Miracle-dz/UniFAR-FTUs huggingface dataset:
Poster: Static Detection of API Call Vulnerabilities in iOS Executables

We propose a static analysis technique for iOS executables for checking API call vulnerabilities that can cause 1) app behaviors tobe altered by malicious external inputs, and 2) sensitive user data to be illegally accessed by apps with stealthy private API calls that use string obfuscation. We identify sensitive functions that dynamically load classes/frameworks, and, for each parameter that corresponds to a dynamically loaded class/framework, we construct a dependency graph that shows the set of values that flow to that parameter. A sensitive function that has its class name or framework path parameter depending on externalinputs is considered to contain a vulnerability. We further conduct string analysis on these dependency graphs to determine all potential string values that these parameters can take, which identifies the set of dynamically loaded classes/frameworks. Taking the intersection of these values with patterns that characterize Apple's API policies (such as restricted use of private/sensitive APIs), we are able to detect potential policy violations and vulnerabilities.
TrQuery: An Embedding-based Framework for Recommanding SPARQL Queries

In this paper, we present an embedding-based framework (TrQuery) for recommending solutions of a SPARQL query, including approximate solutions when exact querying solutions are not available due to incompleteness or inconsistencies of real-world RDF data. Within this framework, embedding is applied to score solutions together with edit distance so that we could obtain more fine-grained recommendations than those recommendations via edit distance. For instance, graphs of two querying solutions with a similar structure can be distinguished in our proposed framework while the edit distance depending on structural difference becomes unable. To this end, we propose a novel score model built on vector space generated in embedding system to compute the similarity between an approximate subgraph matching and a whole graph matching. Finally, we evaluate our approach on large RDF datasets DBpedia and YAGO, and experimental results show that TrQuery exhibits an excellent behavior in terms of both effectiveness and efficiency.
Hysteresis vs. Natural Rate of Unemployment: One, the Other, or Both?

Hysteresis vs. Natural Rate of Unemployment: One, the Other, or Both? This paper re-examines the empirical validity of the hysteresis hypothesis in unemployment rates by education level in 17 OECD countries. To this end, for an unbalanced panel, we employed Pesaran's Cross-Sectional Dependence (CD) and Cross-Sectionally Augmented ADF (CADF) tests. Our empirical findings provide evidence favorable to the non-stationarity of unemployment rates according to levels of primary and secondary education attainment in total unemployment, and therefore the existence of hysteresis for these levels of education. There is no evidence, however, of hysteresis for unemployment rates by tertiary education.
An Ultra-Low Energy Internally Analog, Externally Digital Vector-Matrix Multiplier Based on NOR Flash Memory Technology

Vector-matrix multiplication (VMM) is a core operation in many signal and data processing algorithms. Previous work showed that analog multipliers based on nonvolatile memories have superior energy efficiency as compared to digital counterparts at low-to-medium computing precision. In this paper, we propose extremely energy efficient analog mode VMM circuit with digital input/output interface and configurable precision. Similar to some previous work, the computation is performed by gate-coupled circuit utilizing embedded floating gate (FG) memories. The main novelty of our approach is an ultra-low power sensing circuitry, which is designed based on translinear Gilbert cell in topological combination with a floating resistor and a low-gain amplifier. Additionally, the digital-to-analog input conversion is merged with VMM, while current-mode algorithmic analog-to-digital circuit is employed at the circuit backend. Such implementations of conversion and sensing allow for circuit operation entirely in a current domain, resulting in high performance and energy efficiency. For example, post-layout simulation results for 400 × 400 5-bit VMM circuit designed in 55 nm process with embedded NOR flash memory, show up to 400 MHz operation, 1.68 POps/J energy efficiency, and 39.45 TOps/mm2 computing throughput. Moreover, the circuit is robust against process-voltage-temperature variations, in part due to inclusion of additional FG cells that are utilized for offset compensation.1
Big Data Analytics for Smart Cities: The H2020 CLASS Project

Applying big-data technologies to field applications has resulted in several new needs. First, processing data across a compute continuum spanning from cloud to edge to devices, with varying capacity, architecture etc. Second, some computations need to be made predictable (real-time response), thus supporting both data-in-motion processing and larger-scale data-at-rest processing. Last, employing an event-driven programming model that supports mixing different APIs and models, such as Map/Reduce, CEP, sequential code, etc.
Efficient and Reliable Power Delivery in Voltage-Stacked Manycore System with Hybrid Charge-Recycling Regulators

