Financing of small and medium enterprises (SMES): facilitation through rotating credit and savings associations (ROSCAs) in Lahore

SMEs are the backbone of Pakistan’s economy but they have limited access to the formal sources of finance. According to the SME Development Authority (SMEDA) of Pakistan, 90% of start-ups exit within four years. The current research was carried out to discover the extent of the contribution of Rotating Credit and Savings Associations (ROSCAs) to the SME, explore their weaknesses and to develop measures to transform them into a significant source of SME finance. The study was conducted in the city of Lahore. Purposive sampling technique was adopted to collect the data from 433 entrepreneurs and eight informants. Nearly 90% of the respondents resort to ROSCAs-financing. The ROSCAs system finance the 386 sampled SMEs to the tune of Rs468 million every cycle. The average contribution per SME is Rs1.08 million per cycle. Only 9.8% of the sampled population had obtained formal loans during the last five years. The current study does not support the findings of SMEDA which reported that 80 to 90% of the start-up's exit within the first four years. The majority of respondents expressed fear of failure of ROSCAs is due to fraud or mismanagement and felt that management of ROSCAs by banks can assist in preventing mismanagement or fraud. Laws and procedures for managing cases of dishonoured checks are very weak. Since ROSCAs are extra-legal and un-registered, ROSCAs-related disputes have to be settled out of courts. Furthermore, the concept of Shirkah al-Wujuh was found to be widely practised in the form of ROSCAs for the interest-free (Islamic) financing of SMEs. The recommendations of the current study can be helpful in fortifying the existing ROSCAs system as well as promoting easy and secure access to finance. Moreover, banks can use these findings to position themselves as guarantor and play effective role in the entrepreneur- driven SME finance market

Enhancing Efficiency and Privacy in Memory-Based Malware Classification through Feature Selection

Malware poses a significant security risk to individuals, organizations, and critical infrastructure by compromising systems and data. Leveraging memory dumps that offer snapshots of computer memory can aid the analysis and detection of malicious content, including malware. To improve the efficacy and address privacy concerns in malware classification systems, feature selection can play a critical role as it is capable of identifying the most relevant features, thus, minimizing the amount of data fed to classifiers. In this study, we employ three feature selection approaches to identify significant features from memory content and use them with a diverse set of classifiers to enhance the performance and privacy of the classification task. Comprehensive experiments are conducted across three levels of malware classification tasks: i) binary-level benign or malware classification, ii) malware type classification (including Trojan horse, ransomware, and spyware), and iii) malware family classification within each family (with varying numbers of classes). Results demonstrate that the feature selection strategy, incorporating mutual information and other methods, enhances classifier performance for all tasks. Notably, selecting only 25\% and 50\% of input features using Mutual Information and then employing the Random Forest classifier yields the best results. Our findings reinforce the importance of feature selection for malware classification and provide valuable insights for identifying appropriate approaches. By advancing the effectiveness and privacy of malware classification systems, this research contributes to safeguarding against security threats posed by malicious software.Comment: Accepted in IEEE ICMLA-2023 Conferenc

AxOMaP: Designing FPGA-based Approximate Arithmetic Operators using Mathematical Programming

With the increasing application of machine learning (ML) algorithms in embedded systems, there is a rising necessity to design low-cost computer arithmetic for these resource-constrained systems. As a result, emerging models of computation, such as approximate and stochastic computing, that leverage the inherent error-resilience of such algorithms are being actively explored for implementing ML inference on resource-constrained systems. Approximate computing (AxC) aims to provide disproportionate gains in the power, performance, and area (PPA) of an application by allowing some level of reduction in its behavioral accuracy (BEHAV). Using approximate operators (AxOs) for computer arithmetic forms one of the more prevalent methods of implementing AxC. AxOs provide the additional scope for finer granularity of optimization, compared to only precision scaling of computer arithmetic. To this end, designing platform-specific and cost-efficient approximate operators forms an important research goal. Recently, multiple works have reported using AI/ML-based approaches for synthesizing novel FPGA-based AxOs. However, most of such works limit usage of AI/ML to designing ML-based surrogate functions used during iterative optimization processes. To this end, we propose a novel data analysis-driven mathematical programming-based approach to synthesizing approximate operators for FPGAs. Specifically, we formulate mixed integer quadratically constrained programs based on the results of correlation analysis of the characterization data and use the solutions to enable a more directed search approach for evolutionary optimization algorithms. Compared to traditional evolutionary algorithms-based optimization, we report up to 21% improvement in the hypervolume, for joint optimization of PPA and BEHAV, in the design of signed 8-bit multipliers.Comment: 23 pages, Under review at ACM TRET

