Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding Farmers' Perceptions of and Adaptations to Climate Change and Variability: The Case of the Maritime, Plateau and Savannah Regions of Togo

Togolese agriculture is predominantly rain-fed and hence fundamentally dependent on the vagaries of weather. Thus, it is negatively affected by climate change. The present study assesses farmers’ perceptions and adaptation to climate change to enhance policy towards tackling the challenges climate change poses to the farmers in the study area. Descriptive statistics and multinomial logit (MNL) were used to analyze data obtained from a cross-sectional survey executed during the 2013/2014 agricultural production year in the maritime, plateau and savannah regions of Togo. The analysis of farmers’ perception to climate change reveals high increase in temperature and decrease in rainfall. These results are in line with the trend analysis of climate data that records from 1961 to 2013 about the study area especially on the temperature. Furthermore, the results show that crop diversification, change in crops, find off-farm jobs, change of the amount of land, change of the planting date and plant short season variety are the adaptation methods employed by the farmers. Moreover, with respect to the multinomial logit analysis, the results highlight that education level, farming experience, access extension services, access to credit and access to climate information are the factors that enhance farmers’ adaptive capacity to climate change and variability. Thus, there is room for better adaptation if government intensifies activities of extension workers and ensures that farmers have access to affordable credit schemes to increase their ability and flexibility to adopt adaptation measures. There is also a need to include climate change communication to facilitate exchange of climatic information that could enable smallholder farmers to adapt to changing planting dates. Finally, investment in education systems and creation of off-farm job opportunities in the rural areas can be underlined as a good policy option.
Predicting population-level socio-economic characteristics using Call Detail Records (CDRs) in Sri Lanka

Prior work has shown that mobile network big data can be used as a high-frequency alternative data source to derive proxy measures that have strong predictive capacity to estimate census and poverty data in developing countries. Given that the observations from these studies can be dependent on local context and regional characteristics, we replicate this work targeting two regions in Sri Lanka. We focus on Northern Province, a post-conflict region with a highly vulnerable population and Western Province, an urban region that has been relatively untouched by the conflict. We analyze the relationship between aggregate features related to consumption, social and mobility behaviors derived from pseudonymized mobile phone CDRs and census data associated with population-level socio-economic characteristics. We show that Northern Province exhibits different social and mobility patterns when compared to populations with similar socio-economic characteristics in Western Province, which highlights the importance of replicating prior research studies under different local contexts. We go on to develop predictive models that estimate the census features using the derived CDR features. Our results confirm the applicability of this methodology in a Sri Lankan, post-conflict setting, and highlight potential areas that need to be addressed in order to improve the accuracy of our prediction models.
Operating Channel Validation: Preventing Multi-Channel Man-in-the-Middle Attacks Against Protected Wi-Fi Networks

We present a backwards compatible extension to the 802.11 standard to prevent multi-channel man-in-the-middle attacks. This extension authenticates parameters that define the currently in-use channel. Recent attacks against WPA2, such as most key reinstallation attacks, require a man-in-the-middle (MitM) position between the client and Access Point (AP). In particular, they all employ a multi-channel technique to obtain the MitM position. In this technique, the adversary acts as a legitimate AP by copying all frames sent by a real AP to a different channel. At the same time, the adversary acts as a legitimate client by copying all frames sent by the client to the channel of the real AP. When copying frames between both channels, the adversary can reliably manipulate (encrypted) traffic. We propose an extension to the 802.11 standard to prevent such multi-channel MitM attacks, making exploitation of future weaknesses in protected Wi-Fi networks harder, to practically infeasible. Additionally, we propose a method to securely verify dynamic channel switches that may occur while already connected to a network. Finally, we implemented a prototype of our extension on Linux for both the client and AP to confirm practical feasibility.
CodeKoan: A Source Code Pattern Search Engine Extracting Crowd Knowledge

Source code search is frequently needed and important in software development. Keyword search for source code is a widely used but a limited approach. This paper presents CodeKoan, a scalable engine for searching millions of online code examples written by the worldwide programmers’ community which uses data parallel processing to achieve horizontal scalability. The search engine relies on a token-based, programming language independent algorithm and, as a proof-of-concept, indexes all code examples from Stack Overflow for two programming languages: Java and Python. This paper demonstrates the benefits of extracting crowd knowledge from Stack Overflow by analyzing well-known open source repositories such as OpenNLP and Elasticsearch: Up to one third of the source code in the examined repositories reuses code patterns from Stack Overflow. It also shows that the proposed approach recognizes similar source code and is resilient to modifications such as insertion, deletion and swapping of statements. Furthermore, evidence is given that the proposed approach returns very few false positives among the search results.
Encrypted Traffic Identification Based on N-gram Entropy and Cumulative Sum Test

Since existing methods using entropy are less effective in characterizing encrypted traffic, this paper proposes an encrypted traffic identification method based on n-gram entropy and cumulative sum. This method analyzes the entropy characteristics of n-gram entropy for text, picture, compressed file, and encrypted traffic in the network. Furthermore, an analysis of cumulative sum is performed to better distinguish compressed file traffic and encrypted traffic. The experiments show that our propsed method reaches high accuracy for encrypted traffic identification and performs well in distinguishing compressed file traffic and encrypted traffic.
Code Review for Newcomers: Is It Different?