Voltage stacking (VS) fundamentally improves power delivery efficiency (PDE) by series-stacking multiple voltage domains to eliminate explicit step-down voltage conversion and reduce energy loss along the power delivery path. However, it suffers from aggravated supply noise, preventing its adoption in mainstream computing systems. In this paper, we investigate a practical approach to enabling efficient and reliable power delivery in voltage-stacked manycore systems that can ensure worst-case supply noise reliability without excessive costly over-design. We start by developing an analytical model to capture the essential noise behaviors in VS. It allows us to identify dominant noise contributor and derive the worst-case conditions. With this in-depth understanding, we propose a hybrid voltage regulation solution to effectively mitigate noise with worst-case guarantees. When evaluated with real-world benchmarks, our solution can achieve 93.8% power delivery efficiency, an improvement of 13.9% over the conventional baseline.
A unified strategy for implementing curiosity and empowerment driven reinforcement learning

Although there are many approaches to implement intrinsically motivated artificial agents, the combined usage of multiple intrinsic drives remains still a relatively unexplored research area. Specifically, we hypothesize that a mechanism capable of quantifying and controlling the evolution of the information flow between the agent and the environment could be the fundamental component for implementing a higher degree of autonomy into artificial intelligent agents. This paper propose a unified strategy for implementing two semantically orthogonal intrinsic motivations: curiosity and empowerment. Curiosity reward informs the agent about the relevance of a recent agent action, whereas empowerment is implemented as the opposite information flow from the agent to the environment that quantifies the agent's potential of controlling its own future. We show that an additional homeostatic drive is derived from the curiosity reward, which generalizes and enhances the information gain of a classical curious/heterostatic reinforcement learning agent. We show how a shared internal model by curiosity and empowerment facilitates a more efficient training of the empowerment function. Finally, we discuss future directions for further leveraging the interplay between these two intrinsic rewards.
ReGuard: Finding Reentrancy Bugs in Smart Contracts

Smart contracts enabled a new way to perform cryptocurrency transactions over blockchains. While this emerging technique introduces free-of-con? icts and transparency, smart contract itself is vulnerable. As a special form of computer program, smart contract can hardly get rid of bugs. Even worse, an exploitable security bug can lead to catastrophic consequences, e.g., loss of cryptocurrency/money. In this demo paper, we focus on the most common type of security bugs in smart contracts, i.e., reentrancy bug, which caused the famous DAO attack with a loss of 60 million US dollars. We presented ReGuard, an fuzzing-based analyzer to automatically detect reentrancy bugs in Ethereum smart contracts. Specifically, ReGuard performs fuzz testing on smart contracts by iteratively generating random but diverse transactions. Based on the runtime traces, ReGuard further dynamically identifes reentrancy vulnerabilities. In the preliminary evaluation, we have analyzed 5 existing Ethereum contracts. ReGuard automatically flagged 7 previously unreported reentrancy bugs. A demo video of ReGuard is at https://youtu.be/XxJ3_-cmUiY.
The Greenish Flower Phenotype of Habenaria radiata (Orchidaceae) Is Caused by a Mutation in the SEPALLATA-Like MADS-Box Gene HrSEP-1

In Arabidopsis thaliana, the E-class SEPALLATA (SEP) genes are generally expressed across all floral whorls. These genes play fundamental roles in floral organ fate determination during development by interacting with other MADS-box gene products, such as those from A-, B-, and C-class genes. However, the function of SEP genes in orchid remains obscure. Here, we analyzed a mutant orchid cultivar with greenish flowers in Habenaria radiata and found that this phenotype is caused by the absence of SEP function. Wild type H. radiata flowers contain a column and two perianth whorls consisting of three greenish sepals, two white petals, and a lip (labellum). By contrast, the flowers of H. radiata cultivar ‘Ryokusei’ appear greenish, with three normal sepals in whorl 1, two greenish petals and a lip in whorl 2, and several sepaloid organs and a ventral column in whorls 3 and 4. We isolated two SEP-like genes (HrSEP-1 and HrSEP-2) and two AGAMOUS-like genes (HrAG-1 and HrAG-2) from wild type H. radiata and compared their expression in the wild type vs. the mutant cultivar. HrAG-1 and HrAG-2 were expressed in the column in the wild type, whereas these genes were expressed in the ventral column and in sepaloid organs that had been converted from a column in ‘Ryokusei. ’ HrSEP-1 and HrSEP-2 were expressed in all floral organs in the wild type. However, in the mutant cultivar, HrSEP-2 was expressed in all floral organs, while HrSEP-1 expression was not detected. Thus, we analyzed the genomic structures of HrSEP-1 in the wild type and ‘Ryokusei’ and identified a retrotransposon-like element in its first exon in ‘Ryokusei. ’ Yeast two-hybrid assays demonstrated that HrSEP-1 interacts with HrDEF, HrAG-1, and HrAG-2. These results indicate that the mutant phenotype of ‘Ryokusei’ flowers is caused by the loss of function of HrSEP-1. Therefore, this gene plays an important role in column, lip, and petal development in H. radiata flowers.
PIMA-Logic: A Novel Processing-in-Memory Architecture for Highly Flexible and Energy-Efficient Logic Computation