High-Performance Accurate and Approximate Multipliers for FPGA-Based Hardware Accelerators

Multiplication is one of the widely used arithmetic operations in a variety of applications, such as image/video processing and machine learning. FPGA vendors provide high-performance multipliers in the form of DSP blocks. These multipliers are not only limited in number and have fixed locations on FPGAs but can also create additional routing delays and may prove inefficient for smaller bit-width multiplications. Therefore, FPGA vendors additionally provide optimized soft IP cores for multiplication. However, in this work, we advocate that these soft multiplier IP cores for FPGAs still need better designs to provide high-performance and resource efficiency. Toward this, we present generic area-optimized, low-latency accurate, and approximate softcore multiplier architectures, which exploit the underlying architectural features of FPGAs, i.e., lookup table (LUT) structures and fast-carry chains to reduce the overall critical path delay (CPD) and resource utilization of multipliers. Compared to Xilinx multiplier LogiCORE IP, our proposed unsigned and signed accurate architecture provides up to 25% and 53% reduction in LUT utilization, respectively, for different sizes of multipliers. Moreover, with our unsigned approximate multiplier architectures, a reduction of up to 51% in the CPD can be achieved with an insignificant loss in output accuracy when compared with the LogiCORE IP. For illustration, we have deployed the proposed multiplier architecture in accelerators used in image and video applications, and evaluated them for area and performance gains. Our library of accurate and approximate multipliers is opensource and available online at https://cfaed.tu-dresden.de/pd-downloads to fuel further research and development in this area, facilitate reproducible research, and thereby enabling a new research direction for the FPGA community

An optimal clustering algorithm based distance-aware routing protocol for wireless sensor networks

Wireless Sensors Networks (WSN) consist of low power devices that are deployed at different geographical isolated areas to monitor physical event. Sensors are arranged in clusters. Each cluster assigns a specific and vital node which is known as a cluster head (CH). Each CH collects the useful information from its sensor member to be transmitted to a sink or Base Station (BS). Sensor have implemented with limited batteries (1.5V) that cannot have replaced. To resolve this issue and improve network stability, the proposed scheme adjust the transmission range between CHs and their members. The proposed approach is evaluated via simulation experiments and compared with some references existing algorithms. Our protocol seemed improved performance in terms of extended lifetime and achieved more than 35% improvements in terms of energy consumptio

ANALYSIS OF SPORTS PRE-COMPETITIVE ANXIETY IN UNIVERSITY LEVEL MALE AND FEMALE ATHLETES

The aim of the study is to investigate difference in levels of pre-competition anxiety in athletes of both sexes. The components of pre competitive anxiety was assessed by using the instrument (Urdu version) of competitive state anxiety inventory -2 (CSAI-2) Martens, Vealeyand Burton (1990) was a set of questionnaire consisting of 27 items equally divided into 3-sub scales of cognitive anxiety, somatic anxiety & self-confidence. The subjects (N=720), included male (360) & female (360) athletes of team sports (games), Volley ball, Basket Ball, Hand Ball and individual sports (games), Table Tennis (single), Badminton (single) & athletics with age 16-27 years. The CSAI-2 was administered one hour before the competition. Results were analyzed using student t-test. These findings showed no significant difference in cognitive anxiety and self-confidences however, significant difference was observed in somatic anxiety level among male and female subjects

AxOCS: Scaling FPGA-based Approximate Operators using Configuration Supersampling