Onboarding is a critical stage in the tenure of software developers with a project, because meaningful contribution requires familiarity with the codebase. Some software teams employ practices, such as mentoring, to help new developers get accustomed faster. Code review, i.e., the manual inspection of code changes, is an opportunity for sharing knowledge and helping with onboarding. In this study, we investigate whether and how contributions from developers with low experience in a project do receive a different treatment during code review. We compare reviewers' experience, metrics of reviewers' attention, and change merge rate between changes from newcomers and from more experienced authors in 60 active open source projects. We find that the only phenomenon that is consistent across the vast majority of projects is a lower merge rate for newcomers' changes.
List-three-coloring graphs with no induced $P_6+rP_3$.

For an integer $r$, the graph $P_6+rP_3$ has $r+1$ components, one of which is a path on $6$ vertices, and each of the others is a path on $3$ vertices. In this paper we provide a polynomial-time algorithm to test if a graph with no induced subgraph isomorphic to $P_6+rP_3$ is three-colorable. We also solve the list version of this problem, where each vertex is assigned a list of possible colors, which is a subset of $\{1,2,3\}$.
Generating Multi-Categorical Samples with Generative Adversarial Networks

We propose a method to train generative adversarial networks on mutivariate feature vectors representing multiple categorical values. In contrast to the continuous domain, where GAN-based methods have delivered considerable results, GANs struggle to perform equally well on discrete data. We propose and compare several architectures based on multiple (Gumbel) softmax output layers taking into account the structure of the data. We evaluate the performance of our architecture on datasets with different sparsity, number of features, ranges of categorical values, and dependencies among the features. Our proposed architecture and method outperforms existing models.
Dependence-Preserving Data Compaction for Scalable Forensic Analysis

Large organizations are increasingly targeted in long-running attack campaigns lasting months or years. When a break-in is eventually discovered, forensic analysis begins. System audit logs provide crucial information that underpins such analysis. Unfortunately, audit data collected over months or years can grow to enormous sizes. Large data size is not only a storage concern: forensic analysis tasks can become very slow when they must sift through billions of records. In this paper, we first present two powerful event reduction techniques that reduce the number of records by a factor of 4.6 to 19 in our experiments. An important benefit of our techniques is that they provably preserve the accuracy of forensic analysis tasks such as backtracking and impact analysis. While providing this guarantee, our techniques reduce on-disk file sizes by an average of 35× across our data sets. On average, our in-memory dependence graph uses just 5 bytes per event in the original data. Our system is able to consume and analyze nearly a million events per second.
Use of Wikipedia categories on information retrieval research: a brief review

Wikipedia categories, a classification scheme built for organizing and describing Wikpedia articles, are being applied in computer science research. This paper adopts a systematic literature review approach, in order to identify different approaches and uses of Wikipedia categories in information retrieval research. Several types of work are identified, depending on the intrinsic study of the categories structure, or its use as a tool for the processing and analysis of other documentary corpus different to Wikipedia. Information retrieval is identified as one of the major areas of use, in particular its application in the refinement and improvement of search expressions, and the construction of textual corpus. However, the set of available works shows that in many cases research approaches applied and results obtained can be integrated into a comprehensive and inclusive concept of information retrieval.
The impact of an emotionally expressive writing intervention on eating pathology in female students

ABSTRACT Introduction: Previous research demonstrating emotional influences on eating and weight suggest that emotionally expressive writing may have a significant impact on reducing risk of eating pathology. This study examined the effects of writing about Intensely Positive Experiences on weight and disordered eating during a naturalistic stressor. Method: Seventy-one female students completed an expressive or a control writing task before a period of exams. Both groups were compared on BMI (kg/m2) and the Eating Disorder Examination – Questionnaire (EDE-Q) before the writing task and at 8-week follow-up. A number of secondary analyses were also examined (to identify potential mediators) including measures of attachment, social rank, self-criticism and self-reassurance, stress and mood. Results: There was a significant effect of intervention on changes in the subscales of the EDE-Q (p = .03). Specifically, expressive writers significantly reduced their dietary restraint while those in the control group did not. There was no significant effect of the intervention on changes in BMI or the other subscales of the EDE-Q (Eating, Weight and Shape Concern). There was also no effect of writing on any of the potential mediators in the secondary analyses. Discussion: Emotionally expressive writing may reduce the risk of dietary restraint in women but these findings should be accepted with caution. It is a simple and light touch intervention that has the potential to be widely applied. However, it remains for future research to replicate these results and to identify the mechanisms of action.
On the effects of seeding strategies: a case for search-based multi-objective service composition