In this paper, we propose PIMA-Logic, as a novel Processing-in-Memory Architecture for highly flexible and efficient Logic computation. Instead of integrating complex logic units in cost-sensitive memory, PIMA-Logic exploits a hardware-friendly approach to implement Boolean logic functions between operands either located in the same row or the same column within entire memory arrays. Furthermore, it can efficiently process more complex logic functions between multiple operands to further reduce the latency and power-hungry data movement. The proposed architecture is developed based on Spin Orbit Torque Magnetic Random Access Memory (SOT-MRAM) array and it can simultaneously work as a non-volatile memory and a reconfigurable in-memory logic. The device-to-architecture co-simulation results show that PIMA-Logic can achieve up to 56% and 31.6% improvements with respect to overall energy and delay on combinational logic benchmarks compared to recent Pinatubo architecture. We further implement an in-memory data encryption engine based on PIMA-Logic as a case study. With AES application, it shows 77.2% and 21% lower energy consumption compared to CMOS-ASIC and recent RIMPA implementation, respectively.
The multifaceted abstract brain

Abstract concepts play a central role in human behaviour and constitute a critical component of the human conceptual system. Here, we investigate the neural basis of four types of abstract concepts, examining their similarities and differences through neuroimaging meta-analyses. We examine numerical and emotional concepts, and two higher-order abstract processes, morality judgements and theory of mind. Three main findings emerge. First, representation of abstract concepts is more widespread than is often assumed. Second, representations of different types of abstract concepts differ in important respects. Each of the domains examined here was associated with some unique areas. Third, some areas were commonly activated across domains and included inferior parietal, posterior cingulate and medial prefrontal cortex. We interpret these regions in terms of their role in episodic recall, event representation and social–emotional processing. We suggest that different types of abstract concepts can be represented and grounded through differing contributions from event-based, interoceptive, introspective and sensory-motor representations. The results underscore the richness and diversity of abstract concepts, argue against single-mechanism accounts for representation of all types of abstract concepts and suggest mechanisms for their direct and indirect grounding. This article is part of the theme issue ‘Varieties of abstract concepts: development, use and representation in the brain’.
Poster: Toward Measuring Software Coupling via Weighted Dynamic Metrics

Coupling metrics are an established way to measure internal software quality with respect to modularity. Dynamic metrics have been used to improve the accuracy of static metrics for object-oriented software. We introduce a dynamic metric NOI that takes into account the number of interactions (method calls) during the run of a system. We used the data collected from an experiment to compute our NOI metric and compared the results to a static coupling analysis. We observed an unexpected level of correlation and significant differences between class- and package-level analyses.
Searching for Truth in a Database of Statistics

The proliferation of falsehood and misinformation, in particular through the Web, has lead to increasing energy being invested into journalistic fact-checking. Fact-checking journalists typically check the accuracy of a claim against some trusted data source. Statistic databases such as those compiled by state agencies are often used as trusted data sources, as they contain valuable, high-quality information. However, their usability is limited when they are shared in a format such as HTML or spreadsheets: this makes it hard to find the most relevant dataset for checking a specific claim, or to quickly extract from a dataset the best answer to a given query. We present a novel algorithm enabling the exploitation of such statistic tables, by (i) identifying the statistic datasets most relevant for a given fact-checking query, and (ii) extracting from each dataset the best specific (precise) query answer it may contain. We have implemented our approach and experimented on the complete corpus of statistics obtained from INSEE, the French national statistic institute. Our experiments and comparisons demonstrate the effectiveness of our proposed method.
Identity and performance in technical Q&A

ABSTRACT Identity and the different roles identities play in social question-answering communities have long been discussed from both a theoretical and empirical standpoint. Identity is usually analysed in ways that emphasise a transaction, and many studies use third-party raters to assign value judgements to these factors, which may not be relevant to a community. In this article, we examine identity in Stack Overflow, a question and answer site dedicated to solving computer-programming problems, which has a competitive community run reputation system. Profiles on Stack Overflow are highly customizable, allowing users to choose the level of personal information they want to share: from extensive to none. We develop a categorisation scheme using grounded theory to develop definitions of identity, and analyse behaviour based on these definitions. We find that the choice of identity is diverse within the community and that there is a correlation with identifiers and increased reputation earning among the general population. An analysis of elite users, however, indicates that identity is closely tied to membership length, but not to performance. The results indicate that allowing identity choices does affect system efficacy or user outcomes.
Automated Systolic Array Architecture Synthesis for High Throughput CNN Inference on AWS F 1 FPGA