The rising usage of AI and ML-based processing across application domains has exacerbated the need for low-cost ML implementation, specifically for resource-constrained embedded systems. To this end, approximate computing, an approach that explores the power, performance, area (PPA), and behavioral accuracy (BEHAV) trade-offs, has emerged as a possible solution for implementing embedded machine learning. Due to the predominance of MAC operations in ML, designing platform-specific approximate arithmetic operators forms one of the major research problems in approximate computing. Recently there has been a rising usage of AI/ML-based design space exploration techniques for implementing approximate operators. However, most of these approaches are limited to using ML-based surrogate functions for predicting the PPA and BEHAV impact of a set of related design decisions. While this approach leverages the regression capabilities of ML methods, it does not exploit the more advanced approaches in ML. To this end, we propose AxOCS, a methodology for designing approximate arithmetic operators through ML-based supersampling. Specifically, we present a method to leverage the correlation of PPA and BEHAV metrics across operators of varying bit-widths for generating larger bit-width operators. The proposed approach involves traversing the relatively smaller design space of smaller bit-width operators and employing its associated Design-PPA-BEHAV relationship to generate initial solutions for metaheuristics-based optimization for larger operators. The experimental evaluation of AxOCS for FPGA-optimized approximate operators shows that the proposed approach significantly improves the quality-resulting hypervolume for multi-objective optimization-of 8x8 signed approximate multipliers.Comment: 11 pages, under review with IEEE TCAS-

The Imperatives of Innovative Sources of Development Finance: Evidence from Nigeria

Innovative source of development finance (ISDF) connote a net additional resources to the total resources involving the application of nontraditional mechanisms for sourcing funds  capable of catalyzing and supporting fund raising through new sources. The objectives of ISDF is to complement the dwindling financial resources for the millennium development goals (MDGs) which was conceived as a complementary financing efforts to drastically reduce poverty level; with its attendant consequences of socio ills like hunger, illiteracy, shelter, HIV/aids, social strife among others by the year 2015. This work is design to shore up support for the good intention of the programme. In the case of Nigeria of which despite the enormous resources endowments, above average of her population are still wallowing in poverty with the visible consequences of these set of the population being subjugated to subhuman live. Wanton corruption had being the bane of meaningful development in the country. Looting of public treasury and misappropriation of funds are widely acknowledging worldwide. Essentially, if the suggested changes in modalities as appertaining to Nigeria are embraced, the programme will go a long way in improving the wellbeing of the average Nigerian populace as envisaged in MDGs. Keywords - Millennium Development Goals MDG), Innovative sources of development finance, Public bad, carbon tax, Special drawing rights (SDR

Identification of the Benefits of the Usage of Information Technology in Managing Warehouses in Supply Chain

The aim of this research is to identify the most important benefits of using the Information technology in managing the warehouses. Knowing that the warehouse departments make use of the Warehouse management Systems (WMS) to manage their warehousing activities, but the system performs limited functions such as contacting the suppliers, as for the rest of operations such as knowing the amount of inventory, information regarding stock movement, knowing the arrival and departure schedules of trucks and many other functions etc., are performed by the warehousing staffs manually by using the papers in order to the record data. The study seeks to highlight the current use of IT in managing warehouse and identifying the shortcomings of WMS in managing warehouse. This paper will make use of literature review methodology, which will identify publications related to study highlighting the benefits of usage of information technology in managing the warehouse. The findings of the study will highlight the important benefits of using information technology in managing the Warehouses leading to more profits and efficiency for the organizations. The use of modern technology, such as big data in stock management improves the pace of work, if there is sufficient knowledge and skill when using these techniques

ANALYSIS OF SPORTS PRE-COMPETITIVE ANXIETY IN UNIVERSITY LEVEL MALE AND FEMALE ATHLETES

The aim of the study is to investigate difference in levels of pre-competition anxiety in athletes of both sexes. The components of pre competitive anxiety was assessed by using the instrument (Urdu version) of competitive state anxiety inventory -2 (CSAI-2) Martens, Vealeyand Burton (1990) was a set of questionnaire consisting of 27 items equally divided into 3-sub scales of cognitive anxiety, somatic anxiety & self-confidence. The subjects (N=720), included male (360) & female (360) athletes of team sports (games), Volley ball, Basket Ball, Hand Ball and individual sports (games), Table Tennis (single), Badminton (single) & athletics with age 16-27 years. The CSAI-2 was administered one hour before the competition. Results were analyzed using student t-test. These findings showed no significant difference in cognitive anxiety and self-confidences however, significant difference was observed in somatic anxiety level among male and female subjects