Service composition aims to search a composition plan of candidate services that produces the optimal results with respect to multiple and possibly conflicting Quality-of-Service (QoS) attributes, e.g., latency, throughput and cost. This leads to a multi-objective optimization problem for which evolutionary algorithm is a promising solution. In this paper, we investigate different ways of injecting knowledge about the problem into the Multi-Objective Evolutionary Algorithm (MOEA) by seeding. Specifically, we propose four alternative seeding strategies to strengthen the quality of the initial population for the MOEA to start working with. By using the real-world WS-DREAM dataset, we conduced experimental evaluations based on 9 different workflows of service composition problems and several metrics. The results confirm the effectiveness and efficiency of those seeding strategies. We also observed that, unlike the discoveries for other problem domains, the implication of the number of seeds on the service composition problems is minimal, for which we investigated and discussed the possible reasons.
Kid Coding Games and Artistic Robots: Attitudes and Gaze Behavior

We present a study in the context of coding activities for children. Our main focus is towards understanding the relation between children's attitudes towards coding and their behaviour while coding. Forty-four school children participated in this study solving different coding problems. We used mixed methods to capture attitude and behaviour of the children participating in the study. The attitudes towards learning to program were recorded using the self-reporting questionnaires and the behaviour was captured using the eye-trackers. The results show that the gaze-behaviour of the children moderates the relationship among the different attitudes towards coding.
The Structure of Software Design Discussions

As a software system is iteratively developed, software developers engage in many discussions, often through written forms. Some of these discussions occur on pull requests and include information about the design of the system to which the code in the pull request is being contributed. Although previous work has shown that design is discussed in forums like pull requests, little is known about the form and content of the discussion about design. In this paper, we report on an in-depth analysis of three pull requests to better understand the form and content of design information in pull request discussions to enable the development of tools to help humans access this information.
Semprola: a semiotic programming language

Most people interested in developing new programming languages or programming environments are looking at how to improve the syntax and semantics of the program text or at tools that help make programmers more productive at crafting the program text. What we need is a more fundamental change to the conception of what a program is. This paper introduces a new, Semiotic Programming environment in which we program with signs in a context, rather than with symbols in a text file and where we treat dialogue rather than functions as the dominant organising principle of our code. All of the information held in this environment is managed in a distributed, semiotic graph that is organized into multiple ontological spaces. Taken together these enable our programs and data to have greater semantic depth. Finally the paper gives a brief introduction to Semprola, a Semiotic Programming Language that can be used in this Semiotic Programming environment.
On the use of sequence mining within spectrum based fault localisation

Spectrum based fault localisation is a widely studied class of heuristics for locating faults within a software program. Unfortunately, the current state of the art ignores the inherent dependencies between the methods leading up to the fault, hence having a limited diagnostic accuracy. In this paper we present a variant of spectrum based fault localisation, which leverages series of method calls by means of sequence mining. We validate our variant (we refer to it as sequenced spectrum analysis) on the Defects4J benchmark, demonstrating that sequenced spectrum analysis gains a 12% points improvement against the state of the art.
Entamoeba histolytica Induce Signaling via Raf/MEK/ERK for Neutrophil Extracellular Trap (NET) Formation

