper, 2007). We narrow in on two opposite HR systems in organizations (compliance and commitment) and three leadership attachment styles perceived by the employees (secure, anxious, and avoidant) to propose that their cross-level interactions have different roles in predicting two different elements of employees’ innovative work behavior: idea generation (creativity) and idea implementation (innovation) behaviors (cf. Baer, 2012), respectively.