FPGAs are more and more deployed in datacenters as reconfigurable hardware accelerators for applications leveraging deep neural networks (DNNs). Meanwhile, in-datacenter FPGAs are prone to adopt the Stacked Silicon Interconnect (SSI) technology to integrate multiple dies together in order to incorporate more resources. However, previous designs are not scalable to efficiently utilize resources on these large FPGAs to further improve performance due to timing violation caused by crossing-die critical paths. In this work, we propose a Stacked Systolic Array (SSA) architecture by placing multiple systolic arrays on different dies and chaining them by non-critical paths. And we further improve the DSP utilization of SSA by individual designs for different layers of DNN models. We also present an automation flow to do design space exploration and accelerator generation. Experiment results show that our accelerator achieves up to 85% performance improvement than previous designs.
SE33 locus as a reliable genetic marker for forensic DNA analysis SE33 locus as a reliable genetic marker for forensic DNA analysis systems systems

Background/aim: Genetic variation, an authentic tool of individual discrimination, is being used for forensic investigations worldwide. A missing result for even one out of 13–17 markers leads to an inconclusive report. Additional reliable markers are required to compensate such deficiencies. The SE33 locus has high genetic variability in different populations and is being used in forensic investigation systems in some countries. The purpose of the study was to assess the viability of use of the SE33 locus as a supportive marker for forensic DNA profiling. Materials and methods: Amplification of the SE33 locus was performed using the PowerPlex ES Monoplex System SE33 (Promega). After genotyping 204 Pakistani individuals, different genetic and forensic parameters for the SE33 locus were studied. Results: Genotyping of the SE33 locus revealed a total of 43 alleles including 3 novel alleles. Significant values of different forensic and genetic parameters including power of discrimination, power of exclusion, and polymorphism information content were observed. Conclusions: Addition of the SE33 locus in forensic DNA profiling may help to produce conclusive reports where results are inconclusive due to degraded evidence samples. The SE33 locus can confidently be used for Pakistani and neighboring populations having common ancestors from Iran to Central Asia, the Middle East, India and Turkey.
Institutions and Economic Performance: An Overview of Empirical Research with the Main Focus on Transition Economies

Institutions and Economic Performance: An Overview of Empirical Research with the Main Focus on Transition Economies The New Institutional Economics integrates the theory of institutions into mainstream economics. Institutions are formally and informally the "rules of the game" in society. As such, institutions structure economic activity and influence its outcomes at both micro and macro levels. This paper reviews the empirical literature, focussing on its relevance to transitional economies. We conclude that institutions matter profoundly for economic growth and development. However, although empirical research has increased understanding of the role of institutions in the economy, many questions remain open, especially for transition economies. Knowledge gaps that invite further investigation include: transmission channels between institutions and national output; the issue of mutual endogeneity between institutions and economic growth; the structure and size of the institutional framework and its influence on economic performance; and the links between success in integration with the EU of transition economies, financial support from the EU and the quality of institutions.
Poster: Exploration of Academic and Industrial Evidence about Architectural Tactics and Patterns in Microservices

Microservices are quickly becoming an outstanding architectural choice in the service-oriented software industry. This approach proposes to develop each application as a collection of small services, each running on its process and inter-communicating with lightweight mechanisms. Currently, there is still no clear perspective of emerging recurrent solutions (architectural patterns) or design decisions (architectural tactics) in microservices both in industry and academia. This article describes a systematic review of the academic and industrial literature on architectural patterns and tactics proposed for microservices. The study reported: 44 architectural patterns of microservices in academia and 80 in the industry; architectural tactics related to microservices dependent on other disciplines; and it was also found that most of architectural patterns and tactics are associated to five quality attributes: scalability, flexibility, testability, performance, and elasticity. Added to that results, it was noticed that most microservices in the academic area are reported in evidence related to DevOps and IoT, but the industry is not interested in associating disciplines. Finally, a new proposal of microservices pattern taxonomy is suggested.
Poster: Efficiently Finding Minimal Failing Input in MapReduce Programs

Debugging of distributed computing model programs like MapReduce is a difficult task. That's why prior studies only focus on finding and fixing bugs in early stages of program development. Delta debugging tries to find minimal failing input in sequential programs by dividing inputs into subsets and testing these subsets one-by-one. But no prior work tries to find minimal failing input in distributed programs like MapReduce. In this paper, we present MapRedDD, a framework to efficiently find minimal failing input in MapReduce programs. MapRedDD employs failing input selection technique, focused on identifying the failing input subset in the single run of MapReduce program with multiple input subsets instead of testing each subset separately. This helps to reduce the number of executions of MapReduce program for each input subset and overcome the overhead of job submission, job scheduling and final outcome retrieval. Our work can efficiently find the minimal failing input in the number of executions equal to the number of inputs to MapReduce program N as opposed to the number of executions of MapReduce program equal to the number of input subsets 2^N -1 in worst case for binary search invariant algorithm to find minimal failing input.