Amoebiasis, the disease caused by Entamoeba histolytica is the third leading cause of human deaths among parasite infections. E. histolytica was reported associated with around 100 million cases of amoebic dysentery, colitis and amoebic liver abscess that lead to almost 50,000 fatalities worldwide in 2010. E. histolytica infection is associated with the induction of inflammation characterized by a large number of infiltrating neutrophils. These neutrophils have been implicated in defense against this parasite, by mechanisms not completely described. The neutrophil antimicrobial mechanisms include phagocytosis, degranulation, and formation of neutrophil extracellular traps (NETs). Recently, our group reported that NETs are also produced in response to E. histolytica trophozoites. But, the mechanism for NETs induction remains unknown. In this report we explored the possibility that E. histolytica leads to NETs formation via a signaling pathway similar to the pathways activated by PMA or the Fc receptor FcγRIIIb. Neutrophils were stimulated by E. histolytica trophozoites and the effect of various pharmacological inhibitors on amoeba-induced NETs formation was assessed. Selective inhibitors of Raf, MEK, and NF-κB prevented E. histolytica-induced NET formation. In contrast, inhibitors of PKC, TAK1, and NADPH-oxidase did not block E. histolytica-induced NETs formation. E. histolytica induced phosphorylation of ERK in a Raf and MEK dependent manner. These data show that E. histolytica activates a signaling pathway to induce NETs formation, that involves Raf/MEK/ERK, but it is independent of PKC, TAK1, and reactive oxygen species (ROS). Thus, amoebas activate neutrophils via a different pathway from the pathways activated by PMA or the IgG receptor FcγRIIIb.
Impact of Malocclusions on the Oral Health-Related Quality of Life of Early Adolescents in Ndola, Zambia

The study aimed to assess the prevalence of malocclusions and its impact on oral health-related quality of life (OHRQoL) among early adolescents in Ndola, Zambia. It used a random sample of 384 primary school children aged 12–14 years. The Child Oral Health Impact Profile-Short Form 19 (COHIP-SF19) was used to assess OHRQoL, and the Dental Aesthetic Index (DAI) was used to examine dentofacial anomalies. The chi-square test was used to study whether there was a statistically significant association between variables and multivariate logistic regression for the influence of sociodemographic and malocclusions on OHRQoL. Statistical significance was set at p < 0.05. Participants' sociodemographics were 53.6% female, 41.7% aged 13 years, and 43.5% from grade six. The overall reported impact on OHRQoL was 11.7%, which was significant (p < 0.001) by age and sex, and higher in females than males. The overall prevalence of malocclusions was 27.9%, which was significant (p=0.005) by sex, and higher in males than females. Children with malocclusions reported significant (p < 0.001) negative oral health impact compared to the children without malocclusions. Spacing, diastema, and crowding were most prevalent malocclusions that showed clear inverse association with OHRQoL. The study findings provide indications that malocclusions are negatively associated with OHRQoL among Zambian early adolescents.
Deep Brain Stimulation Management of Essential Tremor with Dystonic Features

Clinical Vignette A 64-year-old female with essential tremor (ET) presents for evaluation of deep brain stimulation (DBS) candidacy. Examination revealed subtle dystonic features as well as a disabling postural-action tremor. Clinical Dilemma Can dystonia occur in the setting of the diagnosis of ET and can its presence alter DBS target selection? Clinical Solution Unilateral DBS implantation of the ventralis intermedius (Vim) led to improvement in both tremor and dystonic posturing. Gap in Knowledge Case reports of DBS in dystonic tremor suggest Vim, globus pallidus internus (GPi), and subthalamic targets may all be effective, to varying degrees, in improving both tremor and dystonia. More rigorous studies are needed to identify the optimal target(s). Expert Commentary This case underscores the limited evidence available to guide a clinician’s choice of DBS targets in patients with ET and dystonia. The severity of the dystonia and the presence of more generalized dystonia may alter the thinking about optimal targeting. Vim, GPi, and subthalamic targets appear potentially acceptable options, though Vim is usually the first target attempted when postural-action tremor is the chief complaint. Occasionally, a second rescue DBS lead may be necessary.
Scalable Misinformation Prevention in Social Networks

In this work, we consider misinformation propagating through a social network and study the problem of its prevention. In this problem, a "bad" campaign starts propagating from a set of seed nodes in the network and we use the notion of a limiting (or "good") campaign to counteract the effect of misinformation. The goal is then to identify a subset of $k$ users that need to be convinced to adopt the limiting campaign so as to minimize the number of people that adopt the "bad" campaign at the end of both propagation processes. This work presents \emph{RPS} (Reverse Prevention Sampling), an algorithm that provides a scalable solution to the misinformation prevention problem. Our theoretical analysis shows that \emph{RPS} runs in $O((k + l)(n + m)(\frac{1}{1 - \gamma}) \log n / \epsilon^2)$ expected time and returns a $(1 - 1/e - \epsilon)$-approximate solution with at least $1 - n^{-l}$ probability (where $\gamma$ is a typically small network parameter). The time complexity of \emph{RPS} substantially improves upon the previously best-known algorithms that run in time $\Omega(m n k \cdot POLY(\epsilon^{-1}))$. We experimentally evaluate \emph{RPS} on large datasets and show that it outperforms the state-of-the-art solution by several orders of magnitude in terms of running time. This demonstrates that misinformation prevention can be made practical while still offering strong theoretical guarantees.